import gradio as gr import numpy as np import random import uuid from PIL import Image import spaces from diffusers import DiffusionPipeline import torch DESCRIPTIONx = """## SD-3.5 LARGE TURBO """ device = "cuda" if torch.cuda.is_available() else "cpu" model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo" if torch.cuda.is_available(): torch_dtype = torch.bfloat16 else: torch_dtype = torch.float32 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype) pipe = pipe.to(device) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 # Define styles style_list = [ { "name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "Style Zero", "prompt": "{prompt}", "negative_prompt": "", }, ] STYLE_NAMES = [style["name"] for style in style_list] DEFAULT_STYLE_NAME = STYLE_NAMES[0] grid_sizes = { "2x1": (2, 1), "1x2": (1, 2), "2x2": (2, 2), "2x3": (2, 3), "3x2": (3, 2), "1x1": (1, 1) } @spaces.GPU(duration=60, enable_queue=True) def infer( prompt, negative_prompt="", seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, style="Style Zero", grid_size="1x1", progress=gr.Progress(track_tqdm=True), ): selected_style = next(s for s in style_list if s["name"] == style) styled_prompt = selected_style["prompt"].format(prompt=prompt) styled_negative_prompt = selected_style["negative_prompt"] if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) grid_size_x, grid_size_y = grid_sizes.get(grid_size, (2, 2)) num_images = grid_size_x * grid_size_y images = [] for _ in range(num_images): image = pipe( prompt=styled_prompt, negative_prompt=styled_negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, generator=generator, ).images[0] images.append(image) # Create a grid image grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y)) for i, img in enumerate(images[:num_images]): grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height)) # Save the grid image unique_name = str(uuid.uuid4()) + ".png" grid_img.save(unique_name) return unique_name, seed examples = [ "A capybara wearing a suit holding a sign that reads Hello World", ] css = ''' .gradio-container{max-width: 585px !important} h1{text-align:center} footer { visibility: hidden } ''' with gr.Blocks(css=css, theme="prithivMLmods/Minecraft-Theme") as demo: gr.Markdown(DESCRIPTIONx) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Run", scale=0, variant="primary") result = gr.Image(label="Result", show_label=False) with gr.Row(visible=True): grid_size_selection = gr.Dropdown( choices=["2x1", "1x2", "2x2", "2x3", "3x2", "1x1"], value="1x1", label="Grid Size" ) with gr.Row(visible=True): style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Quality Style", ) with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): width = gr.Slider( label="Width", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=7.5, step=0.1, value=0.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=4, ) gr.Examples( examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True, cache_mode="lazy" ) gr.on( triggers=[run_button.click, prompt.submit], fn=infer, inputs=[ prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, style_selection, grid_size_selection, ], outputs=[result, seed], ) if __name__ == "__main__": demo.launch()