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| 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 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 | |
| from huggingface_hub import hf_hub_download | |
| num_frames = 25 | |
| num_steps = 30 | |
| model_config = "scripts/sampling/configs/svd_xt.yaml" | |
| device = "cuda" | |
| hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints", token=os.getenv("HF_TOKEN")) | |
| 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 | |
| model, filter = load_model( | |
| model_config, | |
| device, | |
| num_frames, | |
| num_steps, | |
| ) | |
| def sample( | |
| image: Image.Image, | |
| num_frames: Optional[int] = 25, | |
| num_steps: Optional[int] = 30, | |
| version: str = "svd_xt", | |
| fps_id: int = 6, | |
| motion_bucket_id: int = 127, | |
| cond_aug: float = 0.02, | |
| seed: int = 23, | |
| decoding_t: int = 7, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary. | |
| ): | |
| output_folder = str(uuid.uuid4()) | |
| torch.manual_seed(seed) | |
| all_img_paths = [image] | |
| for input_img_path in all_img_paths: | |
| 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(*'avc1'), | |
| 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 | |
| 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 | |
| import gradio as gr | |
| import uuid | |
| def resize_image(image, output_size=(1024, 576)): | |
| # 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.ANTIALIAS) | |
| # 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.ANTIALIAS) | |
| # 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('''# Stable Video Diffusion - Image2Video - XT | |
| Generate 25 frames of video from a single image using SDV-XT. | |
| ''') | |
| with gr.Column(): | |
| image = gr.Image(label="Upload your image (it will be center cropped to 1024x576)", type="pil") | |
| generate_btn = gr.Button("Generate") | |
| with gr.Accordion("Advanced options", open=False): | |
| cond_aug = gr.Slider(label="Conditioning augmentation", value=0.02, minimum=0.0) | |
| seed = gr.Slider(label="Seed", value=42, minimum=0, maximum=int(1e9), step=1) | |
| #decoding_t = gr.Slider(label="Decode frames at a time", value=6, minimum=1, maximum=14, interactive=False) | |
| saving_fps = gr.Slider(label="Saving FPS", value=6, minimum=6, maximum=48, step=6) | |
| with gr.Column(): | |
| video = gr.Video() | |
| image.upload(fn=resize_image, inputs=image, outputs=image) | |
| generate_btn.click(fn=sample, inputs=[image], outputs=video, api_name="video") | |
| demo.launch() | |