Update app.py
Browse files
app.py
CHANGED
@@ -1,8 +1,10 @@
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import gradio as gr
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import numpy as np
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import random
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from diffusers import DiffusionPipeline
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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@@ -37,11 +39,10 @@ def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt
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generator=generator
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).images[0]
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print("Image generated successfully.") # Debug: Confirm image generation
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except Exception as e:
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print(f"Error generating image: {e}")
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return None
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return image
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examples = [
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["red", "t-shirt", "yellow stripes"],
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@@ -88,10 +89,23 @@ with gr.Blocks(css=css) as demo:
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gr.Examples(examples=examples, inputs=[prompt_part2, prompt_part3, prompt_part4])
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demo.queue().launch()
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import numpy as np
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import random
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import torch
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import gradio as gr
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from diffusers import DiffusionPipeline
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from PIL import Image
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import io
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device = "cuda" if torch.cuda.is_available() else "cpu"
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generator=generator
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).images[0]
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print("Image generated successfully.") # Debug: Confirm image generation
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return image
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except Exception as e:
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print(f"Error generating image: {e}")
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return None
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examples = [
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["red", "t-shirt", "yellow stripes"],
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gr.Examples(examples=examples, inputs=[prompt_part2, prompt_part3, prompt_part4])
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def run_infer():
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output_image = infer(
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prompt_part1.value,
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prompt_part2.value,
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prompt_part3.value,
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prompt_part4.value,
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prompt_part5.value,
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negative_prompt.value,
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seed.value,
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randomize_seed.value,
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width.value,
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height.value,
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guidance_scale.value,
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num_inference_steps.value
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)
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return output_image
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run_button.click(fn=run_infer, outputs=result)
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demo.queue(api_name="/infer").launch()
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