app.py

import spaces
import random
import torch
import cv2
import gradio as gr
import numpy as np
from huggingface_hub import snapshot_download
from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor
from diffusers.utils import load_image
from kolors.pipelines.pipeline_controlnet_xl_kolors_img2img import StableDiffusionXLControlNetImg2ImgPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from kolors.models.controlnet import ControlNetModel
from diffusers import  AutoencoderKL
from kolors.models.unet_2d_condition import UNet2DConditionModel
from diffusers import EulerDiscreteScheduler
from PIL import Image
from annotator.midas import MidasDetector
from annotator.util import resize_image, HWC3


device = "cuda"
ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
ckpt_dir_depth = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Depth")
ckpt_dir_canny = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Canny")

text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device)
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device)
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
controlnet_depth = ControlNetModel.from_pretrained(f"{ckpt_dir_depth}", revision=None).half().to(device)
controlnet_canny = ControlNetModel.from_pretrained(f"{ckpt_dir_canny}", revision=None).half().to(device)

pipe_depth = StableDiffusionXLControlNetImg2ImgPipeline(
    vae=vae,
    controlnet = controlnet_depth,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    unet=unet,
    scheduler=scheduler,
    force_zeros_for_empty_prompt=False
)

pipe_canny = StableDiffusionXLControlNetImg2ImgPipeline(
    vae=vae,
    controlnet = controlnet_canny,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    unet=unet,
    scheduler=scheduler,
    force_zeros_for_empty_prompt=False
)

@spaces.GPU
def process_canny_condition(image, canny_threods=[100,200]):
    np_image = image.copy()
    np_image = cv2.Canny(np_image, canny_threods[0], canny_threods[1])
    np_image = np_image[:, :, None]
    np_image = np.concatenate([np_image, np_image, np_image], axis=2)
    np_image = HWC3(np_image)
    return Image.fromarray(np_image)

model_midas = MidasDetector()

@spaces.GPU
def process_depth_condition_midas(img, res = 1024):
    h,w,_ = img.shape
    img = resize_image(HWC3(img), res)
    result = HWC3(model_midas(img))
    result = cv2.resize(result, (w,h))
    return Image.fromarray(result)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

@spaces.GPU
def infer(prompt, 
          image = None, 
          controlnet_type = "Depth", 
          negative_prompt = "", 
          seed = 0, 
          randomize_seed = False,
          guidance_scale = 6.0, 
          num_inference_steps = 50,
          controlnet_conditioning_scale = 0.7,
          control_guidance_end = 0.9,
          strength = 1.0
        ):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    init_image = resize_image(image,  MAX_IMAGE_SIZE)
    if controlnet_type == "Depth":
        pipe = pipe_depth.to("cuda")
        condi_img = process_depth_condition_midas( np.array(init_image), MAX_IMAGE_SIZE)
    elif controlnet_type == "Canny":
        pipe = pipe_canny.to("cuda")
        condi_img = process_canny_condition(np.array(init_image))
    else:
        return None
    image = pipe(
        prompt= prompt ,
        image = init_image,
        controlnet_conditioning_scale = controlnet_conditioning_scale,
        control_guidance_end = control_guidance_end, 
        strength= strength , 
        control_image = condi_img,
        negative_prompt= negative_prompt , 
        num_inference_steps= num_inference_steps, 
        guidance_scale= guidance_scale,
        num_images_per_prompt=1,
        generator=generator,
    ).images[0]
    return [condi_img, image]
examples = [

]

css="""
#col-left {
    margin: 0 auto;
    max-width: 600px;
}
#col-right {
    margin: 0 auto;
    max-width: 750px;
}
"""

def load_description(fp):
    with open(fp, 'r', encoding='utf-8') as f:
        content = f.read()
    return content

with gr.Blocks(css=css) as Kolors:
    gr.HTML(load_description("assets/title.md"))
    with gr.Row():
        with gr.Column(elem_id="col-left"):
            with gr.Row():
                prompt = gr.Textbox(
                    label="Prompt",
                    placeholder="Enter your prompt",
                    lines=2
                )
            with gr.Row():
                controlnet_type = gr.Dropdown(
                    ["Depth", "Canny"],
                    label = "Controlnet",
                    value="Depth"
                )
            with gr.Row():
                image = gr.Image(label="Image", type="pil")
            with gr.Accordion("Advanced Settings", open=False):
                negative_prompt = gr.Textbox(
                    label="Negative prompt",
                    placeholder="Enter a negative prompt",
                    visible=True,
                    value="nsfw，脸部阴影，低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯"
                )
                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():
                    guidance_scale = gr.Slider(
                        label="Guidance scale",
                        minimum=0.0,
                        maximum=10.0,
                        step=0.1,
                        value=6.0,
                    )
                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=10,
                        maximum=50,
                        step=1,
                        value=30,
                    )
                with gr.Row():
                    controlnet_conditioning_scale = gr.Slider(
                        label="Controlnet Conditioning Scale",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.1,
                        value=0.7,
                    )
                    control_guidance_end = gr.Slider(
                        label="Control Guidance End",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.1,
                        value=0.9,
                    )
                with gr.Row():
                    strength = gr.Slider(
                        label="Strength",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.1,
                        value=1.0,
                    )
            with gr.Row():
                run_button = gr.Button("Run")
            
        with gr.Column(elem_id="col-right"):
            result = gr.Gallery(label="Result", show_label=False, columns=2)
    
    with gr.Row():
        gr.Examples(
                fn = infer,
                examples = examples,
                inputs = [prompt, image, controlnet_type],
                outputs = [result]
            )

    run_button.click(
        fn = infer,
        inputs = [prompt, image, controlnet_type, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
        outputs = [result]
    )

Kolors.queue().launch(debug=True)