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Update app.py
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app.py
CHANGED
@@ -8,7 +8,7 @@ if torch.cuda.is_available():
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PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000}
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torch.cuda.max_memory_allocated(device=device)
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torch.cuda.empty_cache()
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pipe = DiffusionPipeline.from_pretrained("stabilityai/
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pipe.enable_xformers_memory_efficient_attention()
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pipe = pipe.to(device)
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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@@ -18,7 +18,7 @@ if torch.cuda.is_available():
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refiner.enable_sequential_cpu_offload()
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refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
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else:
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pipe = DiffusionPipeline.from_pretrained("stabilityai/
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pipe = pipe.to(device)
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
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@@ -27,7 +27,7 @@ else:
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def genie (prompt, negative_prompt, height, width, scale, steps, seed, prompt_2, negative_prompt_2, high_noise_frac):
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generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
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int_image = pipe(prompt, prompt_2=prompt_2,
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image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0]
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return image
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PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000}
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torch.cuda.max_memory_allocated(device=device)
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torch.cuda.empty_cache()
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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pipe.enable_xformers_memory_efficient_attention()
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pipe = pipe.to(device)
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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refiner.enable_sequential_cpu_offload()
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refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
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else:
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
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pipe = pipe.to(device)
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
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def genie (prompt, negative_prompt, height, width, scale, steps, seed, prompt_2, negative_prompt_2, high_noise_frac):
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generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
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int_image = pipe(prompt, prompt_2=prompt_2, height=height, width=width, num_inference_steps=steps, num_images_per_prompt=1, generator=generator, output_type="latent").images
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image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0]
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return image
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