import os import copy import torch import random import gradio as gr from glob import glob from omegaconf import OmegaConf from safetensors import safe_open from diffusers import AutoencoderKL from diffusers import EulerDiscreteScheduler, DDIMScheduler from diffusers.utils.import_utils import is_xformers_available from transformers import CLIPTextModel, CLIPTokenizer from utils.unet import UNet3DConditionModel from utils.pipeline_magictime import MagicTimePipeline from utils.util import save_videos_grid, convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint, load_diffusers_lora_unet, convert_ldm_clip_text_model # import spaces pretrained_model_path = "./ckpts/Base_Model/stable-diffusion-v1-5" inference_config_path = "./sample_configs/RealisticVision.yaml" magic_adapter_s_path = "./ckpts/Magic_Weights/magic_adapter_s/magic_adapter_s.ckpt" magic_adapter_t_path = "./ckpts/Magic_Weights/magic_adapter_t" magic_text_encoder_path = "./ckpts/Magic_Weights/magic_text_encoder" if torch.cuda.is_available(): device = "cuda" elif torch.backends.mps.is_available(): device = "mps" else: device = "cpu" css = """ .toolbutton { margin-buttom: 0em 0em 0em 0em; max-width: 2.5em; min-width: 2.5em !important; height: 2.5em; } """ examples = [ # 1-RealisticVision [ "RealisticVisionV60B1_v51VAE.safetensors", "motion_module.ckpt", "Cherry blossoms transitioning from tightly closed buds to a peak state of bloom. The progression moves through stages of bud swelling, petal exposure, and gradual opening, culminating in a full and vibrant display of open blossoms.", "worst quality, low quality, letterboxed", 512, 512, "2038801077" ], # 2-RCNZ [ "RcnzCartoon.safetensors", "motion_module.ckpt", "Time-lapse of a simple modern house's construction in a Minecraft virtual environment: beginning with an avatar laying a white foundation, progressing through wall erection and interior furnishing, to adding roof and exterior details, and completed with landscaping and a tall chimney.", "worst quality, low quality, letterboxed", 512, 512, "1268480012" ], # 3-ToonYou [ "ToonYou_beta6.safetensors", "motion_module.ckpt", "Bean sprouts grow and mature from seeds.", "worst quality, low quality, letterboxed", 512, 512, "1496541313" ] ] # clean Grdio cache print(f"### Cleaning cached examples ...") os.system(f"rm -rf gradio_cached_examples/") # @spaces.GPU(duration=300) class MagicTimeController: def __init__(self): # config dirs self.basedir = os.getcwd() self.stable_diffusion_dir = os.path.join(self.basedir, "ckpts", "Base_Model") self.motion_module_dir = os.path.join(self.basedir, "ckpts", "Base_Model", "motion_module") self.personalized_model_dir = os.path.join(self.basedir, "ckpts", "DreamBooth") self.savedir = os.path.join(self.basedir, "outputs") os.makedirs(self.savedir, exist_ok=True) self.dreambooth_list = [] self.motion_module_list = [] self.selected_dreambooth = None self.selected_motion_module = None self.refresh_motion_module() self.refresh_personalized_model() # config models self.inference_config = OmegaConf.load(inference_config_path)[1] self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").to(device) self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").to(device) self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).to(device) self.text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") # self.tokenizer = tokenizer # self.text_encoder = text_encoder # self.vae = vae # self.unet = unet # self.text_model = text_model self.update_motion_module(self.motion_module_list[0]) self.update_dreambooth(self.dreambooth_list[0]) def refresh_motion_module(self): motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt")) self.motion_module_list = [os.path.basename(p) for p in motion_module_list] def refresh_personalized_model(self): dreambooth_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors")) self.dreambooth_list = [os.path.basename(p) for p in dreambooth_list] def update_dreambooth(self, dreambooth_dropdown): self.selected_dreambooth = dreambooth_dropdown dreambooth_dropdown = os.path.join(self.personalized_model_dir, dreambooth_dropdown) dreambooth_state_dict = {} with safe_open(dreambooth_dropdown, framework="pt", device="cpu") as f: for key in f.keys(): dreambooth_state_dict[key] = f.get_tensor(key) converted_vae_checkpoint = convert_ldm_vae_checkpoint(dreambooth_state_dict, self.vae.config) self.vae.load_state_dict(converted_vae_checkpoint) converted_unet_checkpoint = convert_ldm_unet_checkpoint(dreambooth_state_dict, self.unet.config) self.unet.load_state_dict(converted_unet_checkpoint, strict=False) text_model = copy.deepcopy(self.text_model) self.text_encoder = convert_ldm_clip_text_model(text_model, dreambooth_state_dict) from swift import Swift magic_adapter_s_state_dict = torch.load(magic_adapter_s_path, map_location="cpu") self.unet = load_diffusers_lora_unet(self.unet, magic_adapter_s_state_dict, alpha=1.0) self.unet = Swift.from_pretrained(self.unet, magic_adapter_t_path) self.text_encoder = Swift.from_pretrained(self.text_encoder, magic_text_encoder_path) return gr.Dropdown() def update_motion_module(self, motion_module_dropdown): self.selected_motion_module = motion_module_dropdown motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown) motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu") _, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False) assert len(unexpected) == 0 return gr.Dropdown() def magictime( self, dreambooth_dropdown, motion_module_dropdown, prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox, ): if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown) if self.selected_dreambooth != dreambooth_dropdown: self.update_dreambooth(dreambooth_dropdown) if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention() pipeline = MagicTimePipeline( vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet, scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs)) ).to(device) if int(seed_textbox) > 0: seed = int(seed_textbox) else: seed = random.randint(1, 1e16) torch.manual_seed(int(seed)) assert seed == torch.initial_seed() print(f"### seed: {seed}") generator = torch.Generator(device=device) generator.manual_seed(seed) sample = pipeline( prompt_textbox, negative_prompt = negative_prompt_textbox, num_inference_steps = 25, guidance_scale = 8., width = width_slider, height = height_slider, video_length = 16, generator = generator, ).videos save_sample_path = os.path.join(self.savedir, f"sample.mp4") save_videos_grid(sample, save_sample_path) json_config = { "prompt": prompt_textbox, "n_prompt": negative_prompt_textbox, "width": width_slider, "height": height_slider, "seed": seed, "dreambooth": dreambooth_dropdown, } return gr.Video(value=save_sample_path), gr.Json(value=json_config) # inference_config = OmegaConf.load(inference_config_path)[1] # tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") # text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda() # vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda() # unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)).cuda() # text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") # controller = MagicTimeController(tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, unet=unet, text_model=text_model) controller = MagicTimeController() def ui(): with gr.Blocks(css=css) as demo: gr.Markdown( """

MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators

If you like our project, please give us a star ⭐ on GitHub for the latest update.
[GitHub](https://img.shields.io/github/stars/PKU-YuanGroup/MagicTime) | [arXiv](https://arxiv.org/abs/2404.05014) | [Home Page](https://pku-yuangroup.github.io/MagicTime/) | [Dataset](https://drive.google.com/drive/folders/1WsomdkmSp3ql3ImcNsmzFuSQ9Qukuyr8?usp=sharing) """ ) with gr.Row(): with gr.Column(): dreambooth_dropdown = gr.Dropdown( label="DreamBooth Model", choices=controller.dreambooth_list, value=controller.dreambooth_list[0], interactive=True ) motion_module_dropdown = gr.Dropdown( label="Motion Module", choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True ) dreambooth_dropdown.change(fn=controller.update_dreambooth, inputs=[dreambooth_dropdown], outputs=[dreambooth_dropdown]) motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown]) prompt_textbox = gr.Textbox( label="Prompt", lines=3 ) negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo") with gr.Accordion("Advance", open=False): with gr.Row(): width_slider = gr.Slider( label="Width", value=512, minimum=256, maximum=1024, step=64 ) height_slider = gr.Slider( label="Height", value=512, minimum=256, maximum=1024, step=64 ) with gr.Row(): seed_textbox = gr.Textbox(label="Seed", value=-1, interactive=True) seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton") seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox]) generate_button = gr.Button( value="Generate", variant='primary' ) with gr.Column(): result_video = gr.Video( label="Generated Animation", interactive=False ) json_config = gr.Json( label="Config", value=None ) inputs = [dreambooth_dropdown, motion_module_dropdown, prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox] outputs = [result_video, json_config] generate_button.click( fn=controller.magictime, inputs=inputs, outputs=outputs ) #gr.Examples( fn=controller.magictime, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True ) gr.Examples( fn=controller.magictime, examples=examples, inputs=inputs, outputs=outputs) return demo if __name__ == "__main__": demo = ui() demo.queue(max_size=20) demo.launch()