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Running on Zero
Running on Zero
| import imageio, os, torch, warnings, torchvision, argparse, json, random | |
| from peft import LoraConfig, inject_adapter_in_model | |
| from PIL import Image | |
| import pandas as pd | |
| from tqdm import tqdm | |
| from accelerate import Accelerator | |
| class ImageDataset(torch.utils.data.Dataset): | |
| def __init__( | |
| self, | |
| base_path=None, metadata_path=None, | |
| max_pixels=1920*1080, height=None, width=None, | |
| height_division_factor=16, width_division_factor=16, | |
| data_file_keys=("image",), | |
| image_file_extension=("jpg", "jpeg", "png", "webp"), | |
| repeat=1, | |
| args=None, | |
| ): | |
| if args is not None: | |
| base_path = args.dataset_base_path | |
| metadata_path = args.dataset_metadata_path | |
| height = args.height | |
| width = args.width | |
| max_pixels = args.max_pixels | |
| data_file_keys = args.data_file_keys.split(",") | |
| repeat = args.dataset_repeat | |
| self.base_path = base_path | |
| self.max_pixels = max_pixels | |
| self.height = height | |
| self.width = width | |
| self.height_division_factor = height_division_factor | |
| self.width_division_factor = width_division_factor | |
| self.data_file_keys = data_file_keys | |
| self.image_file_extension = image_file_extension | |
| self.repeat = repeat | |
| if height is not None and width is not None: | |
| print("Height and width are fixed. Setting `dynamic_resolution` to False.") | |
| self.dynamic_resolution = False | |
| elif height is None and width is None: | |
| print("Height and width are none. Setting `dynamic_resolution` to True.") | |
| self.dynamic_resolution = True | |
| if metadata_path is None: | |
| print("No metadata. Trying to generate it.") | |
| metadata = self.generate_metadata(base_path) | |
| print(f"{len(metadata)} lines in metadata.") | |
| self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] | |
| elif metadata_path.endswith(".json"): | |
| with open(metadata_path, "r") as f: | |
| metadata = json.load(f) | |
| self.data = metadata | |
| else: | |
| metadata = pd.read_csv(metadata_path) | |
| # Ensure prompt column is string type to avoid float conversion for NaN values | |
| if 'prompt' in metadata.columns: | |
| metadata['prompt'] = metadata['prompt'].astype(str) | |
| # Replace 'nan' string (from NaN) with empty string | |
| metadata['prompt'] = metadata['prompt'].replace('nan', '') | |
| self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] | |
| def generate_metadata(self, folder): | |
| image_list, prompt_list = [], [] | |
| file_set = set(os.listdir(folder)) | |
| for file_name in file_set: | |
| if "." not in file_name: | |
| continue | |
| file_ext_name = file_name.split(".")[-1].lower() | |
| file_base_name = file_name[:-len(file_ext_name)-1] | |
| if file_ext_name not in self.image_file_extension: | |
| continue | |
| prompt_file_name = file_base_name + ".txt" | |
| if prompt_file_name not in file_set: | |
| continue | |
| with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: | |
| prompt = f.read().strip() | |
| image_list.append(file_name) | |
| prompt_list.append(prompt) | |
| metadata = pd.DataFrame() | |
| metadata["image"] = image_list | |
| metadata["prompt"] = prompt_list | |
| return metadata | |
| def crop_and_resize(self, image, target_height, target_width): | |
| width, height = image.size | |
| scale = max(target_width / width, target_height / height) | |
| image = torchvision.transforms.functional.resize( | |
| image, | |
| (round(height*scale), round(width*scale)), | |
| interpolation=torchvision.transforms.InterpolationMode.BILINEAR | |
| ) | |
| image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) | |
| return image | |
| def get_height_width(self, image): | |
| if self.dynamic_resolution: | |
| width, height = image.size | |
| if width * height > self.max_pixels: | |
| scale = (width * height / self.max_pixels) ** 0.5 | |
| height, width = int(height / scale), int(width / scale) | |
| height = height // self.height_division_factor * self.height_division_factor | |
| width = width // self.width_division_factor * self.width_division_factor | |
| else: | |
| height, width = self.height, self.width | |
| return height, width | |
| def load_image(self, file_path): | |
| image = Image.open(file_path).convert("RGB") | |
| image = self.crop_and_resize(image, *self.get_height_width(image)) | |
| return image | |
| def load_data(self, file_path): | |
| return self.load_image(file_path) | |
| def __getitem__(self, data_id): | |
| data = self.data[data_id % len(self.data)].copy() | |
| for key in self.data_file_keys: | |
| if key in data: | |
| path = os.path.join(self.base_path, data[key]) | |
| data[key] = self.load_data(path) | |
| if data[key] is None: | |
| warnings.warn(f"cannot load file {data[key]}.") | |
| return None | |
| return data | |
| def __len__(self): | |
| return len(self.data) * self.repeat | |
| class VideoDataset(torch.utils.data.Dataset): | |
| def __init__( | |
| self, | |
| base_path=None, metadata_path=None, | |
| num_frames=81, | |
| time_division_factor=4, time_division_remainder=1, | |
| max_pixels=1920*1080, height=None, width=None, | |
| height_division_factor=16, width_division_factor=16, | |
| data_file_keys=("video",), | |
| image_file_extension=("jpg", "jpeg", "png", "webp"), | |
| video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), | |
| repeat=1, | |
| args=None, | |
| action_base_path=None, | |
| enable_icl=False, | |
| icl_num_examples=2, | |
| icl_context_frames=8, | |
| ): | |
| if args is not None: | |
| base_path = args.dataset_base_path | |
| metadata_path = args.dataset_metadata_path | |
| height = args.height | |
| width = args.width | |
| max_pixels = args.max_pixels | |
| num_frames = args.num_frames | |
