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import gradio as gr
from gradio_client import Client
def get_instantID(portrait_in, prompt):
client = Client("https://instantx-instantid.hf.space/")
negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green"
result = client.predict(
portrait_in, # filepath in 'Upload a photo of your face' Image component
None, # filepath in 'Upload a reference pose image (optional)' Image component
prompt, # str in 'Prompt' Textbox component
negative_prompt, # str in 'Negative Prompt' Textbox component
"(No style)", # Literal['(No style)', 'Watercolor', 'Film Noir', 'Neon', 'Jungle', 'Mars', 'Vibrant Color', 'Snow', 'Line art'] in 'Style template' Dropdown component
True, # bool in 'Enhance non-face region' Checkbox component
20, # float (numeric value between 20 and 100) in 'Number of sample steps' Slider component
0.8, # float (numeric value between 0 and 1.5) in 'IdentityNet strength (for fedility)' Slider component
0.8, # float (numeric value between 0 and 1.5) in 'Image adapter strength (for detail)' Slider component
5, # float (numeric value between 0.1 and 10.0) in 'Guidance scale' Slider component
0, # float (numeric value between 0 and 2147483647) in 'Seed' Slider component
api_name="/generate_image"
)
print(result)
return result[0]
def get_video(image_in, prompt):
client = Client("https://modelscope-i2vgen-xl.hf.space/")
result = client.predict(
image_in,
prompt
)
print(result)
return result
def infer(image_in, prompt):
iid_img = get_instantID(image_in, prompt)
video_res = get_video(iid_img, prompt)
print(video_res)
return video_res
gr.Interface(
fn = infer,
inputs = [
gr.Image(type="filepath"),
gr.Textbox()
],
outputs = [
gr.Video()
]
).launch() |