fix: prevent --cpu flag from allocating GPU memory

Two root causes fixed:

1. soft_empty_cache() and synchronize() in model_management.py lacked a
   cpu_state == CPUState.CPU guard. They fell through to torch.cuda calls
   that initialize a CUDA context (150-500MB VRAM) even in CPU-only mode.

2. comfy_kitchen is imported unconditionally at startup via quant_ops.py.
   The import chain triggers torch.cuda.is_available() -> cuInit, which
   initializes the CUDA driver. Now gated behind args.cpu check.

Also adds missing QuantizedLayout and register_layout_op fallback stubs
that were absent from the original ImportError handler.

Amp-Thread-ID: https://ampcode.com/threads/T-019cbd03-433e-7601-93ff-3887227496b4

.github/scripts/check-ai-co-authors.sh

@@ -1,103 +0,0 @@
-#!/usr/bin/env bash
-# Checks pull request commits for AI agent Co-authored-by trailers.
-# Exits non-zero when any are found and prints fix instructions.
-set -euo pipefail
-
-base_sha="${1:?usage: check-ai-co-authors.sh <base_sha> <head_sha>}"
-head_sha="${2:?usage: check-ai-co-authors.sh <base_sha> <head_sha>}"
-
-# Known AI coding-agent trailer patterns (case-insensitive).
-# Each entry is an extended-regex fragment matched against Co-authored-by lines.
-AGENT_PATTERNS=(
-    # Anthropic — Claude Code / Amp
-    'noreply@anthropic\.com'
-    # Cursor
-    'cursoragent@cursor\.com'
-    # GitHub Copilot
-    'copilot-swe-agent\[bot\]'
-    'copilot@github\.com'
-    # OpenAI Codex
-    'noreply@openai\.com'
-    'codex@openai\.com'
-    # Aider
-    'aider@aider\.chat'
-    # Google — Gemini / Jules
-    'gemini@google\.com'
-    'jules@google\.com'
-    # Windsurf / Codeium
-    '@codeium\.com'
-    # Devin
-    'devin-ai-integration\[bot\]'
-    'devin@cognition\.ai'
-    'devin@cognition-labs\.com'
-    # Amazon Q Developer
-    'amazon-q-developer'
-    '@amazon\.com.*[Qq].[Dd]eveloper'
-    # Cline
-    'cline-bot'
-    'cline@cline\.ai'
-    # Continue
-    'continue-agent'
-    'continue@continue\.dev'
-    # Sourcegraph
-    'noreply@sourcegraph\.com'
-    # Generic catch-alls for common agent name patterns
-    'Co-authored-by:.*\b[Cc]laude\b'
-    'Co-authored-by:.*\b[Cc]opilot\b'
-    'Co-authored-by:.*\b[Cc]ursor\b'
-    'Co-authored-by:.*\b[Cc]odex\b'
-    'Co-authored-by:.*\b[Gg]emini\b'
-    'Co-authored-by:.*\b[Aa]ider\b'
-    'Co-authored-by:.*\b[Dd]evin\b'
-    'Co-authored-by:.*\b[Ww]indsurf\b'
-    'Co-authored-by:.*\b[Cc]line\b'
-    'Co-authored-by:.*\b[Aa]mazon Q\b'
-    'Co-authored-by:.*\b[Jj]ules\b'
-    'Co-authored-by:.*\bOpenCode\b'
-)
-
-# Build a single alternation regex from all patterns.
-regex=""
-for pattern in "${AGENT_PATTERNS[@]}"; do
-    if [[ -n "$regex" ]]; then
-        regex="${regex}|${pattern}"
-    else
-        regex="$pattern"
-    fi
-done
-
-# Collect Co-authored-by lines from every commit in the PR range.
-violations=""
-while IFS= read -r sha; do
-    message="$(git log -1 --format='%B' "$sha")"
-    matched_lines="$(echo "$message" | grep -iE "^Co-authored-by:" || true)"
-    if [[ -z "$matched_lines" ]]; then
-        continue
-    fi
-
-    while IFS= read -r line; do
-        if echo "$line" | grep -iqE "$regex"; then
-            short="$(git log -1 --format='%h' "$sha")"
-            violations="${violations}  ${short}: ${line}"$'\n'
-        fi
-    done <<< "$matched_lines"
-done < <(git rev-list "${base_sha}..${head_sha}")
-
-if [[ -n "$violations" ]]; then
-    echo "::error::AI agent Co-authored-by trailers detected in PR commits."
-    echo ""
-    echo "The following commits contain Co-authored-by trailers from AI coding agents:"
-    echo ""
-    echo "$violations"
-    echo "These trailers should be removed before merging."
-    echo ""
-    echo "To fix, rewrite the commit messages with:"
-    echo "  git rebase -i ${base_sha}"
-    echo ""
-    echo "and remove the Co-authored-by lines, then force-push your branch."
-    echo ""
-    echo "If you believe this is a false positive, please open an issue."
-    exit 1
-fi
-
-echo "No AI agent Co-authored-by trailers found."

.github/workflows/check-ai-co-authors.yml

@@ -1,19 +0,0 @@
-name: Check AI Co-Authors
-
-on:
-  pull_request:
-    branches: ['*']
-
-jobs:
-  check-ai-co-authors:
-    name: Check for AI agent co-author trailers
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Check commits for AI co-author trailers
-        run: bash .github/scripts/check-ai-co-authors.sh "${{ github.event.pull_request.base.sha }}" "${{ github.event.pull_request.head.sha }}"

README.md

@@ -38,8 +38,6 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 
 ## Get Started
 
-### Local
-
 #### [Desktop Application](https://www.comfy.org/download)
 - The easiest way to get started.
 - Available on Windows & macOS.
@@ -51,13 +49,8 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 #### [Manual Install](#manual-install-windows-linux)
 Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
 
-### Cloud
-
-#### [Comfy Cloud](https://www.comfy.org/cloud)
-- Our official paid cloud version for those who can't afford local hardware.
-
-## Examples
-See what ComfyUI can do with the [newer template workflows](https://comfy.org/workflows) or old [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
+## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.

alembic_db/env.py

@@ -8,7 +8,7 @@ from alembic import context
 config = context.config
 
 
-from app.database.models import Base, NAMING_CONVENTION
+from app.database.models import Base
 target_metadata = Base.metadata
 
 # other values from the config, defined by the needs of env.py,
@@ -51,10 +51,7 @@ def run_migrations_online() -> None:
 
     with connectable.connect() as connection:
         context.configure(
-            connection=connection,
-            target_metadata=target_metadata,
-            render_as_batch=True,
-            naming_convention=NAMING_CONVENTION,
+            connection=connection, target_metadata=target_metadata
         )
 
         with context.begin_transaction():

alembic_db/versions/0002_merge_to_asset_references.py

@@ -1,267 +0,0 @@
-"""
-Merge AssetInfo and AssetCacheState into unified asset_references table.
-
-This migration drops old tables and creates the new unified schema.
-All existing data is discarded.
-
-Revision ID: 0002_merge_to_asset_references
-Revises: 0001_assets
-Create Date: 2025-02-11
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-revision = "0002_merge_to_asset_references"
-down_revision = "0001_assets"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Drop old tables (order matters due to FK constraints)
-    op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta")
-    op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta")
-    op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta")
-    op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta")
-    op.drop_table("asset_info_meta")
-
-    op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags")
-    op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags")
-    op.drop_table("asset_info_tags")
-
-    op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state")
-    op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state")
-    op.drop_table("asset_cache_state")
-
-    op.drop_index("ix_assets_info_owner_name", table_name="assets_info")
-    op.drop_index("ix_assets_info_last_access_time", table_name="assets_info")
-    op.drop_index("ix_assets_info_created_at", table_name="assets_info")
-    op.drop_index("ix_assets_info_name", table_name="assets_info")
-    op.drop_index("ix_assets_info_asset_id", table_name="assets_info")
-    op.drop_index("ix_assets_info_owner_id", table_name="assets_info")
-    op.drop_table("assets_info")
-
-    # Truncate assets table (cascades handled by dropping dependent tables first)
-    op.execute("DELETE FROM assets")
-
-    # Create asset_references table
-    op.create_table(
-        "asset_references",
-        sa.Column("id", sa.String(length=36), primary_key=True),
-        sa.Column(
-            "asset_id",
-            sa.String(length=36),
-            sa.ForeignKey("assets.id", ondelete="CASCADE"),
-            nullable=False,
-        ),
-        sa.Column("file_path", sa.Text(), nullable=True),
-        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
-        sa.Column(
-            "needs_verify",
-            sa.Boolean(),
-            nullable=False,
-            server_default=sa.text("false"),
-        ),
-        sa.Column(
-            "is_missing", sa.Boolean(), nullable=False, server_default=sa.text("false")
-        ),
-        sa.Column("enrichment_level", sa.Integer(), nullable=False, server_default="0"),
-        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
-        sa.Column("name", sa.String(length=512), nullable=False),
-        sa.Column(
-            "preview_id",
-            sa.String(length=36),
-            sa.ForeignKey("assets.id", ondelete="SET NULL"),
-            nullable=True,
-        ),
-        sa.Column("user_metadata", sa.JSON(), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("deleted_at", sa.DateTime(timezone=False), nullable=True),
-        sa.CheckConstraint(
-            "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg"
-        ),
-        sa.CheckConstraint(
-            "enrichment_level >= 0 AND enrichment_level <= 2",
-            name="ck_ar_enrichment_level_range",
-        ),
-    )
-    op.create_index(
-        "uq_asset_references_file_path", "asset_references", ["file_path"], unique=True
-    )
-    op.create_index("ix_asset_references_asset_id", "asset_references", ["asset_id"])
-    op.create_index("ix_asset_references_owner_id", "asset_references", ["owner_id"])
-    op.create_index("ix_asset_references_name", "asset_references", ["name"])
-    op.create_index("ix_asset_references_is_missing", "asset_references", ["is_missing"])
-    op.create_index(
-        "ix_asset_references_enrichment_level", "asset_references", ["enrichment_level"]
-    )
-    op.create_index("ix_asset_references_created_at", "asset_references", ["created_at"])
-    op.create_index(
-        "ix_asset_references_last_access_time", "asset_references", ["last_access_time"]
-    )
-    op.create_index(
-        "ix_asset_references_owner_name", "asset_references", ["owner_id", "name"]
-    )
-    op.create_index("ix_asset_references_deleted_at", "asset_references", ["deleted_at"])
-
-    # Create asset_reference_tags table
-    op.create_table(
-        "asset_reference_tags",
-        sa.Column(
-            "asset_reference_id",
-            sa.String(length=36),
-            sa.ForeignKey("asset_references.id", ondelete="CASCADE"),
-            nullable=False,
-        ),
-        sa.Column(
-            "tag_name",
-            sa.String(length=512),
-            sa.ForeignKey("tags.name", ondelete="RESTRICT"),
-            nullable=False,
-        ),
-        sa.Column(
-            "origin", sa.String(length=32), nullable=False, server_default="manual"
-        ),
-        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
-        sa.PrimaryKeyConstraint(
-            "asset_reference_id", "tag_name", name="pk_asset_reference_tags"
-        ),
-    )
-    op.create_index(
-        "ix_asset_reference_tags_tag_name", "asset_reference_tags", ["tag_name"]
-    )
-    op.create_index(
-        "ix_asset_reference_tags_asset_reference_id",
-        "asset_reference_tags",
-        ["asset_reference_id"],
-    )
-
-    # Create asset_reference_meta table
-    op.create_table(
-        "asset_reference_meta",
-        sa.Column(
-            "asset_reference_id",
-            sa.String(length=36),
-            sa.ForeignKey("asset_references.id", ondelete="CASCADE"),
-            nullable=False,
-        ),
-        sa.Column("key", sa.String(length=256), nullable=False),
-        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
-        sa.Column("val_str", sa.String(length=2048), nullable=True),
-        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
-        sa.Column("val_bool", sa.Boolean(), nullable=True),
-        sa.Column("val_json", sa.JSON(), nullable=True),
-        sa.PrimaryKeyConstraint(
-            "asset_reference_id", "key", "ordinal", name="pk_asset_reference_meta"
-        ),
-    )
-    op.create_index("ix_asset_reference_meta_key", "asset_reference_meta", ["key"])
-    op.create_index(
-        "ix_asset_reference_meta_key_val_str", "asset_reference_meta", ["key", "val_str"]
-    )
-    op.create_index(
-        "ix_asset_reference_meta_key_val_num", "asset_reference_meta", ["key", "val_num"]
-    )
-    op.create_index(
-        "ix_asset_reference_meta_key_val_bool",
-        "asset_reference_meta",
-        ["key", "val_bool"],
-    )
-
-
-def downgrade() -> None:
-    """Reverse 0002_merge_to_asset_references: drop new tables, recreate old schema.
-
-    NOTE: Data is not recoverable. The upgrade discards all rows from the old
-    tables and truncates assets. After downgrade the old schema will be empty.
-    A filesystem rescan will repopulate data once the older code is running.
-    """
-    # Drop new tables (order matters due to FK constraints)
-    op.drop_index("ix_asset_reference_meta_key_val_bool", table_name="asset_reference_meta")
-    op.drop_index("ix_asset_reference_meta_key_val_num", table_name="asset_reference_meta")
-    op.drop_index("ix_asset_reference_meta_key_val_str", table_name="asset_reference_meta")
-    op.drop_index("ix_asset_reference_meta_key", table_name="asset_reference_meta")
-    op.drop_table("asset_reference_meta")
-
-    op.drop_index("ix_asset_reference_tags_asset_reference_id", table_name="asset_reference_tags")
-    op.drop_index("ix_asset_reference_tags_tag_name", table_name="asset_reference_tags")
-    op.drop_table("asset_reference_tags")
-
-    op.drop_index("ix_asset_references_deleted_at", table_name="asset_references")
-    op.drop_index("ix_asset_references_owner_name", table_name="asset_references")
-    op.drop_index("ix_asset_references_last_access_time", table_name="asset_references")
-    op.drop_index("ix_asset_references_created_at", table_name="asset_references")
-    op.drop_index("ix_asset_references_enrichment_level", table_name="asset_references")
-    op.drop_index("ix_asset_references_is_missing", table_name="asset_references")
-    op.drop_index("ix_asset_references_name", table_name="asset_references")
-    op.drop_index("ix_asset_references_owner_id", table_name="asset_references")
-    op.drop_index("ix_asset_references_asset_id", table_name="asset_references")
-    op.drop_index("uq_asset_references_file_path", table_name="asset_references")
-    op.drop_table("asset_references")
-
-    # Truncate assets (upgrade deleted all rows; downgrade starts fresh too)
-    op.execute("DELETE FROM assets")
-
-    # Recreate old tables from 0001_assets schema
-    op.create_table(
-        "assets_info",
-        sa.Column("id", sa.String(length=36), primary_key=True),
-        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
-        sa.Column("name", sa.String(length=512), nullable=False),
-        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False),
-        sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True),
-        sa.Column("user_metadata", sa.JSON(), nullable=True),
-        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
-        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
-        sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
-    )
-    op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"])
-    op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"])
-    op.create_index("ix_assets_info_name", "assets_info", ["name"])
-    op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"])
-    op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"])
-    op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"])
-
-    op.create_table(
-        "asset_cache_state",
-        sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
-        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False),
-        sa.Column("file_path", sa.Text(), nullable=False),
-        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
-        sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")),
-        sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
-        sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
-    )
-    op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"])
-    op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"])
-
-    op.create_table(
-        "asset_info_tags",
-        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
-        sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False),
-        sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"),
-        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
-        sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"),
-    )
-    op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"])
-    op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"])
-
-    op.create_table(
-        "asset_info_meta",
-        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
-        sa.Column("key", sa.String(length=256), nullable=False),
-        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
-        sa.Column("val_str", sa.String(length=2048), nullable=True),
-        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
-        sa.Column("val_bool", sa.Boolean(), nullable=True),
-        sa.Column("val_json", sa.JSON(), nullable=True),
-        sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"),
-    )
-    op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"])
-    op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"])
-    op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"])
-    op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"])

alembic_db/versions/0003_add_metadata_job_id.py

@@ -1,98 +0,0 @@
-"""
-Add system_metadata and job_id columns to asset_references.
-Change preview_id FK from assets.id to asset_references.id.
-
-Revision ID: 0003_add_metadata_job_id
-Revises: 0002_merge_to_asset_references
-Create Date: 2026-03-09
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-from app.database.models import NAMING_CONVENTION
-
-revision = "0003_add_metadata_job_id"
-down_revision = "0002_merge_to_asset_references"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    with op.batch_alter_table("asset_references") as batch_op:
-        batch_op.add_column(
-            sa.Column("system_metadata", sa.JSON(), nullable=True)
-        )
-        batch_op.add_column(
-            sa.Column("job_id", sa.String(length=36), nullable=True)
-        )
-
-    # Change preview_id FK from assets.id to asset_references.id (self-ref).
-    # Existing values are asset-content IDs that won't match reference IDs,
-    # so null them out first.
-    op.execute("UPDATE asset_references SET preview_id = NULL WHERE preview_id IS NOT NULL")
-    with op.batch_alter_table(
-        "asset_references", naming_convention=NAMING_CONVENTION
-    ) as batch_op:
-        batch_op.drop_constraint(
-            "fk_asset_references_preview_id_assets", type_="foreignkey"
-        )
-        batch_op.create_foreign_key(
-            "fk_asset_references_preview_id_asset_references",
-            "asset_references",
-            ["preview_id"],
-            ["id"],
-            ondelete="SET NULL",
-        )
-        batch_op.create_index(
-            "ix_asset_references_preview_id", ["preview_id"]
-        )
-
-    # Purge any all-null meta rows before adding the constraint
-    op.execute(
-        "DELETE FROM asset_reference_meta"
-        " WHERE val_str IS NULL AND val_num IS NULL AND val_bool IS NULL AND val_json IS NULL"
-    )
-    with op.batch_alter_table("asset_reference_meta") as batch_op:
-        batch_op.create_check_constraint(
-            "ck_asset_reference_meta_has_value",
-            "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
-        )
-
-
-def downgrade() -> None:
-    # SQLite doesn't reflect CHECK constraints, so we must declare it
-    # explicitly via table_args for the batch recreate to find it.
-    # Use the fully-rendered constraint name to avoid the naming convention
-    # doubling the prefix.
-    with op.batch_alter_table(
-        "asset_reference_meta",
-        table_args=[
-            sa.CheckConstraint(
-                "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
-                name="ck_asset_reference_meta_has_value",
-            ),
-        ],
-    ) as batch_op:
-        batch_op.drop_constraint(
-            "ck_asset_reference_meta_has_value", type_="check"
-        )
-
-    with op.batch_alter_table(
-        "asset_references", naming_convention=NAMING_CONVENTION
-    ) as batch_op:
-        batch_op.drop_index("ix_asset_references_preview_id")
-        batch_op.drop_constraint(
-            "fk_asset_references_preview_id_asset_references", type_="foreignkey"
-        )
-        batch_op.create_foreign_key(
-            "fk_asset_references_preview_id_assets",
-            "assets",
-            ["preview_id"],
-            ["id"],
-            ondelete="SET NULL",
-        )
-
-    with op.batch_alter_table("asset_references") as batch_op:
-        batch_op.drop_column("job_id")
-        batch_op.drop_column("system_metadata")

app/assets/api/schemas_in.py

@@ -1,8 +1,6 @@
 import json
-from dataclasses import dataclass
 from typing import Any, Literal
 
-from app.assets.helpers import validate_blake3_hash
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -12,43 +10,6 @@ from pydantic import (
     model_validator,
 )
 
-
-class UploadError(Exception):
-    """Error during upload parsing with HTTP status and code."""
-
-    def __init__(self, status: int, code: str, message: str):
-        super().__init__(message)
-        self.status = status
-        self.code = code
-        self.message = message
-
-
-class AssetValidationError(Exception):
-    """Validation error in asset processing (invalid tags, metadata, etc.)."""
-
-    def __init__(self, code: str, message: str):
-        super().__init__(message)
-        self.code = code
-        self.message = message
-
-
-@dataclass
-class ParsedUpload:
-    """Result of parsing a multipart upload request."""
-
-    file_present: bool
-    file_written: int
-    file_client_name: str | None
-    tmp_path: str | None
-    tags_raw: list[str]
-    provided_name: str | None
-    user_metadata_raw: str | None
-    provided_hash: str | None
-    provided_hash_exists: bool | None
-    provided_mime_type: str | None = None
-    provided_preview_id: str | None = None
-
-
 class ListAssetsQuery(BaseModel):
     include_tags: list[str] = Field(default_factory=list)
     exclude_tags: list[str] = Field(default_factory=list)
@@ -60,9 +21,7 @@ class ListAssetsQuery(BaseModel):
     limit: conint(ge=1, le=500) = 20
     offset: conint(ge=0) = 0
 
-    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = (
-        "created_at"
-    )
+    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = "created_at"
     order: Literal["asc", "desc"] = "desc"
 
     @field_validator("include_tags", "exclude_tags", mode="before")
@@ -100,17 +59,11 @@ class ListAssetsQuery(BaseModel):
 class UpdateAssetBody(BaseModel):
     name: str | None = None
     user_metadata: dict[str, Any] | None = None
-    preview_id: str | None = None  # references an asset_reference id, not an asset id
 
     @model_validator(mode="after")
-    def _validate_at_least_one_field(self):
-        if all(
-            v is None
-            for v in (self.name, self.user_metadata, self.preview_id)
-        ):
-            raise ValueError(
-                "Provide at least one of: name, user_metadata, preview_id."
-            )
+    def _at_least_one(self):
+        if self.name is None and self.user_metadata is None:
+            raise ValueError("Provide at least one of: name, user_metadata.")
         return self
 
 
@@ -118,20 +71,26 @@ class CreateFromHashBody(BaseModel):
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
     hash: str
-    name: str | None = None
+    name: str
     tags: list[str] = Field(default_factory=list)
     user_metadata: dict[str, Any] = Field(default_factory=dict)
-    mime_type: str | None = None
-    preview_id: str | None = None  # references an asset_reference id, not an asset id
 
     @field_validator("hash")
     @classmethod
     def _require_blake3(cls, v):
-        return validate_blake3_hash(v or "")
+        s = (v or "").strip().lower()
+        if ":" not in s:
+            raise ValueError("hash must be 'blake3:<hex>'")
+        algo, digest = s.split(":", 1)
+        if algo != "blake3":
+            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
+        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
+            raise ValueError("hash digest must be lowercase hex")
+        return s
 
     @field_validator("tags", mode="before")
     @classmethod
-    def _normalize_tags_field(cls, v):
+    def _tags_norm(cls, v):
         if v is None:
             return []
         if isinstance(v, list):
@@ -148,44 +107,6 @@ class CreateFromHashBody(BaseModel):
         return []
 
 
-class TagsRefineQuery(BaseModel):
-    include_tags: list[str] = Field(default_factory=list)
-    exclude_tags: list[str] = Field(default_factory=list)
-    name_contains: str | None = None
-    metadata_filter: dict[str, Any] | None = None
-    limit: conint(ge=1, le=1000) = 100
-
-    @field_validator("include_tags", "exclude_tags", mode="before")
-    @classmethod
-    def _split_csv_tags(cls, v):
-        if v is None:
-            return []
-        if isinstance(v, str):
-            return [t.strip() for t in v.split(",") if t.strip()]
-        if isinstance(v, list):
-            out: list[str] = []
-            for item in v:
-                if isinstance(item, str):
-                    out.extend([t.strip() for t in item.split(",") if t.strip()])
-            return out
-        return v
-
-    @field_validator("metadata_filter", mode="before")
-    @classmethod
-    def _parse_metadata_json(cls, v):
-        if v is None or isinstance(v, dict):
-            return v
-        if isinstance(v, str) and v.strip():
-            try:
-                parsed = json.loads(v)
-            except Exception as e:
-                raise ValueError(f"metadata_filter must be JSON: {e}") from e
-            if not isinstance(parsed, dict):
-                raise ValueError("metadata_filter must be a JSON object")
-            return parsed
-        return None
-
-
 class TagsListQuery(BaseModel):
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
@@ -233,36 +154,38 @@ class TagsRemove(TagsAdd):
 
 class UploadAssetSpec(BaseModel):
     """Upload Asset operation.
-
-    - tags: optional list; if provided, first is root ('models'|'input'|'output');
-            if root == 'models', second must be a valid category
+    - tags: ordered; first is root ('models'|'input'|'output');
+            if root == 'models', second must be a valid category from folder_paths.folder_names_and_paths
     - name: display name
     - user_metadata: arbitrary JSON object (optional)
-    - hash: optional canonical 'blake3:<hex>' for validation / fast-path
-    - mime_type: optional MIME type override
-    - preview_id: optional asset_reference ID for preview
+    - hash: optional canonical 'blake3:<hex>' provided by the client for validation / fast-path
 
-    Files are stored using the content hash as filename stem.
+    Files created via this endpoint are stored on disk using the **content hash** as the filename stem
+    and the original extension is preserved when available.
     """
-
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
-    tags: list[str] = Field(default_factory=list)
+    tags: list[str] = Field(..., min_length=1)
     name: str | None = Field(default=None, max_length=512, description="Display Name")
     user_metadata: dict[str, Any] = Field(default_factory=dict)
     hash: str | None = Field(default=None)
-    mime_type: str | None = Field(default=None)
-    preview_id: str | None = Field(default=None)  # references an asset_reference id
 
     @field_validator("hash", mode="before")
     @classmethod
     def _parse_hash(cls, v):
         if v is None:
             return None
-        s = str(v).strip()
+        s = str(v).strip().lower()
         if not s:
             return None
-        return validate_blake3_hash(s)
+        if ":" not in s:
+            raise ValueError("hash must be 'blake3:<hex>'")
+        algo, digest = s.split(":", 1)
+        if algo != "blake3":
+            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
+        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
+            raise ValueError("hash digest must be lowercase hex")
+        return f"{algo}:{digest}"
 
     @field_validator("tags", mode="before")
     @classmethod
@@ -331,13 +254,11 @@ class UploadAssetSpec(BaseModel):
     @model_validator(mode="after")
     def _validate_order(self):
         if not self.tags:
-            raise ValueError("at least one tag is required for uploads")
+            raise ValueError("tags must be provided and non-empty")
         root = self.tags[0]
         if root not in {"models", "input", "output"}:
             raise ValueError("first tag must be one of: models, input, output")
         if root == "models":
             if len(self.tags) < 2:
-                raise ValueError(
-                    "models uploads require a category tag as the second tag"
-                )
+                raise ValueError("models uploads require a category tag as the second tag")
         return self

app/assets/api/schemas_out.py

@@ -4,10 +4,7 @@ from typing import Any
 from pydantic import BaseModel, ConfigDict, Field, field_serializer
 
 
-class Asset(BaseModel):
-    """API view of an asset. Maps to DB ``AssetReference`` joined with its ``Asset`` blob;
-    ``id`` here is the AssetReference id, not the content-addressed Asset id."""
-
+class AssetSummary(BaseModel):
     id: str
     name: str
     asset_hash: str | None = None
@@ -15,33 +12,61 @@ class Asset(BaseModel):
     mime_type: str | None = None
     tags: list[str] = Field(default_factory=list)
     preview_url: str | None = None
-    preview_id: str | None = None  # references an asset_reference id, not an asset id
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    is_immutable: bool = False
-    metadata: dict[str, Any] | None = None
-    job_id: str | None = None
-    prompt_id: str | None = None  # deprecated: use job_id
-    created_at: datetime
-    updated_at: datetime
+    created_at: datetime | None = None
+    updated_at: datetime | None = None
     last_access_time: datetime | None = None
 
     model_config = ConfigDict(from_attributes=True)
 
     @field_serializer("created_at", "updated_at", "last_access_time")
-    def _serialize_datetime(self, v: datetime | None, _info):
+    def _ser_dt(self, v: datetime | None, _info):
         return v.isoformat() if v else None
 
 
-class AssetCreated(Asset):
-    created_new: bool
-
-
 class AssetsList(BaseModel):
-    assets: list[Asset]
+    assets: list[AssetSummary]
     total: int
     has_more: bool
 
 
+class AssetUpdated(BaseModel):
+    id: str
+    name: str
+    asset_hash: str | None = None
+    tags: list[str] = Field(default_factory=list)
+    user_metadata: dict[str, Any] = Field(default_factory=dict)
+    updated_at: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+    @field_serializer("updated_at")
+    def _ser_updated(self, v: datetime | None, _info):
+        return v.isoformat() if v else None
+
+
+class AssetDetail(BaseModel):
+    id: str
+    name: str
+    asset_hash: str | None = None
+    size: int | None = None
+    mime_type: str | None = None
+    tags: list[str] = Field(default_factory=list)
+    user_metadata: dict[str, Any] = Field(default_factory=dict)
+    preview_id: str | None = None
+    created_at: datetime | None = None
+    last_access_time: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+    @field_serializer("created_at", "last_access_time")
+    def _ser_dt(self, v: datetime | None, _info):
+        return v.isoformat() if v else None
+
+
+class AssetCreated(AssetDetail):
+    created_new: bool
+
+
 class TagUsage(BaseModel):
     name: str
     count: int
@@ -66,7 +91,3 @@ class TagsRemove(BaseModel):
     removed: list[str] = Field(default_factory=list)
     not_present: list[str] = Field(default_factory=list)
     total_tags: list[str] = Field(default_factory=list)
-
-
-class TagHistogram(BaseModel):
-    tag_counts: dict[str, int]

app/assets/api/upload.py

@@ -1,185 +0,0 @@
-import logging
-import os
-import uuid
-from typing import Callable
-
-from aiohttp import web
-
-import folder_paths
-from app.assets.api.schemas_in import ParsedUpload, UploadError
-from app.assets.helpers import validate_blake3_hash
-
-
-def normalize_and_validate_hash(s: str) -> str:
-    """Validate and normalize a hash string.
-
-    Returns canonical 'blake3:<hex>' or raises UploadError.
-    """
-    try:
-        return validate_blake3_hash(s)
-    except ValueError:
-        raise UploadError(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-
-
-async def parse_multipart_upload(
-    request: web.Request,
-    check_hash_exists: Callable[[str], bool],
-) -> ParsedUpload:
-    """
-    Parse a multipart/form-data upload request.
-
-    Args:
-        request: The aiohttp request
-        check_hash_exists: Callable(hash_str) -> bool to check if a hash exists
-
-    Returns:
-        ParsedUpload with parsed fields and temp file path
-
-    Raises:
-        UploadError: On validation or I/O errors
-    """
-    if not (request.content_type or "").lower().startswith("multipart/"):
-        raise UploadError(
-            415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads."
-        )
-
-    reader = await request.multipart()
-
-    file_present = False
-    file_client_name: str | None = None
-    tags_raw: list[str] = []
-    provided_name: str | None = None
-    user_metadata_raw: str | None = None
-    provided_hash: str | None = None
-    provided_hash_exists: bool | None = None
-    provided_mime_type: str | None = None
-    provided_preview_id: str | None = None
-
-    file_written = 0
-    tmp_path: str | None = None
-
-    while True:
-        field = await reader.next()
-        if field is None:
-            break
-
-        fname = getattr(field, "name", "") or ""
-
-        if fname == "hash":
-            try:
-                s = ((await field.text()) or "").strip().lower()
-            except Exception:
-                raise UploadError(
-                    400, "INVALID_HASH", "hash must be like 'blake3:<hex>'"
-                )
-
-            if s:
-                provided_hash = normalize_and_validate_hash(s)
-                try:
-                    provided_hash_exists = check_hash_exists(provided_hash)
-                except Exception as e:
-                    logging.exception(
-                        "check_hash_exists failed for hash=%s: %s", provided_hash, e
-                    )
-                    raise UploadError(
-                        500,
-                        "HASH_CHECK_FAILED",
-                        "Backend error while checking asset hash.",
-                    )
-
-        elif fname == "file":
-            file_present = True
-            file_client_name = (field.filename or "").strip()
-
-            if provided_hash and provided_hash_exists is True:
-                # Hash exists - drain file but don't write to disk
-                try:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        file_written += len(chunk)
-                except Exception:
-                    raise UploadError(
-                        500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file."
-                    )
-                continue
-
-            uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads")
-            unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)
-            os.makedirs(unique_dir, exist_ok=True)
-            tmp_path = os.path.join(unique_dir, ".upload.part")
-
-            try:
-                with open(tmp_path, "wb") as f:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        f.write(chunk)
-                        file_written += len(chunk)
-            except Exception:
-                delete_temp_file_if_exists(tmp_path)
-                raise UploadError(
-                    500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file."
-                )
-
-        elif fname == "tags":
-            tags_raw.append((await field.text()) or "")
-        elif fname == "name":
-            provided_name = (await field.text()) or None
-        elif fname == "user_metadata":
-            user_metadata_raw = (await field.text()) or None
-        elif fname == "id":
-            raise UploadError(
-                400,
-                "UNSUPPORTED_FIELD",
-                "Client-provided 'id' is not supported. Asset IDs are assigned by the server.",
-            )
-        elif fname == "mime_type":
-            provided_mime_type = ((await field.text()) or "").strip() or None
-        elif fname == "preview_id":
-            provided_preview_id = ((await field.text()) or "").strip() or None
-
-    if not file_present and not (provided_hash and provided_hash_exists):
-        raise UploadError(
-            400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'."
-        )
-
-    if (
-        file_present
-        and file_written == 0
-        and not (provided_hash and provided_hash_exists)
-    ):
-        delete_temp_file_if_exists(tmp_path)
-        raise UploadError(400, "EMPTY_UPLOAD", "Uploaded file is empty.")
-
-    return ParsedUpload(
-        file_present=file_present,
-        file_written=file_written,
-        file_client_name=file_client_name,
-        tmp_path=tmp_path,
-        tags_raw=tags_raw,
-        provided_name=provided_name,
-        user_metadata_raw=user_metadata_raw,
-        provided_hash=provided_hash,
-        provided_hash_exists=provided_hash_exists,
-        provided_mime_type=provided_mime_type,
-        provided_preview_id=provided_preview_id,
-    )
-
-
-def delete_temp_file_if_exists(tmp_path: str | None) -> None:
-    """Safely remove a temp file and its parent directory if empty."""
-    if tmp_path:
-        try:
-            if os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        except OSError as e:
-            logging.debug("Failed to delete temp file %s: %s", tmp_path, e)
-        try:
-            parent = os.path.dirname(tmp_path)
-            if parent and os.path.isdir(parent):
-                os.rmdir(parent)  # only succeeds if empty
-        except OSError:
-            pass

app/assets/database/bulk_ops.py

@@ -0,0 +1,204 @@
+import os
+import uuid
+import sqlalchemy
+from typing import Iterable
+from sqlalchemy.orm import Session
+from sqlalchemy.dialects import sqlite
+
+from app.assets.helpers import utcnow
+from app.assets.database.models import Asset, AssetCacheState, AssetInfo, AssetInfoTag, AssetInfoMeta
+
+MAX_BIND_PARAMS = 800
+
+def _chunk_rows(rows: list[dict], cols_per_row: int, max_bind_params: int) -> Iterable[list[dict]]:
+    if not rows:
+        return []
+    rows_per_stmt = max(1, max_bind_params // max(1, cols_per_row))
+    for i in range(0, len(rows), rows_per_stmt):
+        yield rows[i:i + rows_per_stmt]
+
+def _iter_chunks(seq, n: int):
+    for i in range(0, len(seq), n):
+        yield seq[i:i + n]
+
+def _rows_per_stmt(cols: int) -> int:
+    return max(1, MAX_BIND_PARAMS // max(1, cols))
+
+
+def seed_from_paths_batch(
+    session: Session,
+    *,
+    specs: list[dict],
+    owner_id: str = "",
+) -> dict:
+    """Each spec is a dict with keys:
+      - abs_path: str
+      - size_bytes: int
+      - mtime_ns: int
+      - info_name: str
+      - tags: list[str]
+      - fname: Optional[str]
+    """
+    if not specs:
+        return {"inserted_infos": 0, "won_states": 0, "lost_states": 0}
+
+    now = utcnow()
+    asset_rows: list[dict] = []
+    state_rows: list[dict] = []
+    path_to_asset: dict[str, str] = {}
+    asset_to_info: dict[str, dict] = {}  # asset_id -> prepared info row
+    path_list: list[str] = []
+
+    for sp in specs:
+        ap = os.path.abspath(sp["abs_path"])
+        aid = str(uuid.uuid4())
+        iid = str(uuid.uuid4())
+        path_list.append(ap)
+        path_to_asset[ap] = aid
+
+        asset_rows.append(
+            {
+                "id": aid,
+                "hash": None,
+                "size_bytes": sp["size_bytes"],
+                "mime_type": None,
+                "created_at": now,
+            }
+        )
+        state_rows.append(
+            {
+                "asset_id": aid,
+                "file_path": ap,
+                "mtime_ns": sp["mtime_ns"],
+            }
+        )
+        asset_to_info[aid] = {
+            "id": iid,
+            "owner_id": owner_id,
+            "name": sp["info_name"],
+            "asset_id": aid,
+            "preview_id": None,
+            "user_metadata": {"filename": sp["fname"]} if sp["fname"] else None,
+            "created_at": now,
+            "updated_at": now,
+            "last_access_time": now,
+            "_tags": sp["tags"],
+            "_filename": sp["fname"],
+        }
+
+    # insert all seed Assets (hash=NULL)
+    ins_asset = sqlite.insert(Asset)
+    for chunk in _iter_chunks(asset_rows, _rows_per_stmt(5)):
+        session.execute(ins_asset, chunk)
+
+    # try to claim AssetCacheState (file_path)
+    # Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
+    ins_state = (
+        sqlite.insert(AssetCacheState)
+        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
+    )
+    for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
+        session.execute(ins_state, chunk)
+
+    # Query to find which of our paths won (were actually inserted)
+    winners_by_path: set[str] = set()
+    for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
+        result = session.execute(
+            sqlalchemy.select(AssetCacheState.file_path)
+            .where(AssetCacheState.file_path.in_(chunk))
+            .where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
+        )
+        winners_by_path.update(result.scalars().all())
+
+    all_paths_set = set(path_list)
+    losers_by_path = all_paths_set - winners_by_path
+    lost_assets = [path_to_asset[p] for p in losers_by_path]
+    if lost_assets:  # losers get their Asset removed
+        for id_chunk in _iter_chunks(lost_assets, MAX_BIND_PARAMS):
+            session.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(id_chunk)))
+
+    if not winners_by_path:
+        return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
+
+    # insert AssetInfo only for winners
+    # Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
+    winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
+    ins_info = (
+        sqlite.insert(AssetInfo)
+        .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
+    )
+    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
+        session.execute(ins_info, chunk)
+
+    # Query to find which info rows were actually inserted (by matching our generated IDs)
+    all_info_ids = [row["id"] for row in winner_info_rows]
+    inserted_info_ids: set[str] = set()
+    for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
+        )
+        inserted_info_ids.update(result.scalars().all())
+
+    # build and insert tag + meta rows for the AssetInfo
+    tag_rows: list[dict] = []
+    meta_rows: list[dict] = []
+    if inserted_info_ids:
+        for row in winner_info_rows:
+            iid = row["id"]
+            if iid not in inserted_info_ids:
+                continue
+            for t in row["_tags"]:
+                tag_rows.append({
+                    "asset_info_id": iid,
+                    "tag_name": t,
+                    "origin": "automatic",
+                    "added_at": now,
+                })
+            if row["_filename"]:
+                meta_rows.append(
+                    {
+                        "asset_info_id": iid,
+                        "key": "filename",
+                        "ordinal": 0,
+                        "val_str": row["_filename"],
+                        "val_num": None,
+                        "val_bool": None,
+                        "val_json": None,
+                    }
+                )
+
+    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=meta_rows, max_bind_params=MAX_BIND_PARAMS)
+    return {
+        "inserted_infos": len(inserted_info_ids),
+        "won_states": len(winners_by_path),
+        "lost_states": len(losers_by_path),
+    }
+
+
+def bulk_insert_tags_and_meta(
+    session: Session,
+    *,
+    tag_rows: list[dict],
+    meta_rows: list[dict],
+    max_bind_params: int,
+) -> None:
+    """Batch insert into asset_info_tags and asset_info_meta with ON CONFLICT DO NOTHING.
+    - tag_rows keys: asset_info_id, tag_name, origin, added_at
+    - meta_rows keys: asset_info_id, key, ordinal, val_str, val_num, val_bool, val_json
+    """
+    if tag_rows:
+        ins_links = (
+            sqlite.insert(AssetInfoTag)
+            .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
+        )
+        for chunk in _chunk_rows(tag_rows, cols_per_row=4, max_bind_params=max_bind_params):
+            session.execute(ins_links, chunk)
+    if meta_rows:
+        ins_meta = (
+            sqlite.insert(AssetInfoMeta)
+            .on_conflict_do_nothing(
+                index_elements=[AssetInfoMeta.asset_info_id, AssetInfoMeta.key, AssetInfoMeta.ordinal]
+            )
+        )
+        for chunk in _chunk_rows(meta_rows, cols_per_row=7, max_bind_params=max_bind_params):
+            session.execute(ins_meta, chunk)

app/assets/database/models.py

@@ -2,8 +2,8 @@ from __future__ import annotations
 
 import uuid
 from datetime import datetime
-from typing import Any
 
+from typing import Any
 from sqlalchemy import (
     JSON,
     BigInteger,
@@ -16,36 +16,47 @@ from sqlalchemy import (
     Numeric,
     String,
     Text,
+    UniqueConstraint,
 )
 from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
 
-from app.assets.helpers import get_utc_now
-from app.database.models import Base
+from app.assets.helpers import utcnow
+from app.database.models import to_dict, Base
 
 
 class Asset(Base):
     __tablename__ = "assets"
 
-    id: Mapped[str] = mapped_column(
-        String(36), primary_key=True, default=lambda: str(uuid.uuid4())
-    )
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
     hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
     size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
     mime_type: Mapped[str | None] = mapped_column(String(255))
     created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=get_utc_now
+        DateTime(timezone=False), nullable=False, default=utcnow
     )
 
-    references: Mapped[list[AssetReference]] = relationship(
-        "AssetReference",
+    infos: Mapped[list[AssetInfo]] = relationship(
+        "AssetInfo",
         back_populates="asset",
-        primaryjoin=lambda: Asset.id == foreign(AssetReference.asset_id),
-        foreign_keys=lambda: [AssetReference.asset_id],
+        primaryjoin=lambda: Asset.id == foreign(AssetInfo.asset_id),
+        foreign_keys=lambda: [AssetInfo.asset_id],
         cascade="all,delete-orphan",
         passive_deletes=True,
     )
 
-    # preview_id on AssetReference is a self-referential FK to asset_references.id
+    preview_of: Mapped[list[AssetInfo]] = relationship(
+        "AssetInfo",
+        back_populates="preview_asset",
+        primaryjoin=lambda: Asset.id == foreign(AssetInfo.preview_id),
+        foreign_keys=lambda: [AssetInfo.preview_id],
+        viewonly=True,
+    )
+
+    cache_states: Mapped[list[AssetCacheState]] = relationship(
+        back_populates="asset",
+        cascade="all, delete-orphan",
+        passive_deletes=True,
+    )
 
     __table_args__ = (
         Index("uq_assets_hash", "hash", unique=True),
@@ -53,126 +64,108 @@ class Asset(Base):
         CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
     )
 
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        return to_dict(self, include_none=include_none)
+
     def __repr__(self) -> str:
         return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
 
 
-class AssetReference(Base):
-    """Unified model combining file cache state and user-facing metadata.
+class AssetCacheState(Base):
+    __tablename__ = "asset_cache_state"
 
-    Each row represents either:
-    - A filesystem reference (file_path is set) with cache state
-    - An API-created reference (file_path is NULL) without cache state
-    """
-
-    __tablename__ = "asset_references"
-
-    id: Mapped[str] = mapped_column(
-        String(36), primary_key=True, default=lambda: str(uuid.uuid4())
-    )
-    asset_id: Mapped[str] = mapped_column(
-        String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False
-    )
-
-    # Cache state fields (from former AssetCacheState)
-    file_path: Mapped[str | None] = mapped_column(Text, nullable=True)
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False)
+    file_path: Mapped[str] = mapped_column(Text, nullable=False)
     mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
     needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
-    is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
-    enrichment_level: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
 
-    # Info fields (from former AssetInfo)
+    asset: Mapped[Asset] = relationship(back_populates="cache_states")
+
+    __table_args__ = (
+        Index("ix_asset_cache_state_file_path", "file_path"),
+        Index("ix_asset_cache_state_asset_id", "asset_id"),
+        CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
+        UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        return to_dict(self, include_none=include_none)
+
+    def __repr__(self) -> str:
+        return f"<AssetCacheState id={self.id} asset_id={self.asset_id} path={self.file_path!r}>"
+
+
+class AssetInfo(Base):
+    __tablename__ = "assets_info"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
     owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
     name: Mapped[str] = mapped_column(String(512), nullable=False)
-    preview_id: Mapped[str | None] = mapped_column(
-        String(36), ForeignKey("asset_references.id", ondelete="SET NULL")
-    )
-    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(
-        JSON(none_as_null=True)
-    )
-    system_metadata: Mapped[dict[str, Any] | None] = mapped_column(
-        JSON(none_as_null=True), nullable=True, default=None
-    )
-    job_id: Mapped[str | None] = mapped_column(String(36), nullable=True, default=None)
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=get_utc_now
-    )
-    updated_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=get_utc_now
-    )
-    last_access_time: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=get_utc_now
-    )
-    deleted_at: Mapped[datetime | None] = mapped_column(
-        DateTime(timezone=False), nullable=True, default=None
-    )
+    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False)
+    preview_id: Mapped[str | None] = mapped_column(String(36), ForeignKey("assets.id", ondelete="SET NULL"))
+    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(JSON(none_as_null=True))
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    last_access_time: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
 
     asset: Mapped[Asset] = relationship(
         "Asset",
-        back_populates="references",
+        back_populates="infos",
         foreign_keys=[asset_id],
         lazy="selectin",
     )
-    preview_ref: Mapped[AssetReference | None] = relationship(
-        "AssetReference",
+    preview_asset: Mapped[Asset | None] = relationship(
+        "Asset",
+        back_populates="preview_of",
         foreign_keys=[preview_id],
-        remote_side=lambda: [AssetReference.id],
     )
 
-    metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship(
-        back_populates="asset_reference",
+    metadata_entries: Mapped[list[AssetInfoMeta]] = relationship(
+        back_populates="asset_info",
         cascade="all,delete-orphan",
         passive_deletes=True,
     )
 
-    tag_links: Mapped[list[AssetReferenceTag]] = relationship(
-        back_populates="asset_reference",
+    tag_links: Mapped[list[AssetInfoTag]] = relationship(
+        back_populates="asset_info",
         cascade="all,delete-orphan",
         passive_deletes=True,
-        overlaps="tags,asset_references",
+        overlaps="tags,asset_infos",
     )
 
     tags: Mapped[list[Tag]] = relationship(
-        secondary="asset_reference_tags",
-        back_populates="asset_references",
+        secondary="asset_info_tags",
+        back_populates="asset_infos",
         lazy="selectin",
         viewonly=True,
-        overlaps="tag_links,asset_reference_links,asset_references,tag",
+        overlaps="tag_links,asset_info_links,asset_infos,tag",
     )
 
     __table_args__ = (
-        Index("uq_asset_references_file_path", "file_path", unique=True),
-        Index("ix_asset_references_asset_id", "asset_id"),
-        Index("ix_asset_references_owner_id", "owner_id"),
-        Index("ix_asset_references_name", "name"),
-        Index("ix_asset_references_is_missing", "is_missing"),
-        Index("ix_asset_references_enrichment_level", "enrichment_level"),
-        Index("ix_asset_references_created_at", "created_at"),
-        Index("ix_asset_references_last_access_time", "last_access_time"),
-        Index("ix_asset_references_deleted_at", "deleted_at"),
-        Index("ix_asset_references_preview_id", "preview_id"),
-        Index("ix_asset_references_owner_name", "owner_id", "name"),
-        CheckConstraint(
-            "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg"
-        ),
-        CheckConstraint(
-            "enrichment_level >= 0 AND enrichment_level <= 2",
-            name="ck_ar_enrichment_level_range",
-        ),
+        UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
+        Index("ix_assets_info_owner_name", "owner_id", "name"),
+        Index("ix_assets_info_owner_id", "owner_id"),
+        Index("ix_assets_info_asset_id", "asset_id"),
+        Index("ix_assets_info_name", "name"),
+        Index("ix_assets_info_created_at", "created_at"),
+        Index("ix_assets_info_last_access_time", "last_access_time"),
     )
 
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        data = to_dict(self, include_none=include_none)
+        data["tags"] = [t.name for t in self.tags]
+        return data
+
     def __repr__(self) -> str:
-        path_part = f" path={self.file_path!r}" if self.file_path else ""
-        return f"<AssetReference id={self.id} name={self.name!r}{path_part}>"
+        return f"<AssetInfo id={self.id} name={self.name!r} asset_id={self.asset_id}>"
 
 
-class AssetReferenceMeta(Base):
-    __tablename__ = "asset_reference_meta"
+class AssetInfoMeta(Base):
+    __tablename__ = "asset_info_meta"
 
-    asset_reference_id: Mapped[str] = mapped_column(
-        String(36),
-        ForeignKey("asset_references.id", ondelete="CASCADE"),
-        primary_key=True,
+    asset_info_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
     )
     key: Mapped[str] = mapped_column(String(256), primary_key=True)
     ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
@@ -182,44 +175,36 @@ class AssetReferenceMeta(Base):
     val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
     val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
 
-    asset_reference: Mapped[AssetReference] = relationship(
-        back_populates="metadata_entries"
-    )
+    asset_info: Mapped[AssetInfo] = relationship(back_populates="metadata_entries")
 
     __table_args__ = (
-        Index("ix_asset_reference_meta_key", "key"),
-        Index("ix_asset_reference_meta_key_val_str", "key", "val_str"),
-        Index("ix_asset_reference_meta_key_val_num", "key", "val_num"),
-        Index("ix_asset_reference_meta_key_val_bool", "key", "val_bool"),
-        CheckConstraint(
-            "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
-            name="has_value",
-        ),
+        Index("ix_asset_info_meta_key", "key"),
+        Index("ix_asset_info_meta_key_val_str", "key", "val_str"),
+        Index("ix_asset_info_meta_key_val_num", "key", "val_num"),
+        Index("ix_asset_info_meta_key_val_bool", "key", "val_bool"),
     )
 
 
-class AssetReferenceTag(Base):
-    __tablename__ = "asset_reference_tags"
+class AssetInfoTag(Base):
+    __tablename__ = "asset_info_tags"
 
-    asset_reference_id: Mapped[str] = mapped_column(
-        String(36),
-        ForeignKey("asset_references.id", ondelete="CASCADE"),
-        primary_key=True,
+    asset_info_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
     )
     tag_name: Mapped[str] = mapped_column(
         String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
     )
     origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
     added_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=False), nullable=False, default=get_utc_now
+        DateTime(timezone=False), nullable=False, default=utcnow
     )
 
-    asset_reference: Mapped[AssetReference] = relationship(back_populates="tag_links")
-    tag: Mapped[Tag] = relationship(back_populates="asset_reference_links")
+    asset_info: Mapped[AssetInfo] = relationship(back_populates="tag_links")
+    tag: Mapped[Tag] = relationship(back_populates="asset_info_links")
 
     __table_args__ = (
-        Index("ix_asset_reference_tags_tag_name", "tag_name"),
-        Index("ix_asset_reference_tags_asset_reference_id", "asset_reference_id"),
+        Index("ix_asset_info_tags_tag_name", "tag_name"),
+        Index("ix_asset_info_tags_asset_info_id", "asset_info_id"),
     )
 
 
@@ -229,18 +214,20 @@ class Tag(Base):
     name: Mapped[str] = mapped_column(String(512), primary_key=True)
     tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
 
-    asset_reference_links: Mapped[list[AssetReferenceTag]] = relationship(
+    asset_info_links: Mapped[list[AssetInfoTag]] = relationship(
         back_populates="tag",
-        overlaps="asset_references,tags",
+        overlaps="asset_infos,tags",
     )
-    asset_references: Mapped[list[AssetReference]] = relationship(
-        secondary="asset_reference_tags",
+    asset_infos: Mapped[list[AssetInfo]] = relationship(
+        secondary="asset_info_tags",
         back_populates="tags",
         viewonly=True,
-        overlaps="asset_reference_links,tag_links,tags,asset_reference",
+        overlaps="asset_info_links,tag_links,tags,asset_info",
     )
 
