200 lines
8.0 KiB
Markdown
200 lines
8.0 KiB
Markdown
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# Documentation
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- Class name: SUPIR_model_loader
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- Category: SUPIR
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- Output node: False
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SUPIR_model_loader节点负责加载SUPIR模型，这是SUPIR框架中用于图像处理和增强的关键组件。它处理模型的初始化和配置，确保模型为后续的图像处理任务做好准备。
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# Input types
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## Required
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- supir_model
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- 指定SUPIR模型checkpoint文件的路径，对于加载模型的状态以进行图像处理任务至关重要。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: List[str]
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- sdxl_model
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- 指定SDXL模型checkpoint文件的路径，这些文件将与SUPIR模型合并以增强其功能。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: List[str]
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- fp8_unet
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- 一个标志，用于确定是否将UNet权重转换为较低精度格式以节省VRAM，这可能会对质量产生轻微影响。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- diffusion_dtype
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- 指定扩散操作的数据类型，提供了优化性能或兼容性的选项。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- SUPIR_model
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- 加载和配置完毕的SUPIR模型，可以立即用于图像处理任务。
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- Comfy dtype: SUPIRMODEL
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- Python dtype: torch.nn.Module
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- SUPIR_VAE
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- SUPIR模型的VAE组件，对某些图像处理操作至关重要。
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- Comfy dtype: SUPIRVAE
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- Python dtype: torch.nn.Module
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class SUPIR_model_loader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"supir_model": (folder_paths.get_filename_list("checkpoints"),),
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"sdxl_model": (folder_paths.get_filename_list("checkpoints"),),
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"fp8_unet": ("BOOLEAN", {"default": False}),
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"diffusion_dtype": (
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[
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'fp16',
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'bf16',
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'fp32',
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'auto'
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], {
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"default": 'auto'
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}),
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},
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}
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RETURN_TYPES = ("SUPIRMODEL", "SUPIRVAE")
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RETURN_NAMES = ("SUPIR_model","SUPIR_VAE",)
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FUNCTION = "process"
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CATEGORY = "SUPIR"
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DESCRIPTION = """
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Old loader, not recommended to be used.
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Loads the SUPIR model and the selected SDXL model and merges them.
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"""
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def process(self, supir_model, sdxl_model, diffusion_dtype, fp8_unet):
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device = mm.get_torch_device()
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mm.unload_all_models()
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SUPIR_MODEL_PATH = folder_paths.get_full_path("checkpoints", supir_model)
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SDXL_MODEL_PATH = folder_paths.get_full_path("checkpoints", sdxl_model)
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config_path = os.path.join(script_directory, "options/SUPIR_v0.yaml")
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clip_config_path = os.path.join(script_directory, "configs/clip_vit_config.json")
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tokenizer_path = os.path.join(script_directory, "configs/tokenizer")
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custom_config = {
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'sdxl_model': sdxl_model,
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'diffusion_dtype': diffusion_dtype,
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'supir_model': supir_model,
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'fp8_unet': fp8_unet,
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}
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if diffusion_dtype == 'auto':
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try:
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if mm.should_use_fp16():
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print("Diffusion using fp16")
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dtype = torch.float16
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model_dtype = 'fp16'
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elif mm.should_use_bf16():
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print("Diffusion using bf16")
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dtype = torch.bfloat16
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model_dtype = 'bf16'
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else:
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print("Diffusion using fp32")
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dtype = torch.float32
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model_dtype = 'fp32'
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except:
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raise AttributeError("ComfyUI version too old, can't autodetect properly. Set your dtypes manually.")
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else:
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print(f"Diffusion using {diffusion_dtype}")
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dtype = convert_dtype(diffusion_dtype)
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model_dtype = diffusion_dtype
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if not hasattr(self, "model") or self.model is None or self.current_config != custom_config:
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self.current_config = custom_config
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self.model = None
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mm.soft_empty_cache()
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config = OmegaConf.load(config_path)
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if mm.XFORMERS_IS_AVAILABLE:
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print("Using XFORMERS")
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config.model.params.control_stage_config.params.spatial_transformer_attn_type = "softmax-xformers"
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config.model.params.network_config.params.spatial_transformer_attn_type = "softmax-xformers"
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config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla-xformers"
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config.model.params.diffusion_dtype = model_dtype
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config.model.target = ".SUPIR.models.SUPIR_model_v2.SUPIRModel"
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pbar = comfy.utils.ProgressBar(5)
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self.model = instantiate_from_config(config.model).cpu()
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self.model.model.dtype = dtype
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pbar.update(1)
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try:
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print(f"Attempting to load SDXL model: [{SDXL_MODEL_PATH}]")
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sdxl_state_dict = load_state_dict(SDXL_MODEL_PATH)
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self.model.load_state_dict(sdxl_state_dict, strict=False)
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if fp8_unet:
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self.model.model.to(torch.float8_e4m3fn)
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else:
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self.model.model.to(dtype)
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pbar.update(1)
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except:
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raise Exception("Failed to load SDXL model")
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#first clip model from SDXL checkpoint
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try:
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print("Loading first clip model from SDXL checkpoint")
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replace_prefix = {}
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replace_prefix["conditioner.embedders.0.transformer."] = ""
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sd = comfy.utils.state_dict_prefix_replace(sdxl_state_dict, replace_prefix, filter_keys=False)
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clip_text_config = CLIPTextConfig.from_pretrained(clip_config_path)
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self.model.conditioner.embedders[0].tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
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self.model.conditioner.embedders[0].transformer = CLIPTextModel(clip_text_config)
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self.model.conditioner.embedders[0].transformer.load_state_dict(sd, strict=False)
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self.model.conditioner.embedders[0].eval()
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self.model.conditioner.embedders[0].to(dtype)
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for param in self.model.conditioner.embedders[0].parameters():
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param.requires_grad = False
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pbar.update(1)
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except:
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raise Exception("Failed to load first clip model from SDXL checkpoint")
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del sdxl_state_dict
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#second clip model from SDXL checkpoint
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try:
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print("Loading second clip model from SDXL checkpoint")
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replace_prefix2 = {}
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replace_prefix2["conditioner.embedders.1.model."] = ""
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sd = comfy.utils.state_dict_prefix_replace(sd, replace_prefix2, filter_keys=True)
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clip_g = build_text_model_from_openai_state_dict(sd, device, cast_dtype=dtype)
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self.model.conditioner.embedders[1].model = clip_g
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self.model.conditioner.embedders[1].to(dtype)
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pbar.update(1)
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except:
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raise Exception("Failed to load second clip model from SDXL checkpoint")
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del sd, clip_g
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try:
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print(f'Attempting to load SUPIR model: [{SUPIR_MODEL_PATH}]')
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supir_state_dict = load_state_dict(SUPIR_MODEL_PATH)
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self.model.load_state_dict(supir_state_dict, strict=False)
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if fp8_unet:
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self.model.model.to(torch.float8_e4m3fn)
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else:
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self.model.model.to(dtype)
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del supir_state_dict
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pbar.update(1)
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except:
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raise Exception("Failed to load SUPIR model")
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mm.soft_empty_cache()
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return (self.model, self.model.first_stage_model,)
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```
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