83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
import comfy.supported_models_base
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import comfy.latent_formats
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import comfy.model_patcher
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import comfy.model_base
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import comfy.utils
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import comfy.conds
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import torch
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from comfy import model_management
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from tqdm import tqdm
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class EXM_HYDiT(comfy.supported_models_base.BASE):
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unet_config = {}
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unet_extra_config = {}
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latent_format = comfy.latent_formats.SDXL
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def __init__(self, model_conf):
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self.unet_config = model_conf.get("unet_config", {})
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self.sampling_settings = model_conf.get("sampling_settings", {})
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self.latent_format = self.latent_format()
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# UNET is handled by extension
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self.unet_config["disable_unet_model_creation"] = True
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def model_type(self, state_dict, prefix=""):
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return comfy.model_base.ModelType.V_PREDICTION
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class EXM_HYDiT_Model(comfy.model_base.BaseModel):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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for name in ["context_t5", "context_mask", "context_t5_mask"]:
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out[name] = comfy.conds.CONDRegular(kwargs[name])
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src_size_cond = kwargs.get("src_size_cond", None)
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if src_size_cond is not None:
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out["src_size_cond"] = comfy.conds.CONDRegular(torch.tensor(src_size_cond))
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return out
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def load_hydit(model_path, model_conf):
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state_dict = comfy.utils.load_torch_file(model_path)
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state_dict = state_dict.get("model", state_dict)
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parameters = comfy.utils.calculate_parameters(state_dict)
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unet_dtype = model_management.unet_dtype(model_params=parameters)
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load_device = comfy.model_management.get_torch_device()
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offload_device = comfy.model_management.unet_offload_device()
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# ignore fp8/etc and use directly for now
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manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
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if manual_cast_dtype:
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print(f"HunYuanDiT: falling back to {manual_cast_dtype}")
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unet_dtype = manual_cast_dtype
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model_conf = EXM_HYDiT(model_conf)
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model = EXM_HYDiT_Model(
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model_conf,
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model_type=comfy.model_base.ModelType.V_PREDICTION,
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device=model_management.get_torch_device()
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)
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from .models.models import HunYuanDiT
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model.diffusion_model = HunYuanDiT(
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**model_conf.unet_config,
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log_fn=tqdm.write,
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)
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model.diffusion_model.load_state_dict(state_dict)
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model.diffusion_model.dtype = unet_dtype
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model.diffusion_model.eval()
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model.diffusion_model.to(unet_dtype)
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model_patcher = comfy.model_patcher.ModelPatcher(
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model,
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load_device = load_device,
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offload_device = offload_device,
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current_device = "cpu",
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size = 6 * (1024**3),
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)
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return model_patcher
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