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import gradio as gr | |
from gradio_client import Client | |
def get_caption(image_in): | |
client = Client("https://vikhyatk-moondream1.hf.space/") | |
result = client.predict( | |
image_in, | |
"Describe the image", | |
api_name="/answer_question" | |
) | |
caption = result['choices'][0]['text'] | |
return caption | |
def get_lcm(prompt): | |
client = Client("https://latent-consistency-lcm-lora-for-sdxl.hf.space/") | |
images = [] | |
for _ in range(4): # 4개의 이미지 생성 | |
result = client.predict( | |
prompt, # str in 'parameter_5' Textbox component | |
0.3, # float (numeric value between 0.0 and 5) in 'Guidance' Slider component | |
8, # float (numeric value between 2 and 10) in 'Steps' Slider component | |
0, # float (numeric value between 0 and 12013012031030) in 'Seed' Slider component | |
True, # bool in 'Randomize' Checkbox component | |
api_name="/predict" | |
) | |
images.append(result[0]) | |
return images | |
def infer(image_in): | |
caption = get_caption(image_in) | |
img_vars = get_lcm(caption) | |
return caption, img_vars | |
gr.Interface( | |
title="ArXivGPT Image", | |
description=" Image2Image Variation - LCM SDXL & Moondream1 using for image generation", | |
fn=infer, | |
inputs=[ | |
gr.Image(type="filepath", label="Image input") | |
], | |
outputs=[ | |
gr.Textbox(label="Caption"), | |
gr.Gallery(label="LCM Image variations") | |
] | |
).queue(max_size=25).launch() | |