Merge branch 'comfyanonymous:master' into master

.pylintrc

@@ -1,3 +1,3 @@
 [MESSAGES CONTROL]
 disable=all
-enable=eval-used
+enable=eval-used, unused-import

comfy/cldm/cldm.py

@@ -2,11 +2,9 @@
 #and modified
 
 import torch
-import torch as th
 import torch.nn as nn
 
 from ..ldm.modules.diffusionmodules.util import (
-    zero_module,
     timestep_embedding,
 )
 

comfy/cldm/dit_embedder.py

@@ -1,10 +1,8 @@
 import math
 from typing import List, Optional, Tuple
 
-import numpy as np
 import torch
 import torch.nn as nn
-from einops import rearrange
 from torch import Tensor
 
 from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch

comfy/cldm/mmdit.py

@@ -1,5 +1,5 @@
 import torch
-from typing import Dict, Optional
+from typing import Optional
 import comfy.ldm.modules.diffusionmodules.mmdit
 
 class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):

comfy/extra_samplers/uni_pc.py

@@ -1,10 +1,9 @@
 #code taken from: https://github.com/wl-zhao/UniPC and modified
 
 import torch
-import torch.nn.functional as F
 import math
 
-from tqdm.auto import trange, tqdm
+from tqdm.auto import trange
 
 
 class NoiseScheduleVP:

comfy/ldm/audio/autoencoder.py

@@ -2,7 +2,7 @@
 
 import torch
 from torch import nn
-from typing import Literal, Dict, Any
+from typing import Literal
 import math
 import comfy.ops
 ops = comfy.ops.disable_weight_init

comfy/ldm/audio/embedders.py

@@ -2,8 +2,8 @@
 
 import torch
 import torch.nn as nn
-from torch import Tensor, einsum
+from torch import Tensor
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
+from typing import List, Union
 from einops import rearrange
 import math
 import comfy.ops

comfy/ldm/cascade/controlnet.py

@@ -16,7 +16,6 @@
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
 
-import torch
 import torchvision
 from torch import nn
 from .common import LayerNorm2d_op

comfy/ldm/flux/controlnet.py

@@ -6,9 +6,7 @@ import math
 from torch import Tensor, nn
 from einops import rearrange, repeat
 
-from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
+from .layers import (timestep_embedding)
-                                 MLPEmbedder, SingleStreamBlock,
-                                 timestep_embedding)
 
 from .model import Flux
 import comfy.ldm.common_dit

comfy/ldm/genmo/joint_model/utils.py

@@ -1,7 +1,7 @@
 #original code from https://github.com/genmoai/models under apache 2.0 license
 #adapted to ComfyUI
 
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 import torch.nn as nn

comfy/ldm/genmo/vae/model.py

@@ -1,7 +1,7 @@
 #original code from https://github.com/genmoai/models under apache 2.0 license
 #adapted to ComfyUI
 
-from typing import Callable, List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 from functools import partial
 import math
 

comfy/ldm/hydit/controlnet.py

@@ -1,24 +1,17 @@
-from typing import Any, Optional
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
-from torch.utils import checkpoint
 
 from comfy.ldm.modules.diffusionmodules.mmdit import (
-    Mlp,
     TimestepEmbedder,
     PatchEmbed,
-    RMSNorm,
 )
-from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
 from .poolers import AttentionPool
 
 import comfy.latent_formats
 from .models import HunYuanDiTBlock, calc_rope
 
-from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
 
 
 class HunYuanControlNet(nn.Module):

comfy/ldm/hydit/models.py

@@ -1,8 +1,6 @@
-from typing import Any
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 import comfy.ops
 from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm

comfy/ldm/hydit/poolers.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.ops
 

