feat: switch SubgraphManager to index.json-driven discovery with distribution filtering

Amp-Thread-ID: https://ampcode.com/threads/T-019c30d2-a605-708d-824f-35e8f3a0c2f5

.github/workflows/release-webhook.yml

@@ -7,8 +7,6 @@ on:
 jobs:
   send-webhook:
     runs-on: ubuntu-latest
-    env:
-      DESKTOP_REPO_DISPATCH_TOKEN: ${{ secrets.DESKTOP_REPO_DISPATCH_TOKEN }}
     steps:
       - name: Send release webhook
         env:
@@ -108,37 +106,3 @@ jobs:
             --fail --silent --show-error
           
           echo "✅ Release webhook sent successfully"
-
-      - name: Send repository dispatch to desktop
-        env:
-          DISPATCH_TOKEN: ${{ env.DESKTOP_REPO_DISPATCH_TOKEN }}
-          RELEASE_TAG: ${{ github.event.release.tag_name }}
-          RELEASE_URL: ${{ github.event.release.html_url }}
-        run: |
-          set -euo pipefail
-
-          if [ -z "${DISPATCH_TOKEN:-}" ]; then
-            echo "::error::DESKTOP_REPO_DISPATCH_TOKEN is required but not set."
-            exit 1
-          fi
-
-          PAYLOAD="$(jq -n \
-            --arg release_tag "$RELEASE_TAG" \
-            --arg release_url "$RELEASE_URL" \
-            '{
-              event_type: "comfyui_release_published",
-              client_payload: {
-                release_tag: $release_tag,
-                release_url: $release_url
-              }
-            }')"
-
-          curl -fsSL \
-            -X POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "Content-Type: application/json" \
-            -H "Authorization: Bearer ${DISPATCH_TOKEN}" \
-            https://api.github.com/repos/Comfy-Org/desktop/dispatches \
-            -d "$PAYLOAD"
-
-          echo "✅ Dispatched ComfyUI release ${RELEASE_TAG} to Comfy-Org/desktop"

app/subgraph_manager.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 from typing import TypedDict
+import json
+import logging
 import os
 import folder_paths
 import glob
@@ -40,6 +42,7 @@ class SubgraphManager:
     def __init__(self):
         self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None
         self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None
+        self.distribution = os.environ.get("DISTRIBUTION", "localhost")
 
     def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]:
         """Create a subgraph entry from a file path. Expects normalized path (forward slashes)."""
@@ -90,15 +93,56 @@ class SubgraphManager:
         return subgraphs_dict
 
     async def get_blueprint_subgraphs(self, force_reload=False):
-        """Load subgraphs from the blueprints directory."""
+        """Load subgraphs from the blueprints directory using index.json for discovery."""
         if not force_reload and self.cached_blueprint_subgraphs is not None:
             return self.cached_blueprint_subgraphs
 
         subgraphs_dict: dict[SubgraphEntry] = {}
         blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')
 
-        if os.path.exists(blueprints_dir):
+        index_path = os.path.join(blueprints_dir, "index.json")
+        if os.path.isfile(index_path):
+            try:
+                with open(index_path, "r", encoding="utf-8") as f:
+                    categories = json.load(f)
+            except (json.JSONDecodeError, OSError) as e:
+                logging.error("Failed to load blueprint index %s: %s", index_path, e)
+                categories = []
+
+            if not isinstance(categories, list):
+                logging.error("Blueprint index.json is not a list: %s", index_path)
+                categories = []
+
+            for category in categories:
+                module_name = category.get("moduleName", "default")
+                for blueprint in category.get("blueprints", []):
+                    name = blueprint.get("name")
+                    if not name:
+                        logging.warning("Blueprint entry missing 'name' in category '%s', skipping", module_name)
+                        continue
+
+                    include_on = blueprint.get("includeOnDistributions")
+                    if include_on is not None and self.distribution not in include_on:
+                        continue
+
+                    file_by_dist = blueprint.get("fileByDistribution", {})
+                    filename = file_by_dist.get(self.distribution, f"{name}.json")
+                    filepath = os.path.realpath(os.path.join(blueprints_dir, filename))
+                    if not filepath.startswith(os.path.realpath(blueprints_dir) + os.sep):
+                        logging.warning("Blueprint path escapes blueprints directory: %s", filepath)
+                        continue
+
+                    if not os.path.isfile(filepath):
+                        logging.warning("Blueprint file not found: %s", filepath)
+                        continue
+
+                    entry_id, entry = self._create_entry(filepath, Source.templates, module_name)
+                    subgraphs_dict[entry_id] = entry
+        elif os.path.exists(blueprints_dir):
+            logging.warning("No blueprint index.json found at %s, falling back to glob", index_path)
             for file in glob.glob(os.path.join(blueprints_dir, "*.json")):
+                if os.path.basename(file) == "index.json":
+                    continue
                 file = file.replace('\\', '/')
                 entry_id, entry = self._create_entry(file, Source.templates, "comfyui")
                 subgraphs_dict[entry_id] = entry

comfy/k_diffusion/sampling.py

@@ -1,11 +1,12 @@
 import math
+import time
 from functools import partial
 
 from scipy import integrate
 import torch
 from torch import nn
 import torchsde
-from tqdm.auto import tqdm
+from tqdm.auto import trange as trange_, tqdm
 
 from . import utils
 from . import deis
@@ -14,7 +15,34 @@ import comfy.model_patcher
 import comfy.model_sampling
 
 import comfy.memory_management
-from comfy.utils import model_trange as trange
+
+
+def trange(*args, **kwargs):
+    if comfy.memory_management.aimdo_allocator is None:
+        return trange_(*args, **kwargs)
+
+    pbar = trange_(*args, **kwargs, smoothing=1.0)
+    pbar._i = 0
+    pbar.set_postfix_str("  Model Initializing ...  ")
+
+    _update = pbar.update
+
+    def warmup_update(n=1):
+        pbar._i += 1
+        if pbar._i == 1:
+            pbar.i1_time = time.time()
+            pbar.set_postfix_str(" Model Initialization complete!  ")
+        elif pbar._i == 2:
+            #bring forward the effective start time based the the diff between first and second iteration
+            #to attempt to remove load overhead from the final step rate estimate.
+            pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)
+            pbar.set_postfix_str("")
+
+        _update(n)
+
+    pbar.update = warmup_update
+    return pbar
+
 
 def append_zero(x):
     return torch.cat([x, x.new_zeros([1])])

comfy/ldm/ace/ace_step15.py

@@ -7,67 +7,6 @@ from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 from comfy.ldm.flux.layers import timestep_embedding
 
-def get_silence_latent(length, device):
-    head = torch.tensor([[[ 0.5707,  0.0982,  0.6909, -0.5658,  0.6266,  0.6996, -0.1365, -0.1291,
-                        -0.0776, -0.1171, -0.2743, -0.8422, -0.1168,  1.5539, -4.6936,  0.7436,
-                        -1.1846, -0.2637,  0.6933, -6.7266,  0.0966, -0.1187, -0.3501, -1.1736,
-                        0.0587, -2.0517, -1.3651,  0.7508, -0.2490, -1.3548, -0.1290, -0.7261,
-                        1.1132, -0.3249,  0.2337,  0.3004,  0.6605, -0.0298, -0.1989, -0.4041,
-                        0.2843, -1.0963, -0.5519,  0.2639, -1.0436, -0.1183,  0.0640,  0.4460,
-                        -1.1001, -0.6172, -1.3241,  1.1379,  0.5623, -0.1507, -0.1963, -0.4742,
-                        -2.4697,  0.5302,  0.5381,  0.4636, -0.1782, -0.0687,  1.0333,  0.4202],
-                        [ 0.3040, -0.1367,  0.6200,  0.0665, -0.0642,  0.4655, -0.1187, -0.0440,
-                        0.2941, -0.2753,  0.0173, -0.2421, -0.0147,  1.5603, -2.7025,  0.7907,
-                        -0.9736, -0.0682,  0.1294, -5.0707, -0.2167,  0.3302, -0.1513, -0.8100,
-                        -0.3894, -0.2884, -0.3149,  0.8660, -0.3817, -1.7061,  0.5824, -0.4840,
-                        0.6938,  0.1859,  0.1753,  0.3081,  0.0195,  0.1403, -0.0754, -0.2091,
-                        0.1251, -0.1578, -0.4968, -0.1052, -0.4554, -0.0320,  0.1284,  0.4974,
-                        -1.1889, -0.0344, -0.8313,  0.2953,  0.5445, -0.6249, -0.1595, -0.0682,
-                        -3.1412,  0.0484,  0.4153,  0.8260, -0.1526, -0.0625,  0.5366,  0.8473],
-                        [ 5.3524e-02, -1.7534e-01,  5.4443e-01, -4.3501e-01, -2.1317e-03,
-                        3.7200e-01, -4.0143e-03, -1.5516e-01, -1.2968e-01, -1.5375e-01,
-                        -7.7107e-02, -2.0593e-01, -3.2780e-01,  1.5142e+00, -2.6101e+00,
-                        5.8698e-01, -1.2716e+00, -2.4773e-01, -2.7933e-02, -5.0799e+00,
-                        1.1601e-01,  4.0987e-01, -2.2030e-02, -6.6495e-01, -2.0995e-01,
-                        -6.3474e-01, -1.5893e-01,  8.2745e-01, -2.2992e-01, -1.6816e+00,
-                        5.4440e-01, -4.9579e-01,  5.5128e-01,  3.0477e-01,  8.3052e-02,
-                        -6.1782e-02,  5.9036e-03,  2.9553e-01, -8.0645e-02, -1.0060e-01,
-                        1.9144e-01, -3.8124e-01, -7.2949e-01,  2.4520e-02, -5.0814e-01,
-                        2.3977e-01,  9.2943e-02,  3.9256e-01, -1.1993e+00, -3.2752e-01,
-                        -7.2707e-01,  2.9476e-01,  4.3542e-01, -8.8597e-01, -4.1686e-01,
-                        -8.5390e-02, -2.9018e+00,  6.4988e-02,  5.3945e-01,  9.1988e-01,
-                        5.8762e-02, -7.0098e-02,  6.4772e-01,  8.9118e-01],
-                        [-3.2225e-02, -1.3195e-01,  5.6411e-01, -5.4766e-01, -5.2170e-03,
-                        3.1425e-01, -5.4367e-02, -1.9419e-01, -1.3059e-01, -1.3660e-01,
-                        -9.0984e-02, -1.9540e-01, -2.5590e-01,  1.5440e+00, -2.6349e+00,
-                        6.8273e-01, -1.2532e+00, -1.9810e-01, -2.2793e-02, -5.0506e+00,
-                        1.8818e-01,  5.0109e-01,  7.3546e-03, -6.8771e-01, -3.0676e-01,
-                        -7.3257e-01, -1.6687e-01,  9.2232e-01, -1.8987e-01, -1.7267e+00,
-                        5.3355e-01, -5.3179e-01,  4.4953e-01,  2.8820e-01,  1.3012e-01,
-                        -2.0943e-01, -1.1348e-01,  3.3929e-01, -1.5069e-01, -1.2919e-01,
-                        1.8929e-01, -3.6166e-01, -8.0756e-01,  6.6387e-02, -5.8867e-01,
-                        1.6978e-01,  1.0134e-01,  3.3877e-01, -1.2133e+00, -3.2492e-01,
-                        -8.1237e-01,  3.8101e-01,  4.3765e-01, -8.0596e-01, -4.4531e-01,
-                        -4.7513e-02, -2.9266e+00,  1.1741e-03,  4.5123e-01,  9.3075e-01,
-                        5.3688e-02, -1.9621e-01,  6.4530e-01,  9.3870e-01]]], device=device).movedim(-1, 1)
-
-    silence_latent = torch.tensor([[[-1.3672e-01, -1.5820e-01,  5.8594e-01, -5.7422e-01,  3.0273e-02,
-                                2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
-                                -2.7710e-02, -1.8066e-01, -2.9688e-01,  1.6016e+00, -2.6719e+00,
-                                7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
-                                2.4316e-01,  4.7266e-01,  4.6387e-02, -6.6406e-01, -2.1973e-01,
-                                -6.7578e-01, -1.5723e-01,  9.5312e-01, -2.0020e-01, -1.7109e+00,
-                                5.8984e-01, -5.7422e-01,  5.1562e-01,  2.8320e-01,  1.4551e-01,
-                                -1.8750e-01, -5.9814e-02,  3.6719e-01, -1.0059e-01, -1.5723e-01,
-                                2.0605e-01, -4.3359e-01, -8.2812e-01,  4.5654e-02, -6.6016e-01,
-                                1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
-                                -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
-                                -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
-                                7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, length)
-    silence_latent[:, :, :head.shape[-1]] = head
-    return silence_latent
-
-
 def get_layer_class(operations, layer_name):
     if operations is not None and hasattr(operations, layer_name):
         return getattr(operations, layer_name)
@@ -738,7 +677,7 @@ class AttentionPooler(nn.Module):
     def forward(self, x):
         B, T, P, D = x.shape
         x = self.embed_tokens(x)
-        special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1)
+        special = self.special_token.expand(B, T, 1, -1)
         x = torch.cat([special, x], dim=2)
         x = x.view(B * T, P + 1, D)
 
@@ -789,7 +728,7 @@ class FSQ(nn.Module):
         self.register_buffer('implicit_codebook', implicit_codebook, persistent=False)
 
     def bound(self, z):
-        levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1)
+        levels_minus_1 = (self._levels - 1).to(z.dtype)
         scale = 2. / levels_minus_1
         bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5
 
@@ -804,8 +743,8 @@ class FSQ(nn.Module):
         return codes_non_centered.float() * (2. / (self._levels.float() - 1)) - 1.
 
