Remove unused enable_safetensors parameter from extract_file_metadata

Amp-Thread-ID: https://ampcode.com/threads/T-019ccb0b-2980-74fc-b62f-5fce0f658d8e
Co-authored-by: Amp <amp@ampcode.com>

app/assets/services/hashing.py

@@ -3,12 +3,8 @@ import os
 from contextlib import contextmanager
 from dataclasses import dataclass
 from typing import IO, Any, Callable, Iterator
-import logging
 
-try:
+from blake3 import blake3
-    from blake3 import blake3
-except ModuleNotFoundError:
-    logging.warning("WARNING: blake3 package not installed")
 
 DEFAULT_CHUNK = 8 * 1024 * 1024
 

comfy/ldm/flux/layers.py

@@ -223,19 +223,12 @@ class DoubleStreamBlock(nn.Module):
         del txt_k, img_k
         v = torch.cat((txt_v, img_v), dim=2)
         del txt_v, img_v
-
-        extra_options["img_slice"] = [txt.shape[1], q.shape[2]]
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
-                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
-
         # run actual attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
 
         if "attn1_output_patch" in transformer_patches:
+            extra_options["img_slice"] = [txt.shape[1], attn.shape[1]]
             patch = transformer_patches["attn1_output_patch"]
             for p in patch:
                 attn = p(attn, extra_options)
@@ -328,12 +321,6 @@ class SingleStreamBlock(nn.Module):
         del qkv
         q, k = self.norm(q, k, v)
 
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
-                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
-
         # compute attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v

comfy/ldm/flux/math.py

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

comfy/ldm/flux/model.py

@@ -170,7 +170,7 @@ class Flux(nn.Module):
 
         if "post_input" in patches:
             for p in patches["post_input"]:
-                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
+                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
                 img = out["img"]
                 txt = out["txt"]
                 img_ids = out["img_ids"]

comfy/ldm/modules/attention.py

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

comfy/ldm/modules/diffusionmodules/model.py

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

comfy/ldm/modules/sub_quadratic_attention.py

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

comfy/ldm/qwen_image/model.py

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

comfy/lora.py

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

comfy/model_detection.py

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

comfy/model_management.py

@@ -270,23 +270,6 @@ try:
 except:
     OOM_EXCEPTION = Exception
 
-try:
-    ACCELERATOR_ERROR = torch.AcceleratorError
-except AttributeError:
-    ACCELERATOR_ERROR = RuntimeError
-
-def is_oom(e):
-    if isinstance(e, OOM_EXCEPTION):
-        return True
-    if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or "out of memory" in str(e).lower()):
-        discard_cuda_async_error()
-        return True
-    return False
-
-def raise_non_oom(e):
-    if not is_oom(e):
-        raise e
-
 XFORMERS_VERSION = ""
 XFORMERS_ENABLED_VAE = True
 if args.disable_xformers:
@@ -1280,7 +1263,7 @@ def discard_cuda_async_error():
         b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
         _ = a + b
         synchronize()
-    except RuntimeError:
+    except torch.AcceleratorError:
         #Dump it! We already know about it from the synchronous return
         pass
 

comfy/model_patcher.py

@@ -599,27 +599,6 @@ class ModelPatcher:
 
         return models
 
-    def model_patches_call_function(self, function_name="cleanup", arguments={}):
-        to = self.model_options["transformer_options"]
-        if "patches" in to:
-            patches = to["patches"]
-            for name in patches:
-                patch_list = patches[name]
-                for i in range(len(patch_list)):
-                    if hasattr(patch_list[i], function_name):
-                        getattr(patch_list[i], function_name)(**arguments)
-        if "patches_replace" in to:
-            patches = to["patches_replace"]
-            for name in patches:
-                patch_list = patches[name]
-                for k in patch_list:
-                    if hasattr(patch_list[k], function_name):
-                        getattr(patch_list[k], function_name)(**arguments)
-        if "model_function_wrapper" in self.model_options:
-            wrap_func = self.model_options["model_function_wrapper"]
-            if hasattr(wrap_func, function_name):
-                getattr(wrap_func, function_name)(**arguments)
-
     def model_dtype(self):
         if hasattr(self.model, "get_dtype"):
             return self.model.get_dtype()
@@ -1083,7 +1062,6 @@ class ModelPatcher:
         return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
 
     def cleanup(self):
-        self.model_patches_call_function(function_name="cleanup")
         self.clean_hooks()
         if hasattr(self.model, "current_patcher"):
             self.model.current_patcher = None

comfy/sd.py

@@ -954,8 +954,7 @@ class VAE:
                 if pixel_samples is None:
                     pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 pixel_samples[x:x+batch_number] = out
-        except Exception as e:
+        except model_management.OOM_EXCEPTION:
-            model_management.raise_non_oom(e)
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.
@@ -1030,8 +1029,7 @@ class VAE:
                     samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 samples[x:x + batch_number] = out
 
