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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.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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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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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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@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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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(prompt, |
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image = None, |
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controlnet_type = "Depth", |
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negative_prompt = "", |
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seed = 0, |
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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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if controlnet_type == "Depth": |
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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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elif controlnet_type == "Canny": |
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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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else: |
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return None |
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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] |
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examples = [ |
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["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", |
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"woman_1.png", "Canny"], |
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["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", |
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"dog.png", "Canny"], |
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["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", |
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"woman_2.png", "Depth"], |
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["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", |
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"bird.png", "Depth"] |
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] |
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css=""" |
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#col-left { |
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margin: 0 auto; |
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max-width: 600px; |
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} |
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#col-right { |
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margin: 0 auto; |
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max-width: 750px; |
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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(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="Prompt", |
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placeholder="Enter your prompt", |
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lines=2 |
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) |
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with gr.Row(): |
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controlnet_type = gr.Dropdown( |
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["Depth", "Canny"], |
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label = "Controlnet", |
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value="Depth" |
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) |
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with gr.Row(): |
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image = gr.Image(label="Image", type="pil") |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Textbox( |
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label="Negative prompt", |
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placeholder="Enter a negative prompt", |
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visible=True, |
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value="nsfw，脸部阴影，低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯" |
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) |
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seed = gr.Slider( |
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label="Seed", |
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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="Randomize seed", value=True) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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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="Number of inference steps", |
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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="Controlnet Conditioning Scale", |
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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="Control Guidance End", |
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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="Strength", |
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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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run_button = gr.Button("Run") |
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with gr.Column(elem_id="col-right"): |
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result = gr.Gallery(label="Result", show_label=False, columns=2) |
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with gr.Row(): |
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gr.Examples( |
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fn = infer, |
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examples = examples, |
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inputs = [prompt, image, controlnet_type], |
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outputs = [result] |
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) |
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run_button.click( |
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fn = infer, |
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inputs = [prompt, image, controlnet_type, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength], |
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outputs = [result] |
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) |
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Kolors.queue().launch(debug=True) |
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