     def codes_to_indices(self, zhat):
-        zhat_normalized = (zhat + 1.) / (2. / (comfy.model_management.cast_to(self._levels, device=zhat.device, dtype=zhat.dtype) - 1))
-        return (zhat_normalized * comfy.model_management.cast_to(self._basis, device=zhat.device, dtype=zhat.dtype)).sum(dim=-1).round().to(torch.int32)
+        zhat_normalized = (zhat + 1.) / (2. / (self._levels.to(zhat.dtype) - 1))
+        return (zhat_normalized * self._basis.to(zhat.dtype)).sum(dim=-1).round().to(torch.int32)
 
     def forward(self, z):
         orig_dtype = z.dtype
@@ -887,7 +826,7 @@ class ResidualFSQ(nn.Module):
         x = self.project_in(x)
 
         if hasattr(self, 'soft_clamp_input_value'):
-            sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype)
+            sc_val = self.soft_clamp_input_value.to(x.dtype)
             x = (x / sc_val).tanh() * sc_val
 
         quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype)
@@ -895,7 +834,7 @@ class ResidualFSQ(nn.Module):
         all_indices = []
 
         for layer, scale in zip(self.layers, self.scales):
-            scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype)
+            scale = scale.to(residual.dtype)
 
             quantized, indices = layer(residual / scale)
             quantized = quantized * scale
@@ -1101,21 +1040,22 @@ class AceStepConditionGenerationModel(nn.Module):
             lm_hints = self.detokenizer(lm_hints_5Hz)
 
         lm_hints = lm_hints[:, :src_latents.shape[1], :]
-        if is_covers is None or is_covers is True:
+        if is_covers is None:
             src_latents = lm_hints
-        elif is_covers is False:
-            src_latents = refer_audio_acoustic_hidden_states_packed
+        else:
+            src_latents = torch.where(is_covers.unsqueeze(-1).unsqueeze(-1) > 0, lm_hints, src_latents)
 
         context_latents = torch.cat([src_latents, chunk_masks.to(src_latents.dtype)], dim=-1)
 
         return encoder_hidden, encoder_mask, context_latents
 
-    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs):
+    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, **kwargs):
         text_attention_mask = None
         lyric_attention_mask = None
         refer_audio_order_mask = None
         attention_mask = None
         chunk_masks = None
+        is_covers = None
         src_latents = None
         precomputed_lm_hints_25Hz = None
         lyric_hidden_states = lyric_embed
@@ -1127,7 +1067,7 @@ class AceStepConditionGenerationModel(nn.Module):
         if refer_audio_order_mask is None:
             refer_audio_order_mask = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long)
 
-        if src_latents is None:
+        if src_latents is None and is_covers is None:
             src_latents = x
 
         if chunk_masks is None:
@@ -1140,9 +1080,6 @@ class AceStepConditionGenerationModel(nn.Module):
             src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes
         )
 
-        if replace_with_null_embeds:
-            enc_hidden[:] = self.null_condition_emb.to(enc_hidden)
-
         out = self.decoder(hidden_states=x,
                            timestep=timestep,
                            timestep_r=timestep,

comfy/ldm/anima/model.py

@@ -195,20 +195,8 @@ class Anima(MiniTrainDIT):
         super().__init__(*args, **kwargs)
         self.llm_adapter = LLMAdapter(device=kwargs.get("device"), dtype=kwargs.get("dtype"), operations=kwargs.get("operations"))
 
-    def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=None):
+    def preprocess_text_embeds(self, text_embeds, text_ids):
         if text_ids is not None:
-            out = self.llm_adapter(text_embeds, text_ids)
-            if t5xxl_weights is not None:
-                out = out * t5xxl_weights
-
-            if out.shape[1] < 512:
-                out = torch.nn.functional.pad(out, (0, 0, 0, 512 - out.shape[1]))
-            return out
+            return self.llm_adapter(text_embeds, text_ids)
         else:
             return text_embeds
-
-    def forward(self, x, timesteps, context, **kwargs):
-        t5xxl_ids = kwargs.pop("t5xxl_ids", None)
-        if t5xxl_ids is not None:
-            context = self.preprocess_text_embeds(context, t5xxl_ids, t5xxl_weights=kwargs.pop("t5xxl_weights", None))
-        return super().forward(x, timesteps, context, **kwargs)

comfy/ldm/cosmos/predict2.py

@@ -335,7 +335,7 @@ class FinalLayer(nn.Module):
         device=None, dtype=None, operations=None
     ):
         super().__init__()
-        self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.linear = operations.Linear(
             hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
         )
@@ -463,8 +463,6 @@ class Block(nn.Module):
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
         transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
-        residual_dtype = x_B_T_H_W_D.dtype
-        compute_dtype = emb_B_T_D.dtype
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
 
@@ -514,7 +512,7 @@ class Block(nn.Module):
         result_B_T_H_W_D = rearrange(
             self.self_attn(
                 # normalized_x_B_T_HW_D,
-                rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
                 transformer_options=transformer_options,
@@ -524,7 +522,7 @@ class Block(nn.Module):
             h=H,
             w=W,
         )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
@@ -538,7 +536,7 @@ class Block(nn.Module):
             )
             _result_B_T_H_W_D = rearrange(
                 self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
                     transformer_options=transformer_options,
@@ -557,7 +555,7 @@ class Block(nn.Module):
             shift_cross_attn_B_T_1_1_D,
             transformer_options=transformer_options,
         )
-        x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D
+        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
@@ -565,8 +563,8 @@ class Block(nn.Module):
             scale_mlp_B_T_1_1_D,
             shift_mlp_B_T_1_1_D,
         )
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
         return x_B_T_H_W_D
 
 
@@ -878,14 +876,6 @@ class MiniTrainDIT(nn.Module):
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
             "transformer_options": kwargs.get("transformer_options", {}),
         }
-
-        # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream
-        # in fp32, but run attention and MLP modules in fp16.
-        # An alternate method that clamps fp16 values "works" in the sense that it makes coherent images, but there is noticeable
-        # quality degradation and visual artifacts.
-        if x_B_T_H_W_D.dtype == torch.float16:
-            x_B_T_H_W_D = x_B_T_H_W_D.float()
-
         for block in self.blocks:
             x_B_T_H_W_D = block(
                 x_B_T_H_W_D,
@@ -894,6 +884,6 @@ class MiniTrainDIT(nn.Module):
                 **block_kwargs,
             )
 
-        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
+        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
         x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]
         return x_B_C_Tt_Hp_Wp

comfy/ldm/flux/math.py

@@ -29,34 +29,19 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     return out.to(dtype=torch.float32, device=pos.device)
 
 
-def _apply_rope1(x: Tensor, freqs_cis: Tensor):
-    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
-
-    x_out = freqs_cis[..., 0] * x_[..., 0]
-    x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
-
-    return x_out.reshape(*x.shape).type_as(x)
-
-
-def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
-
-
 try:
     import comfy.quant_ops
-    q_apply_rope = comfy.quant_ops.ck.apply_rope
-    q_apply_rope1 = comfy.quant_ops.ck.apply_rope1
-    def apply_rope(xq, xk, freqs_cis):
-        if comfy.model_management.in_training:
-            return _apply_rope(xq, xk, freqs_cis)
-        else:
-            return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
-    def apply_rope1(x, freqs_cis):
-        if comfy.model_management.in_training:
-            return _apply_rope1(x, freqs_cis)
-        else:
-            return q_apply_rope1(x, freqs_cis)
+    apply_rope = comfy.quant_ops.ck.apply_rope
+    apply_rope1 = comfy.quant_ops.ck.apply_rope1
 except:
     logging.warning("No comfy kitchen, using old apply_rope functions.")
-    apply_rope = _apply_rope
-    apply_rope1 = _apply_rope1
+    def apply_rope1(x: Tensor, freqs_cis: Tensor):
+        x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
+
+        x_out = freqs_cis[..., 0] * x_[..., 0]
+        x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
+
+        return x_out.reshape(*x.shape).type_as(x)
+
+    def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+        return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)

comfy/model_base.py

@@ -147,11 +147,11 @@ class BaseModel(torch.nn.Module):
                 self.diffusion_model.to(memory_format=torch.channels_last)
                 logging.debug("using channels last mode for diffusion model")
             logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
-            comfy.model_management.archive_model_dtypes(self.diffusion_model)
-
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
+        comfy.model_management.archive_model_dtypes(self.diffusion_model)
+
         self.adm_channels = unet_config.get("adm_in_channels", None)
         if self.adm_channels is None:
             self.adm_channels = 0
@@ -1160,16 +1160,12 @@ class Anima(BaseModel):
         device = kwargs["device"]
         if cross_attn is not None:
             if t5xxl_ids is not None:
+                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype()), t5xxl_ids.unsqueeze(0).to(device=device))
                 if t5xxl_weights is not None:
-                    t5xxl_weights = t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn)
-                t5xxl_ids = t5xxl_ids.unsqueeze(0)
-
-                if torch.is_inference_mode_enabled():  # if not we are training
-                    cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype()), t5xxl_ids.to(device=device), t5xxl_weights=t5xxl_weights.to(device=device, dtype=self.get_dtype()))
-                else:
-                    out['t5xxl_ids'] = comfy.conds.CONDRegular(t5xxl_ids)
-                    out['t5xxl_weights'] = comfy.conds.CONDRegular(t5xxl_weights)
+                    cross_attn *= t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn)
 
+                if cross_attn.shape[1] < 512:
+                    cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, 0, 512 - cross_attn.shape[1]))
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
 
@@ -1556,8 +1552,6 @@ class ACEStep15(BaseModel):
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            if torch.count_nonzero(cross_attn) == 0:
-                out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True)
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         conditioning_lyrics = kwargs.get("conditioning_lyrics", None)
@@ -1566,11 +1560,22 @@ class ACEStep15(BaseModel):
 
         refer_audio = kwargs.get("reference_audio_timbre_latents", None)
         if refer_audio is None or len(refer_audio) == 0:
-            refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
+            refer_audio = torch.tensor([[[-1.3672e-01, -1.5820e-01,  5.8594e-01, -5.7422e-01,  3.0273e-02,
+                                        2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
+                                        -2.7710e-02, -1.8066e-01, -2.9688e-01,  1.6016e+00, -2.6719e+00,
+                                        7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
+                                        2.4316e-01,  4.7266e-01,  4.6387e-02, -6.6406e-01, -2.1973e-01,
+                                        -6.7578e-01, -1.5723e-01,  9.5312e-01, -2.0020e-01, -1.7109e+00,
+                                        5.8984e-01, -5.7422e-01,  5.1562e-01,  2.8320e-01,  1.4551e-01,
+                                        -1.8750e-01, -5.9814e-02,  3.6719e-01, -1.0059e-01, -1.5723e-01,
+                                        2.0605e-01, -4.3359e-01, -8.2812e-01,  4.5654e-02, -6.6016e-01,
+                                        1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
+                                        -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
+                                        -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
+                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, noise.shape[2])
             pass_audio_codes = True
         else:
             refer_audio = refer_audio[-1][:, :, :noise.shape[2]]
-            out['is_covers'] = comfy.conds.CONDConstant(True)
             pass_audio_codes = False
 
         if pass_audio_codes:
@@ -1578,12 +1583,6 @@ class ACEStep15(BaseModel):
             if audio_codes is not None:
                 out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
                 refer_audio = refer_audio[:, :, :750]
-            else:
-                out['is_covers'] = comfy.conds.CONDConstant(False)
-
-        if refer_audio.shape[2] < noise.shape[2]:
-            pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
-            refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2)
 
         out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
         return out

comfy/model_management.py

@@ -19,7 +19,7 @@
 import psutil
 import logging
 from enum import Enum
-from comfy.cli_args import args, PerformanceFeature
+from comfy.cli_args import args, PerformanceFeature, enables_dynamic_vram
 import threading
 import torch
 import sys
@@ -55,11 +55,6 @@ cpu_state = CPUState.GPU
 
 total_vram = 0
 
-
-# Training Related State
-in_training = False
-
-
 def get_supported_float8_types():
     float8_types = []
     try:
@@ -656,7 +651,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
                 soft_empty_cache()
     return unloaded_models
 
-def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
+def load_models_gpu_orig(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
     cleanup_models_gc()
     global vram_state
 
@@ -752,6 +747,26 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         current_loaded_models.insert(0, loaded_model)
     return
 
+def load_models_gpu_thread(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load):
+    with torch.inference_mode():
+        load_models_gpu_orig(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load)
+        soft_empty_cache()
+
+def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
+    #Deliberately load models outside of the Aimdo mempool so they can be retained accross
+    #nodes. Use a dummy thread to do it as pytorch documents that mempool contexts are
+    #thread local. So exploit that to escape context
+    if enables_dynamic_vram():
+        t = threading.Thread(
+            target=load_models_gpu_thread,
+            args=(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load)
+        )
+        t.start()
+        t.join()
+    else:
+        load_models_gpu_orig(models, memory_required=memory_required, force_patch_weights=force_patch_weights,
+                             minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)
+
 def load_model_gpu(model):
     return load_models_gpu([model])
 
@@ -1211,20 +1226,21 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str
         if dtype is None:
             dtype = weight._model_dtype
 
+        r = torch.empty_like(weight, dtype=dtype, device=device)
+
         signature = comfy_aimdo.model_vbar.vbar_fault(weight._v)
         if signature is not None:
-            if comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
-                v_tensor = weight._v_tensor
-            else:
-                raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
-                v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
-                weight._v_tensor = v_tensor
+            raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
+            v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
+            if not comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
                 weight._v_signature = signature
                 #Send it over
                 v_tensor.copy_(weight, non_blocking=non_blocking)
-            return v_tensor.to(dtype=dtype)
-
-        r = torch.empty_like(weight, dtype=dtype, device=device)
+            #always take a deep copy even if _v is good, as we have no reasonable point to unpin
+            #a non comfy weight
+            r.copy_(v_tensor)
+            comfy_aimdo.model_vbar.vbar_unpin(weight._v)
+            return r
 
         if weight.dtype != r.dtype and weight.dtype != weight._model_dtype:
             #Offloaded casting could skip this, however it would make the quantizations

comfy/model_patcher.py

@@ -19,6 +19,7 @@
 from __future__ import annotations
 
 import collections
+import copy
 import inspect
 import logging
 import math
@@ -316,7 +317,7 @@ class ModelPatcher:
 
         n.object_patches = self.object_patches.copy()
         n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
-        n.model_options = comfy.utils.deepcopy_list_dict(self.model_options)
+        n.model_options = copy.deepcopy(self.model_options)
         n.backup = self.backup
         n.object_patches_backup = self.object_patches_backup
         n.parent = self
@@ -1491,9 +1492,7 @@ class ModelPatcherDynamic(ModelPatcher):
             if vbar is not None:
                 vbar.prioritize()
 
