import argparse
import gradio as gr
import numpy as np
import torch
from pulid import attention_processor as attention
from pulid.pipeline_v1_1 import PuLIDPipeline
from pulid.utils import resize_numpy_image_long
torch.set_grad_enabled(False)
parser = argparse.ArgumentParser()
parser.add_argument(
'--base',
type=str,
default='RunDiffusion/Juggernaut-XL-v9',
choices=[
'Lykon/dreamshaper-xl-lightning',
# 'SG161222/RealVisXL_V4.0', will add it later
'RunDiffusion/Juggernaut-XL-v9',
],
)
# parser.add_argument('--sampler', type=str, default='dpmpp_2m', choices=['dpmpp_sde', 'dpmpp_2m'])
parser.add_argument('--port', type=int, default=7860)
args = parser.parse_args()
use_lightning_model = 'lightning' in args.base.lower()
# currently we only support two commonly used sampler
args.sampler = 'dpmpp_sde' if use_lightning_model else 'dpmpp_2m'
if use_lightning_model:
default_cfg = 2.0
default_steps = 5
else:
default_cfg = 7.0
default_steps = 25
pipeline = PuLIDPipeline(sdxl_repo=args.base, sampler=args.sampler)
# other params
DEFAULT_NEGATIVE_PROMPT = (
'flaws in the eyes, flaws in the face, flaws, lowres, non-HDRi, low quality, worst quality,'
'artifacts noise, text, watermark, glitch, deformed, mutated, ugly, disfigured, hands, '
'low resolution, partially rendered objects, deformed or partially rendered eyes, '
'deformed, deformed eyeballs, cross-eyed,blurry'
)
dreamshaper_example_inps = [
['portrait, blacklight', 'example_inputs/liuyifei.png', 42, 0.8, 10],
['pixel art, 1boy', 'example_inputs/lecun.jpg', 42, 0.8, 10],
[
'cinematic film still, close up, photo of redheaded girl near grasses, fictional landscapes, (intense sunlight:1.4), realist detail, brooding mood, ue5, detailed character expressions, light amber and red, amazing quality, wallpaper, analog film grain',
'example_inputs/liuyifei.png',
42,
0.8,
10,
],
[
'A minimalist line art depiction of an Artificial Intelligence being\'s thought process, lines and nodes forming intricate patterns.',
'example_inputs/hinton.jpeg',
42,
0.8,
10,
],
[
'instagram photo, photo of 23 y.o man in black sweater, pale skin, (smile:0.4), hard shadows',
'example_inputs/pengwei.jpg',
42,
0.8,
10,
],
[
'by Tsutomu Nihei,(strange but extremely beautiful:1.4),(masterpiece, best quality:1.4),in the style of nicola samori,The Joker,',
'example_inputs/lecun.jpg',
1675432759740519133,
0.8,
10,
],
]
jugger_example_inps = [
[
'robot,simple robot,robot with glass face,ellipse head robot,(made partially out of glass),hexagonal shapes,ferns growing inside head,butterflies on head,butterflies flying around',
'example_inputs/hinton.jpeg',
15022214902832471291,
0.8,
20,
],
['sticker art, 1girl', 'example_inputs/liuyifei.png', 42, 0.8, 20],
[
'1girl, cute model, Long thick Maxi Skirt, Knit sweater, swept back hair, alluring smile, working at a clothing store, perfect eyes, highly detailed beautiful expressive eyes, detailed eyes, 35mm photograph, film, bokeh, professional, 4k, highly detailed dynamic lighting, photorealistic, 8k, raw, rich, intricate details,',
'example_inputs/liuyifei.png',
42,
0.8,
20,
],
['Chinese paper-cut, 1girl', 'example_inputs/liuyifei.png', 42, 0.8, 20],
['Studio Ghibli, 1boy', 'example_inputs/hinton.jpeg', 42, 0.8, 20],
['1man made of ice sculpture', 'example_inputs/lecun.jpg', 42, 0.8, 20],
['portrait of green-skinned shrek, wearing lacoste purple sweater', 'example_inputs/lecun.jpg', 42, 0.8, 20],
['1990s Japanese anime, 1girl', 'example_inputs/liuyifei.png', 42, 0.8, 20],
['made of little stones, portrait', 'example_inputs/hinton.jpeg', 42, 0.8, 20],
]
@torch.inference_mode()
def run(*args):
id_image = args[0]
supp_images = args[1:4]
prompt, neg_prompt, scale, seed, steps, H, W, id_scale, num_zero, ortho = args[4:]
seed = int(seed)
if seed == -1:
seed = torch.Generator(device="cpu").seed()
pipeline.debug_img_list = []
attention.NUM_ZERO = num_zero
if ortho == 'v2':
attention.ORTHO = False
attention.ORTHO_v2 = True
elif ortho == 'v1':
attention.ORTHO = True
attention.ORTHO_v2 = False
else:
attention.ORTHO = False
attention.ORTHO_v2 = False
if id_image is not None:
id_image = resize_numpy_image_long(id_image, 1024)
supp_id_image_list = [
resize_numpy_image_long(supp_id_image, 1024) for supp_id_image in supp_images if supp_id_image is not None
]
id_image_list = [id_image] + supp_id_image_list
uncond_id_embedding, id_embedding = pipeline.get_id_embedding(id_image_list)
else:
uncond_id_embedding = None
id_embedding = None
img = pipeline.inference(
prompt, (1, H, W), neg_prompt, id_embedding, uncond_id_embedding, id_scale, scale, steps, seed
)[0]
return np.array(img), str(seed), pipeline.debug_img_list
_HEADER_ = '''
Official Gradio Demo
**PuLID** is a tuning-free ID customization approach. PuLID maintains high ID fidelity while effectively reducing interference with the original model’s behavior.
