kolcontrl / app.py
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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 = [
["一个漂亮的女孩,高品质,超清晰,色彩鲜艳,超高分辨率,最佳品质,8k,高清,4K",
"woman_1.png", "Canny"],
["全景,一只可爱的白色小狗坐在杯子里,看向镜头,动漫风格,3d渲染,辛烷值渲染",
"dog.png", "Canny"],
["新海诚风格,丰富的色彩,穿着绿色衬衫的女人站在田野里,唯美风景,清新明亮,斑驳的光影,最好的质量,超细节,8K画质",
"woman_2.png", "Depth"],
["一只颜色鲜艳的小鸟,高品质,超清晰,色彩鲜艳,超高分辨率,最佳品质,8k,高清,4K",
"bird.png", "Depth"]
]
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)