-            #We force reserve VRAM for the non comfy-weight so we dont have to deal
-            #with pin and unpin syncrhonization which can be expensive for small weights
-            #with a high layer rate (e.g. autoregressive LLMs).
+            #We have way more tools for acceleration on comfy weight offloading, so always
             #prioritize the non-comfy weights (note the order reverse).
             loading = self._load_list(prio_comfy_cast_weights=True)
             loading.sort(reverse=True)
@@ -1525,7 +1524,7 @@ class ModelPatcherDynamic(ModelPatcher):
                     setattr(m, param_key + "_function", weight_function)
                     geometry = weight
                     if not isinstance(weight, QuantizedTensor):
-                        model_dtype = getattr(m, param_key + "_comfy_model_dtype", None) or weight.dtype
+                        model_dtype = getattr(m, param_key + "_comfy_model_dtype", weight.dtype)
                         weight._model_dtype = model_dtype
                         geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype)
                     return comfy.memory_management.vram_aligned_size(geometry)
@@ -1551,14 +1550,13 @@ class ModelPatcherDynamic(ModelPatcher):
                         weight.seed_key = key
                         set_dirty(weight, dirty)
                         geometry = weight
-                        model_dtype = getattr(m, param + "_comfy_model_dtype", None) or weight.dtype
+                        model_dtype = getattr(m, param + "_comfy_model_dtype", weight.dtype)
                         geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype)
                         weight_size = geometry.numel() * geometry.element_size()
                         if vbar is not None and not hasattr(weight, "_v"):
                             weight._v = vbar.alloc(weight_size)
                             weight._model_dtype = model_dtype
                         allocated_size += weight_size
-                    vbar.set_watermark_limit(allocated_size)
 
             logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.")
 

comfy/ops.py

@@ -83,18 +83,14 @@ 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):
     offload_stream = None
     xfer_dest = None
+    cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ])
 
     signature = comfy_aimdo.model_vbar.vbar_fault(s._v)
-    resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)
     if signature is not None:
-        if resident:
-            weight = s._v_weight
-            bias = s._v_bias
-        else:
-            xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
+        xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
+    resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)
 
     if not resident:
-        cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ])
         cast_dest = None
 
         xfer_source = [ s.weight, s.bias ]
@@ -144,13 +140,9 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
                     post_cast.copy_(pre_cast)
             xfer_dest = cast_dest
 
-        params = comfy.memory_management.interpret_gathered_like(cast_geometry, xfer_dest)
-        weight = params[0]
-        bias = params[1]
-        if signature is not None:
-            s._v_weight = weight
-            s._v_bias = bias
-        s._v_signature=signature
+    params = comfy.memory_management.interpret_gathered_like(cast_geometry, xfer_dest)
+    weight = params[0]
+    bias = params[1]
 
     def post_cast(s, param_key, x, dtype, resident, update_weight):
         lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
@@ -177,8 +169,8 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
                 if orig.dtype == dtype and len(fns) == 0:
                     #The layer actually wants our freshly saved QT
                     x = y
-            elif update_weight:
-                y = comfy.float.stochastic_rounding(x, orig.dtype, seed = comfy.utils.string_to_seed(s.seed_key))
+            else:
+                y = x
             if update_weight:
                 orig.copy_(y)
         for f in fns:
@@ -190,6 +182,7 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
     weight = post_cast(s, "weight", weight, dtype, resident, update_weight)
     if s.bias is not None:
         bias = post_cast(s, "bias", bias, bias_dtype, resident, update_weight)
+    s._v_signature=signature
 
     #FIXME: weird offload return protocol
     return weight, bias, (offload_stream, device if signature is not None else None, None)

comfy/sampler_helpers.py

@@ -122,26 +122,20 @@ def estimate_memory(model, noise_shape, conds):
     minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
     return memory_required, minimum_memory_required
 
-def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):
+def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
     executor = comfy.patcher_extension.WrapperExecutor.new_executor(
         _prepare_sampling,
         comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
     )
-    return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load, force_offload=force_offload)
+    return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load)
 
-def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):
+def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False):
     real_model: BaseModel = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
-    if force_offload: # In training + offload enabled, we want to force prepare sampling to trigger partial load
-        memory_required = 1e20
-        minimum_memory_required = None
-    else:
-        memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
-        memory_required += inference_memory
-        minimum_memory_required += inference_memory
-    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)
+    memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory, force_full_load=force_full_load)
     real_model = model.model
 
     return real_model, conds, models

comfy/sd.py

@@ -793,6 +793,8 @@ class VAE:
             self.first_stage_model = AutoencoderKL(**(config['params']))
         self.first_stage_model = self.first_stage_model.eval()
 
+        model_management.archive_model_dtypes(self.first_stage_model)
+
         if device is None:
             device = model_management.vae_device()
         self.device = device
@@ -801,7 +803,6 @@ class VAE:
             dtype = model_management.vae_dtype(self.device, self.working_dtypes)
         self.vae_dtype = dtype
         self.first_stage_model.to(self.vae_dtype)
-        model_management.archive_model_dtypes(self.first_stage_model)
         self.output_device = model_management.intermediate_device()
 
         mp = comfy.model_patcher.CoreModelPatcher

comfy/supported_models.py

@@ -993,7 +993,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
@@ -1023,7 +1023,11 @@ class Anima(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Anima(self, device=device)
@@ -1034,12 +1038,6 @@ class Anima(supported_models_base.BASE):
         detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_06b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect))
 
-    def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs):
-        self.memory_usage_factor = (self.unet_config.get("model_channels", 2048) / 2048) * 0.95
-        if dtype is torch.float16:
-            self.memory_usage_factor *= 1.4
-        return super().set_inference_dtype(dtype, manual_cast_dtype, **kwargs)
-
 class CosmosI2VPredict2(CosmosT2IPredict2):
     unet_config = {
         "image_model": "cosmos_predict2",

comfy/text_encoders/ace15.py

@@ -16,7 +16,6 @@ def sample_manual_loop_no_classes(
     temperature: float = 0.85,
     top_p: float = 0.9,
     top_k: int = None,
-    min_p: float = 0.000,
     seed: int = 1,
     min_tokens: int = 1,
     max_new_tokens: int = 2048,
@@ -24,8 +23,6 @@ def sample_manual_loop_no_classes(
     audio_end_id: int = 215669,
     eos_token_id: int = 151645,
 ):
-    if ids is None:
-        return []
     device = model.execution_device
 
     if execution_dtype is None:
@@ -35,7 +32,6 @@ def sample_manual_loop_no_classes(
             execution_dtype = torch.float32
 
     embeds, attention_mask, num_tokens, embeds_info = model.process_tokens(ids, device)
-    embeds_batch = embeds.shape[0]
     for i, t in enumerate(paddings):
         attention_mask[i, :t] = 0
         attention_mask[i, t:] = 1
@@ -45,27 +41,22 @@ def sample_manual_loop_no_classes(
     generator = torch.Generator(device=device)
     generator.manual_seed(seed)
     model_config = model.transformer.model.config
-    past_kv_shape = [embeds_batch, model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim]
 
     for x in range(model_config.num_hidden_layers):
-        past_key_values.append((torch.empty(past_kv_shape, device=device, dtype=execution_dtype), torch.empty(past_kv_shape, device=device, dtype=execution_dtype), 0))
+        past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), torch.empty([embeds.shape[0], model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim], device=device, dtype=execution_dtype), 0))
 
     progress_bar = comfy.utils.ProgressBar(max_new_tokens)
 
-    for step in comfy.utils.model_trange(max_new_tokens, desc="LM sampling"):
+    for step in range(max_new_tokens):
         outputs = model.transformer(None, attention_mask, embeds=embeds.to(execution_dtype), num_tokens=num_tokens, intermediate_output=None, dtype=execution_dtype, embeds_info=embeds_info, past_key_values=past_key_values)
         next_token_logits = model.transformer.logits(outputs[0])[:, -1]
         past_key_values = outputs[2]
 
-        if cfg_scale != 1.0:
-            cond_logits = next_token_logits[0:1]
-            uncond_logits = next_token_logits[1:2]
-            cfg_logits = uncond_logits + cfg_scale * (cond_logits - uncond_logits)
-        else:
-            cfg_logits = next_token_logits[0:1]
+        cond_logits = next_token_logits[0:1]
+        uncond_logits = next_token_logits[1:2]
+        cfg_logits = uncond_logits + cfg_scale * (cond_logits - uncond_logits)
 
-        use_eos_score = eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step
-        if use_eos_score:
+        if eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step:
             eos_score = cfg_logits[:, eos_token_id].clone()
 
         remove_logit_value = torch.finfo(cfg_logits.dtype).min
@@ -73,7 +64,7 @@ def sample_manual_loop_no_classes(
         cfg_logits[:, :audio_start_id] = remove_logit_value
         cfg_logits[:, audio_end_id:] = remove_logit_value
 
-        if use_eos_score:
+        if eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step:
             cfg_logits[:, eos_token_id] = eos_score
 
         if top_k is not None and top_k > 0:
@@ -81,12 +72,6 @@ def sample_manual_loop_no_classes(
             min_val = top_k_vals[..., -1, None]
             cfg_logits[cfg_logits < min_val] = remove_logit_value
 
-        if min_p is not None and min_p > 0:
-            probs = torch.softmax(cfg_logits, dim=-1)
-            p_max = probs.max(dim=-1, keepdim=True).values
-            indices_to_remove = probs < (min_p * p_max)
-            cfg_logits[indices_to_remove] = remove_logit_value
-
         if top_p is not None and top_p < 1.0:
             sorted_logits, sorted_indices = torch.sort(cfg_logits, descending=True)
             cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
@@ -108,8 +93,8 @@ def sample_manual_loop_no_classes(
             break
 
         embed, _, _, _ = model.process_tokens([[token]], device)
-        embeds = embed.repeat(embeds_batch, 1, 1)
-        attention_mask = torch.cat([attention_mask, torch.ones((embeds_batch, 1), device=device, dtype=attention_mask.dtype)], dim=1)
+        embeds = embed.repeat(2, 1, 1)
+        attention_mask = torch.cat([attention_mask, torch.ones((2, 1), device=device, dtype=attention_mask.dtype)], dim=1)
 
         output_audio_codes.append(token - audio_start_id)
         progress_bar.update_absolute(step)
@@ -117,31 +102,24 @@ def sample_manual_loop_no_classes(
     return output_audio_codes
 
 
-def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0, min_p=0.000):
+def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0):
     positive = [[token for token, _ in inner_list] for inner_list in positive]
+    negative = [[token for token, _ in inner_list] for inner_list in negative]
     positive = positive[0]
+    negative = negative[0]
 
-    if cfg_scale != 1.0:
-        negative = [[token for token, _ in inner_list] for inner_list in negative]
-        negative = negative[0]
+    neg_pad = 0
+    if len(negative) < len(positive):
+        neg_pad = (len(positive) - len(negative))
+        negative = [model.special_tokens["pad"]] * neg_pad + negative
 
-        neg_pad = 0
-        if len(negative) < len(positive):
-            neg_pad = (len(positive) - len(negative))
-            negative = [model.special_tokens["pad"]] * neg_pad + negative
+    pos_pad = 0
+    if len(negative) > len(positive):
+        pos_pad = (len(negative) - len(positive))
+        positive = [model.special_tokens["pad"]] * pos_pad + positive
 
-        pos_pad = 0
-        if len(negative) > len(positive):
-            pos_pad = (len(negative) - len(positive))
-            positive = [model.special_tokens["pad"]] * pos_pad + positive
-
-        paddings = [pos_pad, neg_pad]
-        ids = [positive, negative]
-    else:
-        paddings = []
-        ids = [positive]
-
-    return sample_manual_loop_no_classes(model, ids, paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
+    paddings = [pos_pad, neg_pad]
+    return sample_manual_loop_no_classes(model, [positive, negative], paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
 
 
 class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
@@ -151,12 +129,12 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
     def _metas_to_cot(self, *, return_yaml: bool = False, **kwargs) -> str:
         user_metas = {
             k: kwargs.pop(k)
-            for k in ("bpm", "duration", "keyscale", "timesignature")
+            for k in ("bpm", "duration", "keyscale", "timesignature", "language", "caption")
             if k in kwargs
         }
         timesignature = user_metas.get("timesignature")
         if isinstance(timesignature, str) and timesignature.endswith("/4"):
-            user_metas["timesignature"] = timesignature[:-2]
+            user_metas["timesignature"] = timesignature.rsplit("/", 1)[0]
         user_metas = {
             k: v if not isinstance(v, str) or not v.isdigit() else int(v)
             for k, v in user_metas.items()
@@ -169,11 +147,8 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
         return f"<think>\n{meta_yaml}\n</think>" if not return_yaml else meta_yaml
 
     def _metas_to_cap(self, **kwargs) -> str:
-        use_keys = ("bpm", "timesignature", "keyscale", "duration")
+        use_keys = ("bpm", "duration", "keyscale", "timesignature")
         user_metas = { k: kwargs.pop(k, "N/A") for k in use_keys }
-        timesignature = user_metas.get("timesignature")
-        if isinstance(timesignature, str) and timesignature.endswith("/4"):
-            user_metas["timesignature"] = timesignature[:-2]
         duration = user_metas["duration"]
         if duration == "N/A":
             user_metas["duration"] = "30 seconds"
@@ -184,13 +159,9 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
         return "\n".join(f"- {k}: {user_metas[k]}" for k in use_keys)
 
     def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
-        text = text.strip()
-        text_negative = kwargs.get("caption_negative", text).strip()
+        out = {}
         lyrics = kwargs.get("lyrics", "")
-        lyrics_negative = kwargs.get("lyrics_negative", lyrics)
         duration = kwargs.get("duration", 120)
-        if isinstance(duration, str):
-            duration = float(duration.split(None, 1)[0])
         language = kwargs.get("language")
         seed = kwargs.get("seed", 0)
 
