Update README.md

README.md

@@ -122,6 +122,58 @@ 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
 )
 
+```
+
+you can also use a normal IP-Adapter and a normal LoRA to load model:
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
+from PIL import Image
+
+from ip_adapter.ip_adapter_faceid_separate 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"
+lora_ckpt = "ip-adapter-faceid_sd15_lora.safetensors"
+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 lora and fuse
+pipe.load_lora_weights(lora_ckpt)
+pipe.fuse_lora()
+
+# 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
+)
+
+
 ```
 
 ### IP-Adapter-FaceID-SDXL
@@ -188,6 +240,7 @@ images = ip_model.generate(
 
 ```
 
+
 ### IP-Adapter-FaceID-Plus
 
 Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding and face image: