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#!/usr/bin/env python
import gradio as gr
import PIL.Image
import os
from gradio_client import Client, file
lgm_mini_client = Client("dylanebert/LGM-mini")
triposr_client = Client("stabilityai/TripoSR")
crm_client = Client("Zhengyi/CRM")
def run(image, model_name):
file_path = "temp.png"
image.save(file_path)
if model_name=='lgm-mini':
result = lgm_mini_client.predict(
file_path, # filepath in 'image' Image component
api_name="/run"
)
output = result
elif model_name=='triposr':
process_result = triposr_client.predict(
file_path, # filepath in 'Input Image' Image component
True, # bool in 'Remove Background' Checkbox component
0.85, # float (numeric value between 0.5 and 1.0) in 'Foreground Ratio' Slider component
api_name="/preprocess")
result = triposr_client.predict(
process_result, # filepath in 'Processed Image' Image component
256, # float (numeric value between 32 and 320) in 'Marching Cubes Resolution' Slider component
api_name="/generate")
output = result[0]
elif model=='crm':
preprocess_result = crm_client.predict(
file(file_path), # filepath in 'Image input' Image component
"Auto Remove background", # Literal['Alpha as mask', 'Auto Remove background'] in 'backgroud choice' Radio component
1, # float (numeric value between 0.5 and 1.0) in 'Foreground Ratio' Slider component
"#000000", # str in 'Background Color' Colorpicker component
api_name="/preprocess_image"
)
result = crm_client.predict(
file(preprocess_result), # filepath in 'Processed Image' Image component
1234, # float in 'seed' Number component
5.5, # float in 'guidance_scale' Number component
30, # float in 'sample steps' Number component
api_name="/gen_image"
)
output = result[2]
return output
demo = gr.Interface(
fn=run,
inputs=[gr.Image(type="pil"),gr.Textbox(label="Model Name")],
outputs=gr.Model3D(label="3D Model"),
api_name="synthesize"
)
demo.launch() |