import torch import spaces from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor from diffusers.utils import load_image import os,sys import gradio as gr from huggingface_hub import hf_hub_download from kolors.pipelines.pipeline_controlnet_xl_kolors_img2img_face 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 import numpy as np import cv2 from insightface.app import FaceAnalysis from insightface.data import get_image as ins_get_image example_path = os.path.join(os.path.dirname(__file__), 'examples') class FaceInfoGenerator(): def __init__(self, root_dir = "./"): self.app = FaceAnalysis(name = 'antelopev2', root = root_dir, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) self.app.prepare(ctx_id = 0, det_size = (640, 640)) def get_faceinfo_one_img(self, face_image): face_info = self.app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)) if len(face_info) == 0: face_info = None else: face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face return face_info def face_bbox_to_square(bbox): ## l, t, r, b to square l, t, r, b l,t,r,b = bbox cent_x = (l + r) / 2 cent_y = (t + b) / 2 w, h = r - l, b - t r = max(w, h) / 2 l0 = cent_x - r r0 = cent_x + r t0 = cent_y - r b0 = cent_y + r return [l0, t0, r0, b0] text_encoder = ChatGLMModel.from_pretrained("Kwai-Kolors/Kolors",subfolder="text_encoder").to(dtype=torch.bfloat16) tokenizer = ChatGLMTokenizer.from_pretrained("Kwai-Kolors/Kolors",subfolder="text_encoder") vae = AutoencoderKL.from_pretrained("Kwai-Kolors/Kolors",subfolder="vae", revision=None).to(dtype=torch.bfloat16) scheduler = EulerDiscreteScheduler.from_pretrained("Kwai-Kolors/Kolors",subfolder="scheduler") unet = UNet2DConditionModel.from_pretrained("Kwai-Kolors/Kolors",subfolder="unet", revision=None).to(dtype=torch.bfloat16) control_path = "haowu11/Kolors-Controlnet-Pose-Tryon" controlnet = ControlNetModel.from_pretrained( control_path , revision=None).to(dtype=torch.bfloat16) face_info_generator = FaceInfoGenerator(root_dir = "./") clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained("Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus",subfolder="clip-vit-large-patch14-336", ignore_mismatched_sizes=True) clip_image_processor = CLIPImageProcessor(size = 336, crop_size = 336) hf_hub_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus", filename="ipa-faceid-plus.bin",cache_dir='./') snapshotname = os.listdir('./models--Kwai-Kolors--Kolors-IP-Adapter-FaceID-Plus/snapshots')[0] pipe = StableDiffusionXLControlNetImg2ImgPipeline( vae=vae, controlnet = controlnet, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, # image_encoder=image_encoder, # feature_extractor=clip_image_processor, force_zeros_for_empty_prompt=False, face_clip_encoder=clip_image_encoder, face_clip_processor=clip_image_processor, ) if hasattr(pipe.unet, 'encoder_hid_proj'): pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj ip_scale = 0.5 pipe.load_ip_adapter_faceid_plus(f'models--Kwai-Kolors--Kolors-IP-Adapter-FaceID-Plus/snapshots/{snapshotname}/ipa-faceid-plus.bin', device = 'cuda') pipe.set_face_fidelity_scale(ip_scale) pipe = pipe.to("cuda") pipe.enable_model_cpu_offload() @spaces.GPU def infer(face_img,pose_img, garm_img, prompt,negative_prompt, n_samples, n_steps, seed): pipe.face_clip_encoder.to('cuda') face_img = Image.open(face_img) pose_img = Image.open(pose_img) garm_img = Image.open(garm_img) face_img = face_img.resize((336, 336)) pose_img = pose_img.resize((768, 1024)) garm_img = garm_img.resize((768, 1024)) background = Image.new("RGB", (768, 768), (255, 255, 255)) #将face_img粘贴到background中心 background.paste(face_img, (int((768 - 336) / 2), int((768 - 336) / 2))) face_info = face_info_generator.get_faceinfo_one_img(background) face_embeds = torch.from_numpy(np.array([face_info["embedding"]])) face_embeds = face_embeds.to('cuda', dtype = torch.bfloat16) controlnet_conditioning_scale = 1.0 control_guidance_end = 0.9 #strength 越是小，则生成图片越是依赖原始图片。 