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import torch
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
from diffusers import AutoencoderKL, UniPCMultistepScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from huggingface_hub import hf_hub_download
from mgface.pipelines_mgface.pipeline_mgface import MgPipeline
from mgface.pipelines_mgface.unet_ID_2d_condition import UNetID2DConditionModel
from mgface.pipelines_mgface.unet_deno_2d_condition import UNetDeno2DConditionModel
class MagicFaceModel:
def __init__(self, device='cuda'):
self.device = device if torch.cuda.is_available() else 'cpu'
print(f"🖥️ Initializing MagicFace on: {self.device}")
# AU mapping
self.ind_dict = {
'AU1':0, 'AU2':1, 'AU4':2, 'AU5':3, 'AU6':4, 'AU9':5,
'AU12':6, 'AU15':7, 'AU17':8, 'AU20':9, 'AU25':10, 'AU26':11
}
self.weight_dtype = torch.float16 if self.device == 'cuda' else torch.float32
self.load_models()
def load_models(self):
"""Load all MagicFace components"""
print("📥 Loading MagicFace components...")
sd_model = 'runwayml/stable-diffusion-v1-5'
try:
# Load VAE from SD 1.5
print(" - Loading VAE...")
self.vae = AutoencoderKL.from_pretrained(
sd_model,
subfolder="vae",
torch_dtype=self.weight_dtype
).to(self.device)
# Load Text Encoder from SD 1.5
print(" - Loading Text Encoder...")
self.text_encoder = CLIPTextModel.from_pretrained(
sd_model,
subfolder="text_encoder",
torch_dtype=self.weight_dtype
).to(self.device)
# Load Tokenizer
print(" - Loading Tokenizer...")
self.tokenizer = CLIPTokenizer.from_pretrained(
sd_model,
subfolder="tokenizer",
)
# Download YOUR MagicFace model
print(" - Downloading YOUR MagicFace model (79999_iter.pth)...")
magicface_path = hf_hub_download(
repo_id="gauravvjhaa/magicface-affecto-model",
filename="79999_iter.pth",
cache_dir="./models"
)
print(f" - MagicFace model downloaded: {magicface_path}")
# Load MagicFace weights
print(" - Loading MagicFace weights...")
magicface_weights = torch.load(magicface_path, map_location=self.device)
# Initialize UNets (you need to define architecture or load from checkpoint)
print(" - Initializing ID UNet...")
self.unet_ID = UNetID2DConditionModel.from_pretrained(
'mengtingwei/magicface', # Use official architecture
subfolder='ID_enc',
torch_dtype=self.weight_dtype,
use_safetensors=True,
low_cpu_mem_usage=True,
).to(self.device)
print(" - Initializing Denoising UNet...")
self.unet_deno = UNetDeno2DConditionModel.from_pretrained(
'mengtingwei/magicface', # Use official architecture
subfolder='denoising_unet',
torch_dtype=self.weight_dtype,
use_safetensors=True,
low_cpu_mem_usage=True,
).to(self.device)
# Load YOUR weights into the UNets
print(" - Loading YOUR trained weights...")
# This depends on how 79999_iter.pth is structured
# It might contain both UNets or just one
try:
# Try loading as state dict
if isinstance(magicface_weights, dict):
# Check what keys are in the checkpoint
print(f" - Checkpoint keys: {list(magicface_weights.keys())[:5]}...")
# Load weights (adjust based on actual structure)
# Option 1: If it's a full model checkpoint
if 'unet_ID' in magicface_weights:
self.unet_ID.load_state_dict(magicface_weights['unet_ID'])
if 'unet_deno' in magicface_weights:
self.unet_deno.load_state_dict(magicface_weights['unet_deno'])
# Option 2: If it's just one UNet
elif 'state_dict' in magicface_weights:
self.unet_deno.load_state_dict(magicface_weights['state_dict'])
# Option 3: If it's the raw state dict
else:
self.unet_deno.load_state_dict(magicface_weights)
print(" ✅ YOUR weights loaded successfully!")
else:
print(" ⚠️ Unexpected checkpoint format, using default weights")
except Exception as e:
print(f" ⚠️ Could not load your weights: {str(e)}")
print(" ⚠️ Using default pretrained weights from mengtingwei/magicface")
# Set to eval mode
self.vae.requires_grad_(False)
self.text_encoder.requires_grad_(False)
self.unet_ID.requires_grad_(False)
self.unet_deno.requires_grad_(False)
self.vae.eval()
self.text_encoder.eval()
self.unet_ID.eval()
self.unet_deno.eval()
# Create pipeline
print(" - Creating MagicFace pipeline...")
self.pipeline = MgPipeline.from_pretrained(
sd_model,
vae=self.vae,
text_encoder=self.text_encoder,
tokenizer=self.tokenizer,
unet_ID=self.unet_ID,
unet_deno=self.unet_deno,
safety_checker=None,
torch_dtype=self.weight_dtype,
).to(self.device)
# Set scheduler
self.pipeline.scheduler = UniPCMultistepScheduler.from_config(
self.pipeline.scheduler.config
)
self.pipeline.set_progress_bar_config(disable=False)
print("✅ MagicFace loaded successfully!")
except Exception as e:
print(f"❌ Error loading MagicFace: {str(e)}")
import traceback
traceback.print_exc()
raise
def tokenize_caption(self, caption: str):
"""Tokenize text prompt"""
inputs = self.tokenizer(
caption,
max_length=self.tokenizer.model_max_length,
padding="max_length",
truncation=True,
return_tensors="pt"
)
return inputs.input_ids.to(self.device)
def prepare_au_vector(self, au_params: dict):
"""Convert AU parameters dict to tensor"""
au_prompt = np.zeros((12,))
for au_name, value in au_params.items():
if au_name in self.ind_dict:
au_prompt[self.ind_dict[au_name]] = float(value)
print(f" 🎭 AU vector: {au_prompt}")
return torch.from_numpy(au_prompt).float().unsqueeze(0).to(self.device)
@torch.no_grad()
def transform(self, source_image, bg_image, au_params, num_inference_steps=50, seed=424):
"""
Transform facial expression using MagicFace
Args:
source_image: PIL Image (512x512, cropped face)
bg_image: PIL Image (512x512, background)
au_params: dict like {"AU6": 2.0, "AU12": 2.0}
num_inference_steps: number of diffusion steps
seed: random seed
Returns:
PIL Image (transformed)
"""
print(f"🎭 Starting MagicFace transformation...")
print(f" AU params: {au_params}")
print(f" Inference steps: {num_inference_steps}")
try:
# Prepare inputs
transform = transforms.ToTensor()
source = transform(source_image).unsqueeze(0).to(self.device)
bg = transform(bg_image).unsqueeze(0).to(self.device)
# Get text embeddings
prompt = "A close up of a person."
prompt_ids = self.tokenize_caption(prompt)
prompt_embeds = self.text_encoder(prompt_ids)[0]
# Prepare AU vector
au_vector = self.prepare_au_vector(au_params)
# Set seed
generator = torch.Generator(device=self.device).manual_seed(seed)
# Run inference
print(" 🚀 Running diffusion pipeline...")
result = self.pipeline(
prompt_embeds=prompt_embeds,
source=source,
bg=bg,
au=au_vector,
num_inference_steps=num_inference_steps,
generator=generator,
)
print("✅ Transformation complete!")
return result.images[0]
except Exception as e:
print(f"❌ Transformation error: {str(e)}")
import traceback
traceback.print_exc()
raise |