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| import os | |
| import huggingface_hub as hf | |
| from modules import shared, processing, sd_models, devices | |
| def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger, strength, start): # pylint: disable=arguments-differ | |
| from modules.face.photomaker_model import PhotoMakerStableDiffusionXLPipeline | |
| # prepare pipeline | |
| if len(input_images) == 0: | |
| shared.log.warning('PhotoMaker: no input images') | |
| return None | |
| c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else '' | |
| if c != 'StableDiffusionXLPipeline': | |
| shared.log.warning(f'PhotoMaker invalid base model: current={c} required=StableDiffusionXLPipeline') | |
| return None | |
| # validate prompt | |
| trigger_ids = shared.sd_model.tokenizer.encode(trigger) + shared.sd_model.tokenizer_2.encode(trigger) | |
| prompt_ids1 = shared.sd_model.tokenizer.encode(p.all_prompts[0]) | |
| prompt_ids2 = shared.sd_model.tokenizer_2.encode(p.all_prompts[0]) | |
| for t in trigger_ids: | |
| if prompt_ids1.count(t) != 1: | |
| shared.log.error(f'PhotoMaker: trigger word not matched in prompt: {trigger} ids={trigger_ids} prompt={p.all_prompts[0]} ids={prompt_ids1}') | |
| return None | |
| if prompt_ids2.count(t) != 1: | |
| shared.log.error(f'PhotoMaker: trigger word not matched in prompt: {trigger} ids={trigger_ids} prompt={p.all_prompts[0]} ids={prompt_ids1}') | |
| return None | |
| # create new pipeline | |
| orig_pipeline = shared.sd_model # backup current pipeline definition | |
| shared.sd_model = PhotoMakerStableDiffusionXLPipeline( | |
| vae = shared.sd_model.vae, | |
| text_encoder=shared.sd_model.text_encoder, | |
| text_encoder_2=shared.sd_model.text_encoder_2, | |
| tokenizer=shared.sd_model.tokenizer, | |
| tokenizer_2=shared.sd_model.tokenizer_2, | |
| unet=shared.sd_model.unet, | |
| scheduler=shared.sd_model.scheduler, | |
| force_zeros_for_empty_prompt=shared.opts.diffusers_force_zeros, | |
| ) | |
| sd_models.copy_diffuser_options(shared.sd_model, orig_pipeline) # copy options from original pipeline | |
| sd_models.set_diffuser_options(shared.sd_model) # set all model options such as fp16, offload, etc. | |
| sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device | |
| shared.sd_model.to(dtype=devices.dtype) | |
| orig_prompt_attention = shared.opts.prompt_attention | |
| shared.opts.data['prompt_attention'] = 'Fixed attention' # otherwise need to deal with class_tokens_mask | |
| p.task_args['input_id_images'] = input_images | |
| p.task_args['start_merge_step'] = int(start * p.steps) | |
| p.task_args['prompt'] = p.all_prompts[0] # override all logic | |
| photomaker_path = hf.hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model", cache_dir=shared.opts.diffusers_dir) | |
| shared.log.debug(f'PhotoMaker: model={photomaker_path} images={len(input_images)} trigger={trigger} args={p.task_args}') | |
| # load photomaker adapter | |
| shared.sd_model.load_photomaker_adapter( | |
| os.path.dirname(photomaker_path), | |
| subfolder="", | |
| weight_name=os.path.basename(photomaker_path), | |
| trigger_word=trigger | |
| ) | |
| shared.sd_model.set_adapters(["photomaker"], adapter_weights=[strength]) | |
| # run processing | |
| processed: processing.Processed = processing.process_images(p) | |
| p.extra_generation_params['PhotoMaker'] = f'{strength}' | |
| # restore original pipeline | |
| shared.opts.data['prompt_attention'] = orig_prompt_attention | |
| shared.sd_model = orig_pipeline | |
| return processed | |