mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-11-12 12:13:29 +08:00
145 lines
4.1 KiB
Python
145 lines
4.1 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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from comfy import model_management
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from .supported_dit_models import HunYuan_DiT, HYDiT_Model, ModifiedHunYuanDiT
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from .clip import CLIP
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import os
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from .hydit.config_comfyui import get_args
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sampling_settings = {
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"beta_schedule" : "linear",
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"linear_start" : 0.00085,
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"linear_end" : 0.03,
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"timesteps" : 1000,
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}
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hydit_conf = {
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"G/2": { # Seems to be the main one
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"unet_config": {
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"depth" : 40,
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"num_heads" : 16,
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"patch_size" : 2,
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"hidden_size" : 1408,
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"mlp_ratio" : 4.3637,
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"input_size": (1024//8, 1024//8),
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#"disable_unet_model_creation": True,
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},
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"sampling_settings" : sampling_settings,
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},
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}
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def load_dit(model_path, output_clip=True, output_model=True, output_vae=True, MODEL_PATH = None, VAE_PATH = None):
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if MODEL_PATH:
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state_dict = comfy.utils.load_torch_file(MODEL_PATH)
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else:
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state_dict = comfy.utils.load_torch_file(os.path.join(model_path, "t2i", "model", "pytorch_model_ema.pt"))
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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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clip = None,
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vae = None
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model_patcher = None
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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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root = os.path.join(model_path, "t2i")
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if manual_cast_dtype:
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print(f"DiT: falling back to {manual_cast_dtype}")
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unet_dtype = manual_cast_dtype
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#model_conf["unet_config"]["num_classes"] = state_dict["y_embedder.embedding_table.weight"].shape[0] - 1 # adj. for empty
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if output_model:
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model_conf = HunYuan_DiT(hydit_conf["G/2"])
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model = 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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#print(model_conf.unet_config)
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#assert(0)
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all_args =get_args()
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model.diffusion_model = ModifiedHunYuanDiT(model_conf.dit_conf, **model_conf.unet_config).half().to(load_device)
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model.diffusion_model.load_state_dict(state_dict)
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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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)
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if output_clip:
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clip = CLIP(root)
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if output_vae:
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if VAE_PATH:
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vae_path = VAE_PATH
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else:
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vae_path = os.path.join(root, "sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors")
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#print(vae_path)
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sd = comfy.utils.load_torch_file(vae_path)
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vae = comfy.sd.VAE(sd=sd)
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return (model_patcher, clip, vae)
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def load_checkpoint(model_path):
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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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model_patcher = None
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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"DiT: falling back to {manual_cast_dtype}")
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unet_dtype = manual_cast_dtype
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model_conf = HunYuan_DiT(hydit_conf["G/2"])
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model = 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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model.diffusion_model = ModifiedHunYuanDiT(model_conf.dit_conf, **model_conf.unet_config).half().to(load_device)
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model.diffusion_model.load_state_dict(state_dict)
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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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return (model_patcher,)
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def load_vae(vae_path):
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sd = comfy.utils.load_torch_file(vae_path)
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vae = comfy.sd.VAE(sd=sd)
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return (vae,) |