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Update app.py
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app.py
CHANGED
@@ -5,43 +5,26 @@ import numpy as np
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from diffusers import DiffusionPipeline
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device = "cuda" if torch.cuda.is_available() else "cpu"
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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/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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torch.cuda.empty_cache()
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#refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16")
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#refiner.enable_xformers_memory_efficient_attention()
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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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#refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
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#refiner = refiner.to(device)
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#refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
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def genie (prompt, steps, seed):
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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, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
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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 int_image
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gr.Interface(fn=genie, inputs=[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.'),
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#gr.Slider(512, 1024, 768, step=128, label='Height'),
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#gr.Slider(512, 1024, 768, step=128, label='Width'),
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#gr.Slider(1, 15, 10, label='Guidance Scale'),
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gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations'),
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gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True),
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#gr.Textbox(label='Embedded Prompt'),
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#gr.Textbox(label='Embedded Negative Prompt'),
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#gr.Slider(minimum=.7, maximum=.99, value=.95, step=.01, label='Refiner Denoise Start %')
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],
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outputs='image',
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title="Stable Diffusion Turbo CPU or GPU",
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from diffusers import DiffusionPipeline
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if torch.cuda.is_available():
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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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torch.cuda.empty_cache()
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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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def genie (prompt, steps, seed):
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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, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
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return int_image
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gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'),
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gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations'),
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gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True),
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],
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outputs='image',
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title="Stable Diffusion Turbo CPU or GPU",
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