| data_file_keys = args.data_file_keys.split(",") | |
| repeat = args.dataset_repeat | |
| # In-context learning parameters | |
| if hasattr(args, 'enable_icl'): | |
| enable_icl = args.enable_icl | |
| if hasattr(args, 'icl_num_examples'): | |
| icl_num_examples = args.icl_num_examples | |
| if hasattr(args, 'icl_context_frames'): | |
| icl_context_frames = args.icl_context_frames | |
| self.base_path = base_path | |
| self.num_frames = num_frames | |
| self.time_division_factor = time_division_factor | |
| self.time_division_remainder = time_division_remainder | |
| self.max_pixels = max_pixels | |
| self.height = height | |
| self.width = width | |
| self.height_division_factor = height_division_factor | |
| self.width_division_factor = width_division_factor | |
| self.data_file_keys = data_file_keys | |
| self.image_file_extension = image_file_extension | |
| self.video_file_extension = video_file_extension | |
| self.repeat = repeat | |
| # In-context learning parameters | |
| self.enable_icl = enable_icl | |
| self.icl_num_examples = icl_num_examples | |
| self.icl_context_frames = icl_context_frames | |
| if height is not None and width is not None: | |
| print("Height and width are fixed. Setting `dynamic_resolution` to False.") | |
| self.dynamic_resolution = False | |
| elif height is None and width is None: | |
| print("Height and width are none. Setting `dynamic_resolution` to True.") | |
| self.dynamic_resolution = True | |
| if metadata_path is None: | |
| print("No metadata. Trying to generate it.") | |
| metadata = self.generate_metadata(base_path) | |
| print(f"{len(metadata)} lines in metadata.") | |
| self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] | |
| elif metadata_path.endswith(".json"): | |
| with open(metadata_path, "r") as f: | |
| metadata = json.load(f) | |
| self.data = metadata | |
| else: | |
| metadata = pd.read_csv(metadata_path) | |
| # Ensure prompt column is string type to avoid float conversion for NaN values | |
| if 'prompt' in metadata.columns: | |
| metadata['prompt'] = metadata['prompt'].astype(str) | |
| # Replace 'nan' string (from NaN) with empty string | |
| metadata['prompt'] = metadata['prompt'].replace('nan', '') | |
| # CRITICAL FIX: Clean prompt - remove video path prefix if present | |
| # Some CSV prompts start with "video_name.mp4 " prefix, which should be removed | |
| def clean_prompt(prompt_str): | |
| if not isinstance(prompt_str, str) or not prompt_str: | |
| return prompt_str | |
| # Check if prompt starts with a video path (contains .mp4 or /) | |
| # Pattern: "VideoName/1234_5678.mp4 " or "VideoName.mp4 " | |
| import re | |
| # Match pattern: word/word.mp4 or word.mp4 at the start, followed by space | |
| pattern = r'^[A-Za-z0-9_]+(/[A-Za-z0-9_]+)?\.mp4\s+' | |
| cleaned = re.sub(pattern, '', prompt_str) | |
| # Also handle truncated prompts ending with "..." | |
| if cleaned.endswith('...'): | |
| cleaned = cleaned[:-3].rstrip() | |
| return cleaned.strip() | |
| metadata['prompt'] = metadata['prompt'].apply(clean_prompt) | |
| self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] | |
| self.action_base_path = action_base_path | |
| if self.enable_icl: | |
| print(f"In-context learning enabled: {icl_num_examples} examples, {icl_context_frames} context frames each") | |
| def generate_metadata(self, folder): | |
| video_list, prompt_list = [], [] | |
| file_set = set(os.listdir(folder)) | |
| for file_name in file_set: | |
| if "." not in file_name: | |
| continue | |
| file_ext_name = file_name.split(".")[-1].lower() | |
| file_base_name = file_name[:-len(file_ext_name)-1] | |
| if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension: | |
| continue | |
| prompt_file_name = file_base_name + ".txt" | |
| if prompt_file_name not in file_set: | |
| continue | |
| with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: | |
| prompt = f.read().strip() | |
| video_list.append(file_name) | |
| prompt_list.append(prompt) | |
| metadata = pd.DataFrame() | |
| metadata["video"] = video_list | |
| metadata["prompt"] = prompt_list | |
| return metadata | |
| def crop_and_resize(self, image, target_height, target_width): | |
| width, height = image.size | |
| scale = max(target_width / width, target_height / height) | |
| image = torchvision.transforms.functional.resize( | |
| image, | |
| (round(height*scale), round(width*scale)), | |
| interpolation=torchvision.transforms.InterpolationMode.BILINEAR | |
| ) | |
| image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) | |
| return image | |
| def get_height_width(self, image): | |
| if self.dynamic_resolution: | |
| width, height = image.size | |
| if width * height > self.max_pixels: | |
| scale = (width * height / self.max_pixels) ** 0.5 | |
| height, width = int(height / scale), int(width / scale) | |
| height = height // self.height_division_factor * self.height_division_factor | |
| width = width // self.width_division_factor * self.width_division_factor | |
| else: | |
| height, width = self.height, self.width | |
| return height, width | |
| def get_num_frames(self, reader): | |
| num_frames = self.num_frames | |
| if int(reader.count_frames()) < num_frames: | |
| num_frames = int(reader.count_frames()) | |
| while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: | |
| num_frames -= 1 | |
| return num_frames | |
| def load_video(self, file_path): | |
| reader = imageio.get_reader(file_path) | |
| num_frames = self.get_num_frames(reader) | |
| frames = [] | |
| for frame_id in range(num_frames): | |
| frame = reader.get_data(frame_id) | |
| frame = Image.fromarray(frame) | |
| frame = self.crop_and_resize(frame, *self.get_height_width(frame)) | |
| frames.append(frame) | |
| reader.close() | |
| return frames | |
| def load_image(self, file_path): | |
