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#!/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=7.5,
)
adapter_conditioning_scale = gr.Slider(
label="Adapter Conditioning Scale",
minimum=0.5,
maximum=1,
step=0.1,
value=0.8,
)
cond_tau = gr.Slider(
label="Fraction of timesteps for which adapter should be applied",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.8,
)
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()
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