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