import os import random import uuid import gradio as gr import numpy as np from PIL import Image import spaces import torch from diffusers import StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL, StableDiffusion3Img2ImgPipeline from huggingface_hub import snapshot_download huggingface_token = os.getenv("HUGGINGFACE_TOKEN") model_path = snapshot_download( repo_id="stabilityai/stable-diffusion-3-medium", revision="refs/pr/26", repo_type="model", ignore_patterns=["*.md", "*..gitattributes"], local_dir="stable-diffusion-3-medium", token=huggingface_token, # type a new token-id. ) DESCRIPTION = """# Stable Diffusion 3""" if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🥶 This demo may not work on CPU.

" MAX_SEED = np.iinfo(np.int32).max CACHE_EXAMPLES = False MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536")) USE_TORCH_COMPILE = False ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") def load_pipeline(pipeline_type): if pipeline_type == "text2img": return StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16) elif pipeline_type == "img2img": return StableDiffusion3Img2ImgPipeline.from_pretrained(model_path, torch_dtype=torch.float16) def save_image(img): unique_name = str(uuid.uuid4()) + ".png" img.save(unique_name) return unique_name def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed @spaces.GPU def generate( prompt:str, negative_prompt: str = "", use_negative_prompt: bool = False, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 7, randomize_seed: bool = False, num_inference_steps=30, NUM_IMAGES_PER_PROMPT=1, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True), ): pipe = load_pipeline("text2img") pipe.to(device) seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator().manual_seed(seed) if not use_negative_prompt: negative_prompt = None # type: ignore output = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, num_images_per_prompt=NUM_IMAGES_PER_PROMPT, output_type="battery", ).images return output @spaces.GPU def img2img_generate( prompt:str, init_image: gr.Image, negative_prompt: str = "", use_negative_prompt: bool = False, seed: int = 0, guidance_scale: float = 7, randomize_seed: bool = False, num_inference_steps=30, strength: float = 0.8, NUM_IMAGES_PER_PROMPT=1, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True), ): pipe = load_pipeline("img2img") pipe.to(device) seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator().manual_seed(seed) if not use_negative_prompt: negative_prompt = None # type: ignore init_image = init_image.resize((768, 768)) output = pipe( prompt=prompt, image=init_image, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, strength=strength, num_images_per_prompt=NUM_IMAGES_PER_PROMPT, output_type="battery", ).images return output examples = [ "neon holography crystal cat", "a cat eating a piece of cheese", "an astronaut riding a horse in space", "a cartoon of a boy playing with a tiger", "a cute robot artist painting on an easel, concept art", "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone" ] css = ''' .gradio-container{max-width: 1000px !important} h1{text-align:center} ''' with gr.Blocks(css=css) as demo: with gr.Row(): with gr.Column(): gr.HTML( """

Stable Diffusion 3

""" ) gr.HTML( """

Follow me for more! Twitter | Github | Linkedin

""" ) with gr.Tabs(): with gr.TabItem("Text to Image"): with gr.Group(): 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) result = gr.Gallery(label="Result", elem_id="gallery", show_label=False) with gr.Accordion("Advanced options", open=False): with gr.Row(): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) negative_prompt = gr.Text( label="Negative prompt", max_lines=1, value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW", visible=True, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) steps = gr.Slider( label="Steps", minimum=0, maximum=60, step=1, value=25, ) number_image = gr.Slider( label="Number of Images", minimum=1, maximum=4, step=1, value=1, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(visible=True): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=10, step=0.1, value=7.0, ) gr.Examples( examples=examples, inputs=prompt, outputs=[result], fn=generate, cache_examples=CACHE_EXAMPLES, ) use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, api_name=False, ) gr.on( triggers=[ prompt.submit, negative_prompt.submit, run_button.click, ], fn=generate, inputs=[ prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed, steps, number_image, ], outputs=[result], api_name="run", ) with gr.TabItem("Image to Image"): with gr.Group(): with gr.Row(equal_height=True): with gr.Column(scale=1): img2img_prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) init_image = gr.Image(label="Input Image", type="pil") with gr.Row(): img2img_run_button = gr.Button("Generate", variant="primary") with gr.Column(scale=1): img2img_output = gr.Gallery(label="Result", elem_id="gallery") with gr.Accordion("Advanced options", open=False): with gr.Row(): img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) img2img_negative_prompt = gr.Text( label="Negative prompt", max_lines=1, value="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW", visible=True, ) img2img_seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) img2img_steps = gr.Slider( label="Steps", minimum=0, maximum=60, step=1, value=25, ) img2img_number_image = gr.Slider( label="Number of Images", minimum=1, maximum=4, step=1, value=1, ) img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): img2img_guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=10, step=0.1, value=7.0, ) strength = gr.Slider(label="Img2Img Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8) img2img_use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=img2img_use_negative_prompt, outputs=img2img_negative_prompt, api_name=False, ) gr.on( triggers=[ img2img_prompt.submit, img2img_negative_prompt.submit, img2img_run_button.click, ], fn=img2img_generate, inputs=[ img2img_prompt, init_image, img2img_negative_prompt, img2img_use_negative_prompt, img2img_seed, img2img_guidance_scale, img2img_randomize_seed, img2img_steps, strength, img2img_number_image, ], outputs=[img2img_output], api_name="img2img_run", ) if __name__ == "__main__": demo.queue().launch()