Update README.md

README.md

@@ -1,3 +1,69 @@
----
-license: cc-by-nc-4.0
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+# ***ControlNet Head Mesh SDXL***
+
+
+# ControlNet Example(Conditioned on 3D Head Mesh)
+![images_0)](./pncc_example.jpg)
+
+# Head Mesh Processor Install
+
+pip install git+https://github.com/KupynOrest/head_detector.git
+
+# Code to Use Tile blur
+
+
+```python
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers import EulerAncestralDiscreteScheduler
+from PIL import Image
+import torch
+import numpy as np
+import cv2
+
+from head_detector import HeadDetector
+
+detector = HeadDetector()
+
+controlnet_conditioning_scale = 1.0  
+prompt = "your prompt, the longer the better, you can describe it as detail as possible"
+negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
+
+eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
+
+
+controlnet = ControlNetModel.from_pretrained(
+    "okupyn/head-mesh-controlnet-xl",
+    torch_dtype=torch.float16
+)
+
+# when test with other base model, you need to change the vae also.
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    vae=vae,
+    safety_checker=None,
+    torch_dtype=torch.float16,
+    scheduler=eulera_scheduler,
+)
+
+controlnet_img = detector("your original image path").get_pncc()
+controlnet_img = Image.fromarray(controlnet_img)
+
+
+images = pipe(
+    prompt,
+    negative_prompt=negative_prompt,
+    image=controlnet_img,
+    controlnet_conditioning_scale=controlnet_conditioning_scale,
+    num_inference_steps=30,
+    ).images
+
+images[0].save(f"your image save path")
+
+```
+
+---
+license: cc-by-nc-4.0
+library_name: diffusers
+---