Spaces:
Sleeping
Sleeping
File size: 8,663 Bytes
992a789 7543bdf 992a789 7543bdf 992a789 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
import gradio as gr
# import gradio.helpers
import torch
import os
from glob import glob
from pathlib import Path
from typing import Optional
from PIL import Image
from diffusers.utils import load_image, export_to_video
from pipeline import StableVideoDiffusionPipeline
import random
from safetensors import safe_open
from lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler
def get_safetensors_files():
models_dir = "./safetensors"
safetensors_files = [
f for f in os.listdir(models_dir) if f.endswith(".safetensors")
]
return safetensors_files
def model_select(selected_file):
print("load model weights", selected_file)
pipe.unet.cpu()
file_path = os.path.join("./safetensors", selected_file)
state_dict = {}
with safe_open(file_path, framework="pt", device="cpu") as f:
for key in f.keys():
state_dict[key] = f.get_tensor(key)
missing, unexpected = pipe.unet.load_state_dict(state_dict, strict=True)
pipe.unet.cuda()
del state_dict
return
noise_scheduler = AnimateLCMSVDStochasticIterativeScheduler(
num_train_timesteps=40,
sigma_min=0.002,
sigma_max=700.0,
sigma_data=1.0,
s_noise=1.0,
rho=7,
clip_denoised=False,
)
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt",
scheduler=noise_scheduler,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.to("cuda")
pipe.enable_model_cpu_offload() # for smaller cost
model_select("AnimateLCM-SVD-xt.safetensors")
# pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) # for faster inference
max_64_bit_int = 2**63 - 1
def sample(
image: Image,
seed: Optional[int] = 42,
randomize_seed: bool = False,
motion_bucket_id: int = 80,
fps_id: int = 8,
max_guidance_scale: float = 1.2,
min_guidance_scale: float = 1,
width: int = 1024,
height: int = 576,
num_inference_steps: int = 4,
decoding_t: int = 4, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
output_folder: str = "outputs_gradio",
):
if image.mode == "RGBA":
image = image.convert("RGB")
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 = pipe(
image,
decode_chunk_size=decoding_t,
generator=generator,
motion_bucket_id=motion_bucket_id,
height=height,
width=width,
num_inference_steps=num_inference_steps,
min_guidance_scale=min_guidance_scale,
max_guidance_scale=max_guidance_scale,
).frames[0]
export_to_video(frames, video_path, fps=fps_id)
torch.manual_seed(seed)
return video_path, seed
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.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(
"""
# [AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning](https://arxiv.org/abs/2402.00769)
Fu-Yun Wang, Zhaoyang Huang (*Corresponding Author), Xiaoyu Shi, Weikang Bian, Guanglu Song, Yu Liu, Hongsheng Li (*Corresponding Author)<br>
[arXiv Report](https://arxiv.org/abs/2402.00769) | [Project Page](https://animatelcm.github.io/) | [Github](https://github.com/G-U-N/AnimateLCM) | [Civitai](https://civitai.com/models/290375/animatelcm-fast-video-generation) | [Replicate](https://replicate.com/camenduru/animate-lcm)
Related Models:
[AnimateLCM-t2v](https://huggingface.co/wangfuyun/AnimateLCM): Personalized Text-to-Video Generation
[AnimateLCM-SVD-xt](https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt): General Image-to-Video Generation
[AnimateLCM-i2v](https://huggingface.co/wangfuyun/AnimateLCM-I2V): Personalized Image-to-Video Generation
"""
)
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("Advanced options", open=False):
safetensors_dropdown = gr.Dropdown(
label="Choose Safetensors", choices=get_safetensors_files()
)
seed = gr.Slider(
label="Seed",
value=42,
randomize=False,
minimum=0,
maximum=max_64_bit_int,
step=1,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
motion_bucket_id = gr.Slider(
label="Motion bucket id",
info="Controls how much motion to add/remove from the image",
value=80,
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=8,
minimum=5,
maximum=30,
)
width = gr.Slider(
label="Width of input image",
info="It should be divisible by 64",
value=1024,
minimum=576,
maximum=2048,
)
height = gr.Slider(
label="Height of input image",
info="It should be divisible by 64",
value=576,
minimum=320,
maximum=1152,
)
max_guidance_scale = gr.Slider(
label="Max guidance scale",
info="classifier-free guidance strength",
value=1.2,
minimum=1,
maximum=2,
)
min_guidance_scale = gr.Slider(
label="Min guidance scale",
info="classifier-free guidance strength",
value=1,
minimum=1,
maximum=1.5,
)
num_inference_steps = gr.Slider(
label="Num inference steps",
info="steps for inference",
value=4,
minimum=1,
maximum=20,
step=1,
)
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,
max_guidance_scale,
min_guidance_scale,
width,
height,
num_inference_steps,
],
outputs=[video, seed],
api_name="video",
)
safetensors_dropdown.change(fn=model_select, inputs=safetensors_dropdown)
gr.Examples(
examples=[
"test_imgs/ai-generated-8255456_1280.png",
"test_imgs/ai-generated-8496135_1280.jpg",
"test_imgs/dog-7396912_1280.jpg",
"test_imgs/ship-7833921_1280.jpg",
],
inputs=image,
outputs=[video, seed],
fn=sample,
cache_examples=True,
)
if __name__ == "__main__":
demo.queue(max_size=20, api_open=False)
demo.launch(share=True, show_api=False)
|