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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/")
results = client.predict(
prompt, # existing parameters
num_images=4, # New parameter to request 4 images. This parameter may be different for the actual API.
)
# Print and return the results assuming it contains 4 images
print(results)
return results[:4] # Return first 4 images in case the API returns more
def get_lcm(prompt):
client = Client("https://latent-consistency-lcm-lora-for-sdxl.hf.space/")
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"
)
print(result)
return result[0]
def infer(image_in):
caption = get_caption(image_in)
img_var = get_lcm(caption)
return img_var
gr.Interface(
title = "ArXivGPT Image",
description = "Image to Image variation, using LCM SDXL & Moondream1",
fn = infer,
inputs = [
gr.Image(type="filepath", label="Image input")
],
outputs = [
gr.Image(label="Image variation")
]
).queue(max_size=25).launch() |