-    __table_args__ = (Index("ix_tags_tag_type", "tag_type"),)
+    __table_args__ = (
+        Index("ix_tags_tag_type", "tag_type"),
+    )
 
     def __repr__(self) -> str:
         return f"<Tag {self.name}>"

app/assets/database/queries.py

@@ -0,0 +1,976 @@
+import os
+import logging
+import sqlalchemy as sa
+from collections import defaultdict
+from datetime import datetime
+from typing import Iterable, Any
+from sqlalchemy import select, delete, exists, func
+from sqlalchemy.dialects import sqlite
+from sqlalchemy.exc import IntegrityError
+from sqlalchemy.orm import Session, contains_eager, noload
+from app.assets.database.models import Asset, AssetInfo, AssetCacheState, AssetInfoMeta, AssetInfoTag, Tag
+from app.assets.helpers import (
+    compute_relative_filename, escape_like_prefix, normalize_tags, project_kv, utcnow
+)
+from typing import Sequence
+
+
+def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
+    """Build owner visibility predicate for reads. Owner-less rows are visible to everyone."""
+    owner_id = (owner_id or "").strip()
+    if owner_id == "":
+        return AssetInfo.owner_id == ""
+    return AssetInfo.owner_id.in_(["", owner_id])
+
+
+def pick_best_live_path(states: Sequence[AssetCacheState]) -> str:
+    """
+    Return the best on-disk path among cache states:
+      1) Prefer a path that exists with needs_verify == False (already verified).
+      2) Otherwise, pick the first path that exists.
+      3) Otherwise return empty string.
+    """
+    alive = [s for s in states if getattr(s, "file_path", None) and os.path.isfile(s.file_path)]
+    if not alive:
+        return ""
+    for s in alive:
+        if not getattr(s, "needs_verify", False):
+            return s.file_path
+    return alive[0].file_path
+
+
+def apply_tag_filters(
+    stmt: sa.sql.Select,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+) -> sa.sql.Select:
+    """include_tags: every tag must be present; exclude_tags: none may be present."""
+    include_tags = normalize_tags(include_tags)
+    exclude_tags = normalize_tags(exclude_tags)
+
+    if include_tags:
+        for tag_name in include_tags:
+            stmt = stmt.where(
+                exists().where(
+                    (AssetInfoTag.asset_info_id == AssetInfo.id)
+                    & (AssetInfoTag.tag_name == tag_name)
+                )
+            )
+
+    if exclude_tags:
+        stmt = stmt.where(
+            ~exists().where(
+                (AssetInfoTag.asset_info_id == AssetInfo.id)
+                & (AssetInfoTag.tag_name.in_(exclude_tags))
+            )
+        )
+    return stmt
+
+
+def apply_metadata_filter(
+    stmt: sa.sql.Select,
+    metadata_filter: dict | None = None,
+) -> sa.sql.Select:
+    """Apply filters using asset_info_meta projection table."""
+    if not metadata_filter:
+        return stmt
+
+    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
+        return sa.exists().where(
+            AssetInfoMeta.asset_info_id == AssetInfo.id,
+            AssetInfoMeta.key == key,
+            *preds,
+        )
+
+    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
+        if value is None:
+            no_row_for_key = sa.not_(
+                sa.exists().where(
+                    AssetInfoMeta.asset_info_id == AssetInfo.id,
+                    AssetInfoMeta.key == key,
+                )
+            )
+            null_row = _exists_for_pred(
+                key,
+                AssetInfoMeta.val_json.is_(None),
+                AssetInfoMeta.val_str.is_(None),
+                AssetInfoMeta.val_num.is_(None),
+                AssetInfoMeta.val_bool.is_(None),
+            )
+            return sa.or_(no_row_for_key, null_row)
+
+        if isinstance(value, bool):
+            return _exists_for_pred(key, AssetInfoMeta.val_bool == bool(value))
+        if isinstance(value, (int, float)):
+            from decimal import Decimal
+            num = value if isinstance(value, Decimal) else Decimal(str(value))
+            return _exists_for_pred(key, AssetInfoMeta.val_num == num)
+        if isinstance(value, str):
+            return _exists_for_pred(key, AssetInfoMeta.val_str == value)
+        return _exists_for_pred(key, AssetInfoMeta.val_json == value)
+
+    for k, v in metadata_filter.items():
+        if isinstance(v, list):
+            ors = [_exists_clause_for_value(k, elem) for elem in v]
+            if ors:
+                stmt = stmt.where(sa.or_(*ors))
+        else:
+            stmt = stmt.where(_exists_clause_for_value(k, v))
+    return stmt
+
+
+def asset_exists_by_hash(
+    session: Session,
+    *,
+    asset_hash: str,
+) -> bool:
+    """
+    Check if an asset with a given hash exists in database.
+    """
+    row = (
+        session.execute(
+            select(sa.literal(True)).select_from(Asset).where(Asset.hash == asset_hash).limit(1)
+        )
+    ).first()
+    return row is not None
+
+
+def asset_info_exists_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+) -> bool:
+    q = (
+        select(sa.literal(True))
+        .select_from(AssetInfo)
+        .where(AssetInfo.asset_id == asset_id)
+        .limit(1)
+    )
+    return (session.execute(q)).first() is not None
+
+
+def get_asset_by_hash(
+    session: Session,
+    *,
+    asset_hash: str,
+) -> Asset | None:
+    return (
+        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
+    ).scalars().first()
+
+
+def get_asset_info_by_id(
+    session: Session,
+    *,
+    asset_info_id: str,
+) -> AssetInfo | None:
+    return session.get(AssetInfo, asset_info_id)
+
+
+def list_asset_infos_page(
+    session: Session,
+    owner_id: str = "",
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+) -> tuple[list[AssetInfo], dict[str, list[str]], int]:
+    base = (
+        select(AssetInfo)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .options(contains_eager(AssetInfo.asset), noload(AssetInfo.tags))
+        .where(visible_owner_clause(owner_id))
+    )
+
+    if name_contains:
+        escaped, esc = escape_like_prefix(name_contains)
+        base = base.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
+
+    base = apply_tag_filters(base, include_tags, exclude_tags)
+    base = apply_metadata_filter(base, metadata_filter)
+
+    sort = (sort or "created_at").lower()
+    order = (order or "desc").lower()
+    sort_map = {
+        "name": AssetInfo.name,
+        "created_at": AssetInfo.created_at,
+        "updated_at": AssetInfo.updated_at,
+        "last_access_time": AssetInfo.last_access_time,
+        "size": Asset.size_bytes,
+    }
+    sort_col = sort_map.get(sort, AssetInfo.created_at)
+    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
+
+    base = base.order_by(sort_exp).limit(limit).offset(offset)
+
+    count_stmt = (
+        select(sa.func.count())
+        .select_from(AssetInfo)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .where(visible_owner_clause(owner_id))
+    )
+    if name_contains:
+        escaped, esc = escape_like_prefix(name_contains)
+        count_stmt = count_stmt.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
+    count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
+    count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
+
+    total = int((session.execute(count_stmt)).scalar_one() or 0)
+
+    infos = (session.execute(base)).unique().scalars().all()
+
+    id_list: list[str] = [i.id for i in infos]
+    tag_map: dict[str, list[str]] = defaultdict(list)
+    if id_list:
+        rows = session.execute(
+            select(AssetInfoTag.asset_info_id, Tag.name)
+            .join(Tag, Tag.name == AssetInfoTag.tag_name)
+            .where(AssetInfoTag.asset_info_id.in_(id_list))
+            .order_by(AssetInfoTag.added_at)
+        )
+        for aid, tag_name in rows.all():
+            tag_map[aid].append(tag_name)
+
+    return infos, tag_map, total
+
+
+def fetch_asset_info_asset_and_tags(
+    session: Session,
+    asset_info_id: str,
+    owner_id: str = "",
+) -> tuple[AssetInfo, Asset, list[str]] | None:
+    stmt = (
+        select(AssetInfo, Asset, Tag.name)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .join(AssetInfoTag, AssetInfoTag.asset_info_id == AssetInfo.id, isouter=True)
+        .join(Tag, Tag.name == AssetInfoTag.tag_name, isouter=True)
+        .where(
+            AssetInfo.id == asset_info_id,
+            visible_owner_clause(owner_id),
+        )
+        .options(noload(AssetInfo.tags))
+        .order_by(Tag.name.asc())
+    )
+
+    rows = (session.execute(stmt)).all()
+    if not rows:
+        return None
+
+    first_info, first_asset, _ = rows[0]
+    tags: list[str] = []
+    seen: set[str] = set()
+    for _info, _asset, tag_name in rows:
+        if tag_name and tag_name not in seen:
+            seen.add(tag_name)
+            tags.append(tag_name)
+    return first_info, first_asset, tags
+
+
+def fetch_asset_info_and_asset(
+    session: Session,
+    *,
+    asset_info_id: str,
+    owner_id: str = "",
+) -> tuple[AssetInfo, Asset] | None:
+    stmt = (
+        select(AssetInfo, Asset)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .where(
+            AssetInfo.id == asset_info_id,
+            visible_owner_clause(owner_id),
+        )
+        .limit(1)
+        .options(noload(AssetInfo.tags))
+    )
+    row = session.execute(stmt)
+    pair = row.first()
+    if not pair:
+        return None
+    return pair[0], pair[1]
+
+def list_cache_states_by_asset_id(
+    session: Session, *, asset_id: str
+) -> Sequence[AssetCacheState]:
+    return (
+        session.execute(
+            select(AssetCacheState)
+            .where(AssetCacheState.asset_id == asset_id)
+            .order_by(AssetCacheState.id.asc())
+        )
+    ).scalars().all()
+
+
+def touch_asset_info_by_id(
+    session: Session,
+    *,
+    asset_info_id: str,
+    ts: datetime | None = None,
+    only_if_newer: bool = True,
+) -> None:
+    ts = ts or utcnow()
+    stmt = sa.update(AssetInfo).where(AssetInfo.id == asset_info_id)
+    if only_if_newer:
+        stmt = stmt.where(
+            sa.or_(AssetInfo.last_access_time.is_(None), AssetInfo.last_access_time < ts)
+        )
+    session.execute(stmt.values(last_access_time=ts))
+
+
+def create_asset_info_for_existing_asset(
+    session: Session,
+    *,
+    asset_hash: str,
+    name: str,
+    user_metadata: dict | None = None,
+    tags: Sequence[str] | None = None,
+    tag_origin: str = "manual",
+    owner_id: str = "",
+) -> AssetInfo:
+    """Create or return an existing AssetInfo for an Asset identified by asset_hash."""
+    now = utcnow()
+    asset = get_asset_by_hash(session, asset_hash=asset_hash)
+    if not asset:
+        raise ValueError(f"Unknown asset hash {asset_hash}")
+
+    info = AssetInfo(
+        owner_id=owner_id,
+        name=name,
+        asset_id=asset.id,
+        preview_id=None,
+        created_at=now,
+        updated_at=now,
+        last_access_time=now,
+    )
+    try:
+        with session.begin_nested():
+            session.add(info)
+            session.flush()
+    except IntegrityError:
+        existing = (
+            session.execute(
+                select(AssetInfo)
+                .options(noload(AssetInfo.tags))
+                .where(
+                    AssetInfo.asset_id == asset.id,
+                    AssetInfo.name == name,
+                    AssetInfo.owner_id == owner_id,
+                )
+                .limit(1)
+            )
+        ).unique().scalars().first()
+        if not existing:
+            raise RuntimeError("AssetInfo upsert failed to find existing row after conflict.")
+        return existing
+
+    # metadata["filename"] hack
+    new_meta = dict(user_metadata or {})
+    computed_filename = None
+    try:
+        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
+        if p:
+            computed_filename = compute_relative_filename(p)
+    except Exception:
+        computed_filename = None
+    if computed_filename:
+        new_meta["filename"] = computed_filename
+    if new_meta:
+        replace_asset_info_metadata_projection(
+            session,
+            asset_info_id=info.id,
+            user_metadata=new_meta,
+        )
+
+    if tags is not None:
+        set_asset_info_tags(
+            session,
+            asset_info_id=info.id,
+            tags=tags,
+            origin=tag_origin,
+        )
+    return info
+
+
+def set_asset_info_tags(
+    session: Session,
+    *,
+    asset_info_id: str,
+    tags: Sequence[str],
+    origin: str = "manual",
+) -> dict:
+    desired = normalize_tags(tags)
+
+    current = set(
+        tag_name for (tag_name,) in (
+            session.execute(select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id))
+        ).all()
+    )
+
+    to_add = [t for t in desired if t not in current]
+    to_remove = [t for t in current if t not in desired]
+
+    if to_add:
+        ensure_tags_exist(session, to_add, tag_type="user")
+        session.add_all([
+            AssetInfoTag(asset_info_id=asset_info_id, tag_name=t, origin=origin, added_at=utcnow())
+            for t in to_add
+        ])
+        session.flush()
+
+    if to_remove:
+        session.execute(
+            delete(AssetInfoTag)
+            .where(AssetInfoTag.asset_info_id == asset_info_id, AssetInfoTag.tag_name.in_(to_remove))
+        )
+        session.flush()
+
+    return {"added": to_add, "removed": to_remove, "total": desired}
+
+
+def replace_asset_info_metadata_projection(
+    session: Session,
+    *,
+    asset_info_id: str,
+    user_metadata: dict | None = None,
+) -> None:
+    info = session.get(AssetInfo, asset_info_id)
+    if not info:
+        raise ValueError(f"AssetInfo {asset_info_id} not found")
+
+    info.user_metadata = user_metadata or {}
+    info.updated_at = utcnow()
+    session.flush()
+
+    session.execute(delete(AssetInfoMeta).where(AssetInfoMeta.asset_info_id == asset_info_id))
+    session.flush()
+
+    if not user_metadata:
+        return
+
+    rows: list[AssetInfoMeta] = []
+    for k, v in user_metadata.items():
+        for r in project_kv(k, v):
+            rows.append(
+                AssetInfoMeta(
+                    asset_info_id=asset_info_id,
+                    key=r["key"],
+                    ordinal=int(r["ordinal"]),
+                    val_str=r.get("val_str"),
+                    val_num=r.get("val_num"),
+                    val_bool=r.get("val_bool"),
+                    val_json=r.get("val_json"),
+                )
+            )
+    if rows:
+        session.add_all(rows)
+        session.flush()
+
+
+def ingest_fs_asset(
+    session: Session,
+    *,
+    asset_hash: str,
+    abs_path: str,
+    size_bytes: int,
+    mtime_ns: int,
+    mime_type: str | None = None,
+    info_name: str | None = None,
+    owner_id: str = "",
+    preview_id: str | None = None,
+    user_metadata: dict | None = None,
+    tags: Sequence[str] = (),
+    tag_origin: str = "manual",
+    require_existing_tags: bool = False,
+) -> dict:
+    """
+    Idempotently upsert:
+      - Asset by content hash (create if missing)
+      - AssetCacheState(file_path) pointing to asset_id
+      - Optionally AssetInfo + tag links and metadata projection
+    Returns flags and ids.
+    """
+    locator = os.path.abspath(abs_path)
+    now = utcnow()
+
+    if preview_id:
+        if not session.get(Asset, preview_id):
+            preview_id = None
+
+    out: dict[str, Any] = {
+        "asset_created": False,
+        "asset_updated": False,
+        "state_created": False,
+        "state_updated": False,
+        "asset_info_id": None,
+    }
+
+    # 1) Asset by hash
+    asset = (
+        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
+    ).scalars().first()
+    if not asset:
+        vals = {
+            "hash": asset_hash,
+            "size_bytes": int(size_bytes),
+            "mime_type": mime_type,
+            "created_at": now,
+        }
+        res = session.execute(
+            sqlite.insert(Asset)
+            .values(**vals)
+            .on_conflict_do_nothing(index_elements=[Asset.hash])
+        )
+        if int(res.rowcount or 0) > 0:
+            out["asset_created"] = True
+        asset = (
+            session.execute(
+                select(Asset).where(Asset.hash == asset_hash).limit(1)
+            )
+        ).scalars().first()
+        if not asset:
+            raise RuntimeError("Asset row not found after upsert.")
+    else:
+        changed = False
+        if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
+            asset.size_bytes = int(size_bytes)
+            changed = True
+        if mime_type and asset.mime_type != mime_type:
+            asset.mime_type = mime_type
+            changed = True
+        if changed:
+            out["asset_updated"] = True
+
+    # 2) AssetCacheState upsert by file_path (unique)
+    vals = {
+        "asset_id": asset.id,
+        "file_path": locator,
+        "mtime_ns": int(mtime_ns),
+    }
+    ins = (
+        sqlite.insert(AssetCacheState)
+        .values(**vals)
+        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
+    )
+
+    res = session.execute(ins)
+    if int(res.rowcount or 0) > 0:
+        out["state_created"] = True
+    else:
+        upd = (
+            sa.update(AssetCacheState)
+            .where(AssetCacheState.file_path == locator)
+            .where(
+                sa.or_(
+                    AssetCacheState.asset_id != asset.id,
+                    AssetCacheState.mtime_ns.is_(None),
+                    AssetCacheState.mtime_ns != int(mtime_ns),
+                )
+            )
+            .values(asset_id=asset.id, mtime_ns=int(mtime_ns))
+        )
+        res2 = session.execute(upd)
+        if int(res2.rowcount or 0) > 0:
+            out["state_updated"] = True
+
+    # 3) Optional AssetInfo + tags + metadata
+    if info_name:
+        try:
+            with session.begin_nested():
+                info = AssetInfo(
+                    owner_id=owner_id,
+                    name=info_name,
+                    asset_id=asset.id,
+                    preview_id=preview_id,
+                    created_at=now,
+                    updated_at=now,
+                    last_access_time=now,
+                )
+                session.add(info)
+                session.flush()
+                out["asset_info_id"] = info.id
+        except IntegrityError:
+            pass
+
+        existing_info = (
+            session.execute(
+                select(AssetInfo)
+                .where(
+                    AssetInfo.asset_id == asset.id,
+                    AssetInfo.name == info_name,
+                    (AssetInfo.owner_id == owner_id),
+                )
+                .limit(1)
+            )
+        ).unique().scalar_one_or_none()
+        if not existing_info:
+            raise RuntimeError("Failed to update or insert AssetInfo.")
+
+        if preview_id and existing_info.preview_id != preview_id:
+            existing_info.preview_id = preview_id
+
+        existing_info.updated_at = now
+        if existing_info.last_access_time < now:
+            existing_info.last_access_time = now
+        session.flush()
+        out["asset_info_id"] = existing_info.id
+
+        norm = [t.strip().lower() for t in (tags or []) if (t or "").strip()]
+        if norm and out["asset_info_id"] is not None:
+            if not require_existing_tags:
+                ensure_tags_exist(session, norm, tag_type="user")
+
+            existing_tag_names = set(
+                name for (name,) in (session.execute(select(Tag.name).where(Tag.name.in_(norm)))).all()
+            )
+            missing = [t for t in norm if t not in existing_tag_names]
+            if missing and require_existing_tags:
+                raise ValueError(f"Unknown tags: {missing}")
+
+            existing_links = set(
+                tag_name
+                for (tag_name,) in (
+                    session.execute(
+                        select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == out["asset_info_id"])
+                    )
+                ).all()
+            )
+            to_add = [t for t in norm if t in existing_tag_names and t not in existing_links]
+            if to_add:
+                session.add_all(
+                    [
+                        AssetInfoTag(
+                            asset_info_id=out["asset_info_id"],
+                            tag_name=t,
+                            origin=tag_origin,
+                            added_at=now,
+                        )
+                        for t in to_add
+                    ]
+                )
+                session.flush()
+
+        # metadata["filename"] hack
+        if out["asset_info_id"] is not None:
+            primary_path = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
+            computed_filename = compute_relative_filename(primary_path) if primary_path else None
+
+            current_meta = existing_info.user_metadata or {}
+            new_meta = dict(current_meta)
+            if user_metadata is not None:
+                for k, v in user_metadata.items():
+                    new_meta[k] = v
+            if computed_filename:
+                new_meta["filename"] = computed_filename
+
+            if new_meta != current_meta:
+                replace_asset_info_metadata_projection(
+                    session,
+                    asset_info_id=out["asset_info_id"],
+                    user_metadata=new_meta,
+                )
+
+    try:
+        remove_missing_tag_for_asset_id(session, asset_id=asset.id)
+    except Exception:
+        logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
+    return out
+
+
+def update_asset_info_full(
+    session: Session,
+    *,
+    asset_info_id: str,
+    name: str | None = None,
+    tags: Sequence[str] | None = None,
+    user_metadata: dict | None = None,
+    tag_origin: str = "manual",
+    asset_info_row: Any = None,
+) -> AssetInfo:
+    if not asset_info_row:
+        info = session.get(AssetInfo, asset_info_id)
+        if not info:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+    else:
+        info = asset_info_row
+
+    touched = False
+    if name is not None and name != info.name:
+        info.name = name
+        touched = True
+
+    computed_filename = None
+    try:
+        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=info.asset_id))
+        if p:
+            computed_filename = compute_relative_filename(p)
+    except Exception:
+        computed_filename = None
+
+    if user_metadata is not None:
+        new_meta = dict(user_metadata)
+        if computed_filename:
+            new_meta["filename"] = computed_filename
+        replace_asset_info_metadata_projection(
+            session, asset_info_id=asset_info_id, user_metadata=new_meta
+        )
+        touched = True
+    else:
+        if computed_filename:
+            current_meta = info.user_metadata or {}
+            if current_meta.get("filename") != computed_filename:
+                new_meta = dict(current_meta)
+                new_meta["filename"] = computed_filename
+                replace_asset_info_metadata_projection(
+                    session, asset_info_id=asset_info_id, user_metadata=new_meta
+                )
+                touched = True
+
+    if tags is not None:
+        set_asset_info_tags(
+            session,
+            asset_info_id=asset_info_id,
+            tags=tags,
+            origin=tag_origin,
+        )
+        touched = True
+
+    if touched and user_metadata is None:
+        info.updated_at = utcnow()
+        session.flush()
+
+    return info
+
+
+def delete_asset_info_by_id(
+    session: Session,
+    *,
+    asset_info_id: str,
+    owner_id: str,
+) -> bool:
+    stmt = sa.delete(AssetInfo).where(
+        AssetInfo.id == asset_info_id,
+        visible_owner_clause(owner_id),
+    )
+    return int((session.execute(stmt)).rowcount or 0) > 0
+
+
+def list_tags_with_usage(
+    session: Session,
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    include_zero: bool = True,
+    order: str = "count_desc",
+    owner_id: str = "",
+) -> tuple[list[tuple[str, str, int]], int]:
+    counts_sq = (
+        select(
+            AssetInfoTag.tag_name.label("tag_name"),
+            func.count(AssetInfoTag.asset_info_id).label("cnt"),
+        )
+        .select_from(AssetInfoTag)
+        .join(AssetInfo, AssetInfo.id == AssetInfoTag.asset_info_id)
+        .where(visible_owner_clause(owner_id))
+        .group_by(AssetInfoTag.tag_name)
+        .subquery()
+    )
+
+    q = (
+        select(
+            Tag.name,
+            Tag.tag_type,
+            func.coalesce(counts_sq.c.cnt, 0).label("count"),
+        )
+        .select_from(Tag)
+        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
+    )
+
+    if prefix:
+        escaped, esc = escape_like_prefix(prefix.strip().lower())
+        q = q.where(Tag.name.like(escaped + "%", escape=esc))
+
+    if not include_zero:
+        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
+
+    if order == "name_asc":
+        q = q.order_by(Tag.name.asc())
+    else:
+        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
+
+    total_q = select(func.count()).select_from(Tag)
+    if prefix:
+        escaped, esc = escape_like_prefix(prefix.strip().lower())
+        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
+    if not include_zero:
+        total_q = total_q.where(
+            Tag.name.in_(select(AssetInfoTag.tag_name).group_by(AssetInfoTag.tag_name))
+        )
+
+    rows = (session.execute(q.limit(limit).offset(offset))).all()
+    total = (session.execute(total_q)).scalar_one()
+
+    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
+    return rows_norm, int(total or 0)
+
+
+def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
+    wanted = normalize_tags(list(names))
+    if not wanted:
+        return
+    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
+    ins = (
+        sqlite.insert(Tag)
+        .values(rows)
+        .on_conflict_do_nothing(index_elements=[Tag.name])
+    )
+    session.execute(ins)
+
+
+def get_asset_tags(session: Session, *, asset_info_id: str) -> list[str]:
+    return [
+        tag_name for (tag_name,) in (
+            session.execute(
+                select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
+            )
+        ).all()
+    ]
+
+
+def add_tags_to_asset_info(
+    session: Session,
+    *,
+    asset_info_id: str,
+    tags: Sequence[str],
+    origin: str = "manual",
+    create_if_missing: bool = True,
+    asset_info_row: Any = None,
+) -> dict:
+    if not asset_info_row:
+        info = session.get(AssetInfo, asset_info_id)
+        if not info:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+
+    norm = normalize_tags(tags)
+    if not norm:
+        total = get_asset_tags(session, asset_info_id=asset_info_id)
+        return {"added": [], "already_present": [], "total_tags": total}
+
+    if create_if_missing:
+        ensure_tags_exist(session, norm, tag_type="user")
+
+    current = {
+        tag_name
+        for (tag_name,) in (
+            session.execute(
+                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
+            )
+        ).all()
+    }
+
+    want = set(norm)
+    to_add = sorted(want - current)
+
+    if to_add:
+        with session.begin_nested() as nested:
+            try:
+                session.add_all(
+                    [
+                        AssetInfoTag(
+                            asset_info_id=asset_info_id,
+                            tag_name=t,
+                            origin=origin,
+                            added_at=utcnow(),
+                        )
+                        for t in to_add
+                    ]
+                )
+                session.flush()
+            except IntegrityError:
+                nested.rollback()
+
+    after = set(get_asset_tags(session, asset_info_id=asset_info_id))
+    return {
+        "added": sorted(((after - current) & want)),
+        "already_present": sorted(want & current),
+        "total_tags": sorted(after),
+    }
+
+
+def remove_tags_from_asset_info(
+    session: Session,
+    *,
+    asset_info_id: str,
+    tags: Sequence[str],
+) -> dict:
+    info = session.get(AssetInfo, asset_info_id)
+    if not info:
+        raise ValueError(f"AssetInfo {asset_info_id} not found")
+
+    norm = normalize_tags(tags)
+    if not norm:
+        total = get_asset_tags(session, asset_info_id=asset_info_id)
+        return {"removed": [], "not_present": [], "total_tags": total}
+
+    existing = {
+        tag_name
+        for (tag_name,) in (
+            session.execute(
+                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
+            )
+        ).all()
+    }
+
+    to_remove = sorted(set(t for t in norm if t in existing))
+    not_present = sorted(set(t for t in norm if t not in existing))
+
+    if to_remove:
+        session.execute(
+            delete(AssetInfoTag)
+            .where(
+                AssetInfoTag.asset_info_id == asset_info_id,
+                AssetInfoTag.tag_name.in_(to_remove),
+            )
+        )
+        session.flush()
+
+    total = get_asset_tags(session, asset_info_id=asset_info_id)
+    return {"removed": to_remove, "not_present": not_present, "total_tags": total}
+
+
+def remove_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+) -> None:
+    session.execute(
+        sa.delete(AssetInfoTag).where(
+            AssetInfoTag.asset_info_id.in_(sa.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
+            AssetInfoTag.tag_name == "missing",
+        )
+    )
+
+
+def set_asset_info_preview(
+    session: Session,
+    *,
+    asset_info_id: str,
+    preview_asset_id: str | None = None,
+) -> None:
+    """Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
+    info = session.get(AssetInfo, asset_info_id)
+    if not info:
+        raise ValueError(f"AssetInfo {asset_info_id} not found")
+
+    if preview_asset_id is None:
+        info.preview_id = None
+    else:
+        # validate preview asset exists
+        if not session.get(Asset, preview_asset_id):
+            raise ValueError(f"Preview Asset {preview_asset_id} not found")
+        info.preview_id = preview_asset_id
+
+    info.updated_at = utcnow()
+    session.flush()

app/assets/database/queries/__init__.py

@@ -1,137 +0,0 @@
-from app.assets.database.queries.asset import (
-    asset_exists_by_hash,
-    bulk_insert_assets,
-    create_stub_asset,
-    get_asset_by_hash,
-    get_existing_asset_ids,
-    reassign_asset_references,
-    update_asset_hash_and_mime,
-    upsert_asset,
-)
-from app.assets.database.queries.asset_reference import (
-    CacheStateRow,
-    UnenrichedReferenceRow,
-    bulk_insert_references_ignore_conflicts,
-    bulk_update_enrichment_level,
-    count_active_siblings,
-    bulk_update_is_missing,
-    bulk_update_needs_verify,
-    convert_metadata_to_rows,
-    delete_assets_by_ids,
-    delete_orphaned_seed_asset,
-    delete_reference_by_id,
-    delete_references_by_ids,
-    fetch_reference_and_asset,
-    fetch_reference_asset_and_tags,
-    get_or_create_reference,
-    get_reference_by_file_path,
-    get_reference_by_id,
-    get_reference_with_owner_check,
-    get_reference_ids_by_ids,
-    get_references_by_paths_and_asset_ids,
-    get_references_for_prefixes,
-    get_unenriched_references,
-    get_unreferenced_unhashed_asset_ids,
-    insert_reference,
-    list_all_file_paths_by_asset_id,
-    list_references_by_asset_id,
-    list_references_page,
-    mark_references_missing_outside_prefixes,
-    rebuild_metadata_projection,
-    reference_exists,
-    reference_exists_for_asset_id,
-    restore_references_by_paths,
-    set_reference_metadata,
-    set_reference_preview,
-    set_reference_system_metadata,
-    soft_delete_reference_by_id,
-    update_reference_access_time,
-    update_reference_name,
-    update_is_missing_by_asset_id,
-    update_reference_timestamps,
-    update_reference_updated_at,
-    upsert_reference,
-)
-from app.assets.database.queries.tags import (
-    AddTagsResult,
-    RemoveTagsResult,
-    SetTagsResult,
-    add_missing_tag_for_asset_id,
-    add_tags_to_reference,
-    bulk_insert_tags_and_meta,
-    ensure_tags_exist,
-    get_reference_tags,
-    list_tag_counts_for_filtered_assets,
-    list_tags_with_usage,
-    remove_missing_tag_for_asset_id,
-    remove_tags_from_reference,
-    set_reference_tags,
-    validate_tags_exist,
-)
-
-__all__ = [
-    "AddTagsResult",
-    "CacheStateRow",
-    "RemoveTagsResult",
-    "SetTagsResult",
-    "UnenrichedReferenceRow",
-    "add_missing_tag_for_asset_id",
-    "add_tags_to_reference",
-    "asset_exists_by_hash",
-    "bulk_insert_assets",
-    "bulk_insert_references_ignore_conflicts",
-    "bulk_insert_tags_and_meta",
-    "bulk_update_enrichment_level",
-    "count_active_siblings",
-    "create_stub_asset",
-    "bulk_update_is_missing",
-    "bulk_update_needs_verify",
-    "convert_metadata_to_rows",
-    "delete_assets_by_ids",
-    "delete_orphaned_seed_asset",
-    "delete_reference_by_id",
-    "delete_references_by_ids",
-    "ensure_tags_exist",
-    "fetch_reference_and_asset",
-    "fetch_reference_asset_and_tags",
-    "get_asset_by_hash",
-    "get_existing_asset_ids",
-    "get_or_create_reference",
-    "get_reference_by_file_path",
-    "get_reference_by_id",
-    "get_reference_with_owner_check",
-    "get_reference_ids_by_ids",
-    "get_reference_tags",
-    "get_references_by_paths_and_asset_ids",
-    "get_references_for_prefixes",
-    "get_unenriched_references",
-    "get_unreferenced_unhashed_asset_ids",
-    "insert_reference",
-    "list_all_file_paths_by_asset_id",
-    "list_references_by_asset_id",
-    "list_references_page",
-    "list_tag_counts_for_filtered_assets",
-    "list_tags_with_usage",
-    "mark_references_missing_outside_prefixes",
-    "reassign_asset_references",
-    "rebuild_metadata_projection",
-    "reference_exists",
-    "reference_exists_for_asset_id",
-    "remove_missing_tag_for_asset_id",
-    "remove_tags_from_reference",
-    "restore_references_by_paths",
-    "set_reference_metadata",
-    "set_reference_preview",
-    "set_reference_system_metadata",
-    "soft_delete_reference_by_id",
-    "set_reference_tags",
-    "update_asset_hash_and_mime",
-    "update_is_missing_by_asset_id",
-    "update_reference_access_time",
-    "update_reference_name",
-    "update_reference_timestamps",
-    "update_reference_updated_at",
-    "upsert_asset",
-    "upsert_reference",
-    "validate_tags_exist",
-]

app/assets/database/queries/asset.py

@@ -1,152 +0,0 @@
-import sqlalchemy as sa
-from sqlalchemy import select
-from sqlalchemy.dialects import sqlite
-from sqlalchemy.orm import Session
-
-from app.assets.database.models import Asset, AssetReference
-from app.assets.database.queries.common import MAX_BIND_PARAMS, calculate_rows_per_statement, iter_chunks
-
-
-def asset_exists_by_hash(
-    session: Session,
-    asset_hash: str,
-) -> bool:
-    """
-    Check if an asset with a given hash exists in database.
-    """
-    row = (
-        session.execute(
-            select(sa.literal(True))
-            .select_from(Asset)
-            .where(Asset.hash == asset_hash)
-            .limit(1)
-        )
-    ).first()
-    return row is not None
-
-
-def get_asset_by_hash(
-    session: Session,
-    asset_hash: str,
-) -> Asset | None:
-    return (
-        (session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1)))
-        .scalars()
-        .first()
-    )
-
-
-def upsert_asset(
-    session: Session,
-    asset_hash: str,
-    size_bytes: int,
-    mime_type: str | None = None,
-) -> tuple[Asset, bool, bool]:
-    """Upsert an Asset by hash. Returns (asset, created, updated)."""
-    vals = {"hash": asset_hash, "size_bytes": int(size_bytes)}
-    if mime_type:
-        vals["mime_type"] = mime_type
-
-    ins = (
-        sqlite.insert(Asset)
-        .values(**vals)
-        .on_conflict_do_nothing(index_elements=[Asset.hash])
-    )
-    res = session.execute(ins)
-    created = int(res.rowcount or 0) > 0
-
-    asset = (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-        .scalars()
-        .first()
-    )
-    if not asset:
-        raise RuntimeError("Asset row not found after upsert.")
-
-    updated = False
-    if not created:
-        changed = False
-        if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
-            asset.size_bytes = int(size_bytes)
-            changed = True
-        if mime_type and not asset.mime_type:
-            asset.mime_type = mime_type
-            changed = True
-        if changed:
-            updated = True
-
-    return asset, created, updated
-
-
-def create_stub_asset(
-    session: Session,
-    size_bytes: int,
-    mime_type: str | None = None,
-) -> Asset:
-    """Create a new asset with no hash (stub for later enrichment)."""
-    asset = Asset(size_bytes=size_bytes, mime_type=mime_type, hash=None)
-    session.add(asset)
-    session.flush()
-    return asset
-
-
-def bulk_insert_assets(
-    session: Session,
-    rows: list[dict],
-) -> None:
-    """Bulk insert Asset rows with ON CONFLICT DO NOTHING on hash."""
-    if not rows:
-        return
-    ins = sqlite.insert(Asset).on_conflict_do_nothing(index_elements=[Asset.hash])
-    for chunk in iter_chunks(rows, calculate_rows_per_statement(5)):
-        session.execute(ins, chunk)
-
-
-def get_existing_asset_ids(
-    session: Session,
-    asset_ids: list[str],
-) -> set[str]:
-    """Return the subset of asset_ids that exist in the database."""
-    if not asset_ids:
-        return set()
-    found: set[str] = set()
-    for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS):
-        rows = session.execute(
-            select(Asset.id).where(Asset.id.in_(chunk))
-        ).fetchall()
-        found.update(row[0] for row in rows)
-    return found
-
-
-def update_asset_hash_and_mime(
-    session: Session,
-    asset_id: str,
-    asset_hash: str | None = None,
-    mime_type: str | None = None,
-) -> bool:
-    """Update asset hash and/or mime_type. Returns True if asset was found."""
-    asset = session.get(Asset, asset_id)
-    if not asset:
-        return False
-    if asset_hash is not None:
-        asset.hash = asset_hash
-    if mime_type is not None and not asset.mime_type:
-        asset.mime_type = mime_type
-    return True
-
-
-def reassign_asset_references(
-    session: Session,
-    from_asset_id: str,
-    to_asset_id: str,
-    reference_id: str,
-) -> None:
-    """Reassign a reference from one asset to another.
-
-    Used when merging a stub asset into an existing asset with the same hash.
-    """
-    ref = session.get(AssetReference, reference_id)
-    if ref and ref.asset_id == from_asset_id:
-        ref.asset_id = to_asset_id
-
-    session.flush()

app/assets/database/queries/common.py

@@ -1,127 +0,0 @@
-"""Shared utilities for database query modules."""
-
-import os
-from decimal import Decimal
-from typing import Iterable, Sequence
-
-import sqlalchemy as sa
-from sqlalchemy import exists
-
-from app.assets.database.models import AssetReference, AssetReferenceMeta, AssetReferenceTag
-from app.assets.helpers import escape_sql_like_string, normalize_tags
-
-MAX_BIND_PARAMS = 800
-
-
-def calculate_rows_per_statement(cols: int) -> int:
-    """Calculate how many rows can fit in one statement given column count."""
-    return max(1, MAX_BIND_PARAMS // max(1, cols))
-
-
-def iter_chunks(seq, n: int):
-    """Yield successive n-sized chunks from seq."""
-    for i in range(0, len(seq), n):
-        yield seq[i : i + n]
-
-
-def iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[list[dict]]:
-    """Yield chunks of rows sized to fit within bind param limits."""
-    if not rows:
-        return
-    yield from iter_chunks(rows, calculate_rows_per_statement(cols_per_row))
-
-
-def build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
-    """Build owner visibility predicate for reads.
-
-    Owner-less rows are visible to everyone.
-    """
-    owner_id = (owner_id or "").strip()
-    if owner_id == "":
-        return AssetReference.owner_id == ""
-    return AssetReference.owner_id.in_(["", owner_id])
-
-
-def build_prefix_like_conditions(
-    prefixes: list[str],
-) -> list[sa.sql.ColumnElement]:
-    """Build LIKE conditions for matching file paths under directory prefixes."""
-    conds = []
-    for p in prefixes:
-        base = os.path.abspath(p)
-        if not base.endswith(os.sep):
-            base += os.sep
-        escaped, esc = escape_sql_like_string(base)
-        conds.append(AssetReference.file_path.like(escaped + "%", escape=esc))
-    return conds
-
-
-def apply_tag_filters(
-    stmt: sa.sql.Select,
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-) -> sa.sql.Select:
-    """include_tags: every tag must be present; exclude_tags: none may be present."""
-    include_tags = normalize_tags(include_tags)
-    exclude_tags = normalize_tags(exclude_tags)
-
-    if include_tags:
-        for tag_name in include_tags:
-            stmt = stmt.where(
-                exists().where(
-                    (AssetReferenceTag.asset_reference_id == AssetReference.id)
-                    & (AssetReferenceTag.tag_name == tag_name)
-                )
-            )
-
-    if exclude_tags:
-        stmt = stmt.where(
-            ~exists().where(
-                (AssetReferenceTag.asset_reference_id == AssetReference.id)
-                & (AssetReferenceTag.tag_name.in_(exclude_tags))
-            )
-        )
-    return stmt
-
-
-def apply_metadata_filter(
-    stmt: sa.sql.Select,
-    metadata_filter: dict | None = None,
-) -> sa.sql.Select:
-    """Apply filters using asset_reference_meta projection table."""
-    if not metadata_filter:
-        return stmt
-
-    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
-        return sa.exists().where(
-            AssetReferenceMeta.asset_reference_id == AssetReference.id,
-            AssetReferenceMeta.key == key,
-            *preds,
-        )
-
-    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
-        if value is None:
-            return sa.not_(
-                sa.exists().where(
-                    AssetReferenceMeta.asset_reference_id == AssetReference.id,
-                    AssetReferenceMeta.key == key,
-                )
-            )
-
-        if isinstance(value, bool):
-            return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value))
-        if isinstance(value, (int, float, Decimal)):
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            return _exists_for_pred(key, AssetReferenceMeta.val_num == num)
-        if isinstance(value, str):
-            return _exists_for_pred(key, AssetReferenceMeta.val_str == value)
-        return _exists_for_pred(key, AssetReferenceMeta.val_json == value)
-
-    for k, v in metadata_filter.items():
-        if isinstance(v, list):
-            ors = [_exists_clause_for_value(k, elem) for elem in v]
-            if ors:
-                stmt = stmt.where(sa.or_(*ors))
-        else:
-            stmt = stmt.where(_exists_clause_for_value(k, v))
-    return stmt

app/assets/database/queries/tags.py

@@ -1,418 +0,0 @@
-from dataclasses import dataclass
-from typing import Iterable, Sequence
-
-import sqlalchemy as sa
-from sqlalchemy import delete, func, select
-from sqlalchemy.dialects import sqlite
-from sqlalchemy.exc import IntegrityError
-from sqlalchemy.orm import Session
-
-from app.assets.database.models import (
-    Asset,
-    AssetReference,
-    AssetReferenceMeta,
-    AssetReferenceTag,
-    Tag,
-)
-from app.assets.database.queries.common import (
-    apply_metadata_filter,
-    apply_tag_filters,
-    build_visible_owner_clause,
-    iter_row_chunks,
-)
-from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags
-
-
-@dataclass(frozen=True)
-class AddTagsResult:
-    added: list[str]
-    already_present: list[str]
-    total_tags: list[str]
-
-
-@dataclass(frozen=True)
-class RemoveTagsResult:
-    removed: list[str]
-    not_present: list[str]
-    total_tags: list[str]
-
-
-@dataclass(frozen=True)
-class SetTagsResult:
-    added: list[str]
-    removed: list[str]
-    total: list[str]
-
-
-def validate_tags_exist(session: Session, tags: list[str]) -> None:
-    """Raise ValueError if any of the given tag names do not exist."""
-    existing_tag_names = set(
-        name
-        for (name,) in session.execute(select(Tag.name).where(Tag.name.in_(tags))).all()
-    )
-    missing = [t for t in tags if t not in existing_tag_names]
-    if missing:
-        raise ValueError(f"Unknown tags: {missing}")
-
-
-def ensure_tags_exist(
-    session: Session, names: Iterable[str], tag_type: str = "user"
-) -> None:
-    wanted = normalize_tags(list(names))
-    if not wanted:
-        return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
-    ins = (
-        sqlite.insert(Tag)
-        .values(rows)
-        .on_conflict_do_nothing(index_elements=[Tag.name])
-    )
-    session.execute(ins)
-
-
-def get_reference_tags(session: Session, reference_id: str) -> list[str]:
-    return [
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                select(AssetReferenceTag.tag_name)
-                .where(AssetReferenceTag.asset_reference_id == reference_id)
-                .order_by(AssetReferenceTag.tag_name.asc())
-            )
-        ).all()
-    ]
-
-
-def set_reference_tags(
-    session: Session,
-    reference_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-) -> SetTagsResult:
-    desired = normalize_tags(tags)
-
-    current = set(get_reference_tags(session, reference_id))
-
-    to_add = [t for t in desired if t not in current]
-    to_remove = [t for t in current if t not in desired]
-
-    if to_add:
-        ensure_tags_exist(session, to_add, tag_type="user")
-        session.add_all(
-            [
-                AssetReferenceTag(
-                    asset_reference_id=reference_id,
-                    tag_name=t,
-                    origin=origin,
-                    added_at=get_utc_now(),
-                )
-                for t in to_add
-            ]
-        )
-        session.flush()
-
-    if to_remove:
-        session.execute(
-            delete(AssetReferenceTag).where(
-                AssetReferenceTag.asset_reference_id == reference_id,
-                AssetReferenceTag.tag_name.in_(to_remove),
-            )
-        )
-        session.flush()
-
-    return SetTagsResult(added=sorted(to_add), removed=sorted(to_remove), total=sorted(desired))
-
-
-def add_tags_to_reference(
-    session: Session,
-    reference_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-    create_if_missing: bool = True,
-    reference_row: AssetReference | None = None,
-) -> AddTagsResult:
-    if not reference_row:
-        ref = session.get(AssetReference, reference_id)
-        if not ref:
-            raise ValueError(f"AssetReference {reference_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_reference_tags(session, reference_id=reference_id)
-        return AddTagsResult(added=[], already_present=[], total_tags=total)
-
-    if create_if_missing:
-        ensure_tags_exist(session, norm, tag_type="user")
-
-    current = set(get_reference_tags(session, reference_id))
-
-    want = set(norm)
-    to_add = sorted(want - current)
-
-    if to_add:
-        with session.begin_nested() as nested:
-            try:
-                session.add_all(
-                    [
-                        AssetReferenceTag(
-                            asset_reference_id=reference_id,
-                            tag_name=t,
-                            origin=origin,
-                            added_at=get_utc_now(),
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-            except IntegrityError:
-                nested.rollback()
-
-    after = set(get_reference_tags(session, reference_id=reference_id))
-    return AddTagsResult(
-        added=sorted(((after - current) & want)),
-        already_present=sorted(want & current),
-        total_tags=sorted(after),
-    )
-
-
-def remove_tags_from_reference(
-    session: Session,
-    reference_id: str,
-    tags: Sequence[str],
-) -> RemoveTagsResult:
-    ref = session.get(AssetReference, reference_id)
-    if not ref:
-        raise ValueError(f"AssetReference {reference_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_reference_tags(session, reference_id=reference_id)
-        return RemoveTagsResult(removed=[], not_present=[], total_tags=total)
-
-    existing = set(get_reference_tags(session, reference_id))
-
-    to_remove = sorted(set(t for t in norm if t in existing))
-    not_present = sorted(set(t for t in norm if t not in existing))
-
-    if to_remove:
-        session.execute(
-            delete(AssetReferenceTag).where(
-                AssetReferenceTag.asset_reference_id == reference_id,
-                AssetReferenceTag.tag_name.in_(to_remove),
-            )
-        )
-        session.flush()
-
-    total = get_reference_tags(session, reference_id=reference_id)
-    return RemoveTagsResult(removed=to_remove, not_present=not_present, total_tags=total)
-
-
-def add_missing_tag_for_asset_id(
-    session: Session,
-    asset_id: str,
-    origin: str = "automatic",
-) -> None:
-    select_rows = (
-        sa.select(
-            AssetReference.id.label("asset_reference_id"),
-            sa.literal("missing").label("tag_name"),
-            sa.literal(origin).label("origin"),
-            sa.literal(get_utc_now()).label("added_at"),
-        )
-        .where(AssetReference.asset_id == asset_id)
-        .where(
-            sa.not_(
-                sa.exists().where(
-                    (AssetReferenceTag.asset_reference_id == AssetReference.id)
-                    & (AssetReferenceTag.tag_name == "missing")
-                )
-            )
-        )
-    )
-    session.execute(
-        sqlite.insert(AssetReferenceTag)
-        .from_select(
-            ["asset_reference_id", "tag_name", "origin", "added_at"],
-            select_rows,
-        )
-        .on_conflict_do_nothing(
-            index_elements=[
-                AssetReferenceTag.asset_reference_id,
-                AssetReferenceTag.tag_name,
-            ]
-        )
-    )
-
-
-def remove_missing_tag_for_asset_id(
-    session: Session,
-    asset_id: str,
-) -> None:
-    session.execute(
-        sa.delete(AssetReferenceTag).where(
-            AssetReferenceTag.asset_reference_id.in_(
-                sa.select(AssetReference.id).where(AssetReference.asset_id == asset_id)
-            ),
-            AssetReferenceTag.tag_name == "missing",
-        )
-    )
-
-
-def list_tags_with_usage(
-    session: Session,
-    prefix: str | None = None,
-    limit: int = 100,
-    offset: int = 0,
-    include_zero: bool = True,
-    order: str = "count_desc",
-    owner_id: str = "",
-) -> tuple[list[tuple[str, str, int]], int]:
-    counts_sq = (
-        select(
-            AssetReferenceTag.tag_name.label("tag_name"),
-            func.count(AssetReferenceTag.asset_reference_id).label("cnt"),
-        )
-        .select_from(AssetReferenceTag)
-        .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)
-        .where(build_visible_owner_clause(owner_id))
-        .where(
-            sa.or_(
-                AssetReference.is_missing == False,  # noqa: E712
-                AssetReferenceTag.tag_name == "missing",
-            )
-        )
-        .where(AssetReference.deleted_at.is_(None))
-        .group_by(AssetReferenceTag.tag_name)
-        .subquery()
-    )
-
-    q = (
-        select(
-            Tag.name,
-            Tag.tag_type,
-            func.coalesce(counts_sq.c.cnt, 0).label("count"),
-        )
-        .select_from(Tag)
-        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
-    )
-
-    if prefix:
-        escaped, esc = escape_sql_like_string(prefix.strip().lower())
-        q = q.where(Tag.name.like(escaped + "%", escape=esc))
-
-    if not include_zero:
-        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
-
-    if order == "name_asc":
-        q = q.order_by(Tag.name.asc())
-    else:
-        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
-
-    total_q = select(func.count()).select_from(Tag)
-    if prefix:
-        escaped, esc = escape_sql_like_string(prefix.strip().lower())
-        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
-    if not include_zero:
-        visible_tags_sq = (
-            select(AssetReferenceTag.tag_name)
-            .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)
-            .where(build_visible_owner_clause(owner_id))
-            .where(
-                sa.or_(
-                    AssetReference.is_missing == False,  # noqa: E712
-                    AssetReferenceTag.tag_name == "missing",
-                )
-            )
-            .where(AssetReference.deleted_at.is_(None))
-            .group_by(AssetReferenceTag.tag_name)
-        )
-        total_q = total_q.where(Tag.name.in_(visible_tags_sq))
-
-    rows = (session.execute(q.limit(limit).offset(offset))).all()
-    total = (session.execute(total_q)).scalar_one()
-
-    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
-    return rows_norm, int(total or 0)
-
-
-def list_tag_counts_for_filtered_assets(
-    session: Session,
-    owner_id: str = "",
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 100,
-) -> dict[str, int]:
-    """Return tag counts for assets matching the given filters.
-
-    Uses the same filtering logic as list_references_page but returns
-    {tag_name: count} instead of paginated references.
-    """
-    # Build a subquery of matching reference IDs
-    ref_sq = (
-        select(AssetReference.id)
-        .join(Asset, Asset.id == AssetReference.asset_id)
-        .where(build_visible_owner_clause(owner_id))
-        .where(AssetReference.is_missing == False)  # noqa: E712
-        .where(AssetReference.deleted_at.is_(None))
-    )
-
-    if name_contains:
-        escaped, esc = escape_sql_like_string(name_contains)
-        ref_sq = ref_sq.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc))
-
-    ref_sq = apply_tag_filters(ref_sq, include_tags, exclude_tags)
-    ref_sq = apply_metadata_filter(ref_sq, metadata_filter)
-    ref_sq = ref_sq.subquery()
-
-    # Count tags across those references
-    q = (
-        select(
-            AssetReferenceTag.tag_name,
-            func.count(AssetReferenceTag.asset_reference_id).label("cnt"),
-        )
-        .where(AssetReferenceTag.asset_reference_id.in_(select(ref_sq.c.id)))
-        .group_by(AssetReferenceTag.tag_name)
-        .order_by(func.count(AssetReferenceTag.asset_reference_id).desc(), AssetReferenceTag.tag_name.asc())
-        .limit(limit)
-    )
-
-    rows = session.execute(q).all()
-    return {tag_name: int(cnt) for tag_name, cnt in rows}
-
-
-def bulk_insert_tags_and_meta(
-    session: Session,
-    tag_rows: list[dict],
-    meta_rows: list[dict],
-) -> None:
-    """Batch insert into asset_reference_tags and asset_reference_meta.
-
-    Uses ON CONFLICT DO NOTHING.
-
-    Args:
-        session: Database session
-        tag_rows: Dicts with: asset_reference_id, tag_name, origin, added_at
-        meta_rows: Dicts with: asset_reference_id, key, ordinal, val_*
-    """
-    if tag_rows:
-        ins_tags = sqlite.insert(AssetReferenceTag).on_conflict_do_nothing(
-            index_elements=[
-                AssetReferenceTag.asset_reference_id,
-                AssetReferenceTag.tag_name,
-            ]
-        )
-        for chunk in iter_row_chunks(tag_rows, cols_per_row=4):
-            session.execute(ins_tags, chunk)
-
-    if meta_rows:
-        ins_meta = sqlite.insert(AssetReferenceMeta).on_conflict_do_nothing(
-            index_elements=[
-                AssetReferenceMeta.asset_reference_id,
-                AssetReferenceMeta.key,
-                AssetReferenceMeta.ordinal,
-            ]
-        )
-        for chunk in iter_row_chunks(meta_rows, cols_per_row=7):
-            session.execute(ins_meta, chunk)

app/assets/database/tags.py

@@ -0,0 +1,62 @@
+from typing import Iterable
+
+import sqlalchemy
+from sqlalchemy.orm import Session
+from sqlalchemy.dialects import sqlite
+
+from app.assets.helpers import normalize_tags, utcnow
+from app.assets.database.models import Tag, AssetInfoTag, AssetInfo
+
+
+def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
+    wanted = normalize_tags(list(names))
+    if not wanted:
+        return
+    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
+    ins = (
+            sqlite.insert(Tag)
+            .values(rows)
+            .on_conflict_do_nothing(index_elements=[Tag.name])
+        )
+    return session.execute(ins)
+
+def add_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+    origin: str = "automatic",
+) -> None:
+    select_rows = (
+        sqlalchemy.select(
+            AssetInfo.id.label("asset_info_id"),
+            sqlalchemy.literal("missing").label("tag_name"),
+            sqlalchemy.literal(origin).label("origin"),
+            sqlalchemy.literal(utcnow()).label("added_at"),
+        )
+        .where(AssetInfo.asset_id == asset_id)
+        .where(
+            sqlalchemy.not_(
+                sqlalchemy.exists().where((AssetInfoTag.asset_info_id == AssetInfo.id) & (AssetInfoTag.tag_name == "missing"))
+            )
+        )
+    )
+    session.execute(
+        sqlite.insert(AssetInfoTag)
+        .from_select(
+            ["asset_info_id", "tag_name", "origin", "added_at"],
+            select_rows,
+        )
+        .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
+    )
+
+def remove_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+) -> None:
+    session.execute(
+        sqlalchemy.delete(AssetInfoTag).where(
+            AssetInfoTag.asset_info_id.in_(sqlalchemy.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
+            AssetInfoTag.tag_name == "missing",
+        )
+    )

app/assets/hashing.py

@@ -0,0 +1,75 @@
+from blake3 import blake3
+from typing import IO
+import os
+import asyncio
+
+
+DEFAULT_CHUNK = 8 * 1024 *1024 # 8MB
+
+# NOTE: this allows hashing different representations of a file-like object
+def blake3_hash(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+) -> str:
+    """
+    Returns a BLAKE3 hex digest for ``fp``, which may be:
+      - a filename (str/bytes) or PathLike
+      - an open binary file object
+    If ``fp`` is a file object, it must be opened in **binary** mode and support
+    ``read``, ``seek``, and ``tell``. The function will seek to the start before
+    reading and will attempt to restore the original position afterward.
+    """
+    # duck typing to check if input is a file-like object
+    if hasattr(fp, "read"):
+        return _hash_file_obj(fp, chunk_size)
+
+    with open(os.fspath(fp), "rb") as f:
+        return _hash_file_obj(f, chunk_size)
+
+
+async def blake3_hash_async(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+) -> str:
+    """Async wrapper for ``blake3_hash_sync``.
+    Uses a worker thread so the event loop remains responsive.
+    """
+    # If it is a path, open inside the worker thread to keep I/O off the loop.
+    if hasattr(fp, "read"):
+        return await asyncio.to_thread(blake3_hash, fp, chunk_size)
+
+    def _worker() -> str:
+        with open(os.fspath(fp), "rb") as f:
+            return _hash_file_obj(f, chunk_size)
+
+    return await asyncio.to_thread(_worker)
+
+
+def _hash_file_obj(file_obj: IO, chunk_size: int = DEFAULT_CHUNK) -> str:
+    """
+    Hash an already-open binary file object by streaming in chunks.
+    - Seeks to the beginning before reading (if supported).
+    - Restores the original position afterward (if tell/seek are supported).
+    """
+    if chunk_size <= 0:
+        chunk_size = DEFAULT_CHUNK
+
+    # in case file object is already open and not at the beginning, track so can be restored after hashing
+    orig_pos = file_obj.tell()
+
+    try:
+        # seek to the beginning before reading
+        if orig_pos != 0:
+            file_obj.seek(0)
+
+        h = blake3()
+        while True:
+            chunk = file_obj.read(chunk_size)
+            if not chunk:
+                break
+            h.update(chunk)
+        return h.hexdigest()
+    finally:
+        # restore original position in file object, if needed
+        if orig_pos != 0:
+            file_obj.seek(orig_pos)

app/assets/helpers.py

@@ -1,42 +1,226 @@
+import contextlib
 import os
+from decimal import Decimal
+from aiohttp import web
 from datetime import datetime, timezone
-from typing import Sequence
+from pathlib import Path
+from typing import Literal, Any
+
+import folder_paths
 