strength = 1.0 im1 = np.array(pose_img) im2 = np.array(garm_img) condi_img = Image.fromarray( np.concatenate( (im1, im2), axis=1 ) ) generator = torch.Generator(device="cpu").manual_seed(seed) image = pipe( prompt= prompt , # image = init_image, controlnet_conditioning_scale = controlnet_conditioning_scale, control_guidance_end = control_guidance_end, # ip_adapter_image=[ ip_adapter_img ], face_crop_image = face_img, face_insightface_embeds = face_embeds, strength= strength , control_image = condi_img, negative_prompt= negative_prompt , num_inference_steps=n_steps , guidance_scale= 5.0, num_images_per_prompt=n_samples, generator=generator, ).images return image block = gr.Blocks().queue() with block: with gr.Row(): gr.Markdown("# KolorsControlnerTryon Demo") with gr.Row(): with gr.Column(): pose_img = gr.Image(label="Pose", sources='upload', type="filepath", height=768, value=os.path.join(example_path, 'pose/1.jpg')) example = gr.Examples( inputs=pose_img, examples_per_page=10, examples=[ os.path.join(example_path, 'pose/1.jpg'), os.path.join(example_path, 'pose/2.jpg'), os.path.join(example_path, 'pose/3.jpg'), os.path.join(example_path, 'pose/4.jpg'), os.path.join(example_path, 'pose/5.jpg'), os.path.join(example_path, 'pose/6.jpg'), os.path.join(example_path, 'pose/7.jpg'), os.path.join(example_path, 'pose/8.jpg'), os.path.join(example_path, 'pose/9.jpg'), os.path.join(example_path, 'pose/10.jpg'), ]) with gr.Column(): garm_img = gr.Image(label="Garment", sources='upload', type="filepath", height=768, value=os.path.join(example_path, 'garment/1.jpg'),) example = gr.Examples( inputs=garm_img, examples_per_page=10, examples=[ os.path.join(example_path, 'garment/1.jpg'), os.path.join(example_path, 'garment/2.jpg'), os.path.join(example_path, 'garment/3.jpg'), os.path.join(example_path, 'garment/4.jpg'), os.path.join(example_path, 'garment/5.jpg'), os.path.join(example_path, 'garment/6.jpg'), os.path.join(example_path, 'garment/7.jpg'), os.path.join(example_path, 'garment/8.jpg'), os.path.join(example_path, 'garment/9.jpg'), os.path.join(example_path, 'garment/10.jpg'), ]) with gr.Row(): with gr.Column(): face_img = gr.Image(label="Face", sources='upload', type="filepath", height=336, value=os.path.join(example_path, 'face/1.png'),) example = gr.Examples( inputs=face_img, examples_per_page=10, examples=[ os.path.join(example_path, 'face/1.png'), os.path.join(example_path, 'face/2.png'), os.path.join(example_path, 'face/3.png'), os.path.join(example_path, 'face/4.png'), os.path.join(example_path, 'face/5.png'), os.path.join(example_path, 'face/6.png'), os.path.join(example_path, 'face/7.png'), os.path.join(example_path, 'face/8.png'), os.path.join(example_path, 'face/9.png'), os.path.join(example_path, 'face/10.png'), ]) with gr.Column(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True, scale=1) with gr.Column(): prompt = gr.Textbox(value="这张图片上的模特穿着一件黑色的长袖T恤，T恤上印着彩色的字母'OBEY'。她还穿着一条牛仔裤。", show_label=False, elem_id="prompt") negative_prompt = gr.Textbox(value="nsfw，脸部阴影，低分辨率，糟糕的解剖结构、糟糕的手，缺失手指、质量最差、低质量、jpeg伪影、模糊、糟糕，黑脸，霓虹灯", show_label=False, elem_id="negative_prompt") n_samples = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1) n_steps = gr.Slider(label="Steps", minimum=20, maximum=50, value=20, step=1) seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1) run_button = gr.Button(value="Run") ips = [face_img,pose_img, garm_img, prompt,negative_prompt, n_samples, n_steps, seed] run_button.click(fn=infer, inputs=ips, outputs=[result_gallery]) if __name__ == "__main__": block.launch(server_name='0.0.0.0')