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Running
on
Zero
File size: 2,814 Bytes
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import yaml
import torch
import os
from diffusers import StableDiffusionXLPipeline
from utils import PhotoMakerStableDiffusionXLPipeline
def get_models_dict(config_path='config/models.yaml', verbose=False):
"""
Loads model configuration from a YAML file.
Args:
config_path (str): Path to the YAML configuration file.
verbose (bool): If True, prints the loaded configuration.
Returns:
dict: Parsed YAML data.
"""
if not os.path.exists(config_path):
raise FileNotFoundError(f"Config file '{config_path}' not found.")
with open(config_path, 'r') as stream:
try:
data = yaml.safe_load(stream)
if verbose:
print("Loaded model configuration:", data)
return data
except yaml.YAMLError as exc:
raise RuntimeError(f"Error parsing YAML file: {exc}")
def load_models(model_info, device="cuda", photomaker_path=None):
"""
Loads a Stable Diffusion XL model or a PhotoMaker variant based on the provided info.
Args:
model_info (dict): Model configuration dictionary.
device (str): Target device ('cuda' or 'cpu').
photomaker_path (str, optional): Path to PhotoMaker adapter weights if using Photomaker.
Returns:
DiffusionPipeline: Loaded diffusion pipeline.
"""
path = model_info.get("path")
single_file = model_info.get("single_files", False)
use_safetensors = model_info.get("use_safetensors", True)
model_type = model_info.get("model_type", "original")
if not path:
raise ValueError("Model path must be specified in the model_info.")
if model_type == "original":
pipeline_cls = StableDiffusionXLPipeline
elif model_type == "Photomaker":
pipeline_cls = PhotoMakerStableDiffusionXLPipeline
else:
raise NotImplementedError(
f"Unsupported model type '{model_type}'. Choose either 'original' or 'Photomaker'."
)
# Load model
if single_file:
print(f"Loading model from a single file: {path}")
pipe = pipeline_cls.from_single_file(path, torch_dtype=torch.float16)
else:
print(f"Loading model from a directory: {path}")
pipe = pipeline_cls.from_pretrained(path, torch_dtype=torch.float16, use_safetensors=use_safetensors)
pipe = pipe.to(device)
# Load PhotoMaker adapter if needed
if model_type == "Photomaker":
if not photomaker_path:
raise ValueError("Photomaker model type requires a valid 'photomaker_path'.")
pipe.load_photomaker_adapter(
os.path.dirname(photomaker_path),
subfolder="",
weight_name=os.path.basename(photomaker_path),
trigger_word="img"
)
pipe.fuse_lora()
return pipe |