 
-def select_best_live_path(states: Sequence) -> str:
+RootType = Literal["models", "input", "output"]
+ALLOWED_ROOTS: tuple[RootType, ...] = ("models", "input", "output")
+
+def get_query_dict(request: web.Request) -> dict[str, Any]:
     """
-    Return the best on-disk path among cache states:
-      1) Prefer a path that exists with needs_verify == False (already verified).
-      2) Otherwise, pick the first path that exists.
-      3) Otherwise return empty string.
+    Gets a dictionary of query parameters from the request.
+
+    'request.query' is a MultiMapping[str], needs to be converted to a dictionary to be validated by Pydantic.
     """
-    alive = [
-        s
-        for s in states
-        if getattr(s, "file_path", None) and os.path.isfile(s.file_path)
-    ]
-    if not alive:
-        return ""
-    for s in alive:
-        if not getattr(s, "needs_verify", False):
-            return s.file_path
-    return alive[0].file_path
+    query_dict = {
+        key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key)
+        for key in request.query.keys()
+    }
+    return query_dict
 
+def list_tree(base_dir: str) -> list[str]:
+    out: list[str] = []
+    base_abs = os.path.abspath(base_dir)
+    if not os.path.isdir(base_abs):
+        return out
+    for dirpath, _subdirs, filenames in os.walk(base_abs, topdown=True, followlinks=False):
+        for name in filenames:
+            out.append(os.path.abspath(os.path.join(dirpath, name)))
+    return out
 
-def escape_sql_like_string(s: str, escape: str = "!") -> tuple[str, str]:
-    """Escapes %, _ and the escape char in a LIKE prefix.
+def prefixes_for_root(root: RootType) -> list[str]:
+    if root == "models":
+        bases: list[str] = []
+        for _bucket, paths in get_comfy_models_folders():
+            bases.extend(paths)
+        return [os.path.abspath(p) for p in bases]
+    if root == "input":
+        return [os.path.abspath(folder_paths.get_input_directory())]
+    if root == "output":
+        return [os.path.abspath(folder_paths.get_output_directory())]
+    return []
 
-    Returns (escaped_prefix, escape_char).
+def escape_like_prefix(s: str, escape: str = "!") -> tuple[str, str]:
+    """Escapes %, _ and the escape char itself in a LIKE prefix.
+    Returns (escaped_prefix, escape_char). Caller should append '%' and pass escape=escape_char to .like().
     """
     s = s.replace(escape, escape + escape)  # escape the escape char first
     s = s.replace("%", escape + "%").replace("_", escape + "_")  # escape LIKE wildcards
     return s, escape
 
+def fast_asset_file_check(
+    *,
+    mtime_db: int | None,
+    size_db: int | None,
+    stat_result: os.stat_result,
+) -> bool:
+    if mtime_db is None:
+        return False
+    actual_mtime_ns = getattr(stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000))
+    if int(mtime_db) != int(actual_mtime_ns):
+        return False
+    sz = int(size_db or 0)
+    if sz > 0:
+        return int(stat_result.st_size) == sz
+    return True
 
-def get_utc_now() -> datetime:
+def utcnow() -> datetime:
     """Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC."""
     return datetime.now(timezone.utc).replace(tzinfo=None)
 
+def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
+    """Build a list of (folder_name, base_paths[]) categories that are configured for model locations.
+
+    We trust `folder_paths.folder_names_and_paths` and include a category if
+    *any* of its base paths lies under the Comfy `models_dir`.
+    """
+    targets: list[tuple[str, list[str]]] = []
+    models_root = os.path.abspath(folder_paths.models_dir)
+    for name, values in folder_paths.folder_names_and_paths.items():
+        paths, _exts = values[0], values[1]  # NOTE: this prevents nodepacks that hackily edit folder_... from breaking ComfyUI
+        if any(os.path.abspath(p).startswith(models_root + os.sep) for p in paths):
+            targets.append((name, paths))
+    return targets
+
+def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
+    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
+    root = tags[0]
+    if root == "models":
+        if len(tags) < 2:
+            raise ValueError("at least two tags required for model asset")
+        try:
+            bases = folder_paths.folder_names_and_paths[tags[1]][0]
+        except KeyError:
+            raise ValueError(f"unknown model category '{tags[1]}'")
+        if not bases:
+            raise ValueError(f"no base path configured for category '{tags[1]}'")
+        base_dir = os.path.abspath(bases[0])
+        raw_subdirs = tags[2:]
+    else:
+        base_dir = os.path.abspath(
+            folder_paths.get_input_directory() if root == "input" else folder_paths.get_output_directory()
+        )
+        raw_subdirs = tags[1:]
+    for i in raw_subdirs:
+        if i in (".", ".."):
+            raise ValueError("invalid path component in tags")
+
+    return base_dir, raw_subdirs if raw_subdirs else []
+
+def ensure_within_base(candidate: str, base: str) -> None:
+    cand_abs = os.path.abspath(candidate)
+    base_abs = os.path.abspath(base)
+    try:
+        if os.path.commonpath([cand_abs, base_abs]) != base_abs:
+            raise ValueError("destination escapes base directory")
+    except Exception:
+        raise ValueError("invalid destination path")
+
+def compute_relative_filename(file_path: str) -> str | None:
+    """
+    Return the model's path relative to the last well-known folder (the model category),
+    using forward slashes, eg:
+      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
+      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
+
+    For non-model paths, returns None.
+    NOTE: this is a temporary helper, used only for initializing metadata["filename"] field.
+    """
+    try:
+        root_category, rel_path = get_relative_to_root_category_path_of_asset(file_path)
+    except ValueError:
+        return None
+
+    p = Path(rel_path)
+    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
+    if not parts:
+        return None
+
+    if root_category == "models":
+        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
+        inside = parts[1:] if len(parts) > 1 else [parts[0]]
+        return "/".join(inside)
+    return "/".join(parts)  # input/output: keep all parts
+
+def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
+    """Given an absolute or relative file path, determine which root category the path belongs to:
+      - 'input' if the file resides under `folder_paths.get_input_directory()`
+      - 'output' if the file resides under `folder_paths.get_output_directory()`
+      - 'models' if the file resides under any base path of categories returned by `get_comfy_models_folders()`
+
+    Returns:
+        (root_category, relative_path_inside_that_root)
+        For 'models', the relative path is prefixed with the category name:
+            e.g. ('models', 'vae/test/sub/ae.safetensors')
+
+    Raises:
+        ValueError: if the path does not belong to input, output, or configured model bases.
+    """
+    fp_abs = os.path.abspath(file_path)
+
+    def _is_within(child: str, parent: str) -> bool:
+        try:
+            return os.path.commonpath([child, parent]) == parent
+        except Exception:
+            return False
+
+    def _rel(child: str, parent: str) -> str:
+        return os.path.relpath(os.path.join(os.sep, os.path.relpath(child, parent)), os.sep)
+
+    # 1) input
+    input_base = os.path.abspath(folder_paths.get_input_directory())
+    if _is_within(fp_abs, input_base):
+        return "input", _rel(fp_abs, input_base)
+
+    # 2) output
+    output_base = os.path.abspath(folder_paths.get_output_directory())
+    if _is_within(fp_abs, output_base):
+        return "output", _rel(fp_abs, output_base)
+
+    # 3) models (check deepest matching base to avoid ambiguity)
+    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
+    for bucket, bases in get_comfy_models_folders():
+        for b in bases:
+            base_abs = os.path.abspath(b)
+            if not _is_within(fp_abs, base_abs):
+                continue
+            cand = (len(base_abs), bucket, _rel(fp_abs, base_abs))
+            if best is None or cand[0] > best[0]:
+                best = cand
+
+    if best is not None:
+        _, bucket, rel_inside = best
+        combined = os.path.join(bucket, rel_inside)
+        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
+
+    raise ValueError(f"Path is not within input, output, or configured model bases: {file_path}")
+
+def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
+    """Return a tuple (name, tags) derived from a filesystem path.
+
+    Semantics:
+      - Root category is determined by `get_relative_to_root_category_path_of_asset`.
+      - The returned `name` is the base filename with extension from the relative path.
+      - The returned `tags` are:
+            [root_category] + parent folders of the relative path (in order)
+        For 'models', this means:
+            file '/.../ModelsDir/vae/test_tag/ae.safetensors'
+            -> root_category='models', some_path='vae/test_tag/ae.safetensors'
+            -> name='ae.safetensors', tags=['models', 'vae', 'test_tag']
+
+    Raises:
+        ValueError: if the path does not belong to input, output, or configured model bases.
+    """
+    root_category, some_path = get_relative_to_root_category_path_of_asset(file_path)
+    p = Path(some_path)
+    parent_parts = [part for part in p.parent.parts if part not in (".", "..", p.anchor)]
+    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
 
 def normalize_tags(tags: list[str] | None) -> list[str]:
     """
@@ -44,22 +228,85 @@ def normalize_tags(tags: list[str] | None) -> list[str]:
       - Stripping whitespace and converting to lowercase.
       - Removing duplicates.
     """
-    return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or "").strip()))
+    return [t.strip().lower() for t in (tags or []) if (t or "").strip()]
 
+def collect_models_files() -> list[str]:
+    out: list[str] = []
+    for folder_name, bases in get_comfy_models_folders():
+        rel_files = folder_paths.get_filename_list(folder_name) or []
+        for rel_path in rel_files:
+            abs_path = folder_paths.get_full_path(folder_name, rel_path)
+            if not abs_path:
+                continue
+            abs_path = os.path.abspath(abs_path)
+            allowed = False
+            for b in bases:
+                base_abs = os.path.abspath(b)
+                with contextlib.suppress(Exception):
+                    if os.path.commonpath([abs_path, base_abs]) == base_abs:
+                        allowed = True
+                        break
+            if allowed:
+                out.append(abs_path)
+    return out
 
-def validate_blake3_hash(s: str) -> str:
-    """Validate and normalize a blake3 hash string.
+def is_scalar(v):
+    if v is None:
+        return True
+    if isinstance(v, bool):
+        return True
+    if isinstance(v, (int, float, Decimal, str)):
+        return True
+    return False
 
-    Returns canonical 'blake3:<hex>' or raises ValueError.
+def project_kv(key: str, value):
     """
-    s = s.strip().lower()
-    if not s or ":" not in s:
-        raise ValueError("hash must be 'blake3:<hex>'")
-    algo, digest = s.split(":", 1)
-    if (
-        algo != "blake3"
-        or len(digest) != 64
-        or any(c for c in digest if c not in "0123456789abcdef")
-    ):
-        raise ValueError("hash must be 'blake3:<hex>'")
-    return f"{algo}:{digest}"
+    Turn a metadata key/value into typed projection rows.
+    Returns list[dict] with keys:
+      key, ordinal, and one of val_str / val_num / val_bool / val_json (others None)
+    """
+    rows: list[dict] = []
+
+    def _null_row(ordinal: int) -> dict:
+        return {
+            "key": key, "ordinal": ordinal,
+            "val_str": None, "val_num": None, "val_bool": None, "val_json": None
+        }
+
+    if value is None:
+        rows.append(_null_row(0))
+        return rows
+
+    if is_scalar(value):
+        if isinstance(value, bool):
+            rows.append({"key": key, "ordinal": 0, "val_bool": bool(value)})
+        elif isinstance(value, (int, float, Decimal)):
+            num = value if isinstance(value, Decimal) else Decimal(str(value))
+            rows.append({"key": key, "ordinal": 0, "val_num": num})
+        elif isinstance(value, str):
+            rows.append({"key": key, "ordinal": 0, "val_str": value})
+        else:
+            rows.append({"key": key, "ordinal": 0, "val_json": value})
+        return rows
+
+    if isinstance(value, list):
+        if all(is_scalar(x) for x in value):
+            for i, x in enumerate(value):
+                if x is None:
+                    rows.append(_null_row(i))
+                elif isinstance(x, bool):
+                    rows.append({"key": key, "ordinal": i, "val_bool": bool(x)})
+                elif isinstance(x, (int, float, Decimal)):
+                    num = x if isinstance(x, Decimal) else Decimal(str(x))
+                    rows.append({"key": key, "ordinal": i, "val_num": num})
+                elif isinstance(x, str):
+                    rows.append({"key": key, "ordinal": i, "val_str": x})
+                else:
+                    rows.append({"key": key, "ordinal": i, "val_json": x})
+            return rows
+        for i, x in enumerate(value):
+            rows.append({"key": key, "ordinal": i, "val_json": x})
+        return rows
+
+    rows.append({"key": key, "ordinal": 0, "val_json": value})
+    return rows

app/assets/manager.py

@@ -0,0 +1,516 @@
+import os
+import mimetypes
+import contextlib
+from typing import Sequence
+
+from app.database.db import create_session
+from app.assets.api import schemas_out, schemas_in
+from app.assets.database.queries import (
+    asset_exists_by_hash,
+    asset_info_exists_for_asset_id,
+    get_asset_by_hash,
+    get_asset_info_by_id,
+    fetch_asset_info_asset_and_tags,
+    fetch_asset_info_and_asset,
+    create_asset_info_for_existing_asset,
+    touch_asset_info_by_id,
+    update_asset_info_full,
+    delete_asset_info_by_id,
+    list_cache_states_by_asset_id,
+    list_asset_infos_page,
+    list_tags_with_usage,
+    get_asset_tags,
+    add_tags_to_asset_info,
+    remove_tags_from_asset_info,
+    pick_best_live_path,
+    ingest_fs_asset,
+    set_asset_info_preview,
+)
+from app.assets.helpers import resolve_destination_from_tags, ensure_within_base
+from app.assets.database.models import Asset
+
+
+def _safe_sort_field(requested: str | None) -> str:
+    if not requested:
+        return "created_at"
+    v = requested.lower()
+    if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
+        return v
+    return "created_at"
+
+
+def _get_size_mtime_ns(path: str) -> tuple[int, int]:
+    st = os.stat(path, follow_symlinks=True)
+    return st.st_size, getattr(st, "st_mtime_ns", int(st.st_mtime * 1_000_000_000))
+
+
+def _safe_filename(name: str | None, fallback: str) -> str:
+    n = os.path.basename((name or "").strip() or fallback)
+    if n:
+        return n
+    return fallback
+
+
+def asset_exists(*, asset_hash: str) -> bool:
+    """
+    Check if an asset with a given hash exists in database.
+    """
+    with create_session() as session:
+        return asset_exists_by_hash(session, asset_hash=asset_hash)
+
+
+def list_assets(
+    *,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+    owner_id: str = "",
+) -> schemas_out.AssetsList:
+    sort = _safe_sort_field(sort)
+    order = "desc" if (order or "desc").lower() not in {"asc", "desc"} else order.lower()
+
+    with create_session() as session:
+        infos, tag_map, total = list_asset_infos_page(
+            session,
+            owner_id=owner_id,
+            include_tags=include_tags,
+            exclude_tags=exclude_tags,
+            name_contains=name_contains,
+            metadata_filter=metadata_filter,
+            limit=limit,
+            offset=offset,
+            sort=sort,
+            order=order,
+        )
+
+    summaries: list[schemas_out.AssetSummary] = []
+    for info in infos:
+        asset = info.asset
+        tags = tag_map.get(info.id, [])
+        summaries.append(
+            schemas_out.AssetSummary(
+                id=info.id,
+                name=info.name,
+                asset_hash=asset.hash if asset else None,
+                size=int(asset.size_bytes) if asset else None,
+                mime_type=asset.mime_type if asset else None,
+                tags=tags,
+                created_at=info.created_at,
+                updated_at=info.updated_at,
+                last_access_time=info.last_access_time,
+            )
+        )
+
+    return schemas_out.AssetsList(
+        assets=summaries,
+        total=total,
+        has_more=(offset + len(summaries)) < total,
+    )
+
+
+def get_asset(
+    *,
+    asset_info_id: str,
+    owner_id: str = "",
+) -> schemas_out.AssetDetail:
+    with create_session() as session:
+        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
+        if not res:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        info, asset, tag_names = res
+        preview_id = info.preview_id
+
+    return schemas_out.AssetDetail(
+        id=info.id,
+        name=info.name,
+        asset_hash=asset.hash if asset else None,
+        size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
+        mime_type=asset.mime_type if asset else None,
+        tags=tag_names,
+        user_metadata=info.user_metadata or {},
+        preview_id=preview_id,
+        created_at=info.created_at,
+        last_access_time=info.last_access_time,
+    )
+
+
+def resolve_asset_content_for_download(
+    *,
+    asset_info_id: str,
+    owner_id: str = "",
+) -> tuple[str, str, str]:
+    with create_session() as session:
+        pair = fetch_asset_info_and_asset(session, asset_info_id=asset_info_id, owner_id=owner_id)
+        if not pair:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+
+        info, asset = pair
+        states = list_cache_states_by_asset_id(session, asset_id=asset.id)
+        abs_path = pick_best_live_path(states)
+        if not abs_path:
+            raise FileNotFoundError
+
+        touch_asset_info_by_id(session, asset_info_id=asset_info_id)
+        session.commit()
+
+        ctype = asset.mime_type or mimetypes.guess_type(info.name or abs_path)[0] or "application/octet-stream"
+        download_name = info.name or os.path.basename(abs_path)
+        return abs_path, ctype, download_name
+
+
+def upload_asset_from_temp_path(
+    spec: schemas_in.UploadAssetSpec,
+    *,
+    temp_path: str,
+    client_filename: str | None = None,
+    owner_id: str = "",
+    expected_asset_hash: str | None = None,
+) -> schemas_out.AssetCreated:
+    """
+    Create new asset or update existing asset from a temporary file path.
+    """
+    try:
+        # NOTE: blake3 is not required right now, so this will fail if blake3 is not installed in local environment
+        import app.assets.hashing as hashing
+        digest = hashing.blake3_hash(temp_path)
+    except Exception as e:
+        raise RuntimeError(f"failed to hash uploaded file: {e}")
+    asset_hash = "blake3:" + digest
+
+    if expected_asset_hash and asset_hash != expected_asset_hash.strip().lower():
+        raise ValueError("HASH_MISMATCH")
+
+    with create_session() as session:
+        existing = get_asset_by_hash(session, asset_hash=asset_hash)
+        if existing is not None:
+            with contextlib.suppress(Exception):
+                if temp_path and os.path.exists(temp_path):
+                    os.remove(temp_path)
+
+            display_name = _safe_filename(spec.name or (client_filename or ""), fallback=digest)
+            info = create_asset_info_for_existing_asset(
+                session,
+                asset_hash=asset_hash,
+                name=display_name,
+                user_metadata=spec.user_metadata or {},
+                tags=spec.tags or [],
+                tag_origin="manual",
+                owner_id=owner_id,
+            )
+            tag_names = get_asset_tags(session, asset_info_id=info.id)
+            session.commit()
+
+            return schemas_out.AssetCreated(
+                id=info.id,
+                name=info.name,
+                asset_hash=existing.hash,
+                size=int(existing.size_bytes) if existing.size_bytes is not None else None,
+                mime_type=existing.mime_type,
+                tags=tag_names,
+                user_metadata=info.user_metadata or {},
+                preview_id=info.preview_id,
+                created_at=info.created_at,
+                last_access_time=info.last_access_time,
+                created_new=False,
+            )
+
+    base_dir, subdirs = resolve_destination_from_tags(spec.tags)
+    dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
+    os.makedirs(dest_dir, exist_ok=True)
+
+    src_for_ext = (client_filename or spec.name or "").strip()
+    _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
+    ext = _ext if 0 < len(_ext) <= 16 else ""
+    hashed_basename = f"{digest}{ext}"
+    dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
+    ensure_within_base(dest_abs, base_dir)
+
+    content_type = (
+        mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
+        or mimetypes.guess_type(hashed_basename, strict=False)[0]
+        or "application/octet-stream"
+    )
+
+    try:
+        os.replace(temp_path, dest_abs)
+    except Exception as e:
+        raise RuntimeError(f"failed to move uploaded file into place: {e}")
+
+    try:
+        size_bytes, mtime_ns = _get_size_mtime_ns(dest_abs)
+    except OSError as e:
+        raise RuntimeError(f"failed to stat destination file: {e}")
+
+    with create_session() as session:
+        result = ingest_fs_asset(
+            session,
+            asset_hash=asset_hash,
+            abs_path=dest_abs,
+            size_bytes=size_bytes,
+            mtime_ns=mtime_ns,
+            mime_type=content_type,
+            info_name=_safe_filename(spec.name or (client_filename or ""), fallback=digest),
+            owner_id=owner_id,
+            preview_id=None,
+            user_metadata=spec.user_metadata or {},
+            tags=spec.tags,
+            tag_origin="manual",
+            require_existing_tags=False,
+        )
+        info_id = result["asset_info_id"]
+        if not info_id:
+            raise RuntimeError("failed to create asset metadata")
+
+        pair = fetch_asset_info_and_asset(session, asset_info_id=info_id, owner_id=owner_id)
+        if not pair:
+            raise RuntimeError("inconsistent DB state after ingest")
+        info, asset = pair
+        tag_names = get_asset_tags(session, asset_info_id=info.id)
+        created_result = schemas_out.AssetCreated(
+            id=info.id,
+            name=info.name,
+            asset_hash=asset.hash,
+            size=int(asset.size_bytes),
+            mime_type=asset.mime_type,
+            tags=tag_names,
+            user_metadata=info.user_metadata or {},
+            preview_id=info.preview_id,
+            created_at=info.created_at,
+            last_access_time=info.last_access_time,
+            created_new=result["asset_created"],
+        )
+        session.commit()
+
+    return created_result
+
+
+def update_asset(
+    *,
+    asset_info_id: str,
+    name: str | None = None,
+    tags: list[str] | None = None,
+    user_metadata: dict | None = None,
+    owner_id: str = "",
+) -> schemas_out.AssetUpdated:
+    with create_session() as session:
+        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
+        if not info_row:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        if info_row.owner_id and info_row.owner_id != owner_id:
+            raise PermissionError("not owner")
+
+        info = update_asset_info_full(
+            session,
+            asset_info_id=asset_info_id,
+            name=name,
+            tags=tags,
+            user_metadata=user_metadata,
+            tag_origin="manual",
+            asset_info_row=info_row,
+        )
+
+        tag_names = get_asset_tags(session, asset_info_id=asset_info_id)
+        result = schemas_out.AssetUpdated(
+            id=info.id,
+            name=info.name,
+            asset_hash=info.asset.hash if info.asset else None,
+            tags=tag_names,
+            user_metadata=info.user_metadata or {},
+            updated_at=info.updated_at,
+        )
+        session.commit()
+
+    return result
+
+
+def set_asset_preview(
+    *,
+    asset_info_id: str,
+    preview_asset_id: str | None = None,
+    owner_id: str = "",
+) -> schemas_out.AssetDetail:
+    with create_session() as session:
+        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
+        if not info_row:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        if info_row.owner_id and info_row.owner_id != owner_id:
+            raise PermissionError("not owner")
+
+        set_asset_info_preview(
+            session,
+            asset_info_id=asset_info_id,
+            preview_asset_id=preview_asset_id,
+        )
+
+        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
+        if not res:
+            raise RuntimeError("State changed during preview update")
+        info, asset, tags = res
+        result = schemas_out.AssetDetail(
+            id=info.id,
+            name=info.name,
+            asset_hash=asset.hash if asset else None,
+            size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
+            mime_type=asset.mime_type if asset else None,
+            tags=tags,
+            user_metadata=info.user_metadata or {},
+            preview_id=info.preview_id,
+            created_at=info.created_at,
+            last_access_time=info.last_access_time,
+        )
+        session.commit()
+
+    return result
+
+
+def delete_asset_reference(*, asset_info_id: str, owner_id: str, delete_content_if_orphan: bool = True) -> bool:
+    with create_session() as session:
+        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
+        asset_id = info_row.asset_id if info_row else None
+        deleted = delete_asset_info_by_id(session, asset_info_id=asset_info_id, owner_id=owner_id)
+        if not deleted:
+            session.commit()
+            return False
+
+        if not delete_content_if_orphan or not asset_id:
+            session.commit()
+            return True
+
+        still_exists = asset_info_exists_for_asset_id(session, asset_id=asset_id)
+        if still_exists:
+            session.commit()
+            return True
+
+        states = list_cache_states_by_asset_id(session, asset_id=asset_id)
+        file_paths = [s.file_path for s in (states or []) if getattr(s, "file_path", None)]
+
+        asset_row = session.get(Asset, asset_id)
+        if asset_row is not None:
+            session.delete(asset_row)
+
+        session.commit()
+        for p in file_paths:
+            with contextlib.suppress(Exception):
+                if p and os.path.isfile(p):
+                    os.remove(p)
+    return True
+
+
+def create_asset_from_hash(
+    *,
+    hash_str: str,
+    name: str,
+    tags: list[str] | None = None,
+    user_metadata: dict | None = None,
+    owner_id: str = "",
+) -> schemas_out.AssetCreated | None:
+    canonical = hash_str.strip().lower()
+    with create_session() as session:
+        asset = get_asset_by_hash(session, asset_hash=canonical)
+        if not asset:
+            return None
+
+        info = create_asset_info_for_existing_asset(
+            session,
+            asset_hash=canonical,
+            name=_safe_filename(name, fallback=canonical.split(":", 1)[1]),
+            user_metadata=user_metadata or {},
+            tags=tags or [],
+            tag_origin="manual",
+            owner_id=owner_id,
+        )
+        tag_names = get_asset_tags(session, asset_info_id=info.id)
+        result = schemas_out.AssetCreated(
+            id=info.id,
+            name=info.name,
+            asset_hash=asset.hash,
+            size=int(asset.size_bytes),
+            mime_type=asset.mime_type,
+            tags=tag_names,
+            user_metadata=info.user_metadata or {},
+            preview_id=info.preview_id,
+            created_at=info.created_at,
+            last_access_time=info.last_access_time,
+            created_new=False,
+        )
+        session.commit()
+
+    return result
+
+
+def add_tags_to_asset(
+    *,
+    asset_info_id: str,
+    tags: list[str],
+    origin: str = "manual",
+    owner_id: str = "",
+) -> schemas_out.TagsAdd:
+    with create_session() as session:
+        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
+        if not info_row:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        if info_row.owner_id and info_row.owner_id != owner_id:
+            raise PermissionError("not owner")
+        data = add_tags_to_asset_info(
+            session,
+            asset_info_id=asset_info_id,
+            tags=tags,
+            origin=origin,
+            create_if_missing=True,
+            asset_info_row=info_row,
+        )
+        session.commit()
+    return schemas_out.TagsAdd(**data)
+
+
+def remove_tags_from_asset(
+    *,
+    asset_info_id: str,
+    tags: list[str],
+    owner_id: str = "",
+) -> schemas_out.TagsRemove:
+    with create_session() as session:
+        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
+        if not info_row:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        if info_row.owner_id and info_row.owner_id != owner_id:
+            raise PermissionError("not owner")
+
+        data = remove_tags_from_asset_info(
+            session,
+            asset_info_id=asset_info_id,
+            tags=tags,
+        )
+        session.commit()
+    return schemas_out.TagsRemove(**data)
+
+
+def list_tags(
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    order: str = "count_desc",
+    include_zero: bool = True,
+    owner_id: str = "",
+) -> schemas_out.TagsList:
+    limit = max(1, min(1000, limit))
+    offset = max(0, offset)
+
+    with create_session() as session:
+        rows, total = list_tags_with_usage(
+            session,
+            prefix=prefix,
+            limit=limit,
+            offset=offset,
+            include_zero=include_zero,
+            order=order,
+            owner_id=owner_id,
+        )
+
+    tags = [schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows]
+    return schemas_out.TagsList(tags=tags, total=total, has_more=(offset + len(tags)) < total)

app/assets/scanner.py

@@ -1,582 +1,263 @@
+import contextlib
+import time
 import logging
 import os
-from pathlib import Path
-from typing import Callable, Literal, TypedDict
+import sqlalchemy
 
 import folder_paths
-from app.assets.database.queries import (
-    add_missing_tag_for_asset_id,
-    bulk_update_enrichment_level,
-    bulk_update_is_missing,
-    bulk_update_needs_verify,
-    delete_orphaned_seed_asset,
-    delete_references_by_ids,
-    ensure_tags_exist,
-    get_asset_by_hash,
-    get_reference_by_id,
-    get_references_for_prefixes,
-    get_unenriched_references,
-    mark_references_missing_outside_prefixes,
-    reassign_asset_references,
-    remove_missing_tag_for_asset_id,
-    set_reference_system_metadata,
-    update_asset_hash_and_mime,
+from app.database.db import create_session, dependencies_available
+from app.assets.helpers import (
+    collect_models_files, compute_relative_filename, fast_asset_file_check, get_name_and_tags_from_asset_path,
+    list_tree,prefixes_for_root, escape_like_prefix,
+    RootType
 )
-from app.assets.services.bulk_ingest import (
-    SeedAssetSpec,
-    batch_insert_seed_assets,
-)
-from app.assets.services.file_utils import (
-    get_mtime_ns,
-    is_visible,
-    list_files_recursively,
-    verify_file_unchanged,
-)
-from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash
-from app.assets.services.metadata_extract import extract_file_metadata
-from app.assets.services.path_utils import (
-    compute_relative_filename,
-    get_comfy_models_folders,
-    get_name_and_tags_from_asset_path,
-)
-from app.database.db import create_session
+from app.assets.database.tags import add_missing_tag_for_asset_id, ensure_tags_exist, remove_missing_tag_for_asset_id
+from app.assets.database.bulk_ops import seed_from_paths_batch
+from app.assets.database.models import Asset, AssetCacheState, AssetInfo
 
 
-class _RefInfo(TypedDict):
-    ref_id: str
-    file_path: str
-    exists: bool
-    stat_unchanged: bool
-    needs_verify: bool
+def seed_assets(roots: tuple[RootType, ...], enable_logging: bool = False) -> None:
+    """
+    Scan the given roots and seed the assets into the database.
+    """
+    if not dependencies_available():
+        if enable_logging:
+            logging.warning("Database dependencies not available, skipping assets scan")
+        return
+    t_start = time.perf_counter()
+    created = 0
+    skipped_existing = 0
+    orphans_pruned = 0
+    paths: list[str] = []
+    try:
+        existing_paths: set[str] = set()
+        for r in roots:
+            try:
+                survivors: set[str] = _fast_db_consistency_pass(r, collect_existing_paths=True, update_missing_tags=True)
+                if survivors:
+                    existing_paths.update(survivors)
+            except Exception as e:
+                logging.exception("fast DB scan failed for %s: %s", r, e)
 
+        try:
+            orphans_pruned = _prune_orphaned_assets(roots)
+        except Exception as e:
+            logging.exception("orphan pruning failed: %s", e)
 
-class _AssetAccumulator(TypedDict):
-    hash: str | None
-    size_db: int
-    refs: list[_RefInfo]
+        if "models" in roots:
+            paths.extend(collect_models_files())
+        if "input" in roots:
+            paths.extend(list_tree(folder_paths.get_input_directory()))
+        if "output" in roots:
+            paths.extend(list_tree(folder_paths.get_output_directory()))
 
-
-RootType = Literal["models", "input", "output"]
-
-
-def get_prefixes_for_root(root: RootType) -> list[str]:
-    if root == "models":
-        bases: list[str] = []
-        for _bucket, paths in get_comfy_models_folders():
-            bases.extend(paths)
-        return [os.path.abspath(p) for p in bases]
-    if root == "input":
-        return [os.path.abspath(folder_paths.get_input_directory())]
-    if root == "output":
-        return [os.path.abspath(folder_paths.get_output_directory())]
-    return []
-
-
-def get_all_known_prefixes() -> list[str]:
-    """Get all known asset prefixes across all root types."""
-    all_roots: tuple[RootType, ...] = ("models", "input", "output")
-    return [p for root in all_roots for p in get_prefixes_for_root(root)]
-
-
-def collect_models_files() -> list[str]:
-    out: list[str] = []
-    for folder_name, bases in get_comfy_models_folders():
-        rel_files = folder_paths.get_filename_list(folder_name) or []
-        for rel_path in rel_files:
-            if not all(is_visible(part) for part in Path(rel_path).parts):
+        specs: list[dict] = []
+        tag_pool: set[str] = set()
+        for p in paths:
+            abs_p = os.path.abspath(p)
+            if abs_p in existing_paths:
+                skipped_existing += 1
                 continue
-            abs_path = folder_paths.get_full_path(folder_name, rel_path)
-            if not abs_path:
+            try:
+                stat_p = os.stat(abs_p, follow_symlinks=False)
+            except OSError:
                 continue
-            abs_path = os.path.abspath(abs_path)
-            allowed = False
-            abs_p = Path(abs_path)
-            for b in bases:
-                if abs_p.is_relative_to(os.path.abspath(b)):
-                    allowed = True
-                    break
-            if allowed:
-                out.append(abs_path)
-    return out
+            # skip empty files
+            if not stat_p.st_size:
+                continue
+            name, tags = get_name_and_tags_from_asset_path(abs_p)
+            specs.append(
+                {
+                    "abs_path": abs_p,
+                    "size_bytes": stat_p.st_size,
+                    "mtime_ns": getattr(stat_p, "st_mtime_ns", int(stat_p.st_mtime * 1_000_000_000)),
+                    "info_name": name,
+                    "tags": tags,
+                    "fname": compute_relative_filename(abs_p),
+                }
+            )
+            for t in tags:
+                tag_pool.add(t)
+        # if no file specs, nothing to do
+        if not specs:
+            return
+        with create_session() as sess:
+            if tag_pool:
+                ensure_tags_exist(sess, tag_pool, tag_type="user")
+
+            result = seed_from_paths_batch(sess, specs=specs, owner_id="")
+            created += result["inserted_infos"]
+            sess.commit()
+    finally:
+        if enable_logging:
+            logging.info(
+                "Assets scan(roots=%s) completed in %.3fs (created=%d, skipped_existing=%d, orphans_pruned=%d, total_seen=%d)",
+                roots,
+                time.perf_counter() - t_start,
+                created,
+                skipped_existing,
+                orphans_pruned,
+                len(paths),
+            )
 
 
-def sync_references_with_filesystem(
-    session,
+def _prune_orphaned_assets(roots: tuple[RootType, ...]) -> int:
+    """Prune cache states outside configured prefixes, then delete orphaned seed assets."""
+    all_prefixes = [os.path.abspath(p) for r in roots for p in prefixes_for_root(r)]
+    if not all_prefixes:
+        return 0
+
+    def make_prefix_condition(prefix: str):
+        base = prefix if prefix.endswith(os.sep) else prefix + os.sep
+        escaped, esc = escape_like_prefix(base)
+        return AssetCacheState.file_path.like(escaped + "%", escape=esc)
+
+    matches_valid_prefix = sqlalchemy.or_(*[make_prefix_condition(p) for p in all_prefixes])
+
+    orphan_subq = (
+        sqlalchemy.select(Asset.id)
+        .outerjoin(AssetCacheState, AssetCacheState.asset_id == Asset.id)
+        .where(Asset.hash.is_(None), AssetCacheState.id.is_(None))
+    ).scalar_subquery()
+
+    with create_session() as sess:
+        sess.execute(sqlalchemy.delete(AssetCacheState).where(~matches_valid_prefix))
+        sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id.in_(orphan_subq)))
+        result = sess.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(orphan_subq)))
+        sess.commit()
+        return result.rowcount
+
+
+def _fast_db_consistency_pass(
     root: RootType,
+    *,
     collect_existing_paths: bool = False,
     update_missing_tags: bool = False,
 ) -> set[str] | None:
-    """Reconcile asset references with filesystem for a root.
-
-    - Toggle needs_verify per reference using mtime/size stat check
-    - For hashed assets with at least one stat-unchanged ref: delete stale missing refs
-    - For seed assets with all refs missing: delete Asset and its references
-    - Optionally add/remove 'missing' tags based on stat check in this root
-    - Optionally return surviving absolute paths
-
-    Args:
-        session: Database session
-        root: Root type to scan
-        collect_existing_paths: If True, return set of surviving file paths
-        update_missing_tags: If True, update 'missing' tags based on file status
-
-    Returns:
-        Set of surviving absolute paths if collect_existing_paths=True, else None
+    """Fast DB+FS pass for a root:
+      - Toggle needs_verify per state using fast check
+      - For hashed assets with at least one fast-ok state in this root: delete stale missing states
+      - For seed assets with all states missing: delete Asset and its AssetInfos
+      - Optionally add/remove 'missing' tags based on fast-ok in this root
+      - Optionally return surviving absolute paths
     """
-    prefixes = get_prefixes_for_root(root)
+    prefixes = prefixes_for_root(root)
     if not prefixes:
         return set() if collect_existing_paths else None
 
-    rows = get_references_for_prefixes(
-        session, prefixes, include_missing=update_missing_tags
-    )
-
-    by_asset: dict[str, _AssetAccumulator] = {}
-    for row in rows:
-        acc = by_asset.get(row.asset_id)
-        if acc is None:
-            acc = {"hash": row.asset_hash, "size_db": row.size_bytes, "refs": []}
-            by_asset[row.asset_id] = acc
-
-        stat_unchanged = False
-        try:
-            exists = True
-            stat_unchanged = verify_file_unchanged(
-                mtime_db=row.mtime_ns,
-                size_db=acc["size_db"],
-                stat_result=os.stat(row.file_path, follow_symlinks=True),
-            )
-        except FileNotFoundError:
-            exists = False
-        except PermissionError:
-            exists = True
-            logging.debug("Permission denied accessing %s", row.file_path)
-        except OSError as e:
-            exists = False
-            logging.debug("OSError checking %s: %s", row.file_path, e)
-
-        acc["refs"].append(
-            {
-                "ref_id": row.reference_id,
-                "file_path": row.file_path,
-                "exists": exists,
-                "stat_unchanged": stat_unchanged,
-                "needs_verify": row.needs_verify,
-            }
-        )
-
-    to_set_verify: list[str] = []
-    to_clear_verify: list[str] = []
-    stale_ref_ids: list[str] = []
-    to_mark_missing: list[str] = []
-    to_clear_missing: list[str] = []
-    survivors: set[str] = set()
-
-    for aid, acc in by_asset.items():
-        a_hash = acc["hash"]
-        refs = acc["refs"]
-        any_unchanged = any(r["stat_unchanged"] for r in refs)
-        all_missing = all(not r["exists"] for r in refs)
-
-        for r in refs:
-            if not r["exists"]:
-                to_mark_missing.append(r["ref_id"])
-                continue
-            if r["stat_unchanged"]:
-                to_clear_missing.append(r["ref_id"])
-                if r["needs_verify"]:
-                    to_clear_verify.append(r["ref_id"])
-            if not r["stat_unchanged"] and not r["needs_verify"]:
-                to_set_verify.append(r["ref_id"])
-
-        if a_hash is None:
-            if refs and all_missing:
-                delete_orphaned_seed_asset(session, aid)
-            else:
-                for r in refs:
-                    if r["exists"]:
-                        survivors.add(os.path.abspath(r["file_path"]))
-            continue
-
-        if any_unchanged:
-            for r in refs:
-                if not r["exists"]:
-                    stale_ref_ids.append(r["ref_id"])
-            if update_missing_tags:
-                try:
-                    remove_missing_tag_for_asset_id(session, asset_id=aid)
-                except Exception as e:
-                    logging.warning(
-                        "Failed to remove missing tag for asset %s: %s", aid, e
-                    )
-        elif update_missing_tags:
-            try:
-                add_missing_tag_for_asset_id(session, asset_id=aid, origin="automatic")
-            except Exception as e:
-                logging.warning("Failed to add missing tag for asset %s: %s", aid, e)
-
-        for r in refs:
-            if r["exists"]:
-                survivors.add(os.path.abspath(r["file_path"]))
-
-    delete_references_by_ids(session, stale_ref_ids)
-    stale_set = set(stale_ref_ids)
-    to_mark_missing = [ref_id for ref_id in to_mark_missing if ref_id not in stale_set]
-    bulk_update_is_missing(session, to_mark_missing, value=True)
-    bulk_update_is_missing(session, to_clear_missing, value=False)
-    bulk_update_needs_verify(session, to_set_verify, value=True)
-    bulk_update_needs_verify(session, to_clear_verify, value=False)
-
-    return survivors if collect_existing_paths else None
-
-
-def sync_root_safely(root: RootType) -> set[str]:
-    """Sync a single root's references with the filesystem.
-
-    Returns survivors (existing paths) or empty set on failure.
-    """
-    try:
-        with create_session() as sess:
-            survivors = sync_references_with_filesystem(
-                sess,
-                root,
-                collect_existing_paths=True,
-                update_missing_tags=True,
-            )
-            sess.commit()
-            return survivors or set()
-    except Exception as e:
-        logging.exception("fast DB scan failed for %s: %s", root, e)
-        return set()
-
-
-def mark_missing_outside_prefixes_safely(prefixes: list[str]) -> int:
-    """Mark references as missing when outside the given prefixes.
-
-    This is a non-destructive soft-delete. Returns count marked or 0 on failure.
-    """
-    try:
-        with create_session() as sess:
-            count = mark_references_missing_outside_prefixes(sess, prefixes)
-            sess.commit()
-            return count
-    except Exception as e:
-        logging.exception("marking missing assets failed: %s", e)
-        return 0
-
-
-def collect_paths_for_roots(roots: tuple[RootType, ...]) -> list[str]:
-    """Collect all file paths for the given roots."""
-    paths: list[str] = []
-    if "models" in roots:
-        paths.extend(collect_models_files())
-    if "input" in roots:
-        paths.extend(list_files_recursively(folder_paths.get_input_directory()))
-    if "output" in roots:
-        paths.extend(list_files_recursively(folder_paths.get_output_directory()))
-    return paths
-
-
-def build_asset_specs(
-    paths: list[str],
-    existing_paths: set[str],
-    enable_metadata_extraction: bool = True,
-    compute_hashes: bool = False,
-) -> tuple[list[SeedAssetSpec], set[str], int]:
-    """Build asset specs from paths, returning (specs, tag_pool, skipped_count).
-
-    Args:
-        paths: List of file paths to process
-        existing_paths: Set of paths that already exist in the database
-        enable_metadata_extraction: If True, extract tier 1 & 2 metadata
-        compute_hashes: If True, compute blake3 hashes (slow for large files)
-    """
-    specs: list[SeedAssetSpec] = []
-    tag_pool: set[str] = set()
-    skipped = 0
-
-    for p in paths:
-        abs_p = os.path.abspath(p)
-        if abs_p in existing_paths:
-            skipped += 1
-            continue
-        try:
-            stat_p = os.stat(abs_p, follow_symlinks=True)
-        except OSError:
-            continue
-        if not stat_p.st_size:
-            continue
-        name, tags = get_name_and_tags_from_asset_path(abs_p)
-        rel_fname = compute_relative_filename(abs_p)
-
-        # Extract metadata (tier 1: filesystem, tier 2: safetensors header)
-        metadata = None
-        if enable_metadata_extraction:
-            metadata = extract_file_metadata(
-                abs_p,
-                stat_result=stat_p,
-                relative_filename=rel_fname,
-            )
-
-        # Compute hash if requested
-        asset_hash: str | None = None
-        if compute_hashes:
-            try:
-                digest, _ = compute_blake3_hash(abs_p)
-                asset_hash = "blake3:" + digest
-            except Exception as e:
-                logging.warning("Failed to hash %s: %s", abs_p, e)
-
-        mime_type = metadata.content_type if metadata else None
-        specs.append(
-            {
-                "abs_path": abs_p,
-                "size_bytes": stat_p.st_size,
-                "mtime_ns": get_mtime_ns(stat_p),
-                "info_name": name,
-                "tags": tags,
-                "fname": rel_fname,
-                "metadata": metadata,
-                "hash": asset_hash,
-                "mime_type": mime_type,
-                "job_id": None,
-            }
-        )
-        tag_pool.update(tags)
-
-    return specs, tag_pool, skipped
-
-
-
-def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -> int:
-    """Insert asset specs into database, returning count of created refs."""
-    if not specs:
-        return 0
-    with create_session() as sess:
-        if tag_pool:
-            ensure_tags_exist(sess, tag_pool, tag_type="user")
-        result = batch_insert_seed_assets(sess, specs=specs, owner_id="")
-        sess.commit()
-        return result.inserted_refs
-
-
-# Enrichment level constants
-ENRICHMENT_STUB = 0  # Fast scan: path, size, mtime only
-ENRICHMENT_METADATA = 1  # Metadata extracted (safetensors header, mime type)
-ENRICHMENT_HASHED = 2  # Hash computed (blake3)
-
-
-def get_unenriched_assets_for_roots(
-    roots: tuple[RootType, ...],
-    max_level: int = ENRICHMENT_STUB,
-    limit: int = 1000,
-) -> list:
-    """Get assets that need enrichment for the given roots.
-
-    Args:
-        roots: Tuple of root types to scan
-        max_level: Maximum enrichment level to include
-        limit: Maximum number of rows to return
-
-    Returns:
-        List of UnenrichedReferenceRow
-    """
-    prefixes: list[str] = []
-    for root in roots:
-        prefixes.extend(get_prefixes_for_root(root))
-
-    if not prefixes:
-        return []
+    conds = []
+    for p in prefixes:
+        base = os.path.abspath(p)
+        if not base.endswith(os.sep):
+            base += os.sep
+        escaped, esc = escape_like_prefix(base)
+        conds.append(AssetCacheState.file_path.like(escaped + "%", escape=esc))
 
     with create_session() as sess:
-        return get_unenriched_references(
-            sess, prefixes, max_level=max_level, limit=limit
-        )
-
-
-def enrich_asset(
-    session,
-    file_path: str,
-    reference_id: str,
-    asset_id: str,
-    extract_metadata: bool = True,
-    compute_hash: bool = False,
-    interrupt_check: Callable[[], bool] | None = None,
-    hash_checkpoints: dict[str, HashCheckpoint] | None = None,
-) -> int:
-    """Enrich a single asset with metadata and/or hash.
-
-    Args:
-        session: Database session (caller manages lifecycle)
-        file_path: Absolute path to the file
-        reference_id: ID of the reference to update
-        asset_id: ID of the asset to update (for mime_type and hash)
-        extract_metadata: If True, extract safetensors header and mime type
-        compute_hash: If True, compute blake3 hash
-        interrupt_check: Optional non-blocking callable that returns True if
-            the operation should be interrupted (e.g. paused or cancelled)
-        hash_checkpoints: Optional dict for saving/restoring hash progress
-            across interruptions, keyed by file path
-
-    Returns:
-        New enrichment level achieved
-    """
-    new_level = ENRICHMENT_STUB
-
-    try:
-        stat_p = os.stat(file_path, follow_symlinks=True)
-    except OSError:
-        return new_level
-
-    initial_mtime_ns = get_mtime_ns(stat_p)
-    rel_fname = compute_relative_filename(file_path)
-    mime_type: str | None = None
-    metadata = None
-
-    if extract_metadata:
-        metadata = extract_file_metadata(
-            file_path,
-            stat_result=stat_p,
-            relative_filename=rel_fname,
-        )
-        if metadata:
-            mime_type = metadata.content_type
-            new_level = ENRICHMENT_METADATA
-
-    full_hash: str | None = None
-    if compute_hash:
-        try:
-            mtime_before = get_mtime_ns(stat_p)
-            size_before = stat_p.st_size
-
-            # Restore checkpoint if available and file unchanged
-            checkpoint = None
-            if hash_checkpoints is not None:
-                checkpoint = hash_checkpoints.get(file_path)
-                if checkpoint is not None:
-                    cur_stat = os.stat(file_path, follow_symlinks=True)
-                    if (checkpoint.mtime_ns != get_mtime_ns(cur_stat)
-                            or checkpoint.file_size != cur_stat.st_size):
-                        checkpoint = None
-                        hash_checkpoints.pop(file_path, None)
-                    else:
-                        mtime_before = get_mtime_ns(cur_stat)
-
-            digest, new_checkpoint = compute_blake3_hash(
-                file_path,
-                interrupt_check=interrupt_check,
-                checkpoint=checkpoint,
-            )
-
-            if digest is None:
-                # Interrupted — save checkpoint for later resumption
-                if hash_checkpoints is not None and new_checkpoint is not None:
-                    new_checkpoint.mtime_ns = mtime_before
-                    new_checkpoint.file_size = size_before
-                    hash_checkpoints[file_path] = new_checkpoint
-                return new_level
-
-            # Completed — clear any saved checkpoint
-            if hash_checkpoints is not None:
-                hash_checkpoints.pop(file_path, None)
-
-            stat_after = os.stat(file_path, follow_symlinks=True)
-            mtime_after = get_mtime_ns(stat_after)
-            if mtime_before != mtime_after:
-                logging.warning("File modified during hashing, discarding hash: %s", file_path)
-            else:
-                full_hash = f"blake3:{digest}"
-                metadata_ok = not extract_metadata or metadata is not None
-                if metadata_ok:
-                    new_level = ENRICHMENT_HASHED
-        except Exception as e:
-            logging.warning("Failed to hash %s: %s", file_path, e)
-
-    # Optimistic guard: if the reference's mtime_ns changed since we
-    # started (e.g. ingest_existing_file updated it), our results are
-    # stale — discard them to avoid overwriting fresh registration data.
-    ref = get_reference_by_id(session, reference_id)
-    if ref is None or ref.mtime_ns != initial_mtime_ns:
-        session.rollback()
-        logging.info(
-            "Ref %s mtime changed during enrichment, discarding stale result",
-            reference_id,
-        )
-        return ENRICHMENT_STUB
-
-    if extract_metadata and metadata:
-        system_metadata = metadata.to_user_metadata()
-        set_reference_system_metadata(session, reference_id, system_metadata)
-
-    if full_hash:
-        existing = get_asset_by_hash(session, full_hash)
-        if existing and existing.id != asset_id:
-            reassign_asset_references(session, asset_id, existing.id, reference_id)
-            delete_orphaned_seed_asset(session, asset_id)
-            if mime_type:
-                update_asset_hash_and_mime(session, existing.id, mime_type=mime_type)
-        else:
-            update_asset_hash_and_mime(session, asset_id, full_hash, mime_type)
-    elif mime_type:
-        update_asset_hash_and_mime(session, asset_id, mime_type=mime_type)
-
-    bulk_update_enrichment_level(session, [reference_id], new_level)
-    session.commit()
-
-    return new_level
-
-
-def enrich_assets_batch(
-    rows: list,
-    extract_metadata: bool = True,
-    compute_hash: bool = False,
-    interrupt_check: Callable[[], bool] | None = None,
-    hash_checkpoints: dict[str, HashCheckpoint] | None = None,
-) -> tuple[int, list[str]]:
-    """Enrich a batch of assets.
-
-    Uses a single DB session for the entire batch, committing after each
-    individual asset to avoid long-held transactions while eliminating
-    per-asset session creation overhead.
-
-    Args:
-        rows: List of UnenrichedReferenceRow from get_unenriched_assets_for_roots
-        extract_metadata: If True, extract metadata for each asset
-        compute_hash: If True, compute hash for each asset
-        interrupt_check: Optional non-blocking callable that returns True if
-            the operation should be interrupted (e.g. paused or cancelled)
-        hash_checkpoints: Optional dict for saving/restoring hash progress
-            across interruptions, keyed by file path
-
-    Returns:
-        Tuple of (enriched_count, failed_reference_ids)
-    """
-    enriched = 0
-    failed_ids: list[str] = []
-
-    with create_session() as sess:
-        for row in rows:
-            if interrupt_check is not None and interrupt_check():
-                break
-
-            try:
-                new_level = enrich_asset(
-                    sess,
-                    file_path=row.file_path,
-                    reference_id=row.reference_id,
-                    asset_id=row.asset_id,
-                    extract_metadata=extract_metadata,
-                    compute_hash=compute_hash,
-                    interrupt_check=interrupt_check,
-                    hash_checkpoints=hash_checkpoints,
+        rows = (
+            sess.execute(
+                sqlalchemy.select(
+                    AssetCacheState.id,
+                    AssetCacheState.file_path,
+                    AssetCacheState.mtime_ns,
+                    AssetCacheState.needs_verify,
+                    AssetCacheState.asset_id,
+                    Asset.hash,
+                    Asset.size_bytes,
                 )
-                if new_level > row.enrichment_level:
-                    enriched += 1
-                else:
-                    failed_ids.append(row.reference_id)
-            except Exception as e:
-                logging.warning("Failed to enrich %s: %s", row.file_path, e)
-                sess.rollback()
-                failed_ids.append(row.reference_id)
+                .join(Asset, Asset.id == AssetCacheState.asset_id)
+                .where(sqlalchemy.or_(*conds))
+                .order_by(AssetCacheState.asset_id.asc(), AssetCacheState.id.asc())
+            )
+        ).all()
 
