Update README.md

README.md

@@ -26,10 +26,76 @@ An experimental version of IP-Adapter-FaceID: we use face ID embedding from a fa
 
 ![results](./ip-adapter-faceid.jpg)
 
+## Usage
+
+Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding:
+
+```python
+
+import cv2
+from insightface.app import FaceAnalysis
+
+
+app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+image = cv2.imread("person.jpg")
+faces = app.get(image)
+
+faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
+```
+
+Then, you can generate images conditioned on the face embeddings:
+
+```python
+
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
+from PIL import Image
+
+from ip_adapter.ip_adapter_faceid import IPAdapterFaceID
+
+base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE"
+vae_model_path = "stabilityai/sd-vae-ft-mse"
+ip_ckpt = "ip-adapter-faceid_sd15.bin"
+device = "cuda"
+
+noise_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained(
+    base_model_path,
+    torch_dtype=torch.float16,
+    scheduler=noise_scheduler,
+    vae=vae,
+    feature_extractor=None,
+    safety_checker=None
+)
+
+# load ip-adapter
+ip_model = IPAdapterFaceID(pipe, ip_ckpt, device)
+
+# generate image
+prompt = "photo of a woman in red dress in a garden"
+negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry"
+
+images = ip_model.generate(
+    prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023
+)
+
+```
+
 
 ## Limitations and Bias
 - The model does not achieve perfect photorealism and ID consistency.
-- The generalization of the model is limited due to limitations of the training model, generative model and face recognition model.
+- The generalization of the model is limited due to limitations of the training data, base model and face recognition model.