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Runtime error
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1e47ffe
1
Parent(s):
29f021f
Update app.py
Browse files
app.py
CHANGED
@@ -75,7 +75,7 @@ current_model_path = current_model.path
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if is_colab:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model.path,
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-
torch_dtype=torch.
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
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safety_checker=lambda images, clip_input: (images, False)
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)
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@@ -83,13 +83,13 @@ if is_colab:
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else:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model.path,
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-
torch_dtype=torch.
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
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)
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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@@ -164,14 +164,14 @@ def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width,
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if is_colab or current_model == custom_model:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model_path,
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-
torch_dtype=torch.
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
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safety_checker=lambda images, clip_input: (images, False)
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)
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else:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model_path,
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torch_dtype=torch.
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
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)
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# pipe = pipe.to("cpu")
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@@ -213,19 +213,19 @@ def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance
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if is_colab or current_model == custom_model:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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current_model_path,
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torch_dtype=torch.
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
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safety_checker=lambda images, clip_input: (images, False)
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)
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else:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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current_model_path,
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torch_dtype=torch.
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
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)
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# pipe = pipe.to("cpu")
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# pipe = current_model.pipe_i2i
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-
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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pipe.enable_xformers_memory_efficient_attention()
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@@ -285,8 +285,8 @@ with gr.Blocks(css="style.css") as demo:
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</div>
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"""
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)
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with gr.Column(scale=55):
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with gr.Group():
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model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
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if is_colab:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model.path,
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torch_dtype=torch.float16,
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
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safety_checker=lambda images, clip_input: (images, False)
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)
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else:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model.path,
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+
torch_dtype=torch.float16,
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
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)
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+
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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pipe.enable_xformers_memory_efficient_attention()
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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if is_colab or current_model == custom_model:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model_path,
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+
torch_dtype=torch.float16,
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
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safety_checker=lambda images, clip_input: (images, False)
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)
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else:
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pipe = StableDiffusionPipeline.from_pretrained(
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current_model_path,
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torch_dtype=torch.float16,
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
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)
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# pipe = pipe.to("cpu")
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if is_colab or current_model == custom_model:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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current_model_path,
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torch_dtype=torch.float16,
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
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safety_checker=lambda images, clip_input: (images, False)
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)
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else:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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current_model_path,
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torch_dtype=torch.float16,
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
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)
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# pipe = pipe.to("cpu")
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# pipe = current_model.pipe_i2i
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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pipe.enable_xformers_memory_efficient_attention()
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</div>
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"""
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)
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+
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with gr.Row():
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with gr.Column(scale=55):
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with gr.Group():
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model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
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