app.py

import sys
sys.path.append('./')
import spaces
import gradio as gr
import torch
from ip_adapter.utils import BLOCKS as BLOCKS
from ip_adapter.utils import controlnet_BLOCKS as controlnet_BLOCKS
from ip_adapter.utils import resize_content
import cv2
import numpy as np
import random
from PIL import Image
from transformers import AutoImageProcessor, AutoModel
from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    StableDiffusionXLControlNetPipeline,

)
from ip_adapter import CSGO
from transformers import BlipProcessor, BlipForConditionalGeneration

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
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")

from huggingface_hub import hf_hub_download

# hf_hub_download(repo_id="h94/IP-Adapter", filename="sdxl_models/image_encoder", local_dir="./sdxl_models/image_encoder")
hf_hub_download(repo_id="InstantX/CSGO", filename="csgo_4_32.bin", local_dir="./CSGO/")
os.system('rm -rf IP-Adapter/models')
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
image_encoder_path = "sdxl_models/image_encoder"
csgo_ckpt ='./CSGO/csgo_4_32.bin'
pretrained_vae_name_or_path ='madebyollin/sdxl-vae-fp16-fix'
controlnet_path = "TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic"
weight_dtype = torch.float16


os.system("git clone https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic")
os.system("mv TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v2_fp16.safetensors TTPLanet_SDXL_Controlnet_Tile_Realistic/diffusion_pytorch_model.safetensors")
os.system('rm -rf TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v1_fp16.safetensors')
os.system('rm -rf TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v1_fp16.safetensors')
controlnet_path = "./TTPLanet_SDXL_Controlnet_Tile_Realistic"


# os.system('git clone https://huggingface.co/InstantX/CSGO')
# os.system('rm -rf CSGO/csgo.bin')



vae = AutoencoderKL.from_pretrained(pretrained_vae_name_or_path,torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16,use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    base_model_path,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    add_watermarker=False,
    vae=vae
)
pipe.enable_vae_tiling()


blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)

target_content_blocks = BLOCKS['content']
target_style_blocks = BLOCKS['style']
controlnet_target_content_blocks = controlnet_BLOCKS['content']
controlnet_target_style_blocks = controlnet_BLOCKS['style']

csgo = CSGO(pipe, image_encoder_path, csgo_ckpt, device, num_content_tokens=4, num_style_tokens=32,
            target_content_blocks=target_content_blocks, target_style_blocks=target_style_blocks,
            controlnet_adapter=True,
            controlnet_target_content_blocks=controlnet_target_content_blocks,
            controlnet_target_style_blocks=controlnet_target_style_blocks,
            content_model_resampler=True,
            style_model_resampler=True,
            )

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





def get_example():
    case = [
        [
            "./assets/img_0.png",
            './assets/img_1.png',
            "Image-Driven Style Transfer",
            "there is a small house with a sheep statue on top of it",
            0.6,
            1.0,
            7.0,
            42
        ],
        [
         None,
         './assets/img_1.png',
            "Text-Driven Style Synthesis",
         "a cat",
         0.01,
            1.0,
            7.0,
            42
         ],
        [
            None,
            './assets/img_2.png',
            "Text-Driven Style Synthesis",
            "a cat",
            0.01,
            1.0,
            7.0,
            42,
        ],
        [
            "./assets/img_0.png",
            './assets/img_1.png',
            "Text Edit-Driven Style Synthesis",
            "there is a small house",
            0.4,
            1.0,
            7.0,
            42,
        ],
    ]
    return case


def run_for_examples(content_image_pil,style_image_pil,target, prompt, scale_c, scale_s,guidance_scale,seed):
    return create_image(
        content_image_pil=content_image_pil,
        style_image_pil=style_image_pil,
        prompt=prompt,
        scale_c=scale_c,
        scale_s=scale_s,
        guidance_scale=guidance_scale,
        num_samples=2,
        num_inference_steps=50,
        seed=seed,
        target=target,
    )
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

def image_grid(imgs, rows, cols):
    assert len(imgs) == rows * cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols * w, rows * h))
    grid_w, grid_h = grid.size

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
    return grid
@spaces.GPU
def create_image(content_image_pil,
                 style_image_pil,
                 prompt,
                 scale_c,
                 scale_s,
                 guidance_scale,
                 num_samples,
                 num_inference_steps,
                 seed,
                 target="Image-Driven Style Transfer",
):


    if content_image_pil is None:
        content_image_pil = Image.fromarray(
            np.zeros((1024, 1024, 3), dtype=np.uint8)).convert('RGB')

    if prompt == '':

        inputs = blip_processor(content_image_pil, return_tensors="pt").to(device)
        out = blip_model.generate(**inputs)
        prompt = blip_processor.decode(out[0], skip_special_tokens=True)
    width, height, content_image = resize_content(content_image_pil)
    style_image = style_image_pil
    neg_content_prompt='text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry'
    if target =="Image-Driven Style Transfer":

        images = csgo.generate(pil_content_image=content_image, pil_style_image=style_image,
                               prompt=prompt,
                               negative_prompt=neg_content_prompt,
                               height=height,
                               width=width,
                               content_scale=1.0,
                               style_scale=scale_s,
                               guidance_scale=guidance_scale,
                               num_images_per_prompt=num_samples,
                               num_inference_steps=num_inference_steps,
                               num_samples=1,
                               seed=seed,
                               image=content_image.convert('RGB'),
                               controlnet_conditioning_scale=scale_c,
                               )