| image = Image.open(file_path).convert("RGB") | |
| image = self.crop_and_resize(image, *self.get_height_width(image)) | |
| frames = [image] | |
| return frames | |
| def is_image(self, file_path): | |
| file_ext_name = file_path.split(".")[-1] | |
| return file_ext_name.lower() in self.image_file_extension | |
| def is_video(self, file_path): | |
| file_ext_name = file_path.split(".")[-1] | |
| return file_ext_name.lower() in self.video_file_extension | |
| def load_data(self, file_path): | |
| # Handle multiple frame paths separated by '|' (for frame sequences) | |
| if '|' in str(file_path): | |
| # Split the path by '|' to get individual frame paths | |
| frame_paths = str(file_path).split('|') | |
| frames = [] | |
| # Get base_path (dataset root) | |
| if not hasattr(self, 'base_path') or not self.base_path: | |
| warnings.warn(f"Cannot determine base directory for frame sequence: {file_path}") | |
| return None | |
| base_dir = self.base_path # This is the dataset root | |
| # Check the first path to determine the format | |
| first_frame = frame_paths[0].strip() if frame_paths else "" | |
| # If first frame is already an absolute path (from __getitem__ joining), | |
| # extract the base directory from it | |
| if os.path.isabs(first_frame): | |
| # Extract base directory from first frame path | |
| # First frame format: /path/to/dataset/frames/video_name/frame.png | |
| # We need to get /path/to/dataset | |
| parts = first_frame.split(os.sep) | |
| # Find 'frames' in the path and get everything before it | |
| if 'frames' in parts: | |
| frames_idx = parts.index('frames') | |
| base_dir = os.sep.join(parts[:frames_idx]) | |
| else: | |
| # Fallback: use self.base_path | |
| base_dir = self.base_path | |
| for frame_path in frame_paths: | |
| frame_path = frame_path.strip() | |
| if not frame_path: | |
| continue | |
| # Construct full path | |
| if os.path.isabs(frame_path): | |
| # Already absolute path (from __getitem__) | |
| full_frame_path = frame_path | |
| else: | |
| # Relative path - need to construct full path | |
| # Remove 'frames/' prefix if present (we'll add it consistently) | |
| if frame_path.startswith('frames/'): | |
| frame_path = frame_path[7:] # Remove 'frames/' prefix | |
| # Always join with base_dir + 'frames/' since base_dir is dataset root | |
| full_frame_path = os.path.join(base_dir, 'frames', frame_path) | |
| # Load individual frame | |
| if os.path.exists(full_frame_path): | |
| if self.is_image(full_frame_path): | |
| frame_data = self.load_image(full_frame_path) | |
| if frame_data: | |
| frames.extend(frame_data) | |
| else: | |
| warnings.warn(f"Frame is not an image: {full_frame_path}") | |
| else: | |
| warnings.warn(f"Frame not found: {full_frame_path}") | |
| if frames: | |
| return frames | |
| else: | |
| warnings.warn(f"No frames loaded from sequence: {file_path}") | |
| return None | |
| # Handle single file (image or video) | |
| if self.is_image(file_path): | |
| return self.load_image(file_path) | |
| elif self.is_video(file_path): | |
| return self.load_video(file_path) | |
| else: | |
| return None | |
| def __getitem__(self, data_id): | |
| data = self.data[data_id % len(self.data)].copy() | |
| for key in self.data_file_keys: | |
| if key in ["video_name", "start_frame", "end_frame"]: | |
| if "actions" in data: | |
| continue | |
| try: | |
| video_name = data.get("video_name") | |
| if video_name is None: | |
| warnings.warn(f"video_name is missing in metadata for data_id {data_id}. Skipping action loading.") | |
| continue | |
| if video_name.endswith(".mp4"): | |
| video_name = ".".join(video_name.split(".")[:-1]) | |
| if "_" in video_name: | |
| video_name = "_".join(video_name.split("_")[:4]) | |
| import json | |
| json_path = os.path.join(self.action_base_path, video_name + ".json") | |
| # Check if action file exists | |
| if not os.path.exists(json_path): | |
| warnings.warn(f"Action file does not exist: {json_path}. Skipping action loading for data_id {data_id}.") | |
| continue | |
| start_frame = data.get("start_frame") | |
| end_frame = data.get("end_frame") | |
| if start_frame is None or end_frame is None: | |
| warnings.warn(f"start_frame or end_frame is missing in metadata for data_id {data_id}. Skipping action loading.") | |
| continue | |
| json_data = json.load(open(json_path, "r"))['actions'] | |
| actions = [] | |
| current_yaw = 0.0 | |
| for frame_id in range(start_frame+1, end_frame+1): | |
| frame_str = str(frame_id) | |
| if frame_str not in json_data: | |
| warnings.warn(f"Frame {frame_id} not found in action file {json_path}. Skipping this frame.") | |
| continue | |
| action = json_data[frame_str] | |
| new_action = [0.0] * (2 + 2 + 3 + 1 + 2) | |
| if action['ws'] == 1: | |
| new_action[0] = 1 | |
| elif action['ws'] == 2: | |
| new_action[1] = 1 | |
| if action['ad'] == 1: | |
| new_action[2] = 1 | |
| elif action['ad'] == 2: | |
| new_action[3] = 1 | |
| if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: | |
| new_action[4] = 1 | |
| elif action['scs'] == 2: | |
| new_action[5] = 1 | |
| elif action['scs'] == 3: | |
| new_action[6] = 1 | |
| if action.get('collision', 0) == 1: | |
| new_action[7] = 1 | |
| new_action[0] = 0 | |
| new_action[1] = 0 | |
| new_action[2] = 0 | |
| new_action[3] = 0 | |
| pre_pitch = action.get('pre_pitch', 0.0) | |
| current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 | |
| current_yaw += action.get('yaw_delta', 0.0) * 15.0 | |
| new_action[8] = current_pitch | |
| new_action[9] = current_yaw | |
| actions.append(new_action) | |
| data["actions"] = actions | |
| except Exception as e: | |
| warnings.warn(f"Exception while loading actions for data_id {data_id}: {e}. Continuing without actions.") | |
| # Don't return None, just continue without actions | |
| continue | |