@@ -199,55 +170,28 @@ class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
         temperature = kwargs.get("temperature", 0.85)
         top_p = kwargs.get("top_p", 0.9)
         top_k = kwargs.get("top_k", 0.0)
-        min_p = kwargs.get("min_p", 0.000)
+
 
         duration = math.ceil(duration)
         kwargs["duration"] = duration
-        tokens_duration = duration * 5
-        min_tokens = int(kwargs.get("min_tokens", tokens_duration))
-        max_tokens = int(kwargs.get("max_tokens", tokens_duration))
 
-        metas_negative = {
-            k.rsplit("_", 1)[0]: kwargs.pop(k)
-            for k in ("bpm_negative", "duration_negative", "keyscale_negative", "timesignature_negative", "language_negative", "caption_negative")
-            if k in kwargs
-        }
-        if not kwargs.get("use_negative_caption"):
-            _ = metas_negative.pop("caption", None)
-
-        cot_text = self._metas_to_cot(caption=text, **kwargs)
-        cot_text_negative = "<think>\n\n</think>" if not metas_negative else self._metas_to_cot(**metas_negative)
+        cot_text = self._metas_to_cot(caption = text, **kwargs)
         meta_cap = self._metas_to_cap(**kwargs)
 
-        lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n\n# Lyric\n{}\n<|im_end|>\n<|im_start|>assistant\n{}\n\n<|im_end|>\n"
-        lyrics_template = "# Languages\n{}\n\n# Lyric\n{}<|endoftext|><|endoftext|>"
-        qwen3_06b_template = "# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}\n\n# Metas\n{}\n<|endoftext|>\n<|endoftext|>"
+        lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n# Lyric\n{}\n<|im_end|>\n<|im_start|>assistant\n{}\n<|im_end|>\n"
 
-        llm_prompts = {
-            "lm_prompt": lm_template.format(text, lyrics.strip(), cot_text),
-            "lm_prompt_negative": lm_template.format(text_negative, lyrics_negative.strip(), cot_text_negative),
-            "lyrics": lyrics_template.format(language if language is not None else "", lyrics),
-            "qwen3_06b": qwen3_06b_template.format(text, meta_cap),
-        }
+        out["lm_prompt"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, cot_text), disable_weights=True)
+        out["lm_prompt_negative"] = self.qwen3_06b.tokenize_with_weights(lm_template.format(text, lyrics, "<think>\n</think>"), disable_weights=True)
 
-        out = {
-            prompt_key: self.qwen3_06b.tokenize_with_weights(
-                prompt,
-                prompt_key == "qwen3_06b" and return_word_ids,
-                disable_weights = True,
-                **kwargs,
-            )
-            for prompt_key, prompt in llm_prompts.items()
-        }
-        out["lm_metadata"] = {"min_tokens": min_tokens,
-                              "max_tokens": max_tokens,
+        out["lyrics"] = self.qwen3_06b.tokenize_with_weights("# Languages\n{}\n\n# Lyric\n{}<|endoftext|><|endoftext|>".format(language if language is not None else "", lyrics), return_word_ids, disable_weights=True, **kwargs)
+        out["qwen3_06b"] = self.qwen3_06b.tokenize_with_weights("# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}\n# Metas\n{}\n<|endoftext|>\n<|endoftext|>".format(text, meta_cap), return_word_ids, **kwargs)
+        out["lm_metadata"] = {"min_tokens": duration * 5,
                               "seed": seed,
                               "generate_audio_codes": generate_audio_codes,
                               "cfg_scale": cfg_scale,
                               "temperature": temperature,
                               "top_p": top_p,
                               "top_k": top_k,
-                              "min_p": min_p,
                               }
         return out
 
@@ -308,7 +252,7 @@ class ACE15TEModel(torch.nn.Module):
 
         lm_metadata = token_weight_pairs["lm_metadata"]
         if lm_metadata["generate_audio_codes"]:
-            audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"], min_p=lm_metadata["min_p"])
+            audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"])
             out["audio_codes"] = [audio_codes]
 
         return base_out, None, out

comfy/text_encoders/anima.py

@@ -23,7 +23,7 @@ class AnimaTokenizer:
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["qwen3_06b"] = [[(k[0], 1.0, k[2]) if return_word_ids else (k[0], 1.0) for k in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
+        out["qwen3_06b"] = [[(token, 1.0) for token, _ in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
         out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 

comfy/text_encoders/lt.py

@@ -25,7 +25,7 @@ def ltxv_te(*args, **kwargs):
 class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}

comfy/utils.py

@@ -27,7 +27,6 @@ from PIL import Image
 import logging
 import itertools
 from torch.nn.functional import interpolate
-from tqdm.auto import trange
 from einops import rearrange
 from comfy.cli_args import args, enables_dynamic_vram
 import json
@@ -1156,32 +1155,6 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
 def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
     return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
 
-def model_trange(*args, **kwargs):
-    if comfy.memory_management.aimdo_allocator is None:
-        return trange(*args, **kwargs)
-
-    pbar = trange(*args, **kwargs, smoothing=1.0)
-    pbar._i = 0
-    pbar.set_postfix_str("  Model Initializing ...  ")
-
-    _update = pbar.update
-
-    def warmup_update(n=1):
-        pbar._i += 1
-        if pbar._i == 1:
-            pbar.i1_time = time.time()
-            pbar.set_postfix_str(" Model Initialization complete!  ")
-        elif pbar._i == 2:
-            #bring forward the effective start time based the the diff between first and second iteration
-            #to attempt to remove load overhead from the final step rate estimate.
-            pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)
-            pbar.set_postfix_str("")
-
-        _update(n)
-
-    pbar.update = warmup_update
-    return pbar
-
 PROGRESS_BAR_ENABLED = True
 def set_progress_bar_enabled(enabled):
     global PROGRESS_BAR_ENABLED
@@ -1403,21 +1376,3 @@ def string_to_seed(data):
             else:
                 crc >>= 1
     return crc ^ 0xFFFFFFFF
-
-def deepcopy_list_dict(obj, memo=None):
-    if memo is None:
-        memo = {}
-
-    obj_id = id(obj)
-    if obj_id in memo:
-        return memo[obj_id]
-
-    if isinstance(obj, dict):
-        res = {deepcopy_list_dict(k, memo): deepcopy_list_dict(v, memo) for k, v in obj.items()}
-    elif isinstance(obj, list):
-        res = [deepcopy_list_dict(i, memo) for i in obj]
-    else:
-        res = obj
-
-    memo[obj_id] = res
-    return res

comfy/weight_adapter/bypass.py

@@ -21,7 +21,6 @@ from typing import Optional, Union
 import torch
 import torch.nn as nn
 
-import comfy.model_management
 from .base import WeightAdapterBase, WeightAdapterTrainBase
 from comfy.patcher_extension import PatcherInjection
 
@@ -182,21 +181,18 @@ class BypassForwardHook:
             )
             return  # Already injected
 
-        # Move adapter weights to compute device (GPU)
-        # Use get_torch_device() instead of module.weight.device because
-        # with offloading, module weights may be on CPU while compute happens on GPU
-        device = comfy.model_management.get_torch_device()
-
-        # Get dtype from module weight if available
+        # Move adapter weights to module's device to avoid CPU-GPU transfer on every forward
+        device = None
         dtype = None
         if hasattr(self.module, "weight") and self.module.weight is not None:
+            device = self.module.weight.device
             dtype = self.module.weight.dtype
+        elif hasattr(self.module, "W_q"):  # Quantized layers might use different attr
+            device = self.module.W_q.device
+            dtype = self.module.W_q.dtype
 
-        # Only use dtype if it's a standard float type, not quantized
-        if dtype is not None and dtype not in (torch.float32, torch.float16, torch.bfloat16):
-            dtype = None
-
-        self._move_adapter_weights_to_device(device, dtype)
+        if device is not None:
+            self._move_adapter_weights_to_device(device, dtype)
 
         self.original_forward = self.module.forward
         self.module.forward = self._bypass_forward

comfy_api/latest/_input/video_types.py

@@ -34,21 +34,6 @@ class VideoInput(ABC):
         """
         pass
 
-    @abstractmethod
-    def as_trimmed(
-        self,
-        start_time: float | None = None,
-        duration: float | None = None,
-        strict_duration: bool = False,
-    ) -> VideoInput | None:
-        """
-        Create a new VideoInput which is trimmed to have the corresponding start_time and duration
-
-        Returns:
-            A new VideoInput, or None if the result would have negative duration
-        """
-        pass
-
     def get_stream_source(self) -> Union[str, io.BytesIO]:
         """
         Get a streamable source for the video. This allows processing without

comfy_api/latest/_input_impl/video_types.py

@@ -6,7 +6,6 @@ from typing import Optional
 from .._input import AudioInput, VideoInput
 import av
 import io
-import itertools
 import json
 import numpy as np
 import math
@@ -30,6 +29,7 @@ def container_to_output_format(container_format: str | None) -> str | None:
     formats = container_format.split(",")
     return formats[0]
 
+
 def get_open_write_kwargs(
     dest: str | io.BytesIO, container_format: str, to_format: str | None
 ) -> dict:
@@ -57,14 +57,12 @@ class VideoFromFile(VideoInput):
     Class representing video input from a file.
     """
 
-    def __init__(self, file: str | io.BytesIO, *, start_time: float=0, duration: float=0):
+    def __init__(self, file: str | io.BytesIO):
         """
         Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object
         containing the file contents.
         """
         self.__file = file
-        self.__start_time = start_time
-        self.__duration = duration
 
     def get_stream_source(self) -> str | io.BytesIO:
         """
@@ -98,16 +96,6 @@ class VideoFromFile(VideoInput):
         Returns:
             Duration in seconds
         """
-        raw_duration = self._get_raw_duration()
-        if self.__start_time < 0:
-            duration_from_start = min(raw_duration, -self.__start_time)
-        else:
-            duration_from_start = raw_duration - self.__start_time
-        if self.__duration:
-            return min(self.__duration, duration_from_start)
-        return duration_from_start
-
-    def _get_raw_duration(self) -> float:
         if isinstance(self.__file, io.BytesIO):
             self.__file.seek(0)
         with av.open(self.__file, mode="r") as container:
@@ -125,13 +113,9 @@ class VideoFromFile(VideoInput):
             if video_stream and video_stream.average_rate:
                 frame_count = 0
                 container.seek(0)
-                frame_iterator = (
-                    container.decode(video_stream)
-                    if video_stream.codec.capabilities & 0x100
-                    else container.demux(video_stream)
-                )
-                for packet in frame_iterator:
-                    frame_count += 1
+                for packet in container.demux(video_stream):
+                    for _ in packet.decode():
+                        frame_count += 1
                 if frame_count > 0:
                     return float(frame_count / video_stream.average_rate)
 
@@ -147,54 +131,36 @@ class VideoFromFile(VideoInput):
 
         with av.open(self.__file, mode="r") as container:
             video_stream = self._get_first_video_stream(container)
-            # 1. Prefer the frames field if available and usable
-            if (
-                video_stream.frames
-                and video_stream.frames > 0
-                and not self.__start_time
-                and not self.__duration
-            ):
+            # 1. Prefer the frames field if available
+            if video_stream.frames and video_stream.frames > 0:
                 return int(video_stream.frames)
 
             # 2. Try to estimate from duration and average_rate using only metadata
+            if container.duration is not None and video_stream.average_rate:
+                duration_seconds = float(container.duration / av.time_base)
+                estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
+                if estimated_frames > 0:
+                    return estimated_frames
+
             if (
                 getattr(video_stream, "duration", None) is not None
                 and getattr(video_stream, "time_base", None) is not None
                 and video_stream.average_rate
             ):
-                raw_duration = float(video_stream.duration * video_stream.time_base)
-                if self.__start_time < 0:
-                    duration_from_start = min(raw_duration, -self.__start_time)
-                else:
-                    duration_from_start = raw_duration - self.__start_time
-                duration_seconds = min(self.__duration, duration_from_start)
+                duration_seconds = float(video_stream.duration * video_stream.time_base)
                 estimated_frames = int(round(duration_seconds * float(video_stream.average_rate)))
                 if estimated_frames > 0:
                     return estimated_frames
 
             # 3. Last resort: decode frames and count them (streaming)
-            if self.__start_time < 0:
-                start_time = max(self._get_raw_duration() + self.__start_time, 0)
-            else:
-                start_time = self.__start_time
-            frame_count = 1
-            start_pts = int(start_time / video_stream.time_base)
-            end_pts = int((start_time + self.__duration) / video_stream.time_base)
-            container.seek(start_pts, stream=video_stream)
-            frame_iterator = (
-                container.decode(video_stream)
-                if video_stream.codec.capabilities & 0x100
-                else container.demux(video_stream)
-            )
-            for frame in frame_iterator:
-                if frame.pts >= start_pts:
-                    break
-            else:
-                raise ValueError(f"Could not determine frame count for file '{self.__file}'\nNo frames exist for start_time {self.__start_time}")
-            for frame in frame_iterator:
-                if frame.pts >= end_pts:
-                    break
-                frame_count += 1
+            frame_count = 0
+            container.seek(0)
+            for packet in container.demux(video_stream):
+                for _ in packet.decode():
+                    frame_count += 1
+
+            if frame_count == 0:
+                raise ValueError(f"Could not determine frame count for file '{self.__file}'")
             return frame_count
 
     def get_frame_rate(self) -> Fraction:
@@ -233,21 +199,9 @@ class VideoFromFile(VideoInput):
             return container.format.name
 
     def get_components_internal(self, container: InputContainer) -> VideoComponents:
-        video_stream = self._get_first_video_stream(container)
-        if self.__start_time < 0:
-            start_time = max(self._get_raw_duration() + self.__start_time, 0)
-        else:
-            start_time = self.__start_time
         # Get video frames
         frames = []
-        start_pts = int(start_time / video_stream.time_base)
-        end_pts = int((start_time + self.__duration) / video_stream.time_base)
-        container.seek(start_pts, stream=video_stream)
-        for frame in container.decode(video_stream):
-            if frame.pts < start_pts:
-                continue
-            if self.__duration and frame.pts >= end_pts:
-                break
+        for frame in container.decode(video=0):
             img = frame.to_ndarray(format='rgb24')  # shape: (H, W, 3)
             img = torch.from_numpy(img) / 255.0  # shape: (H, W, 3)
             frames.append(img)
@@ -255,44 +209,31 @@ class VideoFromFile(VideoInput):
         images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0)
 
