Upload app.py

app.py

@@ -1,154 +1,209 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor
+from diffusers.utils import load_image
+import os,sys
+import gradio as gr
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+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
 
-    generator = torch.Generator().manual_seed(seed)
+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]
+
+
+ckpt_dir = f'weights/Kolors'
+text_encoder = ChatGLMModel.from_pretrained(
+    f'{ckpt_dir}/text_encoder').to(dtype=torch.bfloat16)
+tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
+vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).to(dtype=torch.bfloat16)
+scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
+unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).to(dtype=torch.bfloat16)
+
+control_path = f'weights/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(f'weights/Kolors-IP-Adapter-FaceID-Plus/clip-vit-large-patch14-336', ignore_mismatched_sizes=True)
+clip_image_encoder.to('cuda')
+clip_image_processor = CLIPImageProcessor(size = 336, crop_size = 336)
+
+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'weights/Kolors-IP-Adapter-FaceID-Plus/ipa-faceid-plus.bin', device = 'cuda')
+pipe.set_face_fidelity_scale(ip_scale)
+pipe = pipe.to("cuda")
+pipe.enable_model_cpu_offload()
+
+def infer(face_img,pose_img, garm_img, prompt,negative_prompt, n_samples, n_steps, seed):
+    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,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
+        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[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            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():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+    ).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=40, 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])
+
+block.launch(server_name='0.0.0.0', server_port=7865)