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111 lines
4.5 KiB
111 lines
4.5 KiB
import json |
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import os |
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import yaml |
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import folder_paths |
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from comfy.ldm.util import instantiate_from_config |
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from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE |
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import os.path as osp |
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import re |
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import torch |
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from safetensors.torch import load_file, save_file |
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import diffusers_convert |
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def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None): |
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diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json"))) |
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diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json"))) |
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# magic |
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v2 = diffusers_unet_conf["sample_size"] == 96 |
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if 'prediction_type' in diffusers_scheduler_conf: |
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v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction' |
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if v2: |
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if v_pred: |
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config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml') |
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else: |
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config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml') |
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else: |
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config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml') |
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with open(config_path, 'r') as stream: |
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config = yaml.safe_load(stream) |
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model_config_params = config['model']['params'] |
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clip_config = model_config_params['cond_stage_config'] |
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scale_factor = model_config_params['scale_factor'] |
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vae_config = model_config_params['first_stage_config'] |
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vae_config['scale_factor'] = scale_factor |
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model_config_params["unet_config"]["params"]["use_fp16"] = fp16 |
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unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors") |
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vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors") |
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text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors") |
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# Load models from safetensors if it exists, if it doesn't pytorch |
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if osp.exists(unet_path): |
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unet_state_dict = load_file(unet_path, device="cpu") |
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else: |
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unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin") |
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unet_state_dict = torch.load(unet_path, map_location="cpu") |
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if osp.exists(vae_path): |
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vae_state_dict = load_file(vae_path, device="cpu") |
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else: |
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vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin") |
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vae_state_dict = torch.load(vae_path, map_location="cpu") |
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if osp.exists(text_enc_path): |
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text_enc_dict = load_file(text_enc_path, device="cpu") |
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else: |
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text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin") |
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text_enc_dict = torch.load(text_enc_path, map_location="cpu") |
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# Convert the UNet model |
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unet_state_dict = diffusers_convert.convert_unet_state_dict(unet_state_dict) |
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unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} |
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# Convert the VAE model |
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vae_state_dict = diffusers_convert.convert_vae_state_dict(vae_state_dict) |
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vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} |
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# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper |
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is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict |
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if is_v20_model: |
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# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm |
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text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} |
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text_enc_dict = diffusers_convert.convert_text_enc_state_dict_v20(text_enc_dict) |
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text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} |
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else: |
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text_enc_dict = diffusers_convert.convert_text_enc_state_dict(text_enc_dict) |
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text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} |
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# Put together new checkpoint |
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sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict} |
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clip = None |
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vae = None |
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class WeightsLoader(torch.nn.Module): |
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pass |
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w = WeightsLoader() |
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load_state_dict_to = [] |
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if output_vae: |
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vae = VAE(scale_factor=scale_factor, config=vae_config) |
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w.first_stage_model = vae.first_stage_model |
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load_state_dict_to = [w] |
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if output_clip: |
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clip = CLIP(config=clip_config, embedding_directory=embedding_directory) |
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w.cond_stage_model = clip.cond_stage_model |
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load_state_dict_to = [w] |
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model = instantiate_from_config(config["model"]) |
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model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to) |
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if fp16: |
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model = model.half() |
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return ModelPatcher(model), clip, vae
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