import sys
sys.path.append('./')
import os
import cv2
import random
import numpy as np
from PIL import Image
import spaces
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
import torch
import gradio as gr
from huggingface_hub import hf_hub_download
from ip_adapter import IPAdapterXL
import os
os.system("git lfs install")
os.system("git clone https://huggingface.co/h94/IP-Adapter")
os.system("mv IP-Adapter/sdxl_models sdxl_models")
# global variable
MAX_SEED = np.iinfo(np.int32).max
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
# initialization
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
image_encoder_path = "sdxl_models/image_encoder"
ip_ckpt = "sdxl_models/ip-adapter_sdxl.bin"
controlnet_path = "diffusers/controlnet-canny-sdxl-1.0"
controlnet = ControlNetModel.from_pretrained(controlnet_path, use_safetensors=False, torch_dtype=torch.float16).to(device)
# load SDXL pipeline
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
add_watermarker=False,
)
# load ip-adapter
# target_blocks=["block"] for original IP-Adapter
# target_blocks=["up_blocks.0.attentions.1"] for style blocks only
# target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks
ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["up_blocks.0.attentions.1"])
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def resize_img(
input_image,
max_side=1280,
min_side=1024,
size=None,
pad_to_max_side=False,
mode=Image.BILINEAR,
base_pixel_number=64,
):
w, h = input_image.size
if size is not None:
w_resize_new, h_resize_new = size
else:
ratio = min_side / min(h, w)
w, h = round(ratio * w), round(ratio * h)
ratio = max_side / max(h, w)
input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
input_image = input_image.resize([w_resize_new, h_resize_new], mode)
if pad_to_max_side:
res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
offset_x = (max_side - w_resize_new) // 2
offset_y = (max_side - h_resize_new) // 2
res[
offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
] = np.array(input_image)
input_image = Image.fromarray(res)
return input_image
def get_example():
case = [
[
"./assets/0.jpg",
None,
"a cat, masterpiece, best quality, high quality",
1.0,
0.0
],
[
"./assets/1.jpg",
None,
"a cat, masterpiece, best quality, high quality",
1.0,
0.0
],
[
"./assets/2.jpg",
None,
"a cat, masterpiece, best quality, high quality",
1.0,
0.0
],
[
"./assets/3.jpg",
None,
"a cat, masterpiece, best quality, high quality",
1.0,
0.0
],
[
"./assets/2.jpg",
"./assets/yann-lecun.jpg",
"a man, masterpiece, best quality, high quality",
1.0,
0.6
],
]
return case
def run_for_examples(style_image, source_image, prompt, scale, control_scale):
return create_image(
image_pil=style_image,
input_image=source_image,
prompt=prompt,
n_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
scale=scale,
control_scale=control_scale,
guidance_scale=5,
num_samples=1,
num_inference_steps=20,
seed=42,
target="Load only style blocks",
neg_content_prompt="",
neg_content_scale=0,
)
@spaces.GPU(enable_queue=True)
def create_image(image_pil,
input_image,
prompt,
n_prompt,
scale,
control_scale,
guidance_scale,
num_samples,
num_inference_steps,
seed,
target="Load only style blocks",
neg_content_prompt=None,
neg_content_scale=0):
if target =="Load original IP-Adapter":
# target_blocks=["blocks"] for original IP-Adapter
ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["blocks"])
elif target=="Load only style blocks":
# target_blocks=["up_blocks.0.attentions.1"] for style blocks only
ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["up_blocks.0.attentions.1"])
elif target == "Load style+layout block":
# target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks
ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device, target_blocks=["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"])
if input_image is not None:
input_image = resize_img(input_image, max_side=1024)
cv_input_image = pil_to_cv2(input_image)
detected_map = cv2.Canny(cv_input_image, 50, 200)
canny_map = Image.fromarray(cv2.cvtColor(detected_map, cv2.COLOR_BGR2RGB))
else:
canny_map = Image.new('RGB', (1024, 1024), color=(255, 255, 255))
control_scale = 0
if float(control_scale) == 0:
canny_map = canny_map.resize((1024,1024))
if len(neg_content_prompt) > 0 and neg_content_scale != 0:
images = ip_model.generate(pil_image=image_pil,
prompt=prompt,
negative_prompt=n_prompt,
scale=scale,
guidance_scale=guidance_scale,
num_samples=num_samples,
num_inference_steps=num_inference_steps,
seed=seed,
image=canny_map,
controlnet_conditioning_scale=float(control_scale),
neg_content_prompt=neg_content_prompt,
neg_content_scale=neg_content_scale
)
else:
images = ip_model.generate(pil_image=image_pil,
prompt=prompt,
negative_prompt=n_prompt,
scale=scale,
guidance_scale=guidance_scale,
num_samples=num_samples,
num_inference_steps=num_inference_steps,
seed=seed,
image=canny_map,
controlnet_conditioning_scale=float(control_scale),
)
return images
def pil_to_cv2(image_pil):
image_np = np.array(image_pil)
image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
return image_cv2
# Description
title = r"""
<h1 align="center">InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation</h1>
"""
description = r"""
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantStyle/InstantStyle' target='_blank'><b>InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation</b></a>.<br>
