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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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if torch.cuda.is_available(): |
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torch.cuda.max_memory_allocated(device=device) |
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
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pipe.enable_xformers_memory_efficient_attention() |
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pipe = pipe.to(device) |
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else: |
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True) |
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pipe = pipe.to(device) |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): |
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prompt = f"{prompt_part1} {color} colored plain {dress_type} with {design} design, {prompt_part5}" |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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try: |
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image = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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width=width, |
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height=height, |
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generator=generator |
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).images[0] |
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print("Image generated successfully.") |
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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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["blue", "hoodie", "minimalist"], |
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["red", "sweatshirt", "geometric design"], |
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] |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 520px; |
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} |
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""" |
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power_device = "GPU" if torch.cuda.is_available() else "CPU" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f""" |
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# Text-to-Image Gradio Template |
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Currently running on {power_device}. |
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""") |
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with gr.Row(): |
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prompt_part1 = gr.Textbox(value="a single", label="Prompt Part 1", show_label=False, interactive=False, container=False, elem_id="prompt_part1", visible=False) |
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prompt_part2 = gr.Textbox(label="color", show_label=False, max_lines=1, placeholder="color (e.g., color category)", container=False) |
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prompt_part3 = gr.Textbox(label="dress_type", show_label=False, max_lines=1, placeholder="dress_type (e.g., t-shirt, sweatshirt, shirt, hoodie)", container=False) |
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prompt_part4 = gr.Textbox(label="design", show_label=False, max_lines=1, placeholder="design", container=False) |
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prompt_part5 = gr.Textbox(value="hanging on the plain grey wall", label="Prompt Part 5", show_label=False, interactive=False, container=False, elem_id="prompt_part5", visible=False) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Textbox(label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False) |
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512) |
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height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512) |
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with gr.Row(): |
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guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0) |
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num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=12, step=1, value=2) |
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gr.Examples(examples=examples, inputs=[prompt_part2, prompt_part3, prompt_part4]) |
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run_button.click( |
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fn=infer, |
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inputs=[prompt_part1, prompt_part2, prompt_part3, prompt_part4, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], |
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outputs=[result] |
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) |
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demo.queue().launch() |
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