| import torch |
| from diffusers import DiffusionPipeline |
| import gradio as gr |
| import numpy as np |
| import random |
|
|
| |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| dtype = torch.float16 |
|
|
| repo = "stabilityai/stable-diffusion-3-medium-diffusers" |
| pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium") |
|
|
| MAX_SEED = np.iinfo(np.int32).max |
| MAX_IMAGE_SIZE = 1344 |
|
|
| def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): |
| if randomize_seed: |
| seed = random.randint(0, MAX_SEED) |
|
|
| generator = torch.Generator(device=device).manual_seed(seed) |
| image = pipeline( |
| prompt=prompt, |
| negative_prompt=negative_prompt, |
| width=width, |
| height=height, |
| guidance_scale=guidance_scale, |
| num_inference_steps=num_inference_steps, |
| generator=generator |
| ).images[0] |
|
|
| return image, seed |
|
|
| examples = [ |
| ["Astronaut in the forest, cool color palette, calm colors, detailed, 8k"], |
| ["An astronaut riding a green horse"], |
| ["A delicious piece of ceviche cheesecake"] |
| ] |
|
|
| |
| css = """ |
| body { |
| font-family: 'Arial', sans-serif; |
| background-color: #f0f0f0; |
| color: #333; |
| margin: 0; |
| padding: 0; |
| display: flex; |
| flex-direction: column; |
| align-items: center; |
| justify-content: center; |
| height: 100vh; |
| } |
| |
| #col-container { |
| max-width: 800px; |
| width: 100%; |
| margin: 20px; |
| padding: 20px; |
| background-color: #fff; |
| border-radius: 10px; |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); |
| } |
| |
| #title { |
| font-size: 28px; |
| font-weight: bold; |
| text-align: center; |
| margin-bottom: 20px; |
| } |
| |
| .input-box { |
| margin-bottom: 20px; |
| } |
| |
| .input-box label { |
| font-size: 18px; |
| font-weight: bold; |
| display: block; |
| margin-bottom: 5px; |
| } |
| |
| .input-box input, |
| .input-box select, |
| .input-box textarea { |
| width: calc(100% - 20px); |
| padding: 10px; |
| font-size: 16px; |
| border: 1px solid #ccc; |
| border-radius: 4px; |
| margin-bottom: 10px; |
| } |
| |
| .input-box input:focus, |
| .input-box select:focus, |
| .input-box textarea:focus { |
| outline: none; |
| border-color: #007BFF; |
| } |
| |
| .button { |
| background-color: #007BFF; |
| color: white; |
| padding: 10px 20px; |
| border: none; |
| border-radius: 4px; |
| cursor: pointer; |
| font-size: 16px; |
| width: 100%; |
| } |
| |
| .button:hover { |
| background-color: #0056b3; |
| } |
| |
| .result { |
| margin-top: 20px; |
| text-align: center; |
| } |
| |
| .result img { |
| max-width: 100%; |
| border-radius: 8px; |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); |
| } |
| """ |
|
|
| with gr.Blocks(css=css) as demo: |
| gr.Markdown(""" |
| # Stable Diffusion 3 Medium Demo |
| Generate images using Stable Diffusion 3. Try different prompts and settings to see the results. |
| """, elem_id="title") |
|
|
| with gr.Column(elem_id="col-container"): |
| prompt = gr.Textbox(label="Prompt", lines=2, placeholder="Enter your prompt here...", elem_classes="input-box") |
| negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, placeholder="Enter negative prompt (optional)", elem_classes="input-box") |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, elem_classes="input-box") |
| randomize_seed = gr.Checkbox(label="Randomize Seed", value=True, elem_classes="input-box") |
| width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=512, elem_classes="input-box") |
| height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=512, elem_classes="input-box") |
| guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, step=0.5, value=7.5, elem_classes="input-box") |
| num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=100, step=1, value=28, elem_classes="input-box") |
|
|
| generate_button = gr.Button("Generate Image", elem_classes="button") |
| result_image = gr.Image(elem_classes="result") |
|
|
| generate_button.click(infer, inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs=[result_image, seed]) |
|
|
| gr.Examples(examples=examples, inputs=[prompt]) |
|
|
| demo.launch() |
|
|
