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Runtime error
Runtime error
Update app.py
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app.py
CHANGED
@@ -40,16 +40,16 @@ if torch.cuda.is_available():
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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def inference(model, img, strength, prompt, guidance, steps, seed):
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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if img is not None:
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return
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else:
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return
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def
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global current_model
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global pipe
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@@ -63,14 +63,15 @@ def text_inference(model, prompt, guidance, steps, generator=None):
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prompt = prompt_prefixes[current_model] + prompt
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image = pipe(
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prompt,
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num_inference_steps=int(steps),
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guidance_scale=guidance,
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width=
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height=
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generator=generator).images[0]
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return image
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def
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global current_model
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global pipe
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@@ -82,16 +83,17 @@ def img_inference(model, prompt, img, strength, guidance, steps, generator):
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pipe = pipe.to("cuda")
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prompt = prompt_prefixes[current_model] + prompt
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ratio = min(
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img = img.resize((int(img.width * ratio), int(img.height * ratio)))
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image = pipe(
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prompt,
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init_image=img,
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num_inference_steps=int(steps),
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strength=strength,
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guidance_scale=guidance,
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width=
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height=
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generator=generator).images[0]
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return image
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@@ -139,32 +141,35 @@ with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column():
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model = gr.Dropdown(label="Model", choices=models, value=models[0])
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prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically")
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with gr.
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run = gr.Button(value="Run")
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gr.Markdown(f"Running on: {device}")
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with gr.Column():
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image_out = gr.Image(height=512)
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gr.Examples([
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[models[0], "jason bateman disassembling the demon core", 7.5, 50],
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[models[3], "portrait of dwayne johnson", 7.0, 75],
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[models[4], "portrait of a beautiful alyx vance half life", 10, 50],
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[models[5], "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7, 45],
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[models[4], "fantasy portrait painting, digital art", 4, 30],
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], [model, prompt, guidance, steps], image_out,
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gr.Markdown('''
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Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br>
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Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe)
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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def inference(model, img, strength, prompt, neg_prompt, guidance, steps, width, height, seed):
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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if img is not None:
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return txt_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
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else:
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return img_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator)
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def img_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator=None):
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global current_model
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global pipe
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prompt = prompt_prefixes[current_model] + prompt
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image = pipe(
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prompt,
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negative_prompt=neg_prompt,
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num_inference_steps=int(steps),
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guidance_scale=guidance,
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width=width,
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height=height,
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generator=generator).images[0]
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return image
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def txt_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
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global current_model
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global pipe
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pipe = pipe.to("cuda")
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prompt = prompt_prefixes[current_model] + prompt
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ratio = min(height / img.height, width / img.width)
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img = img.resize((int(img.width * ratio), int(img.height * ratio)))
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image = pipe(
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prompt,
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negative_prompt=neg_prompt,
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init_image=img,
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num_inference_steps=int(steps),
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strength=strength,
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guidance_scale=guidance,
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width=width,
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height=height,
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generator=generator).images[0]
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return image
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with gr.Row():
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with gr.Column():
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model = gr.Dropdown(label="Model", choices=models, value=models[0])
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prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically")
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with gr.Tab("Options"):
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neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
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steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2)
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width = gr.Slider(label="Width", value=512, maximum=1024, minimum=64)
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height = gr.Slider(label="Height", value=512, maximum=1024, minimum=64)
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
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with gr.Tab("Image to image"):
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image = gr.Image(label="Image", height=256, tool="editor", type="pil")
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
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with gr.Column():
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image_out = gr.Image(height=512)
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run = gr.Button(value="Run")
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gr.Markdown(f"Running on: {device}")
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inputs = [model, image, strength, prompt, neg_prompt, guidance, steps, width, height, seed]
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prompt.submit(inference, inputs=inputs, outputs=image_out)
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run.click(inference, inputs=inputs, outputs=image_out)
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gr.Examples([
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[models[0], "jason bateman disassembling the demon core", 7.5, 50],
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[models[3], "portrait of dwayne johnson", 7.0, 75],
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[models[4], "portrait of a beautiful alyx vance half life", 10, 50],
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[models[5], "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7, 45],
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[models[4], "fantasy portrait painting, digital art", 4, 30],
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], [model, prompt, guidance, steps], image_out, img_to_img, cache_examples=False)
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gr.Markdown('''
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Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br>
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Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe)
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