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Running
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Running
on
Zero
File size: 4,458 Bytes
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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
@spaces.GPU(duration=5)
def infer(prompt, model_size, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=4.5, 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", "1.6B"],
["🐶 Wearing 🕶 flying on the 🌈", "1.6B"],
["an anime illustration of a wiener schnitzel", "0.6B"],
["a photorealistic landscape of mountains at sunset", "0.6B"],
]
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=4.5,
)
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() |