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--- |
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tags: |
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- text-to-image |
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- stable-diffusion |
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language: |
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- en |
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library_name: diffusers |
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--- |
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# IP-Adapter-FaceID Model Card |
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<div align="center"> |
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[**Project Page**](https://ip-adapter.github.io) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2308.06721) **|** [**Code**](https://github.com/tencent-ailab/IP-Adapter) |
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</div> |
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--- |
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## Introduction |
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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. |
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**Update 2023/12/27**: |
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IP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure) |
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<div align="center"> |
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</div> |
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**Update 2023/12/28**: |
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IP-Adapter-FaceID-PlusV2: face ID embedding (for face ID) + controllable CLIP image embedding (for face structure) |
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You can adjust the weight of the face structure to get different generation! |
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<div align="center"> |
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</div> |
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**Update 2024/01/04**: |
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IP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID |
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<div align="center"> |
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</div> |
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**Update 2024/01/17**: |
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IP-Adapter-FaceID-PlusV2-SDXL: An experimental SDXL version of IP-Adapter-FaceID-PlusV2 |
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**Update 2024/01/19**: |
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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). |
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<div align="center"> |
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</div> |
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## Usage |
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### IP-Adapter-FaceID |
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Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding: |
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```python |
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import cv2 |
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from insightface.app import FaceAnalysis |
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import torch |
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app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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app.prepare(ctx_id=0, det_size=(640, 640)) |
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image = cv2.imread("person.jpg") |
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faces = app.get(image) |
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faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) |
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``` |
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Then, you can generate images conditioned on the face embeddings: |
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```python |
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import torch |
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from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL |
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from PIL import Image |
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from ip_adapter.ip_adapter_faceid import IPAdapterFaceID |
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base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" |
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vae_model_path = "stabilityai/sd-vae-ft-mse" |
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ip_ckpt = "ip-adapter-faceid_sd15.bin" |
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device = "cuda" |
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noise_scheduler = DDIMScheduler( |
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num_train_timesteps=1000, |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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clip_sample=False, |
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set_alpha_to_one=False, |
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steps_offset=1, |
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) |
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vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) |
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pipe = StableDiffusionPipeline.from_pretrained( |
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base_model_path, |
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torch_dtype=torch.float16, |
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scheduler=noise_scheduler, |
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vae=vae, |
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feature_extractor=None, |
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safety_checker=None |
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) |
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# load ip-adapter |
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ip_model = IPAdapterFaceID(pipe, ip_ckpt, device) |
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# generate image |
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prompt = "photo of a woman in red dress in a garden" |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" |
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images = ip_model.generate( |
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prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023 |
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) |
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``` |
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you can also use a normal IP-Adapter and a normal LoRA to load model: |
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```python |
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import torch |
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from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL |
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from PIL import Image |
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from ip_adapter.ip_adapter_faceid_separate import IPAdapterFaceID |
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base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" |
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vae_model_path = "stabilityai/sd-vae-ft-mse" |
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ip_ckpt = "ip-adapter-faceid_sd15.bin" |
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lora_ckpt = "ip-adapter-faceid_sd15_lora.safetensors" |
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device = "cuda" |
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noise_scheduler = DDIMScheduler( |
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num_train_timesteps=1000, |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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clip_sample=False, |
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set_alpha_to_one=False, |
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steps_offset=1, |
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) |
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vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) |
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pipe = StableDiffusionPipeline.from_pretrained( |
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base_model_path, |
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torch_dtype=torch.float16, |
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scheduler=noise_scheduler, |
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vae=vae, |
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feature_extractor=None, |
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safety_checker=None |
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) |
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# load lora and fuse |
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pipe.load_lora_weights(lora_ckpt) |
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pipe.fuse_lora() |
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# load ip-adapter |
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ip_model = IPAdapterFaceID(pipe, ip_ckpt, device) |
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# generate image |
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prompt = "photo of a woman in red dress in a garden" |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" |
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images = ip_model.generate( |
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prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023 |
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) |
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``` |
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### IP-Adapter-FaceID-SDXL |
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Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding: |
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```python |
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import cv2 |
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from insightface.app import FaceAnalysis |
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import torch |
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app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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app.prepare(ctx_id=0, det_size=(640, 640)) |
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image = cv2.imread("person.jpg") |
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faces = app.get(image) |
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faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) |
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``` |
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Then, you can generate images conditioned on the face embeddings: |
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```python |
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import torch |
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from diffusers import StableDiffusionXLPipeline, DDIMScheduler |
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from PIL import Image |
