Create depth_app.py

depth_app.py

@@ -0,0 +1,218 @@
+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.util import resize_image, HWC3
+
+device = "cuda"
+ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
+ckpt_dir_ipa = snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus")
+ckpt_dir_depth = snapshot_download(repo_id="Kwai-Kolors/Kolors-ControlNet-Depth")
+
+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)
+
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_dir_ipa}/image_encoder', ignore_mismatched_sizes=True).to(dtype=torch.float16, device=device)
+ip_img_size = 336
+clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
+
+pipe_depth = StableDiffusionXLControlNetImg2ImgPipeline(
+    vae=vae,
+    controlnet=controlnet_depth,
+    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
+)
+
+pipe_depth.load_ip_adapter(f'{ckpt_dir_ipa}', subfolder="", weight_name=["ip_adapter_plus_general.bin"])
+
+model_midas = MidasDetector()
+
+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)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+def infer_depth(prompt,
+                image=None,
+                ipa_img=None,
+                negative_prompt="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",
+                seed=66,
+                randomize_seed=False,
+                guidance_scale=5.0,
+                num_inference_steps=50,
+                controlnet_conditioning_scale=0.5,
+                control_guidance_end=0.9,
+                strength=1.0,
+                ip_scale=0.5,
+                ):
+    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")
+    pipe.set_ip_adapter_scale([ip_scale])
+    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,
+        ip_adapter_image=[ipa_img],
+        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
+
+depth_examples = [
+    ["一个漂亮的女孩，最好的质量，超细节，8K画质",
+     "image/1.png", "image/woman_1.png"],
+]
+
+css = """
+#col-left {
+    margin: 0 auto;
+    max-width: 600px;
+}
+#col-right {
+    margin: 0 auto;
+    max-width: 750px;
+}
+#button {
+    color: blue;
+}
+"""
+
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+with gr.Blocks(css=css) as DepthApp:
+    gr.HTML(load_description("assets/title.md"))
+    with gr.Row():
+        with gr.Column(elem_id="col-left"):
+            with gr.Row():
+                prompt = gr.Textbox(
+                    label="Prompt",
+                    placeholder="Enter your prompt",
+                    lines=2
+                )
+            with gr.Row():
+                image = gr.Image(label="Image", type="pil")
+                ipa_image = gr.Image(label="IP-Adapter-Image", type="pil")
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt = gr.Textbox(
+                    label="Negative prompt",
+                    placeholder="Enter a negative prompt",
+                    visible=True,
+                    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"
+                )
+                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():
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.0,
+                        maximum=10.0,
+                        step=0.1,
+                        value=5.0,
+                    )
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=10,
+                        maximum=50,
+                        step=1,
+                        value=30,
+                    )
+                with gr.Row():
+                    controlnet_conditioning_scale = gr.Slider(
+                        label="Controlnet Conditioning Scale",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.5,
+                    )
+                    control_guidance_end = gr.Slider(
+                        label="Control Guidance End",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.9,
+                    )
+                with gr.Row():
+                    strength = gr.Slider(
+                        label="Strength",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=1.0,
+                    )
+                    ip_scale = gr.Slider(
+                        label="IP_Scale",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.5,
+                    )
+            with gr.Row():
+                depth_button = gr.Button("Depth", elem_id="button")
+
+        with gr.Column(elem_id="col-right"):
+            result = gr.Gallery(label="Result", show_label=False, columns=2)
+            seed_used = gr.Number(label="Seed Used")
+
+    with gr.Row():
+        gr.Examples(
+            fn=infer_depth,
+            examples=depth_examples,
+            inputs=[prompt, image, ipa_image],
+            outputs=[result, seed_used],
+            label="Depth"
+        )
+
+    depth_button.click(
+        fn=infer_depth,
+        inputs=[prompt, image, ipa_image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength, ip_scale],
+        outputs=[result, seed_used]
+    )
+
+DepthApp.queue().launch(debug=True, share=True)