IP Adapter FaceID

Table of Contents

Model Overview

The IP-Adapter-FaceID model is a cutting-edge AI tool that generates images of faces based on text prompts and face embeddings. This model is an experimental version that uses face ID embedding from a face recognition model instead of CLIP image embedding, and also utilizes LoRA to improve ID consistency.

Tasks and rank

Image-to-Image Translation 24th

Image Manipulation 11th

Image Generation 25th

Facial Recognition 3rd

Face Generation 2nd

Learn More About the Ranking System

Capabilities

The model is capable of generating various style images conditioned on a face with only text prompts. It uses face ID embedding from a face recognition model instead of CLIP image embedding, and LoRA to improve ID consistency.

• Generate images of a person with a specific face, based on a text prompt
• Condition the generated image on a face ID embedding, to ensure the generated face matches the target face
• Use LoRA to improve the consistency of the generated face

How does it work?

1. Extract face ID embedding from a face recognition model
2. Use the face ID embedding to condition the generated image
3. Use LoRA to improve the consistency of the generated face

Alternatives

MediaPipe Holistic 26000 Full-body landmark detection

MediaPipe Face Mesh 26000 Estimates 3D face landmarks

Grounded SAM 13900 Visual task model

FLUX.1 Dev 5413 Text-to-image model

Vit Base Patch16 224 In21k 220 Image classification

Variations of the Model

There are several variations of the IP-Adapter-FaceID model, including:

• IP-Adapter-FaceID-Plus: uses face ID embedding and CLIP image embedding to generate images
• IP-Adapter-FaceID-PlusV2: uses face ID embedding and controllable CLIP image embedding to generate images
• IP-Adapter-FaceID-SDXL: an experimental SDXL version of IP-Adapter-FaceID
• IP-Adapter-FaceID-PlusV2-SDXL: an experimental SDXL version of IP-Adapter-FaceID-PlusV2
• IP-Adapter-FaceID-Portrait: generates portrait images based on multiple facial images

Performance

The model showcases remarkable performance in generating high-quality images conditioned on face embeddings. Let’s dive into its speed, accuracy, and efficiency in various tasks.

Speed

The model’s speed is notable, with the ability to generate images in a relatively short amount of time. For instance, it can produce high-quality images with a resolution of 512x768 in just 30 inference steps.

Accuracy

The model demonstrates impressive accuracy in preserving the face ID consistency, even when generating images with different styles and structures. This is particularly evident in the IP-Adapter-FaceID-Plus variant, which utilizes both face ID embedding and CLIP image embedding to achieve better results.

Efficiency

The model’s efficiency is also worth highlighting, as it can generate multiple images with different prompts and face embeddings in a single run. For example, the IP-Adapter-FaceID-Portrait variant can generate 4 images with a resolution of 512x512 in a single inference step.

Examples

Generate an image of a woman in a red dress in a garden, using the provided face ID embedding. Image generated: A realistic image of a woman in a red dress in a garden, with the specified face ID embedding.

Create a portrait of a woman based on multiple facial images. Image generated: A portrait of a woman, with enhanced similarity to the provided facial images.

Produce an image of a close-up shot of a beautiful Asian teenage girl in a white dress, wearing small silver earrings in the garden, under the soft morning light, using the provided face ID embedding. Image generated: A realistic close-up shot of a beautiful Asian teenage girl in a white dress, wearing small silver earrings in the garden, under the soft morning light, with the specified face ID embedding.

Comparison with Other Models

Compared to ==Other Face Generation Models==, the IP-Adapter-FaceID model stands out for its ability to generate high-quality images with face ID consistency. While ==Other Models== may struggle with preserving the face ID, the IP-Adapter-FaceID model achieves this with remarkable accuracy.

Limitations and Bias

The model is not perfect and has some limitations. The generalization of the model is limited due to the limitations of the training data, base model, and face recognition model. Additionally, the model may not achieve perfect photorealism and ID consistency.

Format

The IP-Adapter-FaceID model uses a unique architecture that combines face ID embedding with a stable diffusion pipeline. It accepts input in the form of text prompts and face ID embeddings, which are extracted from facial images using the InsightFace model.

Supported Data Formats

• Text prompts: The model accepts text prompts that describe the desired output image.
• Face ID embeddings: The model requires face ID embeddings, which are extracted from facial images using the InsightFace model.

Special Requirements

• Face ID embedding extraction: To use the model, you need to extract face ID embeddings from facial images using the InsightFace model.
• Text prompt formatting: The text prompt should be a string that describes the desired output image.

Example Code

Here’s an example of how to use the IP-Adapter-FaceID model:

import cv2
from insightface.app import FaceAnalysis
import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
from PIL import Image
from ip_adapter.ip_adapter_faceid import IPAdapterFaceID

# Extract face ID embedding from a facial image
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)

# Load the IP-Adapter-FaceID model
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)
ip_model = IPAdapterFaceID(pipe, ip_ckpt, device)

# Generate an image conditioned on the face ID embedding and a text prompt
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)