# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Conversion script for the LDM checkpoints.""" import argparse import importlib import torch from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." ) # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml parser.add_argument( "--original_config_file", default=None, type=str, help="The YAML config file corresponding to the original architecture.", ) parser.add_argument( "--config_files", default=None, type=str, help="The YAML config file corresponding to the architecture.", ) parser.add_argument( "--num_in_channels", default=None, type=int, help="The number of input channels. If `None` number of input channels will be automatically inferred.", ) parser.add_argument( "--scheduler_type", default="pndm", type=str, help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", ) parser.add_argument( "--pipeline_type", default=None, type=str, help=( "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'" ". If `None` pipeline will be automatically inferred." ), ) parser.add_argument( "--image_size", default=None, type=int, help=( "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2" " Base. Use 768 for Stable Diffusion v2." ), ) parser.add_argument( "--prediction_type", default=None, type=str, help=( "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2." ), ) parser.add_argument( "--extract_ema", action="store_true", help=( "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." ), ) parser.add_argument( "--upcast_attention", action="store_true", help=( "Whether the attention computation should always be upcasted. This is necessary when running stable" " diffusion 2.1." ), ) parser.add_argument( "--from_safetensors", action="store_true", help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", ) parser.add_argument( "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not.", ) parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") parser.add_argument( "--stable_unclip", type=str, default=None, required=False, help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.", ) parser.add_argument( "--stable_unclip_prior", type=str, default=None, required=False, help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.", ) parser.add_argument( "--clip_stats_path", type=str, help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.", required=False, ) parser.add_argument( "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint." ) parser.add_argument("--half", action="store_true", help="Save weights in half precision.") parser.add_argument( "--vae_path", type=str, default=None, required=False, help="Set to a path, hub id to an already converted vae to not convert it again.", ) parser.add_argument( "--pipeline_class_name", type=str, default=None, required=False, help="Specify the pipeline class name", ) args = parser.parse_args() if args.pipeline_class_name is not None: library = importlib.import_module("diffusers") class_obj = getattr(library, args.pipeline_class_name) pipeline_class = class_obj else: pipeline_class = None pipe = download_from_original_stable_diffusion_ckpt( checkpoint_path_or_dict=args.checkpoint_path, original_config_file=args.original_config_file, config_files=args.config_files, image_size=args.image_size, prediction_type=args.prediction_type, model_type=args.pipeline_type, extract_ema=args.extract_ema, scheduler_type=args.scheduler_type, num_in_channels=args.num_in_channels, upcast_attention=args.upcast_attention, from_safetensors=args.from_safetensors, device=args.device, stable_unclip=args.stable_unclip, stable_unclip_prior=args.stable_unclip_prior, clip_stats_path=args.clip_stats_path, controlnet=args.controlnet, vae_path=args.vae_path, pipeline_class=pipeline_class, ) if args.half: pipe.to(dtype=torch.float16) if args.controlnet: # only save the controlnet model pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) else: pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)