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -3,7 +3,7 @@ from torch import nn
 from functools import partial
 import math
 from einops import rearrange
-from typing import Any, Mapping, Optional, Tuple, Union, List
+from typing import Optional, Tuple, Union
 from .conv_nd_factory import make_conv_nd, make_linear_nd
 from .pixel_norm import PixelNorm
 

comfy/ldm/lightricks/vae/conv_nd_factory.py

@@ -1,6 +1,5 @@
 from typing import Tuple, Union
 
-import torch
 
 from .dual_conv3d import DualConv3d
 from .causal_conv3d import CausalConv3d

comfy/ldm/models/autoencoder.py

@@ -1,6 +1,6 @@
 import torch
 from contextlib import contextmanager
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Tuple, Union
 
 from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
 

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -1,5 +1,3 @@
-import logging
-import math
 from typing import Dict, Optional, List
 
 import numpy as np

comfy/ldm/modules/diffusionmodules/model.py

@@ -3,7 +3,6 @@ import math
 import torch
 import torch.nn as nn
 import numpy as np
-from typing import Optional, Any
 import logging
 
 from comfy import model_management

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -9,7 +9,6 @@ import logging
 from .util import (
     checkpoint,
     avg_pool_nd,
-    zero_module,
     timestep_embedding,
     AlphaBlender,
 )

comfy/ldm/modules/diffusionmodules/upscaling.py

@@ -4,7 +4,6 @@ import numpy as np
 from functools import partial
 
 from .util import extract_into_tensor, make_beta_schedule
-from comfy.ldm.util import default
 
 
 class AbstractLowScaleModel(nn.Module):

comfy/ldm/modules/diffusionmodules/util.py

@@ -8,7 +8,6 @@
 # thanks!
 
 
-import os
 import math
 import torch
 import torch.nn as nn

comfy/ldm/modules/temporal_ae.py

@@ -1,5 +1,5 @@
 import functools
-from typing import Callable, Iterable, Union
+from typing import Iterable, Union
 
 import torch
 from einops import rearrange, repeat

comfy/ops.py

@@ -269,7 +269,7 @@ def fp8_linear(self, input):
 
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
-            inn = input.reshape(-1, input.shape[2]).to(dtype)
+            inn = torch.clamp(input, min=-448, max=448).reshape(-1, input.shape[2]).to(dtype)
         else:
             scale_input = scale_input.to(input.device)
             inn = (input * (1.0 / scale_input).to(input.dtype)).reshape(-1, input.shape[2]).to(dtype)

comfy/sampler_helpers.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 import uuid
-import torch
 import comfy.model_management
 import comfy.conds
 import comfy.utils

comfy/text_encoders/spiece_tokenizer.py

@@ -1,4 +1,3 @@
-import os
 import torch
 
 class SPieceTokenizer:

comfy/text_encoders/t5.py

@@ -209,6 +209,11 @@ class T5Stack(torch.nn.Module):
         intermediate = None
         optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
         past_bias = None
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.block) + intermediate_output
+
         for i, l in enumerate(self.block):
             x, past_bias = l(x, mask, past_bias, optimized_attention)
             if i == intermediate_output:

comfy_extras/nodes_advanced_samplers.py

@@ -2,8 +2,7 @@ import comfy.samplers
 import comfy.utils
 import torch
 import numpy as np
-from tqdm.auto import trange, tqdm
+from tqdm.auto import trange
-import math
 
 
 @torch.no_grad()

comfy_extras/nodes_clip_sdxl.py

@@ -1,4 +1,3 @@
-import torch
 from nodes import MAX_RESOLUTION
 
 class CLIPTextEncodeSDXLRefiner:

comfy_extras/nodes_compositing.py

@@ -1,4 +1,3 @@
-import numpy as np
 import torch
 import comfy.utils
 from enum import Enum

comfy_extras/nodes_hooks.py

@@ -4,7 +4,6 @@ import torch
 from collections.abc import Iterable
 
 if TYPE_CHECKING:
-    from comfy.model_patcher import ModelPatcher
     from comfy.sd import CLIP
 
 import comfy.hooks

comfy_extras/nodes_model_advanced.py

@@ -1,4 +1,3 @@
-import folder_paths
 import comfy.sd
 import comfy.model_sampling
 import comfy.latent_formats

comfy_extras/nodes_model_downscale.py

@@ -1,4 +1,3 @@
-import torch
 import comfy.utils
 
 class PatchModelAddDownscale:

