Update README.md

README.md

@@ -17,10 +17,10 @@ pipeline_tag: text-to-image
 
 ## Introduction
 
-InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image.
+InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks.
 
 <div  align="center">
-<img src='examples/0.png'>
+<img src='examples/applications.png'>
 </div>
 
 
@@ -31,11 +31,86 @@ You also can download the model in python script:
 
 ```python
 from huggingface_hub import hf_hub_download
-hf_hub_download(repo_id="InstantX/InstantID", local_dir="./checkpoints")
+hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
+hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
+hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
+```
+
+For face encoder, you need to manutally download via this [URL](https://github.com/deepinsight/insightface/issues/1896#issuecomment-1023867304) to `models/antelopev2`.
+
+```python
+# !pip install opencv-python transformers accelerate insightface
+import diffusers
+from diffusers.utils import load_image
+from diffusers.models import ControlNetModel
+
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+
+from insightface.app import FaceAnalysis
+from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
+
+# prepare 'antelopev2' under ./models
+app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+# prepare models under ./checkpoints
+face_adapter = f'./checkpoints/ip-adapter.bin'
+controlnet_path = f'./checkpoints/ControlNetModel'
+
+# load IdentityNet
+controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+
+pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
+... )
+pipe.cuda()
+
+# load adapter
+pipe.load_ip_adapter_instantid(face_adapter)
+```
+
+Then, you can customized your own face images
+
+```python
+# load an image
+image = load_image("your-example.jpg")
+
+# prepare face emb
+face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]
+face_emb = face_info['embedding']
+face_kps = draw_kps(face_image, face_info['kps'])
+
+pipe.set_ip_adapter_scale(0.8)
+
+prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"
+negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"
+
+# generate image
+image = pipe(
+...     prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8
+... ).images[0]
 ```
 
 For more details, please follow the instructions in our [GitHub repository](https://github.com/InstantID/InstantID). 
 
+## Usage Tips
+1. If you're not satisfied with the similarity, try to increase the weight of "IdentityNet Strength" and "Adapter Strength".
+2. If you feel that the saturation is too high, first decrease the Adapter strength. If it is still too high, then decrease the IdentityNet strength.
+3. If you find that text control is not as expected, decrease Adapter strength.
+4. If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model.
+
+## Demos
+
+<div  align="center">
+<img src='examples/0.png'>
+</div>
+
+<div  align="center">
+<img src='examples/1.png'>
+</div>
 
 ## Disclaimer