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Running
on
Zero
| import gradio as gr | |
| import spaces | |
| import numpy as np | |
| import random | |
| import spaces | |
| import torch | |
| from diffusers import SanaSprintPipeline | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = SanaSprintPipeline.from_pretrained( | |
| "Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers", | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| pipe2 = SanaSprintPipeline.from_pretrained( | |
| "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| pipe.to(device) | |
| pipe2.to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer(prompt, model_size, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=2, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| # Choose the appropriate model based on selected model size | |
| selected_pipe = pipe if model_size == "0.6B" else pipe2 | |
| img = selected_pipe( | |
| prompt=prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| output_type="pil" | |
| ) | |
| print(img) | |
| return img.images[0], seed | |
| examples = [ | |
| ["a tiny astronaut hatching from an egg on the moon", "0.6B"], | |
| ["a cat holding a sign that says hello world", "1.6B"], | |
| ["an anime illustration of a wiener schnitzel", "0.6B"], | |
| ["a photorealistic landscape of mountains at sunset", "1.6B"], | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f"""# Sana Sprint""") | |
| gr.Markdown("Demo for the real-time [Sana Sprint](https://huggingface.co/collections/Efficient-Large-Model/sana-sprint-67d6810d65235085b3b17c76) model") | |
| 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) | |
| model_size = gr.Radio( | |
| label="Model Size", | |
| choices=["0.6B", "1.6B"], | |
| value="1.6B", | |
| interactive=True | |
| ) | |
| with gr.Accordion("Advanced Settings", open=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=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=1, | |
| maximum=15, | |
| step=0.1, | |
| value=1, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=2, | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| fn = infer, | |
| inputs = [prompt, model_size], # Add model_size to inputs | |
| outputs = [result, seed], | |
| cache_examples="lazy" | |
| ) | |
| gr.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn = infer, | |
| inputs = [prompt, model_size, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], # Add model_size to inputs | |
| outputs = [result, seed] | |
| ) | |
| demo.launch() |