Merge branch 'master' into assets-redo-part2

* Not sure about this one, but try removing assets from old sessions.

* Simplify _prune_orphaned_assets: merge functions, use list comprehensions

Amp-Thread-ID: https://ampcode.com/threads/T-019c0917-0dc3-75ab-870d-a32b3fdc1927
Co-authored-by: Amp <amp@ampcode.com>

* Refactor _prune_orphaned_assets for readability

Amp-Thread-ID: https://ampcode.com/threads/T-019c0917-0dc3-75ab-870d-a32b3fdc1927
Co-authored-by: Amp <amp@ampcode.com>

* Add unit tests for pruning

* Add unit tests for _prune_orphaned_assets

Tests cover:

- Orphaned seed assets pruned when file removed

- Seed assets with valid files survive

- Hashed assets not pruned even without file

- Multi-root pruning

- SQL LIKE escape handling for %, _, spaces

Amp-Thread-ID: https://ampcode.com/threads/T-019c0c7a-5c8a-7548-b6c3-823e9829ce74
Co-authored-by: Amp <amp@ampcode.com>

* Ruff fix

---------

Co-authored-by: Amp <amp@ampcode.com>

comfy/text_encoders/llama.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from dataclasses import dataclass
-from typing import Optional, Any, Tuple
+from typing import Optional, Any
 import math
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
@@ -32,7 +32,6 @@ class Llama2Config:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Mistral3Small24BConfig:
@@ -55,7 +54,6 @@ class Mistral3Small24BConfig:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen25_3BConfig:
@@ -78,7 +76,6 @@ class Qwen25_3BConfig:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_06BConfig:
@@ -101,7 +98,6 @@ class Qwen3_06BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_4BConfig:
@@ -124,7 +120,6 @@ class Qwen3_4BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_8BConfig:
@@ -147,7 +142,6 @@ class Qwen3_8BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Ovis25_2BConfig:
@@ -170,7 +164,6 @@ class Ovis25_2BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen25_7BVLI_Config:
@@ -193,7 +186,6 @@ class Qwen25_7BVLI_Config:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma2_2B_Config:
@@ -217,7 +209,6 @@ class Gemma2_2B_Config:
     sliding_attention = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma3_4B_Config:
@@ -241,7 +232,6 @@ class Gemma3_4B_Config:
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma3_12B_Config:
@@ -265,7 +255,6 @@ class Gemma3_12B_Config:
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
-    lm_head: bool = False
     vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
     mm_tokens_per_image = 256
 
@@ -367,7 +356,6 @@ class Attention(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
         xq = self.q_proj(hidden_states)
@@ -385,30 +373,11 @@ class Attention(nn.Module):
 
         xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)
 
-        present_key_value = None
-        if past_key_value is not None:
-            index = 0
-            num_tokens = xk.shape[2]
-            if len(past_key_value) > 0:
-                past_key, past_value, index = past_key_value
-                if past_key.shape[2] >= (index + num_tokens):
-                    past_key[:, :, index:index + xk.shape[2]] = xk
-                    past_value[:, :, index:index + xv.shape[2]] = xv
-                    xk = past_key[:, :, :index + xk.shape[2]]
-                    xv = past_value[:, :, :index + xv.shape[2]]
-                    present_key_value = (past_key, past_value, index + num_tokens)
-                else:
-                    xk = torch.cat((past_key[:, :, :index], xk), dim=2)
-                    xv = torch.cat((past_value[:, :, :index], xv), dim=2)
-                    present_key_value = (xk, xv, index + num_tokens)
-            else:
-                present_key_value = (xk, xv, index + num_tokens)
-
         xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
         xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
 
         output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
-        return self.o_proj(output), present_key_value
+        return self.o_proj(output)
 
 class MLP(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -439,17 +408,15 @@ class TransformerBlock(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
-        x, present_key_value = self.self_attn(
+        x = self.self_attn(
             hidden_states=x,
             attention_mask=attention_mask,
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
-            past_key_value=past_key_value,
         )
         x = residual + x
 
@@ -459,7 +426,7 @@ class TransformerBlock(nn.Module):
         x = self.mlp(x)
         x = residual + x
 