         # Get frame rate
-        frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
+        video_stream = next(s for s in container.streams if s.type == 'video')
+        frame_rate = Fraction(video_stream.average_rate) if video_stream and video_stream.average_rate else Fraction(1)
 
         # Get audio if available
         audio = None
-        container.seek(start_pts, stream=video_stream)
-        # Use last stream for consistency
-        if len(container.streams.audio):
-            audio_stream = container.streams.audio[-1]
-            audio_frames = []
-            resample = av.audio.resampler.AudioResampler(format='fltp').resample
-            frames = itertools.chain.from_iterable(
-                map(resample, container.decode(audio_stream))
-            )
-
-            has_first_frame = False
-            for frame in frames:
-                offset_seconds = start_time - frame.pts * audio_stream.time_base
-                to_skip = int(offset_seconds * audio_stream.sample_rate)
-                if to_skip < frame.samples:
-                    has_first_frame = True
-                    break
-            if has_first_frame:
-                audio_frames.append(frame.to_ndarray()[..., to_skip:])
-
-            for frame in frames:
-                if frame.time > start_time + self.__duration:
-                    break
-                audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
-            if len(audio_frames) > 0:
-                audio_data = np.concatenate(audio_frames, axis=1)  # shape: (channels, total_samples)
-                if self.__duration:
-                    audio_data = audio_data[..., :int(self.__duration * audio_stream.sample_rate)]
-
-                audio_tensor = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, channels, total_samples)
-                audio = AudioInput({
-                    "waveform": audio_tensor,
-                    "sample_rate": int(audio_stream.sample_rate) if audio_stream.sample_rate else 1,
-                })
+        try:
+            container.seek(0)  # Reset the container to the beginning
+            for stream in container.streams:
+                if stream.type != 'audio':
+                    continue
+                assert isinstance(stream, av.AudioStream)
+                audio_frames = []
+                for packet in container.demux(stream):
+                    for frame in packet.decode():
+                        assert isinstance(frame, av.AudioFrame)
+                        audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
+                if len(audio_frames) > 0:
+                    audio_data = np.concatenate(audio_frames, axis=1)  # shape: (channels, total_samples)
+                    audio_tensor = torch.from_numpy(audio_data).unsqueeze(0)  # shape: (1, channels, total_samples)
+                    audio = AudioInput({
+                        "waveform": audio_tensor,
+                        "sample_rate": int(stream.sample_rate) if stream.sample_rate else 1,
+                    })
+        except StopIteration:
+            pass  # No audio stream
 
         metadata = container.metadata
         return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
@@ -309,7 +250,7 @@ class VideoFromFile(VideoInput):
         path: str | io.BytesIO,
         format: VideoContainer = VideoContainer.AUTO,
         codec: VideoCodec = VideoCodec.AUTO,
-        metadata: Optional[dict] = None,
+        metadata: Optional[dict] = None
     ):
         if isinstance(self.__file, io.BytesIO):
             self.__file.seek(0)  # Reset the BytesIO object to the beginning
@@ -321,14 +262,15 @@ class VideoFromFile(VideoInput):
                 reuse_streams = False
             if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None:
                 reuse_streams = False
-            if self.__start_time or self.__duration:
-                reuse_streams = False
 
             if not reuse_streams:
                 components = self.get_components_internal(container)
                 video = VideoFromComponents(components)
                 return video.save_to(
-                    path, format=format, codec=codec, metadata=metadata
+                    path,
+                    format=format,
+                    codec=codec,
+                    metadata=metadata
                 )
 
             streams = container.streams
@@ -362,21 +304,10 @@ class VideoFromFile(VideoInput):
                         output_container.mux(packet)
 
     def _get_first_video_stream(self, container: InputContainer):
-        if len(container.streams.video):
-            return container.streams.video[0]
-        raise ValueError(f"No video stream found in file '{self.__file}'")
-
-    def as_trimmed(
-        self, start_time: float = 0, duration: float = 0, strict_duration: bool = True
-    ) -> VideoInput | None:
-        trimmed = VideoFromFile(
-            self.get_stream_source(),
-            start_time=start_time + self.__start_time,
-            duration=duration,
-        )
-        if trimmed.get_duration() < duration and strict_duration:
-            return None
-        return trimmed
+        video_stream = next((s for s in container.streams if s.type == "video"), None)
+        if video_stream is None:
+            raise ValueError(f"No video stream found in file '{self.__file}'")
+        return video_stream
 
 
 class VideoFromComponents(VideoInput):
@@ -391,7 +322,7 @@ class VideoFromComponents(VideoInput):
         return VideoComponents(
             images=self.__components.images,
             audio=self.__components.audio,
-            frame_rate=self.__components.frame_rate,
+            frame_rate=self.__components.frame_rate
         )
 
     def save_to(
@@ -399,7 +330,7 @@ class VideoFromComponents(VideoInput):
         path: str,
         format: VideoContainer = VideoContainer.AUTO,
         codec: VideoCodec = VideoCodec.AUTO,
-        metadata: Optional[dict] = None,
+        metadata: Optional[dict] = None
     ):
         if format != VideoContainer.AUTO and format != VideoContainer.MP4:
             raise ValueError("Only MP4 format is supported for now")
@@ -426,10 +357,7 @@ class VideoFromComponents(VideoInput):
             audio_stream: Optional[av.AudioStream] = None
             if self.__components.audio:
                 audio_sample_rate = int(self.__components.audio['sample_rate'])
-                waveform = self.__components.audio['waveform']
-                waveform = waveform[0, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])]
-                layout = {1: 'mono', 2: 'stereo', 6: '5.1'}.get(waveform.shape[0], 'stereo')
-                audio_stream = output.add_stream('aac', rate=audio_sample_rate, layout=layout)
+                audio_stream = output.add_stream('aac', rate=audio_sample_rate)
 
             # Encode video
             for i, frame in enumerate(self.__components.images):
@@ -444,21 +372,12 @@ class VideoFromComponents(VideoInput):
             output.mux(packet)
 
             if audio_stream and self.__components.audio:
-                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().numpy(), format='fltp', layout=layout)
+                waveform = self.__components.audio['waveform']
+                waveform = waveform[:, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])]
+                frame = av.AudioFrame.from_ndarray(waveform.movedim(2, 1).reshape(1, -1).float().cpu().numpy(), format='flt', layout='mono' if waveform.shape[1] == 1 else 'stereo')
                 frame.sample_rate = audio_sample_rate
                 frame.pts = 0
                 output.mux(audio_stream.encode(frame))
 
                 # Flush encoder
                 output.mux(audio_stream.encode(None))
-
-    def as_trimmed(
-        self,
-        start_time: float | None = None,
-        duration: float | None = None,
-        strict_duration: bool = True,
-    ) -> VideoInput | None:
-        if self.get_duration() < start_time + duration:
-            return None
-        #TODO Consider tracking duration and trimming at time of save?
-        return VideoFromFile(self.get_stream_source(), start_time=start_time, duration=duration)

comfy_api_nodes/apis/__init__.py

@@ -1197,6 +1197,12 @@ class KlingImageGenImageReferenceType(str, Enum):
     face = 'face'
 
 
+class KlingImageGenModelName(str, Enum):
+    kling_v1 = 'kling-v1'
+    kling_v1_5 = 'kling-v1-5'
+    kling_v2 = 'kling-v2'
+
+
 class KlingImageGenerationsRequest(BaseModel):
     aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9'
     callback_url: Optional[AnyUrl] = Field(
@@ -1212,7 +1218,7 @@ class KlingImageGenerationsRequest(BaseModel):
         0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0
     )
     image_reference: Optional[KlingImageGenImageReferenceType] = None
-    model_name: str = Field(...)
+    model_name: Optional[KlingImageGenModelName] = 'kling-v1'
     n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9)
     negative_prompt: Optional[str] = Field(
         None, description='Negative text prompt', max_length=200

comfy_api_nodes/apis/kling.py

@@ -1,22 +1,12 @@
 from pydantic import BaseModel, Field
 
 
-class MultiPromptEntry(BaseModel):
-    index: int = Field(...)
-    prompt: str = Field(...)
-    duration: str = Field(...)
-
-
 class OmniProText2VideoRequest(BaseModel):
     model_name: str = Field(..., description="kling-video-o1")
     aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
     duration: str = Field(..., description="'5' or '10'")
     prompt: str = Field(...)
     mode: str = Field("pro")
-    multi_shot: bool | None = Field(None)
-    multi_prompt: list[MultiPromptEntry] | None = Field(None)
-    shot_type: str | None = Field(None)
-    sound: str = Field(..., description="'on' or 'off'")
 
 
 class OmniParamImage(BaseModel):
@@ -36,10 +26,6 @@ class OmniProFirstLastFrameRequest(BaseModel):
     duration: str = Field(..., description="'5' or '10'")
     prompt: str = Field(...)
     mode: str = Field("pro")
-    sound: str | None = Field(None, description="'on' or 'off'")
-    multi_shot: bool | None = Field(None)
-    multi_prompt: list[MultiPromptEntry] | None = Field(None)
-    shot_type: str | None = Field(None)
 
 
 class OmniProReferences2VideoRequest(BaseModel):
@@ -52,10 +38,6 @@ class OmniProReferences2VideoRequest(BaseModel):
     duration: str | None = Field(..., description="From 3 to 10.")
     prompt: str = Field(...)
     mode: str = Field("pro")
-    sound: str | None = Field(None, description="'on' or 'off'")
-    multi_shot: bool | None = Field(None)
-    multi_prompt: list[MultiPromptEntry] | None = Field(None)
-    shot_type: str | None = Field(None)
 
 
 class TaskStatusVideoResult(BaseModel):
@@ -72,7 +54,6 @@ class TaskStatusImageResult(BaseModel):
 class TaskStatusResults(BaseModel):
     videos: list[TaskStatusVideoResult] | None = Field(None)
     images: list[TaskStatusImageResult] | None = Field(None)
-    series_images: list[TaskStatusImageResult] | None = Field(None)
 
 
 class TaskStatusResponseData(BaseModel):
@@ -96,42 +77,31 @@ class OmniImageParamImage(BaseModel):
 
 
 class OmniProImageRequest(BaseModel):
-    model_name: str = Field(...)
-    resolution: str = Field(...)
+    model_name: str = Field(..., description="kling-image-o1")
+    resolution: str = Field(..., description="'1k' or '2k'")
     aspect_ratio: str | None = Field(...)
     prompt: str = Field(...)
     mode: str = Field("pro")
     n: int | None = Field(1, le=9)
     image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
-    result_type: str | None = Field(None, description="Set to 'series' for series generation")
-    series_amount: int | None = Field(None, ge=2, le=9, description="Number of images in a series")
 
 
 class TextToVideoWithAudioRequest(BaseModel):
-    model_name: str = Field(...)
+    model_name: str = Field(..., description="kling-v2-6")
     aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
-    duration: str = Field(...)
-    prompt: str | None = Field(...)
-    negative_prompt: str | None = Field(None)
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
     mode: str = Field("pro")
     sound: str = Field(..., description="'on' or 'off'")
-    multi_shot: bool | None = Field(None)
-    multi_prompt: list[MultiPromptEntry] | None = Field(None)
-    shot_type: str | None = Field(None)
 
 
 class ImageToVideoWithAudioRequest(BaseModel):
-    model_name: str = Field(...)
+    model_name: str = Field(..., description="kling-v2-6")
     image: str = Field(...)
-    image_tail: str | None = Field(None)
-    duration: str = Field(...)
-    prompt: str | None = Field(...)
-    negative_prompt: str | None = Field(None)
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
     mode: str = Field("pro")
     sound: str = Field(..., description="'on' or 'off'")
-    multi_shot: bool | None = Field(None)
-    multi_prompt: list[MultiPromptEntry] | None = Field(None)
-    shot_type: str | None = Field(None)
 
 
 class MotionControlRequest(BaseModel):

comfy_api_nodes/nodes_magnific.py

@@ -30,30 +30,6 @@ from comfy_api_nodes.util import (
     validate_image_dimensions,
 )
 
-_EUR_TO_USD = 1.19
-
-
-def _tier_price_eur(megapixels: float) -> float:
-    """Price in EUR for a single Magnific upscaling step based on input megapixels."""
-    if megapixels <= 1.3:
-        return 0.143
-    if megapixels <= 3.0:
-        return 0.286
-    if megapixels <= 6.4:
-        return 0.429
-    return 1.716
-
-
-def _calculate_magnific_upscale_price_usd(width: int, height: int, scale: int) -> float:
-    """Calculate total Magnific upscale price in USD for given input dimensions and scale factor."""
-    num_steps = int(math.log2(scale))
-    total_eur = 0.0
-    pixels = width * height
-    for _ in range(num_steps):
-        total_eur += _tier_price_eur(pixels / 1_000_000)
-        pixels *= 4
-    return round(total_eur * _EUR_TO_USD, 2)
-
 
 class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
     @classmethod
@@ -127,20 +103,11 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["scale_factor", "auto_downscale"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
                 expr="""
                 (
-                  $ad := widgets.auto_downscale;
-                  $mins := $ad
-                    ? {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.515}
-                    : {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.844};
-                  $maxs := {"2x": 0.515, "4x": 0.844, "8x": 1.015, "16x": 1.187};
-                  {
-                    "type": "range_usd",
-                    "min_usd": $lookup($mins, widgets.scale_factor),
-                    "max_usd": $lookup($maxs, widgets.scale_factor),
-                    "format": { "approximate": true }
-                  }
+                  $max := widgets.scale_factor = "2x" ? 1.326 : 1.657;
+                  {"type": "range_usd", "min_usd": 0.11, "max_usd": $max}
                 )
                 """,
             ),
@@ -201,10 +168,6 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
                     f"Use a smaller input image or lower scale factor."
                 )
 
