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import spaces |
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import random |
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import torch |
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import cv2 |
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import gradio as gr |
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import numpy as np |
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from huggingface_hub import snapshot_download |
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from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor |
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from diffusers.utils import load_image |
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from kolors.pipelines.pipeline_controlnet_xl_kolors_img2img import StableDiffusionXLControlNetImg2ImgPipeline |
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from kolors.models.modeling_chatglm import ChatGLMModel |
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from kolors.models.tokenization_chatglm import ChatGLMTokenizer |
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from kolors.models.controlnet import ControlNetModel |
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from diffusers import AutoencoderKL |
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from kolors.models.unet_2d_condition import UNet2DConditionModel |
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from diffusers import EulerDiscreteScheduler |
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from PIL import Image |
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from annotator.midas import MidasDetector |
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from annotator.dwpose import DWposeDetector |
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from annotator.util import resize_image, HWC3 |
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device = "cuda" |
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ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors") |
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ckpt_dir_depth = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Depth") |
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ckpt_dir_canny = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Canny") |
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ckpt_dir_pose = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Pose") |
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text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device) |
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tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder') |
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vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device) |
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scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler") |
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unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device) |
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controlnet_depth = ControlNetModel.from_pretrained(f"{ckpt_dir_depth}", revision=None).half().to(device) |
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controlnet_canny = ControlNetModel.from_pretrained(f"{ckpt_dir_canny}", revision=None).half().to(device) |
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controlnet_pose = ControlNetModel.from_pretrained(f"{ckpt_dir_pose}", revision=None).half().to(device) |
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pipe_depth = StableDiffusionXLControlNetImg2ImgPipeline( |
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vae=vae, |
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controlnet = controlnet_depth, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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unet=unet, |
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scheduler=scheduler, |
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force_zeros_for_empty_prompt=False |
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) |
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pipe_canny = StableDiffusionXLControlNetImg2ImgPipeline( |
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vae=vae, |
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controlnet = controlnet_canny, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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unet=unet, |
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scheduler=scheduler, |
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force_zeros_for_empty_prompt=False |
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) |
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pipe_pose = StableDiffusionXLControlNetImg2ImgPipeline( |
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vae=vae, |
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controlnet = controlnet_pose, |
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text_encoder=text_encoder, |
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tokenizer=tokenizer, |
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unet=unet, |
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scheduler=scheduler, |
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force_zeros_for_empty_prompt=False |
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) |
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@spaces.GPU |
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def process_canny_condition(image, canny_threods=[100,200]): |
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np_image = image.copy() |
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np_image = cv2.Canny(np_image, canny_threods[0], canny_threods[1]) |
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np_image = np_image[:, :, None] |
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np_image = np.concatenate([np_image, np_image, np_image], axis=2) |
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np_image = HWC3(np_image) |
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return Image.fromarray(np_image) |
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model_midas = MidasDetector() |
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@spaces.GPU |
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def process_depth_condition_midas(img, res = 1024): |
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h,w,_ = img.shape |
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img = resize_image(HWC3(img), res) |
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result = HWC3(model_midas(img)) |
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result = cv2.resize(result, (w,h)) |
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return Image.fromarray(result) |
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model_dwpose = DWposeDetector() |
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@spaces.GPU |
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def process_dwpose_condition(image, res=1024): |
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h,w,_ = image.shape |
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img = resize_image(HWC3(image), res) |
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out_res, out_img = model_dwpose(image) |
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result = HWC3(out_img) |
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result = cv2.resize( result, (w,h) ) |
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return Image.fromarray(result) |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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@spaces.GPU |
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def infer_depth(prompt, |
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image = None, |
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negative_prompt = "nsfw๏ผ่ธ้จ้ดๅฝฑ๏ผไฝๅ่พจ็๏ผjpegไผชๅฝฑใๆจก็ณใ็ณ็ณ๏ผ้ป่ธ๏ผ้่น็ฏ", |
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seed = 397886929, |
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randomize_seed = False, |
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guidance_scale = 6.0, |
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num_inference_steps = 50, |
