import spaces import gradio as gr import torch import torchvision as tv import random, os from diffusers import StableVideoDiffusionPipeline from PIL import Image from glob import glob from typing import Optional from tdd_svd_scheduler import TDDSVDStochasticIterativeScheduler from utils import load_lora_weights, save_video # LOCAL = True LOCAL = False if LOCAL: svd_path = '/share2/duanyuxuan/diff_playground/diffusers_models/stable-video-diffusion-img2vid-xt-1-1' lora_file_path = '/share2/duanyuxuan/diff_playground/SVD-TDD/svd-xt-1-1_tdd_lora_weights.safetensors' else: svd_path = 'stabilityai/stable-video-diffusion-img2vid-xt-1-1' lora_repo_path = 'RED-AIGC/TDD' lora_weight_name = 'svd-xt-1-1_tdd_lora_weights.safetensors' if torch.cuda.is_available(): noise_scheduler = TDDSVDStochasticIterativeScheduler(num_train_timesteps = 250, sigma_min = 0.002, sigma_max = 700.0, sigma_data = 1.0, s_noise = 1.0, rho = 7, clip_denoised = False) pipeline = StableVideoDiffusionPipeline.from_pretrained(svd_path, scheduler = noise_scheduler, torch_dtype = torch.float16, variant = "fp16").to('cuda') if LOCAL: load_lora_weights(pipeline.unet, lora_file_path) else: load_lora_weights(pipeline.unet, lora_repo_path, weight_name = lora_weight_name) max_64_bit_int = 2**63 - 1 @spaces.GPU def sample( image: Image, seed: Optional[int] = 1, randomize_seed: bool = False, num_inference_steps: int = 4, eta: float = 0.3, min_guidance_scale: float = 1.0, max_guidance_scale: float = 1.0, fps: int = 7, width: int = 512, height: int = 512, num_frames: int = 25, motion_bucket_id: int = 127, output_folder: str = "outputs_gradio", ): pipeline.scheduler.set_eta(eta) if randomize_seed: seed = random.randint(0, max_64_bit_int) generator = torch.manual_seed(seed) os.makedirs(output_folder, exist_ok=True) base_count = len(glob(os.path.join(output_folder, "*.mp4"))) video_path = os.path.join(output_folder, f"{base_count:06d}.mp4") with torch.autocast("cuda"): frames = pipeline( image, height = height, width = width, num_inference_steps = num_inference_steps, min_guidance_scale = min_guidance_scale, max_guidance_scale = max_guidance_scale, num_frames = num_frames, fps = fps, motion_bucket_id = motion_bucket_id, decode_chunk_size = 8, noise_aug_strength = 0.02, generator = generator, ).frames[0] save_video(frames, video_path, fps = fps, quality = 5.0) torch.manual_seed(seed) return video_path, seed def preprocess_image(image, height = 512, width = 512): image = image.convert('RGB') if image.size[0] != image.size[1]: image = tv.transforms.functional.pil_to_tensor(image) image = tv.transforms.functional.center_crop(image, min(image.shape[-2:])) image = tv.transforms.functional.to_pil_image(image) image = image.resize((width, height)) return image css = """ h1 { text-align: center; display:block; } .gradio-container { max-width: 70.5rem !important; } """ with gr.Blocks(css = css) as demo: gr.Markdown( """ # Stable Video Diffusion distilled by ✨Target-Driven Distillation✨ Target-Driven Distillation (TDD) is a state-of-the-art consistency distillation model that largely accelerates the inference processes of diffusion models. Using its delicate strategies of *target timestep selection* and *decoupled guidance*, models distilled by TDD can generated highly detailed images with only a few steps. Besides, TDD is also available for distilling video generation models. This space presents TDD-distilled [SVD-xt 1.1](https://huggingface.co./stabilityai/stable-video-diffusion-img2vid-xt-1-1). [**Project Page**](https://redaigc.github.io/TDD/) **|** [**Paper**](https://arxiv.org/abs/2409.01347) **|** [**Code**](https://github.com/RedAIGC/Target-Driven-Distillation) **|** [**Model**](https://huggingface.co./RED-AIGC/TDD) **|** [🤗 **TDD-SDXL Demo**](https://huggingface.co./spaces/RED-AIGC/TDD) **|** [🤗 **TDD-SVD Demo**](https://huggingface.co./spaces/RED-AIGC/SVD-TDD) The codes of this space are built on [AnimateLCM-SVD](https://huggingface.co./spaces/wangfuyun/AnimateLCM-SVD) and we acknowledge their contribution. """ ) with gr.Row(): with gr.Column(): image = gr.Image(label="Upload your image", type="pil") generate_btn = gr.Button("Generate") video = gr.Video() with gr.Accordion("Options", open = True): seed = gr.Slider( label="Seed", value=1, randomize=False, minimum=0, maximum=max_64_bit_int, step=1, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=False) min_guidance_scale = gr.Slider( label="Min guidance scale", info="min strength of classifier-free guidance", value=1.0, minimum=1.0, maximum=1.5, ) max_guidance_scale = gr.Slider( label="Max guidance scale", info="max strength of classifier-free guidance, it should not be less than Min guidance scale", value=1.0, minimum=1.0, maximum=3.0, ) num_inference_steps = gr.Slider( label="Num inference steps", info="steps for inference", value=4, minimum=4, maximum=8, step=1, ) eta = gr.Slider( label = "Eta", info = "the value of gamma in gamma-sampling", value = 0.3, minimum = 0.0, maximum = 1.0, step = 0.1, ) image.upload(fn = preprocess_image, inputs = image, outputs = image, queue = False) generate_btn.click( fn = sample, inputs = [ image, seed, randomize_seed, num_inference_steps, eta, min_guidance_scale, max_guidance_scale, ], outputs = [video, seed], api_name = "video", ) # safetensors_dropdown.change(fn=model_select, inputs=safetensors_dropdown) # gr.Examples( # examples=[ # ["examples/ipadapter_cat.jpg"], # ], # inputs=[image], # outputs=[video, seed], # fn=sample, # cache_examples=True, # ) if __name__ == "__main__": if LOCAL: demo.queue().launch(share=True, server_name='0.0.0.0') else: demo.queue(api_open=False).launch(show_api=False)