zhiweili commited on
Commit
2ceb133
·
1 Parent(s): 32b3396

test image to image

Browse files
Files changed (2) hide show
  1. app.py +39 -0
  2. requirements.txt +3 -0
app.py ADDED
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+ import gradio as gr
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+ import torch
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+ from diffusers import AutoPipelineForImage2Image, DPMSolverMultistepScheduler
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+
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+ base_model = "SG161222/RealVisXL_V4.0"
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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+ pipeline = AutoPipelineForImage2Image.from_pretrained(
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+ base_model, torch_dtype=torch.float16, use_safetensors=True
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+ )
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+ pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
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+ pipeline.to(device)
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+ generator = torch.Generator(device).manual_seed(0)
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+
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+ def image_to_image(input_image, prompt, guidance_scale, num_inference_steps):
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+ # Generate the output image
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+ output_image = pipeline(
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+ generator=generator,
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+ prompt=prompt, image=input_image,
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+ guidance_scale=guidance_scale, num_inference_steps = num_inference_steps
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+ ).images[0]
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+
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+ return output_image
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+
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+ with gr.Block() as grApp:
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+ input_image = gr.Image(label="Input Image")
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+ prompt = gr.Textbox(lines=3, label="Prompt")
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+ guidance_scale = gr.Slider(minimum=0, maximum=1, default=0.75, label="Guidance Scale")
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+ num_inference_steps = gr.Slider(minimum=10, maximum=100, default=25, label="Number of Inference Steps")
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+ output_image = gr.Image()
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+ generate_btn = gr.Button("Generate Image")
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+ generate_btn.click(
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+ fn=image_to_image,
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+ inputs=[input_image, prompt, guidance_scale, num_inference_steps],
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+ outputs=output_image,
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+ )
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+
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+ grApp.launch()
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+
requirements.txt ADDED
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+ gradio
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+ torch
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+ diffusers