-        final_height, final_width = get_image_dimensions(image)
-        actual_scale = int(scale_factor.rstrip("x"))
-        price_usd = _calculate_magnific_upscale_price_usd(final_width, final_height, actual_scale)
-
         initial_res = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler", method="POST"),
@@ -226,7 +189,6 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler/{initial_res.task_id}"),
             response_model=TaskResponse,
             status_extractor=lambda x: x.status,
-            price_extractor=lambda _: price_usd,
             poll_interval=10.0,
             max_poll_attempts=480,
         )
@@ -295,14 +257,8 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
                 expr="""
                 (
-                  $mins := {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.844};
-                  $maxs := {"2x": 2.045, "4x": 2.545, "8x": 2.889, "16x": 3.06};
-                  {
-                    "type": "range_usd",
-                    "min_usd": $lookup($mins, widgets.scale_factor),
-                    "max_usd": $lookup($maxs, widgets.scale_factor),
-                    "format": { "approximate": true }
-                  }
+                  $max := widgets.scale_factor = "2x" ? 1.326 : 1.657;
+                  {"type": "range_usd", "min_usd": 0.11, "max_usd": $max}
                 )
                 """,
             ),
@@ -365,9 +321,6 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
                     f"Use a smaller input image or lower scale factor."
                 )
 
-        final_height, final_width = get_image_dimensions(image)
-        price_usd = _calculate_magnific_upscale_price_usd(final_width, final_height, requested_scale)
-
         initial_res = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler-precision-v2", method="POST"),
@@ -386,7 +339,6 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler-precision-v2/{initial_res.task_id}"),
             response_model=TaskResponse,
             status_extractor=lambda x: x.status,
-            price_extractor=lambda _: price_usd,
             poll_interval=10.0,
             max_poll_attempts=480,
         )
@@ -925,8 +877,8 @@ class MagnificExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
-            MagnificImageUpscalerCreativeNode,
-            MagnificImageUpscalerPreciseV2Node,
+            # MagnificImageUpscalerCreativeNode,
+            # MagnificImageUpscalerPreciseV2Node,
             MagnificImageStyleTransferNode,
             MagnificImageRelightNode,
             MagnificImageSkinEnhancerNode,

comfy_api_nodes/nodes_moonvalley.py

@@ -219,8 +219,8 @@ class MoonvalleyImg2VideoNode(IO.ComfyNode):
                 ),
                 IO.Int.Input(
                     "steps",
-                    default=80,
-                    min=75,  # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
+                    default=33,
+                    min=1,
                     max=100,
                     step=1,
                     tooltip="Number of denoising steps",
@@ -340,8 +340,8 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
                 ),
                 IO.Int.Input(
                     "steps",
-                    default=60,
-                    min=60,  # steps should be greater or equal to cooldown_steps(36) + warmup_steps(24)
+                    default=33,
+                    min=1,
                     max=100,
                     step=1,
                     display_mode=IO.NumberDisplay.number,
@@ -370,7 +370,7 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
         video: Input.Video | None = None,
         control_type: str = "Motion Transfer",
         motion_intensity: int | None = 100,
-        steps=60,
+        steps=33,
         prompt_adherence=4.5,
     ) -> IO.NodeOutput:
         validated_video = validate_video_to_video_input(video)
@@ -465,8 +465,8 @@ class MoonvalleyTxt2VideoNode(IO.ComfyNode):
                 ),
                 IO.Int.Input(
                     "steps",
-                    default=80,
-                    min=75,  # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
+                    default=33,
+                    min=1,
                     max=100,
                     step=1,
                     tooltip="Inference steps",

comfy_api_nodes/util/client.py

@@ -143,9 +143,9 @@ async def poll_op(
     poll_interval: float = 5.0,
     max_poll_attempts: int = 160,
     timeout_per_poll: float = 120.0,
-    max_retries_per_poll: int = 10,
+    max_retries_per_poll: int = 3,
     retry_delay_per_poll: float = 1.0,
-    retry_backoff_per_poll: float = 1.4,
+    retry_backoff_per_poll: float = 2.0,
     estimated_duration: int | None = None,
     cancel_endpoint: ApiEndpoint | None = None,
     cancel_timeout: float = 10.0,
@@ -240,9 +240,9 @@ async def poll_op_raw(
     poll_interval: float = 5.0,
     max_poll_attempts: int = 160,
     timeout_per_poll: float = 120.0,
-    max_retries_per_poll: int = 10,
+    max_retries_per_poll: int = 3,
     retry_delay_per_poll: float = 1.0,
-    retry_backoff_per_poll: float = 1.4,
+    retry_backoff_per_poll: float = 2.0,
     estimated_duration: int | None = None,
     cancel_endpoint: ApiEndpoint | None = None,
     cancel_timeout: float = 10.0,

comfy_execution/jobs.py

@@ -20,60 +20,10 @@ class JobStatus:
 
 
 # Media types that can be previewed in the frontend
-PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d'})
+PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio'})
 
 # 3D file extensions for preview fallback (no dedicated media_type exists)
-THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
-
-
-def has_3d_extension(filename: str) -> bool:
-    lower = filename.lower()
-    return any(lower.endswith(ext) for ext in THREE_D_EXTENSIONS)
-
-
-def normalize_output_item(item):
-    """Normalize a single output list item for the jobs API.
-
-    Returns the normalized item, or None to exclude it.
-    String items with 3D extensions become {filename, type, subfolder} dicts.
-    """
-    if item is None:
-        return None
-    if isinstance(item, str):
-        if has_3d_extension(item):
-            return {'filename': item, 'type': 'output', 'subfolder': '', 'mediaType': '3d'}
-        return None
-    if isinstance(item, dict):
-        return item
-    return None
-
-
-def normalize_outputs(outputs: dict) -> dict:
-    """Normalize raw node outputs for the jobs API.
-
-    Transforms string 3D filenames into file output dicts and removes
-    None items. All other items (non-3D strings, dicts, etc.) are
-    preserved as-is.
-    """
-    normalized = {}
-    for node_id, node_outputs in outputs.items():
-        if not isinstance(node_outputs, dict):
-            normalized[node_id] = node_outputs
-            continue
-        normalized_node = {}
-        for media_type, items in node_outputs.items():
-            if media_type == 'animated' or not isinstance(items, list):
-                normalized_node[media_type] = items
-                continue
-            normalized_items = []
-            for item in items:
-                if item is None:
-                    continue
-                norm = normalize_output_item(item)
-                normalized_items.append(norm if norm is not None else item)
-            normalized_node[media_type] = normalized_items
-        normalized[node_id] = normalized_node
-    return normalized
+THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb'})
 
 
 def _extract_job_metadata(extra_data: dict) -> tuple[Optional[int], Optional[str]]:
@@ -95,9 +45,9 @@ def is_previewable(media_type: str, item: dict) -> bool:
     Maintains backwards compatibility with existing logic.
 
     Priority:
-    1. media_type is 'images', 'video', 'audio', or '3d'
+    1. media_type is 'images', 'video', or 'audio'
     2. format field starts with 'video/' or 'audio/'
-    3. filename has a 3D extension (.obj, .fbx, .gltf, .glb, .usdz)
+    3. filename has a 3D extension (.obj, .fbx, .gltf, .glb)
     """
     if media_type in PREVIEWABLE_MEDIA_TYPES:
         return True
@@ -189,7 +139,7 @@ def normalize_history_item(prompt_id: str, history_item: dict, include_outputs:
     })
 
     if include_outputs:
-        job['outputs'] = normalize_outputs(outputs)
+        job['outputs'] = outputs
         job['execution_status'] = status_info
         job['workflow'] = {
             'prompt': prompt,
@@ -221,23 +171,18 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                 continue
 
             for item in items:
-                normalized = normalize_output_item(item)
-                if normalized is None:
-                    continue
-
                 count += 1
 
-                if preview_output is not None:
+                if not isinstance(item, dict):
                     continue
 
-                if isinstance(normalized, dict) and is_previewable(media_type, normalized):
+                if preview_output is None and is_previewable(media_type, item):
                     enriched = {
-                        **normalized,
+                        **item,
                         'nodeId': node_id,
+                        'mediaType': media_type
                     }
-                    if 'mediaType' not in normalized:
-                        enriched['mediaType'] = media_type
-                    if normalized.get('type') == 'output':
+                    if item.get('type') == 'output':
                         preview_output = enriched
                     elif fallback_preview is None:
                         fallback_preview = enriched

comfy_execution/progress.py

@@ -164,7 +164,21 @@ class WebUIProgressHandler(ProgressHandler):
         if self.server_instance is None:
             return
 
-        active_nodes = self.registry.get_serialized_state()
+        # Only send info for non-pending nodes
+        active_nodes = {
+            node_id: {
+                "value": state["value"],
+                "max": state["max"],
+                "state": state["state"].value,
+                "node_id": node_id,
+                "prompt_id": prompt_id,
+                "display_node_id": self.registry.dynprompt.get_display_node_id(node_id),
+                "parent_node_id": self.registry.dynprompt.get_parent_node_id(node_id),
+                "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+            }
+            for node_id, state in nodes.items()
+            if state["state"] != NodeState.Pending
+        }
 
         # Send a combined progress_state message with all node states
         # Include client_id to ensure message is only sent to the initiating client
@@ -300,24 +314,6 @@ class ProgressRegistry:
             if handler.enabled:
                 handler.finish_handler(node_id, entry, self.prompt_id)
 
-    def get_serialized_state(self) -> Dict[str, dict]:
-        """Return current node progress as a dict suitable for WS progress_state."""
-        active: Dict[str, dict] = {}
-        for nid, state in self.nodes.items():
-            if state["state"] == NodeState.Pending:
-                continue
-            active[nid] = {
-                "value": state["value"],
-                "max": state["max"],
-                "state": state["state"].value,
-                "node_id": nid,
-                "prompt_id": self.prompt_id,
-                "display_node_id": self.dynprompt.get_display_node_id(nid),
-                "parent_node_id": self.dynprompt.get_parent_node_id(nid),
-                "real_node_id": self.dynprompt.get_real_node_id(nid),
-            }
-        return active
-
     def reset_handlers(self) -> None:
         """Reset all handlers"""
         for handler in self.handlers.values():

comfy_extras/nodes_ace.py

@@ -49,14 +49,13 @@ class TextEncodeAceStepAudio15(io.ComfyNode):
                 io.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
                 io.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
                 io.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
-                io.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[io.Conditioning.Output()],
         )
 
     @classmethod
-    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> io.NodeOutput:
-        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p)
+    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k) -> io.NodeOutput:
+        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k)
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         return io.NodeOutput(conditioning)
 

comfy_extras/nodes_custom_sampler.py

@@ -622,7 +622,6 @@ class SamplerSASolver(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSASolver",
-            search_aliases=["sde"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
@@ -667,7 +666,6 @@ class SamplerSEEDS2(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSEEDS2",
-            search_aliases=["sde", "exp heun"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),

comfy_extras/nodes_easycache.py

@@ -9,14 +9,6 @@ if TYPE_CHECKING:
     from uuid import UUID
 
 
-def _extract_tensor(data, output_channels):
-    """Extract tensor from data, handling both single tensors and lists."""
-    if isinstance(data, list):
-        # LTX2 AV tensors: [video, audio]
-        return data[0][:, :output_channels], data[1][:, :output_channels]
-    return data[:, :output_channels], None
-
-
 def easycache_forward_wrapper(executor, *args, **kwargs):
     # get values from args
     transformer_options: dict[str] = args[-1]
@@ -25,7 +17,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
         if not transformer_options:
             transformer_options = args[-2]
     easycache: EasyCacheHolder = transformer_options["easycache"]
-    x, ax = _extract_tensor(args[0], easycache.output_channels)
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
     sigmas = transformer_options["sigmas"]
     uuids = transformer_options["uuids"]
     if sigmas is not None and easycache.is_past_end_timestep(sigmas):
@@ -43,11 +35,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
         if easycache.skip_current_step and can_apply_cache_diff:
             if easycache.verbose:
                 logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
-            result = easycache.apply_cache_diff(x, uuids)
-            if ax is not None:
-                result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
-                return [result, result_audio]
-            return result
+            return easycache.apply_cache_diff(x, uuids)
         if easycache.initial_step:
             easycache.first_cond_uuid = uuids[0]
             has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
@@ -63,18 +51,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
                         logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     # other conds should also skip this step, and instead use their cached values
                     easycache.skip_current_step = True
-                    result = easycache.apply_cache_diff(x, uuids)
-                    if ax is not None:
-                        result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
-                        return [result, result_audio]
-                    return result
+                    return easycache.apply_cache_diff(x, uuids)
                 else:
                     if easycache.verbose:
                         logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     easycache.cumulative_change_rate = 0.0
 
