| |
| |
|
|
| from diffusers import DiffusionPipeline |
| from huggingface_hub import upload_folder |
| from peft import LoraConfig |
| import argparse |
| import torch |
|
|
| from peft.utils import get_peft_model_state_dict |
| from diffusers.utils import convert_state_dict_to_diffusers |
| from diffusers.loaders import StableDiffusionXLLoraLoaderMixin, LoraLoaderMixin |
| from huggingface_hub import create_repo, upload_folder |
|
|
|
|
| mapping = { |
| "hf-internal-testing/tiny-sd-pipe": "hf-internal-testing/tiny-sd-lora-peft", |
| "hf-internal-testing/tiny-sdxl-pipe": "hf-internal-testing/tiny-sdxl-lora-peft", |
| } |
|
|
|
|
| def load_pipeline(pipeline_id): |
| pipe = DiffusionPipeline.from_pretrained(pipeline_id) |
| return pipe |
|
|
|
|
| def get_lora_config(): |
| rank = 4 |
|
|
| torch.manual_seed(0) |
| text_lora_config = LoraConfig( |
| r=rank, |
| lora_alpha=rank, |
| target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], |
| init_lora_weights=False, |
| ) |
|
|
| torch.manual_seed(0) |
| unet_lora_config = LoraConfig( |
| r=rank, |
| lora_alpha=rank, |
| target_modules=["to_q", "to_k", "to_v", "to_out.0"], |
| init_lora_weights=False, |
| ) |
| return text_lora_config, unet_lora_config |
|
|
|
|
| def get_dummy_inputs(): |
| pipeline_inputs = { |
| "prompt": "A painting of a squirrel eating a burger", |
| "num_inference_steps": 2, |
| "guidance_scale": 6.0, |
| "output_type": "np", |
| "generator": torch.manual_seed(0), |
| } |
| return pipeline_inputs |
|
|
|
|
| def run_inference(args): |
| has_two_text_encoders = False |
| pipe = load_pipeline(pipeline_id=args.pipeline_id) |
| text_lora_config, unet_lora_config = get_lora_config() |
|
|
| pipe.text_encoder.add_adapter(text_lora_config) |
| pipe.unet.add_adapter(unet_lora_config) |
| if hasattr(pipe, "text_encoder_2"): |
| pipe.text_encoder_2.add_adapter(text_lora_config) |
| has_two_text_encoders = True |
|
|
| inputs = get_dummy_inputs() |
| outputs = pipe(**inputs).images |
| predicted_slice = outputs[0, -3:, -3:, -1].flatten().tolist() |
|
|
| print(", ".join([str(round(x, 4)) for x in predicted_slice])) |
|
|
| if args.push_to_hub: |
| text_encoder_state_dict = convert_state_dict_to_diffusers( |
| get_peft_model_state_dict(pipe.text_encoder) |
| ) |
| unet_state_dict = convert_state_dict_to_diffusers( |
| get_peft_model_state_dict(pipe.unet) |
| ) |
| if has_two_text_encoders: |
| text_encoder_2_state_dict = convert_state_dict_to_diffusers( |
| get_peft_model_state_dict(pipe.text_encoder_2) |
| ) |
|
|
| serialization_cls = ( |
| StableDiffusionXLLoraLoaderMixin |
| if has_two_text_encoders |
| else LoraLoaderMixin |
| ) |
| output_dir = mapping[args.pipeline_id].split("/")[-1] |
|
|
| if not has_two_text_encoders: |
| serialization_cls.save_lora_weights( |
| save_directory=output_dir, |
| unet_lora_layers=unet_state_dict, |
| text_encoder_lora_layers=text_encoder_state_dict, |
| ) |
| else: |
| serialization_cls.save_lora_weights( |
| save_directory=output_dir, |
| unet_lora_layers=unet_state_dict, |
| text_encoder_lora_layers=text_encoder_state_dict, |
| text_encoder_2_lora_layers=text_encoder_2_state_dict, |
| ) |
|
|
| repo_id = create_repo(repo_id=mapping[args.pipeline_id], exist_ok=True).repo_id |
| upload_folder(repo_id=repo_id, folder_path=output_dir) |
|
|
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| parser.add_argument( |
| "--pipeline_id", |
| type=str, |
| default="hf-internal-testing/tiny-sd-pipe", |
| choices=[ |
| "hf-internal-testing/tiny-sd-pipe", |
| "hf-internal-testing/tiny-sdxl-pipe", |
| ], |
| ) |
| parser.add_argument("--push_to_hub", action="store_true") |
| args = parser.parse_args() |
|
|
| run_inference(args) |
|
|