comfy_extras/nodes_upscale_model.py

@@ -1,4 +1,3 @@
-import os
 import logging
 from spandrel import ModelLoader, ImageModelDescriptor
 from comfy import model_management

custom_nodes/websocket_image_save.py

@@ -1,7 +1,5 @@
-from PIL import Image, ImageOps
+from PIL import Image
-from io import BytesIO
 import numpy as np
-import struct
 import comfy.utils
 import time
 

execution.py

@@ -17,7 +17,6 @@ from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt,
 from comfy_execution.graph_utils import is_link, GraphBuilder
 from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
 from comfy_execution.validation import validate_node_input
-from comfy.cli_args import args
 
 class ExecutionResult(Enum):
     SUCCESS = 0

fix_torch.py

@@ -5,15 +5,19 @@ import ctypes
 import logging
 
 
-torch_spec = importlib.util.find_spec("torch")
+def fix_pytorch_libomp():
-for folder in torch_spec.submodule_search_locations:
+    """
+    Fix PyTorch libomp DLL issue on Windows by copying the correct DLL file if needed.
+    """
+    torch_spec = importlib.util.find_spec("torch")
+    for folder in torch_spec.submodule_search_locations:
         lib_folder = os.path.join(folder, "lib")
         test_file = os.path.join(lib_folder, "fbgemm.dll")
         dest = os.path.join(lib_folder, "libomp140.x86_64.dll")
         if os.path.exists(dest):
             break
 
-    with open(test_file, 'rb') as f:
+        with open(test_file, "rb") as f:
             contents = f.read()
             if b"libomp140.x86_64.dll" not in contents:
                 break

folder_paths.py

@@ -4,7 +4,7 @@ import os
 import time
 import mimetypes
 import logging
-from typing import Set, List, Dict, Tuple, Literal
+from typing import Literal
 from collections.abc import Collection
 
 supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
@@ -133,7 +133,7 @@ def get_directory_by_type(type_name: str) -> str | None:
         return get_input_directory()
     return None
 
-def filter_files_content_types(files: List[str], content_types: Literal["image", "video", "audio"]) -> List[str]:
+def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio"]) -> list[str]:
     """
     Example:
         files = os.listdir(folder_paths.get_input_directory())

latent_preview.py

@@ -1,7 +1,5 @@
 import torch
 from PIL import Image
-import struct
-import numpy as np
 from comfy.cli_args import args, LatentPreviewMethod
 from comfy.taesd.taesd import TAESD
 import comfy.model_management

main.py

@@ -8,6 +8,11 @@ import time
 from comfy.cli_args import args
 from app.logger import setup_logger
 
+if __name__ == "__main__":
+    #NOTE: These do not do anything on core ComfyUI which should already have no communication with the internet, they are for custom nodes.
+    os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
+    os.environ['DO_NOT_TRACK'] = '1'
+
 
 setup_logger(log_level=args.verbose)
 
@@ -82,7 +87,8 @@ if __name__ == "__main__":
 
 if args.windows_standalone_build:
     try:
-        import fix_torch
+        from fix_torch import fix_pytorch_libomp
+        fix_pytorch_libomp()
     except:
         pass
 

nodes.py

@@ -11,7 +11,7 @@ import time
 import random
 import logging
 
-from PIL import Image, ImageOps, ImageSequence, ImageFile
+from PIL import Image, ImageOps, ImageSequence
 from PIL.PngImagePlugin import PngInfo
 
 import numpy as np
@@ -644,9 +644,7 @@ class LoraLoader:
             if self.loaded_lora[0] == lora_path:
                 lora = self.loaded_lora[1]
             else:
-                temp = self.loaded_lora
                 self.loaded_lora = None
-                del temp
 
         if lora is None:
             lora = comfy.utils.load_torch_file(lora_path, safe_load=True)

script_examples/basic_api_example.py

@@ -1,6 +1,5 @@
 import json
-from urllib import request, parse
+from urllib import request
-import random
 
 #This is the ComfyUI api prompt format.
 

tests/inference/test_inference.py

@@ -1,6 +1,5 @@
 from copy import deepcopy
 from io import BytesIO
-from urllib import request
 import numpy
 import os
 from PIL import Image