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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, # filepath in 'image' Image component
"Describe the image", # str in 'Question' Textbox component
api_name="/answer_question"
)
print(result)
return result
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
css = """
footer {
visibility: hidden;
}
"""
gr.Interface(css=css,
fn = infer,
inputs = [
gr.Image(type="filepath", label="Image input")
],
outputs = [
gr.Image(label="LCM Image variation")
]
).queue(max_size=25).launch()
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