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| 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 AnimationFreeInitPipeline | |
| 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 | |
| from diffusers.training_utils import set_seed | |
| from animatediff.utils.freeinit_utils import get_freq_filter | |
| from collections import namedtuple | |
| pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5" | |
| inference_config_path = "configs/inference/inference-v1.yaml" | |
| css = """ | |
| .toolbutton { | |
| margin-buttom: 0em 0em 0em 0em; | |
| max-width: 2.5em; | |
| min-width: 2.5em !important; | |
| height: 2.5em; | |
| } | |
| """ | |
| examples = [ | |
| # 0-RealisticVision | |
| [ | |
| "realisticVisionV51_v20Novae.safetensors", | |
| "mm_sd_v14.ckpt", | |
| "A panda standing on a surfboard in the ocean under moonlight.", | |
| "worst quality, low quality, nsfw, logo", | |
| 512, 512, "2005563494988190", | |
| "butterworth", 0.25, 0.25, 3, | |
| ["use_fp16"] | |
| ], | |
| # 1-ToonYou | |
| [ | |
| "toonyou_beta3.safetensors", | |
| "mm_sd_v14.ckpt", | |
| "(best quality, masterpiece), 1girl, looking at viewer, blurry background, upper body, contemporary, dress", | |
| "(worst quality, low quality)", | |
| 512, 512, "478028150728261", | |
| "butterworth", 0.25, 0.25, 3, | |
| ["use_fp16"] | |
| ], | |
| # 2-Lyriel | |
| [ | |
| "lyriel_v16.safetensors", | |
| "mm_sd_v14.ckpt", | |
| "hypercars cyberpunk moving, muted colors, swirling color smokes, legend, cityscape, space", | |
| "3d, cartoon, anime, sketches, worst quality, low quality, nsfw, logo", | |
| 512, 512, "1566149281915957", | |
| "butterworth", 0.25, 0.25, 3, | |
| ["use_fp16"] | |
| ], | |
| # 3-RCNZ | |
| [ | |
| "rcnzCartoon3d_v10.safetensors", | |
| "mm_sd_v14.ckpt", | |
| "A cute raccoon playing guitar in a boat on the ocean", | |
| "worst quality, low quality, nsfw, logo", | |
| 512, 512, "1566149281915957", | |
| "butterworth", 0.25, 0.25, 3, | |
| ["use_fp16"] | |
| ], | |
| # 4-MajicMix | |
| [ | |
| "majicmixRealistic_v5Preview.safetensors", | |
| "mm_sd_v14.ckpt", | |
| "1girl, reading book", | |
| "(ng_deepnegative_v1_75t:1.2), (badhandv4:1), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, watermark, moles", | |
| 512, 512, "2005563494988190", | |
| "butterworth", 0.25, 0.25, 3, | |
| ["use_fp16"] | |
| ], | |
| # # 5-RealisticVision | |
| # [ | |
| # "realisticVisionV51_v20Novae.safetensors", | |
| # "mm_sd_v14.ckpt", | |
| # "A panda standing on a surfboard in the ocean in sunset.", | |
| # "worst quality, low quality, nsfw, logo", | |
| # 512, 512, "2005563494988190", | |
| # "butterworth", 0.25, 0.25, 3, | |
| # ["use_fp16"] | |
| # ] | |
| ] | |
| # clean unrelated ckpts | |
| # ckpts = [ | |
| # "realisticVisionV40_v20Novae.safetensors", | |
| # "majicmixRealistic_v5Preview.safetensors", | |
| # "rcnzCartoon3d_v10.safetensors", | |
| # "lyriel_v16.safetensors", | |
| # "toonyou_beta3.safetensors" | |
| # ] | |
| # for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")): | |
| # for ckpt in ckpts: | |
| # if path.endswith(ckpt): break | |
| # else: | |
| # print(f"### Cleaning {path} ...") | |
| # os.system(f"rm -rf {path}") | |
| # os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}") | |
| # os.system(f"bash download_bashscripts/1-ToonYou.sh") | |
| # os.system(f"bash download_bashscripts/2-Lyriel.sh") | |
| # os.system(f"bash download_bashscripts/3-RcnzCartoon.sh") | |
| # os.system(f"bash download_bashscripts/4-MajicMix.sh") | |
| # os.system(f"bash download_bashscripts/5-RealisticVision.sh") | |
| # # clean Gradio cache | |
| # 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.base_model_list = [] | |
| self.motion_module_list = [] | |
| self.filter_type_list = [ | |
| "butterworth", | |
| "gaussian", | |
| "box", | |
