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import gradio as gr
import os
img_to_text = gr.Interface.load("spaces/pharma/CLIP-Interrogator")
stable_diffusion = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion")
def get_images(prompt):
gallery_dir = stable_diffusion(prompt, fn_index=2)
return [os.path.join(gallery_dir, image) for image in os.listdir(gallery_dir)]
def get_prompts(uploaded_image):
return img_to_text(uploaded_image)
with gr.Blocks() as demo:
gr.Markdown(
"""
## Stable Diffusion Perception 🎆🌌
Input an image and see how the model perceives it! 👀
"""
)
with gr.Row():
with gr.Column():
input_img = gr.Image(type="filepath")
with gr.Row():
see_prompts = gr.Button("Check how your image prompts your model!", elem_id="check_btn_1")
with gr.Column():
img2text_output = gr.Textbox(
label="Convert your image to text!",
lines=4,
elem_id="translated"
)
with gr.Row():
diffuse_btn = gr.Button(value="Diffuse it!", elem_id="diffuse_btn")
with gr.Column():
sd_output = gr.Gallery().style(grid=2, height="auto")
def translate_directly(img):
images = get_images(get_prompts(img))
return images
see_prompts.click(img_to_text,
inputs = input_img,
outputs = [
img2text_output
])
diffuse_btn.click(get_images,
inputs = [
img2text_output
],
outputs = sd_output
)
demo.launch()
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