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README.md

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+---
+tags:
+- text-to-image
+- stable-diffusion
+
+language:
+- en
+library_name: diffusers
+---
+
+# IP-Adapter-FaceID Model Card
+
+
+<div align="center">
+
+[**Project Page**](https://ip-adapter.github.io) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2308.06721) **|** [**Code**](https://github.com/tencent-ailab/IP-Adapter)
+</div>
+
+---
+
+
+
+## Introduction
+
+An experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts. 
+
+![results](./ip-adapter-faceid.jpg)
+
+
+**Update 2023/12/27**: 
+
+IP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure)
+
+<div  align="center">    
+
+![results](./faceid-plus.jpg)
+</div>
+
+**Update 2023/12/28**: 
+
+IP-Adapter-FaceID-PlusV2: face ID embedding (for face ID) + controllable CLIP image embedding (for face structure)
+
+You can adjust the weight of the face structure to get different generation!
+
+<div  align="center">    
+
+![results](./faceid_plusv2.jpg)
+</div>
+
+**Update 2024/01/04**: 
+
+IP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID
+
+<div  align="center">    
+
+![results](./sdxl_faceid.jpg)
+</div>
+
+## Usage
+
+### IP-Adapter-FaceID
+
+Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding:
+
+```python
+
+import cv2
+from insightface.app import FaceAnalysis
+import torch
+
+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
+)
+
+```
+
+### IP-Adapter-FaceID-SDXL
+
+Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding:
+
+```python
+
+import cv2
+from insightface.app import FaceAnalysis
+import torch
+
+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 StableDiffusionXLPipeline, DDIMScheduler
+from PIL import Image
+
+from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDXL
+
+base_model_path = "SG161222/RealVisXL_V3.0"
+ip_ckpt = "ip-adapter-faceid_sdxl.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,
+)
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    base_model_path,
+    torch_dtype=torch.float16,
+    scheduler=noise_scheduler,
+    add_watermarker=False,
+)
+
+# load ip-adapter
+ip_model = IPAdapterFaceIDXL(pipe, ip_ckpt, device)
+
+# generate image
+prompt = "A closeup shot of a beautiful Asian teenage girl in a white dress wearing small silver earrings in the garden, under the soft morning light"
+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=2,
+    width=1024, height=1024,
+    num_inference_steps=30, guidance_scale=7.5, seed=2023
+)
+
+```
+
+### IP-Adapter-FaceID-Plus
+
+Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding and face image:
+
+```python
+
+import cv2
+from insightface.app import FaceAnalysis
+from insightface.utils import face_align
+import torch
+
+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)
+face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) # you can also segment the face
+```
+
+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 IPAdapterFaceIDPlus
+
+v2 = False
+base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE"
+vae_model_path = "stabilityai/sd-vae-ft-mse"
+image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ip_ckpt = "ip-adapter-faceid-plus_sd15.bin" if not v2 else "ip-adapter-faceid-plusv2_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 = IPAdapterFaceIDPlus(pipe, image_encoder_path, 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, face_image=face_image, faceid_embeds=faceid_embeds, shortcut=v2, s_scale=1.0,
+     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 data, base model and face recognition model.
+
+
+
+## Non-commercial use
+**This model is released exclusively for research purposes and is not intended for commercial use.**
+

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