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from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDXL |
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base_model_path = "SG161222/RealVisXL_V3.0" |
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ip_ckpt = "ip-adapter-faceid_sdxl.bin" |
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device = "cuda" |
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noise_scheduler = DDIMScheduler( |
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num_train_timesteps=1000, |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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clip_sample=False, |
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set_alpha_to_one=False, |
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steps_offset=1, |
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) |
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pipe = StableDiffusionXLPipeline.from_pretrained( |
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base_model_path, |
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torch_dtype=torch.float16, |
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scheduler=noise_scheduler, |
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add_watermarker=False, |
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) |
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# load ip-adapter |
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ip_model = IPAdapterFaceIDXL(pipe, ip_ckpt, device) |
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# generate image |
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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" |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" |
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images = ip_model.generate( |
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prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=2, |
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width=1024, height=1024, |
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num_inference_steps=30, guidance_scale=7.5, seed=2023 |
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) |
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``` |
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### IP-Adapter-FaceID-Plus |
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Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding and face image: |
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```python |
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import cv2 |
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from insightface.app import FaceAnalysis |
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from insightface.utils import face_align |
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import torch |
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app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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app.prepare(ctx_id=0, det_size=(640, 640)) |
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image = cv2.imread("person.jpg") |
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faces = app.get(image) |
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faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) |
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face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) # you can also segment the face |
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``` |
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Then, you can generate images conditioned on the face embeddings: |
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```python |
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import torch |
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from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL |
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from PIL import Image |
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from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDPlus |
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v2 = False |
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base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" |
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vae_model_path = "stabilityai/sd-vae-ft-mse" |
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image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" |
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ip_ckpt = "ip-adapter-faceid-plus_sd15.bin" if not v2 else "ip-adapter-faceid-plusv2_sd15.bin" |
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device = "cuda" |
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noise_scheduler = DDIMScheduler( |
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num_train_timesteps=1000, |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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clip_sample=False, |
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set_alpha_to_one=False, |
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steps_offset=1, |
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) |
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vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) |
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pipe = StableDiffusionPipeline.from_pretrained( |
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base_model_path, |
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torch_dtype=torch.float16, |
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scheduler=noise_scheduler, |
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vae=vae, |
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feature_extractor=None, |
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safety_checker=None |
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) |
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# load ip-adapter |
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ip_model = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_ckpt, device) |
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# generate image |
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prompt = "photo of a woman in red dress in a garden" |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" |
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images = ip_model.generate( |
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prompt=prompt, negative_prompt=negative_prompt, face_image=face_image, faceid_embeds=faceid_embeds, shortcut=v2, s_scale=1.0, |
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num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023 |
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) |
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``` |
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### IP-Adapter-FaceID-Portrait |
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```python |
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import cv2 |
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from insightface.app import FaceAnalysis |
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import torch |
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app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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app.prepare(ctx_id=0, det_size=(640, 640)) |
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images = ["1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg"] |
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faceid_embeds = [] |
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for image in images: |
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image = cv2.imread("person.jpg") |
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faces = app.get(image) |
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faceid_embeds.append(torch.from_numpy(faces[0].normed_embedding).unsqueeze(0).unsqueeze(0)) |
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faceid_embeds = torch.cat(faceid_embeds, dim=1) |
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``` |
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```python |
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import torch |
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from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL |
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from PIL import Image |
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from ip_adapter.ip_adapter_faceid_separate import IPAdapterFaceID |
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base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" |
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vae_model_path = "stabilityai/sd-vae-ft-mse" |
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ip_ckpt = "ip-adapter-faceid-portrait_sd15.bin" |
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device = "cuda" |
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noise_scheduler = DDIMScheduler( |
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num_train_timesteps=1000, |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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clip_sample=False, |
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set_alpha_to_one=False, |
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steps_offset=1, |
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) |
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vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) |
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pipe = StableDiffusionPipeline.from_pretrained( |
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base_model_path, |
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torch_dtype=torch.float16, |
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scheduler=noise_scheduler, |
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vae=vae, |
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feature_extractor=None, |
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safety_checker=None |
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) |
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# load ip-adapter |
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ip_model = IPAdapterFaceID(pipe, ip_ckpt, device, num_tokens=16, n_cond=5) |
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# generate image |
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prompt = "photo of a woman in red dress in a garden" |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" |
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images = ip_model.generate( |
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prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=512, num_inference_steps=30, seed=2023 |
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) |
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``` |
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## Limitations and Bias |
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- The models do not achieve perfect photorealism and ID consistency. |
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- The generalization of the models is limited due to limitations of the training data, base model and face recognition model. |
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## Non-commercial use |
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**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.** |
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