Spaces:
Runtime error
Runtime error
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, | |
version: str = "svd_xt", | |
fps_id: int = 6, | |
motion_bucket_id: int = 127, | |
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('''# Stable Video Diffusion - Img2Vid - XT | |
## Research release (non-commercial): 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 a native web experience for video. | |
''') | |
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(open=False): | |
seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int) | |
randomize_seed = gr.Checkbox("Randomize seed") | |
image.upload(fn=resize_image, inputs=image, outputs=image, queue=False) | |
generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed], outputs=[video, seed], api_name="video") | |
if __name__ == "__main__": | |
demo.launch(share=True) |