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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 | |
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() | |