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Running
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A10G
Running
on
A10G
#!/usr/bin/env python | |
import os | |
import random | |
import gradio as gr | |
import numpy as np | |
import torch | |
from model import ADAPTER_NAMES, Model | |
DESCRIPTION = "# T2I-Adapter-SDXL" | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
MAX_SEED = np.iinfo(np.int32).max | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
model = Model(ADAPTER_NAMES[0]) | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
gr.DuplicateButton( | |
value="Duplicate Space for private use", | |
elem_id="duplicate-button", | |
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
) | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Group(): | |
image = gr.Image(label="Input image", type="pil", height=600) | |
prompt = gr.Textbox(label="Prompt") | |
adapter_name = gr.Dropdown(label="Adapter", choices=ADAPTER_NAMES, value=ADAPTER_NAMES[0]) | |
run_button = gr.Button("Run") | |
with gr.Accordion("Advanced options", open=False): | |
apply_preprocess = gr.Checkbox(label="Apply preprocess", value=True) | |
negative_prompt = gr.Textbox( | |
label="Negative prompt", | |
value="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of steps", | |
minimum=1, | |
maximum=Model.MAX_NUM_INFERENCE_STEPS, | |
step=1, | |
value=30, | |
) | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.1, | |
maximum=30.0, | |
step=0.1, | |
value=5.0, | |
) | |
adapter_conditioning_scale = gr.Slider( | |
label="Adapter Conditioning Scale", | |
minimum=0.5, | |
maximum=1, | |
step=0.1, | |
value=1.0, | |
) | |
cond_tau = gr.Slider( | |
label="Fraction of timesteps for which adapter should be applied", | |
minimum=0.5, | |
maximum=1.0, | |
step=0.1, | |
value=1.0, | |
) | |
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.Column(): | |
result = gr.Gallery(label="Result", columns=2, height=600, object_fit="scale-down", show_label=False) | |
inputs = [ | |
image, | |
prompt, | |
negative_prompt, | |
num_inference_steps, | |
guidance_scale, | |
adapter_conditioning_scale, | |
cond_tau, | |
seed, | |
apply_preprocess, | |
] | |
prompt.submit( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=model.change_adapter, | |
inputs=adapter_name, | |
api_name=False, | |
).success( | |
fn=model.run, | |
inputs=inputs, | |
outputs=result, | |
api_name=False, | |
) | |
negative_prompt.submit( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=model.change_adapter, | |
inputs=adapter_name, | |
api_name=False, | |
).success( | |
fn=model.run, | |
inputs=inputs, | |
outputs=result, | |
api_name=False, | |
) | |
run_button.click( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=model.change_adapter, | |
inputs=adapter_name, | |
api_name=False, | |
).success( | |
fn=model.run, | |
inputs=inputs, | |
outputs=result, | |
api_name="run", | |
) | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() | |