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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
@staticmethod
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]
@staticmethod
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()
@staticmethod
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
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