How to use:<br>
1. Upload a style image.
2. Set stylization mode, only use style block by default.
2. Enter a text prompt, as done in normal text-to-image models.
3. Click the <b>Submit</b> button to begin customization.
4. Share your stylized photo with your friends and enjoy! 😊
Advanced usage:<br>
1. Click advanced options.
2. Upload another source image for image-based stylization using ControlNet.
3. Enter negative content prompt to avoid content leakage.
"""
article = r"""
---
📝 **Citation**
<br>
If our work is helpful for your research or applications, please cite us via:
```bibtex
@article{wang2024instantstyle,
title={InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation},
author={Wang, Haofan and Wang, Qixun and Bai, Xu and Qin, Zekui and Chen, Anthony},
journal={arXiv preprint arXiv:2404.02733},
year={2024}
}
```
📧 **Contact**
<br>
If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>.
"""
block = gr.Blocks(css="footer {visibility: hidden}").queue(max_size=10, api_open=False)
with block:
# description
gr.Markdown(title)
gr.Markdown(description)
with gr.Tabs():
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
image_pil = gr.Image(label="Style Image", type='pil')
target = gr.Radio(["Load only style blocks", "Load style+layout block", "Load original IP-Adapter"],
value="Load only style blocks",
label="Style mode")
prompt = gr.Textbox(label="Prompt",
value="a cat, masterpiece, best quality, high quality")
scale = gr.Slider(minimum=0,maximum=2.0, step=0.01,value=1.0, label="Scale")
with gr.Accordion(open=False, label="Advanced Options"):
with gr.Column():
src_image_pil = gr.Image(label="Source Image (optional)", type='pil')
control_scale = gr.Slider(minimum=0,maximum=1.0, step=0.01,value=0.5, label="Controlnet conditioning scale")
n_prompt = gr.Textbox(label="Neg Prompt", value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry")
neg_content_prompt = gr.Textbox(label="Neg Content Prompt", value="")
neg_content_scale = gr.Slider(minimum=0, maximum=1.0, step=0.01,value=0.5, label="Neg Content Scale")
guidance_scale = gr.Slider(minimum=1,maximum=15.0, step=0.01,value=5.0, label="guidance scale")
num_samples= gr.Slider(minimum=1,maximum=4.0, step=1.0,value=1.0, label="num samples")
num_inference_steps = gr.Slider(minimum=5,maximum=50.0, step=1.0,value=20, label="num inference steps")
seed = gr.Slider(minimum=-1000000,maximum=1000000,value=1, step=1, label="Seed Value")
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
generate_button = gr.Button("Generate Image")
with gr.Column():
generated_image = gr.Gallery(label="Generated Image")
generate_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=create_image,
inputs=[image_pil,
src_image_pil,
prompt,
n_prompt,
scale,
control_scale,
guidance_scale,
num_samples,
num_inference_steps,
seed,
target,
neg_content_prompt,
neg_content_scale],
outputs=[generated_image])
gr.Examples(
examples=get_example(),
inputs=[image_pil, src_image_pil, prompt, scale, control_scale],
fn=run_for_examples,
outputs=[generated_image],
cache_examples=True,
)
gr.Markdown(article)
block.launch()