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from diffusers import StableDiffusionPipeline |
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import torch |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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from diffusers import StableDiffusionImg2ImgPipeline |
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device = "cpu" |
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model_id = "rikdas/weights" |
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pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device) |
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pipe2 = StableDiffusionImg2ImgPipeline.from_pretrained(model_id).to(device) |
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import gradio as gr |
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def generate_txt2img(prompt): |
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return pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0] |
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def generate_img2img(img, prompt): |
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image = Image.fromarray(img) |
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return pipe2(prompt=prompt, image=image, strength=0.75, guidance_scale=7.5).images[0] |
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with gr.Blocks() as demo: |
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with gr.Tab("Text2Image"): |
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inp_txt = gr.Text(showlabel=False, placeholder="Enter your prompt here...") |
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btn = gr.Button("Generate") |
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out_img = gr.Image() |
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btn.click(fn=generate_txt2img, inputs=[inp_txt], outputs=[out_img]) |
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with gr.Tab("Image2Image"): |
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inp_img = gr.Image() |
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inp_txt2 = gr.Text(showlabel=False,placeholder="Enter your prompt here...") |
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btn2 = gr.Button("Generate") |
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out_img2 = gr.Image() |
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btn2.click(fn=generate_img2img, inputs=[inp_img, inp_txt2], outputs=[out_img2]) |
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demo.launch(debug=True) |
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