from typing import Dict, Any import torch from PIL import Image import base64 from io import BytesIO import numpy as np from diffusers import AutoencoderKL, DDIMScheduler from einops import repeat from omegaconf import OmegaConf from transformers import CLIPVisionModelWithProjection import cv2 import os import sys import skvideo.io from src.models.pose_guider import PoseGuider from src.models.unet_2d_condition import UNet2DConditionModel from src.models.unet_3d import UNet3DConditionModel from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline from src.utils.util import read_frames, get_fps, save_videos_grid import roop.globals from roop.core import start, decode_execution_providers, suggest_max_memory, suggest_execution_threads from roop.utilities import normalize_output_path from roop.processors.frame.core import get_frame_processors_modules import onnxruntime as ort import gc import subprocess device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device.type != 'cuda': raise ValueError("The model requires a GPU for inference.") class EndpointHandler(): def __init__(self, path=""): base_dir = os.path.dirname(os.path.abspath(__file__)) config_path = os.path.join(base_dir, 'configs', 'prompts', 'animation.yaml') if not os.path.exists(config_path): raise FileNotFoundError(f"The configuration file was not found at: {config_path}") self.config = OmegaConf.load(config_path) self.weight_dtype = torch.float16 self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.pipeline = None self._initialize_pipeline() def _initialize_pipeline(self): base_dir = os.path.dirname(os.path.abspath(__file__)) config_path = os.path.join(base_dir, 'pretrained_weights', 'sd-vae-ft-mse') if not os.path.exists(config_path): raise FileNotFoundError(f"The sd-vae-ft-mse folder was not found at: {config_path}") vae = AutoencoderKL.from_pretrained(config_path).to(self.device, dtype=self.weight_dtype) pretrained_base_model_path_unet = os.path.join(base_dir, 'pretrained_weights', 'stable-diffusion-v1-5', 'unet') print("model path is " + pretrained_base_model_path_unet) reference_unet = UNet2DConditionModel.from_pretrained( pretrained_base_model_path_unet ).to(dtype=self.weight_dtype, device=self.device) inference_config_path = os.path.join(base_dir, 'configs', 'inference', 'inference_v2.yaml') motion_module_path = os.path.join(base_dir, 'pretrained_weights', 'motion_module.pth') denoising_unet_path = os.path.join(base_dir, 'pretrained_weights', 'denoising_unet.pth') reference_unet_path = os.path.join(base_dir, 'pretrained_weights', 'reference_unet.pth') pose_guider_path = os.path.join(base_dir, 'pretrained_weights', 'pose_guider.pth') image_encoder_path = os.path.join(base_dir, 'pretrained_weights', 'image_encoder') infer_config = OmegaConf.load(inference_config_path) denoising_unet = UNet3DConditionModel.from_pretrained_2d( pretrained_base_model_path_unet, motion_module_path, unet_additional_kwargs=infer_config.unet_additional_kwargs, ).to(self.device, dtype=self.weight_dtype) pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(self.device, dtype=self.weight_dtype) image_enc = CLIPVisionModelWithProjection.from_pretrained(image_encoder_path).to(self.device, dtype=self.weight_dtype) sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs) scheduler = DDIMScheduler(**sched_kwargs) denoising_unet.load_state_dict(torch.load(denoising_unet_path, map_location="cpu"), strict=False) reference_unet.load_state_dict(torch.load(reference_unet_path, map_location="cpu")) pose_guider.load_state_dict(torch.load(pose_guider_path, map_location="cpu")) self.pipeline = Pose2VideoPipeline( vae=vae, image_encoder=image_enc, reference_unet=reference_unet, denoising_unet=denoising_unet, pose_guider=pose_guider, scheduler=scheduler ).to(self.device, dtype=self.weight_dtype) def _crop_face(self, image, save_path="cropped_face.jpg", margin=0.5): # Convert