| elif key == "video": | |
| # Check if data[key] exists and is not None | |
| if key not in data or data[key] is None: | |
| warnings.warn(f"Video key '{key}' is missing or None in metadata for data_id {data_id}. Skipping this sample.") | |
| return None | |
| # Handle frame sequences (paths with '|' separator) | |
| video_path_str = str(data[key]) | |
| if '|' in video_path_str: | |
| # For frame sequences, pass the full path string to load_data | |
| # load_data will handle splitting and loading individual frames | |
| path = os.path.join(self.base_path, video_path_str) | |
| # Don't check path existence here for frame sequences | |
| # load_data will handle individual frame loading | |
| else: | |
| path = os.path.join(self.base_path, data[key]) | |
| # Check if path exists (only for single files) | |
| if not os.path.exists(path): | |
| warnings.warn(f"Video file does not exist: {path}. Skipping this sample.") | |
| return None | |
| try: | |
| data[key] = self.load_data(path) | |
| if data[key] is None: | |
| warnings.warn(f"Failed to load video file: {path}. load_data returned None.") | |
| return None | |
| except Exception as e: | |
| warnings.warn(f"Exception while loading video file {path}: {e}. Skipping this sample.") | |
| return None | |
| # In-context learning: sample context examples from dataset | |
| if self.enable_icl and len(self.data) > 1: | |
| context_frames_list = [] | |
| context_actions_list = [] | |
| # Sample random examples from dataset (excluding current one) | |
| current_idx = data_id % len(self.data) | |
| candidate_indices = [i for i in range(len(self.data)) if i != current_idx] | |
| if len(candidate_indices) > 0: | |
| num_samples = min(self.icl_num_examples, len(candidate_indices)) | |
| sampled_indices = random.sample(candidate_indices, num_samples) | |
| for sample_idx in sampled_indices: | |
| sample_data = self.data[sample_idx].copy() | |
| # Load video for context | |
| if "video" in self.data_file_keys and "video" in sample_data: | |
| video_path = os.path.join(self.base_path, sample_data["video"]) | |
| sample_video = self.load_data(video_path) | |
| if sample_video is not None and len(sample_video) >= self.icl_context_frames: | |
| # Sample context_frames from the video | |
| start_idx = random.randint(0, max(0, len(sample_video) - self.icl_context_frames)) | |
| context_frames = sample_video[start_idx:start_idx + self.icl_context_frames] | |
| context_frames_list.extend(context_frames) | |
| # Load corresponding actions if available | |
| if self.action_base_path is not None and "video_name" in sample_data: | |
| try: | |
| sample_video_name = sample_data["video_name"] | |
| if sample_video_name.endswith(".mp4"): | |
| sample_video_name = ".".join(sample_video_name.split(".")[:-1]) | |
| if "_" in sample_video_name: | |
| sample_video_name = "_".join(sample_video_name.split("_")[:4]) | |
| sample_json_path = os.path.join(self.action_base_path, sample_video_name + ".json") | |
| if os.path.exists(sample_json_path): | |
| sample_json_data = json.load(open(sample_json_path, "r"))['actions'] | |
| sample_start_frame = sample_data.get("start_frame", 0) | |
| sample_end_frame = sample_data.get("end_frame", len(sample_video)) | |
| # Get actions for the context frames | |
| context_actions = [] | |
| context_yaw = 0.0 | |
| for frame_idx in range(sample_start_frame + start_idx + 1, | |
| min(sample_start_frame + start_idx + self.icl_context_frames + 1, sample_end_frame + 1)): | |
| if str(frame_idx) in sample_json_data: | |
| action = sample_json_data[str(frame_idx)] | |
| new_action = [0.0] * (2 + 2 + 3 + 1 + 2) | |
| if action['ws'] == 1: | |
| new_action[0] = 1 | |
| elif action['ws'] == 2: | |
| new_action[1] = 1 | |
| if action['ad'] == 1: | |
| new_action[2] = 1 | |
| elif action['ad'] == 2: | |
| new_action[3] = 1 | |
| if action['scs'] == 1 and action.get("jump_invalid", 0) == 0: | |
| new_action[4] = 1 | |
| elif action['scs'] == 2: | |
| new_action[5] = 1 | |
| elif action['scs'] == 3: | |
| new_action[6] = 1 | |
| if action.get('collision', 0) == 1: | |
| new_action[7] = 1 | |
| new_action[0] = 0 | |
| new_action[1] = 0 | |
| new_action[2] = 0 | |
| new_action[3] = 0 | |
| pre_pitch = action.get('pre_pitch', 0.0) | |
| current_pitch = pre_pitch + action.get('pitch_delta', 0.0) * 15.0 | |
| context_yaw += action.get('yaw_delta', 0.0) * 15.0 | |
| new_action[8] = current_pitch | |
| new_action[9] = context_yaw | |
| context_actions.append(new_action) | |
| context_actions_list.extend(context_actions[:len(context_frames)]) | |
| except Exception as e: | |
| # If loading actions fails, just skip | |
| pass | |
| if context_frames_list: | |
| data["context_frames"] = context_frames_list | |
| if context_actions_list and len(context_actions_list) == len(context_frames_list): | |
| data["context_actions"] = context_actions_list | |
| return data | |
| def __len__(self): | |
| return len(self.data) * self.repeat | |
| def get_one_hot(action, range=2): | |
| one_hot = [0] * (range + 1) | |
| one_hot[action] = 1 | |
| return one_hot | |
| import numpy as np | |
| class CamVideoDataset(torch.utils.data.Dataset): | |
| """Dataset for Context-as-Memory camera pose conditioned training (ported from VWM). | |
| Loads 81 PNG frames from UE scenes with random temporal cropping and extracts | |
| corresponding camera poses as 12-dim relative RT vectors subsampled to match | |
| the 21 latent frames. | |
| """ | |
| def __init__( | |
| self, | |
| base_path=None, | |
| num_frames=81, | |
| height=None, width=None, | |
| max_pixels=1920*1080, | |
| height_division_factor=16, width_division_factor=16, | |
| repeat=1, | |
| args=None, | |
| cam_position_scale=None, | |
| ): | |
| if args is not None: | |
| base_path = args.dataset_base_path | |
| height = args.height | |
| width = args.width | |