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controlnet_conditioning_scale = 0.7, |
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control_guidance_end = 0.9, |
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strength = 1.0 |
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): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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init_image = resize_image(image, MAX_IMAGE_SIZE) |
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pipe = pipe_depth.to("cuda") |
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condi_img = process_depth_condition_midas( np.array(init_image), MAX_IMAGE_SIZE) |
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image = pipe( |
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prompt= prompt , |
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image = init_image, |
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controlnet_conditioning_scale = controlnet_conditioning_scale, |
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control_guidance_end = control_guidance_end, |
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strength= strength , |
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control_image = condi_img, |
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negative_prompt= negative_prompt , |
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num_inference_steps= num_inference_steps, |
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guidance_scale= guidance_scale, |
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num_images_per_prompt=1, |
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generator=generator, |
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).images[0] |
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return [condi_img, image], seed |
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@spaces.GPU |
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def infer_canny(prompt, |
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image = None, |
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negative_prompt = "nsfw๏ผ่ธ้จ้ดๅฝฑ๏ผไฝๅ่พจ็๏ผjpegไผชๅฝฑใๆจก็ณใ็ณ็ณ๏ผ้ป่ธ๏ผ้่น็ฏ", |
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seed = 397886929, |
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randomize_seed = False, |
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guidance_scale = 6.0, |
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num_inference_steps = 50, |
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controlnet_conditioning_scale = 0.7, |
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control_guidance_end = 0.9, |
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strength = 1.0 |
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): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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init_image = resize_image(image, MAX_IMAGE_SIZE) |
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pipe = pipe_canny.to("cuda") |
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condi_img = process_canny_condition(np.array(init_image)) |
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image = pipe( |
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prompt= prompt , |
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image = init_image, |
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controlnet_conditioning_scale = controlnet_conditioning_scale, |
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control_guidance_end = control_guidance_end, |
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strength= strength , |
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control_image = condi_img, |
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negative_prompt= negative_prompt , |
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num_inference_steps= num_inference_steps, |
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guidance_scale= guidance_scale, |
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num_images_per_prompt=1, |
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generator=generator, |
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).images[0] |
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return [condi_img, image], seed |
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@spaces.GPU |
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def infer_pose(prompt, |
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image = None, |
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negative_prompt = "nsfw๏ผ่ธ้จ้ดๅฝฑ๏ผไฝๅ่พจ็๏ผjpegไผชๅฝฑใๆจก็ณใ็ณ็ณ๏ผ้ป่ธ๏ผ้่น็ฏ", |
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seed = 66, |
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randomize_seed = False, |
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guidance_scale = 6.0, |
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num_inference_steps = 50, |
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controlnet_conditioning_scale = 0.7, |
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control_guidance_end = 0.9, |
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strength = 1.0 |
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): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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init_image = resize_image(image, MAX_IMAGE_SIZE) |
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pipe = pipe_pose.to("cuda") |
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condi_img = process_dwpose_condition(np.array(init_image), MAX_IMAGE_SIZE) |
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image = pipe( |
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prompt= prompt , |
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image = init_image, |
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controlnet_conditioning_scale = controlnet_conditioning_scale, |
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control_guidance_end = control_guidance_end, |
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strength= strength , |
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control_image = condi_img, |
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negative_prompt= negative_prompt , |
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num_inference_steps= num_inference_steps, |
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guidance_scale= guidance_scale, |
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num_images_per_prompt=1, |
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generator=generator, |
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).images[0] |
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return [condi_img, image], seed |
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canny_examples = [ |
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["์๋ฆ๋ค์ด ์๋
, ๊ณ ํ์ง, ๋งค์ฐ ์ ๋ช
, ์์ํ ์์, ์ด๊ณ ํด์๋, ์ต์์ ํ์ง, 8k, ๊ณ ํ์ง, 4K", |
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"image/woman_1.png"], |
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["ํ๋
ธ๋ผ๋ง, ์ปต ์์ ์์์๋ ๊ท์ฌ์ด ํฐ ๊ฐ์์ง, ์นด๋ฉ๋ผ๋ฅผ ๋ฐ๋ผ๋ณด๋, ์ ๋๋ฉ์ด์
์คํ์ผ, 3D ๋ ๋๋ง, ์ฅํ
์ธ ๋ ๋", |
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"image/dog.png"] |
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] |
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depth_examples = [ |
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["์ ์นด์ด ๋ง์ฝํ ์คํ์ผ, ํ๋ถํ ์๊ฐ, ์ด๋ก ์
์ธ ๋ฅผ ์
์ ์ฌ์ฑ์ด ๋คํ์ ์ ์๋, ์๋ฆ๋ค์ด ํ๊ฒฝ, ๋ง๊ณ ๋ฐ์, ์ผ๋ฃฉ์ง ๋น๊ณผ ๊ทธ๋ฆผ์, ์ต๊ณ ์ ํ์ง, ์ด์ธ๋ฐ, 8K ํ์ง", |
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"image/woman_2.png"], |
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["ํ๋ คํ ์์์ ์์ ์, ๊ณ ํ์ง, ๋งค์ฐ ์ ๋ช
, ์์ํ ์์, ์ด๊ณ ํด์๋, ์ต์์ ํ์ง, 8k, ๊ณ ํ์ง, 4K", |
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"image/bird.png"] |
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] |
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pose_examples = [ |
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["๋ณด๋ผ์ ํผํ ์ฌ๋ฆฌ๋ธ ๋๋ ์ค๋ฅผ ์
๊ณ ์๊ด๊ณผ ํฐ์ ๋ ์ด์ค ์ฅ๊ฐ์ ๋ ์๋
๊ฐ ์ ์์ผ๋ก ์ผ๊ตด์ ๊ฐ์ธ๊ณ ์๋, ๊ณ ํ์ง, ๋งค์ฐ ์ ๋ช
, ์์ํ ์์, ์ด๊ณ ํด์๋, ์ต์์ ํ์ง, 8k, ๊ณ ํ์ง, 4K", |
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"image/woman_3.png"], |
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["๊ฒ์์ ์คํฌ์ธ ์ฌํท๊ณผ ํฐ์ ์ด๋๋ฅผ ์