image to OpenCV format cv_image = np.array(image) cv_image = cv_image[:, :, ::-1].copy() # Load OpenCV face detector face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # Detect faces gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 4) if len(faces) == 0: raise ValueError("No faces detected in the reference image.") # Crop the first face found with a margin x, y, w, h = faces[0] x_margin = int(margin * w) y_margin = int(margin * h) x1 = max(0, x - x_margin) y1 = max(0, y - y_margin // 2) # Less margin at the top x2 = min(cv_image.shape[1], x + w + x_margin) y2 = min(cv_image.shape[0], y + h + y_margin) # More margin at the bottom cropped_face = cv_image[y1:y2, x1:x2] # Convert back to PIL format cropped_face = Image.fromarray(cropped_face[:, :, ::-1]).convert("RGB") # Save the cropped face cropped_face.save(save_path, format="JPEG", quality=95) return cropped_face def _swap_face(self, source_image, target_video_path): source_path = "input.jpg" source_image.save(source_path, format="JPEG", quality=95) output_path = "output.mp4" roop.globals.source_path = source_path roop.globals.target_path = target_video_path roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, output_path) roop.globals.frame_processors = ["face_swapper", "face_enhancer"] roop.globals.headless = True roop.globals.keep_fps = True roop.globals.keep_audio = True roop.globals.keep_frames = False roop.globals.many_faces = False roop.globals.video_encoder = "libx264" roop.globals.video_quality = 100 roop.globals.max_memory = suggest_max_memory() # Set GPU execution provider roop.globals.execution_providers = decode_execution_providers(["CUDAExecutionProvider"]) roop.globals.execution_threads = suggest_execution_threads() # Ensure onnxruntime is using the GPU ort.set_default_logger_severity(3) # Suppress verbose logging providers = ['CUDAExecutionProvider'] options = ort.SessionOptions() options.intra_op_num_threads = 1 for frame_processor in get_frame_processors_modules(roop.globals.frame_processors): if hasattr(frame_processor, 'onnx_session'): frame_processor.onnx_session.set_providers(providers, options) # Clear CUDA cache before starting the face swapping process torch.cuda.empty_cache() start() # Clear CUDA cache after the face swapping process for frame_processor in roop.globals.frame_processors: del frame_processor torch.cuda.empty_cache() return os.path.join(os.getcwd(), output_path) def print_memory_stat_for_stuff(self, phase, log_file="memory_stats.log"): with open(log_file, "a") as f: f.write(f"Memory Stats - {phase}:\n") f.write(f"Allocated memory: {torch.cuda.memory_allocated() / 1024**2:.2f} MB\n") f.write(f"Reserved memory: {torch.cuda.memory_reserved() / 1024**2:.2f} MB\n") f.write(f"Max allocated memory: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB\n") f.write(f"Max reserved memory: {torch.cuda.max_memory_reserved() / 1024**2:.2f} MB\n") f.write("="*30 + "\n") def convert_to_playable_format(self, input_path, output_path): command = [ "ffmpeg", "-i", input_path, "-c:v", "libx264", "-preset", "fast", "-crf", "18", "-y", # Overwrite output file if it exists output_path ] result = subprocess.run(command, capture_output=True, text=True) print("Conversion STDOUT:", result.stdout) print("Conversion STDERR:", result.stderr) if result.returncode != 0: raise RuntimeError(f"FFmpeg conversion failed with exit code {result.returncode}") def run_rife_interpolation(self, video_path, output_path, multi=2, scale=1.0): base_dir = os.path.dirname(os.path.abspath(__file__)) directory = os.path.join(base_dir, "Practical-RIFE", "inference_video.py") model_directory = os.path.join(base_dir, "Practical-RIFE", "train_log") command = [ "python", directory, f"--video={video_path}", f"--output={output_path}", f"--multi={multi}", f"--scale={scale}", f"--model={model_directory}", ] result = subprocess.run(command, capture_output=True, text=True) print(result) print(result.stdout) print(result.stderr) if result.returncode != 0: raise RuntimeError(f"RIFE interpolation failed with exit code {result.returncode}") self.convert_to_playable_format(output_path, "completed_playable.mp4") def speed_up_video(self, input_path, output_path, factor=4): command = [ "ffmpeg", "-i", input_path, "-filter:v", f"setpts=PTS/{factor}", "-an", # Remove audio output_path ] result = subprocess.run(command, capture_output=True, text=True) print("Speed Up Video STDOUT:", result.stdout) print("Speed Up Video STDERR:", result.stderr) if result.returncode != 0: raise RuntimeError(f"FFmpeg speed up failed with exit code {result.returncode}") def slow_down_video(self, input_path, output_path, factor=4): command = [ "ffmpeg", "-i", input_path, "-filter:v", f"setpts={factor}*PTS", "-an", # Remove audio output_path ] result = subprocess.run(command, capture_output=True, text=True) print("Slow Down Video STDOUT:", result.stdout) print("Slow Down Video STDERR:", result.stderr) if result.returncode != 0: raise RuntimeError(f"FFmpeg slow down failed with exit code {result.returncode}") def __call__(self, data: Any) -> Dict[str, str]: inputs = data.get("inputs", {}) ref_image_base64 = inputs.get("ref_image", "") pose_video_path = inputs.get("pose_video_path", "") width = inputs.get("width", 512) height = inputs.get("height", 768) length = inputs.get("length", 24) num_inference_steps = inputs.get("num_inference_steps", 25) cfg = inputs.get("cfg", 3.5) seed = inputs.get("seed", 123) ref_image = Image.open(BytesIO(base64.b64decode(ref_image_base64))) # Get the base directory of the current file base_dir = os.path.dirname(os.path.abspath(__file__)) # Update pose_video_path to use the base directory pose_video_path = os.path.join(base_dir, pose_video_path) if not os.path.exists(pose_video_path): raise FileNotFoundError(f"The pose video was not found at: {pose_video_path}") # Speed up the pose video by 4x sped_up_pose_video_path = os.path.join(base_dir, "sped_up_pose_video.mp4") self.speed_up_video(pose_video_path, sped_up_pose_video_path, factor=4) torch.manual_seed(seed) pose_images = read_frames(sped_up_pose_video_path) src_fps = get_fps(sped_up_pose_video_path) pose_list = [] total_length = min(length, len(pose_images)) for pose_image_pil in pose_images[:total_length]: pose_list.append(pose_image_pil) video = self.pipeline( ref_image, pose_list, width=width, height=height, video_length=total_length, num_inference_steps=num_inference_steps, guidance_scale=cfg ).videos save_dir = os.path.join(base_dir, "output", "gradio") if not os.path.exists(save_dir): os.makedirs(save_dir, exist_ok=True) animation_path = os.path.join(save_dir, "animation_output.mp4") save_videos_grid(video, animation_path, n_rows=1, fps=src_fps) # Crop the face from the reference image and save it cropped_face_path = os.path.join(save_dir, "cropped_face.jpg") cropped_face = self._crop_face(ref_image, save_path=cropped_face_path) # Delete the pipeline and clear CUDA cache to free up memory del self.pipeline torch.cuda.empty_cache() # Perform face swapping swapped_face_video_path = self._swap_face(cropped_face, animation_path) # Slow down the produced video by 4x slowed_down_animation_path = os.path.join(save_dir, "slowed_down_animation_output.mp4") self.slow_down_video(swapped_face_video_path, slowed_down_animation_path, factor=4) # Clear CUDA cache before RIFE interpolation torch.cuda.empty_cache() # Perform RIFE interpolation rife_output_path = os.path.join(save_dir, "completed_result.mp4") self.run_rife_interpolation(slowed_down_animation_path, rife_output_path, multi=2, scale=0.5) # Encode the final video in base64 with open(rife_output_path, "rb") as video_file: video_base64 = base64.b64encode(video_file.read()).decode("utf-8") torch.cuda.empty_cache() return {"video": video_base64}