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import gradio as gr
from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys
pipe = pipeline(task='image-to-video', model='damo/Image-to-Video', model_revision='v1.1.0')
def infer (image_in):
# IMG_PATH: your image path (url or local file)
IMG_PATH = image_in
output_video_path = pipe(IMG_PATH, output_video='output.mp4')[OutputKeys.OUTPUT_VIDEO]
print(output_video_path)
return output_video_path
with gr.Blocks() as demo:
gr.Markdown("""
<p>
You are currently viewing a micro-service API meant to be used by robots.<br/>
For the human UI, please check out the <a href="https://huggingface.co/spaces/fffiloni/MS-Image2Video">original Space by Sylvain Filoni</a>.
</p>
""")
image_in = gr.Image(
label = "Source Image",
source = "upload",
type = "filepath",
elem_id = "image-in"
)
submit_btn = gr.Button("Submit")
video_out = gr.Video(label = "Video Result", elem_id = "video-out")
submit_btn.click(fn = infer, inputs = [image_in], outputs = [video_out])
demo.queue(max_size=6).launch() |