Upload app (14).py

app (14).py

@@ -0,0 +1,351 @@
+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.dwpose import DWposeDetector
+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")
+ckpt_dir_pose = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Pose")
+
+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)
+controlnet_pose = ControlNetModel.from_pretrained(f"{ckpt_dir_pose}", 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
+)
+
+pipe_pose = StableDiffusionXLControlNetImg2ImgPipeline(
+    vae=vae,
+    controlnet = controlnet_pose,
+    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)
+
+model_dwpose = DWposeDetector()
+@spaces.GPU
+def process_dwpose_condition(image, res=1024):
+    h,w,_ = image.shape
+    img = resize_image(HWC3(image), res)
+    out_res, out_img = model_dwpose(image) 
+    result = HWC3(out_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_depth(prompt, 
+          image = None, 
+          negative_prompt = "nsfw，脸部阴影，低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯", 
+          seed = 397886929, 
+          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)
+    pipe = pipe_depth.to("cuda")
+    condi_img = process_depth_condition_midas( np.array(init_image), MAX_IMAGE_SIZE)
+    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], seed
+
+@spaces.GPU
+def infer_canny(prompt, 
+          image = None, 
+          negative_prompt = "nsfw，脸部阴影���低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯", 
+          seed = 397886929, 
+          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)
+    pipe = pipe_canny.to("cuda")
+    condi_img = process_canny_condition(np.array(init_image))
+    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], seed
+
+@spaces.GPU
+def infer_pose(prompt, 
+          image = None, 
+          negative_prompt = "nsfw，脸部阴影，低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯", 
+          seed = 66, 
+          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)
+    pipe = pipe_pose.to("cuda")
+    condi_img = process_dwpose_condition(np.array(init_image), MAX_IMAGE_SIZE)
+    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], seed
+
+
+canny_examples = [
+    ["아름다운 소녀, 고품질, 매우 선명, 생생한 색상, 초고해상도, 최상의 품질, 8k, 고화질, 4K",
+     "image/woman_1.png"],
+    ["파노라마, 컵 안에 앉아있는 귀여운 흰 강아지, 카메라를 바라보는, 애니메이션 스타일, 3D 렌더링, 옥테인 렌더",
+    "image/dog.png"]
+]
+
+depth_examples = [
+    ["신카이 마코토 스타일, 풍부한 색감, 초록 셔츠를 입은 여성이 들판에 서 있는, 아름다운 풍경, 맑고 밝은, 얼룩진 빛과 그림자, 최고의 품질, 초세밀, 8K 화질",
+     "image/woman_2.png"],
+    ["화려한 색상의 작은 새, 고품질, 매우 선명, 생생한 색상, 초고해상도, 최상의 품질, 8k, 고화질, 4K",
+     "image/bird.png"]
+]
+
+pose_examples = [
+    ["보라색 퍼프 슬리브 드레스를 입고 왕관과 흰색 레이스 장갑을 낀 소녀가 양 손으로 얼굴을 감싸고 있는, 고품질, 매우 선명, 생생한 색상, 초고해상도, 최상의 품질, 8k, 고화질, 4K",
+     "image/woman_3.png"],
+    ["검은색 스포츠 재킷과 흰색 이너를 입고 목걸이를 한 여성이 거리에 서 있는, 배경은 빨간 건물과 녹색 나무, 고품질, 매우 선명, 생생한 색상, 초고해상도, 최상의 품질, 8k, 고화질, 4K",
+     "image/woman_4.png"]
+]
+
+css = """
+footer {
+    visibility: hidden;
+}
+"""
+
+
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as Kolors:
+    with gr.Row():
+        with gr.Column(elem_id="col-left"):
+            with gr.Row():
+                prompt = gr.Textbox(
+                    label="프롬프트",
+                    placeholder="프롬프트를 입력하세요",
+                    lines=2
+                )
+            with gr.Row():
+                image = gr.Image(label="이미지", type="pil")
+            with gr.Accordion("고급 설정", open=False):
+                negative_prompt = gr.Textbox(
+                    label="네거티브 프롬프트",
+                    placeholder="네거티브 프롬프트를 입력하세요",
+                    visible=True,
+                    value="nsfw, 얼굴 그림자, 저해상도, jpeg 아티팩트, 흐릿함, 열악함, 검은 얼굴, 네온 조명"
+                )
+                seed = gr.Slider(
+                    label="시드",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(label="시드 무작위화", value=True)
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="가이던스 스케일",
+                        minimum=0.0,
+                        maximum=10.0,
+                        step=0.1,
+                        value=6.0,
+                    )
+                    num_inference_steps = gr.Slider(
+                        label="추론 단계 수",
+                        minimum=10,
+                        maximum=50,
+                        step=1,
+                        value=30,
+                    )
+                with gr.Row():
+                    controlnet_conditioning_scale = gr.Slider(
+                        label="컨트롤넷 컨디셔닝 스케일",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.7,
+                    )
+                    control_guidance_end = gr.Slider(
+                        label="컨트롤 가이던스 종료",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.9,
+                    )
+                with gr.Row():
+                    strength = gr.Slider(
+                        label="강도",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=1.0,
+                    )
+            with gr.Row():
+                canny_button = gr.Button("캐니", elem_id="button")
+                depth_button = gr.Button("깊이", elem_id="button")
+                pose_button = gr.Button("포즈", elem_id="button")
+            
+        with gr.Column(elem_id="col-right"):
+            result = gr.Gallery(label="결과", show_label=False, columns=2)
+            seed_used = gr.Number(label="사용된 시드")
+    
+
+    
+    with gr.Row():
+        gr.Examples(
+                fn = infer_canny,
+                examples = canny_examples,
+                inputs = [prompt, image],
+                outputs = [result, seed_used],
+                label = "Canny"
+            )
+    with gr.Row():
+        gr.Examples(
+                fn = infer_depth,
+                examples = depth_examples,
+                inputs = [prompt, image],
+                outputs = [result, seed_used],
+                label = "Depth"
+            )
+        
+    with gr.Row():
+        gr.Examples(
+                fn = infer_pose,
+                examples = pose_examples,
+                inputs = [prompt, image],
+                outputs = [result, seed_used],
+                label = "Pose"
+            )
+
+    canny_button.click(
+        fn = infer_canny,
+        inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
+        outputs = [result, seed_used]
+    )
+
+    depth_button.click(
+        fn = infer_depth,
+        inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
+        outputs = [result, seed_used]
+    )
+
+    pose_button.click(
+        fn = infer_pose,
+        inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
+        outputs = [result, seed_used]
+    )
+
+Kolors.queue().launch(debug=True)