-    return enriched, failed_ids
+        by_asset: dict[str, dict] = {}
+        for sid, fp, mtime_db, needs_verify, aid, a_hash, a_size in rows:
+            acc = by_asset.get(aid)
+            if acc is None:
+                acc = {"hash": a_hash, "size_db": int(a_size or 0), "states": []}
+                by_asset[aid] = acc
+
+            fast_ok = False
+            try:
+                exists = True
+                fast_ok = fast_asset_file_check(
+                    mtime_db=mtime_db,
+                    size_db=acc["size_db"],
+                    stat_result=os.stat(fp, follow_symlinks=True),
+                )
+            except FileNotFoundError:
+                exists = False
+            except OSError:
+                exists = False
+
+            acc["states"].append({
+                "sid": sid,
+                "fp": fp,
+                "exists": exists,
+                "fast_ok": fast_ok,
+                "needs_verify": bool(needs_verify),
+            })
+
+        to_set_verify: list[int] = []
+        to_clear_verify: list[int] = []
+        stale_state_ids: list[int] = []
+        survivors: set[str] = set()
+
+        for aid, acc in by_asset.items():
+            a_hash = acc["hash"]
+            states = acc["states"]
+            any_fast_ok = any(s["fast_ok"] for s in states)
+            all_missing = all(not s["exists"] for s in states)
+
+            for s in states:
+                if not s["exists"]:
+                    continue
+                if s["fast_ok"] and s["needs_verify"]:
+                    to_clear_verify.append(s["sid"])
+                if not s["fast_ok"] and not s["needs_verify"]:
+                    to_set_verify.append(s["sid"])
+
+            if a_hash is None:
+                if states and all_missing:  # remove seed Asset completely, if no valid AssetCache exists
+                    sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id == aid))
+                    asset = sess.get(Asset, aid)
+                    if asset:
+                        sess.delete(asset)
+                else:
+                    for s in states:
+                        if s["exists"]:
+                            survivors.add(os.path.abspath(s["fp"]))
+                continue
+
+            if any_fast_ok:  # if Asset has at least one valid AssetCache record, remove any invalid AssetCache records
+                for s in states:
+                    if not s["exists"]:
+                        stale_state_ids.append(s["sid"])
+                if update_missing_tags:
+                    with contextlib.suppress(Exception):
+                        remove_missing_tag_for_asset_id(sess, asset_id=aid)
+            elif update_missing_tags:
+                with contextlib.suppress(Exception):
+                    add_missing_tag_for_asset_id(sess, asset_id=aid, origin="automatic")
+
+            for s in states:
+                if s["exists"]:
+                    survivors.add(os.path.abspath(s["fp"]))
+
+        if stale_state_ids:
+            sess.execute(sqlalchemy.delete(AssetCacheState).where(AssetCacheState.id.in_(stale_state_ids)))
+        if to_set_verify:
+            sess.execute(
+                sqlalchemy.update(AssetCacheState)
+                .where(AssetCacheState.id.in_(to_set_verify))
+                .values(needs_verify=True)
+            )
+        if to_clear_verify:
+            sess.execute(
+                sqlalchemy.update(AssetCacheState)
+                .where(AssetCacheState.id.in_(to_clear_verify))
+                .values(needs_verify=False)
+            )
+        sess.commit()
+        return survivors if collect_existing_paths else None

app/assets/seeder.py

@@ -1,846 +0,0 @@
-"""Background asset seeder with thread management and cancellation support."""
-
-import logging
-import os
-import threading
-import time
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import Callable
-
-from app.assets.scanner import (
-    ENRICHMENT_METADATA,
-    ENRICHMENT_STUB,
-    RootType,
-    build_asset_specs,
-    collect_paths_for_roots,
-    enrich_assets_batch,
-    get_all_known_prefixes,
-    get_prefixes_for_root,
-    get_unenriched_assets_for_roots,
-    insert_asset_specs,
-    mark_missing_outside_prefixes_safely,
-    sync_root_safely,
-)
-from app.database.db import dependencies_available
-
-
-class ScanInProgressError(Exception):
-    """Raised when an operation cannot proceed because a scan is running."""
-
-
-class State(Enum):
-    """Seeder state machine states."""
-
-    IDLE = "IDLE"
-    RUNNING = "RUNNING"
-    PAUSED = "PAUSED"
-    CANCELLING = "CANCELLING"
-
-
-class ScanPhase(Enum):
-    """Scan phase options."""
-
-    FAST = "fast"  # Phase 1: filesystem only (stubs)
-    ENRICH = "enrich"  # Phase 2: metadata + hash
-    FULL = "full"  # Both phases sequentially
-
-
-@dataclass
-class Progress:
-    """Progress information for a scan operation."""
-
-    scanned: int = 0
-    total: int = 0
-    created: int = 0
-    skipped: int = 0
-
-
-@dataclass
-class ScanStatus:
-    """Current status of the asset seeder."""
-
-    state: State
-    progress: Progress | None
-    errors: list[str] = field(default_factory=list)
-
-
-ProgressCallback = Callable[[Progress], None]
-
-
-class _AssetSeeder:
-    """Background asset scanning manager.
-
-    Spawns ephemeral daemon threads for scanning.
-    Each scan creates a new thread that exits when complete.
-    Use the module-level ``asset_seeder`` instance.
-    """
-
-    def __init__(self) -> None:
-        # RLock is required because _run_scan() drains pending work while
-        # holding _lock and re-enters start() which also acquires _lock.
-        self._lock = threading.RLock()
-        self._state = State.IDLE
-        self._progress: Progress | None = None
-        self._last_progress: Progress | None = None
-        self._errors: list[str] = []
-        self._thread: threading.Thread | None = None
-        self._cancel_event = threading.Event()
-        self._run_gate = threading.Event()
-        self._run_gate.set()  # Start unpaused (set = running, clear = paused)
-        self._roots: tuple[RootType, ...] = ()
-        self._phase: ScanPhase = ScanPhase.FULL
-        self._compute_hashes: bool = False
-        self._prune_first: bool = False
-        self._progress_callback: ProgressCallback | None = None
-        self._disabled: bool = False
-        self._pending_enrich: dict | None = None
-
-    def disable(self) -> None:
-        """Disable the asset seeder, preventing any scans from starting."""
-        self._disabled = True
-        logging.info("Asset seeder disabled")
-
-    def is_disabled(self) -> bool:
-        """Check if the asset seeder is disabled."""
-        return self._disabled
-
-    def start(
-        self,
-        roots: tuple[RootType, ...] = ("models", "input", "output"),
-        phase: ScanPhase = ScanPhase.FULL,
-        progress_callback: ProgressCallback | None = None,
-        prune_first: bool = False,
-        compute_hashes: bool = False,
-    ) -> bool:
-        """Start a background scan for the given roots.
-
-        Args:
-            roots: Tuple of root types to scan (models, input, output)
-            phase: Scan phase to run (FAST, ENRICH, or FULL for both)
-            progress_callback: Optional callback called with progress updates
-            prune_first: If True, prune orphaned assets before scanning
-            compute_hashes: If True, compute blake3 hashes (slow)
-
-        Returns:
-            True if scan was started, False if already running
-        """
-        if self._disabled:
-            logging.debug("Asset seeder is disabled, skipping start")
-            return False
-        logging.info("Seeder start (roots=%s, phase=%s)", roots, phase.value)
-        with self._lock:
-            if self._state != State.IDLE:
-                logging.info("Asset seeder already running, skipping start")
-                return False
-            self._state = State.RUNNING
-            self._progress = Progress()
-            self._errors = []
-            self._roots = roots
-            self._phase = phase
-            self._prune_first = prune_first
-            self._compute_hashes = compute_hashes
-            self._progress_callback = progress_callback
-            self._cancel_event.clear()
-            self._run_gate.set()  # Ensure unpaused when starting
-            self._thread = threading.Thread(
-                target=self._run_scan,
-                name="_AssetSeeder",
-                daemon=True,
-            )
-            self._thread.start()
-            return True
-
-    def start_fast(
-        self,
-        roots: tuple[RootType, ...] = ("models", "input", "output"),
-        progress_callback: ProgressCallback | None = None,
-        prune_first: bool = False,
-    ) -> bool:
-        """Start a fast scan (phase 1 only) - creates stub records.
-
-        Args:
-            roots: Tuple of root types to scan
-            progress_callback: Optional callback for progress updates
-            prune_first: If True, prune orphaned assets before scanning
-
-        Returns:
-            True if scan was started, False if already running
-        """
-        return self.start(
-            roots=roots,
-            phase=ScanPhase.FAST,
-            progress_callback=progress_callback,
-            prune_first=prune_first,
-            compute_hashes=False,
-        )
-
-    def start_enrich(
-        self,
-        roots: tuple[RootType, ...] = ("models", "input", "output"),
-        progress_callback: ProgressCallback | None = None,
-        compute_hashes: bool = False,
-    ) -> bool:
-        """Start an enrichment scan (phase 2 only) - extracts metadata and hashes.
-
-        Args:
-            roots: Tuple of root types to scan
-            progress_callback: Optional callback for progress updates
-            compute_hashes: If True, compute blake3 hashes
-
-        Returns:
-            True if scan was started, False if already running
-        """
-        return self.start(
-            roots=roots,
-            phase=ScanPhase.ENRICH,
-            progress_callback=progress_callback,
-            prune_first=False,
-            compute_hashes=compute_hashes,
-        )
-
-    def enqueue_enrich(
-        self,
-        roots: tuple[RootType, ...] = ("models", "input", "output"),
-        compute_hashes: bool = False,
-    ) -> bool:
-        """Start an enrichment scan now, or queue it for after the current scan.
-
-        If the seeder is idle, starts immediately. Otherwise, the enrich
-        request is stored and will run automatically when the current scan
-        finishes.
-
-        Args:
-            roots: Tuple of root types to scan
-            compute_hashes: If True, compute blake3 hashes
-
-        Returns:
-            True if started immediately, False if queued for later
-        """
-        with self._lock:
-            if self.start_enrich(roots=roots, compute_hashes=compute_hashes):
-                return True
-            if self._pending_enrich is not None:
-                existing_roots = set(self._pending_enrich["roots"])
-                existing_roots.update(roots)
-                self._pending_enrich["roots"] = tuple(existing_roots)
-                self._pending_enrich["compute_hashes"] = (
-                    self._pending_enrich["compute_hashes"] or compute_hashes
-                )
-            else:
-                self._pending_enrich = {
-                    "roots": roots,
-                    "compute_hashes": compute_hashes,
-                }
-            logging.info("Enrich scan queued (roots=%s)", self._pending_enrich["roots"])
-        return False
-
-    def cancel(self) -> bool:
-        """Request cancellation of the current scan.
-
-        Returns:
-            True if cancellation was requested, False if not running or paused
-        """
-        with self._lock:
-            if self._state not in (State.RUNNING, State.PAUSED):
-                return False
-            logging.info("Asset seeder cancelling (was %s)", self._state.value)
-            self._state = State.CANCELLING
-            self._cancel_event.set()
-            self._run_gate.set()  # Unblock if paused so thread can exit
-            return True
-
-    def stop(self) -> bool:
-        """Stop the current scan (alias for cancel).
-
-        Returns:
-            True if stop was requested, False if not running
-        """
-        return self.cancel()
-
-    def pause(self) -> bool:
-        """Pause the current scan.
-
-        The scan will complete its current batch before pausing.
-
-        Returns:
-            True if pause was requested, False if not running
-        """
-        with self._lock:
-            if self._state != State.RUNNING:
-                return False
-            logging.info("Asset seeder pausing")
-            self._state = State.PAUSED
-            self._run_gate.clear()
-            return True
-
-    def resume(self) -> bool:
-        """Resume a paused scan.
-
-        This is a noop if the scan is not in the PAUSED state
-
-        Returns:
-            True if resumed, False if not paused
-        """
-        with self._lock:
-            if self._state != State.PAUSED:
-                return False
-            logging.info("Asset seeder resuming")
-            self._state = State.RUNNING
-            self._run_gate.set()
-        self._emit_event("assets.seed.resumed", {})
-        return True
-
-    def restart(
-        self,
-        roots: tuple[RootType, ...] | None = None,
-        phase: ScanPhase | None = None,
-        progress_callback: ProgressCallback | None = None,
-        prune_first: bool | None = None,
-        compute_hashes: bool | None = None,
-        timeout: float = 5.0,
-    ) -> bool:
-        """Cancel any running scan and start a new one.
-
-        Args:
-            roots: Roots to scan (defaults to previous roots)
-            phase: Scan phase (defaults to previous phase)
-            progress_callback: Progress callback (defaults to previous)
-            prune_first: Prune before scan (defaults to previous)
-            compute_hashes: Compute hashes (defaults to previous)
-            timeout: Max seconds to wait for current scan to stop
-
-        Returns:
-            True if new scan was started, False if failed to stop previous
-        """
-        logging.info("Asset seeder restart requested")
-        with self._lock:
-            prev_roots = self._roots
-            prev_phase = self._phase
-            prev_callback = self._progress_callback
-            prev_prune = self._prune_first
-            prev_hashes = self._compute_hashes
-
-        self.cancel()
-        if not self.wait(timeout=timeout):
-            return False
-
-        cb = progress_callback if progress_callback is not None else prev_callback
-        return self.start(
-            roots=roots if roots is not None else prev_roots,
-            phase=phase if phase is not None else prev_phase,
-            progress_callback=cb,
-            prune_first=prune_first if prune_first is not None else prev_prune,
-            compute_hashes=(
-                compute_hashes if compute_hashes is not None else prev_hashes
-            ),
-        )
-
-    def wait(self, timeout: float | None = None) -> bool:
-        """Wait for the current scan to complete.
-
-        Args:
-            timeout: Maximum seconds to wait, or None for no timeout
-
-        Returns:
-            True if scan completed, False if timeout expired or no scan running
-        """
-        with self._lock:
-            thread = self._thread
-        if thread is None:
-            return True
-        thread.join(timeout=timeout)
-        return not thread.is_alive()
-
-    def get_status(self) -> ScanStatus:
-        """Get the current status and progress of the seeder."""
-        with self._lock:
-            src = self._progress or self._last_progress
-            return ScanStatus(
-                state=self._state,
-                progress=Progress(
-                    scanned=src.scanned,
-                    total=src.total,
-                    created=src.created,
-                    skipped=src.skipped,
-                )
-                if src
-                else None,
-                errors=list(self._errors),
-            )
-
-    def shutdown(self, timeout: float = 5.0) -> None:
-        """Gracefully shutdown: cancel any running scan and wait for thread.
-
-        Args:
-            timeout: Maximum seconds to wait for thread to exit
-        """
-        self.cancel()
-        self.wait(timeout=timeout)
-        with self._lock:
-            self._thread = None
-
-    def mark_missing_outside_prefixes(self) -> int:
-        """Mark references as missing when outside all known root prefixes.
-
-        This is a non-destructive soft-delete operation. Assets and their
-        metadata are preserved, but references are flagged as missing.
-        They can be restored if the file reappears in a future scan.
-
-        This operation is decoupled from scanning to prevent partial scans
-        from accidentally marking assets belonging to other roots.
-
-        Should be called explicitly when cleanup is desired, typically after
-        a full scan of all roots or during maintenance.
-
-        Returns:
-            Number of references marked as missing
-
-        Raises:
-            ScanInProgressError: If a scan is currently running
-        """
-        with self._lock:
-            if self._state != State.IDLE:
-                raise ScanInProgressError(
-                    "Cannot mark missing assets while scan is running"
-                )
-            self._state = State.RUNNING
-
-        try:
-            if not dependencies_available():
-                logging.warning(
-                    "Database dependencies not available, skipping mark missing"
-                )
-                return 0
-
-            all_prefixes = get_all_known_prefixes()
-            marked = mark_missing_outside_prefixes_safely(all_prefixes)
-            if marked > 0:
-                logging.info("Marked %d references as missing", marked)
-            return marked
-        finally:
-            with self._lock:
-                self._reset_to_idle()
-
-    def _reset_to_idle(self) -> None:
-        """Reset state to IDLE, preserving last progress. Caller must hold _lock."""
-        self._last_progress = self._progress
-        self._state = State.IDLE
-        self._progress = None
-
-    def _is_cancelled(self) -> bool:
-        """Check if cancellation has been requested."""
-        return self._cancel_event.is_set()
-
-    def _is_paused_or_cancelled(self) -> bool:
-        """Non-blocking check: True if paused or cancelled.
-
-        Use as interrupt_check for I/O-bound work (e.g. hashing) so that
-        file handles are released immediately on pause rather than held
-        open while blocked. The caller is responsible for blocking on
-        _check_pause_and_cancel() afterward.
-        """
-        return not self._run_gate.is_set() or self._cancel_event.is_set()
-
-    def _check_pause_and_cancel(self) -> bool:
-        """Block while paused, then check if cancelled.
-
-        Call this at checkpoint locations in scan loops. It will:
-        1. Block indefinitely while paused (until resume or cancel)
-        2. Return True if cancelled, False to continue
-
-        Returns:
-            True if scan should stop, False to continue
-        """
-        if not self._run_gate.is_set():
-            self._emit_event("assets.seed.paused", {})
-        self._run_gate.wait()  # Blocks if paused
-        return self._is_cancelled()
-
-    def _emit_event(self, event_type: str, data: dict) -> None:
-        """Emit a WebSocket event if server is available."""
-        try:
-            from server import PromptServer
-
-            if hasattr(PromptServer, "instance") and PromptServer.instance:
-                PromptServer.instance.send_sync(event_type, data)
-        except Exception:
-            pass
-
-    def _update_progress(
-        self,
-        scanned: int | None = None,
-        total: int | None = None,
-        created: int | None = None,
-        skipped: int | None = None,
-    ) -> None:
-        """Update progress counters (thread-safe)."""
-        callback: ProgressCallback | None = None
-        progress: Progress | None = None
-
-        with self._lock:
-            if self._progress is None:
-                return
-            if scanned is not None:
-                self._progress.scanned = scanned
-            if total is not None:
-                self._progress.total = total
-            if created is not None:
-                self._progress.created = created
-            if skipped is not None:
-                self._progress.skipped = skipped
-            if self._progress_callback:
-                callback = self._progress_callback
-                progress = Progress(
-                    scanned=self._progress.scanned,
-                    total=self._progress.total,
-                    created=self._progress.created,
-                    skipped=self._progress.skipped,
-                )
-
-        if callback and progress:
-            try:
-                callback(progress)
-            except Exception:
-                pass
-
-    _MAX_ERRORS = 200
-
-    def _add_error(self, message: str) -> None:
-        """Add an error message (thread-safe), capped at _MAX_ERRORS."""
-        with self._lock:
-            if len(self._errors) < self._MAX_ERRORS:
-                self._errors.append(message)
-
-    def _log_scan_config(self, roots: tuple[RootType, ...]) -> None:
-        """Log the directories that will be scanned."""
-        import folder_paths
-
-        for root in roots:
-            if root == "models":
-                logging.info(
-                    "Asset scan [models] directory: %s",
-                    os.path.abspath(folder_paths.models_dir),
-                )
-            else:
-                prefixes = get_prefixes_for_root(root)
-                if prefixes:
-                    logging.info("Asset scan [%s] directories: %s", root, prefixes)
-
-    def _run_scan(self) -> None:
-        """Main scan loop running in background thread."""
-        t_start = time.perf_counter()
-        roots = self._roots
-        phase = self._phase
-        cancelled = False
-        total_created = 0
-        total_enriched = 0
-        skipped_existing = 0
-        total_paths = 0
-
-        try:
-            if not dependencies_available():
-                self._add_error("Database dependencies not available")
-                self._emit_event(
-                    "assets.seed.error",
-                    {"message": "Database dependencies not available"},
-                )
-                return
-
-            if self._prune_first:
-                all_prefixes = get_all_known_prefixes()
-                marked = mark_missing_outside_prefixes_safely(all_prefixes)
-                if marked > 0:
-                    logging.info("Marked %d refs as missing before scan", marked)
-
-            if self._check_pause_and_cancel():
-                logging.info("Asset scan cancelled after pruning phase")
-                cancelled = True
-                return
-
-            self._log_scan_config(roots)
-
-            # Phase 1: Fast scan (stub records)
-            if phase in (ScanPhase.FAST, ScanPhase.FULL):
-                created, skipped, paths = self._run_fast_phase(roots)
-                total_created, skipped_existing, total_paths = created, skipped, paths
-
-                if self._check_pause_and_cancel():
-                    cancelled = True
-                    return
-
-                self._emit_event(
-                    "assets.seed.fast_complete",
-                    {
-                        "roots": list(roots),
-                        "created": total_created,
-                        "skipped": skipped_existing,
-                        "total": total_paths,
-                    },
-                )
-
-            # Phase 2: Enrichment scan (metadata + hashes)
-            if phase in (ScanPhase.ENRICH, ScanPhase.FULL):
-                if self._check_pause_and_cancel():
-                    cancelled = True
-                    return
-
-                enrich_cancelled, total_enriched = self._run_enrich_phase(roots)
-
-                if enrich_cancelled:
-                    cancelled = True
-                    return
-
-                self._emit_event(
-                    "assets.seed.enrich_complete",
-                    {
-                        "roots": list(roots),
-                        "enriched": total_enriched,
-                    },
-                )
-
-            elapsed = time.perf_counter() - t_start
-            logging.info(
-                "Scan(%s, %s) done %.3fs: created=%d enriched=%d skipped=%d",
-                roots,
-                phase.value,
-                elapsed,
-                total_created,
-                total_enriched,
-                skipped_existing,
-            )
-
-            self._emit_event(
-                "assets.seed.completed",
-                {
-                    "phase": phase.value,
-                    "total": total_paths,
-                    "created": total_created,
-                    "enriched": total_enriched,
-                    "skipped": skipped_existing,
-                    "elapsed": round(elapsed, 3),
-                },
-            )
-
-        except Exception as e:
-            self._add_error(f"Scan failed: {e}")
-            logging.exception("Asset scan failed")
-            self._emit_event("assets.seed.error", {"message": str(e)})
-        finally:
-            if cancelled:
-                self._emit_event(
-                    "assets.seed.cancelled",
-                    {
-                        "scanned": self._progress.scanned if self._progress else 0,
-                        "total": total_paths,
-                        "created": total_created,
-                    },
-                )
-            with self._lock:
-                self._reset_to_idle()
-                pending = self._pending_enrich
-                if pending is not None:
-                    self._pending_enrich = None
-                    if not self.start_enrich(
-                        roots=pending["roots"],
-                        compute_hashes=pending["compute_hashes"],
-                    ):
-                        logging.warning(
-                            "Pending enrich scan could not start (roots=%s)",
-                            pending["roots"],
-                        )
-
-    def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]:
-        """Run phase 1: fast scan to create stub records.
-
-        Returns:
-            Tuple of (total_created, skipped_existing, total_paths)
-        """
-        t_fast_start = time.perf_counter()
-        total_created = 0
-        skipped_existing = 0
-
-        existing_paths: set[str] = set()
-        t_sync = time.perf_counter()
-        for r in roots:
-            if self._check_pause_and_cancel():
-                return total_created, skipped_existing, 0
-            existing_paths.update(sync_root_safely(r))
-        logging.debug(
-            "Fast scan: sync_root phase took %.3fs (%d existing paths)",
-            time.perf_counter() - t_sync,
-            len(existing_paths),
-        )
-
-        if self._check_pause_and_cancel():
-            return total_created, skipped_existing, 0
-
-        t_collect = time.perf_counter()
-        paths = collect_paths_for_roots(roots)
-        logging.debug(
-            "Fast scan: collect_paths took %.3fs (%d paths found)",
-            time.perf_counter() - t_collect,
-            len(paths),
-        )
-        total_paths = len(paths)
-        self._update_progress(total=total_paths)
-
-        self._emit_event(
-            "assets.seed.started",
-            {"roots": list(roots), "total": total_paths, "phase": "fast"},
-        )
-
-        # Use stub specs (no metadata extraction, no hashing)
-        t_specs = time.perf_counter()
-        specs, tag_pool, skipped_existing = build_asset_specs(
-            paths,
-            existing_paths,
-            enable_metadata_extraction=False,
-            compute_hashes=False,
-        )
-        logging.debug(
-            "Fast scan: build_asset_specs took %.3fs (%d specs, %d skipped)",
-            time.perf_counter() - t_specs,
-            len(specs),
-            skipped_existing,
-        )
-        self._update_progress(skipped=skipped_existing)
-
-        if self._check_pause_and_cancel():
-            return total_created, skipped_existing, total_paths
-
-        batch_size = 500
-        last_progress_time = time.perf_counter()
-        progress_interval = 1.0
-
-        for i in range(0, len(specs), batch_size):
-            if self._check_pause_and_cancel():
-                logging.info(
-                    "Fast scan cancelled after %d/%d files (created=%d)",
-                    i,
-                    len(specs),
-                    total_created,
-                )
-                return total_created, skipped_existing, total_paths
-
-            batch = specs[i : i + batch_size]
-            batch_tags = {t for spec in batch for t in spec["tags"]}
-            try:
-                created = insert_asset_specs(batch, batch_tags)
-                total_created += created
-            except Exception as e:
-                self._add_error(f"Batch insert failed at offset {i}: {e}")
-                logging.exception("Batch insert failed at offset %d", i)
-
-            scanned = i + len(batch)
-            now = time.perf_counter()
-            self._update_progress(scanned=scanned, created=total_created)
-
-            if now - last_progress_time >= progress_interval:
-                self._emit_event(
-                    "assets.seed.progress",
-                    {
-                        "phase": "fast",
-                        "scanned": scanned,
-                        "total": len(specs),
-                        "created": total_created,
-                    },
-                )
-                last_progress_time = now
-
-        self._update_progress(scanned=len(specs), created=total_created)
-        logging.info(
-            "Fast scan complete: %.3fs total (created=%d, skipped=%d, total_paths=%d)",
-            time.perf_counter() - t_fast_start,
-            total_created,
-            skipped_existing,
-            total_paths,
-        )
-        return total_created, skipped_existing, total_paths
-
-    def _run_enrich_phase(self, roots: tuple[RootType, ...]) -> tuple[bool, int]:
-        """Run phase 2: enrich existing records with metadata and hashes.
-
-        Returns:
-            Tuple of (cancelled, total_enriched)
-        """
-        total_enriched = 0
-        batch_size = 100
-        last_progress_time = time.perf_counter()
-        progress_interval = 1.0
-
-        # Get the target enrichment level based on compute_hashes
-        if not self._compute_hashes:
-            target_max_level = ENRICHMENT_STUB
-        else:
-            target_max_level = ENRICHMENT_METADATA
-
-        self._emit_event(
-            "assets.seed.started",
-            {"roots": list(roots), "phase": "enrich"},
-        )
-
-        skip_ids: set[str] = set()
-        consecutive_empty = 0
-        max_consecutive_empty = 3
-
-        # Hash checkpoints survive across batches so interrupted hashes
-        # can be resumed without re-reading the entire file.
-        hash_checkpoints: dict[str, object] = {}
-
-        while True:
-            if self._check_pause_and_cancel():
-                logging.info("Enrich scan cancelled after %d assets", total_enriched)
-                return True, total_enriched
-
-            # Fetch next batch of unenriched assets
-            unenriched = get_unenriched_assets_for_roots(
-                roots,
-                max_level=target_max_level,
-                limit=batch_size,
-            )
-
-            # Filter out previously failed references
-            if skip_ids:
-                unenriched = [r for r in unenriched if r.reference_id not in skip_ids]
-
-            if not unenriched:
-                break
-
-            enriched, failed_ids = enrich_assets_batch(
-                unenriched,
-                extract_metadata=True,
-                compute_hash=self._compute_hashes,
-                interrupt_check=self._is_paused_or_cancelled,
-                hash_checkpoints=hash_checkpoints,
-            )
-            total_enriched += enriched
-            skip_ids.update(failed_ids)
-
-            if enriched == 0:
-                consecutive_empty += 1
-                if consecutive_empty >= max_consecutive_empty:
-                    logging.warning(
-                        "Enrich phase stopping: %d consecutive batches with no progress (%d skipped)",
-                        consecutive_empty,
-                        len(skip_ids),
-                    )
-                    break
-            else:
-                consecutive_empty = 0
-
-            now = time.perf_counter()
-            if now - last_progress_time >= progress_interval:
-                self._emit_event(
-                    "assets.seed.progress",
-                    {
-                        "phase": "enrich",
-                        "enriched": total_enriched,
-                    },
-                )
-                last_progress_time = now
-
-        return False, total_enriched
-
-
-asset_seeder = _AssetSeeder()

app/assets/services/__init__.py

@@ -1,91 +0,0 @@
-from app.assets.services.asset_management import (
-    asset_exists,
-    delete_asset_reference,
-    get_asset_by_hash,
-    get_asset_detail,
-    list_assets_page,
-    resolve_asset_for_download,
-    set_asset_preview,
-    update_asset_metadata,
-)
-from app.assets.services.bulk_ingest import (
-    BulkInsertResult,
-    batch_insert_seed_assets,
-    cleanup_unreferenced_assets,
-)
-from app.assets.services.file_utils import (
-    get_mtime_ns,
-    get_size_and_mtime_ns,
-    list_files_recursively,
-    verify_file_unchanged,
-)
-from app.assets.services.ingest import (
-    DependencyMissingError,
-    HashMismatchError,
-    create_from_hash,
-    ingest_existing_file,
-    register_output_files,
-    upload_from_temp_path,
-)
-from app.assets.database.queries import (
-    AddTagsResult,
-    RemoveTagsResult,
-)
-from app.assets.services.schemas import (
-    AssetData,
-    AssetDetailResult,
-    AssetSummaryData,
-    DownloadResolutionResult,
-    IngestResult,
-    ListAssetsResult,
-    ReferenceData,
-    RegisterAssetResult,
-    TagUsage,
-    UploadResult,
-    UserMetadata,
-)
-from app.assets.services.tagging import (
-    apply_tags,
-    list_tags,
-    remove_tags,
-)
-
-__all__ = [
-    "AddTagsResult",
-    "AssetData",
-    "AssetDetailResult",
-    "AssetSummaryData",
-    "ReferenceData",
-    "BulkInsertResult",
-    "DependencyMissingError",
-    "DownloadResolutionResult",
-    "HashMismatchError",
-    "IngestResult",
-    "ListAssetsResult",
-    "RegisterAssetResult",
-    "RemoveTagsResult",
-    "TagUsage",
-    "UploadResult",
-    "UserMetadata",
-    "apply_tags",
-    "asset_exists",
-    "batch_insert_seed_assets",
-    "create_from_hash",
-    "delete_asset_reference",
-    "get_asset_by_hash",
-    "get_asset_detail",
-    "ingest_existing_file",
-    "register_output_files",
-    "get_mtime_ns",
-    "get_size_and_mtime_ns",
-    "list_assets_page",
-    "list_files_recursively",
-    "list_tags",
-    "cleanup_unreferenced_assets",
-    "remove_tags",
-    "resolve_asset_for_download",
-    "set_asset_preview",
-    "update_asset_metadata",
-    "upload_from_temp_path",
-    "verify_file_unchanged",
-]

app/assets/services/asset_management.py

@@ -1,367 +0,0 @@
-import contextlib
-import mimetypes
-import os
-from typing import Sequence
-
-
-from app.assets.database.models import Asset
-from app.assets.database.queries import (
-    asset_exists_by_hash,
-    reference_exists_for_asset_id,
-    delete_reference_by_id,
-    fetch_reference_and_asset,
-    soft_delete_reference_by_id,
-    fetch_reference_asset_and_tags,
-    get_asset_by_hash as queries_get_asset_by_hash,
-    get_reference_by_id,
-    get_reference_with_owner_check,
-    list_references_page,
-    list_all_file_paths_by_asset_id,
-    list_references_by_asset_id,
-    set_reference_metadata,
-    set_reference_preview,
-    set_reference_tags,
-    update_asset_hash_and_mime,
-    update_reference_access_time,
-    update_reference_name,
-    update_reference_updated_at,
-)
-from app.assets.helpers import select_best_live_path
-from app.assets.services.path_utils import compute_relative_filename
-from app.assets.services.schemas import (
-    AssetData,
-    AssetDetailResult,
-    AssetSummaryData,
-    DownloadResolutionResult,
-    ListAssetsResult,
-    UserMetadata,
-    extract_asset_data,
-    extract_reference_data,
-)
-from app.database.db import create_session
-
-
-def get_asset_detail(
-    reference_id: str,
-    owner_id: str = "",
-) -> AssetDetailResult | None:
-    with create_session() as session:
-        result = fetch_reference_asset_and_tags(
-            session,
-            reference_id=reference_id,
-            owner_id=owner_id,
-        )
-        if not result:
-            return None
-
-        ref, asset, tags = result
-        return AssetDetailResult(
-            ref=extract_reference_data(ref),
-            asset=extract_asset_data(asset),
-            tags=tags,
-        )
-
-
-def update_asset_metadata(
-    reference_id: str,
-    name: str | None = None,
-    tags: Sequence[str] | None = None,
-    user_metadata: UserMetadata = None,
-    tag_origin: str = "manual",
-    owner_id: str = "",
-    mime_type: str | None = None,
-    preview_id: str | None = None,
-) -> AssetDetailResult:
-    with create_session() as session:
-        ref = get_reference_with_owner_check(session, reference_id, owner_id)
-
-        touched = False
-        if name is not None and name != ref.name:
-            update_reference_name(session, reference_id=reference_id, name=name)
-            touched = True
-
-        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
-
-        new_meta: dict | None = None
-        if user_metadata is not None:
-            new_meta = dict(user_metadata)
-        elif computed_filename:
-            current_meta = ref.user_metadata or {}
-            if current_meta.get("filename") != computed_filename:
-                new_meta = dict(current_meta)
-
-        if new_meta is not None:
-            if computed_filename:
-                new_meta["filename"] = computed_filename
-            set_reference_metadata(
-                session, reference_id=reference_id, user_metadata=new_meta
-            )
-            touched = True
-
-        if tags is not None:
-            set_reference_tags(
-                session,
-                reference_id=reference_id,
-                tags=tags,
-                origin=tag_origin,
-            )
-            touched = True
-
-        if mime_type is not None:
-            updated = update_asset_hash_and_mime(
-                session, asset_id=ref.asset_id, mime_type=mime_type
-            )
-            if updated:
-                touched = True
-
-        if preview_id is not None:
-            set_reference_preview(
-                session,
-                reference_id=reference_id,
-                preview_reference_id=preview_id,
-            )
-            touched = True
-
-        if touched and user_metadata is None:
-            update_reference_updated_at(session, reference_id=reference_id)
-
-        result = fetch_reference_asset_and_tags(
-            session,
-            reference_id=reference_id,
-            owner_id=owner_id,
-        )
-        if not result:
-            raise RuntimeError("State changed during update")
-
-        ref, asset, tag_list = result
-        detail = AssetDetailResult(
-            ref=extract_reference_data(ref),
-            asset=extract_asset_data(asset),
-            tags=tag_list,
-        )
-        session.commit()
-
-        return detail
-
-
-def delete_asset_reference(
-    reference_id: str,
-    owner_id: str,
-    delete_content_if_orphan: bool = True,
-) -> bool:
-    with create_session() as session:
-        if not delete_content_if_orphan:
-            # Soft delete: mark the reference as deleted but keep everything
-            deleted = soft_delete_reference_by_id(
-                session, reference_id=reference_id, owner_id=owner_id
-            )
-            session.commit()
-            return deleted
-
-        ref_row = get_reference_by_id(session, reference_id=reference_id)
-        asset_id = ref_row.asset_id if ref_row else None
-        file_path = ref_row.file_path if ref_row else None
-
-        deleted = delete_reference_by_id(
-            session, reference_id=reference_id, owner_id=owner_id
-        )
-        if not deleted:
-            session.commit()
-            return False
-
-        if not asset_id:
-            session.commit()
-            return True
-
-        still_exists = reference_exists_for_asset_id(session, asset_id=asset_id)
-        if still_exists:
-            session.commit()
-            return True
-
-        # Orphaned asset - gather ALL file paths (including
-        # soft-deleted / missing refs) so their on-disk files get cleaned up.
-        file_paths = list_all_file_paths_by_asset_id(session, asset_id=asset_id)
-        # Also include the just-deleted file path
-        if file_path:
-            file_paths.append(file_path)
-
-        asset_row = session.get(Asset, asset_id)
-        if asset_row is not None:
-            session.delete(asset_row)
-
-        session.commit()
-
-        # Delete files after commit
-        for p in file_paths:
-            with contextlib.suppress(Exception):
-                if p and os.path.isfile(p):
-                    os.remove(p)
-
-    return True
-
-
-def set_asset_preview(
-    reference_id: str,
-    preview_reference_id: str | None = None,
-    owner_id: str = "",
-) -> AssetDetailResult:
-    with create_session() as session:
-        get_reference_with_owner_check(session, reference_id, owner_id)
-
-        set_reference_preview(
-            session,
-            reference_id=reference_id,
-            preview_reference_id=preview_reference_id,
-        )
-
-        result = fetch_reference_asset_and_tags(
-            session, reference_id=reference_id, owner_id=owner_id
-        )
-        if not result:
-            raise RuntimeError("State changed during preview update")
-
-        ref, asset, tags = result
-        detail = AssetDetailResult(
-            ref=extract_reference_data(ref),
-            asset=extract_asset_data(asset),
-            tags=tags,
-        )
-        session.commit()
-
-        return detail
-
-
-def asset_exists(asset_hash: str) -> bool:
-    with create_session() as session:
-        return asset_exists_by_hash(session, asset_hash=asset_hash)
-
-
-def get_asset_by_hash(asset_hash: str) -> AssetData | None:
-    with create_session() as session:
-        asset = queries_get_asset_by_hash(session, asset_hash=asset_hash)
-        return extract_asset_data(asset)
-
-
-def list_assets_page(
-    owner_id: str = "",
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 20,
-    offset: int = 0,
-    sort: str = "created_at",
-    order: str = "desc",
-) -> ListAssetsResult:
-    with create_session() as session:
-        refs, tag_map, total = list_references_page(
-            session,
-            owner_id=owner_id,
-            include_tags=include_tags,
-            exclude_tags=exclude_tags,
-            name_contains=name_contains,
-            metadata_filter=metadata_filter,
-            limit=limit,
-            offset=offset,
-            sort=sort,
-            order=order,
-        )
-
-        items: list[AssetSummaryData] = []
-        for ref in refs:
-            items.append(
-                AssetSummaryData(
-                    ref=extract_reference_data(ref),
-                    asset=extract_asset_data(ref.asset),
-                    tags=tag_map.get(ref.id, []),
-                )
-            )
-
-        return ListAssetsResult(items=items, total=total)
-
-
-def resolve_hash_to_path(
-    asset_hash: str,
-    owner_id: str = "",
-) -> DownloadResolutionResult | None:
-    """Resolve a blake3 hash to an on-disk file path.
-
-    Only references visible to *owner_id* are considered (owner-less
-    references are always visible).
-
-    Returns a DownloadResolutionResult with abs_path, content_type, and
-    download_name, or None if no asset or live path is found.
-    """
-    with create_session() as session:
-        asset = queries_get_asset_by_hash(session, asset_hash)
-        if not asset:
-            return None
-        refs = list_references_by_asset_id(session, asset_id=asset.id)
-        visible = [
-            r for r in refs
-            if r.owner_id == "" or r.owner_id == owner_id
-        ]
-        abs_path = select_best_live_path(visible)
-        if not abs_path:
-            return None
-        display_name = os.path.basename(abs_path)
-        for ref in visible:
-            if ref.file_path == abs_path and ref.name:
-                display_name = ref.name
-                break
-        ctype = (
-            asset.mime_type
-            or mimetypes.guess_type(display_name)[0]
-            or "application/octet-stream"
-        )
-    return DownloadResolutionResult(
-        abs_path=abs_path,
-        content_type=ctype,
-        download_name=display_name,
-    )
-
-
-def resolve_asset_for_download(
-    reference_id: str,
-    owner_id: str = "",
-) -> DownloadResolutionResult:
-    with create_session() as session:
-        pair = fetch_reference_and_asset(
-            session, reference_id=reference_id, owner_id=owner_id
-        )
-        if not pair:
-            raise ValueError(f"AssetReference {reference_id} not found")
-
-        ref, asset = pair
-
-        # For references with file_path, use that directly
-        if ref.file_path and os.path.isfile(ref.file_path):
-            abs_path = ref.file_path
-        else:
-            # For API-created refs without file_path, find a path from other refs
-            refs = list_references_by_asset_id(session, asset_id=asset.id)
-            abs_path = select_best_live_path(refs)
-            if not abs_path:
-                raise FileNotFoundError(
-                    f"No live path for AssetReference {reference_id} "
-                    f"(asset id={asset.id}, name={ref.name})"
-                )
-
-        # Capture ORM attributes before commit (commit expires loaded objects)
-        ref_name = ref.name
-        asset_mime = asset.mime_type
-
-        update_reference_access_time(session, reference_id=reference_id)
-        session.commit()
-
-        ctype = (
-            asset_mime
-            or mimetypes.guess_type(ref_name or abs_path)[0]
-            or "application/octet-stream"
-        )
-        download_name = ref_name or os.path.basename(abs_path)
-        return DownloadResolutionResult(
-            abs_path=abs_path,
-            content_type=ctype,
-            download_name=download_name,
-        )

app/assets/services/bulk_ingest.py

@@ -1,283 +0,0 @@
-from __future__ import annotations
-
-import os
-import uuid
-from dataclasses import dataclass
-from datetime import datetime
-from typing import TYPE_CHECKING, Any, TypedDict
-
-from sqlalchemy.orm import Session
-
-from app.assets.database.queries import (
-    bulk_insert_assets,
-    bulk_insert_references_ignore_conflicts,
-    bulk_insert_tags_and_meta,
-    delete_assets_by_ids,
-    get_existing_asset_ids,
-    get_reference_ids_by_ids,
-    get_references_by_paths_and_asset_ids,
-    get_unreferenced_unhashed_asset_ids,
-    restore_references_by_paths,
-)
-from app.assets.helpers import get_utc_now
-
-if TYPE_CHECKING:
-    from app.assets.services.metadata_extract import ExtractedMetadata
-
-
-class SeedAssetSpec(TypedDict):
-    """Spec for seeding an asset from filesystem."""
-
-    abs_path: str
-    size_bytes: int
-    mtime_ns: int
-    info_name: str
-    tags: list[str]
-    fname: str
-    metadata: ExtractedMetadata | None
-    hash: str | None
-    mime_type: str | None
-    job_id: str | None
-
-
-class AssetRow(TypedDict):
-    """Row data for inserting an Asset."""
-
-    id: str
-    hash: str | None
-    size_bytes: int
-    mime_type: str | None
-    created_at: datetime
-
-
-class ReferenceRow(TypedDict):
-    """Row data for inserting an AssetReference."""
-
-    id: str
-    asset_id: str
-    file_path: str
-    mtime_ns: int
-    owner_id: str
-    name: str
-    preview_id: str | None
-    user_metadata: dict[str, Any] | None
-    job_id: str | None
-    created_at: datetime
-    updated_at: datetime
-    last_access_time: datetime
-
-
-class TagRow(TypedDict):
-    """Row data for inserting a Tag."""
-
-    asset_reference_id: str
-    tag_name: str
-    origin: str
-    added_at: datetime
-
-
-class MetadataRow(TypedDict):
-    """Row data for inserting asset metadata."""
-
-    asset_reference_id: str
-    key: str
-    ordinal: int
-    val_str: str | None
-    val_num: float | None
-    val_bool: bool | None
-    val_json: dict[str, Any] | None
-
-
-@dataclass
-class BulkInsertResult:
-    """Result of bulk asset insertion."""
-
-    inserted_refs: int
-    won_paths: int
-    lost_paths: int
-
-
-def batch_insert_seed_assets(
-    session: Session,
-    specs: list[SeedAssetSpec],
-    owner_id: str = "",
-) -> BulkInsertResult:
-    """Seed assets from filesystem specs in batch.
-
-    Each spec is a dict with keys:
-      - abs_path: str
-      - size_bytes: int
-      - mtime_ns: int
-      - info_name: str
-      - tags: list[str]
-      - fname: Optional[str]
-
-    This function orchestrates:
-    1. Insert seed Assets (hash=NULL)
-    2. Claim references with ON CONFLICT DO NOTHING on file_path
-    3. Query to find winners (paths where our asset_id was inserted)
-    4. Delete Assets for losers (path already claimed by another asset)
-    5. Insert tags and metadata for successfully inserted references
-
-    Returns:
-        BulkInsertResult with inserted_refs, won_paths, lost_paths
-    """
-    if not specs:
-        return BulkInsertResult(inserted_refs=0, won_paths=0, lost_paths=0)
-
-    current_time = get_utc_now()
-    asset_rows: list[AssetRow] = []
-    reference_rows: list[ReferenceRow] = []
-    path_to_asset_id: dict[str, str] = {}
-    asset_id_to_ref_data: dict[str, dict] = {}
-    absolute_path_list: list[str] = []
-
-    for spec in specs:
-        absolute_path = os.path.abspath(spec["abs_path"])
-        asset_id = str(uuid.uuid4())
-        reference_id = str(uuid.uuid4())
-        absolute_path_list.append(absolute_path)
-        path_to_asset_id[absolute_path] = asset_id
-
-        mime_type = spec.get("mime_type")
-        asset_rows.append(
-            {
-                "id": asset_id,
-                "hash": spec.get("hash"),
-                "size_bytes": spec["size_bytes"],
-                "mime_type": mime_type,
-                "created_at": current_time,
-            }
-        )
-
-        # Build user_metadata from extracted metadata or fallback to filename
-        extracted_metadata = spec.get("metadata")
-        if extracted_metadata:
-            user_metadata: dict[str, Any] | None = extracted_metadata.to_user_metadata()
-        elif spec["fname"]:
-            user_metadata = {"filename": spec["fname"]}
-        else:
-            user_metadata = None
-
-        reference_rows.append(
-            {
-                "id": reference_id,
-                "asset_id": asset_id,
-                "file_path": absolute_path,
-                "mtime_ns": spec["mtime_ns"],
-                "owner_id": owner_id,
-                "name": spec["info_name"],
-                "preview_id": None,
-                "user_metadata": user_metadata,
-                "job_id": spec.get("job_id"),
-                "created_at": current_time,
-                "updated_at": current_time,
-                "last_access_time": current_time,
-            }
-        )
-
-        asset_id_to_ref_data[asset_id] = {
-            "reference_id": reference_id,
-            "tags": spec["tags"],
-            "filename": spec["fname"],
-            "extracted_metadata": extracted_metadata,
-        }
-
-    bulk_insert_assets(session, asset_rows)
-
-    # Filter reference rows to only those whose assets were actually inserted
-    # (assets with duplicate hashes are silently dropped by ON CONFLICT DO NOTHING)
-    inserted_asset_ids = get_existing_asset_ids(
-        session, [r["asset_id"] for r in reference_rows]
-    )
-    reference_rows = [r for r in reference_rows if r["asset_id"] in inserted_asset_ids]
-
-    bulk_insert_references_ignore_conflicts(session, reference_rows)
-    restore_references_by_paths(session, absolute_path_list)
-    winning_paths = get_references_by_paths_and_asset_ids(session, path_to_asset_id)
-
-    inserted_paths = {
-        path
-        for path in absolute_path_list
-        if path_to_asset_id[path] in inserted_asset_ids
-    }
-    losing_paths = inserted_paths - winning_paths
-    lost_asset_ids = [path_to_asset_id[path] for path in losing_paths]
-
-    if lost_asset_ids:
-        delete_assets_by_ids(session, lost_asset_ids)
-
-    if not winning_paths:
-        return BulkInsertResult(
-            inserted_refs=0,
-            won_paths=0,
-            lost_paths=len(losing_paths),
-        )
-
-    # Get reference IDs for winners
-    winning_ref_ids = [
-        asset_id_to_ref_data[path_to_asset_id[path]]["reference_id"]
-        for path in winning_paths
-    ]
-    inserted_ref_ids = get_reference_ids_by_ids(session, winning_ref_ids)
-
-    tag_rows: list[TagRow] = []
-    metadata_rows: list[MetadataRow] = []
-
-    if inserted_ref_ids:
-        for path in winning_paths:
-            asset_id = path_to_asset_id[path]
-            ref_data = asset_id_to_ref_data[asset_id]
-            ref_id = ref_data["reference_id"]
-
-            if ref_id not in inserted_ref_ids:
-                continue
-
-            for tag in ref_data["tags"]:
-                tag_rows.append(
-                    {
-                        "asset_reference_id": ref_id,
-                        "tag_name": tag,
-                        "origin": "automatic",
-                        "added_at": current_time,
-                    }
-                )
-
-            # Use extracted metadata for meta rows if available
-            extracted_metadata = ref_data.get("extracted_metadata")
-            if extracted_metadata:
-                metadata_rows.extend(extracted_metadata.to_meta_rows(ref_id))
-            elif ref_data["filename"]:
-                # Fallback: just store filename
-                metadata_rows.append(
-                    {
-                        "asset_reference_id": ref_id,
-                        "key": "filename",
-                        "ordinal": 0,
-                        "val_str": ref_data["filename"],
-                        "val_num": None,
-                        "val_bool": None,
-                        "val_json": None,
-                    }
-                )
-
-    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=metadata_rows)
-
-    return BulkInsertResult(
-        inserted_refs=len(inserted_ref_ids),
-        won_paths=len(winning_paths),
-        lost_paths=len(losing_paths),
-    )
-
-
-def cleanup_unreferenced_assets(session: Session) -> int:
-    """Hard-delete unhashed assets with no active references.
-
-    This is a destructive operation intended for explicit cleanup.
-    Only deletes assets where hash=None and all references are missing.
-
-    Returns:
-        Number of assets deleted
-    """
-    unreferenced_ids = get_unreferenced_unhashed_asset_ids(session)
-    return delete_assets_by_ids(session, unreferenced_ids)

app/assets/services/file_utils.py

@@ -1,70 +0,0 @@
-import os
-
-
-def get_mtime_ns(stat_result: os.stat_result) -> int:
-    """Extract mtime in nanoseconds from a stat result."""
-    return getattr(
-        stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000)
-    )
-
-
-def get_size_and_mtime_ns(path: str, follow_symlinks: bool = True) -> tuple[int, int]:
-    """Get file size in bytes and mtime in nanoseconds."""
-    st = os.stat(path, follow_symlinks=follow_symlinks)
-    return st.st_size, get_mtime_ns(st)
-
-
-def verify_file_unchanged(
-    mtime_db: int | None,
-    size_db: int | None,
-    stat_result: os.stat_result,
-) -> bool:
-    """Check if a file is unchanged based on mtime and size.
-
-    Returns True if the file's mtime and size match the database values.
-    Returns False if mtime_db is None or values don't match.
-
-    size_db=None means don't check size; 0 is a valid recorded size.
-    """
-    if mtime_db is None:
-        return False
-    actual_mtime_ns = get_mtime_ns(stat_result)
-    if int(mtime_db) != int(actual_mtime_ns):
-        return False
-    if size_db is not None:
-        return int(stat_result.st_size) == int(size_db)
-    return True
-
-
-def is_visible(name: str) -> bool:
-    """Return True if a file or directory name is visible (not hidden)."""
-    return not name.startswith(".")
-
-
-def list_files_recursively(base_dir: str) -> list[str]:
-    """Recursively list all files in a directory, following symlinks."""
-    out: list[str] = []
-    base_abs = os.path.abspath(base_dir)
-    if not os.path.isdir(base_abs):
-        return out
-    # Track seen real directory identities to prevent circular symlink loops
-    seen_dirs: set[tuple[int, int]] = set()
-    for dirpath, subdirs, filenames in os.walk(
-        base_abs, topdown=True, followlinks=True
-    ):
-        try:
-            st = os.stat(dirpath)
-            dir_id = (st.st_dev, st.st_ino)
-        except OSError:
-            subdirs.clear()
-            continue
-        if dir_id in seen_dirs:
-            subdirs.clear()
-            continue
-        seen_dirs.add(dir_id)
-        subdirs[:] = [d for d in subdirs if is_visible(d)]
-        for name in filenames:
-            if not is_visible(name):
-                continue
-            out.append(os.path.abspath(os.path.join(dirpath, name)))
-    return out

app/assets/services/hashing.py

@@ -1,99 +0,0 @@
-import io
-import os
-from contextlib import contextmanager
-from dataclasses import dataclass
-from typing import IO, Any, Callable, Iterator
-import logging
-
-try:
-    from blake3 import blake3
-except ModuleNotFoundError:
-    logging.warning("WARNING: blake3 package not installed")
-
-DEFAULT_CHUNK = 8 * 1024 * 1024
-
-InterruptCheck = Callable[[], bool]
-
-
-@dataclass
-class HashCheckpoint:
-    """Saved state for resuming an interrupted hash computation."""
-
-    bytes_processed: int
-    hasher: Any  # blake3 hasher instance
-    mtime_ns: int = 0
-    file_size: int = 0
-
-
-@contextmanager
-def _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], bool]]:
-    """Yield (file_object, is_path) with appropriate setup/teardown."""
-    if hasattr(fp, "read"):
-        seekable = getattr(fp, "seekable", lambda: False)()
-        orig_pos = None
-        if seekable:
-            try:
-                orig_pos = fp.tell()
-                if orig_pos != 0:
-                    fp.seek(0)
-            except io.UnsupportedOperation:
-                orig_pos = None
-        try:
-            yield fp, False
-        finally:
-            if orig_pos is not None:
-                fp.seek(orig_pos)
-    else:
-        with open(os.fspath(fp), "rb") as f:
-            yield f, True
-
-
-def compute_blake3_hash(
-    fp: str | IO[bytes],
-    chunk_size: int = DEFAULT_CHUNK,
-    interrupt_check: InterruptCheck | None = None,
-    checkpoint: HashCheckpoint | None = None,
-) -> tuple[str | None, HashCheckpoint | None]:
-    """Compute BLAKE3 hash of a file, with optional checkpoint support.
-
-    Args:
-        fp: File path or file-like object
-        chunk_size: Size of chunks to read at a time
-        interrupt_check: Optional callable that returns True if the operation
-            should be interrupted (e.g. paused or cancelled). Must be
-            non-blocking so file handles are released immediately. Checked
-            between chunk reads.
-        checkpoint: Optional checkpoint to resume from (file paths only)
-
-    Returns:
-        Tuple of (hex_digest, None) on completion, or
-        (None, checkpoint) on interruption (file paths only), or
-        (None, None) on interruption of a file object
-    """
-    if chunk_size <= 0:
-        chunk_size = DEFAULT_CHUNK
-
-    with _open_for_hashing(fp) as (f, is_path):
-        if checkpoint is not None and is_path:
-            f.seek(checkpoint.bytes_processed)
-            h = checkpoint.hasher
-            bytes_processed = checkpoint.bytes_processed
-        else:
-            h = blake3()
-            bytes_processed = 0
-
-        while True:
-            if interrupt_check is not None and interrupt_check():
-                if is_path:
-                    return None, HashCheckpoint(
-                        bytes_processed=bytes_processed,
-                        hasher=h,
-                    )
-                return None, None
-            chunk = f.read(chunk_size)
-            if not chunk:
-                break
-            h.update(chunk)
-            bytes_processed += len(chunk)
-
-        return h.hexdigest(), None