๊ณ ๋ชฉ๊ฑธ์ด๋ฅผ ํ ์ฌ์ฑ์ด ๊ฑฐ๋ฆฌ์ ์ ์๋, ๋ฐฐ๊ฒฝ์ ๋นจ๊ฐ ๊ฑด๋ฌผ๊ณผ ๋
น์ ๋๋ฌด, ๊ณ ํ์ง, ๋งค์ฐ ์ ๋ช
, ์์ํ ์์, ์ด๊ณ ํด์๋, ์ต์์ ํ์ง, 8k, ๊ณ ํ์ง, 4K", |
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"image/woman_4.png"] |
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] |
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css = """ |
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footer { |
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visibility: hidden; |
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} |
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""" |
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def load_description(fp): |
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with open(fp, 'r', encoding='utf-8') as f: |
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content = f.read() |
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return content |
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with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as Kolors: |
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gr.HTML(load_description("assets/title.md")) |
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with gr.Row(): |
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with gr.Column(elem_id="col-left"): |
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with gr.Row(): |
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prompt = gr.Textbox( |
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label="ํ๋กฌํํธ", |
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placeholder="ํ๋กฌํํธ๋ฅผ ์
๋ ฅํ์ธ์", |
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lines=2 |
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) |
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with gr.Row(): |
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image = gr.Image(label="์ด๋ฏธ์ง", type="pil") |
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with gr.Accordion("๊ณ ๊ธ ์ค์ ", open=False): |
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negative_prompt = gr.Textbox( |
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label="๋ค๊ฑฐํฐ๋ธ ํ๋กฌํํธ", |
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placeholder="๋ค๊ฑฐํฐ๋ธ ํ๋กฌํํธ๋ฅผ ์
๋ ฅํ์ธ์", |
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visible=True, |
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value="nsfw, ์ผ๊ตด ๊ทธ๋ฆผ์, ์ ํด์๋, jpeg ์ํฐํฉํธ, ํ๋ฆฟํจ, ์ด์
ํจ, ๊ฒ์ ์ผ๊ตด, ๋ค์จ ์กฐ๋ช
" |
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) |
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seed = gr.Slider( |
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label="์๋", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="์๋ ๋ฌด์์ํ", value=True) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="๊ฐ์ด๋์ค ์ค์ผ์ผ", |
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minimum=0.0, |
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maximum=10.0, |
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step=0.1, |
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value=6.0, |
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) |
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num_inference_steps = gr.Slider( |
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label="์ถ๋ก ๋จ๊ณ ์", |
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minimum=10, |
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maximum=50, |
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step=1, |
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value=30, |
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) |
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with gr.Row(): |
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controlnet_conditioning_scale = gr.Slider( |
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label="์ปจํธ๋กค๋ท ์ปจ๋์
๋ ์ค์ผ์ผ", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=0.7, |
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) |
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control_guidance_end = gr.Slider( |
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label="์ปจํธ๋กค ๊ฐ์ด๋์ค ์ข
๋ฃ", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=0.9, |
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) |
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with gr.Row(): |
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strength = gr.Slider( |
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label="๊ฐ๋", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=1.0, |
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) |
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with gr.Row(): |
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canny_button = gr.Button("์บ๋", elem_id="button") |
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depth_button = gr.Button("๊น์ด", elem_id="button") |
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pose_button = gr.Button("ํฌ์ฆ", elem_id="button") |
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with gr.Column(elem_id="col-right"): |
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result = gr.Gallery(label="๊ฒฐ๊ณผ", show_label=False, columns=2) |
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seed_used = gr.Number(label="์ฌ์ฉ๋ ์๋") |
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with gr.Row(): |
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gr.Examples( |
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fn = infer_canny, |
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examples = canny_examples, |
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inputs = [prompt, image], |
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outputs = [result, seed_used], |
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label = "Canny" |
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) |
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with gr.Row(): |
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gr.Examples( |
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fn = infer_depth, |
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examples = depth_examples, |
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inputs = [prompt, image], |
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outputs = [result, seed_used], |
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label = "Depth" |
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) |
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with gr.Row(): |
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gr.Examples( |
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fn = infer_pose, |
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examples = pose_examples, |
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inputs = [prompt, image], |
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outputs = [result, seed_used], |
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label = "Pose" |
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) |
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canny_button.click( |
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fn = infer_canny, |
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inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength], |
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outputs = [result, seed_used] |
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) |
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depth_button.click( |
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fn = infer_depth, |
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inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength], |
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outputs = [result, seed_used] |
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) |
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pose_button.click( |
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fn = infer_pose, |
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inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength], |
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outputs = [result, seed_used] |
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) |
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Kolors.queue().launch(debug=True) |
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