-    full_output: torch.Tensor = executor(*args, **kwargs)
-    output, audio_output = _extract_tensor(full_output, easycache.output_channels)
+    output: torch.Tensor = executor(*args, **kwargs)
     if has_first_cond_uuid and easycache.has_output_prev_norm():
         output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
         if easycache.verbose:
@@ -91,15 +74,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
             logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
     # TODO: allow cache_diff to be offloaded
     easycache.update_cache_diff(output, next_x_prev, uuids)
-    if audio_output is not None:
-        easycache.update_cache_diff(audio_output, ax, uuids, is_audio=True)
     if has_first_cond_uuid:
         easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids)
         easycache.output_prev_subsampled = easycache.subsample(output, uuids)
         easycache.output_prev_norm = output.flatten().abs().mean()
         if easycache.verbose:
             logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
-    return full_output
+    return output
 
 def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     # get values from args
@@ -108,8 +89,8 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
     if easycache.is_past_end_timestep(timestep):
         return executor(*args, **kwargs)
-    x: torch.Tensor = args[0][:, :easycache.output_channels]
     # prepare next x_prev
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
     next_x_prev = x
     input_change = None
     do_easycache = easycache.should_do_easycache(timestep)
@@ -216,7 +197,6 @@ class EasyCacheHolder:
         self.output_prev_subsampled: torch.Tensor = None
         self.output_prev_norm: torch.Tensor = None
         self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {}
-        self.uuid_cache_diffs_audio: dict[UUID, torch.Tensor] = {}
         self.output_change_rates = []
         self.approx_output_change_rates = []
         self.total_steps_skipped = 0
@@ -265,21 +245,20 @@ class EasyCacheHolder:
     def can_apply_cache_diff(self, uuids: list[UUID]) -> bool:
         return all(uuid in self.uuid_cache_diffs for uuid in uuids)
 
-    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
-        if self.first_cond_uuid in uuids and not is_audio:
+    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]):
+        if self.first_cond_uuid in uuids:
             self.total_steps_skipped += 1
-        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
         batch_offset = x.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
             # slice out only what is relevant to this cond
             batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)]
             # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
-            if x.shape[1:] != cache_diffs[uuid].shape[1:]:
+            if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]:
                 if not self.allow_mismatch:
                     raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good")
                 slicing = []
                 skip_this_dim = True
-                for dim_u, dim_x in zip(cache_diffs[uuid].shape, x.shape):
+                for dim_u, dim_x in zip(self.uuid_cache_diffs[uuid].shape, x.shape):
                     if skip_this_dim:
                         skip_this_dim = False
                         continue
@@ -291,11 +270,10 @@ class EasyCacheHolder:
                     else:
                         slicing.append(slice(None))
                 batch_slice = batch_slice + slicing
-            x[tuple(batch_slice)] += cache_diffs[uuid].to(x.device)
+            x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device)
         return x
 
-    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
-        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
+    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
         # if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
         if output.shape[1:] != x.shape[1:]:
             if not self.allow_mismatch:
@@ -315,7 +293,7 @@ class EasyCacheHolder:
         diff = output - x
         batch_offset = diff.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
-            cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
+            self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
 
     def has_first_cond_uuid(self, uuids: list[UUID]) -> bool:
         return self.first_cond_uuid in uuids
@@ -346,8 +324,6 @@ class EasyCacheHolder:
         self.output_prev_norm = None
         del self.uuid_cache_diffs
         self.uuid_cache_diffs = {}
-        del self.uuid_cache_diffs_audio
-        self.uuid_cache_diffs_audio = {}
         self.total_steps_skipped = 0
         self.state_metadata = None
         return self

comfy_extras/nodes_latent.py

@@ -391,9 +391,8 @@ class LatentOperationTonemapReinhard(io.ComfyNode):
             latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
             normalized_latent = latent / latent_vector_magnitude
 
-            dims = list(range(1, latent_vector_magnitude.ndim))
-            mean = torch.mean(latent_vector_magnitude, dim=dims, keepdim=True)
-            std = torch.std(latent_vector_magnitude, dim=dims, keepdim=True)
+            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
 
             top = (std * 5 + mean) * multiplier
 

comfy_extras/nodes_train.py

@@ -4,7 +4,6 @@ import os
 import numpy as np
 import safetensors
 import torch
-import torch.nn as nn
 import torch.utils.checkpoint
 from tqdm.auto import trange
 from PIL import Image, ImageDraw, ImageFont
@@ -28,11 +27,6 @@ class TrainGuider(comfy_extras.nodes_custom_sampler.Guider_Basic):
     """
     CFGGuider with modifications for training specific logic
     """
-
-    def __init__(self, *args, offloading=False, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.offloading = offloading
-
     def outer_sample(
         self,
         noise,
@@ -51,11 +45,9 @@ class TrainGuider(comfy_extras.nodes_custom_sampler.Guider_Basic):
                 noise.shape,
                 self.conds,
                 self.model_options,
-                force_full_load=not self.offloading,
-                force_offload=self.offloading,
+                force_full_load=True,  # mirror behavior in TrainLoraNode.execute() to keep model loaded
             )
         )
-        torch.cuda.empty_cache()
         device = self.model_patcher.load_device
 
         if denoise_mask is not None:
@@ -412,97 +404,16 @@ def find_all_highest_child_module_with_forward(
     return result
 
 
-def find_modules_at_depth(
-    model: nn.Module, depth: int = 1, result=None, current_depth=0, name=None
-) -> list[nn.Module]:
-    """
-    Find modules at a specific depth level for gradient checkpointing.
-
-    Args:
-        model: The model to search
-        depth: Target depth level (1 = top-level blocks, 2 = their children, etc.)
-        result: Accumulator for results
-        current_depth: Current recursion depth
-        name: Current module name for logging
-
-    Returns:
-        List of modules at the target depth
-    """
-    if result is None:
-        result = []
-    name = name or "root"
-
-    # Skip container modules (they don't have meaningful forward)
-    is_container = isinstance(model, (nn.ModuleList, nn.Sequential, nn.ModuleDict))
-    has_forward = hasattr(model, "forward") and not is_container
-
-    if has_forward:
-        current_depth += 1
-        if current_depth == depth:
-            result.append(model)
-            logging.debug(f"Found module at depth {depth}: {name} ({model.__class__.__name__})")
-            return result
-
-    # Recurse into children
-    for next_name, child in model.named_children():
-        find_modules_at_depth(child, depth, result, current_depth, f"{name}.{next_name}")
-
-    return result
-
-
-class OffloadCheckpointFunction(torch.autograd.Function):
-    """
-    Gradient checkpointing that works with weight offloading.
-
-    Forward: no_grad -> compute -> weights can be freed
-    Backward: enable_grad -> recompute -> backward -> weights can be freed
-
-    For single input, single output modules (Linear, Conv*).
-    """
-
-    @staticmethod
-    def forward(ctx, x: torch.Tensor, forward_fn):
-        ctx.save_for_backward(x)
-        ctx.forward_fn = forward_fn
-        with torch.no_grad():
-            return forward_fn(x)
-
-    @staticmethod
-    def backward(ctx, grad_out: torch.Tensor):
-        x, = ctx.saved_tensors
-        forward_fn = ctx.forward_fn
-
-        # Clear context early
-        ctx.forward_fn = None
-
-        with torch.enable_grad():
-            x_detached = x.detach().requires_grad_(True)
-            y = forward_fn(x_detached)
-            y.backward(grad_out)
-            grad_x = x_detached.grad
-
-        # Explicit cleanup
-        del y, x_detached, forward_fn
-
-        return grad_x, None
-
-
-def patch(m, offloading=False):
+def patch(m):
     if not hasattr(m, "forward"):
         return
     org_forward = m.forward
 
-    # Branch 1: Linear/Conv* -> offload-compatible checkpoint (single input/output)
-    if offloading and isinstance(m, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)):
-        def checkpointing_fwd(x):
-            return OffloadCheckpointFunction.apply(x, org_forward)
-    # Branch 2: Others -> standard checkpoint
-    else:
-        def fwd(args, kwargs):
-            return org_forward(*args, **kwargs)
+    def fwd(args, kwargs):
+        return org_forward(*args, **kwargs)
 
-        def checkpointing_fwd(*args, **kwargs):
-            return torch.utils.checkpoint.checkpoint(fwd, args, kwargs, use_reentrant=False)
+    def checkpointing_fwd(*args, **kwargs):
+        return torch.utils.checkpoint.checkpoint(fwd, args, kwargs, use_reentrant=False)
 
     m.org_forward = org_forward
     m.forward = checkpointing_fwd
@@ -1025,18 +936,6 @@ class TrainLoraNode(io.ComfyNode):
                     default=True,
                     tooltip="Use gradient checkpointing for training.",
                 ),
-                io.Int.Input(
-                    "checkpoint_depth",
-                    default=1,
-                    min=1,
-                    max=5,
-                    tooltip="Depth level for gradient checkpointing.",
-                ),
-                io.Boolean.Input(
-                    "offloading",
-                    default=False,
-                    tooltip="Depth level for gradient checkpointing.",
-                ),
                 io.Combo.Input(
                     "existing_lora",
                     options=folder_paths.get_filename_list("loras") + ["[None]"],
@@ -1083,8 +982,6 @@ class TrainLoraNode(io.ComfyNode):
         lora_dtype,
         algorithm,
         gradient_checkpointing,
-        checkpoint_depth,
-        offloading,
         existing_lora,
         bucket_mode,
         bypass_mode,
@@ -1103,8 +1000,6 @@ class TrainLoraNode(io.ComfyNode):
         lora_dtype = lora_dtype[0]
         algorithm = algorithm[0]
         gradient_checkpointing = gradient_checkpointing[0]
-        offloading = offloading[0]
-        checkpoint_depth = checkpoint_depth[0]
         existing_lora = existing_lora[0]
         bucket_mode = bucket_mode[0]
         bypass_mode = bypass_mode[0]
@@ -1159,18 +1054,16 @@ class TrainLoraNode(io.ComfyNode):
 
             # Setup gradient checkpointing
             if gradient_checkpointing:
-                modules_to_patch = find_modules_at_depth(
-                    mp.model.diffusion_model, depth=checkpoint_depth
-                )
-                logging.info(f"Gradient checkpointing: patching {len(modules_to_patch)} modules at depth {checkpoint_depth}")
-                for m in modules_to_patch:
-                    patch(m, offloading=offloading)
+                for m in find_all_highest_child_module_with_forward(
+                    mp.model.diffusion_model
+                ):
+                    patch(m)
 
             torch.cuda.empty_cache()
             # With force_full_load=False we should be able to have offloading
             # But for offloading in training we need custom AutoGrad hooks for fwd/bwd
             comfy.model_management.load_models_gpu(
-                [mp], memory_required=1e20, force_full_load=not offloading
+                [mp], memory_required=1e20, force_full_load=True
             )
             torch.cuda.empty_cache()
 
@@ -1207,7 +1100,7 @@ class TrainLoraNode(io.ComfyNode):
                 )
 
             # Setup guider
-            guider = TrainGuider(mp, offloading=offloading)
+            guider = TrainGuider(mp)
             guider.set_conds(positive)
 
             # Inject bypass hooks if bypass mode is enabled
@@ -1220,7 +1113,6 @@ class TrainLoraNode(io.ComfyNode):
 
             # Run training loop
             try:
-                comfy.model_management.in_training = True
                 _run_training_loop(
                     guider,
                     train_sampler,
@@ -1231,7 +1123,6 @@ class TrainLoraNode(io.ComfyNode):
                     multi_res,
                 )
             finally:
-                comfy.model_management.in_training = False
                 # Eject bypass hooks if they were injected
                 if bypass_injections is not None:
                     for injection in bypass_injections:
@@ -1241,20 +1132,19 @@ class TrainLoraNode(io.ComfyNode):
                     unpatch(m)
             del train_sampler, optimizer
 
-            for param in lora_sd:
-                lora_sd[param] = lora_sd[param].to(lora_dtype).detach()
-
+            # Finalize adapters
             for adapter in all_weight_adapters:
                 adapter.requires_grad_(False)
-                del adapter
-            del all_weight_adapters
+
+            for param in lora_sd:
+                lora_sd[param] = lora_sd[param].to(lora_dtype)
 
             # mp in train node is highly specialized for training
             # use it in inference will result in bad behavior so we don't return it
             return io.NodeOutput(lora_sd, loss_map, steps + existing_steps)
 
 
-class LoraModelLoader(io.ComfyNode):
+class LoraModelLoader(io.ComfyNode):#
     @classmethod
     def define_schema(cls):
         return io.Schema(
@@ -1276,11 +1166,6 @@ class LoraModelLoader(io.ComfyNode):
                     max=100.0,
                     tooltip="How strongly to modify the diffusion model. This value can be negative.",
                 ),
-                io.Boolean.Input(
-                    "bypass",
-                    default=False,
-                    tooltip="When enabled, applies LoRA in bypass mode without modifying base model weights. Useful for training and when model weights are offloaded.",
-                ),
             ],
             outputs=[
                 io.Model.Output(
@@ -1290,18 +1175,13 @@ class LoraModelLoader(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, model, lora, strength_model, bypass=False):
+    def execute(cls, model, lora, strength_model):
         if strength_model == 0:
             return io.NodeOutput(model)
 
-        if bypass:
-            model_lora, _ = comfy.sd.load_bypass_lora_for_models(
-                model, None, lora, strength_model, 0
-            )
-        else:
-            model_lora, _ = comfy.sd.load_lora_for_models(
-                model, None, lora, strength_model, 0
-            )
+        model_lora, _ = comfy.sd.load_lora_for_models(
+            model, None, lora, strength_model, 0
+        )
         return io.NodeOutput(model_lora)
 
