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README.md
10
README.md
@@ -46,7 +46,7 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
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#### [Manual Install](#manual-install-windows-linux)
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Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
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## Examples
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## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
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See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
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@@ -68,6 +68,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
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- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
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- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
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- [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
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- [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
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- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
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- Asynchronous Queue system
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- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
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@@ -260,6 +261,13 @@ For models compatible with Ascend Extension for PyTorch (torch_npu). To get star
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3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
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4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
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#### Cambricon MLUs
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For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
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1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)
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2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
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3. Launch ComfyUI by running `python main.py`
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# Running
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@@ -421,7 +421,7 @@ class WanModel(torch.nn.Module):
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e0 = self.time_projection(e).unflatten(1, (6, self.dim))
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# context
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context = self.text_embedding(torch.cat([context, context.new_zeros(context.size(0), self.text_len - context.size(1), context.size(2))], dim=1))
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context = self.text_embedding(context)
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if clip_fea is not None and self.img_emb is not None:
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context_clip = self.img_emb(clip_fea) # bs x 257 x dim
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@@ -95,6 +95,13 @@ try:
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except:
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npu_available = False
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try:
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import torch_mlu # noqa: F401
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_ = torch.mlu.device_count()
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mlu_available = torch.mlu.is_available()
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except:
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mlu_available = False
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if args.cpu:
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cpu_state = CPUState.CPU
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@@ -112,6 +119,12 @@ def is_ascend_npu():
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return True
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return False
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def is_mlu():
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global mlu_available
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if mlu_available:
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return True
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return False
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def get_torch_device():
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global directml_enabled
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global cpu_state
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@@ -127,6 +140,8 @@ def get_torch_device():
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return torch.device("xpu", torch.xpu.current_device())
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elif is_ascend_npu():
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return torch.device("npu", torch.npu.current_device())
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elif is_mlu():
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return torch.device("mlu", torch.mlu.current_device())
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else:
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return torch.device(torch.cuda.current_device())
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@@ -153,6 +168,12 @@ def get_total_memory(dev=None, torch_total_too=False):
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_, mem_total_npu = torch.npu.mem_get_info(dev)
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mem_total_torch = mem_reserved
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mem_total = mem_total_npu
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elif is_mlu():
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stats = torch.mlu.memory_stats(dev)
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mem_reserved = stats['reserved_bytes.all.current']
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_, mem_total_mlu = torch.mlu.mem_get_info(dev)
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mem_total_torch = mem_reserved
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mem_total = mem_total_mlu
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else:
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stats = torch.cuda.memory_stats(dev)
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mem_reserved = stats['reserved_bytes.all.current']
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@@ -232,7 +253,7 @@ try:
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if torch_version_numeric[0] >= 2:
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if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
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ENABLE_PYTORCH_ATTENTION = True
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if is_intel_xpu() or is_ascend_npu():
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if is_intel_xpu() or is_ascend_npu() or is_mlu():
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if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
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ENABLE_PYTORCH_ATTENTION = True
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except:
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@@ -316,6 +337,8 @@ def get_torch_device_name(device):
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return "{} {}".format(device, torch.xpu.get_device_name(device))
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elif is_ascend_npu():
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return "{} {}".format(device, torch.npu.get_device_name(device))
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elif is_mlu():
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return "{} {}".format(device, torch.mlu.get_device_name(device))
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else:
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return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
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@@ -905,6 +928,8 @@ def xformers_enabled():
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return False
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if is_ascend_npu():
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return False
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if is_mlu():
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return False
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if directml_enabled:
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return False
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return XFORMERS_IS_AVAILABLE
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@@ -936,6 +961,8 @@ def pytorch_attention_flash_attention():
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return True
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if is_ascend_npu():
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return True
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if is_mlu():
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return True
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if is_amd():
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return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
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return False
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@@ -984,6 +1011,13 @@ def get_free_memory(dev=None, torch_free_too=False):
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mem_free_npu, _ = torch.npu.mem_get_info(dev)
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_npu + mem_free_torch
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elif is_mlu():
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stats = torch.mlu.memory_stats(dev)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_mlu, _ = torch.mlu.mem_get_info(dev)
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_mlu + mem_free_torch
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else:
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stats = torch.cuda.memory_stats(dev)
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mem_active = stats['active_bytes.all.current']
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@@ -1053,6 +1087,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
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if is_ascend_npu():
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return True
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if is_mlu():
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return True
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if torch.version.hip:
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return True
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@@ -1121,6 +1158,11 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
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return False
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props = torch.cuda.get_device_properties(device)
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if is_mlu():
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if props.major > 3:
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return True
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if props.major >= 8:
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return True
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@@ -11,7 +11,7 @@ class UMT5XXlModel(sd1_clip.SDClipModel):
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class UMT5XXlTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer = tokenizer_data.get("spiece_model", None)
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super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0)
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super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0)
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def state_dict(self):
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return {"spiece_model": self.tokenizer.serialize_model()}
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@@ -1,3 +1,3 @@
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# This file is automatically generated by the build process when version is
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# updated in pyproject.toml.
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__version__ = "0.3.17"
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__version__ = "0.3.18"
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@@ -1,6 +1,6 @@
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[project]
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name = "ComfyUI"
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version = "0.3.17"
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version = "0.3.18"
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readme = "README.md"
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license = { file = "LICENSE" }
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requires-python = ">=3.9"
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