h94/IP-Adapter-FaceID
huggingface.co/h94/IP-Adapter-FaceID
Model card
IP-Adapter-FaceID Model Card
Project Page | Paper (ArXiv) | Code
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
Update 2023/12/27:
IP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure)
!results
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!
!results
Update 2024/01/04:
IP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID
!results
Update 2024/01/17:
IP-Adapter-FaceID-PlusV2-SDXL: An experimental SDXL version of IP-Adapter-FaceID-PlusV2
Update 2024/01/19:
IP-Adapter-FaceID-Portrait: same with IP-Adapter-FaceID but for portrait generation (no lora! no controlnet!). Specifically, it accepts multiple facial images to enhance similarity (the default is 5).
!results
Usage
IP-Adapter-FaceID
Firstly, you should use 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 )
```
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
Firstly, you should use 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 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 )
```
IP-Adapter-FaceID-Portrait
```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))
images = ["1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg"]
faceid_embeds = [] for image in images: image = cv2.imread("person.jpg") faces = app.get(image) faceid_embeds.append(torch.from_numpy(faces[0].normed_embedding).unsqueeze(0).unsqueeze(0)) faceid_embeds = torch.cat(faceid_embeds, dim=1) ```
```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-portrait_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, num_tokens=16, n_cond=5)
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=512, num_inference_steps=30, seed=2023 )
```
Limitations and Bias
- The models do not achieve perfect photorealism and ID consistency.
- The generalization of the models is limited due to limitations of the training data, base model and face recognition model.
Non-commercial use
AS InsightFace pretrained models are available for non-commercial research purposes, IP-Adapter-FaceID models are released exclusively for research purposes and is not intended for commercial use.