 

comfy_extras/nodes_video.py

@@ -202,56 +202,6 @@ class LoadVideo(io.ComfyNode):
 
         return True
 
-class VideoSlice(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="Video Slice",
-            display_name="Video Slice",
-            search_aliases=[
-                "trim video duration",
-                "skip first frames",
-                "frame load cap",
-                "start time",
-            ],
-            category="image/video",
-            inputs=[
-                io.Video.Input("video"),
-                io.Float.Input(
-                    "start_time",
-                    default=0.0,
-                    max=1e5,
-                    min=-1e5,
-                    step=0.001,
-                    tooltip="Start time in seconds",
-                ),
-                io.Float.Input(
-                    "duration",
-                    default=0.0,
-                    min=0.0,
-                    step=0.001,
-                    tooltip="Duration in seconds, or 0 for unlimited duration",
-                ),
-                io.Boolean.Input(
-                    "strict_duration",
-                    default=False,
-                    tooltip="If True, when the specified duration is not possible, an error will be raised.",
-                ),
-            ],
-            outputs=[
-                io.Video.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, video: io.Video.Type, start_time: float, duration: float, strict_duration: bool) -> io.NodeOutput:
-        trimmed = video.as_trimmed(start_time, duration, strict_duration=strict_duration)
-        if trimmed is not None:
-            return io.NodeOutput(trimmed)
-        raise ValueError(
-            f"Failed to slice video:\nSource duration: {video.get_duration()}\nStart time: {start_time}\nTarget duration: {duration}"
-        )
-
 
 class VideoExtension(ComfyExtension):
     @override
@@ -262,7 +212,6 @@ class VideoExtension(ComfyExtension):
             CreateVideo,
             GetVideoComponents,
             LoadVideo,
-            VideoSlice,
         ]
 
 async def comfy_entrypoint() -> VideoExtension:

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.13.0"
+__version__ = "0.12.3"

execution.py

@@ -13,11 +13,8 @@ from contextlib import nullcontext
 
 import torch
 
-from comfy.cli_args import args
 import comfy.memory_management
 import comfy.model_management
-import comfy_aimdo.model_vbar
-
 from latent_preview import set_preview_method
 import nodes
 from comfy_execution.caching import (
@@ -530,10 +527,8 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                 finally:
                     if allocator is not None:
-                        if args.verbose == "DEBUG":
-                            comfy_aimdo.model_vbar.vbars_analyze()
                         comfy.model_management.reset_cast_buffers()
-                        comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
+                        torch.cuda.synchronize()
 
             if has_pending_tasks:
                 pending_async_nodes[unique_id] = output_data
@@ -701,8 +696,6 @@ class PromptExecutor:
         else:
             self.server.client_id = None
 
-        self.server.current_prompt_id = prompt_id
-
         self.status_messages = []
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
@@ -724,13 +717,10 @@ class PromptExecutor:
             self.add_message("execution_cached",
                           { "nodes": cached_nodes, "prompt_id": prompt_id},
                           broadcast=False)
-            self.server.current_cached_nodes = cached_nodes
             pending_subgraph_results = {}
             pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
             ui_node_outputs = {}
             executed = set()
-            self.server.current_executed_nodes = executed
-            self.server.current_outputs_to_execute = list(execute_outputs)
             execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
             current_outputs = self.caches.outputs.all_node_ids()
             for node_id in list(execute_outputs):
@@ -767,10 +757,6 @@ class PromptExecutor:
                 "meta": meta_outputs,
             }
             self.server.last_node_id = None
-            self.server.current_prompt_id = None
-            self.server.current_outputs_to_execute = None
-            self.server.current_cached_nodes = None
-            self.server.current_executed_nodes = None
             if comfy.model_management.DISABLE_SMART_MEMORY:
                 comfy.model_management.unload_all_models()
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.13.0"
+version = "0.12.3"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.38.13
-comfyui-workflow-templates==0.8.38
-comfyui-embedded-docs==0.4.1
+comfyui-workflow-templates==0.8.31
+comfyui-embedded-docs==0.4.0
 torch
 torchsde
 torchvision
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.1.8
+comfy-aimdo>=0.1.7
 requests
 
 #non essential dependencies:

server.py

@@ -242,10 +242,6 @@ class PromptServer():
         self.routes = routes
         self.last_node_id = None
         self.client_id = None
-        self.current_prompt_id = None
-        self.current_outputs_to_execute = None
-        self.current_cached_nodes = None
-        self.current_executed_nodes = None
 
         self.on_prompt_handlers = []
 
@@ -268,38 +264,9 @@ class PromptServer():
             try:
                 # Send initial state to the new client
                 await self.send("status", {"status": self.get_queue_info(), "sid": sid}, sid)
-                # On reconnect if we are the currently executing client, replay catch-up events
-                if self.client_id == sid and self.current_prompt_id is not None:
-                    await self.send("execution_start", {
-                        "prompt_id": self.current_prompt_id,
-                        "timestamp": int(time.time() * 1000),
-                        "outputs_to_execute": self.current_outputs_to_execute or [],
-                        "executed_node_ids": list(self.current_executed_nodes) if self.current_executed_nodes else [],
-                    }, sid)
-
-                    if self.current_cached_nodes:
-                        await self.send("execution_cached", {
-                            "nodes": self.current_cached_nodes,
-                            "prompt_id": self.current_prompt_id,
-                            "timestamp": int(time.time() * 1000),
-                        }, sid)
-
-                    from comfy_execution.progress import get_progress_state
-                    progress = get_progress_state()
-                    if progress.prompt_id == self.current_prompt_id:
-                        active_nodes = progress.get_serialized_state()
-                        if active_nodes:
-                            await self.send("progress_state", {
-                                "prompt_id": self.current_prompt_id,
-                                "nodes": active_nodes,
-                            }, sid)
-
-                    if self.last_node_id is not None:
-                        await self.send("executing", {
-                            "node": self.last_node_id,
-                            "display_node": self.last_node_id,
-                            "prompt_id": self.current_prompt_id,
-                        }, sid)
+                # On reconnect if we are the currently executing client send the current node
+                if self.client_id == sid and self.last_node_id is not None:
+                    await self.send("executing", { "node": self.last_node_id }, sid)
 
                 # Flag to track if we've received the first message
                 first_message = True

tests/execution/test_jobs.py

@@ -5,11 +5,8 @@ from comfy_execution.jobs import (
     is_previewable,
     normalize_queue_item,
     normalize_history_item,
-    normalize_output_item,
-    normalize_outputs,
     get_outputs_summary,
     apply_sorting,
-    has_3d_extension,
 )
 
 
@@ -38,8 +35,8 @@ class TestIsPreviewable:
     """Unit tests for is_previewable()"""
 
     def test_previewable_media_types(self):
-        """Images, video, audio, 3d media types should be previewable."""
-        for media_type in ['images', 'video', 'audio', '3d']:
+        """Images, video, audio media types should be previewable."""
+        for media_type in ['images', 'video', 'audio']:
             assert is_previewable(media_type, {}) is True
 
     def test_non_previewable_media_types(self):
@@ -49,7 +46,7 @@ class TestIsPreviewable:
 
     def test_3d_extensions_previewable(self):
         """3D file extensions should be previewable regardless of media_type."""
-        for ext in ['.obj', '.fbx', '.gltf', '.glb', '.usdz']:
+        for ext in ['.obj', '.fbx', '.gltf', '.glb']:
             item = {'filename': f'model{ext}'}
             assert is_previewable('files', item) is True
 
@@ -163,7 +160,7 @@ class TestGetOutputsSummary:
 
     def test_3d_files_previewable(self):
         """3D file extensions should be previewable."""
-        for ext in ['.obj', '.fbx', '.gltf', '.glb', '.usdz']:
+        for ext in ['.obj', '.fbx', '.gltf', '.glb']:
             outputs = {
                 'node1': {
                     'files': [{'filename': f'model{ext}', 'type': 'output'}]
@@ -195,64 +192,6 @@ class TestGetOutputsSummary:
         assert preview['mediaType'] == 'images'
         assert preview['subfolder'] == 'outputs'
 
-    def test_string_3d_filename_creates_preview(self):
-        """String items with 3D extensions should synthesize a preview (Preview3D node output).
-        Only the .glb counts — nulls and non-file strings are excluded."""
-        outputs = {
-            'node1': {
-                'result': ['preview3d_abc123.glb', None, None]
-            }
-        }
-        count, preview = get_outputs_summary(outputs)
-        assert count == 1
-        assert preview is not None
-        assert preview['filename'] == 'preview3d_abc123.glb'
-        assert preview['mediaType'] == '3d'
-        assert preview['nodeId'] == 'node1'
-        assert preview['type'] == 'output'
-
-    def test_string_non_3d_filename_no_preview(self):
-        """String items without 3D extensions should not create a preview."""
-        outputs = {
-            'node1': {
-                'result': ['data.json', None]
-            }
-        }
-        count, preview = get_outputs_summary(outputs)
-        assert count == 0
-        assert preview is None
-
-    def test_string_3d_filename_used_as_fallback(self):
-        """String 3D preview should be used when no dict items are previewable."""
-        outputs = {
-            'node1': {
-                'latents': [{'filename': 'latent.safetensors'}],
-            },
-            'node2': {
-                'result': ['model.glb', None]
-            }
-        }
-        count, preview = get_outputs_summary(outputs)
-        assert preview is not None
-        assert preview['filename'] == 'model.glb'
-        assert preview['mediaType'] == '3d'
-
-
-class TestHas3DExtension:
-    """Unit tests for has_3d_extension()"""
-
-    def test_recognized_extensions(self):
-        for ext in ['.obj', '.fbx', '.gltf', '.glb', '.usdz']:
-            assert has_3d_extension(f'model{ext}') is True
-
-    def test_case_insensitive(self):
-        assert has_3d_extension('MODEL.GLB') is True
-        assert has_3d_extension('Scene.GLTF') is True
-
-    def test_non_3d_extensions(self):
-        for name in ['photo.png', 'video.mp4', 'data.json', 'model']:
-            assert has_3d_extension(name) is False
-
 
 class TestApplySorting:
     """Unit tests for apply_sorting()"""
@@ -456,142 +395,3 @@ class TestNormalizeHistoryItem:
             'prompt': {'nodes': {'1': {}}},
             'extra_data': {'create_time': 1234567890, 'client_id': 'abc'},
         }
-
-    def test_include_outputs_normalizes_3d_strings(self):
-        """Detail view should transform string 3D filenames into file output dicts."""
-        history_item = {
-            'prompt': (
-                5,
-                'prompt-3d',
-                {'nodes': {}},
-                {'create_time': 1234567890},
-                ['node1'],
-            ),
-            'status': {'status_str': 'success', 'completed': True, 'messages': []},
-            'outputs': {
-                'node1': {
-                    'result': ['preview3d_abc123.glb', None, None]
-                }
-            },
-        }
-        job = normalize_history_item('prompt-3d', history_item, include_outputs=True)
-
-        assert job['outputs_count'] == 1
-        result_items = job['outputs']['node1']['result']
-        assert len(result_items) == 1
-        assert result_items[0] == {
-            'filename': 'preview3d_abc123.glb',
-            'type': 'output',
-            'subfolder': '',
-            'mediaType': '3d',
-        }
-
-    def test_include_outputs_preserves_dict_items(self):
-        """Detail view normalization should pass dict items through unchanged."""
-        history_item = {
-            'prompt': (
-                5,
-                'prompt-img',
-                {'nodes': {}},
-                {'create_time': 1234567890},
-                ['node1'],
-            ),
-            'status': {'status_str': 'success', 'completed': True, 'messages': []},
-            'outputs': {
-                'node1': {
-                    'images': [
-                        {'filename': 'photo.png', 'type': 'output', 'subfolder': ''},
-                    ]
-                }
-            },
-        }
-        job = normalize_history_item('prompt-img', history_item, include_outputs=True)
-
-        assert job['outputs_count'] == 1
-        assert job['outputs']['node1']['images'] == [
-            {'filename': 'photo.png', 'type': 'output', 'subfolder': ''},
-        ]
-
-
-class TestNormalizeOutputItem:
-    """Unit tests for normalize_output_item()"""
-
-    def test_none_returns_none(self):
-        assert normalize_output_item(None) is None
-
-    def test_string_3d_extension_synthesizes_dict(self):
-        result = normalize_output_item('model.glb')
-        assert result == {'filename': 'model.glb', 'type': 'output', 'subfolder': '', 'mediaType': '3d'}
-
-    def test_string_non_3d_extension_returns_none(self):
-        assert normalize_output_item('data.json') is None
-
-    def test_string_no_extension_returns_none(self):
-        assert normalize_output_item('camera_info_string') is None
-
-    def test_dict_passes_through(self):
-        item = {'filename': 'test.png', 'type': 'output'}
-        assert normalize_output_item(item) is item
-
-    def test_other_types_return_none(self):
-        assert normalize_output_item(42) is None
-        assert normalize_output_item(True) is None
-
-
-class TestNormalizeOutputs:
-    """Unit tests for normalize_outputs()"""
-
-    def test_empty_outputs(self):
-        assert normalize_outputs({}) == {}
-
-    def test_dict_items_pass_through(self):
-        outputs = {
-            'node1': {
-                'images': [{'filename': 'a.png', 'type': 'output'}],
-            }
-        }
-        result = normalize_outputs(outputs)
-        assert result == outputs
-
-    def test_3d_string_synthesized(self):
-        outputs = {
-            'node1': {
-                'result': ['model.glb', None, None],
-            }
-        }
-        result = normalize_outputs(outputs)
-        assert result == {
-            'node1': {
-                'result': [
-                    {'filename': 'model.glb', 'type': 'output', 'subfolder': '', 'mediaType': '3d'},
-                ],
-            }
-        }
-
-    def test_animated_key_preserved(self):
-        outputs = {
-            'node1': {
-                'images': [{'filename': 'a.png', 'type': 'output'}],
-                'animated': [True],
-            }
-        }
-        result = normalize_outputs(outputs)
-        assert result['node1']['animated'] == [True]
-
-    def test_non_dict_node_outputs_preserved(self):
-        outputs = {'node1': 'unexpected_value'}
-        result = normalize_outputs(outputs)
-        assert result == {'node1': 'unexpected_value'}
-
-    def test_none_items_filtered_but_other_types_preserved(self):
-        outputs = {
-            'node1': {
-                'result': ['data.json', None, [1, 2, 3]],
-            }
-        }
-        result = normalize_outputs(outputs)
-        assert result == {
-            'node1': {
-                'result': ['data.json', [1, 2, 3]],
-            }
-        }