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Running
on
T4
Running
on
T4
Update app.py
Browse files
app.py
CHANGED
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@@ -7,23 +7,23 @@ from diffusers import DiffusionPipeline, StableDiffusion3Pipeline
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from huggingface_hub import hf_hub_download
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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torch.cuda.max_memory_allocated(device=device)
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torch.cuda.empty_cache()
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torch.cuda.empty_cache()
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def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
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torch.cuda.empty_cache()
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torch.cuda.max_memory_allocated(device=device)
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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, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
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torch.cuda.empty_cache()
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torch.cuda.max_memory_allocated(device=device)
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refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0").to(device)
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#refiner.enable_xformers_memory_efficient_attention()
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torch.cuda.empty_cache()
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image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
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torch.cuda.empty_cache()
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@@ -31,8 +31,8 @@ def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
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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. 77 Token Limit'),
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gr.Slider(512,
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gr.Slider(512,
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gr.Slider(.5, maximum=15, value=7, step=.25, label='Guidance Scale'),
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gr.Slider(10, maximum=50, value=25, step=5, label='Number of Prior Iterations'),
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gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random')],
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from huggingface_hub import hf_hub_download
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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torch.cuda.max_memory_allocated(device=device)
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torch.cuda.empty_cache()
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torch.cuda.max_memory_allocated(device=device)
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pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16).to(device)
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pipe.enable_xformers_memory_efficient_attention()
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torch.cuda.empty_cache()
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torch.cuda.max_memory_allocated(device=device)
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refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16").to(device)
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refiner.enable_xformers_memory_efficient_attention()
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torch.cuda.empty_cache()
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def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
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generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
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#generator=np.random.seed(0)
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int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
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image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
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torch.cuda.empty_cache()
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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. 77 Token Limit'),
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gr.Slider(512, 1536, 1024, step=128, label='Height'),
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gr.Slider(512, 1536, 1024, step=128, label='Width'),
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gr.Slider(.5, maximum=15, value=7, step=.25, label='Guidance Scale'),
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gr.Slider(10, maximum=50, value=25, step=5, label='Number of Prior Iterations'),
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gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random')],
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