-        return x, present_key_value
+        return x
 
 class TransformerBlockGemma2(nn.Module):
     def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
@@ -484,7 +451,6 @@ class TransformerBlockGemma2(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
@@ -502,12 +468,11 @@ class TransformerBlockGemma2(nn.Module):
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
-        x, present_key_value = self.self_attn(
+        x = self.self_attn(
             hidden_states=x,
             attention_mask=attention_mask,
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
-            past_key_value=past_key_value,
         )
 
         x = self.post_attention_layernorm(x)
@@ -520,7 +485,7 @@ class TransformerBlockGemma2(nn.Module):
         x = self.post_feedforward_layernorm(x)
         x = residual + x
 
-        return x, present_key_value
+        return x
 
 class Llama2_(nn.Module):
     def __init__(self, config, device=None, dtype=None, ops=None):
@@ -551,10 +516,9 @@ class Llama2_(nn.Module):
         else:
             self.norm = None
 
-        if config.lm_head:
-            self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+        # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
-    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None):
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]):
         if embeds is not None:
             x = embeds
         else:
@@ -563,13 +527,8 @@ class Llama2_(nn.Module):
         if self.normalize_in:
             x *= self.config.hidden_size ** 0.5
 
-        seq_len = x.shape[1]
-        past_len = 0
-        if past_key_values is not None and len(past_key_values) > 0:
-            past_len = past_key_values[0][2]
-
         if position_ids is None:
-            position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0)
+            position_ids = torch.arange(0, x.shape[1], device=x.device).unsqueeze(0)
 
         freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          position_ids,
@@ -580,16 +539,14 @@ class Llama2_(nn.Module):
 
         mask = None
         if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1])
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
             mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
-        if seq_len > 1:
-            causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
-            if mask is not None:
-                mask += causal_mask
-            else:
-                mask = causal_mask
-
+        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
+        if mask is not None:
+            mask += causal_mask
+        else:
+            mask = causal_mask
         optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
         intermediate = None
@@ -605,27 +562,16 @@ class Llama2_(nn.Module):
             elif intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
-        next_key_values = []
         for i, layer in enumerate(self.layers):
             if all_intermediate is not None:
                 if only_layers is None or (i in only_layers):
                     all_intermediate.append(x.unsqueeze(1).clone())
-
-            past_kv = None
-            if past_key_values is not None:
-                past_kv = past_key_values[i] if len(past_key_values) > 0 else []
-
-            x, current_kv = layer(
+            x = layer(
                 x=x,
                 attention_mask=mask,
                 freqs_cis=freqs_cis,
                 optimized_attention=optimized_attention,
-                past_key_value=past_kv,
             )
-
-            if current_kv is not None:
-                next_key_values.append(current_kv)
-
             if i == intermediate_output:
                 intermediate = x.clone()
 
@@ -642,10 +588,7 @@ class Llama2_(nn.Module):
         if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
             intermediate = self.norm(intermediate)
 
-        if len(next_key_values) > 0:
-            return x, intermediate, next_key_values
-        else:
-            return x, intermediate
+        return x, intermediate
 
 
 class Gemma3MultiModalProjector(torch.nn.Module):

comfy_api/latest/_io.py

@@ -1248,7 +1248,6 @@ class Hidden(str, Enum):
 class NodeInfoV1:
     input: dict=None
     input_order: dict[str, list[str]]=None
-    is_input_list: bool=None
     output: list[str]=None
     output_is_list: list[bool]=None
     output_name: list[str]=None
@@ -1475,7 +1474,6 @@ class Schema:
         info = NodeInfoV1(
             input=input,
             input_order={key: list(value.keys()) for (key, value) in input.items()},
-            is_input_list=self.is_input_list,
             output=output,
             output_is_list=output_is_list,
             output_name=output_name,

