import math import os from glob import glob from pathlib import Path from typing import Optional import cv2 import numpy as np import torch from einops import rearrange, repeat from fire import Fire from omegaconf import OmegaConf from PIL import Image from torchvision.transforms import ToTensor from scripts.util.detection.nsfw_and_watermark_dectection import \ DeepFloydDataFiltering from sgm.inference.helpers import embed_watermark from sgm.util import default, instantiate_from_config import gradio as gr import uuid import random from huggingface_hub import hf_hub_download hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints") version = "svd_xt" device = "cuda" max_64_bit_int = 2**63 - 1 def load_model( config: str, device: str, num_frames: int, num_steps: int, ): config = OmegaConf.load(config) if device == "cuda": config.model.params.conditioner_config.params.emb_models[ 0 ].params.open_clip_embedding_config.params.init_device = device config.model.params.sampler_config.params.num_steps = num_steps config.model.params.sampler_config.params.guider_config.params.num_frames = ( num_frames ) if device == "cuda": with torch.device(device): model = instantiate_from_config(config.model).to(device).eval() else: model = instantiate_from_config(config.model).to(device).eval() filter = DeepFloydDataFiltering(verbose=False, device=device) return model, filter if version == "svd_xt": num_frames = 25 num_steps = 30 model_config = "scripts/sampling/configs/svd_xt.yaml" else: raise ValueError(f"Version {version} does not exist.") model, filter = load_model( model_config, device, num_frames, num_steps, ) def sample( input_path: str = "assets/test_image.png", # Can either be image file or folder with image files seed: Optional[int] = None, randomize_seed: bool = True, motion_bucket_id: int = 127, fps_id: int = 6, version: str = "svd_xt", cond_aug: float = 0.02, decoding_t: int = 7, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary. device: str = "cuda", output_folder: str = "outputs", progress=gr.Progress(track_tqdm=True) ): """ Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`. """ if(randomize_seed): seed = random.randint(0, max_64_bit_int) torch.manual_seed(seed) path = Path(input_path) all_img_paths = [] if path.is_file(): if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]): all_img_paths = [input_path] else: raise ValueError("Path is not valid image file.") elif path.is_dir(): all_img_paths = sorted( [ f for f in path.iterdir() if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"] ] ) if len(all_img_paths) == 0: raise ValueError("Folder does not contain any images.") else: raise ValueError for input_img_path in all_img_paths: with Image.open(input_img_path) as image: if image.mode == "RGBA": image = image.convert("RGB") w, h = image.size if h % 64 != 0 or w % 64 != 0: width, height = map(lambda x: x - x % 64, (w, h)) image = image.resize((width, height)) print( f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!" ) image = ToTensor()(image) image = image * 2.0 - 1.0 image = image.unsqueeze(0).to(device) H, W = image.shape[2:] assert image.shape[1] == 3 F = 8 C = 4 shape = (num_frames, C, H // F, W // F) if (H, W) != (576, 1024): print( "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`." ) if motion_bucket_id > 255: print( "WARNING: High motion bucket! This may lead to suboptimal performance." ) if fps_id < 5: print("WARNING: Small fps value! This may lead to suboptimal performance.") if fps_id > 30: print("WARNING: Large fps value! This may lead to suboptimal performance.") value_dict = {} value_dict["motion_bucket_id"] = motion_bucket_id value_dict["fps_id"] = fps_id value_dict["cond_aug"] = cond_aug value_dict["cond_frames_without_noise"] = image value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image) value_dict["cond_aug"] = cond_aug with torch.no_grad(): with torch.autocast(device): batch, batch_uc = get_batch( get_unique_embedder_keys_from_conditioner(model.conditioner), value_dict, [1, num_frames], T=num_frames, device=device, ) c, uc = model.conditioner.get_unconditional_conditioning( batch, batch_uc=batch_uc, force_uc_zero_embeddings=[ "cond_frames", "cond_frames_without_noise", ], ) for k in ["crossattn", "concat"]: uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames) uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames) c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames) c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames) randn = torch.randn(shape, device=device) additional_model_inputs = {} additional_model_inputs["image_only_indicator"] = torch.zeros( 2, num_frames ).to(device) additional_model_inputs["num_video_frames"] = batch["num_video_frames"] def denoiser(input, sigma, c): return model.denoiser( model.model, input, sigma, c, **additional_model_inputs ) samples_z = model.sampler(denoiser, randn, cond=c, uc=uc) model.en_and_decode_n_samples_a_time = decoding_t samples_x = model.decode_first_stage(samples_z) samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0) 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") writer = cv2.VideoWriter( video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps_id + 1, (samples.shape[-1], samples.shape[-2]), ) samples = embed_watermark(samples) samples = filter(samples) vid = ( (rearrange(samples, "t c h w -> t h w c") * 255) .cpu() .numpy() .astype(np.uint8) ) for frame in vid: frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) writer.write(frame) writer.release() return video_path, seed def get_unique_embedder_keys_from_conditioner(conditioner): return list(set([x.input_key for x in conditioner.embedders])) def get_batch(keys, value_dict, N, T, device): batch = {} batch_uc = {} for key in keys: if key == "fps_id": batch[key] = ( torch.tensor([value_dict["fps_id"]]) .to(device) .repeat(int(math.prod(N))) ) elif key == "motion_bucket_id": batch[key] = ( torch.tensor([value_dict["motion_bucket_id"]]) .to(device) .repeat(int(math.prod(N))) ) elif key == "cond_aug": batch[key] = repeat( torch.tensor([value_dict["cond_aug"]]).to(device), "1 -> b", b=math.prod(N), ) elif key == "cond_frames": batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0]) elif key == "cond_frames_without_noise": batch[key] = repeat( value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0] ) else: batch[key] = value_dict[key] if T is not None: batch["num_video_frames"] = T for key in batch.keys(): if key not in batch_uc and isinstance(batch[key], torch.Tensor): batch_uc[key] = torch.clone(batch[key]) return batch, batch_uc def resize_image(image_path, output_size=(1024, 576)): image = Image.open(image_path) # Calculate aspect ratios target_aspect = output_size[0] / output_size[1] # Aspect ratio of the desired size image_aspect = image.width / image.height # Aspect ratio of the original image # Resize then crop if the original image is larger if image_aspect > target_aspect: # Resize the image to match the target height, maintaining aspect ratio new_height = output_size[1] new_width = int(new_height * image_aspect) resized_image = image.resize((new_width, new_height), Image.LANCZOS) # Calculate coordinates for cropping left = (new_width - output_size[0]) / 2 top = 0 right = (new_width + output_size[0]) / 2 bottom = output_size[1] else: # Resize the image to match the target width, maintaining aspect ratio new_width = output_size[0] new_height = int(new_width / image_aspect) resized_image = image.resize((new_width, new_height), Image.LANCZOS) # Calculate coordinates for cropping left = 0 top = (new_height - output_size[1]) / 2 right = output_size[0] bottom = (new_height + output_size[1]) / 2 # Crop the image cropped_image = resized_image.crop((left, top, right, bottom)) return cropped_image with gr.Blocks() as demo: gr.Markdown('''# Unofficial demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets)) #### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `25 frames` of video from a single image at `6 fps`. Each generation takes ~60s on the A100. [Join the waitlist](https://stability.ai/contact) for Stability's upcoming web experience. ''') with gr.Row(): with gr.Column(): image = gr.Image(label="Upload your image", type="filepath") generate_btn = gr.Button("Generate") video = gr.Video() with gr.Accordion("Advanced options", open=False): seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int, step=1) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255) fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30) image.upload(fn=resize_image, inputs=image, outputs=image, queue=False) generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video") if __name__ == "__main__": demo.queue(max_size=20) demo.launch(share=True)