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import spaces |
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import gradio as gr |
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import torch |
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from diffusers import DiffusionPipeline, AutoencoderKL |
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import rembg |
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model_id = "dataautogpt3/OpenDalleV1.1" |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = DiffusionPipeline.from_pretrained(model_id, |
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vae=vae, |
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torch_dtype=torch.float16, |
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use_safetensors=True, |
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variant="fp16") |
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pipe.to("cuda") |
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@spaces.GPU |
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def generate_image(prompt, neg_prompt): |
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prompt += "no background, side view, minimalist shot" |
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image = pipe(prompt, negative_prompt=neg_prompt) |
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image2 = rembg.remove(image) |
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return image, image2 |
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_TITLE = "Shoe Generator" |
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with gr.Blocks(_TITLE) as ShoeGen: |
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with gr.Row(): |
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with gr.Column(): |
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prompt = gr.Textbox(label="Enter a discription of a shoe") |
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neg_prompt = gr.Textbox(label="Enter a negative prompt", value="low quality, watermark, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft, closed eyes, text, logo") |
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button_gen = gr.Button("Generate Image") |
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with gr.Column(): |
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image = gr.Image(label="Generated Image", show_download_button=True) |
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image2 = gr.Image(label="Generated Image without background", show_download_button=True) |
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button_gen.click(generate_image, inputs=[prompt], outputs=[image, image2]) |
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ShoeGen.launch() |
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