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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 torch |
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from diffusers import StableDiffusion3Pipeline |
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import os |
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print(os.getenv('HF_TOKEN')) |
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gr.load("models/stabilityai/stable-diffusion-3-diffusers") |
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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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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=4, |
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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, |
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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, |
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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=2.0, |
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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=4, |
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) |
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run_button.click( |
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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] |
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) |
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demo.queue().launch() |
