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import gradio as gr | |
import numpy as np | |
import tempfile | |
import imageio | |
import torch | |
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) | |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
pipe.enable_model_cpu_offload() | |
caption = gr.load(name="spaces/fffiloni/CoCa-clone") | |
def create_image_caption(image_init): | |
cap = caption(image_init, "Nucleus sampling", 1.2, 0.5, 5, 20, fn_index=0) | |
print("cap: " + cap) | |
return cap | |
def export_to_video(frames: np.ndarray, fps: int) -> str: | |
frames = np.clip((frames * 255), 0, 255).astype(np.uint8) | |
out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) | |
writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps) | |
for frame in frames: | |
writer.append_data(frame) | |
writer.close() | |
return out_file.name | |
def infer(image_init): | |
prompt = create_image_caption(image_init) | |
video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames[0] | |
video_path = export_to_video(video_frames, 12) | |
print(video_path) | |
return prompt, video_path | |
css = """ | |
#col-container {max-width: 510px; margin-left: auto; margin-right: auto;} | |
a {text-decoration-line: underline; font-weight: 600;} | |
.animate-spin { | |
animation: spin 1s linear infinite; | |
} | |
@keyframes spin { | |
from { | |
transform: rotate(0deg); | |
} | |
to { | |
transform: rotate(360deg); | |
} | |
} | |
#share-btn-container { | |
display: flex; | |
padding-left: 0.5rem !important; | |
padding-right: 0.5rem !important; | |
background-color: #000000; | |
justify-content: center; | |
align-items: center; | |
border-radius: 9999px !important; | |
max-width: 13rem; | |
} | |
#share-btn-container:hover { | |
background-color: #060606; | |
} | |
#share-btn { | |
all: initial; | |
color: #ffffff; | |
font-weight: 600; | |
cursor:pointer; | |
font-family: 'IBM Plex Sans', sans-serif; | |
margin-left: 0.5rem !important; | |
padding-top: 0.5rem !important; | |
padding-bottom: 0.5rem !important; | |
right:0; | |
} | |
#share-btn * { | |
all: unset; | |
} | |
#share-btn-container div:nth-child(-n+2){ | |
width: auto !important; | |
min-height: 0px !important; | |
} | |
#share-btn-container .wrap { | |
display: none !important; | |
} | |
#share-btn-container.hidden { | |
display: none!important; | |
} | |
img[src*='#center'] { | |
display: block; | |
margin: auto; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown( | |
""" | |
<h1 style="text-align: center;">Zeroscope Image-to-Video</h1> | |
<p style="text-align: center;"> | |
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br /> | |
This demo is a variation that lets you upload an image as reference for video generation. | |
</p> | |
[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center)](https://huggingface.co/spaces/fffiloni/zeroscope-img-to-video?duplicate=true) | |
""" | |
) | |
image_init = gr.Image(label="Image Init",type="filepath", source="upload", elem_id="image-init") | |
#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False) | |
submit_btn = gr.Button("Submit") | |
coca_cap = gr.Textbox(label="Caption", placeholder="CoCa Caption will be displayed here", elem_id="coca-cap-in") | |
video_result = gr.Video(label="Video Output", elem_id="video-output") | |
submit_btn.click(fn=infer, | |
inputs=[image_init], | |
outputs=[coca_cap, video_result]) | |
demo.queue(max_size=12).launch() | |