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https://github.com/comfyanonymous/ComfyUI.git
synced 2025-08-03 23:49:57 +08:00
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5 Commits
Author | SHA1 | Date | |
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a8776ecfff | ||
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13aaa66ec2 | ||
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5f582a9757 | ||
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fbcc23945d | ||
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3dfefc88d0 |
@@ -130,10 +130,21 @@ class ModelFileManager:
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for file_name in filenames:
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try:
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relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
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result.append(relative_path)
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except:
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logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
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full_path = os.path.join(dirpath, file_name)
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relative_path = os.path.relpath(full_path, directory)
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# Get file metadata
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file_info = {
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"name": relative_path,
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"pathIndex": pathIndex,
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"modified": os.path.getmtime(full_path), # Add modification time
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"created": os.path.getctime(full_path), # Add creation time
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"size": os.path.getsize(full_path) # Add file size
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}
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result.append(file_info)
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except Exception as e:
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logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
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continue
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for d in subdirs:
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@@ -144,7 +155,7 @@ class ModelFileManager:
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logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
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continue
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return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter()
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return result, dirs, time.perf_counter()
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def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
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dirname = os.path.dirname(filepath)
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@@ -20,13 +20,15 @@ class FileInfo(TypedDict):
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path: str
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size: int
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modified: int
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created: int
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def get_file_info(path: str, relative_to: str) -> FileInfo:
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return {
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"path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
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"size": os.path.getsize(path),
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"modified": os.path.getmtime(path)
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"modified": os.path.getmtime(path),
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"created": os.path.getctime(path)
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}
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@@ -10,8 +10,8 @@ class CONDRegular:
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def _copy_with(self, cond):
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return self.__class__(cond)
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def process_cond(self, batch_size, device, **kwargs):
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return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
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def process_cond(self, batch_size, **kwargs):
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return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size))
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def can_concat(self, other):
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if self.cond.shape != other.cond.shape:
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@@ -29,14 +29,14 @@ class CONDRegular:
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class CONDNoiseShape(CONDRegular):
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def process_cond(self, batch_size, device, area, **kwargs):
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def process_cond(self, batch_size, area, **kwargs):
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data = self.cond
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if area is not None:
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dims = len(area) // 2
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for i in range(dims):
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data = data.narrow(i + 2, area[i + dims], area[i])
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return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
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return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size))
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class CONDCrossAttn(CONDRegular):
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@@ -73,7 +73,7 @@ class CONDConstant(CONDRegular):
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def __init__(self, cond):
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self.cond = cond
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def process_cond(self, batch_size, device, **kwargs):
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def process_cond(self, batch_size, **kwargs):
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return self._copy_with(self.cond)
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def can_concat(self, other):
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@@ -92,10 +92,10 @@ class CONDList(CONDRegular):
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def __init__(self, cond):
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self.cond = cond
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def process_cond(self, batch_size, device, **kwargs):
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def process_cond(self, batch_size, **kwargs):
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out = []
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for c in self.cond:
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out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
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out.append(comfy.utils.repeat_to_batch_size(c, batch_size))
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return self._copy_with(out)
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@@ -106,10 +106,12 @@ def model_sampling(model_config, model_type):
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return ModelSampling(model_config)
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def convert_tensor(extra, dtype):
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def convert_tensor(extra, dtype, device):
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if hasattr(extra, "dtype"):
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if extra.dtype != torch.int and extra.dtype != torch.long:
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extra = extra.to(dtype)
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extra = comfy.model_management.cast_to_device(extra, device, dtype)
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else:
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extra = comfy.model_management.cast_to_device(extra, device, None)
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return extra
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@@ -169,20 +171,21 @@ class BaseModel(torch.nn.Module):
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dtype = self.manual_cast_dtype
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xc = xc.to(dtype)
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device = xc.device
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t = self.model_sampling.timestep(t).float()
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if context is not None:
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context = context.to(dtype)
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context = comfy.model_management.cast_to_device(context, device, dtype)
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extra_conds = {}
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for o in kwargs:
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extra = kwargs[o]
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if hasattr(extra, "dtype"):
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extra = convert_tensor(extra, dtype)
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extra = convert_tensor(extra, dtype, device)
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elif isinstance(extra, list):
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ex = []
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for ext in extra:
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ex.append(convert_tensor(ext, dtype))
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ex.append(convert_tensor(ext, dtype, device))
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extra = ex
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extra_conds[o] = extra
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@@ -89,7 +89,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
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conditioning = {}
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model_conds = conds["model_conds"]
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for c in model_conds:
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conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
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conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], area=area)
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hooks = conds.get('hooks', None)
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control = conds.get('control', None)
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@@ -1,5 +1,5 @@
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comfyui-frontend-package==1.23.4
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comfyui-workflow-templates==0.1.45
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comfyui-workflow-templates==0.1.47
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comfyui-embedded-docs==0.2.4
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torch
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torchsde
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