Manjushri commited on
Commit
d135db4
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verified ·
1 Parent(s): b50af58

Update app.py

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Files changed (1) hide show
  1. app.py +12 -12
app.py CHANGED
@@ -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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- pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1").to(device)
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- #pipe.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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- 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, 1280, 768, step=128, label='Height'),
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- gr.Slider(512, 1280, 768, 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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  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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+
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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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+
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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')],