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Create app.py

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  1. app.py +184 -0
app.py ADDED
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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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+
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+
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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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+
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+ MAX_SEED = np.iinfo(np.int32).max
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+ MAX_IMAGE_SIZE = 1024
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+
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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"Create fornt view and back view seperately of {prompt_part1} {color} colored plain {dress_type} with {design} design, {prompt_part5}"
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+
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+ if randomize_seed:
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+ seed = random.randint(0, MAX_SEED)
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+
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+ generator = torch.Generator().manual_seed(seed)
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+
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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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+
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+ return image
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+
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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, sweat shirt, geometric design",
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+ ]
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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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+
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+ if torch.cuda.is_available():
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+ power_device = "GPU"
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+ else:
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+ power_device = "CPU"
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+
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+ with gr.Blocks(css=css) as demo:
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+
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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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+
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+ with gr.Row():
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+
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+ prompt_part1 = gr.Textbox(
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+ value="a single",
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+ label="Prompt Part 1",
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+ show_label=False,
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+ interactive=False,
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+ container=False,
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+ elem_id="prompt_part1",
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+ visible=False,
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+ )
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+
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+ prompt_part2 = gr.Textbox(
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+ label="color",
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+ show_label=False,
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+ max_lines=1,
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+ placeholder="color (e.g., color category)",
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+ container=False,
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+ )
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+
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+ prompt_part3 = gr.Textbox(
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+ label="dress_type",
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+ show_label=False,
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+ max_lines=1,
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+ placeholder="dress_type (e.g., t-shirt, sweatshirt, shirt, hoodie)",
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+ container=False,
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+ )
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+
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+ prompt_part4 = gr.Textbox(
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+ label="design",
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+ show_label=False,
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+ max_lines=1,
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+ placeholder="design",
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+ container=False,
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+ )
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+
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+ prompt_part5 = gr.Textbox(
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+ value="hanging on the plain wall",
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+ label="Prompt Part 5",
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+ show_label=False,
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+ interactive=False,
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+ container=False,
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+ elem_id="prompt_part5",
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+ visible=False,
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+ )
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+
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+
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+ run_button = gr.Button("Run", scale=0)
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+
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+ result = gr.Image(label="Result", show_label=False)
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+
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+ with gr.Accordion("Advanced Settings", open=False):
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+
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+ negative_prompt = gr.Textbox(
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+ label="Negative prompt",
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+ max_lines=1,
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+ placeholder="Enter a negative prompt",
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+ visible=False,
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+ )
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+
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+ seed = gr.Slider(
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+ label="Seed",
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+ minimum=0,
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+ maximum=MAX_SEED,
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+ step=1,
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+ value=0,
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+ )
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+
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+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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+
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+ with gr.Row():
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+
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+ width = gr.Slider(
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+ label="Width",
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+ minimum=256,
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+ maximum=MAX_IMAGE_SIZE,
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+ step=32,
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+ value=512,
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+ )
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+
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+ height = gr.Slider(
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+ label="Height",
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+ minimum=256,
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+ maximum=MAX_IMAGE_SIZE,
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+ step=32,
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+ value=512,
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+ )
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+
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+ with gr.Row():
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+
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+ guidance_scale = gr.Slider(
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+ label="Guidance scale",
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+ minimum=0.0,
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+ maximum=10.0,
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+ step=0.1,
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+ value=0.0,
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+ )
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+
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+ num_inference_steps = gr.Slider(
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+ label="Number of inference steps",
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+ minimum=1,
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+ maximum=12,
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+ step=1,
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+ value=2,
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+ )
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+
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+ gr.Examples(
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+ examples=examples,
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+ inputs=[prompt_part2]
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+ )
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+
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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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+
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+ demo.queue().launch()