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README.md
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---
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title:
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emoji: 🖼
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colorFrom: purple
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colorTo: red
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---
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title: Fyp1-pattern Generation
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emoji: 🖼
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colorFrom: purple
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colorTo: red
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app.py
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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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#import spaces #[uncomment to use ZeroGPU]
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from diffusers import DiffusionPipeline
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import torch
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model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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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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#@spaces.GPU #[uncomment to use ZeroGPU]
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def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
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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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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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return image, seed
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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"A delicious ceviche cheesecake slice",
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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: 640px;
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}
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"""
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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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""")
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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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.Text(
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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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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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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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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=1024, #Replace with defaults that work for your model
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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=1024, #Replace with defaults that work for your model
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)
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with gr.Row():
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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, #Replace with defaults that work for your model
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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=50,
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step=1,
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value=2, #Replace with defaults that work for your model
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)
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gr.Examples(
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examples = examples,
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inputs = [prompt]
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)
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn = infer,
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inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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outputs = [result, seed]
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)
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demo.queue().launch()
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import gradio as gr
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gr.load("models/fyp1/pattern_generation").launch()
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