import argparse import gc from importlib.util import find_spec import random import os import re import time import copy from typing import Tuple, Optional, List, Any, Dict import torch from safetensors.torch import load_file, save_file from safetensors import safe_open from PIL import Image import numpy as np from tqdm import tqdm from musubi_tuner.networks import lora_framepack from musubi_tuner.hunyuan_model.autoencoder_kl_causal_3d import AutoencoderKLCausal3D from musubi_tuner.frame_pack import hunyuan from musubi_tuner.frame_pack.hunyuan_video_packed import load_packed_model from musubi_tuner.frame_pack.hunyuan_video_packed_inference import HunyuanVideoTransformer3DModelPackedInference from musubi_tuner.frame_pack.utils import crop_or_pad_yield_mask, soft_append_bcthw from musubi_tuner.frame_pack.clip_vision import hf_clip_vision_encode from musubi_tuner.frame_pack.k_diffusion_hunyuan import sample_hunyuan from musubi_tuner.dataset import image_video_dataset from musubi_tuner.utils import model_utils from musubi_tuner.utils.lora_utils import filter_lora_state_dict lycoris_available = find_spec("lycoris") is not None from musubi_tuner.utils.device_utils import clean_memory_on_device from musubi_tuner.hv_generate_video import ( get_time_flag, save_images_grid, save_videos_grid, synchronize_device, setup_parser_compile, ) from musubi_tuner.wan_generate_video import merge_lora_weights from musubi_tuner.frame_pack.framepack_utils import load_vae, load_text_encoder1, load_text_encoder2, load_image_encoders import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def parse_section_strings(input_string: str) -> dict[int, str]: section_strings = {} if input_string is None: # handle None input for image_path etc. return section_strings if ";;;" in input_string: split_section_strings = input_string.split(";;;") for section_str in split_section_strings: if ":" not in section_str: start = end = 0 section_str = section_str.strip() else: index_str, section_str = section_str.split(":", 1) index_str = index_str.strip() section_str = section_str.strip() m = re.match(r"^(-?\d+)(-\d+)?$", index_str) if m: start = int(m.group(1)) end = int(m.group(2)[1:]) if m.group(2) is not None else start else: start = end = 0 section_str = section_str.strip() for i in range(start, end + 1): section_strings[i] = section_str else: section_strings[0] = input_string if not section_strings: # If input_string was empty or only separators return section_strings if 0 not in section_strings: indices = list(section_strings.keys()) if all(i < 0 for i in indices): section_index = min(indices) else: section_index = min(i for i in indices if i >= 0) section_strings[0] = section_strings[section_index] return section_strings class GenerationSettings: def __init__(self, device: torch.device, dit_weight_dtype: Optional[torch.dtype] = None): self.device = device self.dit_weight_dtype = dit_weight_dtype # not used currently because model may be optimized def parse_args() -> argparse.Namespace: """parse command line arguments""" parser = argparse.ArgumentParser(description="FramePack inference script") # WAN arguments # parser.add_argument("--ckpt_dir", type=str, default=None, help="The path to the checkpoint directory (Wan 2.1 official).") parser.add_argument( "--sample_solver", type=str, default="unipc", choices=["unipc", "dpm++", "vanilla"], help="The solver used to sample." ) parser.add_argument("--dit", type=str, default=None, help="DiT directory or path") parser.add_argument( "--disable_numpy_memmap", action="store_true", help="Disable numpy memmap when loading safetensors. Default is False." ) parser.add_argument("--vae", type=str, default=None, help="VAE directory or path") parser.add_argument("--text_encoder1", type=str, required=True, help="Text Encoder 1 directory or path") parser.add_argument("--text_encoder2", type=str, required=True, help="Text Encoder 2 directory or path") parser.add_argument("--image_encoder", type=str, required=True, help="Image Encoder directory or path") parser.add_argument("--f1", action="store_true", help="Use F1 sampling method") # LoRA parser.add_argument("--lora_weight", type=str, nargs="*", required=False, default=None, help="LoRA weight path") parser.add_argument("--lora_multiplier", type=float, nargs="*", default=1.0, help="LoRA multiplier") parser.add_argument("--include_patterns", type=str, nargs="*", default=None, help="LoRA module include patterns") parser.add_argument("--exclude_patterns", type=str, nargs="*", default=None, help="LoRA module exclude patterns") parser.add_argument( "--save_merged_model", type=str, default=None, help="Save merged model to path. If specified, no inference will be performed.", ) # inference parser.add_argument( "--prompt", type=str, default=None, help="prompt for generation. If `;;;` is used, it will be split into sections. Example: `section_index:prompt` or " "`section_index:prompt;;;section_index:prompt;;;...