-        except Exception as e:
+        except model_management.OOM_EXCEPTION:
-            model_management.raise_non_oom(e)
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.

comfy_api_nodes/apis/reve.py

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

comfy_api_nodes/nodes_reve.py

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

comfy_api_nodes/util/client.py

@@ -67,7 +67,6 @@ class _RequestConfig:
     progress_origin_ts: float | None = None
     price_extractor: Callable[[dict[str, Any]], float | None] | None = None
     is_rate_limited: Callable[[int, Any], bool] | None = None
-    response_header_validator: Callable[[dict[str, str]], None] | None = None
 
 
 @dataclass
@@ -203,13 +202,11 @@ async def sync_op_raw(
     monitor_progress: bool = True,
     max_retries_on_rate_limit: int = 16,
     is_rate_limited: Callable[[int, Any], bool] | None = None,
-    response_header_validator: Callable[[dict[str, str]], None] | None = None,
 ) -> dict[str, Any] | bytes:
     """
     Make a single network request.
       - If as_binary=False (default): returns JSON dict (or {'_raw': '<text>'} if non-JSON).
       - If as_binary=True: returns bytes.
-      - response_header_validator: optional callback receiving response headers dict
     """
     if isinstance(data, BaseModel):
         data = data.model_dump(exclude_none=True)
@@ -235,7 +232,6 @@ async def sync_op_raw(
         price_extractor=price_extractor,
         max_retries_on_rate_limit=max_retries_on_rate_limit,
         is_rate_limited=is_rate_limited,
-        response_header_validator=response_header_validator,
     )
     return await _request_base(cfg, expect_binary=as_binary)
 
@@ -773,12 +769,6 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                                     cfg.node_cls, cfg.wait_label, int(now - start_time), cfg.estimated_total
                                 )
                     bytes_payload = bytes(buff)
-                    resp_headers = {k.lower(): v for k, v in resp.headers.items()}
-                    if cfg.price_extractor:
-                        with contextlib.suppress(Exception):
-                            extracted_price = cfg.price_extractor(resp_headers)
-                    if cfg.response_header_validator:
-                        cfg.response_header_validator(resp_headers)
                     operation_succeeded = True
                     final_elapsed_seconds = int(time.monotonic() - start_time)
                     request_logger.log_request_response(
@@ -786,7 +776,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                         request_method=method,
                         request_url=url,
                         response_status_code=resp.status,
-                        response_headers=resp_headers,
+                        response_headers=dict(resp.headers),
                         response_content=bytes_payload,
                     )
                     return bytes_payload

comfy_extras/nodes_lt_upsampler.py

@@ -1,32 +1,32 @@
 from comfy import model_management
-from comfy_api.latest import ComfyExtension, IO
-from typing_extensions import override
 import math
 
-
+class LTXVLatentUpsampler:
-class LTXVLatentUpsampler(IO.ComfyNode):
     """
     Upsamples a video latent by a factor of 2.
     """
 
     @classmethod
-    def define_schema(cls):
+    def INPUT_TYPES(s):
-        return IO.Schema(
+        return {
-            node_id="LTXVLatentUpsampler",
+            "required": {
-            category="latent/video",
+                "samples": ("LATENT",),
-            is_experimental=True,
+                "upscale_model": ("LATENT_UPSCALE_MODEL",),
-            inputs=[
+                "vae": ("VAE",),
-                IO.Latent.Input("samples"),
+            }
-                IO.LatentUpscaleModel.Input("upscale_model"),
+        }
-                IO.Vae.Input("vae"),
-            ],
-            outputs=[
-                IO.Latent.Output(),
-            ],
-        )
 
-    @classmethod
+    RETURN_TYPES = ("LATENT",)
-    def execute(cls, samples, upscale_model, vae) -> IO.NodeOutput:
+    FUNCTION = "upsample_latent"
+    CATEGORY = "latent/video"
+    EXPERIMENTAL = True
+
+    def upsample_latent(
+        self,
+        samples: dict,
+        upscale_model,
+        vae,
+    ) -> tuple:
         """
         Upsample the input latent using the provided model.
 
