import gradio as gr import numpy as np import random import torch from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler import spaces device = "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.float16 model_file = "https://huggingface.co/silveroxides/sd3-safetensors/blob/main/sd3_medium_XYembmod_incl_creativeclips.fp16.safetensors" pipe = StableDiffusion3Pipeline.from_single_file(model_file, torch_dtype=torch.float16, text_encoder_3=None).to(device) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1536 @spaces.GPU def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) image = pipe( prompt = prompt, negative_prompt = negative_prompt, guidance_scale = guidance_scale, num_inference_steps = num_inference_steps, width = width, height = height, generator = generator, clip_skip = 0 ).images[0] return image, seed examples = [ "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "An astronaut riding a green horse", "A delicious ceviche cheesecake slice", ] css=""" #col-container { margin: 0 auto; max-width: 580px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f""" # Demo [Stable Diffusion 3 Medium](https://huggingface.co/silveroxides/SD3-custom-clip) Learn more about the [Stable Diffusion 3 series](https://stability.ai/news/stable-diffusion-3). Try on [Stability AI API](https://platform.stability.ai/docs/api-reference#tag/Generate/paths/~1v2beta~1stable-image~1generate~1sd3/post), [Stable Assistant](https://stability.ai/stable-assistant), or on Discord via [Stable Artisan](https://stability.ai/stable-artisan). Run locally with [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [diffusers](https://github.com/huggingface/diffusers) """) 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.Image(label="Result", show_label=False) with gr.Accordion("Advanced Settings", open=False): negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", ) 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=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=70, step=1, value=28, ) gr.Examples( examples = examples, inputs = [prompt] ) gr.on( triggers=[run_button.click, prompt.submit, negative_prompt.submit], fn = infer, inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs = [result, seed] ) demo.launch()