Code: GitHub. Paper: ArXiv.
❗️❗️❗️**Tips:**
- we provide some examples in the bottom, you can try these example prompts first
- a single ID image is usually sufficient, you can also supplement with additional auxiliary images
- You can adjust the trade-off between ID fidelity and editability in the advanced options, but generally, the default settings are good enough.
''' # noqa E501
_CITE_ = r"""
If PuLID is helpful, please help to ⭐ the Github Repo. Thanks! [](https://github.com/ToTheBeginning/PuLID)
---
📧 **Contact**
If you have any questions, feel free to open a discussion or contact us at wuyanze123@gmail.com or guozinan.1@bytedance.com.
""" # noqa E501
with gr.Blocks(title="PuLID", css=".gr-box {border-color: #8136e2}") as demo:
gr.Markdown(_HEADER_)
with gr.Row():
with gr.Column():
with gr.Row():
face_image = gr.Image(label="ID image (main)", height=256)
supp_image1 = gr.Image(label="Additional ID image (auxiliary)", height=256)
supp_image2 = gr.Image(label="Additional ID image (auxiliary)", height=256)
supp_image3 = gr.Image(label="Additional ID image (auxiliary)", height=256)
prompt = gr.Textbox(label="Prompt", value='portrait,color,cinematic,in garden,soft light,detailed face')
submit = gr.Button("Generate")
neg_prompt = gr.Textbox(label="Negative Prompt", value=DEFAULT_NEGATIVE_PROMPT)
scale = gr.Slider(
label="CFG (recommend 2 for lightning model and 7 for non-accelerated model)",
value=default_cfg,
minimum=1,
maximum=10,
step=0.1,
)
seed = gr.Textbox(-1, label="Seed (-1 for random)")
steps = gr.Slider(label="Steps", value=default_steps, minimum=1, maximum=30, step=1)
with gr.Row():
H = gr.Slider(label="Height", value=1152, minimum=512, maximum=2024, step=64)
W = gr.Slider(label="Width", value=896, minimum=512, maximum=2024, step=64)
with gr.Row(), gr.Accordion(
"Advanced Options (adjust the trade-off between ID fidelity and editability)", open=False
):
id_scale = gr.Slider(
label="ID scale (Increasing it enhances ID similarity but reduces editability)",
minimum=0,
maximum=5,
step=0.05,
value=0.8,
interactive=True,
)
num_zero = gr.Slider(
label="num zero (Increasing it enhances ID editability but reduces similarity)",
minimum=0,
maximum=80,
step=1,
value=20,
interactive=True,
)
ortho = gr.Dropdown(label="ortho", choices=['off', 'v1', 'v2'], value='v2', visible=False)
with gr.Column():
output = gr.Image(label="Generated Image")
seed_output = gr.Textbox(label="Used Seed")
intermediate_output = gr.Gallery(label='DebugImage', elem_id="gallery", visible=False)
gr.Markdown(_CITE_)
with gr.Row(), gr.Column():
gr.Markdown("## Examples")
if args.base == 'Lykon/dreamshaper-xl-lightning':
gr.Examples(
examples=dreamshaper_example_inps,
inputs=[prompt, face_image, seed, id_scale, num_zero],
label='dreamshaper-xl-lightning examples',
)
elif args.base == 'RunDiffusion/Juggernaut-XL-v9':
gr.Examples(
examples=jugger_example_inps,
inputs=[prompt, face_image, seed, id_scale, num_zero],
label='Juggernaut-XL-v9 examples',
)
inps = [
face_image,
supp_image1,
supp_image2,
supp_image3,
prompt,
neg_prompt,
scale,
seed,
steps,
H,
W,
id_scale,
num_zero,
ortho,
]
submit.click(fn=run, inputs=inps, outputs=[output, seed_output, intermediate_output])
demo.launch(server_name='0.0.0.0', server_port=args.port)