app/assets/services/ingest.py

@@ -1,563 +0,0 @@
-import contextlib
-import logging
-import mimetypes
-import os
-from typing import Any, Sequence
-
-from sqlalchemy.orm import Session
-
-import app.assets.services.hashing as hashing
-from app.assets.database.queries import (
-    add_tags_to_reference,
-    count_active_siblings,
-    create_stub_asset,
-    ensure_tags_exist,
-    fetch_reference_and_asset,
-    get_asset_by_hash,
-    get_reference_by_file_path,
-    get_reference_tags,
-    get_or_create_reference,
-    reference_exists,
-    remove_missing_tag_for_asset_id,
-    set_reference_metadata,
-    set_reference_tags,
-    update_asset_hash_and_mime,
-    upsert_asset,
-    upsert_reference,
-    validate_tags_exist,
-)
-from app.assets.helpers import get_utc_now, normalize_tags
-from app.assets.services.bulk_ingest import batch_insert_seed_assets
-from app.assets.services.file_utils import get_size_and_mtime_ns
-from app.assets.services.path_utils import (
-    compute_relative_filename,
-    get_name_and_tags_from_asset_path,
-    resolve_destination_from_tags,
-    validate_path_within_base,
-)
-from app.assets.services.schemas import (
-    IngestResult,
-    RegisterAssetResult,
-    UploadResult,
-    UserMetadata,
-    extract_asset_data,
-    extract_reference_data,
-)
-from app.database.db import create_session
-
-
-def _ingest_file_from_path(
-    abs_path: str,
-    asset_hash: str,
-    size_bytes: int,
-    mtime_ns: int,
-    mime_type: str | None = None,
-    info_name: str | None = None,
-    owner_id: str = "",
-    preview_id: str | None = None,
-    user_metadata: UserMetadata = None,
-    tags: Sequence[str] = (),
-    tag_origin: str = "manual",
-    require_existing_tags: bool = False,
-) -> IngestResult:
-    locator = os.path.abspath(abs_path)
-    user_metadata = user_metadata or {}
-
-    asset_created = False
-    asset_updated = False
-    ref_created = False
-    ref_updated = False
-    reference_id: str | None = None
-
-    with create_session() as session:
-        if preview_id:
-            if not reference_exists(session, preview_id):
-                preview_id = None
-
-        asset, asset_created, asset_updated = upsert_asset(
-            session,
-            asset_hash=asset_hash,
-            size_bytes=size_bytes,
-            mime_type=mime_type,
-        )
-
-        ref_created, ref_updated = upsert_reference(
-            session,
-            asset_id=asset.id,
-            file_path=locator,
-            name=info_name or os.path.basename(locator),
-            mtime_ns=mtime_ns,
-            owner_id=owner_id,
-        )
-
-        # Get the reference we just created/updated
-        ref = get_reference_by_file_path(session, locator)
-        if ref:
-            reference_id = ref.id
-
-            if preview_id and ref.preview_id != preview_id:
-                ref.preview_id = preview_id
-
-            norm = normalize_tags(list(tags))
-            if norm:
-                if require_existing_tags:
-                    validate_tags_exist(session, norm)
-                add_tags_to_reference(
-                    session,
-                    reference_id=reference_id,
-                    tags=norm,
-                    origin=tag_origin,
-                    create_if_missing=not require_existing_tags,
-                )
-
-            _update_metadata_with_filename(
-                session,
-                reference_id=reference_id,
-                file_path=ref.file_path,
-                current_metadata=ref.user_metadata,
-                user_metadata=user_metadata,
-            )
-
-        try:
-            remove_missing_tag_for_asset_id(session, asset_id=asset.id)
-        except Exception:
-            logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
-
-        session.commit()
-
-    return IngestResult(
-        asset_created=asset_created,
-        asset_updated=asset_updated,
-        ref_created=ref_created,
-        ref_updated=ref_updated,
-        reference_id=reference_id,
-    )
-
-
-def register_output_files(
-    file_paths: Sequence[str],
-    user_metadata: UserMetadata = None,
-    job_id: str | None = None,
-) -> int:
-    """Register a batch of output file paths as assets.
-
-    Returns the number of files successfully registered.
-    """
-    registered = 0
-    for abs_path in file_paths:
-        if not os.path.isfile(abs_path):
-            continue
-        try:
-            if ingest_existing_file(
-                abs_path, user_metadata=user_metadata, job_id=job_id
-            ):
-                registered += 1
-        except Exception:
-            logging.exception("Failed to register output: %s", abs_path)
-    return registered
-
-
-def ingest_existing_file(
-    abs_path: str,
-    user_metadata: UserMetadata = None,
-    extra_tags: Sequence[str] = (),
-    owner_id: str = "",
-    job_id: str | None = None,
-) -> bool:
-    """Register an existing on-disk file as an asset stub.
-
-    If a reference already exists for this path, updates mtime_ns, job_id,
-    size_bytes, and resets enrichment so the enricher will re-hash it.
-
-    For brand-new paths, inserts a stub record (hash=NULL) for immediate
-    UX visibility.
-
-    Returns True if a row was inserted or updated, False otherwise.
-    """
-    locator = os.path.abspath(abs_path)
-    size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path)
-    mime_type = mimetypes.guess_type(abs_path, strict=False)[0]
-    name, path_tags = get_name_and_tags_from_asset_path(abs_path)
-    tags = list(dict.fromkeys(path_tags + list(extra_tags)))
-
-    with create_session() as session:
-        existing_ref = get_reference_by_file_path(session, locator)
-        if existing_ref is not None:
-            now = get_utc_now()
-            existing_ref.mtime_ns = mtime_ns
-            existing_ref.job_id = job_id
-            existing_ref.is_missing = False
-            existing_ref.deleted_at = None
-            existing_ref.updated_at = now
-            existing_ref.enrichment_level = 0
-
-            asset = existing_ref.asset
-            if asset:
-                # If other refs share this asset, detach to a new stub
-                # instead of mutating the shared row.
-                siblings = count_active_siblings(session, asset.id, existing_ref.id)
-                if siblings > 0:
-                    new_asset = create_stub_asset(
-                        session,
-                        size_bytes=size_bytes,
-                        mime_type=mime_type or asset.mime_type,
-                    )
-                    existing_ref.asset_id = new_asset.id
-                else:
-                    asset.hash = None
-                    asset.size_bytes = size_bytes
-                    if mime_type:
-                        asset.mime_type = mime_type
-            session.commit()
-            return True
-
-        spec = {
-            "abs_path": abs_path,
-            "size_bytes": size_bytes,
-            "mtime_ns": mtime_ns,
-            "info_name": name,
-            "tags": tags,
-            "fname": os.path.basename(abs_path),
-            "metadata": None,
-            "hash": None,
-            "mime_type": mime_type,
-            "job_id": job_id,
-        }
-        if tags:
-            ensure_tags_exist(session, tags)
-        result = batch_insert_seed_assets(session, [spec], owner_id=owner_id)
-        session.commit()
-        return result.won_paths > 0
-
-
-def _register_existing_asset(
-    asset_hash: str,
-    name: str,
-    user_metadata: UserMetadata = None,
-    tags: list[str] | None = None,
-    tag_origin: str = "manual",
-    owner_id: str = "",
-    mime_type: str | None = None,
-    preview_id: str | None = None,
-) -> RegisterAssetResult:
-    user_metadata = user_metadata or {}
-
-    with create_session() as session:
-        asset = get_asset_by_hash(session, asset_hash=asset_hash)
-        if not asset:
-            raise ValueError(f"No asset with hash {asset_hash}")
-
-        if mime_type and not asset.mime_type:
-            update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=mime_type)
-
-        if preview_id:
-            if not reference_exists(session, preview_id):
-                preview_id = None
-
-        ref, ref_created = get_or_create_reference(
-            session,
-            asset_id=asset.id,
-            owner_id=owner_id,
-            name=name,
-            preview_id=preview_id,
-        )
-
-        if not ref_created:
-            if preview_id and ref.preview_id != preview_id:
-                ref.preview_id = preview_id
-
-            tag_names = get_reference_tags(session, reference_id=ref.id)
-            result = RegisterAssetResult(
-                ref=extract_reference_data(ref),
-                asset=extract_asset_data(asset),
-                tags=tag_names,
-                created=False,
-            )
-            session.commit()
-            return result
-
-        new_meta = dict(user_metadata)
-        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
-        if computed_filename:
-            new_meta["filename"] = computed_filename
-
-        if new_meta:
-            set_reference_metadata(
-                session,
-                reference_id=ref.id,
-                user_metadata=new_meta,
-            )
-
-        if tags is not None:
-            set_reference_tags(
-                session,
-                reference_id=ref.id,
-                tags=tags,
-                origin=tag_origin,
-            )
-
-        tag_names = get_reference_tags(session, reference_id=ref.id)
-        session.refresh(ref)
-        result = RegisterAssetResult(
-            ref=extract_reference_data(ref),
-            asset=extract_asset_data(asset),
-            tags=tag_names,
-            created=True,
-        )
-        session.commit()
-
-        return result
-
-
-
-def _update_metadata_with_filename(
-    session: Session,
-    reference_id: str,
-    file_path: str | None,
-    current_metadata: dict | None,
-    user_metadata: dict[str, Any],
-) -> None:
-    computed_filename = compute_relative_filename(file_path) if file_path else None
-
-    current_meta = current_metadata or {}
-    new_meta = dict(current_meta)
-    for k, v in user_metadata.items():
-        new_meta[k] = v
-    if computed_filename:
-        new_meta["filename"] = computed_filename
-
-    if new_meta != current_meta:
-        set_reference_metadata(
-            session,
-            reference_id=reference_id,
-            user_metadata=new_meta,
-        )
-
-
-def _sanitize_filename(name: str | None, fallback: str) -> str:
-    n = os.path.basename((name or "").strip() or fallback)
-    return n if n else fallback
-
-
-class HashMismatchError(Exception):
-    pass
-
-
-class DependencyMissingError(Exception):
-    def __init__(self, message: str):
-        self.message = message
-        super().__init__(message)
-
-
-def upload_from_temp_path(
-    temp_path: str,
-    name: str | None = None,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    client_filename: str | None = None,
-    owner_id: str = "",
-    expected_hash: str | None = None,
-    mime_type: str | None = None,
-    preview_id: str | None = None,
-) -> UploadResult:
-    try:
-        digest, _ = hashing.compute_blake3_hash(temp_path)
-    except ImportError as e:
-        raise DependencyMissingError(str(e))
-    except Exception as e:
-        raise RuntimeError(f"failed to hash uploaded file: {e}")
-    asset_hash = "blake3:" + digest
-
-    if expected_hash and asset_hash != expected_hash.strip().lower():
-        raise HashMismatchError("Uploaded file hash does not match provided hash.")
-
-    with create_session() as session:
-        existing = get_asset_by_hash(session, asset_hash=asset_hash)
-
-    if existing is not None:
-        with contextlib.suppress(Exception):
-            if temp_path and os.path.exists(temp_path):
-                os.remove(temp_path)
-
-        display_name = _sanitize_filename(name or client_filename, fallback=digest)
-        result = _register_existing_asset(
-            asset_hash=asset_hash,
-            name=display_name,
-            user_metadata=user_metadata or {},
-            tags=tags or [],
-            tag_origin="manual",
-            owner_id=owner_id,
-            mime_type=mime_type,
-            preview_id=preview_id,
-        )
-        return UploadResult(
-            ref=result.ref,
-            asset=result.asset,
-            tags=result.tags,
-            created_new=False,
-        )
-
-    if not tags:
-        raise ValueError("tags are required for new asset uploads")
-    base_dir, subdirs = resolve_destination_from_tags(tags)
-    dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
-    os.makedirs(dest_dir, exist_ok=True)
-
-    src_for_ext = (client_filename or name or "").strip()
-    _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
-    ext = _ext if 0 < len(_ext) <= 16 else ""
-    hashed_basename = f"{digest}{ext}"
-    dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
-    validate_path_within_base(dest_abs, base_dir)
-
-    content_type = mime_type or (
-        mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
-        or mimetypes.guess_type(hashed_basename, strict=False)[0]
-        or "application/octet-stream"
-    )
-
-    try:
-        os.replace(temp_path, dest_abs)
-    except Exception as e:
-        raise RuntimeError(f"failed to move uploaded file into place: {e}")
-
-    try:
-        size_bytes, mtime_ns = get_size_and_mtime_ns(dest_abs)
-    except OSError as e:
-        raise RuntimeError(f"failed to stat destination file: {e}")
-
-    ingest_result = _ingest_file_from_path(
-        asset_hash=asset_hash,
-        abs_path=dest_abs,
-        size_bytes=size_bytes,
-        mtime_ns=mtime_ns,
-        mime_type=content_type,
-        info_name=_sanitize_filename(name or client_filename, fallback=digest),
-        owner_id=owner_id,
-        preview_id=preview_id,
-        user_metadata=user_metadata or {},
-        tags=tags,
-        tag_origin="manual",
-        require_existing_tags=False,
-    )
-    reference_id = ingest_result.reference_id
-    if not reference_id:
-        raise RuntimeError("failed to create asset reference")
-
-    with create_session() as session:
-        pair = fetch_reference_and_asset(
-            session, reference_id=reference_id, owner_id=owner_id
-        )
-        if not pair:
-            raise RuntimeError("inconsistent DB state after ingest")
-        ref, asset = pair
-        tag_names = get_reference_tags(session, reference_id=ref.id)
-
-    return UploadResult(
-        ref=extract_reference_data(ref),
-        asset=extract_asset_data(asset),
-        tags=tag_names,
-        created_new=ingest_result.asset_created,
-    )
-
-
-def register_file_in_place(
-    abs_path: str,
-    name: str,
-    tags: list[str],
-    owner_id: str = "",
-    mime_type: str | None = None,
-) -> UploadResult:
-    """Register an already-saved file in the asset database without moving it.
-
-    Tags are derived from the filesystem path (root category + subfolder names),
-    merged with any caller-provided tags, matching the behavior of the scanner.
-    If the path is not under a known root, only the caller-provided tags are used.
-    """
-    try:
-        _, path_tags = get_name_and_tags_from_asset_path(abs_path)
-    except ValueError:
-        path_tags = []
-    merged_tags = normalize_tags([*path_tags, *tags])
-
-    try:
-        digest, _ = hashing.compute_blake3_hash(abs_path)
-    except ImportError as e:
-        raise DependencyMissingError(str(e))
-    except Exception as e:
-        raise RuntimeError(f"failed to hash file: {e}")
-    asset_hash = "blake3:" + digest
-
-    size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path)
-    content_type = mime_type or (
-        mimetypes.guess_type(abs_path, strict=False)[0]
-        or "application/octet-stream"
-    )
-
-    ingest_result = _ingest_file_from_path(
-        abs_path=abs_path,
-        asset_hash=asset_hash,
-        size_bytes=size_bytes,
-        mtime_ns=mtime_ns,
-        mime_type=content_type,
-        info_name=_sanitize_filename(name, fallback=digest),
-        owner_id=owner_id,
-        tags=merged_tags,
-        tag_origin="upload",
-        require_existing_tags=False,
-    )
-    reference_id = ingest_result.reference_id
-    if not reference_id:
-        raise RuntimeError("failed to create asset reference")
-
-    with create_session() as session:
-        pair = fetch_reference_and_asset(
-            session, reference_id=reference_id, owner_id=owner_id
-        )
-        if not pair:
-            raise RuntimeError("inconsistent DB state after ingest")
-        ref, asset = pair
-        tag_names = get_reference_tags(session, reference_id=ref.id)
-
-    return UploadResult(
-        ref=extract_reference_data(ref),
-        asset=extract_asset_data(asset),
-        tags=tag_names,
-        created_new=ingest_result.asset_created,
-    )
-
-
-def create_from_hash(
-    hash_str: str,
-    name: str,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-    mime_type: str | None = None,
-    preview_id: str | None = None,
-) -> UploadResult | None:
-    canonical = hash_str.strip().lower()
-
-    try:
-        result = _register_existing_asset(
-            asset_hash=canonical,
-            name=_sanitize_filename(
-                name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical
-            ),
-            user_metadata=user_metadata or {},
-            tags=tags or [],
-            tag_origin="manual",
-            owner_id=owner_id,
-            mime_type=mime_type,
-            preview_id=preview_id,
-        )
-    except ValueError:
-        logging.warning("create_from_hash: no asset found for hash %s", canonical)
-        return None
-
-    return UploadResult(
-        ref=result.ref,
-        asset=result.asset,
-        tags=result.tags,
-        created_new=False,
-    )

app/assets/services/metadata_extract.py

@@ -1,327 +0,0 @@
-"""Metadata extraction for asset scanning.
-
-Tier 1: Filesystem metadata (zero parsing)
-Tier 2: Safetensors header metadata (fast JSON read only)
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import mimetypes
-import os
-import struct
-from dataclasses import dataclass
-from typing import Any
-
-from utils.mime_types import init_mime_types
-
-init_mime_types()
-
-# Supported safetensors extensions
-SAFETENSORS_EXTENSIONS = frozenset({".safetensors", ".sft"})
-
-# Maximum safetensors header size to read (8MB)
-MAX_SAFETENSORS_HEADER_SIZE = 8 * 1024 * 1024
-
-
-@dataclass
-class ExtractedMetadata:
-    """Metadata extracted from a file during scanning."""
-
-    # Tier 1: Filesystem (always available)
-    filename: str = ""
-    file_path: str = ""  # Full absolute path to the file
-    content_length: int = 0
-    content_type: str | None = None
-    format: str = ""  # file extension without dot
-
-    # Tier 2: Safetensors header (if available)
-    base_model: str | None = None
-    trained_words: list[str] | None = None
-    air: str | None = None  # CivitAI AIR identifier
-    has_preview_images: bool = False
-
-    # Source provenance (populated if embedded in safetensors)
-    source_url: str | None = None
-    source_arn: str | None = None
-    repo_url: str | None = None
-    preview_url: str | None = None
-    source_hash: str | None = None
-
-    # HuggingFace specific
-    repo_id: str | None = None
-    revision: str | None = None
-    filepath: str | None = None
-    resolve_url: str | None = None
-
-    def to_user_metadata(self) -> dict[str, Any]:
-        """Convert to user_metadata dict for AssetReference.user_metadata JSON field."""
-        data: dict[str, Any] = {
-            "filename": self.filename,
-            "content_length": self.content_length,
-            "format": self.format,
-        }
-        if self.file_path:
-            data["file_path"] = self.file_path
-        if self.content_type:
-            data["content_type"] = self.content_type
-
-        # Tier 2 fields
-        if self.base_model:
-            data["base_model"] = self.base_model
-        if self.trained_words:
-            data["trained_words"] = self.trained_words
-        if self.air:
-            data["air"] = self.air
-        if self.has_preview_images:
-            data["has_preview_images"] = True
-
-        # Source provenance
-        if self.source_url:
-            data["source_url"] = self.source_url
-        if self.source_arn:
-            data["source_arn"] = self.source_arn
-        if self.repo_url:
-            data["repo_url"] = self.repo_url
-        if self.preview_url:
-            data["preview_url"] = self.preview_url
-        if self.source_hash:
-            data["source_hash"] = self.source_hash
-
-        # HuggingFace
-        if self.repo_id:
-            data["repo_id"] = self.repo_id
-        if self.revision:
-            data["revision"] = self.revision
-        if self.filepath:
-            data["filepath"] = self.filepath
-        if self.resolve_url:
-            data["resolve_url"] = self.resolve_url
-
-        return data
-
-    def to_meta_rows(self, reference_id: str) -> list[dict]:
-        """Convert to asset_reference_meta rows for typed/indexed querying."""
-        rows: list[dict] = []
-
-        def add_str(key: str, val: str | None, ordinal: int = 0) -> None:
-            if val:
-                rows.append({
-                    "asset_reference_id": reference_id,
-                    "key": key,
-                    "ordinal": ordinal,
-                    "val_str": val[:2048] if len(val) > 2048 else val,
-                    "val_num": None,
-                    "val_bool": None,
-                    "val_json": None,
-                })
-
-        def add_num(key: str, val: int | float | None) -> None:
-            if val is not None:
-                rows.append({
-                    "asset_reference_id": reference_id,
-                    "key": key,
-                    "ordinal": 0,
-                    "val_str": None,
-                    "val_num": val,
-                    "val_bool": None,
-                    "val_json": None,
-                })
-
-        def add_bool(key: str, val: bool | None) -> None:
-            if val is not None:
-                rows.append({
-                    "asset_reference_id": reference_id,
-                    "key": key,
-                    "ordinal": 0,
-                    "val_str": None,
-                    "val_num": None,
-                    "val_bool": val,
-                    "val_json": None,
-                })
-
-        # Tier 1
-        add_str("filename", self.filename)
-        add_num("content_length", self.content_length)
-        add_str("content_type", self.content_type)
-        add_str("format", self.format)
-
-        # Tier 2
-        add_str("base_model", self.base_model)
-        add_str("air", self.air)
-        has_previews = self.has_preview_images if self.has_preview_images else None
-        add_bool("has_preview_images", has_previews)
-
-        # trained_words as multiple rows with ordinals
-        if self.trained_words:
-            for i, word in enumerate(self.trained_words[:100]):  # limit to 100 words
-                add_str("trained_words", word, ordinal=i)
-
-        # Source provenance
-        add_str("source_url", self.source_url)
-        add_str("source_arn", self.source_arn)
-        add_str("repo_url", self.repo_url)
-        add_str("preview_url", self.preview_url)
-        add_str("source_hash", self.source_hash)
-
-        # HuggingFace
-        add_str("repo_id", self.repo_id)
-        add_str("revision", self.revision)
-        add_str("filepath", self.filepath)
-        add_str("resolve_url", self.resolve_url)
-
-        return rows
-
-
-def _read_safetensors_header(
-    path: str, max_size: int = MAX_SAFETENSORS_HEADER_SIZE
-) -> dict[str, Any] | None:
-    """Read only the JSON header from a safetensors file.
-
-    This is very fast - reads 8 bytes for header length, then the JSON header.
-    No tensor data is loaded.
-
-    Args:
-        path: Absolute path to safetensors file
-        max_size: Maximum header size to read (default 8MB)
-
-    Returns:
-        Parsed header dict or None if failed
-    """
-    try:
-        with open(path, "rb") as f:
-            header_bytes = f.read(8)
-            if len(header_bytes) < 8:
-                return None
-            length_of_header = struct.unpack("<Q", header_bytes)[0]
-            if length_of_header > max_size:
-                return None
-            header_data = f.read(length_of_header)
-            if len(header_data) < length_of_header:
-                return None
-            return json.loads(header_data.decode("utf-8"))
-    except (OSError, json.JSONDecodeError, UnicodeDecodeError, struct.error):
-        return None
-
-
-def _extract_safetensors_metadata(
-    header: dict[str, Any], meta: ExtractedMetadata
-) -> None:
-    """Extract metadata from safetensors header __metadata__ section.
-
-    Modifies meta in-place.
-    """
-    st_meta = header.get("__metadata__", {})
-    if not isinstance(st_meta, dict):
-        return
-
-    # Common model metadata
-    meta.base_model = (
-        st_meta.get("ss_base_model_version")
-        or st_meta.get("modelspec.base_model")
-        or st_meta.get("base_model")
-    )
-
-    # Trained words / trigger words
-    trained_words = st_meta.get("ss_tag_frequency")
-    if trained_words and isinstance(trained_words, str):
-        try:
-            tag_freq = json.loads(trained_words)
-            # Extract unique tags from all datasets
-            all_tags: set[str] = set()
-            for dataset_tags in tag_freq.values():
-                if isinstance(dataset_tags, dict):
-                    all_tags.update(dataset_tags.keys())
-            if all_tags:
-                meta.trained_words = sorted(all_tags)[:100]
-        except json.JSONDecodeError:
-            pass
-
-    # Direct trained_words field (some formats)
-    if not meta.trained_words:
-        tw = st_meta.get("trained_words")
-        if isinstance(tw, str):
-            try:
-                parsed = json.loads(tw)
-                if isinstance(parsed, list):
-                    meta.trained_words = [str(x) for x in parsed]
-                else:
-                    meta.trained_words = [w.strip() for w in tw.split(",") if w.strip()]
-            except json.JSONDecodeError:
-                meta.trained_words = [w.strip() for w in tw.split(",") if w.strip()]
-        elif isinstance(tw, list):
-            meta.trained_words = [str(x) for x in tw]
-
-    # CivitAI AIR
-    meta.air = st_meta.get("air") or st_meta.get("modelspec.air")
-
-    # Preview images (ssmd_cover_images)
-    cover_images = st_meta.get("ssmd_cover_images")
-    if cover_images:
-        meta.has_preview_images = True
-
-    # Source provenance fields
-    meta.source_url = st_meta.get("source_url")
-    meta.source_arn = st_meta.get("source_arn")
-    meta.repo_url = st_meta.get("repo_url")
-    meta.preview_url = st_meta.get("preview_url")
-    meta.source_hash = st_meta.get("source_hash") or st_meta.get("sshs_model_hash")
-
-    # HuggingFace fields
-    meta.repo_id = st_meta.get("repo_id") or st_meta.get("hf_repo_id")
-    meta.revision = st_meta.get("revision") or st_meta.get("hf_revision")
-    meta.filepath = st_meta.get("filepath") or st_meta.get("hf_filepath")
-    meta.resolve_url = st_meta.get("resolve_url") or st_meta.get("hf_url")
-
-
-def extract_file_metadata(
-    abs_path: str,
-    stat_result: os.stat_result | None = None,
-    relative_filename: str | None = None,
-) -> ExtractedMetadata:
-    """Extract metadata from a file using tier 1 and tier 2 methods.
-
-    Tier 1: Filesystem metadata from path and stat
-    Tier 2: Safetensors header parsing if applicable
-
-    Args:
-        abs_path: Absolute path to the file
-        stat_result: Optional pre-fetched stat result (saves a syscall)
-        relative_filename: Optional relative filename to use instead of basename
-            (e.g., "flux/123/model.safetensors" for model paths)
-
-    Returns:
-        ExtractedMetadata with all available fields populated
-    """
-    meta = ExtractedMetadata()
-
-    # Tier 1: Filesystem metadata
-    meta.filename = relative_filename or os.path.basename(abs_path)
-    meta.file_path = abs_path
-    _, ext = os.path.splitext(abs_path)
-    meta.format = ext.lstrip(".").lower() if ext else ""
-
-    mime_type, _ = mimetypes.guess_type(abs_path)
-    meta.content_type = mime_type
-
-    # Size from stat
-    if stat_result is None:
-        try:
-            stat_result = os.stat(abs_path, follow_symlinks=True)
-        except OSError:
-            pass
-
-    if stat_result:
-        meta.content_length = stat_result.st_size
-
-    # Tier 2: Safetensors header (if applicable and enabled)
-    if ext.lower() in SAFETENSORS_EXTENSIONS:
-        header = _read_safetensors_header(abs_path)
-        if header:
-            try:
-                _extract_safetensors_metadata(header, meta)
-            except Exception as e:
-                logging.debug("Safetensors meta extract failed %s: %s", abs_path, e)
-
-    return meta

app/assets/services/path_utils.py

@@ -1,167 +0,0 @@
-import os
-from pathlib import Path
-from typing import Literal
-
-import folder_paths
-from app.assets.helpers import normalize_tags
-
-
-_NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
-
-
-def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
-    """Build list of (folder_name, base_paths[]) for all model locations.
-
-    Includes every category registered in folder_names_and_paths,
-    regardless of whether its paths are under the main models_dir,
-    but excludes non-model entries like custom_nodes.
-    """
-    targets: list[tuple[str, list[str]]] = []
-    for name, values in folder_paths.folder_names_and_paths.items():
-        if name in _NON_MODEL_FOLDER_NAMES:
-            continue
-        paths, _exts = values[0], values[1]
-        if paths:
-            targets.append((name, paths))
-    return targets
-
-
-def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
-    if not tags:
-        raise ValueError("tags must not be empty")
-    root = tags[0].lower()
-    if root == "models":
-        if len(tags) < 2:
-            raise ValueError("at least two tags required for model asset")
-        try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
-        except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
-        if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
-        base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
-    elif root == "input":
-        base_dir = os.path.abspath(folder_paths.get_input_directory())
-        raw_subdirs = tags[1:]
-    elif root == "output":
-        base_dir = os.path.abspath(folder_paths.get_output_directory())
-        raw_subdirs = tags[1:]
-    else:
-        raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'")
-    _sep_chars = frozenset(("/", "\\", os.sep))
-    for i in raw_subdirs:
-        if i in (".", "..") or _sep_chars & set(i):
-            raise ValueError("invalid path component in tags")
-
-    return base_dir, raw_subdirs if raw_subdirs else []
-
-
-def validate_path_within_base(candidate: str, base: str) -> None:
-    cand_abs = Path(os.path.abspath(candidate))
-    base_abs = Path(os.path.abspath(base))
-    if not cand_abs.is_relative_to(base_abs):
-        raise ValueError("destination escapes base directory")
-
-
-def compute_relative_filename(file_path: str) -> str | None:
-    """
-    Return the model's path relative to the last well-known folder (the model category),
-    using forward slashes, eg:
-      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
-      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
-
-    For non-model paths, returns None.
-    """
-    try:
-        root_category, rel_path = get_asset_category_and_relative_path(file_path)
-    except ValueError:
-        return None
-
-    p = Path(rel_path)
-    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
-    if not parts:
-        return None
-
-    if root_category == "models":
-        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
-        inside = parts[1:] if len(parts) > 1 else [parts[0]]
-        return "/".join(inside)
-    return "/".join(parts)  # input/output: keep all parts
-
-
-def get_asset_category_and_relative_path(
-    file_path: str,
-) -> tuple[Literal["input", "output", "models"], str]:
-    """Determine which root category a file path belongs to.
-
-    Categories:
-      - 'input': under folder_paths.get_input_directory()
-      - 'output': under folder_paths.get_output_directory()
-      - 'models': under any base path from get_comfy_models_folders()
-
-    Returns:
-        (root_category, relative_path_inside_that_root)
-
-    Raises:
-        ValueError: path does not belong to any known root.
-    """
-    fp_abs = os.path.abspath(file_path)
-
-    def _check_is_within(child: str, parent: str) -> bool:
-        return Path(child).is_relative_to(parent)
-
-    def _compute_relative(child: str, parent: str) -> str:
-        # Normalize relative path, stripping any leading ".." components
-        # by anchoring to root (os.sep) then computing relpath back from it.
-        return os.path.relpath(
-            os.path.join(os.sep, os.path.relpath(child, parent)), os.sep
-        )
-
-    # 1) input
-    input_base = os.path.abspath(folder_paths.get_input_directory())
-    if _check_is_within(fp_abs, input_base):
-        return "input", _compute_relative(fp_abs, input_base)
-
-    # 2) output
-    output_base = os.path.abspath(folder_paths.get_output_directory())
-    if _check_is_within(fp_abs, output_base):
-        return "output", _compute_relative(fp_abs, output_base)
-
-    # 3) models (check deepest matching base to avoid ambiguity)
-    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
-    for bucket, bases in get_comfy_models_folders():
-        for b in bases:
-            base_abs = os.path.abspath(b)
-            if not _check_is_within(fp_abs, base_abs):
-                continue
-            cand = (len(base_abs), bucket, _compute_relative(fp_abs, base_abs))
-            if best is None or cand[0] > best[0]:
-                best = cand
-
-    if best is not None:
-        _, bucket, rel_inside = best
-        combined = os.path.join(bucket, rel_inside)
-        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
-
-    raise ValueError(
-        f"Path is not within input, output, or configured model bases: {file_path}"
-    )
-
-
-def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
-    """Return (name, tags) derived from a filesystem path.
-
-    - name: base filename with extension
-    - tags: [root_category] + parent folder names in order
-
-    Raises:
-        ValueError: path does not belong to any known root.
-    """
-    root_category, some_path = get_asset_category_and_relative_path(file_path)
-    p = Path(some_path)
-    parent_parts = [
-        part for part in p.parent.parts if part not in (".", "..", p.anchor)
-    ]
-    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))

app/assets/services/schemas.py

@@ -1,113 +0,0 @@
-from dataclasses import dataclass
-from datetime import datetime
-from typing import Any, NamedTuple
-
-from app.assets.database.models import Asset, AssetReference
-
-UserMetadata = dict[str, Any] | None
-
-
-@dataclass(frozen=True)
-class AssetData:
-    hash: str | None
-    size_bytes: int | None
-    mime_type: str | None
-
-
-@dataclass(frozen=True)
-class ReferenceData:
-    """Data transfer object for AssetReference."""
-
-    id: str
-    name: str
-    file_path: str | None
-    user_metadata: UserMetadata
-    preview_id: str | None
-    created_at: datetime
-    updated_at: datetime
-    system_metadata: dict[str, Any] | None = None
-    job_id: str | None = None
-    last_access_time: datetime | None = None
-
-
-@dataclass(frozen=True)
-class AssetDetailResult:
-    ref: ReferenceData
-    asset: AssetData | None
-    tags: list[str]
-
-
-@dataclass(frozen=True)
-class RegisterAssetResult:
-    ref: ReferenceData
-    asset: AssetData
-    tags: list[str]
-    created: bool
-
-
-@dataclass(frozen=True)
-class IngestResult:
-    asset_created: bool
-    asset_updated: bool
-    ref_created: bool
-    ref_updated: bool
-    reference_id: str | None
-
-
-class TagUsage(NamedTuple):
-    name: str
-    tag_type: str
-    count: int
-
-
-@dataclass(frozen=True)
-class AssetSummaryData:
-    ref: ReferenceData
-    asset: AssetData | None
-    tags: list[str]
-
-
-@dataclass(frozen=True)
-class ListAssetsResult:
-    items: list[AssetSummaryData]
-    total: int
-
-
-@dataclass(frozen=True)
-class DownloadResolutionResult:
-    abs_path: str
-    content_type: str
-    download_name: str
-
-
-@dataclass(frozen=True)
-class UploadResult:
-    ref: ReferenceData
-    asset: AssetData
-    tags: list[str]
-    created_new: bool
-
-
-def extract_reference_data(ref: AssetReference) -> ReferenceData:
-    return ReferenceData(
-        id=ref.id,
-        name=ref.name,
-        file_path=ref.file_path,
-        user_metadata=ref.user_metadata,
-        preview_id=ref.preview_id,
-        system_metadata=ref.system_metadata,
-        job_id=ref.job_id,
-        created_at=ref.created_at,
-        updated_at=ref.updated_at,
-        last_access_time=ref.last_access_time,
-    )
-
-
-def extract_asset_data(asset: Asset | None) -> AssetData | None:
-    if asset is None:
-        return None
-    return AssetData(
-        hash=asset.hash,
-        size_bytes=asset.size_bytes,
-        mime_type=asset.mime_type,
-    )

app/assets/services/tagging.py

@@ -1,98 +0,0 @@
-from typing import Sequence
-
-from app.assets.database.queries import (
-    AddTagsResult,
-    RemoveTagsResult,
-    add_tags_to_reference,
-    get_reference_with_owner_check,
-    list_tags_with_usage,
-    remove_tags_from_reference,
-)
-from app.assets.database.queries.tags import list_tag_counts_for_filtered_assets
-from app.assets.services.schemas import TagUsage
-from app.database.db import create_session
-
-
-def apply_tags(
-    reference_id: str,
-    tags: list[str],
-    origin: str = "manual",
-    owner_id: str = "",
-) -> AddTagsResult:
-    with create_session() as session:
-        ref_row = get_reference_with_owner_check(session, reference_id, owner_id)
-
-        result = add_tags_to_reference(
-            session,
-            reference_id=reference_id,
-            tags=tags,
-            origin=origin,
-            create_if_missing=True,
-            reference_row=ref_row,
-        )
-        session.commit()
-
-    return result
-
-
-def remove_tags(
-    reference_id: str,
-    tags: list[str],
-    owner_id: str = "",
-) -> RemoveTagsResult:
-    with create_session() as session:
-        get_reference_with_owner_check(session, reference_id, owner_id)
-
-        result = remove_tags_from_reference(
-            session,
-            reference_id=reference_id,
-            tags=tags,
-        )
-        session.commit()
-
-    return result
-
-
-def list_tags(
-    prefix: str | None = None,
-    limit: int = 100,
-    offset: int = 0,
-    order: str = "count_desc",
-    include_zero: bool = True,
-    owner_id: str = "",
-) -> tuple[list[TagUsage], int]:
-    limit = max(1, min(1000, limit))
-    offset = max(0, offset)
-
-    with create_session() as session:
-        rows, total = list_tags_with_usage(
-            session,
-            prefix=prefix,
-            limit=limit,
-            offset=offset,
-            include_zero=include_zero,
-            order=order,
-            owner_id=owner_id,
-        )
-
-    return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total
-
-
-def list_tag_histogram(
-    owner_id: str = "",
-    include_tags: Sequence[str] | None = None,
-    exclude_tags: Sequence[str] | None = None,
-    name_contains: str | None = None,
-    metadata_filter: dict | None = None,
-    limit: int = 100,
-) -> dict[str, int]:
-    with create_session() as session:
-        return list_tag_counts_for_filtered_assets(
-            session,
-            owner_id=owner_id,
-            include_tags=include_tags,
-            exclude_tags=exclude_tags,
-            name_contains=name_contains,
-            metadata_filter=metadata_filter,
-            limit=limit,
-        )

app/database/db.py

@@ -3,7 +3,6 @@ import os
 import shutil
 from app.logger import log_startup_warning
 from utils.install_util import get_missing_requirements_message
-from filelock import FileLock, Timeout
 from comfy.cli_args import args
 
 _DB_AVAILABLE = False
@@ -15,12 +14,8 @@ try:
     from alembic.config import Config
     from alembic.runtime.migration import MigrationContext
     from alembic.script import ScriptDirectory
-    from sqlalchemy import create_engine, event
+    from sqlalchemy import create_engine
     from sqlalchemy.orm import sessionmaker
-    from sqlalchemy.pool import StaticPool
-
-    from app.database.models import Base
-    import app.assets.database.models  # noqa: F401 — register models with Base.metadata
 
     _DB_AVAILABLE = True
 except ImportError as e:
@@ -70,69 +65,9 @@ def get_db_path():
         raise ValueError(f"Unsupported database URL '{url}'.")
 
 
-_db_lock = None
-
-def _acquire_file_lock(db_path):
-    """Acquire an OS-level file lock to prevent multi-process access.
-
-    Uses filelock for cross-platform support (macOS, Linux, Windows).
-    The OS automatically releases the lock when the process exits, even on crashes.
-    """
-    global _db_lock
-    lock_path = db_path + ".lock"
-    _db_lock = FileLock(lock_path)
-    try:
-        _db_lock.acquire(timeout=0)
-    except Timeout:
-        raise RuntimeError(
-            f"Could not acquire lock on database '{db_path}'. "
-            "Another ComfyUI process may already be using it. "
-            "Use --database-url to specify a separate database file."
-        )
-
-
-def _is_memory_db(db_url):
-    """Check if the database URL refers to an in-memory SQLite database."""
-    return db_url in ("sqlite:///:memory:", "sqlite://")
-
-
 def init_db():
     db_url = args.database_url
     logging.debug(f"Database URL: {db_url}")
-
-    if _is_memory_db(db_url):
-        _init_memory_db(db_url)
-    else:
-        _init_file_db(db_url)
-
-
-def _init_memory_db(db_url):
-    """Initialize an in-memory SQLite database using metadata.create_all.
-
-    Alembic migrations don't work with in-memory SQLite because each
-    connection gets its own separate database — tables created by Alembic's
-    internal connection are lost immediately.
-    """
-    engine = create_engine(
-        db_url,
-        poolclass=StaticPool,
-        connect_args={"check_same_thread": False},
-    )
-
-    @event.listens_for(engine, "connect")
-    def set_sqlite_pragma(dbapi_connection, connection_record):
-        cursor = dbapi_connection.cursor()
-        cursor.execute("PRAGMA foreign_keys=ON")
-        cursor.close()
-
-    Base.metadata.create_all(engine)
-
-    global Session
-    Session = sessionmaker(bind=engine)
-
-
-def _init_file_db(db_url):
-    """Initialize a file-backed SQLite database using Alembic migrations."""
     db_path = get_db_path()
     db_exists = os.path.exists(db_path)
 
@@ -140,14 +75,6 @@ def _init_file_db(db_url):
 
     # Check if we need to upgrade
     engine = create_engine(db_url)
-
-    # Enable foreign key enforcement for SQLite
-    @event.listens_for(engine, "connect")
-    def set_sqlite_pragma(dbapi_connection, connection_record):
-        cursor = dbapi_connection.cursor()
-        cursor.execute("PRAGMA foreign_keys=ON")
-        cursor.close()
-
     conn = engine.connect()
 
     context = MigrationContext.configure(conn)
@@ -177,12 +104,6 @@ def _init_file_db(db_url):
             logging.exception("Error upgrading database: ")
             raise e
 
-    # Acquire an OS-level file lock after migrations are complete.
-    # Alembic uses its own connection, so we must wait until it's done
-    # before locking — otherwise our own lock blocks the migration.
-    conn.close()
-    _acquire_file_lock(db_path)
-
     global Session
     Session = sessionmaker(bind=engine)
 

app/database/models.py

@@ -1,18 +1,9 @@
 from typing import Any
 from datetime import datetime
-from sqlalchemy import MetaData
 from sqlalchemy.orm import DeclarativeBase
 
-NAMING_CONVENTION = {
-    "ix": "ix_%(table_name)s_%(column_0_N_name)s",
-    "uq": "uq_%(table_name)s_%(column_0_N_name)s",
-    "ck": "ck_%(table_name)s_%(constraint_name)s",
-    "fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s",
-    "pk": "pk_%(table_name)s",
-}
-
 class Base(DeclarativeBase):
-    metadata = MetaData(naming_convention=NAMING_CONVENTION)
+    pass
 
 def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:
     fields = obj.__table__.columns.keys()

app/user_manager.py

@@ -6,7 +6,6 @@ import uuid
 import glob
 import shutil
 import logging
-import tempfile
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
@@ -378,15 +377,8 @@ class UserManager():
             try:
                 body = await request.read()
 
-                dir_name = os.path.dirname(path)
-                fd, tmp_path = tempfile.mkstemp(dir=dir_name)
-                try:
-                    with os.fdopen(fd, "wb") as f:
-                        f.write(body)
-                    os.replace(tmp_path, path)
-                except:
-                    os.unlink(tmp_path)
-                    raise
+                with open(path, "wb") as f:
+                    f.write(body)
             except OSError as e:
                 logging.warning(f"Error saving file '{path}': {e}")
                 return web.Response(

comfy/audio_encoders/audio_encoders.py

@@ -27,7 +27,6 @@ class AudioEncoderModel():
         self.model.eval()
         self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
         self.model_sample_rate = 16000
-        comfy.model_management.archive_model_dtypes(self.model)
 
     def load_sd(self, sd):
         return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())

comfy/cli_args.py

@@ -83,8 +83,6 @@ fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
 fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
 
-parser.add_argument("--fp16-intermediates", action="store_true", help="Experimental: Use fp16 for intermediate tensors between nodes instead of fp32.")
-
 parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
@@ -149,7 +147,6 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am
 parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
 parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
 parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.")
-parser.add_argument("--enable-dynamic-vram", action="store_true", help="Enable dynamic VRAM on systems where it's not enabled by default.")
 
 parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
 
@@ -235,7 +232,7 @@ database_default_path = os.path.abspath(
     os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
-parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
+parser.add_argument("--disable-assets-autoscan", action="store_true", help="Disable asset scanning on startup for database synchronization.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
@@ -263,6 +260,4 @@ else:
     args.fast = set(args.fast)
 
 def enables_dynamic_vram():
-    if args.enable_dynamic_vram:
-        return True
     return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu

comfy/comfy_types/node_typing.py

@@ -176,8 +176,8 @@ class InputTypeOptions(TypedDict):
     """COMBO type only. Specifies the configuration for a multi-select widget.
     Available after ComfyUI frontend v1.13.4
     https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
-    gradient_stops: NotRequired[list[dict]]
-    """Gradient color stops for gradientslider display mode. Each stop is {"offset": float, "color": [r, g, b]}."""
+    gradient_stops: NotRequired[list[list[float]]]
+    """Gradient color stops for gradientslider display mode. Each stop is [offset, r, g, b] (``FLOAT``)."""
 
 
 class HiddenInputTypeDict(TypedDict):

comfy/context_windows.py

@@ -93,50 +93,6 @@ class IndexListCallbacks:
         return {}
 
 
-def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, device, temporal_dim: int, temporal_scale: int=1, temporal_offset: int=0, retain_index_list: list[int]=[]):
-    if not (hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor)):
-        return None
-    cond_tensor = cond_value.cond
-    if temporal_dim >= cond_tensor.ndim:
-        return None
-
-    cond_size = cond_tensor.size(temporal_dim)
-
-    if temporal_scale == 1:
-        expected_size = x_in.size(window.dim) - temporal_offset
-        if cond_size != expected_size:
-            return None
-
-    if temporal_offset == 0 and temporal_scale == 1:
-        sliced = window.get_tensor(cond_tensor, device, dim=temporal_dim, retain_index_list=retain_index_list)
-        return cond_value._copy_with(sliced)
-
-    # skip leading latent positions that have no corresponding conditioning (e.g. reference frames)
-    if temporal_offset > 0:
-        indices = [i - temporal_offset for i in window.index_list[temporal_offset:]]
-        indices = [i for i in indices if 0 <= i]
-    else:
-        indices = list(window.index_list)
-
-    if not indices:
-        return None
-
-    if temporal_scale > 1:
-        scaled = []
-        for i in indices:
-            for k in range(temporal_scale):
-                si = i * temporal_scale + k
-                if si < cond_size:
-                    scaled.append(si)
-        indices = scaled
-        if not indices:
-            return None
-
-    idx = tuple([slice(None)] * temporal_dim + [indices])
-    sliced = cond_tensor[idx].to(device)
-    return cond_value._copy_with(sliced)
-
-
 @dataclass
 class ContextSchedule:
     name: str
@@ -221,17 +177,10 @@ class IndexListContextHandler(ContextHandlerABC):
                                     new_cond_item[cond_key] = result
                                     handled = True
                                     break
-                            if not handled and self._model is not None:
-                                result = self._model.resize_cond_for_context_window(
-                                    cond_key, cond_value, window, x_in, device,
-                                    retain_index_list=self.cond_retain_index_list)
-                                if result is not None:
-                                    new_cond_item[cond_key] = result
-                                    handled = True
                             if handled:
                                 continue
                             if isinstance(cond_value, torch.Tensor):
-                                if (self.dim < cond_value.ndim and cond_value.size(self.dim) == x_in.size(self.dim)) or \
+                                if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
                                    (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
                                     new_cond_item[cond_key] = window.get_tensor(cond_value, device)
                             # Handle audio_embed (temporal dim is 1)
@@ -275,7 +224,6 @@ class IndexListContextHandler(ContextHandlerABC):
         return context_windows
 
     def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
-        self._model = model
         self.set_step(timestep, model_options)
         context_windows = self.get_context_windows(model, x_in, model_options)
         enumerated_context_windows = list(enumerate(context_windows))

comfy/float.py

@@ -209,39 +209,3 @@ def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed=
         output_block[i:i + slice_size].copy_(block)
 
     return output_fp4, to_blocked(output_block, flatten=False)
-
-
-def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0):
-    def roundup(x_val, multiple):
-        return ((x_val + multiple - 1) // multiple) * multiple
-
-    if pad_32x:
-        rows, cols = x.shape
-        padded_rows = roundup(rows, 32)
-        padded_cols = roundup(cols, 32)
-        if padded_rows != rows or padded_cols != cols:
-            x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))
-
-    F8_E4M3_MAX = 448.0
-    E8M0_BIAS = 127
-    BLOCK_SIZE = 32
-
-    rows, cols = x.shape
-    x_blocked = x.reshape(rows, -1, BLOCK_SIZE)
-    max_abs = torch.amax(torch.abs(x_blocked), dim=-1)
-
-    # E8M0 block scales (power-of-2 exponents)
-    scale_needed = torch.clamp(max_abs.float() / F8_E4M3_MAX, min=2**(-127))
-    exp_biased = torch.clamp(torch.ceil(torch.log2(scale_needed)).to(torch.int32) + E8M0_BIAS, 0, 254)
-    block_scales_e8m0 = exp_biased.to(torch.uint8)
-
-    zero_mask = (max_abs == 0)
-    block_scales_f32 = (block_scales_e8m0.to(torch.int32) << 23).view(torch.float32)
-    block_scales_f32 = torch.where(zero_mask, torch.ones_like(block_scales_f32), block_scales_f32)
-
-    # Scale per-block then stochastic round
-    data_scaled = (x_blocked.float() / block_scales_f32.unsqueeze(-1)).reshape(rows, cols)
-    output_fp8 = stochastic_rounding(data_scaled, torch.float8_e4m3fn, seed=seed)
-
-    block_scales_e8m0 = torch.where(zero_mask, torch.zeros_like(block_scales_e8m0), block_scales_e8m0)
-    return output_fp8, to_blocked(block_scales_e8m0, flatten=False).view(torch.float8_e8m0fnu)

comfy/ldm/cascade/stage_a.py

@@ -136,7 +136,16 @@ class ResBlock(nn.Module):
             ops.Linear(c_hidden, c),
         )
 
-        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=False)
+        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
+
+        # Init weights
+        def _basic_init(module):
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
 
     def _norm(self, x, norm):
         return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

comfy/ldm/flux/layers.py

@@ -144,9 +144,9 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
             return tensor * m_mult
     else:
         for d in modulation_dims:
-            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]:d[2] + 1]
+            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]]
             if m_add is not None:
-                tensor[:, d[0]:d[1]] += m_add[:, d[2]:d[2] + 1]
+                tensor[:, d[0]:d[1]] += m_add[:, d[2]]
         return tensor
 
 
@@ -223,19 +223,12 @@ class DoubleStreamBlock(nn.Module):
         del txt_k, img_k
         v = torch.cat((txt_v, img_v), dim=2)
         del txt_v, img_v
-
-        extra_options["img_slice"] = [txt.shape[1], q.shape[2]]
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
-                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
-
         # run actual attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
 
         if "attn1_output_patch" in transformer_patches:
+            extra_options["img_slice"] = [txt.shape[1], attn.shape[1]]
             patch = transformer_patches["attn1_output_patch"]
             for p in patch:
                 attn = p(attn, extra_options)
@@ -328,12 +321,6 @@ class SingleStreamBlock(nn.Module):
         del qkv
         q, k = self.norm(q, k, v)
 
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
-                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
-
         # compute attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v

comfy/ldm/flux/math.py

@@ -31,8 +31,6 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
 
 def _apply_rope1(x: Tensor, freqs_cis: Tensor):
     x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
-    if x_.shape[2] != 1 and freqs_cis.shape[2] != 1 and x_.shape[2] != freqs_cis.shape[2]:
-        freqs_cis = freqs_cis[:, :, :x_.shape[2]]
 
     x_out = freqs_cis[..., 0] * x_[..., 0]
     x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])

comfy/ldm/flux/model.py

@@ -44,22 +44,6 @@ class FluxParams:
     txt_norm: bool = False
 
 
-def invert_slices(slices, length):
-    sorted_slices = sorted(slices)
-    result = []
-    current = 0
-
-    for start, end in sorted_slices:
-        if current < start:
-            result.append((current, start))
-        current = max(current, end)
-
-    if current < length:
-        result.append((current, length))
-
-    return result
-
-
 class Flux(nn.Module):
     """
     Transformer model for flow matching on sequences.
@@ -154,7 +138,6 @@ class Flux(nn.Module):
         y: Tensor,
         guidance: Tensor = None,
         control = None,
-        timestep_zero_index=None,
         transformer_options={},
         attn_mask: Tensor = None,
     ) -> Tensor:
@@ -181,9 +164,13 @@ class Flux(nn.Module):
             txt = self.txt_norm(txt)
         txt = self.txt_in(txt)
 
+        vec_orig = vec
+        if self.params.global_modulation:
+            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig))
+
         if "post_input" in patches:
             for p in patches["post_input"]:
-                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
+                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
                 img = out["img"]
                 txt = out["txt"]
                 img_ids = out["img_ids"]
@@ -195,24 +182,6 @@ class Flux(nn.Module):
         else:
             pe = None
 
-        vec_orig = vec
-        txt_vec = vec
-        extra_kwargs = {}
-        if timestep_zero_index is not None:
-            modulation_dims = []
-            batch = vec.shape[0] // 2
-            vec_orig = vec_orig.reshape(2, batch, vec.shape[1]).movedim(0, 1)
-            invert = invert_slices(timestep_zero_index, img.shape[1])
-            for s in invert:
-                modulation_dims.append((s[0], s[1], 0))
-            for s in timestep_zero_index:
-                modulation_dims.append((s[0], s[1], 1))
-            extra_kwargs["modulation_dims_img"] = modulation_dims
-            txt_vec = vec[:batch]
-
-        if self.params.global_modulation:
-            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(txt_vec))
-
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.double_blocks)
         transformer_options["block_type"] = "double"
@@ -226,8 +195,7 @@ class Flux(nn.Module):
                                                    vec=args["vec"],
                                                    pe=args["pe"],
                                                    attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args.get("transformer_options"),
-                                                   **extra_kwargs)
+                                                   transformer_options=args.get("transformer_options"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
@@ -245,8 +213,7 @@ class Flux(nn.Module):
                                  vec=vec,
                                  pe=pe,
                                  attn_mask=attn_mask,
-                                 transformer_options=transformer_options,
-                                 **extra_kwargs)
+                                 transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -263,12 +230,6 @@ class Flux(nn.Module):
         if self.params.global_modulation:
             vec, _ = self.single_stream_modulation(vec_orig)
 
-        extra_kwargs = {}
-        if timestep_zero_index is not None:
-            lambda a: 0 if a == 0 else a + txt.shape[1]
-            modulation_dims_combined = list(map(lambda x: (0 if x[0] == 0 else x[0] + txt.shape[1], x[1] + txt.shape[1], x[2]), modulation_dims))
-            extra_kwargs["modulation_dims"] = modulation_dims_combined
-
         transformer_options["total_blocks"] = len(self.single_blocks)
         transformer_options["block_type"] = "single"
         transformer_options["img_slice"] = [txt.shape[1], img.shape[1]]
@@ -281,8 +242,7 @@ class Flux(nn.Module):
                                        vec=args["vec"],
                                        pe=args["pe"],
                                        attn_mask=args.get("attn_mask"),
-                                       transformer_options=args.get("transformer_options"),
-                                       **extra_kwargs)
+                                       transformer_options=args.get("transformer_options"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
@@ -293,7 +253,7 @@ class Flux(nn.Module):
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -304,11 +264,7 @@ class Flux(nn.Module):
 
         img = img[:, txt.shape[1] :, ...]
 
