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import random
import gradio as gr
import numpy as np
import spaces
import torch
from inference_t2mv_sdxl import prepare_pipeline, run_pipeline
import transformers
transformers.utils.move_cache()
# Base model
base_model = "cagliostrolab/animagine-xl-3.1"
# Device and dtype
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
# Hyperparameters
NUM_VIEWS = 6
HEIGHT = 768
WIDTH = 768
MAX_SEED = np.iinfo(np.int32).max
pipe = prepare_pipeline(
base_model=base_model,
vae_model="madebyollin/sdxl-vae-fp16-fix",
unet_model=None,
lora_model=None,
adapter_path="huanngzh/mv-adapter",
scheduler=None,
num_views=NUM_VIEWS,
device=device,
dtype=dtype,
)
@spaces.GPU()
def infer(
prompt,
seed=42,
randomize_seed=False,
guidance_scale=7.0,
num_inference_steps=30,
negative_prompt="watermark, ugly, deformed, noisy, blurry, low contrast",
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
if isinstance(seed, str):
try:
seed = int(seed.strip())
except ValueError:
seed = 42
images = run_pipeline(
pipe,
num_views=NUM_VIEWS,
text=prompt,
height=HEIGHT,
width=WIDTH,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
seed=seed,
negative_prompt=negative_prompt,
device=device,
)
return images, seed
examples = {
"stabilityai/stable-diffusion-xl-base-1.0": [
["An astronaut riding a horse", 0],
["A DSLR photo of a frog wearing a sweater", 21],
],
"cagliostrolab/animagine-xl-3.1": [
[
"1girl, izayoi sakuya, touhou, solo, maid headdress, maid, apron, short sleeves, dress, closed mouth, white apron, serious face, upper body, masterpiece, best quality, very aesthetic, absurdres",
666,
],
[
"1boy, male focus, ikari shinji, neon genesis evangelion, solo, serious face,(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, moist skin, intricate details",
0,
],
[
"1girl, pink hair, pink shirts, smile, shy, masterpiece, anime",
0,
],
],
}
css = """
#col-container {
margin: 0 auto;
max-width: 600px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
f"""# MV-Adapter [Text-to-Multi-View]
Generate 768x768 multi-view images using {base_model} <br>
[[page](https://huanngzh.github.io/MV-Adapter-Page/)] [[repo](https://github.com/huanngzh/MV-Adapter)]
"""
)
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.Gallery(
label="Result",
show_label=False,
columns=[3],
rows=[2],
object_fit="contain",
height="auto",
)
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():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=30,
)
with gr.Row():
guidance_scale = gr.Slider(
label="CFG scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0,
)
with gr.Row():
negative_prompt = gr.Textbox(
label="Negative prompt",
placeholder="Enter your negative prompt",
value="watermark, ugly, deformed, noisy, blurry, low contrast",
)
gr.Examples(
examples=examples[base_model],
fn=infer,
inputs=[prompt, seed],
outputs=[result, seed],
cache_examples=True,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
seed,
randomize_seed,
guidance_scale,
num_inference_steps,
negative_prompt,
],
outputs=[result, seed],
)
demo.launch()
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