Spaces:
Configuration error
Configuration error
File size: 22,869 Bytes
189028d 6454f77 189028d 6454f77 189028d b85533b 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 189028d b85533b 189028d 6454f77 189028d 6454f77 ea69685 6454f77 189028d 6454f77 b85533b 189028d 6454f77 189028d 6454f77 189028d b85533b 189028d 6454f77 189028d 6454f77 189028d 6454f77 b85533b ea69685 189028d 6454f77 189028d 6454f77 b85533b 189028d 6454f77 189028d b85533b 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 b85533b 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 189028d 6454f77 b85533b 6454f77 b85533b 6454f77 b85533b 6454f77 b85533b 6454f77 b85533b 6454f77 189028d 6454f77 b85533b 6454f77 b85533b 189028d 6454f77 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 |
#!/usr/bin/env python3
"""
FaceSpace Studio - Advanced Face Manipulation Platform
Combines face detection, enhancement, swapping, and style transfer
Optimized for Hugging Face Spaces deployment
"""
import gradio as gr
import torch
import cv2
import numpy as np
from PIL import Image
import os
import tempfile
import subprocess
from pathlib import Path
import logging
from functools import lru_cache
from typing import Tuple, Optional, List, Dict
import warnings
import json
import time
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor
import threading
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
warnings.filterwarnings("ignore")
# Configuration
@dataclass
class Config:
"""Configuration for FaceSpace Studio"""
device: str = "cuda" if torch.cuda.is_available() else "cpu"
max_image_size: int = 1024
face_detection_size: Tuple[int, int] = (640, 640)
enhancement_steps: int = 20
video_fps: int = 12
max_video_frames: int = 60
enable_face_swap: bool = True
enable_style_transfer: bool = True
cache_dir: str = "/tmp/facespace_cache"
config = Config()
# Global model registry
models = {
"face_detector": None,
"face_enhancer": None,
"face_swapper": None,
"style_transfer": None,
"upscaler": None
}
# Thread lock for model loading
model_lock = threading.Lock()
def setup_environment():
"""Setup environment and directories"""
os.makedirs(config.cache_dir, exist_ok=True)
if config.device == "cuda":
# GPU optimizations
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512'
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cudnn.benchmark = True
logger.info(f"Device: {config.device}")
if config.device == "cuda":
logger.info(f"GPU: {torch.cuda.get_device_name(0)}")
@lru_cache(maxsize=1)
def load_face_detector():
"""Load InsightFace with fallback options"""
try:
# Try importing InsightFace
from insightface.app import FaceAnalysis
# Try GPU first, fallback to CPU
providers = ['CPUExecutionProvider']
if config.device == "cuda":
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
app = FaceAnalysis(
name='buffalo_l',
providers=providers,
allowed_modules=['detection', 'recognition']
)
app.prepare(ctx_id=0 if config.device == "cuda" else -1,
det_size=config.face_detection_size)
logger.info("InsightFace loaded successfully")
return app
except Exception as e:
logger.warning(f"InsightFace not available: {e}, using OpenCV fallback")
# Fallback to OpenCV face detection
class OpenCVFaceDetector:
def __init__(self):
self.cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)
def get(self, img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = self.cascade.detectMultiScale(gray, 1.1, 4)
# Convert to InsightFace-like format
results = []
for (x, y, w, h) in faces:
face_dict = type('obj', (object,), {
'bbox': np.array([x, y, x+w, y+h]),
'det_score': 0.99,
'landmark': None
})()
results.append(face_dict)
return results
return OpenCVFaceDetector()
@lru_cache(maxsize=1)
def load_enhancement_pipeline():
"""Load Stable Diffusion with optimizations"""
try:
from diffusers import StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16 if config.device == "cuda" else torch.float32,
safety_checker=None,
requires_safety_checker=False
)
# Optimized scheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config,
use_karras_sigmas=True
)
pipe = pipe.to(config.device)
# Memory optimizations
if config.device == "cuda":
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
try:
pipe.enable_xformers_memory_efficient_attention()
except:
pass
logger.info("Enhancement pipeline loaded")
return pipe
except Exception as e:
logger.error(f"Failed to load enhancement pipeline: {e}")
return None
def extract_faces(image: Image.Image, detector) -> List[Dict]:
"""Extract all faces from image with metadata"""
try:
# Convert to CV2 format
img_cv2 = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# Detect faces
faces = detector.get(img_cv2)
if not faces:
return []
# Process each face
face_data = []
for idx, face in enumerate(faces):
bbox = face.bbox.astype(int)
x1, y1, x2, y2 = bbox
# Add padding
height, width = img_cv2.shape[:2]
pad = int(max(x2-x1, y2-y1) * 0.3)
x1 = max(0, x1 - pad)
y1 = max(0, y1 - pad)
x2 = min(width, x2 + pad)
y2 = min(height, y2 + pad)
# Extract face
face_img = img_cv2[y1:y2, x1:x2]
face_pil = Image.fromarray(cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB))
face_data.append({
'id': idx,
'image': face_pil,
'bbox': (x1, y1, x2, y2),
'confidence': getattr(face, 'det_score', 0.99),
'landmarks': getattr(face, 'landmark', None)
})
return face_data
except Exception as e:
logger.error(f"Face extraction error: {e}")
return []
def enhance_face(face_img: Image.Image,
pipe,
prompt: str = "a beautiful person, detailed face, high quality",
strength: float = 0.6) -> Image.Image:
"""Enhance a single face using SD"""
try:
# Resize to optimal size
face_img = face_img.resize((512, 512), Image.LANCZOS)
# Generate
with torch.inference_mode():
result = pipe(
prompt=prompt,
image=face_img,
strength=strength,
guidance_scale=7.5,
num_inference_steps=config.enhancement_steps
).images[0]
return result
except Exception as e:
logger.error(f"Enhancement error: {e}")
return face_img
def blend_face(original: Image.Image,
face: Image.Image,
bbox: Tuple[int, int, int, int],
method: str = "poisson") -> Image.Image:
"""Blend enhanced face back into original image"""
try:
x1, y1, x2, y2 = bbox
face_width = x2 - x1
face_height = y2 - y1
# Resize face to match bbox
face = face.resize((face_width, face_height), Image.LANCZOS)
# Convert to arrays
orig_array = np.array(original)
face_array = np.array(face)
if method == "poisson" and face_array.shape[0] > 5 and face_array.shape[1] > 5:
try:
# Create mask
mask = np.ones(face_array.shape[:2], dtype=np.uint8) * 255
# Calculate center
center = (x1 + face_width // 2, y1 + face_height // 2)
# Apply Poisson blending
orig_cv2 = cv2.cvtColor(orig_array, cv2.COLOR_RGB2BGR)
face_cv2 = cv2.cvtColor(face_array, cv2.COLOR_RGB2BGR)
result = cv2.seamlessClone(
face_cv2, orig_cv2, mask, center, cv2.NORMAL_CLONE
)
result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
return Image.fromarray(result)
except Exception as e:
logger.warning(f"Poisson blend failed: {e}, using alpha blend")
method = "alpha"
if method == "alpha":
# Simple alpha blending with feathering
result = orig_array.copy()
# Create feathered mask
mask = np.ones((face_height, face_width))
y_indices, x_indices = np.ogrid[:face_height, :face_width]
# Distance from edges
dist_from_edge = np.minimum.reduce([
x_indices,
face_width - 1 - x_indices,
y_indices,
face_height - 1 - y_indices
])
# Feather edges
feather_width = min(face_width, face_height) // 8
mask = np.clip(dist_from_edge / feather_width, 0, 1)
mask = mask[:, :, np.newaxis]
# Blend
alpha = 0.8
result[y1:y2, x1:x2] = (
face_array * mask * alpha +
orig_array[y1:y2, x1:x2] * (1 - mask * alpha)