| "ideal" | |
| ] | |
| self.selected_base_model = None | |
| self.selected_motion_module = None | |
| self.selected_filter_type = None | |
| self.set_width = None | |
| self.set_height = None | |
| self.set_d_s = None | |
| self.set_d_t = 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.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda() | |
| self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").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.freq_filter = None | |
| self.update_base_model(self.base_model_list[-2]) | |
| self.update_motion_module(self.motion_module_list[0]) | |
| self.update_filter(512, 512, self.filter_type_list[0], 0.25, 0.25) | |
| def refresh_motion_module(self): | |
| motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt")) | |
| self.motion_module_list = sorted([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 = sorted([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) | |
| return gr.Dropdown.update() | |
| 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.update() | |
| # def update_filter(self, shape, method, n, d_s, d_t): | |
| def update_filter(self, width_slider, height_slider, filter_type_dropdown, d_s_slider, d_t_slider): | |
| self.set_width = width_slider | |
| self.set_height = height_slider | |
| self.selected_filter_type = filter_type_dropdown | |
| self.set_d_s = d_s_slider | |
| self.set_d_t = d_t_slider | |
| vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
| shape = [1, 4, 16, self.set_height//vae_scale_factor, self.set_width//vae_scale_factor] | |
| self.freq_filter = get_freq_filter( | |
| shape, | |
| device="cuda", | |
| filter_type=self.selected_filter_type, | |
| n=4, | |
| d_s=self.set_d_s, | |
| d_t=self.set_d_t | |
| ) | |
| def animate( | |
| self, | |
| base_model_dropdown, | |
| motion_module_dropdown, | |
| prompt_textbox, | |
| negative_prompt_textbox, | |
| width_slider, | |
| height_slider, | |
| seed_textbox, | |
| # freeinit params | |
| filter_type_dropdown, | |
| d_s_slider, | |
| d_t_slider, | |
| num_iters_slider, | |
| # speed up | |
| speed_up_options | |
| ): | |
| # set global seed | |
| set_seed(42) | |
| d_s = float(d_s_slider) | |
| d_t = float(d_t_slider) | |
| num_iters = int(num_iters_slider) | |
| if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown) | |
| if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown) | |
| if self.set_width != width_slider or self.set_height != height_slider or self.selected_filter_type != filter_type_dropdown or self.set_d_s != d_s or self.set_d_t != d_t: | |
| self.update_filter(width_slider, height_slider, filter_type_dropdown, d_s, d_t) | |
| if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention() | |
| pipeline = AnimationFreeInitPipeline( | |
| 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") | |
| # (freeinit) initialize frequency filter for noise reinitialization ------------- | |
| pipeline.freq_filter = self.freq_filter | |
| # ------------------------------------------------------------------------------- | |
| 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_output = pipeline( | |
| prompt_textbox, | |
| negative_prompt = negative_prompt_textbox, | |
| num_inference_steps = 25, | |
| guidance_scale = 7.5, | |
| width = width_slider, | |
| height = height_slider, | |
| video_length = 16, | |
| num_iters = num_iters, | |
| use_fast_sampling = True if "use_coarse_to_fine_sampling" in speed_up_options else False, | |
| save_intermediate = False, | |
| return_orig = True, | |
| use_fp16 = True if "use_fp16" in speed_up_options else False | |
| ) | |
| orig_sample = sample_output.orig_videos | |
| sample = sample_output.videos | |
| save_sample_path = os.path.join(self.savedir, f"sample.mp4") | |
| save_videos_grid(sample, save_sample_path) | |
