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Running
on
Zero
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, | |
) | |
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", | |
0, | |
], | |
[ | |
"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] [Anime] | |
Generate anime-style multi-view images using {base_model} <br> | |
Check our [project page](https://huanngzh.github.io/MV-Adapter-Page/) and [github repo](https://github.com/huanngzh/MV-Adapter) for details <br> | |
Also try our other demos: [Text-to-Multiview (General)](https://huggingface.co./spaces/VAST-AI/MV-Adapter-T2MV-SDXL) | [Text-to-Multiview (Anime)](https://huggingface.co./spaces/huanngzh/MV-Adapter-T2MV-Anime) | [Image-to-Multiview](https://huggingface.co./spaces/VAST-AI/MV-Adapter-I2MV-SDXL) <br> | |
**Tips:** please follow the prompt template in [usage-guidelines](https://huggingface.co./cagliostrolab/animagine-xl-3.1#usage-guidelines) | |
""" | |
) | |
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() | |