).astype(np.uint8)
return Image.fromarray(result)
except Exception as e:
logger.error(f"Blending error: {e}")
return original
def process_image(image: Image.Image,
prompt: str = "beautiful person, detailed face",
strength: float = 0.6,
enhance_all: bool = True,
selected_faces: List[int] = None) -> Tuple[Image.Image, str, List[Dict]]:
"""Main processing function for images"""
if not image:
return None, "Please upload an image", []
try:
# Load models
with model_lock:
if not models["face_detector"]:
models["face_detector"] = load_face_detector()
if not models["face_enhancer"]:
models["face_enhancer"] = load_enhancement_pipeline()
detector = models["face_detector"]
enhancer = models["face_enhancer"]
if not detector or not enhancer:
return None, "Models not loaded properly", []
# Extract faces
faces = extract_faces(image, detector)
if not faces:
return image, "No faces detected", []
# Determine which faces to process
if enhance_all:
faces_to_process = faces
elif selected_faces:
faces_to_process = [f for f in faces if f['id'] in selected_faces]
else:
faces_to_process = [faces[0]] # Process largest face
# Process each face
result = image.copy()
processed_count = 0
for face_data in faces_to_process:
try:
# Enhance face
enhanced = enhance_face(
face_data['image'],
enhancer,
prompt,
strength
)
# Blend back
result = blend_face(
result,
enhanced,
face_data['bbox']
)
processed_count += 1
except Exception as e:
logger.error(f"Error processing face {face_data['id']}: {e}")
# Clear GPU memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
status = f"✅ Enhanced {processed_count}/{len(faces)} faces"
return result, status, faces
except Exception as e:
logger.error(f"Processing error: {e}")
return None, f"Error: {str(e)}", []
def swap_faces(source_image: Image.Image,
target_image: Image.Image,
mode: str = "Single Face",
preserve_expression: bool = True) -> Tuple[Image.Image, str]:
"""Swap faces between images using enhanced source face"""
if not source_image or not target_image:
return None, "Please provide both source and target images"
try:
# Load models
with model_lock:
if not models["face_detector"]:
models["face_detector"] = load_face_detector()
if not models["face_enhancer"]:
models["face_enhancer"] = load_enhancement_pipeline()
detector = models["face_detector"]
enhancer = models["face_enhancer"]
if not detector:
return None, "Face detector not loaded"
# Extract faces
source_faces = extract_faces(source_image, detector)
target_faces = extract_faces(target_image, detector)
if not source_faces:
return None, "No face detected in source image"
if not target_faces:
return None, "No face detected in target image"
# Get source face (use the first/largest)
source_face = source_faces[0]['image']
# Determine which target faces to swap
if mode == "Single Face":
faces_to_swap = [target_faces[0]] # Just the first face
elif mode == "All Faces":
faces_to_swap = target_faces
else:
# For selected faces, just use first for now
faces_to_swap = [target_faces[0]]
# Process swapping
result = target_image.copy()
swapped_count = 0
for target_face in faces_to_swap:
try:
# Resize source face to match target
target_size = target_face['image'].size
source_resized = source_face.resize(target_size, Image.LANCZOS)
if enhancer and preserve_expression:
# Use SD to blend features while preserving expression
prompt = "person, natural expression, photorealistic face"
# Blend source and target for expression preservation
blended = Image.blend(source_resized, target_face['image'], 0.3)
# Enhance the blended face
swapped_face = enhance_face(
blended,
enhancer,
prompt,
strength=0.7
)
else:
# Direct swap without enhancement
swapped_face = source_resized
# Blend back into target image
result = blend_face(
result,
swapped_face,
target_face['bbox'],
method="poisson" if preserve_expression else "alpha"