| max_pixels = args.max_pixels | |
| num_frames = args.num_frames | |
| repeat = args.dataset_repeat | |
| cam_position_scale = getattr(args, "cam_position_scale", 0.01) | |
| self.use_condition_context_frames = getattr(args, "use_condition_context_frames", False) | |
| self.condition_first_frame = getattr(args, "condition_first_frame", False) | |
| self.condition_history_keyframes = getattr(args, "condition_history_keyframes", False) | |
| self.condition_use_camera_pose = getattr(args, "condition_use_camera_pose", True) | |
| self.num_condition_frames = getattr(args, "num_condition_frames", 1) | |
| self.condition_frame_mode = getattr(args, "condition_frame_mode", "first_frame_only") | |
| self.overlap_labels_root = getattr(args, "overlap_labels_root", None) | |
| self.condition_t2v_ratio = getattr(args, "condition_t2v_ratio", 0.10) | |
| self.condition_i2v_ratio = getattr(args, "condition_i2v_ratio", 0.10) | |
| else: | |
| self.use_condition_context_frames = False | |
| self.condition_first_frame = False | |
| self.condition_history_keyframes = False | |
| self.condition_use_camera_pose = True | |
| self.num_condition_frames = 1 | |
| self.condition_frame_mode = "first_frame_only" | |
| self.overlap_labels_root = None | |
| self.condition_t2v_ratio = 0.10 | |
| self.condition_i2v_ratio = 0.10 | |
| if cam_position_scale is None: | |
| cam_position_scale = 0.01 | |
| self.cam_position_scale = float(cam_position_scale) | |
| self.base_path = base_path | |
| self.frames_dir = os.path.join(base_path, "frames") | |
| self.jsons_dir = os.path.join(base_path, "jsons") | |
| self.num_frames = num_frames | |
| self.max_pixels = max_pixels | |
| self.height = height | |
| self.width = width | |
| self.height_division_factor = height_division_factor | |
| self.width_division_factor = width_division_factor | |
| self.repeat = repeat | |
| if height is not None and width is not None: | |
| self.dynamic_resolution = False | |
| else: | |
| self.dynamic_resolution = True | |
| captions_path = os.path.join(base_path, "captions.txt") | |
| self.scene_captions = {} | |
| with open(captions_path, "r") as f: | |
| for line in f: | |
| parts = line.strip().split("\t", 1) | |
| if len(parts) < 2: | |
| continue | |
| clip_path, caption = parts | |
| scene_name = "/".join(clip_path.split("/")[:-1]) | |
| fname = clip_path.split("/")[-1].replace(".mp4", "") | |
| clip_start = int(fname.split("_")[0]) | |
| if scene_name not in self.scene_captions: | |
| self.scene_captions[scene_name] = [] | |
| self.scene_captions[scene_name].append((clip_start, caption)) | |
| for scene_name in self.scene_captions: | |
| self.scene_captions[scene_name].sort(key=lambda x: x[0]) | |
| self.scene_names = sorted(self.scene_captions.keys()) | |
| self.pose_cache = {} | |
| self.overlap_cache = {} | |
| self.invalid_scenes = set() | |
| self.overlap_labels_root = self._resolve_overlap_labels_root(base_path, self.overlap_labels_root) | |
| self._validate_condition_config() | |
| total_scenes = len(self.scene_names) | |
| total_captions = sum(len(v) for v in self.scene_captions.values()) | |
| print(f"CamVideoDataset: {total_scenes} scenes, {total_captions} captions, " | |
| f"repeat={repeat}, cam_position_scale={self.cam_position_scale}, " | |
| f"effective length={total_scenes * repeat}") | |
| def _resolve_overlap_labels_root(self, base_path, overlap_labels_root): | |
| candidate_roots = [] | |
| if overlap_labels_root is not None: | |
| candidate_roots.append(overlap_labels_root) | |
| if base_path is not None: | |
| candidate_roots.append(os.path.join(base_path, "overlap_labels")) | |
| for root in candidate_roots: | |
| if root is not None and os.path.isdir(root): | |
| return root | |
| return overlap_labels_root | |
| def _validate_condition_config(self): | |
| if self.condition_t2v_ratio < 0 or self.condition_i2v_ratio < 0: | |
| raise ValueError("Condition sampling ratios must be non-negative.") | |
| if self.condition_t2v_ratio + self.condition_i2v_ratio >= 1.0: | |
| raise ValueError("condition_t2v_ratio + condition_i2v_ratio must be < 1.0.") | |
| needs_overlap = ( | |
| self.use_condition_context_frames | |
| and self.condition_frame_mode == "first_plus_overlap" | |
| and self.condition_history_keyframes | |
| and self.num_condition_frames > 1 | |
| ) | |
| if needs_overlap and (self.overlap_labels_root is None or not os.path.isdir(self.overlap_labels_root)): | |
| raise FileNotFoundError( | |
| "K-frame condition mode requires overlap_labels_root. " | |
| "Pass --overlap_labels_root or keep overlap_labels under dataset_base_path/overlap_labels." | |
| ) | |
| def _load_scene_poses(self, scene_name): | |
| if scene_name not in self.pose_cache: | |
| json_path = os.path.join(self.jsons_dir, scene_name + ".json") | |
| try: | |
| with open(json_path, "r") as f: | |
| data = json.load(f) | |
| except (FileNotFoundError, json.JSONDecodeError) as e: | |
| raise ValueError(f"Pose JSON for scene '{scene_name}' is missing or corrupt: {e}") | |
| if not isinstance(data, dict) or "CineCameraActor" not in data: | |
| raise ValueError( | |
| f"Pose JSON for scene '{scene_name}' lacks 'CineCameraActor' key " | |
| f"(found keys: {list(data.keys()) if isinstance(data, dict) else type(data).__name__})." | |
| ) | |
| cine = data["CineCameraActor"] | |
| if not isinstance(cine, dict) or len(cine) == 0: | |
| raise ValueError(f"Pose JSON for scene '{scene_name}' has empty 'CineCameraActor' entries.") | |
| self.pose_cache[scene_name] = cine | |
| return self.pose_cache[scene_name] | |
| def _find_nearest_caption(self, scene_name, start_frame): | |
| captions = self.scene_captions[scene_name] | |
| best_idx = 0 | |
| best_dist = abs(captions[0][0] - start_frame) | |
| for i, (clip_start, _) in enumerate(captions): | |
| dist = abs(clip_start - start_frame) | |
