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| from FateZero.test_fatezero import * | |
| import copy | |
| import gradio as gr | |
| class merge_config_then_run(): | |
| def __init__(self) -> None: | |
| # Load the tokenizer | |
| pretrained_model_path = 'FateZero/ckpt/stable-diffusion-v1-4' | |
| self.tokenizer = AutoTokenizer.from_pretrained( | |
| pretrained_model_path, | |
| # 'FateZero/ckpt/stable-diffusion-v1-4', | |
| subfolder="tokenizer", | |
| use_fast=False, | |
| ) | |
| # Load models and create wrapper for stable diffusion | |
| self.text_encoder = CLIPTextModel.from_pretrained( | |
| pretrained_model_path, | |
| subfolder="text_encoder", | |
| ) | |
| self.vae = AutoencoderKL.from_pretrained( | |
| pretrained_model_path, | |
| subfolder="vae", | |
| ) | |
| model_config = { | |
| "lora": 160, | |
| # temporal_downsample_time: 4 | |
| "SparseCausalAttention_index": ['mid'], | |
| "least_sc_channel": 640 | |
| } | |
| self.unet = UNetPseudo3DConditionModel.from_2d_model( | |
| os.path.join(pretrained_model_path, "unet"), model_config=model_config | |
| ) | |
| def run( | |
| self, | |
| # def merge_config_then_run( | |
| model_id, | |
| data_path, | |
| source_prompt, | |
| target_prompt, | |
| cross_replace_steps, | |
| self_replace_steps, | |
| enhance_words, | |
| enhance_words_value, | |
| num_steps, | |
| guidance_scale, | |
| user_input_video=None, | |
| # Temporal and spatial crop of the video | |
| start_sample_frame=0, | |
| n_sample_frame=8, | |
| stride=1, | |
| left_crop=0, | |
| right_crop=0, | |
| top_crop=0, | |
| bottom_crop=0, | |
| ): | |
| # , ] = inputs | |
| default_edit_config='FateZero/config/low_resource_teaser/jeep_watercolor_ddim_10_steps.yaml' | |
| Omegadict_default_edit_config = OmegaConf.load(default_edit_config) | |
| dataset_time_string = get_time_string() | |
| config_now = copy.deepcopy(Omegadict_default_edit_config) | |
| print(f"config_now['pretrained_model_path'] = model_id {model_id}") | |
| # config_now['pretrained_model_path'] = model_id | |
| config_now['train_dataset']['prompt'] = source_prompt | |
| config_now['train_dataset']['path'] = data_path | |
| # ImageSequenceDataset_dict = { } | |
| offset_dict = { | |
| "left": left_crop, | |
| "right": right_crop, | |
| "top": top_crop, | |
| "bottom": bottom_crop, | |
| } | |
| ImageSequenceDataset_dict = { | |
| "start_sample_frame" : start_sample_frame, | |
| "n_sample_frame" : n_sample_frame, | |
| "stride" : stride, | |
| "offset": offset_dict, | |
| } | |
| config_now['train_dataset'].update(ImageSequenceDataset_dict) | |
| if user_input_video and data_path is None: | |
| raise gr.Error('You need to upload a video or choose a provided video') | |
| if user_input_video is not None and user_input_video.name is not None: | |
| config_now['train_dataset']['path'] = user_input_video.name | |
| config_now['validation_sample_logger_config']['prompts'] = [target_prompt] | |
| # fatezero config | |
| p2p_config_now = copy.deepcopy(config_now['validation_sample_logger_config']['p2p_config'][0]) | |
| p2p_config_now['cross_replace_steps']['default_'] = cross_replace_steps | |
| p2p_config_now['self_replace_steps'] = self_replace_steps | |
| p2p_config_now['eq_params']['words'] = enhance_words.split(" ") | |
| p2p_config_now['eq_params']['values'] = [enhance_words_value,]*len(p2p_config_now['eq_params']['words']) | |
| config_now['validation_sample_logger_config']['p2p_config'][0] = copy.deepcopy(p2p_config_now) | |
| # ddim config | |
| config_now['validation_sample_logger_config']['guidance_scale'] = guidance_scale | |
| config_now['validation_sample_logger_config']['num_inference_steps'] = num_steps | |
| logdir = default_edit_config.replace('config', 'result').replace('.yml', '').replace('.yaml', '')+f'_{dataset_time_string}' | |
| config_now['logdir'] = logdir | |
| print(f'Saving at {logdir}') | |
| save_path = test(tokenizer = self.tokenizer, | |
| text_encoder = self.text_encoder, | |
| vae = self.vae, | |
| unet = self.unet, | |
| config=default_edit_config, **config_now) | |
| mp4_path = save_path.replace('_0.gif', '_0_0_0.mp4') | |
| return mp4_path | |