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Configuration error
| import os | |
| 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 animatediff.models.unet import UNet3DConditionModel | |
| from animatediff.pipelines.pipeline_animation import AnimationPipeline | |
| from animatediff.utils.util import save_videos_grid | |
| from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint | |
| pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5" | |
| inference_config_path = "configs/inference/long-inference.yaml" | |
| css = """ | |
| .toolbutton { | |
| margin-buttom: 0em 0em 0em 0em; | |
| max-width: 2.5em; | |
| min-width: 2.5em !important; | |
| height: 2.5em; | |
| } | |
| """ | |
| examples = [ | |
| # 12-EpicRealism | |
| [ | |
| "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3", | |
| "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation", | |
| 512, 512, 32, "1490157606650685400" | |
| ], | |
| # 2-EpicRealism | |
| [ | |
| "a young man is dancing in a paris nice street", | |
| "wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation render, illustration, deformed, distorted, disfigured, doll, poorly drawn, bad anatomy, wrong anatomy deformed, naked, nude, breast (worst quality low quality: 1.4)", | |
| 512, 512, 32, "1" | |
| ], | |
| ] | |
| print(f"### Cleaning cached examples ...") | |
| os.system(f"rm -rf gradio_cached_examples/") | |
| class AnimateController: | |
| def __init__(self): | |
| # config dirs | |
| self.basedir = os.getcwd() | |
| self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion") | |
| self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module") | |
| self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA") | |
| self.savedir = os.path.join(self.basedir, "samples") | |
| os.makedirs(self.savedir, exist_ok=True) | |
| self.selected_base_model = None | |
| self.selected_motion_module = None | |
| self.refresh_motion_module() | |
| self.refresh_personalized_model() | |
| # config models | |
| self.inference_config = OmegaConf.load(inference_config_path) | |
| self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") | |
| self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda() | |
| self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda() | |
| self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda() | |
| self.base_model_list = ['epiCRealismNaturalSin.safetensors'] | |
| self.motion_module_list = ['lt_long_mm_32_frames.ckpt'] | |
| print(self.base_model_list[0]) | |
| self.update_base_model(self.base_model_list[0]) | |
| self.update_motion_module(self.motion_module_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): | |
| base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors")) | |
| self.base_model_list = [os.path.basename(p) for p in base_model_list] | |
| def update_base_model(self, base_model_dropdown): | |
| self.selected_base_model = base_model_dropdown | |
| base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown) | |
| base_model_state_dict = {} | |
| with safe_open(base_model_dropdown, framework="pt", device="cpu") as f: | |
| for key in f.keys(): base_model_state_dict[key] = f.get_tensor(key) | |
| converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config) | |
| self.vae.load_state_dict(converted_vae_checkpoint) | |
| converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config) | |
| self.unet.load_state_dict(converted_unet_checkpoint, strict=False) | |
| self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict) | |
| 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 | |
| def animate( | |
| self, | |
| prompt_textbox, | |
| negative_prompt_textbox, | |
| width_slider, | |
| height_slider, | |
| video_length, | |
| seed_textbox, | |
| ): | |
| # if base_model_dropdown != self.selected_base_model: self.update_base_model(base_model_dropdown) | |
| # if motion_module_dropdown != self.selected_motion_module: self.update_motion_module(motion_module_dropdown) | |
| if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention() | |
| pipeline = AnimationPipeline( | |
| 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("cuda") | |
| 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="cuda") | |
| 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 = video_length, | |
| 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, | |
| } | |
| return save_sample_path, json_config | |
| print(f'gradio version is {gr.__version__}') | |
| controller = AnimateController() | |
| def ui(): | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown( | |
| """ | |
| # [LongAnimateDiff](https://github.com/Lightricks/LongAnimateDiff) | |
| [Sapir Weissbuch](https://github.com/SapirW), [Naomi Ken Korem](https://github.com/Naomi-Ken-Korem), [Daniel Shalem](https://github.com/dshalem), [Yoav HaCohen](https://github.com/yoavhacohen) | Lightricks Research | |
| """ | |
| ) | |
| gr.Markdown( | |
| """ | |
| ### Quick Start | |
| 1. Provide `Prompt` and `Negative Prompt` for each model. You are encouraged to refer to each model's webpage on CivitAI to learn how to write prompts for them. Below are the DreamBooth models in this demo. Click to visit their homepage. | |
| - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775) | |
| - [`epiCRealismNatural.safetensors`](https://civitai.com/models/25694/epicrealism) | |
| 2. Select 'Length' to set the length of the generated video. | |
| (When you are working with ComfyUI try all possible length, with different motion_scale) | |
| 3. Click `Generate`, wait for ~2 min, and enjoy. | |
| 4. In order to effectively utilize 'lt_long_mm_16_64_frames' model, it is highly recommended to use the ComfyUI interface, which enables to easily increase 'motion_scale'. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| # base_model_dropdown = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list, value=controller.base_model_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 ) | |
| # base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_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") | |
| video_length = gr.Slider( label="Length", value=32, minimum=16, maximum=32, step=4 ) | |
| 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) | |
| 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 = [prompt_textbox, negative_prompt_textbox, width_slider, height_slider, video_length, seed_textbox] | |
| outputs = [result_video, json_config] | |
| generate_button.click( fn=controller.animate, inputs=inputs, outputs=outputs ) | |
| gr.Examples( fn=controller.animate, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = ui() | |
| demo.queue(max_size=20) | |
| demo.launch() |