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predict.py
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predict.py
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import os
from cog import BasePredictor, Input, Path
from typing import List
import sys
sys.path.append('/content/Arc2Face-hf')
os.chdir('/content/Arc2Face-hf')
from diffusers import (
StableDiffusionPipeline,
UNet2DConditionModel,
DPMSolverMultistepScheduler,
)
from arc2face import CLIPTextModelWrapper, project_face_embs
import torch
from insightface.app import FaceAnalysis
from PIL import Image
import numpy as np
import random
MAX_SEED = np.iinfo(np.int32).max
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def generate_image(image_path, num_steps, guidance_scale, seed, num_images, device, pipeline, app, dtype):
if image_path is None:
print(f"Cannot find any input face image! Please upload a face image.")
img = np.array(Image.open(image_path))[:,:,::-1]
# Face detection and ID-embedding extraction
faces = app.get(img)
if len(faces) == 0:
print(f"Face detection failed! Please try with another image.")
faces = sorted(faces, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # select largest face (if more than one detected)
id_emb = torch.tensor(faces['embedding'], dtype=dtype)[None].to(device)
id_emb = id_emb/torch.norm(id_emb, dim=1, keepdim=True) # normalize embedding
id_emb = project_face_embs(pipeline, id_emb) # pass throught the encoder
generator = torch.Generator(device=device).manual_seed(seed)
print("Start inference...")
images = pipeline(
prompt_embeds=id_emb,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images,
generator=generator
).images
return images
class Predictor(BasePredictor):
def setup(self) -> None:
# global variable
if torch.cuda.is_available():
self.device = "cuda"
self.dtype = torch.float16
else:
self.device = "cpu"
self.dtype = torch.float32
# Load face detection and recognition package
self.app = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider'])
self.app.prepare(ctx_id=0, det_size=(640, 640))
# Load pipeline
base_model = 'runwayml/stable-diffusion-v1-5'
encoder = CLIPTextModelWrapper.from_pretrained('models', subfolder="encoder", torch_dtype=self.dtype)
unet = UNet2DConditionModel.from_pretrained('models', subfolder="arc2face", torch_dtype=self.dtype)
self.pipeline = StableDiffusionPipeline.from_pretrained(
base_model,
text_encoder=encoder,
unet=unet,
torch_dtype=self.dtype,
safety_checker=None
)
self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config)
self.pipeline = self.pipeline.to(self.device)
def predict(
self,
input_image: Path = Input(description="Input Image"),
num_steps: int = Input(default=25),
guidance_scale: float = Input(default=3.0),
seed: int = Input(default=0),
num_images: int = Input(default=4),
randomize_seed: bool = True
) -> List[Path]:
seed = randomize_seed_fn(seed, randomize_seed)
images = generate_image(input_image, num_steps, guidance_scale, seed, num_images, self.device, self.pipeline, self.app, self.dtype)
for i, img in enumerate(images):
img.save(f'/content/{i+1}.png')
return [Path(f'/content/{i+1}.png') for i in range(num_images)]