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Update app.py
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app.py
CHANGED
@@ -300,10 +300,49 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
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torch.cuda.empty_cache()
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return image
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gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0'], value='PhotoReal', label='Choose Model'),
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gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'),
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gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
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gr.Slider(512, 1024, 768, step=128, label='Height'),
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image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
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torch.cuda.empty_cache()
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if model == 'Test':
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pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
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pipe.enable_xformers_memory_efficient_attention()
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pipe = pipe.to(device)
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torch.cuda.empty_cache()
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if refine == "Yes":
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refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
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refiner.enable_xformers_memory_efficient_attention()
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refiner = refiner.to(device)
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torch.cuda.empty_cache()
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int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
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image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
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torch.cuda.empty_cache()
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if upscale == "Yes":
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refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
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refiner.enable_xformers_memory_efficient_attention()
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refiner = refiner.to(device)
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torch.cuda.empty_cache()
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upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
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torch.cuda.empty_cache()
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return upscaled
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else:
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return image
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else:
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if upscale == "Yes":
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image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
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upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
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upscaler.enable_xformers_memory_efficient_attention()
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upscaler = upscaler.to(device)
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torch.cuda.empty_cache()
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upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
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torch.cuda.empty_cache()
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return upscaled
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else:
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image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
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torch.cuda.empty_cache()
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return image
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return image
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gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'Test'], value='PhotoReal', label='Choose Model'),
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gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'),
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gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
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gr.Slider(512, 1024, 768, step=128, label='Height'),
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