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| from FollowYourPose.test_followyourpose import * | |
| import copy | |
| import gradio as gr | |
| from transformers import AutoTokenizer, CLIPTextModel | |
| def get_time_string() -> str: | |
| x = datetime.datetime.now() | |
| return f"{(x.year - 2000):02d}{x.month:02d}{x.day:02d}-{x.hour:02d}{x.minute:02d}{x.second:02d}" | |
| class merge_config_then_run(): | |
| def __init__(self) -> None: | |
| # Load the tokenizer | |
| # pretrained_model_path = 'FateZero/ckpt/stable-diffusion-v1-4' | |
| self.tokenizer = None | |
| self.text_encoder = None | |
| self.vae = None | |
| self.unet = None | |
| # cache_ckpt = False | |
| # if cache_ckpt: | |
| # 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, | |
| data_path, | |
| target_prompt, | |
| num_steps, | |
| guidance_scale, | |
| user_input_video=None, | |
| start_sample_frame=0, | |
| n_sample_frame=8, | |
| stride=1, | |
| left_crop=0, | |
| right_crop=0, | |
| top_crop=0, | |
| bottom_crop=0, | |
| ): | |
| default_edit_config='FollowYourPose/configs/pose_sample.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}") | |
| 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, | |
| "sampling_rate" : stride, | |
| "offset": offset_dict, | |
| } | |
| config_now['validation_data'].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: | |
| if isinstance(user_input_video, str): | |
| config_now['validation_data']['path'] = user_input_video | |
| elif hasattr(user_input_video, 'name') and user_input_video.name is not None: | |
| config_now['validation_data']['path'] = user_input_video.name | |
| config_now['validation_data']['prompts'] = [target_prompt] | |
| # ddim config | |
| config_now['validation_data']['guidance_scale'] = guidance_scale | |
| config_now['validation_data']['num_inference_steps'] = num_steps | |
| config_now['skeleton_path'] = data_path | |
| save_path = test(**config_now) | |
| mp4_path = save_path.replace('_0.gif', '_0_0_0.mp4') | |
| return mp4_path | |