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)