| if dist < best_dist: | |
| best_dist = dist | |
| best_idx = i | |
| return captions[best_idx][1] | |
| def _compute_rt(position, rotation): | |
| x, y, z = position | |
| yaw_rad = np.radians(rotation[2]) | |
| cos_y, sin_y = np.cos(yaw_rad), np.sin(yaw_rad) | |
| R = np.array([[cos_y, -sin_y, 0], [sin_y, cos_y, 0], [0, 0, 1]]) | |
| return [x, y, z] + R.flatten().tolist() | |
| def _to_relative_rt(rt_list, ref_rt): | |
| R_ref = np.array(ref_rt[3:]).reshape(3, 3) | |
| T_ref = np.array(ref_rt[:3]).reshape(3, 1) | |
| R_ref_inv = R_ref.T | |
| T_ref_inv = -R_ref_inv @ T_ref | |
| result = [] | |
| for rt in rt_list: | |
| R_i = np.array(rt[3:]).reshape(3, 3) | |
| T_i = np.array(rt[:3]).reshape(3, 1) | |
| R_new = R_ref_inv @ R_i | |
| T_new = R_ref_inv @ T_i + T_ref_inv | |
| result.append(T_new.flatten().tolist() + R_new.flatten().tolist()) | |
| return result | |
| def crop_and_resize(self, image, target_height, target_width): | |
| width, height = image.size | |
| scale = max(target_width / width, target_height / height) | |
| image = torchvision.transforms.functional.resize( | |
| image, | |
| (round(height * scale), round(width * scale)), | |
| interpolation=torchvision.transforms.InterpolationMode.BILINEAR | |
| ) | |
| image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) | |
| return image | |
| def get_height_width(self, image): | |
| if self.dynamic_resolution: | |
| width, height = image.size | |
| if width * height > self.max_pixels: | |
| scale = (width * height / self.max_pixels) ** 0.5 | |
| height, width = int(height / scale), int(width / scale) | |
| height = height // self.height_division_factor * self.height_division_factor | |
| width = width // self.width_division_factor * self.width_division_factor | |
| else: | |
| height, width = self.height, self.width | |
| return height, width | |
| def _load_resized_frame(self, scene_name, frame_index, target_height, target_width): | |
| frame_path = os.path.join(self.frames_dir, scene_name, f"{frame_index:04d}.png") | |
| img = Image.open(frame_path).convert("RGB") | |
| return self.crop_and_resize(img, target_height, target_width) | |
| def _load_overlap_frames(self, scene_name, frame_index): | |
| if self.overlap_labels_root is None: | |
| return [] | |
| cache_key = (scene_name, int(frame_index)) | |
| if cache_key not in self.overlap_cache: | |
| overlap_path = os.path.join(self.overlap_labels_root, scene_name, f"{int(frame_index)}.json") | |
| if not os.path.exists(overlap_path): | |
| self.overlap_cache[cache_key] = [] | |
| else: | |
| with open(overlap_path, "r") as f: | |
| overlap_data = json.load(f) | |
| overlaps = overlap_data.get("overlapping_frames", []) | |
| self.overlap_cache[cache_key] = [int(idx) for idx in overlaps] | |
| return self.overlap_cache[cache_key] | |
| def _compute_scene_rt(self, scene_name, frame_index): | |
| frame_data = self._load_scene_poses(scene_name)[str(int(frame_index))] | |
| raw_pos = frame_data["position"] | |
| pos = [float(p) * self.cam_position_scale for p in raw_pos] | |
| return self._compute_rt(pos, frame_data["rotation"]) | |
| def _sample_condition_mode(self): | |
| if not self.use_condition_context_frames: | |
| return "disabled" | |
| if ( | |
| self.condition_frame_mode != "first_plus_overlap" | |
| or not self.condition_history_keyframes | |
| or self.num_condition_frames <= 1 | |
| ): | |
| return "first_frame_only" | |
| sample = random.random() | |
| if sample < self.condition_t2v_ratio: | |
| return "text_only" | |
| if sample < self.condition_t2v_ratio + self.condition_i2v_ratio: | |
| return "first_frame_only" | |
| return "first_plus_overlap" | |
| def _sample_overlap_conditions(self, scene_name, start_frame, ref_rt, target_height, target_width, num_extra_conditions): | |
| if num_extra_conditions <= 0: | |
| return [], [], [] | |
| window_indices = set(range(start_frame, start_frame + self.num_frames)) | |
| target_candidates = list(range(start_frame + 1, start_frame + self.num_frames)) | |
| sampled_target_frames = random.sample(target_candidates, k=min(num_extra_conditions, len(target_candidates))) | |
| overlap_frames = [] | |
| overlap_indices = [] | |
| overlap_actions = [] | |
| used_condition_indices = set() | |
| for target_frame_idx in sampled_target_frames: | |
| candidate_indices = [ | |
| idx for idx in self._load_overlap_frames(scene_name, target_frame_idx) | |
| if idx not in window_indices and idx != target_frame_idx and idx not in used_condition_indices | |
| ] | |
| if len(candidate_indices) == 0: | |
| return None | |
| chosen_idx = random.choice(candidate_indices) | |
| used_condition_indices.add(chosen_idx) | |
| overlap_indices.append(chosen_idx) | |
| overlap_frames.append(self._load_resized_frame(scene_name, chosen_idx, target_height, target_width)) | |
| if self.condition_use_camera_pose: | |
| overlap_rt = self._compute_scene_rt(scene_name, chosen_idx) | |
| overlap_actions.append(self._to_relative_rt([overlap_rt], ref_rt)[0]) | |
| if len(overlap_frames) != num_extra_conditions: | |
| return None | |
| return overlap_frames, overlap_indices, overlap_actions | |
| def _try_get_sample(self, scene_name): | |
| cam_data = self._load_scene_poses(scene_name) | |
| max_start = len(cam_data) - self.num_frames | |
| if max_start < 0: | |
| raise ValueError(f"Scene {scene_name} has fewer than {self.num_frames} frames.") | |
| start_frame = random.randint(0, max_start) | |
| end_frame = start_frame + self.num_frames - 1 | |
| frames = [] | |
| for i in range(start_frame, end_frame + 1): | |
| frame_path = os.path.join(self.frames_dir, scene_name, f"{i:04d}.png") | |
| img = Image.open(frame_path).convert("RGB") | |
| img = self.crop_and_resize(img, *self.get_height_width(img)) | |
| frames.append(img) | |
| prompt = self._find_nearest_caption(scene_name, start_frame) | |