@@ -34,6 +34,7 @@ class LTXVLatentUpsampler(IO.ComfyNode):
             samples (dict): Input latent samples
             upscale_model (LatentUpsampler): Loaded upscale model
             vae: VAE model for normalization
+            auto_tiling (bool): Whether to automatically tile the input for processing
 
         Returns:
             tuple: Tuple containing the upsampled latent
@@ -66,16 +67,9 @@ class LTXVLatentUpsampler(IO.ComfyNode):
         return_dict = samples.copy()
         return_dict["samples"] = upsampled_latents
         return_dict.pop("noise_mask", None)
-        return IO.NodeOutput(return_dict)
+        return (return_dict,)
-
-    upsample_latent = execute  # TODO: remove
 
 
-class LTXVLatentUpsamplerExtension(ComfyExtension):
+NODE_CLASS_MAPPINGS = {
-    @override
+    "LTXVLatentUpsampler": LTXVLatentUpsampler,
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+}
-        return [LTXVLatentUpsampler]
-
-
-async def comfy_entrypoint() -> LTXVLatentUpsamplerExtension:
-    return LTXVLatentUpsamplerExtension()

comfy_extras/nodes_upscale_model.py

@@ -86,8 +86,7 @@ class ImageUpscaleWithModel(io.ComfyNode):
                     pbar = comfy.utils.ProgressBar(steps)
                     s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
                     oom = False
-                except Exception as e:
+                except model_management.OOM_EXCEPTION as e:
-                    model_management.raise_non_oom(e)
                     tile //= 2
                     if tile < 128:
                         raise e

execution.py

@@ -612,7 +612,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
         logging.error(traceback.format_exc())
         tips = ""
 
-        if comfy.model_management.is_oom(ex):
+        if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
             tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
             logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary()))
             logging.error("Got an OOM, unloading all loaded models.")

main.py

@@ -3,16 +3,14 @@ comfy.options.enable_args_parsing()
 
 import os
 import importlib.util
-import shutil
-import importlib.metadata
 import folder_paths
 import time
 from comfy.cli_args import args, enables_dynamic_vram
 from app.logger import setup_logger
+from app.assets.seeder import asset_seeder
 import itertools
 import utils.extra_config
 from utils.mime_types import init_mime_types
-import faulthandler
 import logging
 import sys
 from comfy_execution.progress import get_progress_state
@@ -27,8 +25,6 @@ if __name__ == "__main__":
 
 setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
 
-faulthandler.enable(file=sys.stderr, all_threads=False)
-
 import comfy_aimdo.control
 
 if enables_dynamic_vram():
@@ -68,15 +64,8 @@ if __name__ == "__main__":
 
 
 def handle_comfyui_manager_unavailable():
-    manager_req_path = os.path.join(os.path.dirname(os.path.abspath(folder_paths.__file__)), "manager_requirements.txt")
+    if not args.windows_standalone_build:
-    uv_available = shutil.which("uv") is not None
+        logging.warning(f"\n\nYou appear to be running comfyui-manager from source, this is not recommended. Please install comfyui-manager using the following command:\ncommand:\n\t{sys.executable} -m pip install --pre comfyui_manager\n")
-
-    pip_cmd = f"{sys.executable} -m pip install -r {manager_req_path}"
-    msg = f"\n\nTo use the `--enable-manager` feature, the `comfyui-manager` package must be installed first.\ncommand:\n\t{pip_cmd}"
-    if uv_available:
-        msg += f"\nor using uv:\n\tuv pip install -r {manager_req_path}"
-    msg += "\n"
-    logging.warning(msg)
     args.enable_manager = False
 
 
@@ -184,6 +173,7 @@ execute_prestartup_script()
 
 # Main code
 import asyncio
+import shutil
 import threading
 import gc
 
@@ -192,7 +182,6 @@ if 'torch' in sys.modules:
 
 
 import comfy.utils
-from app.assets.seeder import asset_seeder
 
 import execution
 import server
@@ -462,11 +451,6 @@ if __name__ == "__main__":
     # Running directly, just start ComfyUI.
     logging.info("Python version: {}".format(sys.version))
     logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
-    for package in ("comfy-aimdo", "comfy-kitchen"):
-        try:
-            logging.info("{} version: {}".format(package, importlib.metadata.version(package)))
-        except:
-            pass
 
     if sys.version_info.major == 3 and sys.version_info.minor < 10:
         logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.")

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1b2
+comfyui_manager==4.1b1

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.18
+comfyui-workflow-templates==0.9.11
 comfyui-embedded-docs==0.4.3
 torch
 torchsde
@@ -23,7 +23,7 @@ SQLAlchemy
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.10
+comfy-aimdo>=0.2.7
 requests
 simpleeval>=1.0.0
 blake3