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on
T4
Running
on
T4
Update app.py
Browse files
app.py
CHANGED
@@ -232,7 +232,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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animagine.enable_xformers_memory_efficient_attention()
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animagine = animagine.to(device)
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torch.cuda.empty_cache()
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-
upscaled = animagine(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=
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torch.cuda.empty_cache()
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return upscaled
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else:
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@@ -245,7 +245,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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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=
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torch.cuda.empty_cache()
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return upscaled
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else:
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@@ -280,7 +280,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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sdxl.enable_xformers_memory_efficient_attention()
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sdxl = sdxl.to(device)
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torch.cuda.empty_cache()
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-
upscaled = sdxl(prompt=Prompt, negative_prompt=negative_prompt, image=refined, num_inference_steps=
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torch.cuda.empty_cache()
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return upscaled
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else:
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@@ -294,7 +294,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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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=
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torch.cuda.empty_cache()
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return upscaled
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else:
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@@ -326,7 +326,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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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=
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torch.cuda.empty_cache()
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return upscaled
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else:
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@@ -342,7 +342,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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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=
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torch.cuda.empty_cache()
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return upscaled
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else:
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@@ -355,8 +355,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
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gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL'], 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,
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gr.Slider(512,
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gr.Slider(1, maximum=15, value=5, step=.25, label='Guidance Scale'),
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gr.Slider(5, maximum=100, value=50, step=5, label='Number of Iterations'),
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gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'),
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animagine.enable_xformers_memory_efficient_attention()
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animagine = animagine.to(device)
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torch.cuda.empty_cache()
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upscaled = animagine(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, 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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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=5, 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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sdxl.enable_xformers_memory_efficient_attention()
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sdxl = sdxl.to(device)
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torch.cuda.empty_cache()
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upscaled = sdxl(prompt=Prompt, negative_prompt=negative_prompt, image=refined, num_inference_steps=5, 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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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=5, 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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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=5, 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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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=5, 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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gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL'], 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, 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(1, maximum=15, value=5, step=.25, label='Guidance Scale'),
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gr.Slider(5, maximum=100, value=50, step=5, label='Number of Iterations'),
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gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'),
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