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Running
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Zero
| 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 transformers import T5EncoderModel, BitsAndBytesConfig | |
| from huggingface_hub import login | |
| huggingface_token = os.getenv("HUGGINGFACE_TOKEN") | |
| login(token=huggingface_token) | |
| DESCRIPTION = """# Stable Diffusion 3""" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>" | |
| 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(): | |
| model_id = "stabilityai/stable-diffusion-3-medium-diffusers" | |
| pipe = StableDiffusion3Pipeline.from_pretrained( | |
| model_id, | |
| #device_map="balanced", | |
| torch_dtype=torch.float16 | |
| ) | |
| return pipe | |
| aspect_ratios = { | |
| "21:9": (21, 9), | |
| "2:1": (2, 1), | |
| "16:9": (16, 9), | |
| "5:4": (5, 4), | |
| "4:3": (4, 3), | |
| "3:2": (3, 2), | |
| "1:1": (1, 1), | |
| } | |
| # Function to calculate resolution | |
| def calculate_resolution(aspect_ratio, mode='landscape', total_pixels=1024*1024, divisibility=64): | |
| if aspect_ratio not in aspect_ratios: | |
| raise ValueError(f"Invalid aspect ratio: {aspect_ratio}") | |
| width_multiplier, height_multiplier = aspect_ratios[aspect_ratio] | |
| ratio = width_multiplier / height_multiplier | |
| if mode == 'portrait': | |
| # Swap the ratio for portrait mode | |
| ratio = 1 / ratio | |
| height = int((total_pixels / ratio) ** 0.5) | |
| height -= height % divisibility | |
| width = int(height * ratio) | |
| width -= width % divisibility | |
| while width * height > total_pixels: | |
| height -= divisibility | |
| width = int(height * ratio) | |
| width -= width % divisibility | |
| return width, height | |
| 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 | |
| def generate( | |
| prompt:str, | |
| negative_prompt: str = "", | |
| use_negative_prompt: bool = False, | |
| seed: int = 0, | |
| aspect: str = "1:1", | |
| mode: str = "landscape", | |
| guidance_scale: float = 7.5, | |
| 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() | |
| 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 | |
| width, height = calculate_resolution(aspect, mode) | |
| 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="pil", | |
| ).images | |
| return output | |
| examples = [ | |
| "Beautiful pixel art of a wizard with hovering text \"Achievement unlocked: Diffusion models can spell now\"", | |
| "Frog sitting in a 1950s diner wearing a leather jacket and a top hat. on the table a giant burger and a small sign that says \"froggy fridays\"", | |
| "This dreamlike digital art capture a vibrant kaleidoscopic bird in a rainforest", | |
| "pair of shoes made of dried fruit skins, 3d render, bright colours, clean composition, beautiful artwork, logo saying \"SD3 rocks!\"", | |
| "post-apocalyptic city wasteland, the most delicate beautiful flower with green leaves growing from dust and rubble, vibrant colours, cinematic", | |
| "a dark-armored warrior with ornate golden details, cloaked in a flowing black cape, wielding a radiant, fiery sword, standing amidst an ominous cloudy backdrop with dramatic lighting, exuding a menacing, powerful presence.", | |
| "A wise old wizard with a long white beard, flowing robes, and a gnarled staff, casting a spell, photorealistic style", | |
| "Design a film poster for a noir thriller set in 1940s Los Angeles, featuring a shadowy figure under a streetlamp and a foggy, mysterious ambiance.", | |
| ] | |
| 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( | |
| """ | |
| <h1 style='text-align: center'> | |
| Stable Diffusion 3 | |
| </h1> | |
| """ | |
| ) | |
| 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) | |
| with gr.Row(): | |
| aspect = gr.Dropdown(label='Aspect Ratio', choices=list(aspect_ratios.keys()), value='1:1', interactive=True) | |
| mode = gr.Dropdown(label='Mode', choices=['landscape', 'portrait'], value='landscape') | |
| 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=30, | |
| ) | |
| number_image = gr.Slider( | |
| label="Number of Images", | |
| minimum=1, | |
| maximum=2, | |
| step=1, | |
| value=1, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| 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, | |
| aspect, | |
| mode, | |
| guidance_scale, | |
| randomize_seed, | |
| steps, | |
| number_image, | |
| ], | |
| outputs=[result], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch() |