comfy_api/latest/_ui.py

@@ -146,7 +146,8 @@ class ImageSaveHelper:
         metadata = ImageSaveHelper._create_png_metadata(cls)
         for batch_number, image_tensor in enumerate(images):
             img = ImageSaveHelper._convert_tensor_to_pil(image_tensor)
-            file = folder_paths.format_output_filename(filename, counter, "png", batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
             img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level)
             results.append(SavedResult(file, subfolder, folder_type))
             counter += 1
@@ -175,7 +176,7 @@ class ImageSaveHelper:
         )
         pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images]
         metadata = ImageSaveHelper._create_animated_png_metadata(cls)
-        file = folder_paths.format_output_filename(filename, counter, "png")
+        file = f"{filename}_{counter:05}_.png"
         save_path = os.path.join(full_output_folder, file)
         pil_images[0].save(
             save_path,
@@ -219,7 +220,7 @@ class ImageSaveHelper:
         )
         pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images]
         pil_exif = ImageSaveHelper._create_webp_metadata(pil_images[0], cls)
-        file = folder_paths.format_output_filename(filename, counter, "webp")
+        file = f"{filename}_{counter:05}_.webp"
         pil_images[0].save(
             os.path.join(full_output_folder, file),
             save_all=True,
@@ -283,7 +284,8 @@ class AudioSaveHelper:
 
         results = []
         for batch_number, waveform in enumerate(audio["waveform"].cpu()):
-            file = folder_paths.format_output_filename(filename, counter, format, batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.{format}"
             output_path = os.path.join(full_output_folder, file)
 
             # Use original sample rate initially

comfy_extras/nodes_hunyuan3d.py

@@ -642,7 +642,7 @@ class SaveGLB(IO.ComfyNode):
                     metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
         for i in range(mesh.vertices.shape[0]):
-            f = folder_paths.format_output_filename(filename, counter, "glb")
+            f = f"{filename}_{counter:05}_.glb"
             save_glb(mesh.vertices[i], mesh.faces[i], os.path.join(full_output_folder, f), metadata)
             results.append({
                 "filename": f,

comfy_extras/nodes_images.py

@@ -460,7 +460,8 @@ class SaveSVGNode(IO.ComfyNode):
 
 
         for batch_number, svg_bytes in enumerate(svg.data):
-            file = folder_paths.format_output_filename(filename, counter, "svg", batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.svg"
 
             # Read SVG content
             svg_bytes.seek(0)

comfy_extras/nodes_video.py

@@ -36,7 +36,7 @@ class SaveWEBM(io.ComfyNode):
             filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
         )
 
-        file = folder_paths.format_output_filename(filename, counter, "webm")
+        file = f"{filename}_{counter:05}_.webm"
         container = av.open(os.path.join(full_output_folder, file), mode="w")
 
         if cls.hidden.prompt is not None:
@@ -102,7 +102,7 @@ class SaveVideo(io.ComfyNode):
                 metadata["prompt"] = cls.hidden.prompt
             if len(metadata) > 0:
                 saved_metadata = metadata
-        file = folder_paths.format_output_filename(filename, counter, Types.VideoContainer.get_extension(format))
+        file = f"{filename}_{counter:05}_.{Types.VideoContainer.get_extension(format)}"
         video.save_to(
             os.path.join(full_output_folder, file),
             format=Types.VideoContainer(format),

folder_paths.py

@@ -4,7 +4,6 @@ import os
 import time
 import mimetypes
 import logging
-from datetime import datetime, timezone
 from typing import Literal, List
 from collections.abc import Collection
 
@@ -12,46 +11,6 @@ from comfy.cli_args import args
 
 supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
 
-
-def get_timestamp() -> str:
-    """Generate a filesystem-safe timestamp string for output filenames.
-
-    Returns a UTC timestamp in the format YYYYMMDD-HHMMSS-ffffff (microseconds)
-    which is human-readable, lexicographically sortable, and Windows-safe.
-    """
-    now = datetime.now(timezone.utc)
-    return now.strftime("%Y%m%d-%H%M%S-%f")
-
-
-def format_output_filename(
-    filename: str,
-    counter: int,
-    ext: str,
-    *,
-    batch_num: str | None = None,
-    timestamp: str | None = None,
-) -> str:
-    """Format an output filename with counter and timestamp for cache-busting.
-
-    Args:
-        filename: The base filename prefix
-        counter: The numeric counter for uniqueness
-        ext: The file extension (with or without leading dot)
-        batch_num: Optional batch number to replace %batch_num% placeholder
-        timestamp: Optional timestamp string (defaults to current UTC time)
-
-    Returns:
-        Formatted filename like: filename_00001_20260131-123456-789012_.ext
-    """
-    ext = ext.lstrip(".")
-    if timestamp is None:
-        timestamp = get_timestamp()
-
-    if batch_num is not None:
-        filename = filename.replace("%batch_num%", batch_num)
-
-    return f"{filename}_{counter:05}_{timestamp}_.{ext}"
-
 folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
 