| rt_list_abs = [] | |
| for i in range(start_frame, end_frame + 1): | |
| key = str(i) | |
| if key not in cam_data: | |
| raise ValueError(f"Scene {scene_name} missing pose for frame {i}.") | |
| frame_data = cam_data[key] | |
| raw_pos = frame_data["position"] | |
| pos = [float(p) * self.cam_position_scale for p in raw_pos] | |
| rt = self._compute_rt(pos, frame_data["rotation"]) | |
| rt_list_abs.append(rt) | |
| rt_list = self._to_relative_rt(rt_list_abs, rt_list_abs[0]) | |
| pose_indices = list(range(0, self.num_frames, 4)) | |
| actions = [rt_list[i] for i in pose_indices] | |
| return { | |
| "video": frames, | |
| "prompt": prompt, | |
| "actions": actions, | |
| **self._build_condition_context_payload( | |
| frames=frames, | |
| scene_name=scene_name, | |
| start_frame=start_frame, | |
| ref_rt=rt_list_abs[0], | |
| actions=actions, | |
| ), | |
| } | |
| def __getitem__(self, data_id): | |
| n = len(self.scene_names) | |
| if n == 0: | |
| raise RuntimeError("CamVideoDataset has no scenes.") | |
| max_attempts = min(64, n) | |
| last_error = None | |
| for attempt in range(max_attempts): | |
| idx = (data_id + attempt) % n | |
| scene_name = self.scene_names[idx] | |
| if scene_name in self.invalid_scenes: | |
| continue | |
| try: | |
| return self._try_get_sample(scene_name) | |
| except (ValueError, FileNotFoundError, KeyError, OSError) as e: | |
| self.invalid_scenes.add(scene_name) | |
| last_error = e | |
| if attempt < 3 or attempt % 8 == 0: | |
| print( | |
| f"[CamVideoDataset] Skipping invalid scene '{scene_name}' " | |
| f"({type(e).__name__}: {e}); attempt {attempt + 1}/{max_attempts}" | |
| ) | |
| continue | |
| raise RuntimeError( | |
| f"CamVideoDataset: exhausted {max_attempts} attempts starting from index {data_id}; " | |
| f"last error: {type(last_error).__name__}: {last_error}" | |
| ) | |
| def _build_condition_context_payload(self, frames, scene_name, start_frame, ref_rt, actions): | |
| if not self.use_condition_context_frames: | |
| return {} | |
| payload = { | |
| "use_condition_context_frames": False, | |
| "condition_frames": [], | |
| "condition_frame_indices": [], | |
| "condition_source": None, | |
| "condition_actions": [], | |
| } | |
| condition_mode = self._sample_condition_mode() | |
| payload["condition_source"] = condition_mode | |
| if condition_mode == "text_only": | |
| return payload | |
| payload["use_condition_context_frames"] = True | |
| if self.condition_first_frame: | |
| payload["condition_frames"].append(frames[0]) | |
| payload["condition_frame_indices"].append(start_frame) | |
| payload["condition_source"] = "first_frame_only" | |
| if self.condition_use_camera_pose and actions: | |
| payload["condition_actions"].append(list(actions[0])) | |
| if ( | |
| condition_mode == "first_plus_overlap" | |
| and self.condition_history_keyframes | |
| and self.num_condition_frames > len(payload["condition_frames"]) | |
| ): | |
| num_extra_conditions = self.num_condition_frames - len(payload["condition_frames"]) | |
| overlap_payload = self._sample_overlap_conditions( | |
| scene_name=scene_name, | |
| start_frame=start_frame, | |
| ref_rt=ref_rt, | |
| target_height=frames[0].size[1], | |
| target_width=frames[0].size[0], | |
| num_extra_conditions=num_extra_conditions, | |
| ) | |
| if overlap_payload is None: | |
| return payload | |
| overlap_frames, overlap_indices, overlap_actions = overlap_payload | |
| payload["condition_frames"].extend(overlap_frames) | |
| payload["condition_frame_indices"].extend(overlap_indices) | |
| if self.condition_use_camera_pose: | |
| payload["condition_actions"].extend(overlap_actions) | |
| payload["condition_source"] = "first_plus_overlap" | |
| return payload | |
| def __len__(self): | |
| return len(self.scene_names) * self.repeat | |
| class DiffusionTrainingModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def to(self, *args, **kwargs): | |
| for name, model in self.named_children(): | |
| model.to(*args, **kwargs) | |
| return self | |
| def trainable_modules(self): | |
| trainable_modules = filter(lambda p: p.requires_grad, self.parameters()) | |
| return trainable_modules | |
| def trainable_param_names(self): | |
| trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters())) | |
| trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) | |
| return trainable_param_names | |
| def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None): | |
| if lora_alpha is None: | |
| lora_alpha = lora_rank | |
| lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules) | |
| model = inject_adapter_in_model(lora_config, model) | |
| return model | |
| def export_trainable_state_dict(self, state_dict, remove_prefix=None): | |
| trainable_param_names = self.trainable_param_names() | |
| state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names} | |
| if remove_prefix is not None: | |
| state_dict_ = {} | |
| for name, param in state_dict.items(): | |
| if name.startswith(remove_prefix): | |
| name = name[len(remove_prefix):] | |
| state_dict_[name] = param | |
| state_dict = state_dict_ | |
| return state_dict | |
| class ModelLogger: | |
| def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x): | |
| self.output_path = output_path | |
| self.remove_prefix_in_ckpt = remove_prefix_in_ckpt | |
| self.state_dict_converter = state_dict_converter | |
| def on_step_end(self, loss): | |
| pass | |
| def on_epoch_end(self, accelerator, model, epoch_id): | |
| accelerator.wait_for_everyone() | |
| if accelerator.is_main_process: | |
| state_dict = accelerator.get_state_dict(model) | |
| state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) | |
| state_dict = self.state_dict_converter(state_dict) | |
| os.makedirs(self.output_path, exist_ok=True) | |
| path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") | |