)
swapped_count += 1
except Exception as e:
logger.error(f"Error swapping face: {e}")
# Clear GPU memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
status = f"✅ Swapped {swapped_count} face(s)"
return result, status
except Exception as e:
logger.error(f"Face swap error: {e}")
return None, f"Error: {str(e)}"
def create_interface():
"""Create Gradio interface with all features"""
with gr.Blocks(
title="🎭 FaceSpace Studio",
theme=gr.themes.Soft(
primary_hue="purple",
secondary_hue="blue"
),
css="""
.gradio-container {
max-width: 1200px;
margin: auto;
}
.face-box {
border: 2px solid #9333ea;
border-radius: 8px;
padding: 10px;
margin: 5px;
}
"""
) as demo:
gr.Markdown("""
# 🎭 FaceSpace Studio - Advanced Face Manipulation
**Features**: Face Detection • Enhancement • Style Transfer • Batch Processing
Powered by InsightFace + Stable Diffusion + Advanced Blending
""")
with gr.Tabs():
# Face Enhancement Tab
with gr.TabItem("✨ Face Enhancement"):
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Upload Image",
type="pil"
)
prompt = gr.Textbox(
label="Enhancement Prompt",
value="beautiful person, detailed face, professional photo",
lines=2
)
with gr.Row():
strength = gr.Slider(
label="Enhancement Strength",
minimum=0.1,
maximum=0.9,
value=0.6,
step=0.1
)
enhance_all = gr.Checkbox(
label="Enhance All Faces",
value=True
)
enhance_btn = gr.Button(
"✨ Enhance Faces",
variant="primary",
size="lg"
)
with gr.Column():
output_image = gr.Image(
label="Enhanced Result"
)
status_text = gr.Textbox(
label="Status",
interactive=False
)
face_info = gr.JSON(
label="Detected Faces",
visible=False
)
# Face Swap Tab
with gr.TabItem("🔄 Face Swap"):
with gr.Row():
with gr.Column():
source_img = gr.Image(
label="Source Face (to copy)",
type="pil"
)
target_img = gr.Image(
label="Target Image (to paste into)",
type="pil"
)
swap_mode = gr.Radio(
choices=["Single Face", "All Faces", "Selected Faces"],
value="Single Face",
label="Swap Mode"
)
preserve_expression = gr.Checkbox(
label="Preserve Target Expression",
value=True
)
swap_btn = gr.Button(
"🔄 Swap Faces",
variant="primary",
size="lg"
)
with gr.Column():
swap_result = gr.Image(
label="Swapped Result"
)
swap_status = gr.Textbox(
label="Status",
interactive=False
)
gr.Markdown("""
### Tips:
- Source image should have a clear face
- Works best with similar face angles
- Enable expression preservation for natural results
""")
# Face swap handler
swap_btn.click(
fn=lambda s, t, m, e: swap_faces(s, t, m, e),
inputs=[source_img, target_img, swap_mode, preserve_expression],
outputs=[swap_result, swap_status]
)
# Style Transfer Tab (Placeholder)
with gr.TabItem("🎨 Style Transfer"):
gr.Markdown("""
### Style Transfer - Coming Soon!
Features in development:
- Artistic styles (oil painting, sketch, anime)
- Age progression/regression
- Gender transformation
- Celebrity style transfer
""")
# Batch Processing Tab (Placeholder)
with gr.TabItem("📦 Batch Processing"):
gr.Markdown("""
### Batch Processing - Coming Soon!
Features in development:
- Process multiple images
- Video frame extraction
- Folder upload/download
- Progress tracking
""")
# Event handlers
enhance_btn.click(
fn=process_image,
inputs=[input_image, prompt, strength, enhance_all],
outputs=[output_image, status_text, face_info]
)
gr.Markdown("""
---
### 🔧 Technical Details
- **Face Detection**: InsightFace buffalo_l / OpenCV fallback
- **Enhancement**: Stable Diffusion v1.5 with DPM++ scheduler
- **Blending**: Poisson seamless cloning + Alpha feathering
- **Optimization**: GPU acceleration, XFormers, VAE slicing
Made with ❤️ using advanced AI models
""")
return demo
# Initialize environment
setup_environment()
# Create interface
demo = create_interface()
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
show_error=True
) |