-        extra_kwargs = {}
-        if timestep_zero_index is not None:
-            extra_kwargs["modulation_dims"] = modulation_dims
-
-        img = self.final_layer(img, vec_orig, **extra_kwargs)  # (N, T, patch_size ** 2 * out_channels)
+        img = self.final_layer(img, vec_orig)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
     def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
@@ -356,16 +312,13 @@ class Flux(nn.Module):
         w_len = ((w_orig + (patch_size // 2)) // patch_size)
         img, img_ids = self.process_img(x, transformer_options=transformer_options)
         img_tokens = img.shape[1]
-        timestep_zero_index = None
         if ref_latents is not None:
-            ref_num_tokens = []
             h = 0
             w = 0
             index = 0
             ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method)
-            timestep_zero = ref_latents_method == "index_timestep_zero"
             for ref in ref_latents:
-                if ref_latents_method in ("index", "index_timestep_zero"):
+                if ref_latents_method == "index":
                     index += self.params.ref_index_scale
                     h_offset = 0
                     w_offset = 0
@@ -386,16 +339,9 @@ class Flux(nn.Module):
                     h = max(h, ref.shape[-2] + h_offset)
                     w = max(w, ref.shape[-1] + w_offset)
 
-                kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset, transformer_options=transformer_options)
+                kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 img = torch.cat([img, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
-                ref_num_tokens.append(kontext.shape[1])
-            if timestep_zero:
-                if index > 0:
-                    timestep = torch.cat([timestep, timestep * 0], dim=0)
-                    timestep_zero_index = [[img_tokens, img_ids.shape[1]]]
-            transformer_options = transformer_options.copy()
-            transformer_options["reference_image_num_tokens"] = ref_num_tokens
 
         txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
 
@@ -403,6 +349,6 @@ class Flux(nn.Module):
             for i in self.params.txt_ids_dims:
                 txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
 
-        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, attn_mask=kwargs.get("attention_mask", None))
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]
         return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]

comfy/ldm/hunyuan3dv2_1/hunyuandit.py

@@ -343,7 +343,6 @@ class CrossAttention(nn.Module):
             k.reshape(b, s2, self.num_heads * self.head_dim),
             v,
             heads=self.num_heads,
-            low_precision_attention=False,
         )
 
         out = self.out_proj(x)
@@ -413,7 +412,6 @@ class Attention(nn.Module):
             key.reshape(B, N, self.num_heads * self.head_dim),
             value,
             heads=self.num_heads,
-            low_precision_attention=False,
         )
 
         x = self.out_proj(x)

comfy/ldm/lightricks/av_model.py

@@ -681,33 +681,6 @@ class LTXAVModel(LTXVModel):
         additional_args["has_spatial_mask"] = has_spatial_mask
 
         ax, a_latent_coords = self.a_patchifier.patchify(ax)
-
-        # Inject reference audio for ID-LoRA in-context conditioning
-        ref_audio = kwargs.get("ref_audio", None)
-        ref_audio_seq_len = 0
-        if ref_audio is not None:
-            ref_tokens = ref_audio["tokens"].to(dtype=ax.dtype, device=ax.device)
-            if ref_tokens.shape[0] < ax.shape[0]:
-                ref_tokens = ref_tokens.expand(ax.shape[0], -1, -1)
-            ref_audio_seq_len = ref_tokens.shape[1]
-            B = ax.shape[0]
-
-            # Compute negative temporal positions matching ID-LoRA convention:
-            # offset by -(end_of_last_token + time_per_latent) so reference ends just before t=0
-            p = self.a_patchifier
-            tpl = p.hop_length * p.audio_latent_downsample_factor / p.sample_rate
-            ref_start = p._get_audio_latent_time_in_sec(0, ref_audio_seq_len, torch.float32, ax.device)
-            ref_end = p._get_audio_latent_time_in_sec(1, ref_audio_seq_len + 1, torch.float32, ax.device)
-            time_offset = ref_end[-1].item() + tpl
-            ref_start = (ref_start - time_offset).unsqueeze(0).expand(B, -1).unsqueeze(1)
-            ref_end = (ref_end - time_offset).unsqueeze(0).expand(B, -1).unsqueeze(1)
-            ref_pos = torch.stack([ref_start, ref_end], dim=-1)
-
-            additional_args["ref_audio_seq_len"] = ref_audio_seq_len
-            additional_args["target_audio_seq_len"] = ax.shape[1]
-            ax = torch.cat([ref_tokens, ax], dim=1)
-            a_latent_coords = torch.cat([ref_pos.to(a_latent_coords), a_latent_coords], dim=2)
-
         ax = self.audio_patchify_proj(ax)
 
         # additional_args.update({"av_orig_shape": list(x.shape)})
@@ -748,14 +721,6 @@ class LTXAVModel(LTXVModel):
 
         # Prepare audio timestep
         a_timestep = kwargs.get("a_timestep")
-        ref_audio_seq_len = kwargs.get("ref_audio_seq_len", 0)
-        if ref_audio_seq_len > 0 and a_timestep is not None:
-            # Reference tokens must have timestep=0, expand scalar/1D timestep to per-token so ref=0 and target=sigma.
-            target_len = kwargs.get("target_audio_seq_len")
-            if a_timestep.dim() <= 1:
-                a_timestep = a_timestep.view(-1, 1).expand(batch_size, target_len)
-            ref_ts = torch.zeros(batch_size, ref_audio_seq_len, *a_timestep.shape[2:], device=a_timestep.device, dtype=a_timestep.dtype)
-            a_timestep = torch.cat([ref_ts, a_timestep], dim=1)
         if a_timestep is not None:
             a_timestep_scaled = a_timestep * self.timestep_scale_multiplier
             a_timestep_flat = a_timestep_scaled.flatten()
@@ -990,13 +955,6 @@ class LTXAVModel(LTXVModel):
         v_embedded_timestep = embedded_timestep[0]
         a_embedded_timestep = embedded_timestep[1]
 
-        # Trim reference audio tokens before unpatchification
-        ref_audio_seq_len = kwargs.get("ref_audio_seq_len", 0)
-        if ref_audio_seq_len > 0:
-            ax = ax[:, ref_audio_seq_len:]
-            if a_embedded_timestep.shape[1] > 1:
-                a_embedded_timestep = a_embedded_timestep[:, ref_audio_seq_len:]
-
         # Expand compressed video timestep if needed
         if isinstance(v_embedded_timestep, CompressedTimestep):
             v_embedded_timestep = v_embedded_timestep.expand()

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -23,11 +23,6 @@ class CausalConv3d(nn.Module):
         self.in_channels = in_channels
         self.out_channels = out_channels
 
-        if isinstance(stride, int):
-            self.time_stride = stride
-        else:
-            self.time_stride = stride[0]
-
         kernel_size = (kernel_size, kernel_size, kernel_size)
         self.time_kernel_size = kernel_size[0]
 
@@ -63,25 +58,16 @@ class CausalConv3d(nn.Module):
         pieces = [ cached, x ]
         if is_end and not causal:
             pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1)))
-        input_length = sum([piece.shape[2] for piece in pieces])
-        cache_length = (self.time_kernel_size - self.time_stride) + ((input_length - self.time_kernel_size) % self.time_stride)
 
         needs_caching = not is_end
-        if needs_caching and cache_length == 0:
-            self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False)
+        if needs_caching and x.shape[2] >= self.time_kernel_size - 1:
             needs_caching = False
-        if needs_caching and x.shape[2] >= cache_length:
-            needs_caching = False
-            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
 
         x = torch.cat(pieces, dim=2)
-        del pieces
-        del cached
 
         if needs_caching:
-            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
-        elif is_end:
-            self.temporal_cache_state[tid] = (None, True)
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
 
         return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :]
 

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -11,7 +11,6 @@ from .causal_conv3d import CausalConv3d
 from .pixel_norm import PixelNorm
 from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
 import comfy.ops
-import comfy.model_management
 from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
@@ -233,7 +232,10 @@ class Encoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch.FloatTensor]:
+    def forward_orig(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        r"""The forward method of the `Encoder` class."""
+
+        sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
         sample = self.conv_in(sample)
 
         checkpoint_fn = (
@@ -244,14 +246,10 @@ class Encoder(nn.Module):
 
         for down_block in self.down_blocks:
             sample = checkpoint_fn(down_block)(sample)
-            if sample is None or sample.shape[2] == 0:
-                return None
 
         sample = self.conv_norm_out(sample)
         sample = self.conv_act(sample)
         sample = self.conv_out(sample)
-        if sample is None or sample.shape[2] == 0:
-            return None
 
         if self.latent_log_var == "uniform":
             last_channel = sample[:, -1:, ...]
@@ -283,35 +281,9 @@ class Encoder(nn.Module):
 
         return sample
 
-    def forward_orig(self, sample: torch.FloatTensor, device=None) -> torch.FloatTensor:
-        r"""The forward method of the `Encoder` class."""
-
-        max_chunk_size = get_max_chunk_size(sample.device if device is None else device) * 2  # encoder is more memory-efficient than decoder
-        frame_size = sample[:, :, :1, :, :].numel() * sample.element_size()
-        frame_size = int(frame_size * (self.conv_in.out_channels / self.conv_in.in_channels))
-
-        outputs = []
-        samples = [sample[:, :, :1, :, :]]
-        if sample.shape[2] > 1:
-            chunk_t = max(2, max_chunk_size // frame_size)
-            if chunk_t < 4:
-                chunk_t = 2
-            elif chunk_t < 8:
-                chunk_t = 4
-            else:
-                chunk_t = (chunk_t // 8) * 8
-            samples += list(torch.split(sample[:, :, 1:, :, :], chunk_t, dim=2))
-        for chunk_idx, chunk in enumerate(samples):
-            if chunk_idx == len(samples) - 1:
-                mark_conv3d_ended(self)
-            chunk = patchify(chunk, patch_size_hw=self.patch_size, patch_size_t=1).to(device=device)
-            output = self._forward_chunk(chunk)
-            if output is not None:
-                outputs.append(output)
-
-        return torch_cat_if_needed(outputs, dim=2)
-
     def forward(self, *args, **kwargs):
+        #No encoder support so just flag the end so it doesnt use the cache.
+        mark_conv3d_ended(self)
         try:
             return self.forward_orig(*args, **kwargs)
         finally:
@@ -324,23 +296,7 @@ class Encoder(nn.Module):
                     module.temporal_cache_state.pop(tid, None)
 
 
-MIN_VRAM_FOR_CHUNK_SCALING = 6 * 1024 ** 3
-MAX_VRAM_FOR_CHUNK_SCALING = 24 * 1024 ** 3
-MIN_CHUNK_SIZE = 32 * 1024 ** 2
-MAX_CHUNK_SIZE = 128 * 1024 ** 2
-
-def get_max_chunk_size(device: torch.device) -> int:
-    total_memory = comfy.model_management.get_total_memory(dev=device)
-
-    if total_memory <= MIN_VRAM_FOR_CHUNK_SCALING:
-        return MIN_CHUNK_SIZE
-    if total_memory >= MAX_VRAM_FOR_CHUNK_SCALING:
-        return MAX_CHUNK_SIZE
-
-    interp = (total_memory - MIN_VRAM_FOR_CHUNK_SCALING) / (
-        MAX_VRAM_FOR_CHUNK_SCALING - MIN_VRAM_FOR_CHUNK_SCALING
-    )
-    return int(MIN_CHUNK_SIZE + interp * (MAX_CHUNK_SIZE - MIN_CHUNK_SIZE))
+MAX_CHUNK_SIZE=(128 * 1024 ** 2)
 
 class Decoder(nn.Module):
     r"""
@@ -500,17 +456,6 @@ class Decoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-        # Precompute output scale factors: (channels, (t_scale, h_scale, w_scale), t_offset)
-        ts, hs, ws, to = 1, 1, 1, 0
-        for block in self.up_blocks:
-            if isinstance(block, DepthToSpaceUpsample):
-                ts *= block.stride[0]
-                hs *= block.stride[1]
-                ws *= block.stride[2]
-                if block.stride[0] > 1:
-                    to = to * block.stride[0] + 1
-        self._output_scale = (out_channels // (patch_size ** 2), (ts, hs * patch_size, ws * patch_size), to)
-
         self.timestep_conditioning = timestep_conditioning
 
         if timestep_conditioning:
@@ -532,62 +477,11 @@ class Decoder(nn.Module):
             )
 
 
-    def decode_output_shape(self, input_shape):
-        c, (ts, hs, ws), to = self._output_scale
-        return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws)
-
-    def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size):
-        sample = sample_ref[0]
-        sample_ref[0] = None
-        if idx >= len(self.up_blocks):
-            sample = self.conv_norm_out(sample)
-            if timestep_shift_scale is not None:
-                shift, scale = timestep_shift_scale
-                sample = sample * (1 + scale) + shift
-            sample = self.conv_act(sample)
-            if ended:
-                mark_conv3d_ended(self.conv_out)
-            sample = self.conv_out(sample, causal=self.causal)
-            if sample is not None and sample.shape[2] > 0:
-                sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
-                t = sample.shape[2]
-                output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample)
-                output_offset[0] += t
-            return
-
-        up_block = self.up_blocks[idx]
-        if ended:
-            mark_conv3d_ended(up_block)
-        if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
-            sample = checkpoint_fn(up_block)(
-                sample, causal=self.causal, timestep=scaled_timestep
-            )
-        else:
-            sample = checkpoint_fn(up_block)(sample, causal=self.causal)
-
-        if sample is None or sample.shape[2] == 0:
-            return
-
-        total_bytes = sample.numel() * sample.element_size()
-        num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size
-
-        if num_chunks == 1:
-            # when we are not chunking, detach our x so the callee can free it as soon as they are done
-            next_sample_ref = [sample]
-            del sample
-            self.run_up(idx + 1, next_sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
-            return
-        else:
-            samples = torch.chunk(sample, chunks=num_chunks, dim=2)
-
-            for chunk_idx, sample1 in enumerate(samples):
-                self.run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
-
+    # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
     def forward_orig(
         self,
         sample: torch.FloatTensor,
         timestep: Optional[torch.Tensor] = None,
-        output_buffer: Optional[torch.Tensor] = None,
     ) -> torch.FloatTensor:
         r"""The forward method of the `Decoder` class."""
         batch_size = sample.shape[0]
@@ -602,7 +496,6 @@ class Decoder(nn.Module):
         )
 
         timestep_shift_scale = None
-        scaled_timestep = None
         if self.timestep_conditioning:
             assert (
                 timestep is not None
@@ -630,18 +523,48 @@ class Decoder(nn.Module):
             )
             timestep_shift_scale = ada_values.unbind(dim=1)
 
-        if output_buffer is None:
-            output_buffer = torch.empty(
-                self.decode_output_shape(sample.shape),
-                dtype=sample.dtype, device=comfy.model_management.intermediate_device(),
-            )
-        output_offset = [0]
+        output = []
 
-        max_chunk_size = get_max_chunk_size(sample.device)
+        def run_up(idx, sample, ended):
+            if idx >= len(self.up_blocks):
+                sample = self.conv_norm_out(sample)
+                if timestep_shift_scale is not None:
+                    shift, scale = timestep_shift_scale
+                    sample = sample * (1 + scale) + shift
+                sample = self.conv_act(sample)
+                if ended:
+                    mark_conv3d_ended(self.conv_out)
+                sample = self.conv_out(sample, causal=self.causal)
+                if sample is not None and sample.shape[2] > 0:
+                    output.append(sample)
+                return
 
-        self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
+            up_block = self.up_blocks[idx]
+            if (ended):
+                mark_conv3d_ended(up_block)
+            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
+                sample = checkpoint_fn(up_block)(
+                    sample, causal=self.causal, timestep=scaled_timestep
+                )
+            else:
+                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
 
-        return output_buffer
+            if sample is None or sample.shape[2] == 0:
+                return
+
+            total_bytes = sample.numel() * sample.element_size()
+            num_chunks = (total_bytes + MAX_CHUNK_SIZE - 1) // MAX_CHUNK_SIZE
+            samples = torch.chunk(sample, chunks=num_chunks, dim=2)
+
+            for chunk_idx, sample1 in enumerate(samples):
+                run_up(idx + 1, sample1, ended and chunk_idx == len(samples) - 1)
+
+        run_up(0, sample, True)
+        sample = torch.cat(output, dim=2)
+
+        sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+
+        return sample
 
     def forward(self, *args, **kwargs):
         try:
@@ -765,25 +688,12 @@ class SpaceToDepthDownsample(nn.Module):
             causal=True,
             spatial_padding_mode=spatial_padding_mode,
         )
-        self.temporal_cache_state = {}
 
     def forward(self, x, causal: bool = True):
-        tid = threading.get_ident()
-        cached, pad_first, cached_x, cached_input = self.temporal_cache_state.get(tid, (None, True, None, None))
-        if cached_input is not None:
-            x = torch_cat_if_needed([cached_input, x], dim=2)
-            cached_input = None
-
-        if self.stride[0] == 2 and pad_first:
+        if self.stride[0] == 2:
             x = torch.cat(
                 [x[:, :, :1, :, :], x], dim=2
             )  # duplicate first frames for padding
-            pad_first = False
-
-        if x.shape[2] < self.stride[0]:
-            cached_input = x
-            self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input)
-            return None
 
         # skip connection
         x_in = rearrange(
@@ -798,26 +708,15 @@ class SpaceToDepthDownsample(nn.Module):
 
         # conv
         x = self.conv(x, causal=causal)
-        if self.stride[0] == 2 and x.shape[2] == 1:
-            if cached_x is not None:
-                x = torch_cat_if_needed([cached_x, x], dim=2)
-                cached_x = None
-            else:
-                cached_x = x
-                x = None
+        x = rearrange(
+            x,
+            "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
 
-        if x is not None:
-            x = rearrange(
-                x,
-                "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w",
-                p1=self.stride[0],
-                p2=self.stride[1],
-                p3=self.stride[2],
-            )
-
-        cached = add_exchange_cache(x, cached, x_in, dim=2)
-
-        self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input)
+        x = x + x_in
 
         return x
 
@@ -1150,8 +1049,6 @@ class processor(nn.Module):
         return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
 
 class VideoVAE(nn.Module):
-    comfy_has_chunked_io = True
-
     def __init__(self, version=0, config=None):
         super().__init__()
 
@@ -1294,15 +1191,14 @@ class VideoVAE(nn.Module):
             }
         return config
 
-    def encode(self, x, device=None):
-        x = x[:, :, :max(1, 1 + ((x.shape[2] - 1) // 8) * 8), :, :]
-        means, logvar = torch.chunk(self.encoder(x, device=device), 2, dim=1)
+    def encode(self, x):
+        frames_count = x.shape[2]
+        if ((frames_count - 1) % 8) != 0:
+            raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.")
+        means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
         return self.per_channel_statistics.normalize(means)
 
-    def decode_output_shape(self, input_shape):
-        return self.decoder.decode_output_shape(input_shape)
-
-    def decode(self, x, output_buffer=None):
+    def decode(self, x):
         if self.timestep_conditioning: #TODO: seed
             x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x
-        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep, output_buffer=output_buffer)
+        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep)

comfy/ldm/lightricks/vocoders/vocoder.py

@@ -2,7 +2,6 @@ import torch
 import torch.nn.functional as F
 import torch.nn as nn
 import comfy.ops
-import comfy.model_management
 import numpy as np
 import math
 
@@ -82,7 +81,7 @@ class LowPassFilter1d(nn.Module):
         _, C, _ = x.shape
         if self.padding:
             x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
-        return F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)
+        return F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
 
 
 class UpSample1d(nn.Module):
@@ -126,7 +125,7 @@ class UpSample1d(nn.Module):
         _, C, _ = x.shape
         x = F.pad(x, (self.pad, self.pad), mode="replicate")
         x = self.ratio * F.conv_transpose1d(
-            x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C
+            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
         )
         x = x[..., self.pad_left : -self.pad_right]
         return x
@@ -191,7 +190,7 @@ class Snake(nn.Module):
         self.eps = 1e-9
 
     def forward(self, x):
-        a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)
+        a = self.alpha.unsqueeze(0).unsqueeze(-1)
         if self.alpha_logscale:
             a = torch.exp(a)
         return x + (1.0 / (a + self.eps)) * torch.sin(x * a).pow(2)
@@ -218,8 +217,8 @@ class SnakeBeta(nn.Module):
         self.eps = 1e-9
 
     def forward(self, x):
-        a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)
-        b = comfy.model_management.cast_to(self.beta.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)
+        a = self.alpha.unsqueeze(0).unsqueeze(-1)
+        b = self.beta.unsqueeze(0).unsqueeze(-1)
         if self.alpha_logscale:
             a = torch.exp(a)
             b = torch.exp(b)
@@ -597,7 +596,7 @@ class _STFTFn(nn.Module):
             y = y.unsqueeze(1)                                # (B, 1, T)
         left_pad = max(0, self.win_length - self.hop_length)  # causal: left-only
         y = F.pad(y, (left_pad, 0))
-        spec = F.conv1d(y, comfy.model_management.cast_to(self.forward_basis, dtype=y.dtype, device=y.device), stride=self.hop_length, padding=0)
+        spec = F.conv1d(y, self.forward_basis, stride=self.hop_length, padding=0)
         n_freqs = spec.shape[1] // 2
         real, imag = spec[:, :n_freqs], spec[:, n_freqs:]
         magnitude = torch.sqrt(real ** 2 + imag ** 2)
@@ -648,7 +647,7 @@ class MelSTFT(nn.Module):
         """
         magnitude, phase = self.stft_fn(y)
         energy = torch.norm(magnitude, dim=1)
-        mel = torch.matmul(comfy.model_management.cast_to(self.mel_basis, dtype=magnitude.dtype, device=y.device), magnitude)
+        mel = torch.matmul(self.mel_basis.to(magnitude.dtype), magnitude)
         log_mel = torch.log(torch.clamp(mel, min=1e-5))
         return log_mel, magnitude, phase, energy
 

comfy/ldm/modules/attention.py

@@ -372,8 +372,7 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
                 del s2
             break
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION as e:
             if first_op_done == False:
                 model_management.soft_empty_cache(True)
                 if cleared_cache == False:

comfy/ldm/modules/diffusionmodules/model.py

@@ -258,8 +258,7 @@ def slice_attention(q, k, v):
                 r1[:, :, i:end] = torch.bmm(v, s2)
                 del s2
             break
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION as e:
             model_management.soft_empty_cache(True)
             steps *= 2
             if steps > 128:
@@ -315,8 +314,7 @@ def pytorch_attention(q, k, v):
     try:
         out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
         out = out.transpose(2, 3).reshape(orig_shape)
-    except Exception as e:
-        model_management.raise_non_oom(e)
+    except model_management.OOM_EXCEPTION:
         logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
         oom_fallback = True
     if oom_fallback:

comfy/ldm/modules/sub_quadratic_attention.py

@@ -169,8 +169,7 @@ def _get_attention_scores_no_kv_chunking(
     try:
         attn_probs = attn_scores.softmax(dim=-1)
         del attn_scores
-    except Exception as e:
-        model_management.raise_non_oom(e)
+    except model_management.OOM_EXCEPTION:
         logging.warning("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
         attn_scores -= attn_scores.max(dim=-1, keepdim=True).values # noqa: F821 attn_scores is not defined
         torch.exp(attn_scores, out=attn_scores)

comfy/ldm/qwen_image/model.py

@@ -149,9 +149,6 @@ class Attention(nn.Module):
         seq_img = hidden_states.shape[1]
         seq_txt = encoder_hidden_states.shape[1]
 
-        transformer_patches = transformer_options.get("patches", {})
-        extra_options = transformer_options.copy()
-
         # Project and reshape to BHND format (batch, heads, seq, dim)
         img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
         img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
@@ -170,22 +167,15 @@ class Attention(nn.Module):
         joint_key = torch.cat([txt_key, img_key], dim=2)
         joint_value = torch.cat([txt_value, img_value], dim=2)
 
+        joint_query = apply_rope1(joint_query, image_rotary_emb)
+        joint_key = apply_rope1(joint_key, image_rotary_emb)
+
         if encoder_hidden_states_mask is not None:
             attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device)
             attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask
         else:
             attn_mask = None
 
-        extra_options["img_slice"] = [txt_query.shape[2], joint_query.shape[2]]
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(joint_query, joint_key, joint_value, pe=image_rotary_emb, attn_mask=encoder_hidden_states_mask, extra_options=extra_options)
-                joint_query, joint_key, joint_value, image_rotary_emb, encoder_hidden_states_mask = out.get("q", joint_query), out.get("k", joint_key), out.get("v", joint_value), out.get("pe", image_rotary_emb), out.get("attn_mask", encoder_hidden_states_mask)
-
-        joint_query = apply_rope1(joint_query, image_rotary_emb)
-        joint_key = apply_rope1(joint_key, image_rotary_emb)
-
         joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
                                                          attn_mask, transformer_options=transformer_options,
                                                          skip_reshape=True)
@@ -454,7 +444,6 @@ class QwenImageTransformer2DModel(nn.Module):
 
         timestep_zero_index = None
         if ref_latents is not None:
-            ref_num_tokens = []
             h = 0
             w = 0
             index = 0
@@ -485,16 +474,16 @@ class QwenImageTransformer2DModel(nn.Module):
                 kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 hidden_states = torch.cat([hidden_states, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
-                ref_num_tokens.append(kontext.shape[1])
             if timestep_zero:
                 if index > 0:
                     timestep = torch.cat([timestep, timestep * 0], dim=0)
                     timestep_zero_index = num_embeds
-            transformer_options = transformer_options.copy()
-            transformer_options["reference_image_num_tokens"] = ref_num_tokens
 
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
+        del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states)
         encoder_hidden_states = self.txt_norm(encoder_hidden_states)
@@ -506,18 +495,6 @@ class QwenImageTransformer2DModel(nn.Module):
         patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
 
-        if "post_input" in patches:
-            for p in patches["post_input"]:
-                out = p({"img": hidden_states, "txt": encoder_hidden_states, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
-                hidden_states = out["img"]
-                encoder_hidden_states = out["txt"]
-                img_ids = out["img_ids"]
-                txt_ids = out["txt_ids"]
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
-        del ids, txt_ids, img_ids
-
         transformer_options["total_blocks"] = len(self.transformer_blocks)
         transformer_options["block_type"] = "double"
         for i, block in enumerate(self.transformer_blocks):

comfy/ldm/wan/vae.py

@@ -99,7 +99,7 @@ class Resample(nn.Module):
         else:
             self.resample = nn.Identity()
 
-    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
         b, c, t, h, w = x.size()
         if self.mode == 'upsample3d':
             if feat_cache is not None:
@@ -109,7 +109,22 @@ class Resample(nn.Module):
                     feat_idx[0] += 1
                 else:
 
-                    cache_x = x[:, :, -CACHE_T:, :, :]
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] != 'Rep':
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat([
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device), cache_x
+                        ],
+                                            dim=2)
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] == 'Rep':
+                        cache_x = torch.cat([
+                            torch.zeros_like(cache_x).to(cache_x.device),
+                            cache_x
+                        ],
+                                            dim=2)
                     if feat_cache[idx] == 'Rep':
                         x = self.time_conv(x)
                     else:
@@ -130,24 +145,19 @@ class Resample(nn.Module):
             if feat_cache is not None:
                 idx = feat_idx[0]
                 if feat_cache[idx] is None:
-                    feat_cache[idx] = x
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
                 else:
 
-                    cache_x = x[:, :, -1:, :, :]
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
+                    #     # cache last frame of last two chunk
+                    #     cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+
                     x = self.time_conv(
                         torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
                     feat_cache[idx] = cache_x
-
-                    deferred_x = feat_cache[idx + 1]
-                    if deferred_x is not None:
-                        x = torch.cat([deferred_x, x], 2)
-                        feat_cache[idx + 1] = None
-
-                    if x.shape[2] == 1 and not final:
-                        feat_cache[idx + 1] = x
-                        x = None
-
-                feat_idx[0] += 2
+                    feat_idx[0] += 1
         return x
 
 
@@ -167,12 +177,19 @@ class ResidualBlock(nn.Module):
         self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
             if in_dim != out_dim else nn.Identity()
 
-    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
         old_x = x
         for layer in self.residual:
             if isinstance(layer, CausalConv3d) and feat_cache is not None:
                 idx = feat_idx[0]
-                cache_x = x[:, :, -CACHE_T:, :, :]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
                 x = layer(x, cache_list=feat_cache, cache_idx=idx)
                 feat_cache[idx] = cache_x
                 feat_idx[0] += 1
@@ -196,7 +213,7 @@ class AttentionBlock(nn.Module):
         self.proj = ops.Conv2d(dim, dim, 1)
         self.optimized_attention = vae_attention()
 
-    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
+    def forward(self, x):
         identity = x
         b, c, t, h, w = x.size()
         x = rearrange(x, 'b c t h w -> (b t) c h w')
@@ -266,10 +283,17 @@ class Encoder3d(nn.Module):
             RMS_norm(out_dim, images=False), nn.SiLU(),
             CausalConv3d(out_dim, z_dim, 3, padding=1))
 
-    def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
         if feat_cache is not None:
             idx = feat_idx[0]
-            cache_x = x[:, :, -CACHE_T:, :, :]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
             x = self.conv1(x, feat_cache[idx])
             feat_cache[idx] = cache_x
             feat_idx[0] += 1
@@ -279,16 +303,14 @@ class Encoder3d(nn.Module):
         ## downsamples
         for layer in self.downsamples:
             if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx, final=final)
-                if x is None:
-                    return None
+                x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
         ## middle
         for layer in self.middle:
-            if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx, final=final)
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
@@ -296,7 +318,14 @@ class Encoder3d(nn.Module):
         for layer in self.head:
             if isinstance(layer, CausalConv3d) and feat_cache is not None:
                 idx = feat_idx[0]
-                cache_x = x[:, :, -CACHE_T:, :, :]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
                 x = layer(x, feat_cache[idx])
                 feat_cache[idx] = cache_x
                 feat_idx[0] += 1
@@ -360,48 +389,18 @@ class Decoder3d(nn.Module):
             RMS_norm(out_dim, images=False), nn.SiLU(),
             CausalConv3d(out_dim, output_channels, 3, padding=1))
 
-    def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks):
-        x = x_ref[0]
-        x_ref[0] = None
-        if layer_idx >= len(self.upsamples):
-            for layer in self.head:
-                if isinstance(layer, CausalConv3d) and feat_cache is not None:
-                    cache_x = x[:, :, -CACHE_T:, :, :]
-                    x = layer(x, feat_cache[feat_idx[0]])
-                    feat_cache[feat_idx[0]] = cache_x
-                    feat_idx[0] += 1
-                else:
-                    x = layer(x)
-            out_chunks.append(x)
-            return
-
-        layer = self.upsamples[layer_idx]
-        if feat_cache is not None:
-            x = layer(x, feat_cache, feat_idx)
-        else:
-            x = layer(x)
-
-        if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 2:
-            for frame_idx in range(0, x.shape[2], 2):
-                self.run_up(
-                    layer_idx + 1,
-                    [x[:, :, frame_idx:frame_idx + 2, :, :]],
-                    feat_cache,
-                    feat_idx.copy(),
-                    out_chunks,
-                )
-            del x
-            return
-
-        next_x_ref = [x]
-        del x
-        self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks)
-
     def forward(self, x, feat_cache=None, feat_idx=[0]):
         ## conv1
         if feat_cache is not None:
             idx = feat_idx[0]
-            cache_x = x[:, :, -CACHE_T:, :, :]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
             x = self.conv1(x, feat_cache[idx])
             feat_cache[idx] = cache_x
             feat_idx[0] += 1
@@ -410,21 +409,42 @@ class Decoder3d(nn.Module):
 
         ## middle
         for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
             if feat_cache is not None:
                 x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
-        out_chunks = []
-
-        self.run_up(0, [x], feat_cache, feat_idx, out_chunks)
-        return out_chunks
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
 
 
-def count_cache_layers(model):
+def count_conv3d(model):
     count = 0
     for m in model.modules():
-        if isinstance(m, CausalConv3d) or (isinstance(m, Resample) and m.mode == 'downsample3d'):
+        if isinstance(m, CausalConv3d):
             count += 1
     return count
 
@@ -462,12 +482,11 @@ class WanVAE(nn.Module):
         conv_idx = [0]
         ## cache
         t = x.shape[2]
-        t = 1 + ((t - 1) // 4) * 4
-        iter_ = 1 + (t - 1) // 2
+        iter_ = 1 + (t - 1) // 4
         feat_map = None
         if iter_ > 1:
-            feat_map = [None] * count_cache_layers(self.encoder)
-        ## 对encode输入的x，按时间拆分为1、2、2、2....(总帧数先按4N+1向下取整)
+            feat_map = [None] * count_conv3d(self.encoder)
+        ## 对encode输入的x，按时间拆分为1、4、4、4....
         for i in range(iter_):
             conv_idx = [0]
             if i == 0:
@@ -477,23 +496,20 @@ class WanVAE(nn.Module):
                     feat_idx=conv_idx)
             else:
                 out_ = self.encoder(
-                    x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],
+                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
                     feat_cache=feat_map,
-                    feat_idx=conv_idx,
-                    final=(i == (iter_ - 1)))
-                if out_ is None:
-                    continue
+                    feat_idx=conv_idx)
                 out = torch.cat([out, out_], 2)
-
         mu, log_var = self.conv1(out).chunk(2, dim=1)
         return mu
 
     def decode(self, z):
+        conv_idx = [0]
         # z: [b,c,t,h,w]
-        iter_ = 1 + z.shape[2] // 2
+        iter_ = z.shape[2]
         feat_map = None
         if iter_ > 1:
-            feat_map = [None] * count_cache_layers(self.decoder)
+            feat_map = [None] * count_conv3d(self.decoder)
         x = self.conv2(z)
         for i in range(iter_):
             conv_idx = [0]
@@ -504,8 +520,8 @@ class WanVAE(nn.Module):
                     feat_idx=conv_idx)
             else:
                 out_ = self.decoder(
-                    x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],
+                    x[:, :, i:i + 1, :, :],
                     feat_cache=feat_map,
                     feat_idx=conv_idx)
-                out += out_
-        return torch.cat(out, 2)
+                out = torch.cat([out, out_], 2)
+        return out

comfy/lora.py

@@ -99,9 +99,6 @@ def model_lora_keys_clip(model, key_map={}):
     for k in sdk:
         if k.endswith(".weight"):
             key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
-            tp = k.find(".transformer.") #also map without wrapper prefix for composite text encoder models
-            if tp > 0 and not k.startswith("clip_"):
-                key_map["text_encoders.{}".format(k[tp + 1:-len(".weight")])] = k
 
     text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
     clip_l_present = False

comfy/memory_management.py

@@ -1,71 +1,9 @@
 import math
-import ctypes
-import threading
-import dataclasses
 import torch
 from typing import NamedTuple
 
 from comfy.quant_ops import QuantizedTensor
 
-
-class TensorFileSlice(NamedTuple):
-    file_ref: object
-    thread_id: int
-    offset: int
-    size: int
-
-
-def read_tensor_file_slice_into(tensor, destination):
-
-    if isinstance(tensor, QuantizedTensor):
-        if not isinstance(destination, QuantizedTensor):
-            return False
-        if tensor._layout_cls != destination._layout_cls:
-            return False
-
-        if not read_tensor_file_slice_into(tensor._qdata, destination._qdata):
-            return False
-
-        dst_orig_dtype = destination._params.orig_dtype
-        destination._params.copy_from(tensor._params, non_blocking=False)
-        destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype)
-        return True
-
-    info = getattr(tensor.untyped_storage(), "_comfy_tensor_file_slice", None)
-    if info is None:
-        return False
-
-    file_obj = info.file_ref
-    if (destination.device.type != "cpu"
-            or file_obj is None
-            or threading.get_ident() != info.thread_id
-            or destination.numel() * destination.element_size() < info.size
-            or tensor.numel() * tensor.element_size() != info.size
-            or tensor.storage_offset() != 0
-            or not tensor.is_contiguous()):
-        return False
-
-    if info.size == 0:
-        return True
-
-    buf_type = ctypes.c_ubyte * info.size
-    view = memoryview(buf_type.from_address(destination.data_ptr()))
-
-    try:
-        file_obj.seek(info.offset)
-        done = 0
-        while done < info.size:
-            try:
-                n = file_obj.readinto(view[done:])
-            except OSError:
-                return False
-            if n <= 0:
-                return False
-            done += n
-        return True
-    finally:
-        view.release()
-
 class TensorGeometry(NamedTuple):
     shape: any
     dtype: torch.dtype

comfy/model_base.py

@@ -21,7 +21,6 @@ import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
 import comfy.ldm.lightricks.av_model
-import comfy.context_windows
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from comfy.ldm.cascade.stage_c import StageC
 from comfy.ldm.cascade.stage_b import StageB
@@ -286,12 +285,6 @@ class BaseModel(torch.nn.Module):
             return data
         return None
 
-    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
-        """Override in subclasses to handle model-specific cond slicing for context windows.
-        Return a sliced cond object, or None to fall through to default handling.
-        Use comfy.context_windows.slice_cond() for common cases."""
-        return None
-
     def extra_conds(self, **kwargs):
         out = {}
         concat_cond = self.concat_cond(**kwargs)
@@ -937,10 +930,9 @@ class LongCatImage(Flux):
         transformer_options = transformer_options.copy()
         rope_opts = transformer_options.get("rope_options", {})
         rope_opts = dict(rope_opts)
-        pe_len = float(c_crossattn.shape[1]) if c_crossattn is not None else 512.0
         rope_opts.setdefault("shift_t", 1.0)
-        rope_opts.setdefault("shift_y", pe_len)
-        rope_opts.setdefault("shift_x", pe_len)
+        rope_opts.setdefault("shift_y", 512.0)
+        rope_opts.setdefault("shift_x", 512.0)
         transformer_options["rope_options"] = rope_opts
         return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
 
@@ -1061,10 +1053,6 @@ class LTXAV(BaseModel):
         if guide_attention_entries is not None:
             out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
-        ref_audio = kwargs.get("ref_audio", None)
-        if ref_audio is not None:
-            out['ref_audio'] = comfy.conds.CONDConstant(ref_audio)
-
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
@@ -1387,11 +1375,6 @@ class WAN21_Vace(WAN21):
         out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
         return out
 
-    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
-        if cond_key == "vace_context":
-            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=3, retain_index_list=retain_index_list)
-        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
-
 class WAN21_Camera(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel)
@@ -1444,11 +1427,6 @@ class WAN21_HuMo(WAN21):
 
         return out
 
-    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
-        if cond_key == "audio_embed":
-            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1)
-        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
-
 class WAN22_Animate(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel)
@@ -1466,13 +1444,6 @@ class WAN22_Animate(WAN21):
             out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents))
         return out
 
-    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
-        if cond_key == "face_pixel_values":
-            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_scale=4, temporal_offset=1)
-        if cond_key == "pose_latents":
-            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1)
-        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
-
 class WAN22_S2V(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)
@@ -1509,11 +1480,6 @@ class WAN22_S2V(WAN21):
             out['reference_motion'] = reference_motion.shape
         return out
 
-    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
-        if cond_key == "audio_embed":
-            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1)
-        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
-
 class WAN22(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)

comfy/model_detection.py

@@ -1,5 +1,4 @@
 import json
-import comfy.memory_management
 import comfy.supported_models
 import comfy.supported_models_base
 import comfy.utils
@@ -1119,13 +1118,8 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
                         new[:old_weight.shape[0]] = old_weight
                         old_weight = new
 
-                    if old_weight is out_sd.get(t[0], None) and comfy.memory_management.aimdo_enabled:
-                        old_weight = old_weight.clone()
-
                     w = old_weight.narrow(offset[0], offset[1], offset[2])
                 else:
-                    if comfy.memory_management.aimdo_enabled:
-                        weight = weight.clone()
                     old_weight = weight
                     w = weight
                 w[:] = fun(weight)

comfy/model_management.py

@@ -55,7 +55,6 @@ total_vram = 0
 
 # Training Related State
 in_training = False
-training_fp8_bwd = False
 
 
 def get_supported_float8_types():
@@ -271,23 +270,6 @@ try:
 except:
     OOM_EXCEPTION = Exception
 
-try:
-    ACCELERATOR_ERROR = torch.AcceleratorError
-except AttributeError:
-    ACCELERATOR_ERROR = RuntimeError
-
-def is_oom(e):
-    if isinstance(e, OOM_EXCEPTION):
-        return True
-    if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or "out of memory" in str(e).lower()):
-        discard_cuda_async_error()
-        return True
-    return False
-
-def raise_non_oom(e):
-    if not is_oom(e):
-        raise e
-
 XFORMERS_VERSION = ""
 XFORMERS_ENABLED_VAE = True
 if args.disable_xformers:
@@ -401,7 +383,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):
@@ -506,28 +488,6 @@ def module_size(module):
         module_mem += t.nbytes
     return module_mem
 
-def module_mmap_residency(module, free=False):
-    mmap_touched_mem = 0
-    module_mem = 0
-    bounced_mmaps = set()
-    sd = module.state_dict()
-    for k in sd:
-        t = sd[k]
-        module_mem += t.nbytes
-        storage = t._qdata.untyped_storage() if isinstance(t, comfy.quant_ops.QuantizedTensor) else t.untyped_storage()
-        if not getattr(storage, "_comfy_tensor_mmap_touched", False):
-            continue
-        mmap_touched_mem += t.nbytes
-        if not free:
-            continue
-        storage._comfy_tensor_mmap_touched = False
-        mmap_obj = storage._comfy_tensor_mmap_refs[0]
-        if mmap_obj in bounced_mmaps:
-            continue
-        mmap_obj.bounce()
-        bounced_mmaps.add(mmap_obj)
-    return mmap_touched_mem, module_mem
-
 class LoadedModel:
     def __init__(self, model):
         self._set_model(model)
@@ -542,7 +502,6 @@ class LoadedModel:
         if model.parent is not None:
             self._parent_model = weakref.ref(model.parent)
             self._patcher_finalizer = weakref.finalize(model, self._switch_parent)
-            self._patcher_finalizer.atexit = False
 
     def _switch_parent(self):
         model = self._parent_model()
@@ -556,9 +515,6 @@ class LoadedModel:
     def model_memory(self):
         return self.model.model_size()
 
-    def model_mmap_residency(self, free=False):
-        return self.model.model_mmap_residency(free=free)
-
     def model_loaded_memory(self):
         return self.model.loaded_size()
 
@@ -589,7 +545,6 @@ class LoadedModel:
 
         self.real_model = weakref.ref(real_model)
         self.model_finalizer = weakref.finalize(real_model, cleanup_models)
-        self.model_finalizer.atexit = False
         return real_model
 
     def should_reload_model(self, force_patch_weights=False):
@@ -661,7 +616,7 @@ def extra_reserved_memory():
 def minimum_inference_memory():
     return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()
 
-def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):
+def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_required=0):
     cleanup_models_gc()
     unloaded_model = []
     can_unload = []
@@ -674,14 +629,13 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
-    can_unload_sorted = sorted(can_unload)
-    for x in can_unload_sorted:
+    for x in sorted(can_unload):
         i = x[-1]
         memory_to_free = 1e32
-        pins_to_free = 1e32
+        ram_to_free = 1e32
         if not DISABLE_SMART_MEMORY:
             memory_to_free = memory_required - get_free_memory(device)
-            pins_to_free = pins_required - get_free_ram()
+            ram_to_free = ram_required - get_free_ram()
             if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                 #don't actually unload dynamic models for the sake of other dynamic models
                 #as that works on-demand.
@@ -690,18 +644,9 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
         if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free):
             logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
             unloaded_model.append(i)
-        if pins_to_free > 0:
-            logging.debug(f"PIN Unloading {current_loaded_models[i].model.model.__class__.__name__}")
-            current_loaded_models[i].model.partially_unload_ram(pins_to_free)
-
-    for x in can_unload_sorted:
-        i = x[-1]
-        ram_to_free = ram_required - psutil.virtual_memory().available
-        if ram_to_free <= 0 and i not in unloaded_model:
-            continue
-        resident_memory, _ = current_loaded_models[i].model_mmap_residency(free=True)
-        if resident_memory > 0:
+        if ram_to_free > 0:
             logging.debug(f"RAM Unloading {current_loaded_models[i].model.model.__class__.__name__}")
+            current_loaded_models[i].model.partially_unload_ram(ram_to_free)
 
     for i in sorted(unloaded_model, reverse=True):
         unloaded_models.append(current_loaded_models.pop(i))
@@ -767,27 +712,17 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
 
 
     total_memory_required = {}
-    total_pins_required = {}
     total_ram_required = {}
     for loaded_model in models_to_load:
-        device = loaded_model.device
-        total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device)
-        resident_memory, model_memory = loaded_model.model_mmap_residency()
-        pinned_memory = loaded_model.model.pinned_memory_size()
-        #FIXME: This can over-free the pins as it budgets to pin the entire model. We should
-        #make this JIT to keep as much pinned as possible.
-        pins_required = model_memory - pinned_memory
-        ram_required = model_memory - resident_memory
-        total_pins_required[device] = total_pins_required.get(device, 0) + pins_required
-        total_ram_required[device] = total_ram_required.get(device, 0) + ram_required
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+        #x2, one to make sure the OS can fit the model for loading in disk cache, and for us to do any pinning we
+        #want to do.
+        #FIXME: This should subtract off the to_load current pin consumption.
+        total_ram_required[loaded_model.device] = total_ram_required.get(loaded_model.device, 0) + loaded_model.model_memory() * 2
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
-            free_memory(total_memory_required[device] * 1.1 + extra_mem,
-                        device,
-                        for_dynamic=free_for_dynamic,
-                        pins_required=total_pins_required[device],
-                        ram_required=total_ram_required[device])
+            free_memory(total_memory_required[device] * 1.1 + extra_mem, device, for_dynamic=free_for_dynamic, ram_required=total_ram_required[device])
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
@@ -1004,7 +939,7 @@ def text_encoder_offload_device():
 def text_encoder_device():
     if args.gpu_only:
         return get_torch_device()
-    elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:
+    elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
         if should_use_fp16(prioritize_performance=False):
             return get_torch_device()
         else:
@@ -1053,12 +988,6 @@ def intermediate_device():
     else:
         return torch.device("cpu")
 
-def intermediate_dtype():
-    if args.fp16_intermediates:
-        return torch.float16
-    else:
-        return torch.float32
-
 def vae_device():
     if args.cpu_vae:
         return torch.device("cpu")
@@ -1219,7 +1148,6 @@ def reset_cast_buffers():
     LARGEST_CASTED_WEIGHT = (None, 0)
     for offload_stream in STREAM_CAST_BUFFERS:
         offload_stream.synchronize()
-    synchronize()
     STREAM_CAST_BUFFERS.clear()
     soft_empty_cache()
 
@@ -1279,11 +1207,6 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
             dest_view = dest_views.pop(0)
             if tensor is None:
                 continue
-            if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view):
-                continue
-            storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage()
-            if hasattr(storage, "_comfy_tensor_mmap_touched"):
-                storage._comfy_tensor_mmap_touched = True
             dest_view.copy_(tensor, non_blocking=non_blocking)
 
 
@@ -1339,7 +1262,7 @@ def discard_cuda_async_error():
         b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
         _ = a + b
         synchronize()
-    except RuntimeError:
+    except torch.AcceleratorError:
         #Dump it! We already know about it from the synchronous return
         pass
 
@@ -1721,19 +1644,6 @@ def supports_nvfp4_compute(device=None):
 
     return True
 
-def supports_mxfp8_compute(device=None):
-    if not is_nvidia():
-        return False
-
-    if torch_version_numeric < (2, 10):
-        return False
-
-    props = torch.cuda.get_device_properties(device)
-    if props.major < 10:
-        return False
-
-    return True
-
 def extended_fp16_support():
     # TODO: check why some models work with fp16 on newer torch versions but not on older
     if torch_version_numeric < (2, 7):
@@ -1756,16 +1666,12 @@ def lora_compute_dtype(device):
     return dtype
 
 def synchronize():
-    if cpu_mode():
-        return
     if is_intel_xpu():
         torch.xpu.synchronize()
     elif torch.cuda.is_available():
         torch.cuda.synchronize()
 
 def soft_empty_cache(force=False):
-    if cpu_mode():
-        return
     global cpu_state
     if cpu_state == CPUState.MPS:
         torch.mps.empty_cache()

comfy/model_patcher.py

@@ -297,9 +297,6 @@ class ModelPatcher:
         self.size = comfy.model_management.module_size(self.model)
         return self.size
 
-    def model_mmap_residency(self, free=False):
-        return comfy.model_management.module_mmap_residency(self.model, free=free)
-
     def get_ram_usage(self):
         return self.model_size()
 
@@ -602,27 +599,6 @@ class ModelPatcher:
 
         return models
 
-    def model_patches_call_function(self, function_name="cleanup", arguments={}):
-        to = self.model_options["transformer_options"]
-        if "patches" in to:
-            patches = to["patches"]
-            for name in patches:
-                patch_list = patches[name]
-                for i in range(len(patch_list)):
-                    if hasattr(patch_list[i], function_name):
-                        getattr(patch_list[i], function_name)(**arguments)
-        if "patches_replace" in to:
-            patches = to["patches_replace"]
-            for name in patches:
-                patch_list = patches[name]
-                for k in patch_list:
-                    if hasattr(patch_list[k], function_name):
-                        getattr(patch_list[k], function_name)(**arguments)
-        if "model_function_wrapper" in self.model_options:
-            wrap_func = self.model_options["model_function_wrapper"]
-            if hasattr(wrap_func, function_name):
-                getattr(wrap_func, function_name)(**arguments)
-
     def model_dtype(self):
         if hasattr(self.model, "get_dtype"):
             return self.model.get_dtype()
@@ -739,8 +715,8 @@ class ModelPatcher:
                     default = True # default random weights in non leaf modules
                     break
             if default and default_device is not None:
-                for param_name, param in params.items():
-                    param.data = param.data.to(device=default_device, dtype=getattr(m, param_name + "_comfy_model_dtype", None))
+                for param in params.values():
+                    param.data = param.data.to(device=default_device)
             if not default and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
                 module_mem = comfy.model_management.module_size(m)
                 module_offload_mem = module_mem
@@ -1066,10 +1042,6 @@ class ModelPatcher:
 
             return self.model.model_loaded_weight_memory - current_used
 
-    def pinned_memory_size(self):
-        # Pinned memory pressure tracking is only implemented for DynamicVram loading
-        return 0
-
     def partially_unload_ram(self, ram_to_unload):
         pass
 
@@ -1090,7 +1062,6 @@ class ModelPatcher:
         return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
 
     def cleanup(self):
-        self.model_patches_call_function(function_name="cleanup")
         self.clean_hooks()
         if hasattr(self.model, "current_patcher"):
             self.model.current_patcher = None
@@ -1660,16 +1631,6 @@ class ModelPatcherDynamic(ModelPatcher):
 
         return freed
 
-    def pinned_memory_size(self):
-        total = 0
-        loading = self._load_list(for_dynamic=True)
-        for x in loading:
-            _, _, _, _, m, _ = x
-            pin = comfy.pinned_memory.get_pin(m)
-            if pin is not None:
-                total += pin.numel() * pin.element_size()
-        return total
-
     def partially_unload_ram(self, ram_to_unload):
         loading = self._load_list(for_dynamic=True, default_device=self.offload_device)
         for x in loading:

comfy/ops.py

@@ -80,21 +80,6 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
 
 
 def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):
-
-    #vbar doesn't support CPU weights, but some custom nodes have weird paths
-    #that might switch the layer to the CPU and expect it to work. We have to take
-    #a clone conservatively as we are mmapped and some SFT files are packed misaligned
-    #If you are a custom node author reading this, please move your layer to the GPU
-    #or declare your ModelPatcher as CPU in the first place.
-    if comfy.model_management.is_device_cpu(device):
-        weight = s.weight.to(dtype=dtype, copy=True)
-        if isinstance(weight, QuantizedTensor):
-            weight = weight.dequantize()
-        bias = None
-        if s.bias is not None:
-            bias = s.bias.to(dtype=bias_dtype, copy=True)
-        return weight, bias, (None, None, None)
-
     offload_stream = None
     xfer_dest = None
 
@@ -306,40 +291,10 @@ class CastWeightBiasOp:
     bias_function = []
 
 class disable_weight_init:
-    @staticmethod
-    def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata,
-                                   missing_keys, unexpected_keys, weight_shape,
-                                   bias_shape=None):
-        assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
-        prefix_len = len(prefix)
-        for k, v in state_dict.items():
-            key = k[prefix_len:]
-            if key == "weight":
-                if not assign_to_params_buffers:
-                    v = v.clone()
-                module.weight = torch.nn.Parameter(v, requires_grad=False)
-            elif bias_shape is not None and key == "bias" and v is not None:
-                if not assign_to_params_buffers:
-                    v = v.clone()
-                module.bias = torch.nn.Parameter(v, requires_grad=False)
-            else:
-                unexpected_keys.append(k)
-
-        if module.weight is None:
-            module.weight = torch.nn.Parameter(torch.zeros(weight_shape), requires_grad=False)
-            missing_keys.append(prefix + "weight")
-
-        if bias_shape is not None and module.bias is None and getattr(module, "comfy_need_lazy_init_bias", False):
-            module.bias = torch.nn.Parameter(torch.zeros(bias_shape), requires_grad=False)
-            missing_keys.append(prefix + "bias")
-
     class Linear(torch.nn.Linear, CastWeightBiasOp):
 
         def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            # don't trust subclasses that BYO state dict loader to call us.
-            if (not comfy.model_management.WINDOWS
-                or not comfy.memory_management.aimdo_enabled
-                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 super().__init__(in_features, out_features, bias, device, dtype)
                 return
 
@@ -360,21 +315,32 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if (not comfy.model_management.WINDOWS
-                or not comfy.memory_management.aimdo_enabled
-                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
-            disable_weight_init._lazy_load_from_state_dict(
-                self,
-                state_dict,
-                prefix,
-                local_metadata,
-                missing_keys,
-                unexpected_keys,
-                weight_shape=(self.in_features, self.out_features),
-                bias_shape=(self.out_features,),
-            )
+            assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
+            prefix_len = len(prefix)
+            for k,v in state_dict.items():
+                if k[prefix_len:] == "weight":
+                    if not assign_to_params_buffers:
+                        v = v.clone()
+                    self.weight = torch.nn.Parameter(v, requires_grad=False)
+                elif k[prefix_len:] == "bias" and v is not None:
+                    if not assign_to_params_buffers:
+                        v = v.clone()
+                    self.bias = torch.nn.Parameter(v, requires_grad=False)
+                else:
+                    unexpected_keys.append(k)
+
+            #Reconcile default construction of the weight if its missing.
+            if self.weight is None:
+                v = torch.zeros(self.in_features, self.out_features)
+                self.weight = torch.nn.Parameter(v, requires_grad=False)
+                missing_keys.append(prefix+"weight")
+            if self.bias is None and self.comfy_need_lazy_init_bias:
+                v = torch.zeros(self.out_features,)
+                self.bias = torch.nn.Parameter(v, requires_grad=False)
+                missing_keys.append(prefix+"bias")
 
 
         def reset_parameters(self):
@@ -566,53 +532,6 @@ class disable_weight_init:
                 return super().forward(*args, **kwargs)
 
     class Embedding(torch.nn.Embedding, CastWeightBiasOp):
-        def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,
-                     norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,
-                     _freeze=False, device=None, dtype=None):
-            # don't trust subclasses that BYO state dict loader to call us.
-            if (not comfy.model_management.WINDOWS
-                or not comfy.memory_management.aimdo_enabled
-                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
-                super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
-                                 norm_type, scale_grad_by_freq, sparse, _weight,
-                                 _freeze, device, dtype)
-                return
-
-            torch.nn.Module.__init__(self)
-            self.num_embeddings = num_embeddings
-            self.embedding_dim = embedding_dim
-            self.padding_idx = padding_idx
-            self.max_norm = max_norm
-            self.norm_type = norm_type
-            self.scale_grad_by_freq = scale_grad_by_freq
-            self.sparse = sparse
-            # Keep shape/dtype visible for module introspection without reserving storage.
-            embedding_dtype = dtype if dtype is not None else torch.get_default_dtype()
-            self.weight = torch.nn.Parameter(
-                torch.empty((num_embeddings, embedding_dim), device="meta", dtype=embedding_dtype),
-                requires_grad=False,
-            )
-            self.bias = None
-            self.weight_comfy_model_dtype = dtype
-
-        def _load_from_state_dict(self, state_dict, prefix, local_metadata,
-                                strict, missing_keys, unexpected_keys, error_msgs):
-
-            if (not comfy.model_management.WINDOWS
-                or not comfy.memory_management.aimdo_enabled
-                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
-                return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
-                                                     missing_keys, unexpected_keys, error_msgs)
-            disable_weight_init._lazy_load_from_state_dict(
-                self,
-                state_dict,
-                prefix,
-                local_metadata,
-                missing_keys,
-                unexpected_keys,
-                weight_shape=(self.num_embeddings, self.embedding_dim),
-            )
-
         def reset_parameters(self):
             self.bias = None
             return None
@@ -741,29 +660,23 @@ class fp8_ops(manual_cast):
 