| accelerator.save(state_dict, path, safe_serialization=True) | |
| def launch_training_task( | |
| dataset: torch.utils.data.Dataset, | |
| model: DiffusionTrainingModule, | |
| model_logger: ModelLogger, | |
| optimizer: torch.optim.Optimizer, | |
| scheduler: torch.optim.lr_scheduler.LRScheduler, | |
| num_epochs: int = 1, | |
| gradient_accumulation_steps: int = 1, | |
| ): | |
| dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], drop_last=True) | |
| accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) | |
| model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler) | |
| for epoch_id in range(num_epochs): | |
| for data in tqdm(dataloader): | |
| with accelerator.accumulate(model): | |
| optimizer.zero_grad() | |
| loss = model(data) | |
| accelerator.backward(loss) | |
| optimizer.step() | |
| model_logger.on_step_end(loss) | |
| scheduler.step() | |
| model_logger.on_epoch_end(accelerator, model, epoch_id) | |
| def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"): | |
| dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], drop_last=True) | |
| accelerator = Accelerator() | |
| model, dataloader = accelerator.prepare(model, dataloader) | |
| os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True) | |
| for data_id, data in enumerate(tqdm(dataloader)): | |
| with torch.no_grad(): | |
| inputs = model.forward_preprocess(data) | |
| inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs} | |
| torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth")) | |
| def wan_parser(): | |
| parser = argparse.ArgumentParser(description="Simple example of a training script.") | |
| parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") | |
| parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") | |
| parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..") | |
| parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") | |
| parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") | |
| parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.") | |
| parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.") | |
| parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") | |
| parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") | |
| parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") | |
| parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") | |
| parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") | |
| parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") | |
| parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") | |
| parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") | |
| parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") | |
| parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") | |
| parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") | |
| parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") | |
| parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") | |
| parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") | |
| parser.add_argument("--use_condition_context_frames", default=False, action="store_true", help="Enable appended clean condition latents.") | |
| parser.add_argument("--condition_first_frame", default=False, action="store_true", help="Use the current clip first frame as a clean condition frame.") | |
| parser.add_argument("--condition_history_keyframes", default=False, action="store_true", help="Use overlap-based keyframes as conditions.") | |
| parser.add_argument("--condition_use_camera_pose", default=True, action="store_true", help="Inject camera pose for condition frames.") | |
| parser.add_argument("--num_condition_frames", type=int, default=1, help="Number of condition frames.") | |
| parser.add_argument("--condition_frame_mode", type=str, default="first_frame_only", help="Condition frame selection mode.") | |
| parser.add_argument("--overlap_labels_root", type=str, default=None, help="Root dir for overlap label JSONs.") | |
| parser.add_argument("--condition_t2v_ratio", type=float, default=0.10, help="Ratio of text-only condition samples.") | |
| parser.add_argument("--condition_i2v_ratio", type=float, default=0.10, help="Ratio of first-frame-only condition samples.") | |
| return parser | |
| def flux_parser(): | |
| parser = argparse.ArgumentParser(description="Simple example of a training script.") | |
| parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") | |
| parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") | |
| parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..") | |
| parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.") | |
| parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.") | |
| parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.") | |
| parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") | |
| parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") | |
| parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") | |
| parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") | |
| parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") | |
| parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") | |
| parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") | |
| parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") | |
| parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") | |
| parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") | |
| parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") | |
| parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") | |
| parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.") | |
| parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.") | |
| parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") | |
| parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") | |
| return parser | |