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import gradio as gr
import numpy as np
import random
import spaces
from models import TVARPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "michellemoorre/var-test"
pipe = TVARPipeline.from_pretrained(model_repo_id, device=device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=65)
def infer(
prompt,
negative_prompt="",
seed=42,
randomize_seed=False,
guidance_scale=4.0,
top_k=450,
top_p=0.95,
re=False,
re_max_depth=10,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
image = pipe(
prompt=prompt,
null_prompt=negative_prompt,
cfg=guidance_scale,
top_p=top_p,
top_k=top_k,
re=re,
g_seed=seed,
)[0]
return image, seed
# TODO: add examples from preview
examples = [
"A capybara wearing a suit holding a sign that reads Hello World",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # [OpenTVAR](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)")
gr.Markdown("[Learn more](https://stability.ai/news/introducing-stable-diffusion-3-5) about the OpenTVAR.")
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, variant="primary")
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",
visible=True,
)
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():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=7.5,
step=0.1,
value=4.5,
)
with gr.Row():
top_k = gr.Slider(
label="Sampling top k",
minimum=1,
maximum=1000,
step=10,
value=450,
)
top_p = gr.Slider(
label="Sampling top p",
minimum=0.0,
maximum=1.,
step=0.05,
value=0.95,
)
with gr.Row():
re = gr.Checkbox(label="Rejection Sampling", value=False)
re_max_depth = gr.Slider(
label="Rejection Sampling Depth",
minimum=0,
maximum=20,
step=1,
value=10,
)
gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True)# cache_mode="lazy")
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
guidance_scale,
top_k,
top_p,
re,
re_max_depth,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()