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Create app.py
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app.py
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import os
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import io
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import IPython.display
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from PIL import Image
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import base64
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from diffusers import DiffusionPipeline
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hf_api_key = "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"
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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def get_completion(prompt):
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return pipeline(prompt).images[0]
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import gradio as gr
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#A helper function to convert the PIL image to base64
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#so you can send it to the API
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#A helper function to convert the PIL image to base64
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# so you can send it to the API
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def base64_to_pil(img_base64):
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base64_decoded = base64.b64decode(img_base64)
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byte_stream = io.BytesIO(base64_decoded)
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pil_image = Image.open(byte_stream)
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return pil_image
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def generate(prompt, negative_prompt, steps, guidance, width, height):
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params = {
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"negative_prompt": negative_prompt,
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"num_inference_steps": steps,
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"guidance_scale": guidance,
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"width": width,
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"height": height
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}
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output = get_completion(prompt, params)
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pil_image = base64_to_pil(output)
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return pil_image
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gr.close_all()
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with gr.Blocks() as demo:
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gr.Markdown("# Image Generation with Stable Diffusion")
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with gr.Row():
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with gr.Column(scale=4):
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prompt = gr.Textbox(label="Your prompt") #Give prompt some real estate
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with gr.Column(scale=1, min_width=50):
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btn = gr.Button("Submit") #Submit button side by side!
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with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options!
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negative_prompt = gr.Textbox(label="Negative prompt")
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with gr.Row():
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with gr.Column():
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steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25,
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info="In many steps will the denoiser denoise the image?")
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guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7,
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info="Controls how much the text prompt influences the result")
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with gr.Column():
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width = gr.Slider(label="Width", minimum=64, maximum=512, step=64, value=512)
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height = gr.Slider(label="Height", minimum=64, maximum=512, step=64, value=512)
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output = gr.Image(label="Result") #Move the output up too
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btn.click(fn=generate, inputs=[prompt,negative_prompt,steps,guidance,width,height], outputs=[output])
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gr.close_all()
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demo.launch()
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