 CUBLAS_IS_AVAILABLE = False
 try:
-    from cublas_ops import CublasLinear, cublas_half_matmul
+    from cublas_ops import CublasLinear
     CUBLAS_IS_AVAILABLE = True
 except ImportError:
     pass
 
 if CUBLAS_IS_AVAILABLE:
-    class cublas_ops(manual_cast):
-        class Linear(CublasLinear, manual_cast.Linear):
+    class cublas_ops(disable_weight_init):
+        class Linear(CublasLinear, disable_weight_init.Linear):
             def reset_parameters(self):
                 return None
 
             def forward_comfy_cast_weights(self, input):
-                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-                x = cublas_half_matmul(input, weight, bias, self._epilogue_str, self.has_bias)
-                uncast_bias_weight(self, weight, bias, offload_stream)
-                return x
+                return super().forward(input)
 
             def forward(self, *args, **kwargs):
-                run_every_op()
-                if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
-                    return self.forward_comfy_cast_weights(*args, **kwargs)
-                else:
-                    return super().forward(*args, **kwargs)
+                return super().forward(*args, **kwargs)
+
 
 # ==============================================================================
 # Mixed Precision Operations
@@ -776,104 +689,6 @@ from .quant_ops import (
 )
 
 
-class QuantLinearFunc(torch.autograd.Function):
-    """Custom autograd function for quantized linear: quantized forward, optionally FP8 backward.
-
-    When training_fp8_bwd is enabled:
-      - Forward: quantize input per layout (FP8/NVFP4), use quantized matmul
-      - Backward: all matmuls use FP8 tensor cores via torch.mm dispatch
-      - Cached input is FP8 (half the memory of bf16)
-
-    When training_fp8_bwd is disabled:
-      - Forward: quantize input per layout, use quantized matmul
-      - Backward: dequantize weight to compute_dtype, use standard matmul
-    """
-
-    @staticmethod
-    def forward(ctx, input_float, weight, bias, layout_type, input_scale, compute_dtype):
-        input_shape = input_float.shape
-        inp = input_float.detach().flatten(0, -2)  # zero-cost view to 2D
-
-        # Quantize input for forward (same layout as weight)
-        if layout_type is not None:
-            q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale)
-        else:
-            q_input = inp
-
-        w = weight.detach() if weight.requires_grad else weight
-        b = bias.detach() if bias is not None and bias.requires_grad else bias
-
-        output = torch.nn.functional.linear(q_input, w, b)
-
-        # Unflatten output to match original input shape
-        if len(input_shape) > 2:
-            output = output.unflatten(0, input_shape[:-1])
-
-        # Save for backward
-        ctx.input_shape = input_shape
-        ctx.has_bias = bias is not None
-        ctx.compute_dtype = compute_dtype
-        ctx.weight_requires_grad = weight.requires_grad
-        ctx.fp8_bwd = comfy.model_management.training_fp8_bwd
-
-        if ctx.fp8_bwd:
-            # Cache FP8 quantized input — half the memory of bf16
-            if isinstance(q_input, QuantizedTensor) and layout_type.startswith('TensorCoreFP8'):
-                ctx.q_input = q_input  # already FP8, reuse
-            else:
-                # NVFP4 or other layout — quantize input to FP8 for backward
-                ctx.q_input = QuantizedTensor.from_float(inp, "TensorCoreFP8E4M3Layout")
-            ctx.save_for_backward(weight)
-        else:
-            ctx.q_input = None
-            ctx.save_for_backward(input_float, weight)
-
-        return output
-
-    @staticmethod
-    @torch.autograd.function.once_differentiable
-    def backward(ctx, grad_output):
-        compute_dtype = ctx.compute_dtype
-        grad_2d = grad_output.flatten(0, -2).to(compute_dtype)
-
-        # Value casting — only difference between fp8 and non-fp8 paths
-        if ctx.fp8_bwd:
-            weight, = ctx.saved_tensors
-            # Wrap as FP8 QuantizedTensors → torch.mm dispatches to _scaled_mm
-            grad_mm = QuantizedTensor.from_float(grad_2d, "TensorCoreFP8E5M2Layout")
-            if isinstance(weight, QuantizedTensor) and weight._layout_cls.startswith("TensorCoreFP8"):
-                weight_mm = weight
-            elif isinstance(weight, QuantizedTensor):
-                weight_mm = QuantizedTensor.from_float(weight.dequantize().to(compute_dtype), "TensorCoreFP8E4M3Layout")
-            else:
-                weight_mm = QuantizedTensor.from_float(weight.to(compute_dtype), "TensorCoreFP8E4M3Layout")
-            input_mm = ctx.q_input
-        else:
-            input_float, weight = ctx.saved_tensors
-            # Standard tensors → torch.mm does regular matmul
-            grad_mm = grad_2d
-            if isinstance(weight, QuantizedTensor):
-                weight_mm = weight.dequantize().to(compute_dtype)
-            else:
-                weight_mm = weight.to(compute_dtype)
-            input_mm = input_float.flatten(0, -2).to(compute_dtype) if ctx.weight_requires_grad else None
-
-        # Computation — same for both paths, dispatch handles the rest
-        grad_input = torch.mm(grad_mm, weight_mm)
-        if len(ctx.input_shape) > 2:
-            grad_input = grad_input.unflatten(0, ctx.input_shape[:-1])
-
-        grad_weight = None
-        if ctx.weight_requires_grad:
-            grad_weight = torch.mm(grad_mm.t(), input_mm)
-
-        grad_bias = None
-        if ctx.has_bias:
-            grad_bias = grad_2d.sum(dim=0)
-
-        return grad_input, grad_weight, grad_bias, None, None, None
-
-
 def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):
     class MixedPrecisionOps(manual_cast):
         _quant_config = quant_config
@@ -965,22 +780,6 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             orig_shape=(self.out_features, self.in_features),
                         )
 
-                    elif self.quant_format == "mxfp8":
-                        # MXFP8: E8M0 block scales stored as uint8 in safetensors
-                        block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys,
-                                                             dtype=torch.uint8)
-
-                        if block_scale is None:
-                            raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}")
-
-                        block_scale = block_scale.view(torch.float8_e8m0fnu)
-
-                        params = layout_cls.Params(
-                            scale=block_scale,
-                            orig_dtype=MixedPrecisionOps._compute_dtype,
-                            orig_shape=(self.out_features, self.in_features),
-                        )
-
                     elif self.quant_format == "nvfp4":
                         # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale)
                         tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys)
@@ -1068,37 +867,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
-                _use_quantized = (
-                    getattr(self, 'layout_type', None) is not None and
+                if (getattr(self, 'layout_type', None) is not None and
                     not isinstance(input, QuantizedTensor) and not self._full_precision_mm and
                     not getattr(self, 'comfy_force_cast_weights', False) and
-                    len(self.weight_function) == 0 and len(self.bias_function) == 0
-                )
-
-                # Training path: quantized forward with compute_dtype backward via autograd function
-                if (input.requires_grad and _use_quantized):
-
-                    weight, bias, offload_stream = cast_bias_weight(
-                        self,
-                        input,
-                        offloadable=True,
-                        compute_dtype=compute_dtype,
-                        want_requant=True
-                    )
-
-                    scale = getattr(self, 'input_scale', None)
-                    if scale is not None:
-                        scale = comfy.model_management.cast_to_device(scale, input.device, None)
-
-                    output = QuantLinearFunc.apply(
-                        input, weight, bias, self.layout_type, scale, compute_dtype
-                    )
-
-                    uncast_bias_weight(self, weight, bias, offload_stream)
-                    return output
-
-                # Inference path (unchanged)
-                if _use_quantized:
+                    len(self.weight_function) == 0 and len(self.bias_function) == 0):
 
                     # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
                     input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
@@ -1146,10 +918,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 for key, param in self._parameters.items():
                     if param is None:
                         continue
-                    p = fn(param)
-                    if p.is_inference():
-                        p = p.clone()
-                    self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))
+                    self.register_parameter(key, torch.nn.Parameter(fn(param), requires_grad=False))
                 for key, buf in self._buffers.items():
                     if buf is not None:
                         self._buffers[key] = fn(buf)
@@ -1160,15 +929,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
 def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
     nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device)
-    mxfp8_compute = comfy.model_management.supports_mxfp8_compute(load_device)
 
     if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
         logging.info("Using mixed precision operations")
         disabled = set()
         if not nvfp4_compute:
             disabled.add("nvfp4")
-        if not mxfp8_compute:
-            disabled.add("mxfp8")
         if not fp8_compute:
             disabled.add("float8_e4m3fn")
             disabled.add("float8_e5m2")

comfy/pinned_memory.py

@@ -1,7 +1,6 @@
+import torch
 import comfy.model_management
 import comfy.memory_management
-import comfy_aimdo.host_buffer
-import comfy_aimdo.torch
 
 from comfy.cli_args import args
 
@@ -13,31 +12,18 @@ def pin_memory(module):
         return
     #FIXME: This is a RAM cache trigger event
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
-
-    if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:
+    pin = torch.empty((size,), dtype=torch.uint8)
+    if comfy.model_management.pin_memory(pin):
+        module._pin = pin
+    else:
         module.pin_failed = True
         return False
-
-    try:
-        hostbuf = comfy_aimdo.host_buffer.HostBuffer(size)
-    except RuntimeError:
-        module.pin_failed = True
-        return False
-
-    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)
-    module._pin_hostbuf = hostbuf
-    comfy.model_management.TOTAL_PINNED_MEMORY += size
     return True
 
 def unpin_memory(module):
     if get_pin(module) is None:
         return 0
     size = module._pin.numel() * module._pin.element_size()
-
-    comfy.model_management.TOTAL_PINNED_MEMORY -= size
-    if comfy.model_management.TOTAL_PINNED_MEMORY < 0:
-        comfy.model_management.TOTAL_PINNED_MEMORY = 0
-
+    comfy.model_management.unpin_memory(module._pin)
     del module._pin
-    del module._pin_hostbuf
     return size

comfy/quant_ops.py

@@ -1,33 +1,38 @@
 import torch
 import logging
+from comfy.cli_args import args
 
-try:
-    import comfy_kitchen as ck
-    from comfy_kitchen.tensor import (
-        QuantizedTensor,
-        QuantizedLayout,
-        TensorCoreFP8Layout as _CKFp8Layout,
-        TensorCoreNVFP4Layout as _CKNvfp4Layout,
-        register_layout_op,
-        register_layout_class,
-        get_layout_class,
-    )
-    _CK_AVAILABLE = True
-    if torch.version.cuda is None:
-        ck.registry.disable("cuda")
-    else:
-        cuda_version = tuple(map(int, str(torch.version.cuda).split('.')))
-        if cuda_version < (13,):
-            ck.registry.disable("cuda")
-            logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
-
-    ck.registry.disable("triton")
-    for k, v in ck.list_backends().items():
-        logging.info(f"Found comfy_kitchen backend {k}: {v}")
-except ImportError as e:
-    logging.error(f"Failed to import comfy_kitchen, Error: {e}, fp8 and fp4 support will not be available.")
+if args.cpu:
     _CK_AVAILABLE = False
+else:
+    try:
+        import comfy_kitchen as ck
+        from comfy_kitchen.tensor import (
+            QuantizedTensor,
+            QuantizedLayout,
+            TensorCoreFP8Layout as _CKFp8Layout,
+            TensorCoreNVFP4Layout as _CKNvfp4Layout,
+            register_layout_op,
+            register_layout_class,
+            get_layout_class,
+        )
+        _CK_AVAILABLE = True
+        if torch.version.cuda is None:
+            ck.registry.disable("cuda")
+        else:
+            cuda_version = tuple(map(int, str(torch.version.cuda).split('.')))
+            if cuda_version < (13,):
+                ck.registry.disable("cuda")
+                logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
+        ck.registry.disable("triton")
+        for k, v in ck.list_backends().items():
+            logging.info(f"Found comfy_kitchen backend {k}: {v}")
+    except ImportError as e:
+        logging.error(f"Failed to import comfy_kitchen, Error: {e}, fp8 and fp4 support will not be available.")
+        _CK_AVAILABLE = False
+
+if not _CK_AVAILABLE:
     class QuantizedTensor:
         pass
 
@@ -43,18 +48,6 @@ except ImportError as e:
     def get_layout_class(name):
         return None
 
-_CK_MXFP8_AVAILABLE = False
-if _CK_AVAILABLE:
-    try:
-        from comfy_kitchen.tensor import TensorCoreMXFP8Layout as _CKMxfp8Layout
-        _CK_MXFP8_AVAILABLE = True
-    except ImportError:
-        logging.warning("comfy_kitchen does not support MXFP8, please update comfy_kitchen.")
-
-if not _CK_MXFP8_AVAILABLE:
-    class _CKMxfp8Layout:
-        pass
-
 import comfy.float
 
 # ==============================================================================
@@ -96,31 +89,6 @@ class _TensorCoreFP8LayoutBase(_CKFp8Layout):
         return qdata, params
 
 
-class TensorCoreMXFP8Layout(_CKMxfp8Layout):
-    @classmethod
-    def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
-        if tensor.dim() != 2:
-            raise ValueError(f"MXFP8 requires 2D tensor, got {tensor.dim()}D")
-
-        orig_dtype = tensor.dtype
-        orig_shape = tuple(tensor.shape)
-
-        padded_shape = cls.get_padded_shape(orig_shape)
-        needs_padding = padded_shape != orig_shape
-
-        if stochastic_rounding > 0:
-            qdata, block_scale = comfy.float.stochastic_round_quantize_mxfp8_by_block(tensor, pad_32x=needs_padding, seed=stochastic_rounding)
-        else:
-            qdata, block_scale = ck.quantize_mxfp8(tensor, pad_32x=needs_padding)
-
-        params = cls.Params(
-            scale=block_scale,
-            orig_dtype=orig_dtype,
-            orig_shape=orig_shape,
-        )
-        return qdata, params
-
-
 class TensorCoreNVFP4Layout(_CKNvfp4Layout):
     @classmethod
     def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
@@ -174,8 +142,6 @@ register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout)
 register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
 register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
-if _CK_MXFP8_AVAILABLE:
-    register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 
 QUANT_ALGOS = {
     "float8_e4m3fn": {
@@ -196,14 +162,6 @@ QUANT_ALGOS = {
     },
 }
 
-if _CK_MXFP8_AVAILABLE:
-    QUANT_ALGOS["mxfp8"] = {
-        "storage_t": torch.float8_e4m3fn,
-        "parameters": {"weight_scale", "input_scale"},
-        "comfy_tensor_layout": "TensorCoreMXFP8Layout",
-        "group_size": 32,
-    }
-
 
 # ==============================================================================
 # Re-exports for backward compatibility

comfy/sample.py

@@ -8,12 +8,12 @@ import comfy.nested_tensor
 
 def prepare_noise_inner(latent_image, generator, noise_inds=None):
     if noise_inds is None:
-        return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
+        return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
 
     unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
     noises = []
     for i in range(unique_inds[-1]+1):
-        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
+        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
         if i in unique_inds:
             noises.append(noise)
     noises = [noises[i] for i in inverse]
@@ -64,10 +64,10 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
     sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 
     samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+    samples = samples.to(comfy.model_management.intermediate_device())
     return samples
 
 def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
     samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+    samples = samples.to(comfy.model_management.intermediate_device())
     return samples

comfy/samplers.py

@@ -985,8 +985,8 @@ class CFGGuider:
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
-        noise = noise.to(device=device, dtype=torch.float32)
-        latent_image = latent_image.to(device=device, dtype=torch.float32)
+        noise = noise.to(device)
+        latent_image = latent_image.to(device)
         sigmas = sigmas.to(device)
         cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
 
@@ -1028,7 +1028,6 @@ class CFGGuider:
                 denoise_mask, _ = comfy.utils.pack_latents(denoise_masks)
             else:
                 denoise_mask = denoise_masks[0]
-            denoise_mask = denoise_mask.float()
 
         self.conds = {}
         for k in self.original_conds:

comfy/sd.py

@@ -455,7 +455,7 @@ class VAE:
         self.output_channels = 3
         self.pad_channel_value = None
         self.process_input = lambda image: image * 2.0 - 1.0
-        self.process_output = lambda image: image.add_(1.0).div_(2.0).clamp_(0.0, 1.0)
+        self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
         self.working_dtypes = [torch.bfloat16, torch.float32]
         self.disable_offload = False
         self.not_video = False
@@ -871,16 +871,13 @@ class VAE:
                 pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value)
         return pixels
 
-    def vae_output_dtype(self):
-        return model_management.intermediate_dtype()
-
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
         steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         output = self.process_output(
             (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
             comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
@@ -890,16 +887,16 @@ class VAE:
 
     def decode_tiled_1d(self, samples, tile_x=256, overlap=32):
         if samples.ndim == 3:
-            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         else:
             og_shape = samples.shape
             samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1))
-            decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+            decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float()
 
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
 
     def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
@@ -908,7 +905,7 @@ class VAE:
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
         samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
@@ -917,7 +914,7 @@ class VAE:
 
     def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048):
         if self.latent_dim == 1:
-            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
             out_channels = self.latent_channels
             upscale_amount = 1 / self.downscale_ratio
         else:
@@ -926,7 +923,7 @@ class VAE:
             tile_x = tile_x // extra_channel_size
             overlap = overlap // extra_channel_size
             upscale_amount = 1 / self.downscale_ratio
-            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).to(dtype=self.vae_output_dtype())
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float()
 
         out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device)
         if self.latent_dim == 1:
@@ -935,7 +932,7 @@ class VAE:
             return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1)
 
     def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
     def decode(self, samples_in, vae_options={}):
@@ -951,25 +948,13 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            # Pre-allocate output for VAEs that support direct buffer writes
-            preallocated = False
-            if getattr(self.first_stage_model, 'comfy_has_chunked_io', False):
-                pixel_samples = torch.empty(self.first_stage_model.decode_output_shape(samples_in.shape), device=self.output_device, dtype=self.vae_output_dtype())
-                preallocated = True
-
             for x in range(0, samples_in.shape[0], batch_number):
-                samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype)
-                if preallocated:
-                    self.first_stage_model.decode(samples, output_buffer=pixel_samples[x:x+batch_number], **vae_options)
-                else:
-                    out = self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True)
-                    if pixel_samples is None:
-                        pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
-                    pixel_samples[x:x+batch_number].copy_(out)
-                    del out
-                self.process_output(pixel_samples[x:x+batch_number])
-        except Exception as e:
-            model_management.raise_non_oom(e)
+                samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
+                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
+                if pixel_samples is None:
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                pixel_samples[x:x+batch_number] = out
+        except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.
@@ -978,7 +963,6 @@ class VAE:
             do_tile = True
 
         if do_tile:
-            comfy.model_management.soft_empty_cache()
             dims = samples_in.ndim - 2
             if dims == 1 or self.extra_1d_channel is not None:
                 pixel_samples = self.decode_tiled_1d(samples_in)
@@ -1039,19 +1023,13 @@ class VAE:
             batch_number = max(1, batch_number)
             samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype)
-                if getattr(self.first_stage_model, 'comfy_has_chunked_io', False):
-                    out = self.first_stage_model.encode(pixels_in, device=self.device)
-                else:
-                    pixels_in = pixels_in.to(self.device)
-                    out = self.first_stage_model.encode(pixels_in)
-                out = out.to(self.output_device).to(dtype=self.vae_output_dtype())
+                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
                 if samples is None:
-                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 samples[x:x + batch_number] = out
 
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.
@@ -1060,7 +1038,6 @@ class VAE:
             do_tile = True
 
         if do_tile:
-            comfy.model_management.soft_empty_cache()
             if self.latent_dim == 3:
                 tile = 256
                 overlap = tile // 4

comfy/sd1_clip.py

@@ -46,7 +46,7 @@ class ClipTokenWeightEncoder:
         out, pooled = o[:2]
 
         if pooled is not None:
-            first_pooled = pooled[0:1].to(device=model_management.intermediate_device())
+            first_pooled = pooled[0:1].to(model_management.intermediate_device())
         else:
             first_pooled = pooled
 
@@ -63,16 +63,16 @@ class ClipTokenWeightEncoder:
             output.append(z)
 
         if (len(output) == 0):
-            r = (out[-1:].to(device=model_management.intermediate_device()), first_pooled)
+            r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
         else:
-            r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device()), first_pooled)
+            r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
 
         if len(o) > 2:
             extra = {}
             for k in o[2]:
                 v = o[2][k]
                 if k == "attention_mask":
-                    v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device())
+                    v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
                 extra[k] = v
 
             r = r + (extra,)

comfy/text_encoders/llama.py

@@ -1028,19 +1028,12 @@ class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
                 grid = e.get("extra", None)
                 start = e.get("index")
                 if position_ids is None:
-                    position_ids = torch.ones((3, embeds.shape[1]), device=embeds.device, dtype=torch.long)
+                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
                     position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
                 end = e.get("size") + start
                 len_max = int(grid.max()) // 2
                 start_next = len_max + start
-                if attention_mask is not None:
-                    # Assign compact sequential positions to attended tokens only,
-                    # skipping over padding so post-padding tokens aren't inflated.
-                    after_mask = attention_mask[0, end:]
-                    text_positions = after_mask.cumsum(0) - 1 + start_next + offset
-                    position_ids[:, end:] = torch.where(after_mask.bool(), text_positions, position_ids[0, end:])
-                else:
-                    position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
+                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
                 position_ids[0, start:end] = start + offset
                 max_d = int(grid[0][1]) // 2
                 position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]

comfy/text_encoders/longcat_image.py

@@ -64,13 +64,7 @@ class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):
         return [output]
 
 
-IMAGE_PAD_TOKEN_ID = 151655
-
 class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
-    T2I_PREFIX = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n"
-    EDIT_PREFIX = "<|im_start|>system\nAs an image editing expert, first analyze the content and attributes of the input image(s). Then, based on the user's editing instructions, clearly and precisely determine how to modify the given image(s), ensuring that only the specified parts are altered and all other aspects remain consistent with the original(s).<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
-    SUFFIX = "<|im_end|>\n<|im_start|>assistant\n"
-
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(
             embedding_directory=embedding_directory,
@@ -78,8 +72,10 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
             name="qwen25_7b",
             tokenizer=LongCatImageBaseTokenizer,
         )
+        self.longcat_template_prefix = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n"
+        self.longcat_template_suffix = "<|im_end|>\n<|im_start|>assistant\n"
 
-    def tokenize_with_weights(self, text, return_word_ids=False, images=None, **kwargs):
+    def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
         skip_template = False
         if text.startswith("<|im_start|>"):
             skip_template = True
@@ -94,14 +90,11 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
                 text, return_word_ids=return_word_ids, disable_weights=True, **kwargs
             )
         else:
-            has_images = images is not None and len(images) > 0
-            template_prefix = self.EDIT_PREFIX if has_images else self.T2I_PREFIX
-
             prefix_ids = base_tok.tokenizer(
-                template_prefix, add_special_tokens=False
+                self.longcat_template_prefix, add_special_tokens=False
             )["input_ids"]
             suffix_ids = base_tok.tokenizer(
-                self.SUFFIX, add_special_tokens=False
+                self.longcat_template_suffix, add_special_tokens=False
             )["input_ids"]
 
             prompt_tokens = base_tok.tokenize_with_weights(
@@ -113,14 +106,6 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
             suffix_pairs = [(t, 1.0) for t in suffix_ids]
 
             combined = prefix_pairs + prompt_pairs + suffix_pairs
-
-            if has_images:
-                embed_count = 0
-                for i in range(len(combined)):
-                    if combined[i][0] == IMAGE_PAD_TOKEN_ID and embed_count < len(images):
-                        combined[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"}, combined[i][1])
-                        embed_count += 1
-
             tokens = {"qwen25_7b": [combined]}
 
         return tokens

comfy/text_encoders/qwen_vl.py

@@ -425,7 +425,4 @@ class Qwen2VLVisionTransformer(nn.Module):
             hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention)
 
         hidden_states = self.merger(hidden_states)
-        # Potentially important for spatially precise edits. This is present in the HF implementation.
-        reverse_indices = torch.argsort(window_index)
-        hidden_states = hidden_states[reverse_indices, :]
         return hidden_states

comfy/utils.py

@@ -20,8 +20,6 @@
 import torch
 import math
 import struct
-import ctypes
-import os
 import comfy.memory_management
 import safetensors.torch
 import numpy as np
@@ -34,7 +32,7 @@ from einops import rearrange
 from comfy.cli_args import args
 import json
 import time
-import threading
+import mmap
 import warnings
 
 MMAP_TORCH_FILES = args.mmap_torch_files
@@ -83,17 +81,14 @@ _TYPES = {
 }
 
 def load_safetensors(ckpt):
-    import comfy_aimdo.model_mmap
+    f = open(ckpt, "rb")
+    mapping = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
+    mv = memoryview(mapping)
 
-    f = open(ckpt, "rb", buffering=0)
-    model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt)
-    file_size = os.path.getsize(ckpt)
-    mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get()))
+    header_size = struct.unpack("<Q", mapping[:8])[0]
+    header = json.loads(mapping[8:8+header_size].decode("utf-8"))
 
-    header_size = struct.unpack("<Q", mv[:8])[0]
-    header = json.loads(mv[8:8 + header_size].tobytes().decode("utf-8"))
-
-    mv = mv[(data_base_offset := 8 + header_size):]
+    mv = mv[8 + header_size:]
 
     sd = {}
     for name, info in header.items():
@@ -107,14 +102,7 @@ def load_safetensors(ckpt):
             with warnings.catch_warnings():
                 #We are working with read-only RAM by design
                 warnings.filterwarnings("ignore", message="The given buffer is not writable")
-                tensor = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
-                storage = tensor.untyped_storage()
-                setattr(storage,
-                        "_comfy_tensor_file_slice",
-                        comfy.memory_management.TensorFileSlice(f, threading.get_ident(), data_base_offset + start, end - start))
-                setattr(storage, "_comfy_tensor_mmap_refs", (model_mmap, mv))
-                setattr(storage, "_comfy_tensor_mmap_touched", False)
-                sd[name] = tensor
+                sd[name] = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
 
     return sd, header.get("__metadata__", {}),
 
@@ -897,10 +885,6 @@ def set_attr(obj, attr, value):
     return prev
 
 def set_attr_param(obj, attr, value):
-    # Clone inference tensors (created under torch.inference_mode) since
-    # their version counter is frozen and nn.Parameter() cannot wrap them.
-    if (not torch.is_inference_mode_enabled()) and value.is_inference():
-        value = value.clone()
     return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
 
 def set_attr_buffer(obj, attr, value):
@@ -1135,8 +1119,8 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
                 pbar.update(1)
             continue
 
-        out = output[b:b+1].zero_()
-        out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device)
+        out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
+        out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
 
         positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
 
@@ -1151,7 +1135,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
                 upscaled.append(round(get_pos(d, pos)))
 
             ps = function(s_in).to(output_device)
-            mask = torch.ones([1, 1] + list(ps.shape[2:]), device=output_device)
+            mask = torch.ones_like(ps)
 
             for d in range(2, dims + 2):
                 feather = round(get_scale(d - 2, overlap[d - 2]))
@@ -1174,7 +1158,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
             if pbar is not None:
                 pbar.update(1)
 
-        out.div_(out_div)
+        output[b:b+1] = out/out_div
     return output
 
 def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):

comfy_api/feature_flags.py

@@ -15,7 +15,6 @@ SERVER_FEATURE_FLAGS: dict[str, Any] = {
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
     "extension": {"manager": {"supports_v4": True}},
     "node_replacements": True,
-    "assets": args.enable_assets,
 }
 
 

comfy_api/input/__init__.py

@@ -5,10 +5,6 @@ from comfy_api.latest._input import (
     MaskInput,
     LatentInput,
     VideoInput,
-    CurvePoint,
-    CurveInput,
-    MonotoneCubicCurve,
-    LinearCurve,
 )
 
 __all__ = [
@@ -17,8 +13,4 @@ __all__ = [
     "MaskInput",
     "LatentInput",
     "VideoInput",
-    "CurvePoint",
-    "CurveInput",
-    "MonotoneCubicCurve",
-    "LinearCurve",
 ]

comfy_api/latest/__init__.py

@@ -25,7 +25,6 @@ class ComfyAPI_latest(ComfyAPIBase):
         super().__init__()
         self.node_replacement = self.NodeReplacement()
         self.execution = self.Execution()
-        self.caching = self.Caching()
 
     class NodeReplacement(ProxiedSingleton):
         async def register(self, node_replace: io.NodeReplace) -> None:
@@ -85,36 +84,6 @@ class ComfyAPI_latest(ComfyAPIBase):
                 image=to_display,
             )
 
-    class Caching(ProxiedSingleton):
-        """
-        External cache provider API for sharing cached node outputs
-        across ComfyUI instances.
-
-        Example::
-
-            from comfy_api.latest import Caching
-
-            class MyCacheProvider(Caching.CacheProvider):
-                async def on_lookup(self, context):
-                    ...  # check external storage
-
-                async def on_store(self, context, value):
-                    ...  # store to external storage
-
-            Caching.register_provider(MyCacheProvider())
-        """
-        from ._caching import CacheProvider, CacheContext, CacheValue
-
-        async def register_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
-            """Register an external cache provider. Providers are called in registration order."""
-            from comfy_execution.cache_provider import register_cache_provider
-            register_cache_provider(provider)
-
-        async def unregister_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
-            """Unregister a previously registered cache provider."""
-            from comfy_execution.cache_provider import unregister_cache_provider
-            unregister_cache_provider(provider)
-
 class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """
@@ -147,9 +116,6 @@ class Types:
     VOXEL = VOXEL
     File3D = File3D
 
-
-Caching = ComfyAPI_latest.Caching
-
 ComfyAPI = ComfyAPI_latest
 
 # Create a synchronous version of the API
@@ -169,7 +135,6 @@ __all__ = [
     "Input",
     "InputImpl",
     "Types",
-    "Caching",
     "ComfyExtension",
     "io",
     "IO",

comfy_api/latest/_caching.py

@@ -1,42 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-from dataclasses import dataclass
-
-
-@dataclass
-class CacheContext:
-    node_id: str
-    class_type: str
-    cache_key_hash: str  # SHA256 hex digest
-
-
-@dataclass
-class CacheValue:
-    outputs: list
-    ui: dict = None
-
-
-class CacheProvider(ABC):
-    """Abstract base class for external cache providers.
-    Exceptions from provider methods are caught by the caller and never break execution.
-    """
-
-    @abstractmethod
-    async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
-        """Called on local cache miss. Return CacheValue if found, None otherwise."""
-        pass
-
-    @abstractmethod
-    async def on_store(self, context: CacheContext, value: CacheValue) -> None:
-        """Called after local store. Dispatched via asyncio.create_task."""
-        pass
-
-    def should_cache(self, context: CacheContext, value: Optional[CacheValue] = None) -> bool:
-        """Return False to skip external caching for this node. Default: True."""
-        return True
-
-    def on_prompt_start(self, prompt_id: str) -> None:
-        pass
-
-    def on_prompt_end(self, prompt_id: str) -> None:
-        pass

comfy_api/latest/_input/__init__.py

@@ -1,5 +1,4 @@
 from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
-from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve
 from .video_types import VideoInput
 
 __all__ = [
@@ -8,8 +7,4 @@ __all__ = [
     "VideoInput",
     "MaskInput",
     "LatentInput",
-    "CurvePoint",
-    "CurveInput",
-    "MonotoneCubicCurve",
-    "LinearCurve",
 ]

comfy_api/latest/_input/curve_types.py

@@ -1,219 +0,0 @@
-from __future__ import annotations
-
-import logging
-import math
-from abc import ABC, abstractmethod
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-
-CurvePoint = tuple[float, float]
-
-
-class CurveInput(ABC):
-    """Abstract base class for curve inputs.
-
-    Subclasses represent different curve representations (control-point
-    interpolation, analytical functions, LUT-based, etc.) while exposing a
-    uniform evaluation interface to downstream nodes.
-    """
-
-    @property
-    @abstractmethod
-    def points(self) -> list[CurvePoint]:
-        """The control points that define this curve."""
-
-    @abstractmethod
-    def interp(self, x: float) -> float:
-        """Evaluate the curve at a single *x* value in [0, 1]."""
-
-    def interp_array(self, xs: np.ndarray) -> np.ndarray:
-        """Vectorised evaluation over a numpy array of x values.
-
-        Subclasses should override this for better performance. The default
-        falls back to scalar ``interp`` calls.
-        """
-        return np.fromiter((self.interp(float(x)) for x in xs), dtype=np.float64, count=len(xs))
-
-    def to_lut(self, size: int = 256) -> np.ndarray:
-        """Generate a float64 lookup table of *size* evenly-spaced samples in [0, 1]."""
-        return self.interp_array(np.linspace(0.0, 1.0, size))
-
-    @staticmethod
-    def from_raw(data) -> CurveInput:
-        """Convert raw curve data (dict or point list) to a CurveInput instance.
-
-        Accepts:
-        - A ``CurveInput`` instance (returned as-is).
-        - A dict with ``"points"`` and optional ``"interpolation"`` keys.
-        - A bare list/sequence of ``(x, y)`` pairs (defaults to monotone cubic).
-        """
-        if isinstance(data, CurveInput):
-            return data
-        if isinstance(data, dict):
-            raw_points = data["points"]
-            interpolation = data.get("interpolation", "monotone_cubic")
-        else:
-            raw_points = data
-            interpolation = "monotone_cubic"
-        points = [(float(x), float(y)) for x, y in raw_points]
-        if interpolation == "linear":
-            return LinearCurve(points)
-        if interpolation != "monotone_cubic":
-            logger.warning("Unknown curve interpolation %r, falling back to monotone_cubic", interpolation)
-        return MonotoneCubicCurve(points)
-
-
-class MonotoneCubicCurve(CurveInput):
-    """Monotone cubic Hermite interpolation over control points.
-
-    Mirrors the frontend ``createMonotoneInterpolator`` in
-    ``ComfyUI_frontend/src/components/curve/curveUtils.ts`` so that
-    backend evaluation matches the editor preview exactly.
-
-    All heavy work (sorting, slope computation) happens once at construction.
-    ``interp_array`` is fully vectorised with numpy.
-    """
-
-    def __init__(self, control_points: list[CurvePoint]):
-        sorted_pts = sorted(control_points, key=lambda p: p[0])
-        self._points = [(float(x), float(y)) for x, y in sorted_pts]
-        self._xs = np.array([p[0] for p in self._points], dtype=np.float64)
-        self._ys = np.array([p[1] for p in self._points], dtype=np.float64)
-        self._slopes = self._compute_slopes()
-
-    @property
-    def points(self) -> list[CurvePoint]:
-        return list(self._points)
-
-    def _compute_slopes(self) -> np.ndarray:
-        xs, ys = self._xs, self._ys
-        n = len(xs)
-        if n < 2:
-            return np.zeros(n, dtype=np.float64)
-
-        dx = np.diff(xs)
-        dy = np.diff(ys)
-        dx_safe = np.where(dx == 0, 1.0, dx)
-        deltas = np.where(dx == 0, 0.0, dy / dx_safe)
-
-        slopes = np.empty(n, dtype=np.float64)
-        slopes[0] = deltas[0]
-        slopes[-1] = deltas[-1]
-        for i in range(1, n - 1):
-            if deltas[i - 1] * deltas[i] <= 0:
-                slopes[i] = 0.0
-            else:
-                slopes[i] = (deltas[i - 1] + deltas[i]) / 2
-
-        for i in range(n - 1):
-            if deltas[i] == 0:
-                slopes[i] = 0.0
-                slopes[i + 1] = 0.0
-            else:
-                alpha = slopes[i] / deltas[i]
-                beta = slopes[i + 1] / deltas[i]
-                s = alpha * alpha + beta * beta
-                if s > 9:
-                    t = 3 / math.sqrt(s)
-                    slopes[i] = t * alpha * deltas[i]
-                    slopes[i + 1] = t * beta * deltas[i]
-        return slopes
-
-    def interp(self, x: float) -> float:
-        xs, ys, slopes = self._xs, self._ys, self._slopes
-        n = len(xs)
-        if n == 0:
-            return 0.0
-        if n == 1:
-            return float(ys[0])
-        if x <= xs[0]:
-            return float(ys[0])
-        if x >= xs[-1]:
-            return float(ys[-1])
-
-        hi = int(np.searchsorted(xs, x, side='right'))
-        hi = min(hi, n - 1)
-        lo = hi - 1
-
-        dx = xs[hi] - xs[lo]
-        if dx == 0:
-            return float(ys[lo])
-
-        t = (x - xs[lo]) / dx
-        t2 = t * t
-        t3 = t2 * t
-        h00 = 2 * t3 - 3 * t2 + 1
-        h10 = t3 - 2 * t2 + t
-        h01 = -2 * t3 + 3 * t2
-        h11 = t3 - t2
-        return float(h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi])
-
-    def interp_array(self, xs_in: np.ndarray) -> np.ndarray:
-        """Fully vectorised evaluation using numpy."""
-        xs, ys, slopes = self._xs, self._ys, self._slopes
-        n = len(xs)
-        if n == 0:
-            return np.zeros_like(xs_in, dtype=np.float64)
-        if n == 1:
-            return np.full_like(xs_in, ys[0], dtype=np.float64)
-
-        hi = np.searchsorted(xs, xs_in, side='right').clip(1, n - 1)
-        lo = hi - 1
-
-        dx = xs[hi] - xs[lo]
-        dx_safe = np.where(dx == 0, 1.0, dx)
-        t = np.where(dx == 0, 0.0, (xs_in - xs[lo]) / dx_safe)
-        t2 = t * t
-        t3 = t2 * t
-
-        h00 = 2 * t3 - 3 * t2 + 1
-        h10 = t3 - 2 * t2 + t
-        h01 = -2 * t3 + 3 * t2
-        h11 = t3 - t2
-
-        result = h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi]
-        result = np.where(xs_in <= xs[0], ys[0], result)
-        result = np.where(xs_in >= xs[-1], ys[-1], result)
-        return result
-
-    def __repr__(self) -> str:
-        return f"MonotoneCubicCurve(points={self._points})"
-
-
-class LinearCurve(CurveInput):
-    """Piecewise linear interpolation over control points.
-
-    Mirrors the frontend ``createLinearInterpolator`` in
-    ``ComfyUI_frontend/src/components/curve/curveUtils.ts``.
-    """
-
-    def __init__(self, control_points: list[CurvePoint]):
-        sorted_pts = sorted(control_points, key=lambda p: p[0])
-        self._points = [(float(x), float(y)) for x, y in sorted_pts]
-        self._xs = np.array([p[0] for p in self._points], dtype=np.float64)
-        self._ys = np.array([p[1] for p in self._points], dtype=np.float64)
-
-    @property
-    def points(self) -> list[CurvePoint]:
-        return list(self._points)
-
-    def interp(self, x: float) -> float:
-        xs, ys = self._xs, self._ys
-        n = len(xs)
-        if n == 0:
-            return 0.0
-        if n == 1:
-            return float(ys[0])
-        return float(np.interp(x, xs, ys))
-
-    def interp_array(self, xs_in: np.ndarray) -> np.ndarray:
-        if len(self._xs) == 0:
-            return np.zeros_like(xs_in, dtype=np.float64)
-        if len(self._xs) == 1:
-            return np.full_like(xs_in, self._ys[0], dtype=np.float64)
-        return np.interp(xs_in, self._xs, self._ys)
-
-    def __repr__(self) -> str:
-        return f"LinearCurve(points={self._points})"

comfy_api/latest/_input_impl/video_types.py

@@ -272,7 +272,7 @@ class VideoFromFile(VideoInput):
             has_first_frame = False
             for frame in frames:
                 offset_seconds = start_time - frame.pts * audio_stream.time_base
-                to_skip = max(0, int(offset_seconds * audio_stream.sample_rate))
+                to_skip = int(offset_seconds * audio_stream.sample_rate)
                 if to_skip < frame.samples:
                     has_first_frame = True
                     break
@@ -280,7 +280,7 @@ class VideoFromFile(VideoInput):
                 audio_frames.append(frame.to_ndarray()[..., to_skip:])
 
             for frame in frames:
-                if self.__duration and frame.time > start_time + self.__duration:
+                if frame.time > start_time + self.__duration:
                     break
                 audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
             if len(audio_frames) > 0:

comfy_api/latest/_io.py

@@ -23,7 +23,7 @@ if TYPE_CHECKING:
     from comfy.samplers import CFGGuider, Sampler
     from comfy.sd import CLIP, VAE
     from comfy.sd import StyleModel as StyleModel_
-    from comfy_api.input import VideoInput, CurveInput as CurveInput_
+    from comfy_api.input import VideoInput
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
 from comfy_execution.graph_utils import ExecutionBlocker
@@ -297,7 +297,7 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, gradient_stops: list[dict]=None,
+                    display_mode: NumberDisplay=None, gradient_stops: list[list[float]]=None,
                     socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
             super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min
@@ -1242,9 +1242,8 @@ class BoundingBox(ComfyTypeIO):
 
 @comfytype(io_type="CURVE")
 class Curve(ComfyTypeIO):
-    from comfy_api.input import CurvePoint
-    if TYPE_CHECKING:
-        Type = CurveInput_
+    CurvePoint = tuple[float, float]
+    Type = list[CurvePoint]
 
     class Input(WidgetInput):
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
@@ -1253,18 +1252,6 @@ class Curve(ComfyTypeIO):
             if default is None:
                 self.default = [(0.0, 0.0), (1.0, 1.0)]
 
-        def as_dict(self):
-            d = super().as_dict()
-            if self.default is not None:
-                d["default"] = {"points": [list(p) for p in self.default], "interpolation": "monotone_cubic"}
-            return d
-
-
-@comfytype(io_type="HISTOGRAM")
-class Histogram(ComfyTypeIO):
-    """A histogram represented as a list of bin counts."""
-    Type = list[int]
-
 
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
@@ -2253,6 +2240,5 @@ __all__ = [
     "PriceBadge",
     "BoundingBox",
     "Curve",
-    "Histogram",
     "NodeReplace",
 ]

comfy_api_nodes/apis/gemini.py

@@ -67,7 +67,6 @@ class GeminiPart(BaseModel):
     inlineData: GeminiInlineData | None = Field(None)
     fileData: GeminiFileData | None = Field(None)
     text: str | None = Field(None)
-    thought: bool | None = Field(None)
 
 
 class GeminiTextPart(BaseModel):

comfy_api_nodes/apis/grok.py

@@ -7,8 +7,7 @@ class ImageGenerationRequest(BaseModel):
     aspect_ratio: str = Field(...)
     n: int = Field(...)
     seed: int = Field(...)
-    response_format: str = Field("url")
-    resolution: str = Field(...)
+    response_for: str = Field("url")
 
 
 class InputUrlObject(BaseModel):
@@ -17,33 +16,24 @@ class InputUrlObject(BaseModel):
 
 class ImageEditRequest(BaseModel):
     model: str = Field(...)
-    images: list[InputUrlObject] = Field(...)
+    image: InputUrlObject = Field(...)
     prompt: str = Field(...)
     resolution: str = Field(...)
     n: int = Field(...)
     seed: int = Field(...)
-    response_format: str = Field("url")
-    aspect_ratio: str | None = Field(...)
+    response_for: str = Field("url")
 
 
 class VideoGenerationRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)
-    image: InputUrlObject | None = Field(None)
-    reference_images: list[InputUrlObject] | None = Field(None)
+    image: InputUrlObject | None = Field(...)
     duration: int = Field(...)
     aspect_ratio: str | None = Field(...)
     resolution: str = Field(...)
     seed: int = Field(...)
 
 
-class VideoExtensionRequest(BaseModel):
-    prompt: str = Field(...)
-    video: InputUrlObject = Field(...)
-    duration: int = Field(default=6)
-    model: str | None = Field(default=None)
-
-
 class VideoEditRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)
@@ -57,13 +47,8 @@ class ImageResponseObject(BaseModel):
     revised_prompt: str | None = Field(None)
 
 
-class UsageObject(BaseModel):
-    cost_in_usd_ticks: int | None = Field(None)
-
-
 class ImageGenerationResponse(BaseModel):
     data: list[ImageResponseObject] = Field(...)
-    usage: UsageObject | None = Field(None)
 
 
 class VideoGenerationResponse(BaseModel):
@@ -80,4 +65,3 @@ class VideoStatusResponse(BaseModel):
     status: str | None = Field(None)
     video: VideoResponseObject | None = Field(None)
     model: str | None = Field(None)
-    usage: UsageObject | None = Field(None)

comfy_api_nodes/apis/hunyuan3d.py

@@ -66,17 +66,13 @@ class To3DProTaskQueryRequest(BaseModel):
     JobId: str = Field(...)
 
 
-class TaskFile3DInput(BaseModel):
+class To3DUVFileInput(BaseModel):
     Type: str = Field(..., description="File type: GLB, OBJ, or FBX")
     Url: str = Field(...)
 
 
 class To3DUVTaskRequest(BaseModel):
-    File: TaskFile3DInput = Field(...)
-
-
-class To3DPartTaskRequest(BaseModel):
-    File: TaskFile3DInput = Field(...)
+    File: To3DUVFileInput = Field(...)
 
 
 class TextureEditImageInfo(BaseModel):
@@ -84,13 +80,7 @@ class TextureEditImageInfo(BaseModel):
 
 
 class TextureEditTaskRequest(BaseModel):
-    File3D: TaskFile3DInput = Field(...)
+    File3D: To3DUVFileInput = Field(...)
     Image: TextureEditImageInfo | None = Field(None)
     Prompt: str | None = Field(None)
     EnablePBR: bool | None = Field(None)
-
-
-class SmartTopologyRequest(BaseModel):
-    File3D: TaskFile3DInput = Field(...)
-    PolygonType: str | None = Field(...)
-    FaceLevel: str | None = Field(...)

comfy_api_nodes/apis/kling.py

@@ -148,4 +148,3 @@ class MotionControlRequest(BaseModel):
     keep_original_sound: str = Field(...)
     character_orientation: str = Field(...)
     mode: str = Field(..., description="'pro' or 'std'")
-    model_name: str = Field(...)

comfy_api_nodes/apis/quiver.py

@@ -1,43 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class QuiverImageObject(BaseModel):
-    url: str = Field(...)
-
-
-class QuiverTextToSVGRequest(BaseModel):
-    model: str = Field(default="arrow-preview")
-    prompt: str = Field(...)
-    instructions: str | None = Field(default=None)
-    references: list[QuiverImageObject] | None = Field(default=None, max_length=4)
-    temperature: float | None = Field(default=None, ge=0, le=2)
-    top_p: float | None = Field(default=None, ge=0, le=1)
-    presence_penalty: float | None = Field(default=None, ge=-2, le=2)
-
-
-class QuiverImageToSVGRequest(BaseModel):
-    model: str = Field(default="arrow-preview")
-    image: QuiverImageObject = Field(...)
-    auto_crop: bool | None = Field(default=None)
-    target_size: int | None = Field(default=None, ge=128, le=4096)
-    temperature: float | None = Field(default=None, ge=0, le=2)
-    top_p: float | None = Field(default=None, ge=0, le=1)
-    presence_penalty: float | None = Field(default=None, ge=-2, le=2)
-
-
-class QuiverSVGResponseItem(BaseModel):
-    svg: str = Field(...)
-    mime_type: str | None = Field(default="image/svg+xml")
-
-
-class QuiverSVGUsage(BaseModel):
-    total_tokens: int | None = Field(default=None)
-    input_tokens: int | None = Field(default=None)
-    output_tokens: int | None = Field(default=None)
-
-
-class QuiverSVGResponse(BaseModel):
-    id: str | None = Field(default=None)
-    created: int | None = Field(default=None)
-    data: list[QuiverSVGResponseItem] = Field(...)
-    usage: QuiverSVGUsage | None = Field(default=None)

comfy_api_nodes/apis/reve.py

@@ -1,68 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class RevePostprocessingOperation(BaseModel):
-    process: str = Field(..., description="The postprocessing operation: upscale or remove_background.")
-    upscale_factor: int | None = Field(
-        None,
-        description="Upscale factor (2, 3, or 4). Only used when process is upscale.",
-        ge=2,
-        le=4,
-    )
-
-
-class ReveImageCreateRequest(BaseModel):
-    prompt: str = Field(...)
-    aspect_ratio: str | None = Field(...)
-    version: str = Field(...)
-    test_time_scaling: int = Field(
-        ...,
-        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
-        ge=1,
-        le=15,
-    )
-    postprocessing: list[RevePostprocessingOperation] | None = Field(
-        None, description="Optional postprocessing operations to apply after generation."
-    )
-
-
-class ReveImageEditRequest(BaseModel):
-    edit_instruction: str = Field(...)
-    reference_image: str = Field(..., description="A base64 encoded image to use as reference for the edit.")
-    aspect_ratio: str | None = Field(...)
-    version: str = Field(...)
-    test_time_scaling: int | None = Field(
-        ...,
-        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
-        ge=1,
-        le=15,
-    )
-    postprocessing: list[RevePostprocessingOperation] | None = Field(
-        None, description="Optional postprocessing operations to apply after generation."
-    )
-
-
-class ReveImageRemixRequest(BaseModel):
-    prompt: str = Field(...)
-    reference_images: list[str] = Field(..., description="A list of 1-6 base64 encoded reference images.")
-    aspect_ratio: str | None = Field(...)
-    version: str = Field(...)
-    test_time_scaling: int | None = Field(
-        ...,
-        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
-        ge=1,
-        le=15,
-    )
-    postprocessing: list[RevePostprocessingOperation] | None = Field(
-        None, description="Optional postprocessing operations to apply after generation."
-    )
-
-
-class ReveImageResponse(BaseModel):
-    image: str | None = Field(None, description="The base64 encoded image data.")
-    request_id: str | None = Field(None, description="A unique id for the request.")
-    credits_used: float | None = Field(None, description="The number of credits used for this request.")
-    version: str | None = Field(None, description="The specific model version used.")
-    content_violation: bool | None = Field(
-        None, description="Indicates whether the generated image violates the content policy."
-    )

comfy_api_nodes/nodes_bytedance.py

@@ -47,10 +47,6 @@ SEEDREAM_MODELS = {
 BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
 BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"  # + /{task_id}
 
-DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"}
-
-logger = logging.getLogger(__name__)
-
 
 def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
     if response.error:
@@ -139,7 +135,6 @@ class ByteDanceImageNode(IO.ComfyNode):
             price_badge=IO.PriceBadge(
                 expr="""{"type":"usd","usd":0.03}""",
             ),
-            is_deprecated=True,
         )
 
     @classmethod
@@ -947,7 +942,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
         ]
         return await process_video_task(
             cls,
-            payload=Image2VideoTaskCreationRequest(model=model, content=x, generate_audio=None),
+            payload=Image2VideoTaskCreationRequest(model=model, content=x),
             estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
         )
 
@@ -957,12 +952,6 @@ async def process_video_task(
     payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest,
     estimated_duration: int | None,
 ) -> IO.NodeOutput:
-    if payload.model in DEPRECATED_MODELS:
-        logger.warning(
-            "Model '%s' is deprecated and will be deactivated on May 13, 2026. "
-            "Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.",
-            payload.model,
-        )
     initial_response = await sync_op(
         cls,
         ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),

comfy_api_nodes/nodes_gemini.py

@@ -63,7 +63,7 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
       $m := widgets.model;
       $r := widgets.resolution;
       $isFlash := $contains($m, "nano banana 2");
-      $flashPrices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+      $flashPrices := {"1k": 0.0696, "2k": 0.0696, "4k": 0.123};
       $proPrices := {"1k": 0.134, "2k": 0.134, "4k": 0.24};
       $prices := $isFlash ? $flashPrices : $proPrices;
       {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
@@ -72,6 +72,18 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
 )
 
 
+class GeminiModel(str, Enum):
+    """
+    Gemini Model Names allowed by comfy-api
+    """
+
+    gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06"
+    gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
+    gemini_2_5_pro = "gemini-2.5-pro"
+    gemini_2_5_flash = "gemini-2.5-flash"
+    gemini_3_0_pro = "gemini-3-pro-preview"
+
+
 class GeminiImageModel(str, Enum):
     """
     Gemini Image Model Names allowed by comfy-api
@@ -188,12 +200,10 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
     return "\n".join([part.text for part in parts])
 
 
-async def get_image_from_response(response: GeminiGenerateContentResponse, thought: bool = False) -> Input.Image:
+async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
     image_tensors: list[Input.Image] = []
     parts = get_parts_by_type(response, "image/*")
     for part in parts:
-        if (part.thought is True) != thought:
-            continue
         if part.inlineData:
             image_data = base64.b64decode(part.inlineData.data)
             returned_image = bytesio_to_image_tensor(BytesIO(image_data))
@@ -227,14 +237,10 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 0.30
         output_text_tokens_price = 2.50
         output_image_tokens_price = 30.0
-    elif response.modelVersion in ("gemini-3-pro-preview", "gemini-3.1-pro-preview"):
+    elif response.modelVersion == "gemini-3-pro-preview":
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 0.0
-    elif response.modelVersion == "gemini-3.1-flash-lite-preview":
-        input_tokens_price = 0.25
-        output_text_tokens_price = 1.50
-        output_image_tokens_price = 0.0
     elif response.modelVersion == "gemini-3-pro-image-preview":
         input_tokens_price = 2
         output_text_tokens_price = 12.0
@@ -286,16 +292,8 @@ class GeminiNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=[
-                        "gemini-2.5-pro-preview-05-06",
-                        "gemini-2.5-flash-preview-04-17",
-                        "gemini-2.5-pro",
-                        "gemini-2.5-flash",
-                        "gemini-3-pro-preview",
-                        "gemini-3-1-pro",
-                        "gemini-3-1-flash-lite",
-                    ],
-                    default="gemini-3-1-pro",
+                    options=GeminiModel,
+                    default=GeminiModel.gemini_2_5_pro,
                     tooltip="The Gemini model to use for generating responses.",
                 ),
                 IO.Int.Input(
@@ -365,16 +363,11 @@ class GeminiNode(IO.ComfyNode):
                     "usd": [0.00125, 0.01],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
-                  : ($contains($m, "gemini-3-pro-preview") or $contains($m, "gemini-3-1-pro")) ? {
+                  : $contains($m, "gemini-3-pro-preview") ? {
                     "type": "list_usd",
                     "usd": [0.002, 0.012],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
-                  : $contains($m, "gemini-3-1-flash-lite") ? {
-                    "type": "list_usd",
-                    "usd": [0.00025, 0.0015],
-                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
-                  }
                   : {"type":"text", "text":"Token-based"}
                 )
                 """,
@@ -443,14 +436,12 @@ class GeminiNode(IO.ComfyNode):
         files: list[GeminiPart] | None = None,
         system_prompt: str = "",
     ) -> IO.NodeOutput:
-        if model == "gemini-3-pro-preview":
-            model = "gemini-3.1-pro-preview"  # model "gemini-3-pro-preview" will be soon deprecated by Google
-        elif model == "gemini-3-1-pro":
-            model = "gemini-3.1-pro-preview"
-        elif model == "gemini-3-1-flash-lite":
-            model = "gemini-3.1-flash-lite-preview"
+        validate_string(prompt, strip_whitespace=False)
 