 # --base-directory - Resets all default paths configured in folder_paths with a new base path

nodes.py

@@ -1667,7 +1667,8 @@ class SaveImage:
                     for x in extra_pnginfo:
                         metadata.add_text(x, json.dumps(extra_pnginfo[x]))
 
-            file = folder_paths.format_output_filename(filename, counter, "png", batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
             img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
             results.append({
                 "filename": file,

server.py

@@ -656,7 +656,6 @@ class PromptServer():
             info = {}
             info['input'] = obj_class.INPUT_TYPES()
             info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()}
-            info['is_input_list'] = getattr(obj_class, "INPUT_IS_LIST", False)
             info['output'] = obj_class.RETURN_TYPES
             info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
             info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']

tests-unit/folder_paths_test/output_filename_test.py

@@ -1,83 +0,0 @@
-"""Tests for folder_paths.format_output_filename and get_timestamp functions."""
-
-import sys
-import os
-import unittest
-
-# Add the ComfyUI root to the path for imports
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-import folder_paths
-
-
-class TestGetTimestamp(unittest.TestCase):
-    """Tests for get_timestamp function."""
-
-    def test_returns_string(self):
-        """Should return a string."""
-        result = folder_paths.get_timestamp()
-        self.assertIsInstance(result, str)
-
-    def test_format_matches_expected_pattern(self):
-        """Should return format YYYYMMDD-HHMMSS-ffffff."""
-        result = folder_paths.get_timestamp()
-        # Pattern: 8 digits, hyphen, 6 digits, hyphen, 6 digits
-        pattern = r"^\d{8}-\d{6}-\d{6}$"
-        self.assertRegex(result, pattern)
-
-    def test_is_filesystem_safe(self):
-        """Should not contain characters that are unsafe for filenames."""
-        result = folder_paths.get_timestamp()
-        unsafe_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|', ' ']
-        for char in unsafe_chars:
-            self.assertNotIn(char, result)
-
-
-class TestFormatOutputFilename(unittest.TestCase):
-    """Tests for format_output_filename function."""
-
-    def test_basic_format(self):
-        """Should format filename with counter and timestamp."""
-        result = folder_paths.format_output_filename("test", 1, "png")
-        # Pattern: test_00001_YYYYMMDD-HHMMSS-ffffff_.png
-        pattern = r"^test_00001_\d{8}-\d{6}-\d{6}_\.png$"
-        self.assertRegex(result, pattern)
-
-    def test_counter_padding(self):
-        """Should pad counter to 5 digits."""
-        result = folder_paths.format_output_filename("test", 42, "png")
-        self.assertIn("_00042_", result)
-
-    def test_extension_with_leading_dot(self):
-        """Should handle extension with leading dot."""
-        result = folder_paths.format_output_filename("test", 1, ".png")
-        self.assertTrue(result.endswith("_.png"))
-        self.assertNotIn("..png", result)
-
-    def test_extension_without_leading_dot(self):
-        """Should handle extension without leading dot."""
-        result = folder_paths.format_output_filename("test", 1, "webm")
-        self.assertTrue(result.endswith("_.webm"))
-
-    def test_batch_num_replacement(self):
-        """Should replace %batch_num% placeholder."""
-        result = folder_paths.format_output_filename("test_%batch_num%", 1, "png", batch_num="3")
-        self.assertIn("test_3_", result)
-        self.assertNotIn("%batch_num%", result)
-
-    def test_custom_timestamp(self):
-        """Should use provided timestamp instead of generating one."""
-        custom_ts = "20260101-120000-000000"
-        result = folder_paths.format_output_filename("test", 1, "png", timestamp=custom_ts)
-        self.assertIn(custom_ts, result)
-
-    def test_different_extensions(self):
-        """Should work with various extensions."""
-        extensions = ["png", "webp", "webm", "svg", "glb", "safetensors", "latent"]
-        for ext in extensions:
-            result = folder_paths.format_output_filename("test", 1, ext)
-            self.assertTrue(result.endswith(f"_.{ext}"))
-
-
-if __name__ == "__main__":
-    unittest.main()