    elif target =="Text-Driven Style Synthesis":
        content_image = Image.fromarray(
            np.zeros((1024, 1024, 3), dtype=np.uint8)).convert('RGB')

        images = csgo.generate(pil_content_image=content_image, pil_style_image=style_image,
                               prompt=prompt,
                               negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
                               height=height,
                               width=width,
                               content_scale=0.5,
                               style_scale=scale_s,
                               guidance_scale=7,
                               num_images_per_prompt=num_samples,
                               num_inference_steps=num_inference_steps,
                               num_samples=1,
                               seed=42,
                               image=content_image.convert('RGB'),
                               controlnet_conditioning_scale=scale_c,
                               )
    elif target =="Text Edit-Driven Style Synthesis":


        images = csgo.generate(pil_content_image=content_image, pil_style_image=style_image,
                               prompt=prompt,
                               negative_prompt=neg_content_prompt,
                               height=height,
                               width=width,
                               content_scale=1.0,
                               style_scale=scale_s,
                               guidance_scale=guidance_scale,
                               num_images_per_prompt=num_samples,
                               num_inference_steps=num_inference_steps,
                               num_samples=1,
                               seed=seed,
                               image=content_image.convert('RGB'),
                               controlnet_conditioning_scale=scale_c,
                               )

    return [image_grid(images, 1, num_samples)]


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">CSGO: Content-Style Composition in Text-to-Image Generation</h1>
"""

description = r"""
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/instantX-research/CSGO' target='_blank'><b>CSGO: Content-Style Composition in Text-to-Image Generation</b></a>.<br> 
How to use:<br>
1. Upload a content image if you want to use image-driven style transfer.
2. Upload a style image.
3. Sets the type of task to perform, by default image-driven style transfer is performed. Options are <b>Image-driven style transfer, Text-driven style synthesis, and Text editing-driven style synthesis<b>. 
4. <b>If you choose a text-driven task, enter your desired prompt<b>.  
5. If you don't provide a prompt, the default is to use the BLIP model to generate the caption.  We suggest that by providing detailed prompts for Content images, CSGO is able to effectively guarantee content.
6. Click the <b>Submit</b> button to begin customization.
7. Share your stylized photo with your friends and enjoy! 😊

Advanced usage:<br>
1. Click advanced options.
2. Choose different guidance and steps.
"""

article = r"""
---
📝 **Tips**
In CSGO, the more accurate the text prompts for content images, the better the content retention.
Text-driven style synthesis and text-edit-driven style synthesis are expected to be more stable in the next release.
---
📝 **Citation**
<br>
If our work is helpful for your research or applications, please cite us via:
```bibtex
@article{xing2024csgo,
       title={CSGO: Content-Style Composition in Text-to-Image Generation}, 
       author={Peng Xing and Haofan Wang and Yanpeng Sun and Qixun Wang and Xu Bai and Hao Ai and Renyuan Huang and Zechao Li},
       year={2024},
       journal = {arXiv 2408.16766},
}
```
📧 **Contact**
<br>
If you have any questions, please feel free to open an issue or directly reach us out at <b>[email protected]</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():
                        content_image_pil = gr.Image(label="Content Image (optional)", type='pil')
                        style_image_pil = gr.Image(label="Style Image", type='pil')

                target = gr.Radio(["Image-Driven Style Transfer", "Text-Driven Style Synthesis", "Text Edit-Driven Style Synthesis"],
                                  value="Image-Driven Style Transfer",
                                  label="task")

                # prompt_type = gr.Radio(["caption of Blip", "user input"],
                #     value="caption of Blip",
                #     label="prompt type")

                prompt = gr.Textbox(label="Prompt",
                                    value="there is a small house with a sheep statue on top of it")
                prompt_type = gr.CheckboxGroup(
                    ["caption of Blip", "user input"], label="prompt_type", value=["caption of Blip"],
                    info="Choose to enter more detailed prompts yourself or use the blip model to describe content images."
                )
                if prompt_type == "caption of Blip" and target == "Image-Driven Style Transfer":
                    prompt =''

                scale_c = gr.Slider(minimum=0, maximum=2.0, step=0.01, value=0.6, label="Content Scale")
                scale_s = gr.Slider(minimum=0, maximum=2.0, step=0.01, value=1.0, label="Style Scale")
                with gr.Accordion(open=False, label="Advanced Options"):

                    guidance_scale = gr.Slider(minimum=1, maximum=15.0, step=0.01, value=7.0, 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=100.0, step=1.0, value=50,
                                                    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=[content_image_pil,
                    style_image_pil,
                    prompt,
                    scale_c,
                    scale_s,
                    guidance_scale,
                    num_samples,
                    num_inference_steps,
                    seed,
                    target,],
            outputs=[generated_image])

    gr.Examples(
        examples=get_example(),
        inputs=[content_image_pil,style_image_pil,target, prompt, scale_c, scale_s,guidance_scale,seed],
        fn=run_for_examples,
        outputs=[generated_image],
        cache_examples=False,
    )

    gr.Markdown(article)


block.launch()