+        # Create parts list with text prompt as the first part
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+
+        # Add other modal parts
         if images is not None:
             parts.extend(await create_image_parts(cls, images))
         if audio is not None:
@@ -933,11 +924,6 @@ class GeminiNanoBanana2(IO.ComfyNode):
             outputs=[
                 IO.Image.Output(),
                 IO.String.Output(),
-                IO.Image.Output(
-                    display_name="thought_image",
-                    tooltip="First image from the model's thinking process. "
-                    "Only available with thinking_level HIGH and IMAGE+TEXT modality.",
-                ),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -999,11 +985,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
-        return IO.NodeOutput(
-            await get_image_from_response(response),
-            get_text_from_response(response),
-            await get_image_from_response(response, thought=True),
-        )
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
 
 
 class GeminiExtension(ComfyExtension):

comfy_api_nodes/nodes_grok.py

@@ -8,7 +8,6 @@ from comfy_api_nodes.apis.grok import (
     ImageGenerationResponse,
     InputUrlObject,
     VideoEditRequest,
-    VideoExtensionRequest,
     VideoGenerationRequest,
     VideoGenerationResponse,
     VideoStatusResponse,
@@ -22,26 +21,12 @@ from comfy_api_nodes.util import (
     poll_op,
     sync_op,
     tensor_to_base64_string,
-    upload_images_to_comfyapi,
     upload_video_to_comfyapi,
     validate_string,
     validate_video_duration,
 )
 
 
-def _extract_grok_price(response) -> float | None:
-    if response.usage and response.usage.cost_in_usd_ticks is not None:
-        return response.usage.cost_in_usd_ticks / 10_000_000_000
-    return None
-
-
-def _extract_grok_video_price(response) -> float | None:
-    price = _extract_grok_price(response)
-    if price is not None:
-        return price * 1.43
-    return None
-
-
 class GrokImageNode(IO.ComfyNode):
 
     @classmethod
@@ -52,10 +37,7 @@ class GrokImageNode(IO.ComfyNode):
             category="api node/image/Grok",
             description="Generate images using Grok based on a text prompt",
             inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=["grok-imagine-image-pro", "grok-imagine-image", "grok-imagine-image-beta"],
-                ),
+                IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -99,7 +81,6 @@ class GrokImageNode(IO.ComfyNode):
                     tooltip="Seed to determine if node should re-run; "
                     "actual results are nondeterministic regardless of seed.",
                 ),
-                IO.Combo.Input("resolution", options=["1K", "2K"], optional=True),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -111,13 +92,8 @@ class GrokImageNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images"]),
-                expr="""
-                (
-                  $rate := $contains(widgets.model, "pro") ? 0.07 : 0.02;
-                  {"type":"usd","usd": $rate * widgets.number_of_images}
-                )
-                """,
+                depends_on=IO.PriceBadgeDepends(widgets=["number_of_images"]),
+                expr="""{"type":"usd","usd":0.033 * widgets.number_of_images}""",
             ),
         )
 
@@ -129,7 +105,6 @@ class GrokImageNode(IO.ComfyNode):
         aspect_ratio: str,
         number_of_images: int,
         seed: int,
-        resolution: str = "1K",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         response = await sync_op(
@@ -141,10 +116,8 @@ class GrokImageNode(IO.ComfyNode):
                 aspect_ratio=aspect_ratio,
                 n=number_of_images,
                 seed=seed,
-                resolution=resolution.lower(),
             ),
             response_model=ImageGenerationResponse,
-            price_extractor=_extract_grok_price,
         )
         if len(response.data) == 1:
             return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
@@ -165,17 +138,14 @@ class GrokImageEditNode(IO.ComfyNode):
             category="api node/image/Grok",
             description="Modify an existing image based on a text prompt",
             inputs=[
-                IO.Combo.Input(
-                    "model",
-                    options=["grok-imagine-image-pro", "grok-imagine-image", "grok-imagine-image-beta"],
-                ),
-                IO.Image.Input("image", display_name="images"),
+                IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
+                IO.Image.Input("image"),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
                     tooltip="The text prompt used to generate the image",
                 ),
-                IO.Combo.Input("resolution", options=["1K", "2K"]),
+                IO.Combo.Input("resolution", options=["1K"]),
                 IO.Int.Input(
                     "number_of_images",
                     default=1,
@@ -196,27 +166,6 @@ class GrokImageEditNode(IO.ComfyNode):
                     tooltip="Seed to determine if node should re-run; "
                     "actual results are nondeterministic regardless of seed.",
                 ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=[
-                        "auto",
-                        "1:1",
-                        "2:3",
-                        "3:2",
-                        "3:4",
-                        "4:3",
-                        "9:16",
-                        "16:9",
-                        "9:19.5",
-                        "19.5:9",
-                        "9:20",
-                        "20:9",
-                        "1:2",
-                        "2:1",
-                    ],
-                    optional=True,
-                    tooltip="Only allowed when multiple images are connected to the image input.",
-                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -228,13 +177,8 @@ class GrokImageEditNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images"]),
-                expr="""
-                (
-                  $rate := $contains(widgets.model, "pro") ? 0.07 : 0.02;
-                  {"type":"usd","usd": 0.002 + $rate * widgets.number_of_images}
-                )
-                """,
+                depends_on=IO.PriceBadgeDepends(widgets=["number_of_images"]),
+                expr="""{"type":"usd","usd":0.002 + 0.033 * widgets.number_of_images}""",
             ),
         )
 
@@ -247,32 +191,22 @@ class GrokImageEditNode(IO.ComfyNode):
         resolution: str,
         number_of_images: int,
         seed: int,
-        aspect_ratio: str = "auto",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
-        if model == "grok-imagine-image-pro":
-            if get_number_of_images(image) > 1:
-                raise ValueError("The pro model supports only 1 input image.")
-        elif get_number_of_images(image) > 3:
-            raise ValueError("A maximum of 3 input images is supported.")
-        if aspect_ratio != "auto" and get_number_of_images(image) == 1:
-            raise ValueError(
-                "Custom aspect ratio is only allowed when multiple images are connected to the image input."
-            )
+        if get_number_of_images(image) != 1:
+            raise ValueError("Only one input image is supported.")
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
             data=ImageEditRequest(
                 model=model,
-                images=[InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in image],
+                image=InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(image)}"),
                 prompt=prompt,
                 resolution=resolution.lower(),
                 n=number_of_images,
                 seed=seed,
-                aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
             ),
             response_model=ImageGenerationResponse,
-            price_extractor=_extract_grok_price,
         )
         if len(response.data) == 1:
             return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
@@ -293,7 +227,7 @@ class GrokVideoNode(IO.ComfyNode):
             category="api node/video/Grok",
             description="Generate video from a prompt or an image",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-video", "grok-imagine-video-beta"]),
+                IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -341,11 +275,10 @@ class GrokVideoNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"], inputs=["image"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["duration"], inputs=["image"]),
                 expr="""
                 (
-                  $rate := widgets.resolution = "720p" ? 0.07 : 0.05;
-                  $base := $rate * widgets.duration;
+                  $base := 0.181 * widgets.duration;
                   {"type":"usd","usd": inputs.image.connected ? $base + 0.002 : $base}
                 )
                 """,
@@ -363,8 +296,6 @@ class GrokVideoNode(IO.ComfyNode):
         seed: int,
         image: Input.Image | None = None,
     ) -> IO.NodeOutput:
-        if model == "grok-imagine-video-beta":
-            model = "grok-imagine-video"
         image_url = None
         if image is not None:
             if get_number_of_images(image) != 1:
@@ -390,7 +321,6 @@ class GrokVideoNode(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
             status_extractor=lambda r: r.status if r.status is not None else "complete",
             response_model=VideoStatusResponse,
-            price_extractor=_extract_grok_price,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.video.url))
 
@@ -405,7 +335,7 @@ class GrokVideoEditNode(IO.ComfyNode):
             category="api node/video/Grok",
             description="Edit an existing video based on a text prompt.",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-video", "grok-imagine-video-beta"]),
+                IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -434,7 +364,7 @@ class GrokVideoEditNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd": 0.06, "format": {"suffix": "/sec", "approximate": true}}""",
+                expr="""{"type":"usd","usd": 0.191, "format": {"suffix": "/sec", "approximate": true}}""",
             ),
         )
 
@@ -468,245 +398,6 @@ class GrokVideoEditNode(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
             status_extractor=lambda r: r.status if r.status is not None else "complete",
             response_model=VideoStatusResponse,
-            price_extractor=_extract_grok_price,
-        )
-        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
-
-
-class GrokVideoReferenceNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="GrokVideoReferenceNode",
-            display_name="Grok Reference-to-Video",
-            category="api node/video/Grok",
-            description="Generate video guided by reference images as style and content references.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    tooltip="Text description of the desired video.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "grok-imagine-video",
-                            [
-                                IO.Autogrow.Input(
-                                    "reference_images",
-                                    template=IO.Autogrow.TemplatePrefix(
-                                        IO.Image.Input("image"),
-                                        prefix="reference_",
-                                        min=1,
-                                        max=7,
-                                    ),
-                                    tooltip="Up to 7 reference images to guide the video generation.",
-                                ),
-                                IO.Combo.Input(
-                                    "resolution",
-                                    options=["480p", "720p"],
-                                    tooltip="The resolution of the output video.",
-                                ),
-                                IO.Combo.Input(
-                                    "aspect_ratio",
-                                    options=["16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"],
-                                    tooltip="The aspect ratio of the output video.",
-                                ),
-                                IO.Int.Input(
-                                    "duration",
-                                    default=6,
-                                    min=2,
-                                    max=10,
-                                    step=1,
-                                    tooltip="The duration of the output video in seconds.",
-                                    display_mode=IO.NumberDisplay.slider,
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="The model to use for video generation.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="Seed to determine if node should re-run; "
-                    "actual results are nondeterministic regardless of seed.",
-                ),
-            ],
-            outputs=[
-                IO.Video.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["model.duration", "model.resolution"],
-                    input_groups=["model.reference_images"],
-                ),
-                expr="""
-                (
-                  $res := $lookup(widgets, "model.resolution");
-                  $dur := $lookup(widgets, "model.duration");
-                  $refs := inputGroups["model.reference_images"];
-                  $rate := $res = "720p" ? 0.07 : 0.05;
-                  $price := ($rate * $dur + 0.002 * $refs) * 1.43;
-                  {"type":"usd","usd": $price}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        model: dict,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=True, min_length=1)
-        ref_image_urls = await upload_images_to_comfyapi(
-            cls,
-            list(model["reference_images"].values()),
-            mime_type="image/png",
-            wait_label="Uploading base images",
-            max_images=7,
-        )
-        initial_response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"),
-            data=VideoGenerationRequest(
-                model=model["model"],
-                reference_images=[InputUrlObject(url=i) for i in ref_image_urls],
-                prompt=prompt,
-                resolution=model["resolution"],
-                duration=model["duration"],
-                aspect_ratio=model["aspect_ratio"],
-                seed=seed,
-            ),
-            response_model=VideoGenerationResponse,
-        )
-        response = await poll_op(
-            cls,
-            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
-            status_extractor=lambda r: r.status if r.status is not None else "complete",
-            response_model=VideoStatusResponse,
-            price_extractor=_extract_grok_video_price,
-        )
-        return IO.NodeOutput(await download_url_to_video_output(response.video.url))
-
-
-class GrokVideoExtendNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="GrokVideoExtendNode",
-            display_name="Grok Video Extend",
-            category="api node/video/Grok",
-            description="Extend an existing video with a seamless continuation based on a text prompt.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    tooltip="Text description of what should happen next in the video.",
-                ),
-                IO.Video.Input("video", tooltip="Source video to extend. MP4 format, 2-15 seconds."),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "grok-imagine-video",
-                            [
-                                IO.Int.Input(
-                                    "duration",
-                                    default=8,
-                                    min=2,
-                                    max=10,
-                                    step=1,
-                                    tooltip="Length of the extension in seconds.",
-                                    display_mode=IO.NumberDisplay.slider,
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="The model to use for video extension.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="Seed to determine if node should re-run; "
-                    "actual results are nondeterministic regardless of seed.",
-                ),
-            ],
-            outputs=[
-                IO.Video.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model.duration"]),
-                expr="""
-                (
-                  $dur := $lookup(widgets, "model.duration");
-                  {
-                    "type": "range_usd",
-                    "min_usd": (0.02 + 0.05 * $dur) * 1.43,
-                    "max_usd": (0.15 + 0.05 * $dur) * 1.43
-                  }
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        video: Input.Video,
-        model: dict,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=True, min_length=1)
-        validate_video_duration(video, min_duration=2, max_duration=15)
-        video_size = get_fs_object_size(video.get_stream_source())
-        if video_size > 50 * 1024 * 1024:
-            raise ValueError(f"Video size ({video_size / 1024 / 1024:.1f}MB) exceeds 50MB limit.")
-        initial_response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/xai/v1/videos/extensions", method="POST"),
-            data=VideoExtensionRequest(
-                prompt=prompt,
-                video=InputUrlObject(url=await upload_video_to_comfyapi(cls, video)),
-                duration=model["duration"],
-            ),
-            response_model=VideoGenerationResponse,
-        )
-        response = await poll_op(
-            cls,
-            ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
-            status_extractor=lambda r: r.status if r.status is not None else "complete",
-            response_model=VideoStatusResponse,
-            price_extractor=_extract_grok_video_price,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.video.url))
 
@@ -718,9 +409,7 @@ class GrokExtension(ComfyExtension):
             GrokImageNode,
             GrokImageEditNode,
             GrokVideoNode,
-            GrokVideoReferenceNode,
             GrokVideoEditNode,
-            GrokVideoExtendNode,
         ]
 
 

comfy_api_nodes/nodes_hunyuan3d.py

@@ -1,7 +1,3 @@
-import zipfile
-from io import BytesIO
-
-import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, Types
@@ -9,20 +5,16 @@ from comfy_api_nodes.apis.hunyuan3d import (
     Hunyuan3DViewImage,
     InputGenerateType,
     ResultFile3D,
-    SmartTopologyRequest,
-    TaskFile3DInput,
     TextureEditTaskRequest,
-    To3DPartTaskRequest,
     To3DProTaskCreateResponse,
     To3DProTaskQueryRequest,
     To3DProTaskRequest,
     To3DProTaskResultResponse,
+    To3DUVFileInput,
     To3DUVTaskRequest,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
-    bytesio_to_image_tensor,
-    download_url_to_bytesio,
     download_url_to_file_3d,
     download_url_to_image_tensor,
     downscale_image_tensor_by_max_side,
@@ -43,68 +35,6 @@ def _is_tencent_rate_limited(status: int, body: object) -> bool:
     )
 
 
-class ObjZipResult:
-    __slots__ = ("obj", "texture", "metallic", "normal", "roughness")
-
-    def __init__(
-        self,
-        obj: Types.File3D,
-        texture: Input.Image | None = None,
-        metallic: Input.Image | None = None,
-        normal: Input.Image | None = None,
-        roughness: Input.Image | None = None,
-    ):
-        self.obj = obj
-        self.texture = texture
-        self.metallic = metallic
-        self.normal = normal
-        self.roughness = roughness
-
-
-async def download_and_extract_obj_zip(url: str) -> ObjZipResult:
-    """The Tencent API returns OBJ results as ZIP archives containing the .obj mesh, and texture images.
-
-    When PBR is enabled, the ZIP may contain additional metallic, normal, and roughness maps
-    identified by their filename suffixes.
-    """
-    data = BytesIO()
-    await download_url_to_bytesio(url, data)
-    data.seek(0)
-    if not zipfile.is_zipfile(data):
-        data.seek(0)
-        return ObjZipResult(obj=Types.File3D(source=data, file_format="obj"))
-    data.seek(0)
-    obj_bytes = None
-    textures: dict[str, Input.Image] = {}
-    with zipfile.ZipFile(data) as zf:
-        for name in zf.namelist():
-            lower = name.lower()
-            if lower.endswith(".obj"):
-                obj_bytes = zf.read(name)
-            elif any(lower.endswith(ext) for ext in (".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".webp")):
-                stem = lower.rsplit(".", 1)[0]
-                tensor = bytesio_to_image_tensor(BytesIO(zf.read(name)), mode="RGB")
-                matched_key = "texture"
-                for suffix, key in {
-                    "_metallic": "metallic",
-                    "_normal": "normal",
-                    "_roughness": "roughness",
-                }.items():
-                    if stem.endswith(suffix):
-                        matched_key = key
-                        break
-                textures[matched_key] = tensor
-    if obj_bytes is None:
-        raise ValueError("ZIP archive does not contain an OBJ file.")
-    return ObjZipResult(
-        obj=Types.File3D(source=BytesIO(obj_bytes), file_format="obj"),
-        texture=textures.get("texture"),
-        metallic=textures.get("metallic"),
-        normal=textures.get("normal"),
-        roughness=textures.get("roughness"),
-    )
-
-
 def get_file_from_response(
     response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
 ) -> ResultFile3D | None:
@@ -162,7 +92,6 @@ class TencentTextToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
-                IO.Image.Output(display_name="texture_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -221,14 +150,14 @@ class TencentTextToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            obj_result.obj,
-            obj_result.texture,
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
+            ),
         )
 
 
@@ -281,10 +210,6 @@ class TencentImageToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
-                IO.Image.Output(display_name="texture_image"),
-                IO.Image.Output(display_name="optional_metallic"),
-                IO.Image.Output(display_name="optional_normal"),
-                IO.Image.Output(display_name="optional_roughness"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -378,17 +303,14 @@ class TencentImageToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            obj_result.obj,
-            obj_result.texture,
-            obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3),
-            obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3),
-            obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3),
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
+            ),
         )
 
 
@@ -422,6 +344,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             outputs=[
                 IO.File3DOBJ.Output(display_name="OBJ"),
                 IO.File3DFBX.Output(display_name="FBX"),
+                IO.Image.Output(),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -452,7 +375,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv", method="POST"),
             response_model=To3DProTaskCreateResponse,
             data=To3DUVTaskRequest(
-                File=TaskFile3DInput(
+                File=To3DUVFileInput(
                     Type=file_format.upper(),
                     Url=await upload_3d_model_to_comfyapi(cls, model_3d, file_format),
                 )
@@ -471,6 +394,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "image").Url),
         )
 
 
@@ -508,8 +432,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
             ],
             outputs=[
                 IO.File3DGLB.Output(display_name="GLB"),
-                IO.File3DOBJ.Output(display_name="OBJ"),
-                IO.Image.Output(display_name="texture_image"),
+                IO.File3DFBX.Output(display_name="FBX"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -540,7 +463,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
             ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit", method="POST"),
             response_model=To3DProTaskCreateResponse,
             data=TextureEditTaskRequest(
-                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                File3D=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
                 Prompt=prompt,
                 EnablePBR=True,
             ),
@@ -558,8 +481,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
         )
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
-            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
-            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "texture_image").Url),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
         )
 
 
@@ -616,8 +538,8 @@ class Tencent3DPartNode(IO.ComfyNode):
             cls,
             ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part", method="POST"),
             response_model=To3DProTaskCreateResponse,
-            data=To3DPartTaskRequest(
-                File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+            data=To3DUVTaskRequest(
+                File=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
             ),
             is_rate_limited=_is_tencent_rate_limited,
         )
@@ -635,107 +557,15 @@ class Tencent3DPartNode(IO.ComfyNode):
         )
 
 
-class TencentSmartTopologyNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="TencentSmartTopologyNode",
-            display_name="Hunyuan3D: Smart Topology",
-            category="api node/3d/Tencent",
-            description="Perform smart retopology on a 3D model. "
-            "Supports GLB/OBJ formats; max 200MB; recommended for high-poly models.",
-            inputs=[
-                IO.MultiType.Input(
-                    "model_3d",
-                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DAny],
-                    tooltip="Input 3D model (GLB or OBJ)",
-                ),
-                IO.Combo.Input(
-                    "polygon_type",
-                    options=["triangle", "quadrilateral"],
-                    tooltip="Surface composition type.",
-                ),
-                IO.Combo.Input(
-                    "face_level",
-                    options=["medium", "high", "low"],
-                    tooltip="Polygon reduction level.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[
-                IO.File3DOBJ.Output(display_name="OBJ"),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":1.0}'),
-        )
-
-    SUPPORTED_FORMATS = {"glb", "obj"}
-
-    @classmethod
-    async def execute(
-        cls,
-        model_3d: Types.File3D,
-        polygon_type: str,
-        face_level: str,
-        seed: int,
-    ) -> IO.NodeOutput:
-        _ = seed
-        file_format = model_3d.format.lower()
-        if file_format not in cls.SUPPORTED_FORMATS:
-            raise ValueError(
-                f"Unsupported file format: '{file_format}'. " f"Supported: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
-            )
-        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology", method="POST"),
-            response_model=To3DProTaskCreateResponse,
-            data=SmartTopologyRequest(
-                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
-                PolygonType=polygon_type,
-                FaceLevel=face_level,
-            ),
-            is_rate_limited=_is_tencent_rate_limited,
-        )
-        if response.Error:
-            raise ValueError(f"Task creation failed: [{response.Error.Code}] {response.Error.Message}")
-        result = await poll_op(
-            cls,
-            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology/query", method="POST"),
-            data=To3DProTaskQueryRequest(JobId=response.JobId),
-            response_model=To3DProTaskResultResponse,
-            status_extractor=lambda r: r.Status,
-        )
-        return IO.NodeOutput(
-            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
-        )
-
-
 class TencentHunyuan3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             TencentTextToModelNode,
             TencentImageToModelNode,
-            TencentModelTo3DUVNode,
-            Tencent3DTextureEditNode,
+            # TencentModelTo3DUVNode,
+            # Tencent3DTextureEditNode,
             Tencent3DPartNode,
-            TencentSmartTopologyNode,
         ]
 
 

comfy_api_nodes/nodes_kling.py

@@ -1459,7 +1459,6 @@ class OmniProEditVideoNode(IO.ComfyNode):
             node_id="KlingOmniProEditVideoNode",
             display_name="Kling 3.0 Omni Edit Video",
             category="api node/video/Kling",
-            essentials_category="Video Generation",
             description="Edit an existing video with the latest model from Kling.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
@@ -2748,7 +2747,6 @@ class MotionControl(IO.ComfyNode):
                     "but the character orientation matches the reference image (camera/other details via prompt).",
                 ),
                 IO.Combo.Input("mode", options=["pro", "std"]),
-                IO.Combo.Input("model", options=["kling-v3", "kling-v2-6"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -2779,7 +2777,6 @@ class MotionControl(IO.ComfyNode):
         keep_original_sound: bool,
         character_orientation: str,
         mode: str,
-        model: str = "kling-v2-6",
     ) -> IO.NodeOutput:
         validate_string(prompt, max_length=2500)
         validate_image_dimensions(reference_image, min_width=340, min_height=340)
@@ -2800,7 +2797,6 @@ class MotionControl(IO.ComfyNode):
                 keep_original_sound="yes" if keep_original_sound else "no",
                 character_orientation=character_orientation,
                 mode=mode,
-                model_name=model,
             ),
         )
         if response.code:

comfy_api_nodes/nodes_quiver.py

@@ -1,291 +0,0 @@
-from io import BytesIO
-
-from typing_extensions import override
-
-from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.quiver import (
-    QuiverImageObject,
-    QuiverImageToSVGRequest,
-    QuiverSVGResponse,
-    QuiverTextToSVGRequest,
-)
-from comfy_api_nodes.util import (
-    ApiEndpoint,
-    sync_op,
-    upload_image_to_comfyapi,
-    validate_string,
-)
-from comfy_extras.nodes_images import SVG
-
-
-class QuiverTextToSVGNode(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="QuiverTextToSVGNode",
-            display_name="Quiver Text to SVG",
-            category="api node/image/Quiver",
-            description="Generate an SVG from a text prompt using Quiver AI.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of the desired SVG output.",
-                ),
-                IO.String.Input(
-                    "instructions",
-                    multiline=True,
-                    default="",
-                    tooltip="Additional style or formatting guidance.",
-                    optional=True,
-                ),
-                IO.Autogrow.Input(
-                    "reference_images",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Image.Input("image"),
-                        prefix="ref_",
-                        min=0,
-                        max=4,
-                    ),
-                    tooltip="Up to 4 reference images to guide the generation.",
-                    optional=True,
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "arrow-preview",
-                            [
-                                IO.Float.Input(
-                                    "temperature",
-                                    default=1.0,
-                                    min=0.0,
-                                    max=2.0,
-                                    step=0.1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Randomness control. Higher values increase randomness.",
-                                    advanced=True,
-                                ),
-                                IO.Float.Input(
-                                    "top_p",
-                                    default=1.0,
-                                    min=0.05,
-                                    max=1.0,
-                                    step=0.05,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Nucleus sampling parameter.",
-                                    advanced=True,
-                                ),
-                                IO.Float.Input(
-                                    "presence_penalty",
-                                    default=0.0,
-                                    min=-2.0,
-                                    max=2.0,
-                                    step=0.1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Token presence penalty.",
-                                    advanced=True,
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="Model to use for SVG generation.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed to determine if node should re-run; "
-                    "actual results are nondeterministic regardless of seed.",
-                ),
-            ],
-            outputs=[
-                IO.SVG.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.429}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        model: dict,
-        seed: int,
-        instructions: str = None,
-        reference_images: IO.Autogrow.Type = None,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False, min_length=1)
-
-        references = None
-        if reference_images:
-            references = []
-            for key in reference_images:
-                url = await upload_image_to_comfyapi(cls, reference_images[key])
-                references.append(QuiverImageObject(url=url))
-            if len(references) > 4:
-                raise ValueError("Maximum 4 reference images are allowed.")
-
-        instructions_val = instructions.strip() if instructions else None
-        if instructions_val == "":
-            instructions_val = None
-
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/quiver/v1/svgs/generations", method="POST"),
-            response_model=QuiverSVGResponse,
-            data=QuiverTextToSVGRequest(
-                model=model["model"],
-                prompt=prompt,
-                instructions=instructions_val,
-                references=references,
-                temperature=model.get("temperature"),
-                top_p=model.get("top_p"),
-                presence_penalty=model.get("presence_penalty"),
-            ),
-        )
-
-        svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data]
-        return IO.NodeOutput(SVG(svg_data))
-
-
-class QuiverImageToSVGNode(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="QuiverImageToSVGNode",
-            display_name="Quiver Image to SVG",
-            category="api node/image/Quiver",
-            description="Vectorize a raster image into SVG using Quiver AI.",
-            inputs=[
-                IO.Image.Input(
-                    "image",
-                    tooltip="Input image to vectorize.",
-                ),
-                IO.Boolean.Input(
-                    "auto_crop",
-                    default=False,
-                    tooltip="Automatically crop to the dominant subject.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "arrow-preview",
-                            [
-                                IO.Int.Input(
-                                    "target_size",
-                                    default=1024,
-                                    min=128,
-                                    max=4096,
-                                    tooltip="Square resize target in pixels.",
-                                ),
-                                IO.Float.Input(
-                                    "temperature",
-                                    default=1.0,
-                                    min=0.0,
-                                    max=2.0,
-                                    step=0.1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Randomness control. Higher values increase randomness.",
-                                    advanced=True,
-                                ),
-                                IO.Float.Input(
-                                    "top_p",
-                                    default=1.0,
-                                    min=0.05,
-                                    max=1.0,
-                                    step=0.05,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Nucleus sampling parameter.",
-                                    advanced=True,
-                                ),
-                                IO.Float.Input(
-                                    "presence_penalty",
-                                    default=0.0,
-                                    min=-2.0,
-                                    max=2.0,
-                                    step=0.1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Token presence penalty.",
-                                    advanced=True,
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="Model to use for SVG vectorization.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed to determine if node should re-run; "
-                    "actual results are nondeterministic regardless of seed.",
-                ),
-            ],
-            outputs=[
-                IO.SVG.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.429}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image,
-        auto_crop: bool,
-        model: dict,
-        seed: int,
-    ) -> IO.NodeOutput:
-        image_url = await upload_image_to_comfyapi(cls, image)
-
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/quiver/v1/svgs/vectorizations", method="POST"),
-            response_model=QuiverSVGResponse,
-            data=QuiverImageToSVGRequest(
-                model=model["model"],
-                image=QuiverImageObject(url=image_url),
-                auto_crop=auto_crop if auto_crop else None,
-                target_size=model.get("target_size"),
-                temperature=model.get("temperature"),
-                top_p=model.get("top_p"),
-                presence_penalty=model.get("presence_penalty"),
-            ),
-        )
-
-        svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data]
-        return IO.NodeOutput(SVG(svg_data))
-
-
-class QuiverExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [
-            QuiverTextToSVGNode,
-            QuiverImageToSVGNode,
-        ]
-
-
-async def comfy_entrypoint() -> QuiverExtension:
-    return QuiverExtension()

comfy_api_nodes/nodes_recraft.py

@@ -833,7 +833,6 @@ class RecraftVectorizeImageNode(IO.ComfyNode):
             node_id="RecraftVectorizeImageNode",
             display_name="Recraft Vectorize Image",
             category="api node/image/Recraft",
-            essentials_category="Image Tools",
             description="Generates SVG synchronously from an input image.",
             inputs=[
                 IO.Image.Input("image"),

comfy_api_nodes/nodes_reve.py

@@ -1,395 +0,0 @@
-from io import BytesIO
-
-from typing_extensions import override
-
-from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.reve import (
-    ReveImageCreateRequest,
-    ReveImageEditRequest,
-    ReveImageRemixRequest,
-    RevePostprocessingOperation,
-)
-from comfy_api_nodes.util import (
-    ApiEndpoint,
-    bytesio_to_image_tensor,
-    sync_op_raw,
-    tensor_to_base64_string,
-    validate_string,
-)
-
-
-def _build_postprocessing(upscale: dict, remove_background: bool) -> list[RevePostprocessingOperation] | None:
-    ops = []
-    if upscale["upscale"] == "enabled":
-        ops.append(
-            RevePostprocessingOperation(
-                process="upscale",
-                upscale_factor=upscale["upscale_factor"],
-            )
-        )
-    if remove_background:
-        ops.append(RevePostprocessingOperation(process="remove_background"))
-    return ops or None
-
-
-def _postprocessing_inputs():
-    return [
-        IO.DynamicCombo.Input(
-            "upscale",
-            options=[
-                IO.DynamicCombo.Option("disabled", []),
-                IO.DynamicCombo.Option(
-                    "enabled",
-                    [
-                        IO.Int.Input(
-                            "upscale_factor",
-                            default=2,
-                            min=2,
-                            max=4,
-                            step=1,
-                            tooltip="Upscale factor (2x, 3x, or 4x).",
-                        ),
-                    ],
-                ),
-            ],
-            tooltip="Upscale the generated image. May add additional cost.",
-        ),
-        IO.Boolean.Input(
-            "remove_background",
-            default=False,
-            tooltip="Remove the background from the generated image. May add additional cost.",
-        ),
-    ]
-
-
-def _reve_price_extractor(headers: dict) -> float | None:
-    credits_used = headers.get("x-reve-credits-used")
-    if credits_used is not None:
-        return float(credits_used) / 524.48
-    return None
-
-
-def _reve_response_header_validator(headers: dict) -> None:
-    error_code = headers.get("x-reve-error-code")
-    if error_code:
-        raise ValueError(f"Reve API error: {error_code}")
-    if headers.get("x-reve-content-violation", "").lower() == "true":
-        raise ValueError("The generated image was flagged for content policy violation.")
-
-
-def _model_inputs(versions: list[str], aspect_ratios: list[str]):
-    return [
-        IO.DynamicCombo.Option(
-            version,
-            [
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=aspect_ratios,
-                    tooltip="Aspect ratio of the output image.",
-                ),
-                IO.Int.Input(
-                    "test_time_scaling",
-                    default=1,
-                    min=1,
-                    max=5,
-                    step=1,
-                    tooltip="Higher values produce better images but cost more credits.",
-                    advanced=True,
-                ),
-            ],
-        )
-        for version in versions
-    ]
-
-
-class ReveImageCreateNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ReveImageCreateNode",
-            display_name="Reve Image Create",
-            category="api node/image/Reve",
-            description="Generate images from text descriptions using Reve.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of the desired image. Maximum 2560 characters.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_model_inputs(
-                        ["reve-create@20250915"],
-                        aspect_ratios=["3:2", "16:9", "9:16", "2:3", "4:3", "3:4", "1:1"],
-                    ),
-                    tooltip="Model version to use for generation.",
-                ),
-                *_postprocessing_inputs(),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.03432,"format":{"approximate":true,"note":"(base)"}}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        model: dict,
-        upscale: dict,
-        remove_background: bool,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, min_length=1, max_length=2560)
-        response = await sync_op_raw(
-            cls,
-            ApiEndpoint(
-                path="/proxy/reve/v1/image/create",
-                method="POST",
-                headers={"Accept": "image/webp"},
-            ),
-            as_binary=True,
-            price_extractor=_reve_price_extractor,
-            response_header_validator=_reve_response_header_validator,
-            data=ReveImageCreateRequest(
-                prompt=prompt,
-                aspect_ratio=model["aspect_ratio"],
-                version=model["model"],
-                test_time_scaling=model["test_time_scaling"],
-                postprocessing=_build_postprocessing(upscale, remove_background),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
-
-
-class ReveImageEditNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ReveImageEditNode",
-            display_name="Reve Image Edit",
-            category="api node/image/Reve",
-            description="Edit images using natural language instructions with Reve.",
-            inputs=[
-                IO.Image.Input("image", tooltip="The image to edit."),
-                IO.String.Input(
-                    "edit_instruction",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of how to edit the image. Maximum 2560 characters.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_model_inputs(
-                        ["reve-edit@20250915", "reve-edit-fast@20251030"],
-                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
-                    ),
-                    tooltip="Model version to use for editing.",
-                ),
-                *_postprocessing_inputs(),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["model"],
-                ),
-                expr="""
-                (
-                    $isFast := $contains(widgets.model, "fast");
-                    $base := $isFast ? 0.01001 : 0.0572;
-                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: Input.Image,
-        edit_instruction: str,
-        model: dict,
-        upscale: dict,
-        remove_background: bool,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(edit_instruction, min_length=1, max_length=2560)
-        tts = model["test_time_scaling"]
-        ar = model["aspect_ratio"]
-        response = await sync_op_raw(
-            cls,
-            ApiEndpoint(
-                path="/proxy/reve/v1/image/edit",
-                method="POST",
-                headers={"Accept": "image/webp"},
-            ),
-            as_binary=True,
-            price_extractor=_reve_price_extractor,
-            response_header_validator=_reve_response_header_validator,
-            data=ReveImageEditRequest(
-                edit_instruction=edit_instruction,
-                reference_image=tensor_to_base64_string(image),
-                aspect_ratio=ar if ar != "auto" else None,
-                version=model["model"],
-                test_time_scaling=tts if tts and tts > 1 else None,
-                postprocessing=_build_postprocessing(upscale, remove_background),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
-
-
-class ReveImageRemixNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ReveImageRemixNode",
-            display_name="Reve Image Remix",
-            category="api node/image/Reve",
-            description="Combine reference images with text prompts to create new images using Reve.",
-            inputs=[
-                IO.Autogrow.Input(
-                    "reference_images",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Image.Input("image"),
-                        prefix="image_",
-                        min=1,
-                        max=6,
-                    ),
-                ),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of the desired image. "
-                    "May include XML img tags to reference specific images by index, "
-                    "e.g. <img>0</img>, <img>1</img>, etc.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_model_inputs(
-                        ["reve-remix@20250915", "reve-remix-fast@20251030"],
-                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
-                    ),
-                    tooltip="Model version to use for remixing.",
-                ),
-                *_postprocessing_inputs(),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["model"],
-                ),
-                expr="""
-                (
-                    $isFast := $contains(widgets.model, "fast");
-                    $base := $isFast ? 0.01001 : 0.0572;
-                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        reference_images: IO.Autogrow.Type,
-        prompt: str,
-        model: dict,
-        upscale: dict,
-        remove_background: bool,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, min_length=1, max_length=2560)
-        if not reference_images:
-            raise ValueError("At least one reference image is required.")
-        ref_base64_list = []
-        for key in reference_images:
-            ref_base64_list.append(tensor_to_base64_string(reference_images[key]))
-        if len(ref_base64_list) > 6:
-            raise ValueError("Maximum 6 reference images are allowed.")
-        tts = model["test_time_scaling"]
-        ar = model["aspect_ratio"]
-        response = await sync_op_raw(
-            cls,
-            ApiEndpoint(
-                path="/proxy/reve/v1/image/remix",
-                method="POST",
-                headers={"Accept": "image/webp"},
-            ),
-            as_binary=True,
-            price_extractor=_reve_price_extractor,
-            response_header_validator=_reve_response_header_validator,
-            data=ReveImageRemixRequest(
-                prompt=prompt,
-                reference_images=ref_base64_list,
-                aspect_ratio=ar if ar != "auto" else None,
-                version=model["model"],
-                test_time_scaling=tts if tts and tts > 1 else None,
-                postprocessing=_build_postprocessing(upscale, remove_background),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
-
-
-class ReveExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [
-            ReveImageCreateNode,
-            ReveImageEditNode,
-            ReveImageRemixNode,
-        ]
-
-
-async def comfy_entrypoint() -> ReveExtension:
-    return ReveExtension()

comfy_api_nodes/util/client.py

@@ -67,7 +67,6 @@ class _RequestConfig:
     progress_origin_ts: float | None = None
     price_extractor: Callable[[dict[str, Any]], float | None] | None = None
     is_rate_limited: Callable[[int, Any], bool] | None = None
-    response_header_validator: Callable[[dict[str, str]], None] | None = None
 
 
 @dataclass
@@ -84,7 +83,7 @@ class _PollUIState:
 _RETRY_STATUS = {408, 500, 502, 503, 504}  # status 429 is handled separately
 COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"]
 FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"]
-QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing", "wait"]
+QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing"]
 
 
 async def sync_op(
@@ -203,13 +202,11 @@ async def sync_op_raw(
     monitor_progress: bool = True,
     max_retries_on_rate_limit: int = 16,
     is_rate_limited: Callable[[int, Any], bool] | None = None,
-    response_header_validator: Callable[[dict[str, str]], None] | None = None,
 ) -> dict[str, Any] | bytes:
     """
     Make a single network request.
       - If as_binary=False (default): returns JSON dict (or {'_raw': '<text>'} if non-JSON).
       - If as_binary=True: returns bytes.
-      - response_header_validator: optional callback receiving response headers dict
     """
     if isinstance(data, BaseModel):
         data = data.model_dump(exclude_none=True)
@@ -235,7 +232,6 @@ async def sync_op_raw(
         price_extractor=price_extractor,
         max_retries_on_rate_limit=max_retries_on_rate_limit,
         is_rate_limited=is_rate_limited,
-        response_header_validator=response_header_validator,
     )
     return await _request_base(cfg, expect_binary=as_binary)
 
@@ -773,12 +769,6 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                                     cfg.node_cls, cfg.wait_label, int(now - start_time), cfg.estimated_total
                                 )
                     bytes_payload = bytes(buff)
-                    resp_headers = {k.lower(): v for k, v in resp.headers.items()}
-                    if cfg.price_extractor:
-                        with contextlib.suppress(Exception):
-                            extracted_price = cfg.price_extractor(resp_headers)
-                    if cfg.response_header_validator:
-                        cfg.response_header_validator(resp_headers)
                     operation_succeeded = True
                     final_elapsed_seconds = int(time.monotonic() - start_time)
                     request_logger.log_request_response(
@@ -786,7 +776,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                         request_method=method,
                         request_url=url,
                         response_status_code=resp.status,
-                        response_headers=resp_headers,
+                        response_headers=dict(resp.headers),
                         response_content=bytes_payload,
                     )
                     return bytes_payload

comfy_execution/cache_provider.py

@@ -1,138 +0,0 @@
-from typing import Any, Optional, Tuple, List
-import hashlib
-import json
-import logging
-import threading
-
-# Public types — source of truth is comfy_api.latest._caching
-from comfy_api.latest._caching import CacheProvider, CacheContext, CacheValue  # noqa: F401 (re-exported)
-
-_logger = logging.getLogger(__name__)
-
-
-_providers: List[CacheProvider] = []
-_providers_lock = threading.Lock()
-_providers_snapshot: Tuple[CacheProvider, ...] = ()
-
-
-def register_cache_provider(provider: CacheProvider) -> None:
-    """Register an external cache provider. Providers are called in registration order."""
-    global _providers_snapshot
-    with _providers_lock:
-        if provider in _providers:
-            _logger.warning(f"Provider {provider.__class__.__name__} already registered")
-            return
-        _providers.append(provider)
-        _providers_snapshot = tuple(_providers)
-        _logger.debug(f"Registered cache provider: {provider.__class__.__name__}")
-
-
-def unregister_cache_provider(provider: CacheProvider) -> None:
-    global _providers_snapshot
-    with _providers_lock:
-        try:
-            _providers.remove(provider)
-            _providers_snapshot = tuple(_providers)
-            _logger.debug(f"Unregistered cache provider: {provider.__class__.__name__}")
-        except ValueError:
-            _logger.warning(f"Provider {provider.__class__.__name__} was not registered")
-
-
-def _get_cache_providers() -> Tuple[CacheProvider, ...]:
-    return _providers_snapshot
-
-
-def _has_cache_providers() -> bool:
-    return bool(_providers_snapshot)
-
-
-def _clear_cache_providers() -> None:
-    global _providers_snapshot
-    with _providers_lock:
-        _providers.clear()
-        _providers_snapshot = ()
-
-
-def _canonicalize(obj: Any) -> Any:
-    # Convert to canonical JSON-serializable form with deterministic ordering.
-    # Frozensets have non-deterministic iteration order between Python sessions.
-    # Raises ValueError for non-cacheable types (Unhashable, unknown) so that
-    # _serialize_cache_key returns None and external caching is skipped.
-    if isinstance(obj, frozenset):
-        return ("__frozenset__", sorted(
-            [_canonicalize(item) for item in obj],
-            key=lambda x: json.dumps(x, sort_keys=True)
-        ))
-    elif isinstance(obj, set):
-        return ("__set__", sorted(
-            [_canonicalize(item) for item in obj],
-            key=lambda x: json.dumps(x, sort_keys=True)
-        ))
-    elif isinstance(obj, tuple):
-        return ("__tuple__", [_canonicalize(item) for item in obj])
-    elif isinstance(obj, list):
-        return [_canonicalize(item) for item in obj]
-    elif isinstance(obj, dict):
-        return {"__dict__": sorted(
-            [[_canonicalize(k), _canonicalize(v)] for k, v in obj.items()],
-            key=lambda x: json.dumps(x, sort_keys=True)
-        )}
-    elif isinstance(obj, (int, float, str, bool, type(None))):
-        return (type(obj).__name__, obj)
-    elif isinstance(obj, bytes):
-        return ("__bytes__", obj.hex())
-    else:
-        raise ValueError(f"Cannot canonicalize type: {type(obj).__name__}")
-
-
-def _serialize_cache_key(cache_key: Any) -> Optional[str]:
-    # Returns deterministic SHA256 hex digest, or None on failure.
-    # Uses JSON (not pickle) because pickle is non-deterministic across sessions.
-    try:
-        canonical = _canonicalize(cache_key)
-        json_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
-        return hashlib.sha256(json_str.encode('utf-8')).hexdigest()
-    except Exception as e:
-        _logger.warning(f"Failed to serialize cache key: {e}")
-        return None
-
-
-def _contains_self_unequal(obj: Any) -> bool:
-    # Local cache matches by ==. Values where not (x == x) (NaN, etc.) will
-    # never hit locally, but serialized form would match externally. Skip these.
-    try:
-        if not (obj == obj):
-            return True
-    except Exception:
-        return True
-    if isinstance(obj, (frozenset, tuple, list, set)):
-        return any(_contains_self_unequal(item) for item in obj)
-    if isinstance(obj, dict):
-        return any(_contains_self_unequal(k) or _contains_self_unequal(v) for k, v in obj.items())
-    if hasattr(obj, 'value'):
-        return _contains_self_unequal(obj.value)
-    return False
-
-
-def _estimate_value_size(value: CacheValue) -> int:
-    try:
-        import torch
-    except ImportError:
-        return 0
-
-    total = 0
-
-    def estimate(obj):
-        nonlocal total
-        if isinstance(obj, torch.Tensor):
-            total += obj.numel() * obj.element_size()
-        elif isinstance(obj, dict):
-            for v in obj.values():
-                estimate(v)
-        elif isinstance(obj, (list, tuple)):
-            for item in obj:
-                estimate(item)
-
-    for output in value.outputs:
-        estimate(output)
-    return total

comfy_execution/caching.py

@@ -1,4 +1,3 @@
-import asyncio
 import bisect
 import gc
 import itertools
@@ -148,15 +147,13 @@ class CacheKeySetInputSignature(CacheKeySet):
                     self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
 
 class BasicCache:
-    def __init__(self, key_class, enable_providers=False):
+    def __init__(self, key_class):
         self.key_class = key_class
         self.initialized = False
-        self.enable_providers = enable_providers
         self.dynprompt: DynamicPrompt
         self.cache_key_set: CacheKeySet
         self.cache = {}
         self.subcaches = {}
-        self._pending_store_tasks: set = set()
 
     async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
         self.dynprompt = dynprompt
@@ -199,138 +196,18 @@ class BasicCache:
     def poll(self, **kwargs):
         pass
 
-    def get_local(self, node_id):
+    def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    def _get_immediate(self, node_id):
         if not self.initialized:
             return None
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key in self.cache:
             return self.cache[cache_key]
-        return None
-
-    def set_local(self, node_id, value):
-        assert self.initialized
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        self.cache[cache_key] = value
-
-    async def _set_immediate(self, node_id, value):
-        assert self.initialized
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        self.cache[cache_key] = value
-
-        await self._notify_providers_store(node_id, cache_key, value)
-
-    async def _get_immediate(self, node_id):
-        if not self.initialized:
-            return None
-        cache_key = self.cache_key_set.get_data_key(node_id)
-
-        if cache_key in self.cache:
-            return self.cache[cache_key]
-
-        external_result = await self._check_providers_lookup(node_id, cache_key)
-        if external_result is not None:
-            self.cache[cache_key] = external_result
-            return external_result
-
-        return None
-
-    async def _notify_providers_store(self, node_id, cache_key, value):
-        from comfy_execution.cache_provider import (
-            _has_cache_providers, _get_cache_providers,
-            CacheValue, _contains_self_unequal, _logger
-        )
-
-        if not self.enable_providers:
-            return
-        if not _has_cache_providers():
-            return
-        if not self._is_external_cacheable_value(value):
-            return
-        if _contains_self_unequal(cache_key):
-            return
-
-        context = self._build_context(node_id, cache_key)
-        if context is None:
-            return
-        cache_value = CacheValue(outputs=value.outputs, ui=value.ui)
-
-        for provider in _get_cache_providers():
-            try:
-                if provider.should_cache(context, cache_value):
-                    task = asyncio.create_task(self._safe_provider_store(provider, context, cache_value))
-                    self._pending_store_tasks.add(task)
-                    task.add_done_callback(self._pending_store_tasks.discard)
-            except Exception as e:
-                _logger.warning(f"Cache provider {provider.__class__.__name__} error on store: {e}")
-
-    @staticmethod
-    async def _safe_provider_store(provider, context, cache_value):
-        from comfy_execution.cache_provider import _logger
-        try:
-            await provider.on_store(context, cache_value)
-        except Exception as e:
-            _logger.warning(f"Cache provider {provider.__class__.__name__} async store error: {e}")
-
-    async def _check_providers_lookup(self, node_id, cache_key):
-        from comfy_execution.cache_provider import (
-            _has_cache_providers, _get_cache_providers,
-            CacheValue, _contains_self_unequal, _logger
-        )
-
-        if not self.enable_providers:
-            return None
-        if not _has_cache_providers():
-            return None
-        if _contains_self_unequal(cache_key):
-            return None
-
-        context = self._build_context(node_id, cache_key)
-        if context is None:
-            return None
-
-        for provider in _get_cache_providers():
-            try:
-                if not provider.should_cache(context):
-                    continue
-                result = await provider.on_lookup(context)
-                if result is not None:
-                    if not isinstance(result, CacheValue):
-                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid type")
-                        continue
-                    if not isinstance(result.outputs, (list, tuple)):
-                        _logger.warning(f"Provider {provider.__class__.__name__} returned invalid outputs")
-                        continue
-                    from execution import CacheEntry
-                    return CacheEntry(ui=result.ui, outputs=list(result.outputs))
-            except Exception as e:
-                _logger.warning(f"Cache provider {provider.__class__.__name__} error on lookup: {e}")
-
-        return None
-
-    def _is_external_cacheable_value(self, value):
-        return hasattr(value, 'outputs') and hasattr(value, 'ui')
-
-    def _get_class_type(self, node_id):
-        if not self.initialized or not self.dynprompt:
-            return ''
-        try:
-            return self.dynprompt.get_node(node_id).get('class_type', '')
-        except Exception:
-            return ''
-
-    def _build_context(self, node_id, cache_key):
-        from comfy_execution.cache_provider import CacheContext, _serialize_cache_key, _logger
-        try:
-            cache_key_hash = _serialize_cache_key(cache_key)
-            if cache_key_hash is None:
-                return None
-            return CacheContext(
-                node_id=node_id,
-                class_type=self._get_class_type(node_id),
-                cache_key_hash=cache_key_hash,
-            )
-        except Exception as e:
-            _logger.warning(f"Failed to build cache context for node {node_id}: {e}")
+        else:
             return None
 
     async def _ensure_subcache(self, node_id, children_ids):
@@ -359,8 +236,8 @@ class BasicCache:
         return result
 
 class HierarchicalCache(BasicCache):
-    def __init__(self, key_class, enable_providers=False):
-        super().__init__(key_class, enable_providers=enable_providers)
+    def __init__(self, key_class):
+        super().__init__(key_class)
 
     def _get_cache_for(self, node_id):
         assert self.dynprompt is not None
@@ -380,27 +257,16 @@ class HierarchicalCache(BasicCache):
                 return None
         return cache
 
-    async def get(self, node_id):
+    def get(self, node_id):
         cache = self._get_cache_for(node_id)
         if cache is None:
             return None
-        return await cache._get_immediate(node_id)
+        return cache._get_immediate(node_id)
 
-    def get_local(self, node_id):
-        cache = self._get_cache_for(node_id)
-        if cache is None:
-            return None
-        return BasicCache.get_local(cache, node_id)
-
-    async def set(self, node_id, value):
+    def set(self, node_id, value):
         cache = self._get_cache_for(node_id)
         assert cache is not None
-        await cache._set_immediate(node_id, value)
-
-    def set_local(self, node_id, value):
-        cache = self._get_cache_for(node_id)
-        assert cache is not None
-        BasicCache.set_local(cache, node_id, value)
+        cache._set_immediate(node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         cache = self._get_cache_for(node_id)
@@ -421,24 +287,18 @@ class NullCache:
     def poll(self, **kwargs):
         pass
 
-    async def get(self, node_id):
+    def get(self, node_id):
         return None
 
-    def get_local(self, node_id):
-        return None
-
-    async def set(self, node_id, value):
-        pass
-
-    def set_local(self, node_id, value):
+    def set(self, node_id, value):
         pass
 
     async def ensure_subcache_for(self, node_id, children_ids):
         return self
 
 class LRUCache(BasicCache):
-    def __init__(self, key_class, max_size=100, enable_providers=False):
-        super().__init__(key_class, enable_providers=enable_providers)
+    def __init__(self, key_class, max_size=100):
+        super().__init__(key_class)
         self.max_size = max_size
         self.min_generation = 0
         self.generation = 0
@@ -462,18 +322,18 @@ class LRUCache(BasicCache):
                     del self.children[key]
         self._clean_subcaches()
 
-    async def get(self, node_id):
+    def get(self, node_id):
         self._mark_used(node_id)
-        return await self._get_immediate(node_id)
+        return self._get_immediate(node_id)
 
     def _mark_used(self, node_id):
         cache_key = self.cache_key_set.get_data_key(node_id)
         if cache_key is not None:
             self.used_generation[cache_key] = self.generation
 
-    async def set(self, node_id, value):
+    def set(self, node_id, value):
         self._mark_used(node_id)
-        return await self._set_immediate(node_id, value)
+        return self._set_immediate(node_id, value)
 
     async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
@@ -506,20 +366,20 @@ RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
 class RAMPressureCache(LRUCache):
 
-    def __init__(self, key_class, enable_providers=False):
-        super().__init__(key_class, 0, enable_providers=enable_providers)
+    def __init__(self, key_class):
+        super().__init__(key_class, 0)
         self.timestamps = {}
 
     def clean_unused(self):
         self._clean_subcaches()
 
-    async def set(self, node_id, value):
+    def set(self, node_id, value):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        await super().set(node_id, value)
+        super().set(node_id, value)
 
-    async def get(self, node_id):
+    def get(self, node_id):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
-        return await super().get(node_id)
+        return super().get(node_id)
 
     def poll(self, ram_headroom):
         def _ram_gb():

comfy_execution/graph.py

@@ -204,12 +204,12 @@ class ExecutionList(TopologicalSort):
         self.execution_cache_listeners = {}
 
     def is_cached(self, node_id):
-        return self.output_cache.get_local(node_id) is not None
+        return self.output_cache.get(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
         if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
-        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get_local(from_node_id)
+        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
         if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
@@ -221,7 +221,7 @@ class ExecutionList(TopologicalSort):
         if value is None:
             return None
         #Write back to the main cache on touch.
-        self.output_cache.set_local(from_node_id, value)
+        self.output_cache.set(from_node_id, value)
         return value
 
     def cache_update(self, node_id, value):