Spaces:
Runtime error
Runtime error
keep pipes in cpu until using them
Browse files
app.py
CHANGED
@@ -35,9 +35,9 @@ if device == "cuda":
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scheduler_class = MyEulerAncestralDiscreteScheduler
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pipe_inversion = SDXLDDIMPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
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pipe_inference = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo",
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use_safetensors=True)
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pipe_inference.scheduler = scheduler_class.from_config(pipe_inference.scheduler.config)
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pipe_inversion.scheduler = scheduler_class.from_config(pipe_inversion.scheduler.config)
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pipe_inversion.scheduler_inference = scheduler_class.from_config(pipe_inference.scheduler.config)
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@@ -92,11 +92,6 @@ with gr.Blocks(css="style.css") as demo:
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# @spaces.GPU
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def set_pipe(inversion_state, input_image, description_prompt, edit_guidance_scale, num_inference_steps=4,
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num_inversion_steps=4, inversion_max_step=0.6, rnri_iterations=2, rnri_alpha=0.1, rnri_lr=0.2):
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if device == 'cuda':
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# if image_editor is not None:
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# image_editor = image_editor.to('cpu')
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torch.cuda.empty_cache()
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if input_image is None or not description_prompt:
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return None, "Please set all inputs."
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@@ -113,8 +108,17 @@ with gr.Blocks(css="style.css") as demo:
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num_inversion_steps=num_inversion_steps,
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edit_guidance_scale=edit_guidance_scale,
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inversion_max_step=inversion_max_step)
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inversion_state = ImageEditorDemo.invert(pipe_inversion, input_image, description_prompt, config,
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[rnri_iterations, rnri_alpha, rnri_lr], device)
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return inversion_state, "Input has set!"
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@@ -122,6 +126,7 @@ with gr.Blocks(css="style.css") as demo:
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def edit1(editor, target_prompt):
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if editor is None:
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raise gr.Error("Set inputs before editing.")
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# if device == "cuda":
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# image = editor.to(device).edit(target_prompt)
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# else:
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@@ -136,8 +141,16 @@ with gr.Blocks(css="style.css") as demo:
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# if device == "cuda":
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# image = editor.to(device).edit(target_prompt)
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# else:
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image = ImageEditorDemo.edit(pipe_inference, target_prompt, inversion_state['latent'], inversion_state['noise'],
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inversion_state['cfg'], inversion_state['cfg'].edit_guidance_scale)
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return image
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scheduler_class = MyEulerAncestralDiscreteScheduler
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pipe_inversion = SDXLDDIMPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)#.to(device)
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pipe_inference = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo",
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use_safetensors=True)#.to(device)
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pipe_inference.scheduler = scheduler_class.from_config(pipe_inference.scheduler.config)
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pipe_inversion.scheduler = scheduler_class.from_config(pipe_inversion.scheduler.config)
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pipe_inversion.scheduler_inference = scheduler_class.from_config(pipe_inference.scheduler.config)
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# @spaces.GPU
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def set_pipe(inversion_state, input_image, description_prompt, edit_guidance_scale, num_inference_steps=4,
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num_inversion_steps=4, inversion_max_step=0.6, rnri_iterations=2, rnri_alpha=0.1, rnri_lr=0.2):
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if input_image is None or not description_prompt:
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return None, "Please set all inputs."
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num_inversion_steps=num_inversion_steps,
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edit_guidance_scale=edit_guidance_scale,
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inversion_max_step=inversion_max_step)
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if device == 'cuda':
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torch.cuda.empty_cache()
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pipe_inversion = pipe_inversion.to(device)
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# if image_editor is not None:
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# image_editor = image_editor.to('cpu')
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inversion_state = ImageEditorDemo.invert(pipe_inversion, input_image, description_prompt, config,
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[rnri_iterations, rnri_alpha, rnri_lr], device)
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if device == 'cuda':
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torch.cuda.empty_cache()
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pipe_inversion = pipe_inversion.to('cpu')
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return inversion_state, "Input has set!"
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def edit1(editor, target_prompt):
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if editor is None:
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raise gr.Error("Set inputs before editing.")
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# if device == "cuda":
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# image = editor.to(device).edit(target_prompt)
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# else:
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# if device == "cuda":
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# image = editor.to(device).edit(target_prompt)
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# else:
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if device == 'cuda':
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torch.cuda.empty_cache()
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pipe_inference = pipe_inference.to(device)
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image = ImageEditorDemo.edit(pipe_inference, target_prompt, inversion_state['latent'], inversion_state['noise'],
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inversion_state['cfg'], inversion_state['cfg'].edit_guidance_scale)
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if device == 'cuda':
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pipe_inference = pipe_inference.to('cpu')
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torch.cuda.empty_cache()
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return image
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