| save_orig_sample_path = os.path.join(self.savedir, f"sample_orig.mp4") | |
| save_videos_grid(orig_sample, save_orig_sample_path) | |
| # save_compare_path = os.path.join(self.savedir, f"compare.mp4") | |
| # save_videos_grid(torch.concat([orig_sample, sample]), save_compare_path) | |
| json_config = { | |
| "prompt": prompt_textbox, | |
| "n_prompt": negative_prompt_textbox, | |
| "width": width_slider, | |
| "height": height_slider, | |
| "seed": seed, | |
| "base_model": base_model_dropdown, | |
| "motion_module": motion_module_dropdown, | |
| "filter_type": filter_type_dropdown, | |
| "d_s": d_s, | |
| "d_t": d_t, | |
| "num_iters": num_iters, | |
| "use_fp16": True if "use_fp16" in speed_up_options else False, | |
| "use_coarse_to_fine_sampling": True if "use_coarse_to_fine_sampling" in speed_up_options else False | |
| } | |
| print(json_config) | |
| # return gr.Video.update(value=save_compare_path), gr.Json.update(value=json_config) | |
| # return gr.Video.update(value=save_orig_sample_path), gr.Video.update(value=save_sample_path), gr.Video.update(value=save_compare_path), gr.Json.update(value=json_config) | |
| return gr.Video.update(value=save_orig_sample_path), gr.Video.update(value=save_sample_path), gr.Json.update(value=json_config) | |
| controller = AnimateController() | |
| def ui(): | |
| with gr.Blocks(css=css) as demo: | |
| # gr.Markdown('# FreeInit') | |
| gr.Markdown( | |
| """ | |
| <div align="center"> | |
| <h1>FreeInit</h1> | |
| </div> | |
| """ | |
| ) | |
| gr.Markdown( | |
| """ | |
| <p align="center"> | |
| <a title="Project Page" href="https://tianxingwu.github.io/pages/FreeInit/" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/Project-Website-5B7493?logo=googlechrome&logoColor=5B7493"> | |
| </a> | |
| <a title="arXiv" href="https://arxiv.org/abs/2312.07537" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=b31b1b"> | |
| </a> | |
| <a title="GitHub" href="https://github.com/TianxingWu/FreeInit" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/github/stars/TianxingWu/FreeInit?label=GitHub%20%E2%98%85&&logo=github" alt="badge-github-stars"> | |
| </a> | |
| <a title="Video" href="https://youtu.be/lS5IYbAqriI" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/YouTube-Video-red?logo=youtube&logoColor=red"> | |
| </a> | |
| <a title="Visitor" href="https://hits.seeyoufarm.com" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2FTianxingWu%2FFreeInit&count_bg=%23678F74&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false"> | |
| </a> | |
| </p> | |
| """ | |
| # <a title="Visitor" href="https://hits.seeyoufarm.com" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| # <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2FTianxingWu%2FFreeInit&count_bg=%23678F74&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false"> | |
| # </a> | |
| ) | |
| gr.Markdown( | |
| """ | |
| Official Gradio Demo for ***FreeInit: Bridging Initialization Gap in Video Diffusion Models***. | |
| FreeInit improves time consistency of diffusion-based video generation at inference time. In this demo, we apply FreeInit on [AnimateDiff v1](https://github.com/guoyww/AnimateDiff) as an example. Sampling time: ~ 80s.<br> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| # gr.Markdown( | |
| # """ | |
| # ### Usage | |
| # 1. Select customized model and motion module in `Model Settings`. | |
| # 3. Set `FreeInit Settings`. | |
| # 3. Provide `Prompt` and `Negative Prompt` for your selected model. You can refer to each model's webpage on CivitAI to learn how to write prompts for them: | |
| # - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775) | |
| # - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel) | |
| # - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009) | |
| # - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068) | |
| # - [`realisticVisionV20_v20.safetensors`](https://civitai.com/models/4201?modelVersionId=29460) | |
| # 4. Click `Generate`. | |
| # """ | |
| # ) | |
| prompt_textbox = gr.Textbox( label="Prompt", lines=3, placeholder="Enter your prompt here") | |
| negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo") | |
| gr.Markdown( | |
| """ | |
| *Prompt Tips:* | |
| For each personalized model in `Model Settings`, you can refer to their webpage on CivitAI to learn how to write good prompts for them: | |
| - [`realisticVisionV51_v20Novae.safetensors`](https://civitai.com/models/4201?modelVersionId=130072) | |
| - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775) | |
| - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel) | |
| - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009) | |
| - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068) | |
| """ | |
| ) | |
| with gr.Accordion("Model Settings", open=False): | |
| gr.Markdown( | |
| """ | |
| Select personalized model and motion module for AnimateDiff. | |
| """ | |
| ) | |
| base_model_dropdown = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list, value=controller.base_model_list[-2], interactive=True, | |
| info="Select personalized text-to-image model from community") | |
| motion_module_dropdown = gr.Dropdown( label="Motion Module", choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True, | |
| info="Select motion module. Recommend mm_sd_v14.ckpt for larger movements.") | |
| 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]) | |
| with gr.Accordion("FreeInit Params", open=False): | |
| gr.Markdown( | |
| """ | |
| Adjust to control the smoothness. | |
| """ | |
| ) | |
| filter_type_dropdown = gr.Dropdown( label="Filter Type", choices=controller.filter_type_list, value=controller.filter_type_list[0], interactive=True, | |
| info="Default as Butterworth. To fix large inconsistencies, consider using Gaussian.") | |
| d_s_slider = gr.Slider( label="d_s", value=0.25, minimum=0, maximum=1, step=0.125, | |
| info="Stop frequency for spatial dimensions (0.0-1.0)") | |
| d_t_slider = gr.Slider( label="d_t", value=0.25, minimum=0, maximum=1, step=0.125, | |
| info="Stop frequency for temporal dimension (0.0-1.0)") | |
| # num_iters_textbox = gr.Textbox( label="FreeInit Iterations", value=3, info="Sould be integer >1, larger value leads to smoother results)") | |
| num_iters_slider = gr.Slider( label="FreeInit Iterations", value=3, minimum=2, maximum=5, step=1, | |
| info="Larger value leads to smoother results & longer inference time.") | |
| 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=2005563494988190) | |
| 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]) | |
| with gr.Row(): | |
| speed_up_options = gr.CheckboxGroup( | |
| ["use_fp16", "use_coarse_to_fine_sampling"], | |
| label="Speed-Up Options", | |
| value=["use_fp16"] | |
| ) | |
| 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 ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| orig_video = gr.Video( label="AnimateDiff", interactive=False ) | |
| freeinit_video = gr.Video( label="AnimateDiff + FreeInit", interactive=False ) | |
| # with gr.Row(): | |
| # compare_video = gr.Video( label="Compare", interactive=False ) | |
| with gr.Row(): | |
| json_config = gr.Json( label="Config", value=None ) | |
| inputs = [base_model_dropdown, motion_module_dropdown, | |
| prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox, | |
| filter_type_dropdown, d_s_slider, d_t_slider, num_iters_slider, | |
| speed_up_options | |
| ] | |
| # outputs = [result_video, json_config] | |
| # outputs = [orig_video, freeinit_video, compare_video, json_config] | |
| outputs = [orig_video, freeinit_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() | |