`, section_index can be `0` or `-1` or `0-2`, `-1` means last section, `0-2` means from 0 to 2 (inclusive).", ) parser.add_argument( "--negative_prompt", type=str, default=None, help="negative prompt for generation, default is empty string. should not change.", ) parser.add_argument( "--custom_system_prompt", type=str, default=None, help="Custom system prompt for LLM. If specified, it will override the default system prompt. See hunyuan_model/text_encoder.py for the default system prompt.", ) parser.add_argument("--video_size", type=int, nargs=2, default=[256, 256], help="video size, height and width") parser.add_argument("--video_seconds", type=float, default=5.0, help="video length, default is 5.0 seconds") parser.add_argument( "--video_sections", type=int, default=None, help="number of video sections, Default is None (auto calculate from video seconds)", ) parser.add_argument( "--one_frame_inference", type=str, default=None, help="one frame inference, default is None, comma separated values from 'no_2x', 'no_4x', 'no_post', 'control_indices' and 'target_index'.", ) parser.add_argument( "--one_frame_auto_resize", action="store_true", help="Automatically adjust height and width based on control image size and given size for one frame inference. Default is False.", ) parser.add_argument( "--control_image_path", type=str, default=None, nargs="*", help="path to control (reference) image for one frame inference." ) parser.add_argument( "--control_image_mask_path", type=str, default=None, nargs="*", help="path to control (reference) image mask for one frame inference.", ) parser.add_argument("--fps", type=int, default=30, help="video fps, default is 30") parser.add_argument("--infer_steps", type=int, default=25, help="number of inference steps, default is 25") parser.add_argument("--save_path", type=str, required=True, help="path to save generated video") parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.") # parser.add_argument( # "--cpu_noise", action="store_true", help="Use CPU to generate noise (compatible with ComfyUI). Default is False." # ) parser.add_argument("--latent_window_size", type=int, default=9, help="latent window size, default is 9. should not change.") parser.add_argument( "--embedded_cfg_scale", type=float, default=10.0, help="Embeded CFG scale (distilled CFG Scale), default is 10.0" ) parser.add_argument( "--guidance_scale", type=float, default=1.0, help="Guidance scale for classifier free guidance. Default is 1.0 (no guidance), should not change.", ) parser.add_argument("--guidance_rescale", type=float, default=0.0, help="CFG Re-scale, default is 0.0. Should not change.") # parser.add_argument("--video_path", type=str, default=None, help="path to video for video2video inference") parser.add_argument( "--image_path", type=str, default=None, help="path to image for image2video inference. If `;;;` is used, it will be used as section images. The notation is same as `--prompt`.", ) parser.add_argument("--end_image_path", type=str, default=None, help="path to end image for image2video inference") parser.add_argument( "--latent_paddings", type=str, default=None, help="latent paddings for each section, comma separated values. default is None (FramePack default paddings)", ) # parser.add_argument( # "--control_path", # type=str, # default=None, # help="path to control video for inference with controlnet. video file or directory with images", # ) # parser.add_argument("--trim_tail_frames", type=int, default=0, help="trim tail N frames from the video before saving") # Flow Matching parser.add_argument( "--flow_shift", type=float, default=None, help="Shift factor for flow matching schedulers. Default is None (FramePack default).", ) parser.add_argument("--fp8", action="store_true", help="use fp8 for DiT model") parser.add_argument("--fp8_scaled", action="store_true", help="use scaled fp8 for DiT, only for fp8") # parser.add_argument("--fp8_fast", action="store_true", help="Enable fast FP8 arithmetic (RTX 4XXX+), only for fp8_scaled") parser.add_argument( "--rope_scaling_factor", type=float, default=0.5, help="RoPE scaling factor for high resolution (H/W), default is 0.5" ) parser.add_argument( "--rope_scaling_timestep_threshold", type=int, default=None, help="RoPE scaling timestep threshold, default is None (disable), if set, RoPE scaling will be applied only for timesteps >= threshold, around 800 is good starting point", ) parser.add_argument("--fp8_llm", action="store_true", help="use fp8 for Text Encoder 1 (LLM)") parser.add_argument( "--device", type=str, default=None, help="device to use for inference. If None, use CUDA if available, otherwise use CPU" ) parser.add_argument( "--attn_mode", type=str, default="torch", choices=["flash", "torch", "sageattn", "xformers", "sdpa"], # "flash2", "flash3", help="attention mode", ) parser.add_argument( "--vae_tiling", action="store_true", help="enable spatial tiling for VAE, default is False. If vae_spatial_tile_sample_min_size is set, this is automatically enabled", ) parser.add_argument("--vae_chunk_size", type=int, default=None, help="chunk size for CausalConv3d in VAE") parser.add_argument( "--vae_spatial_tile_sample_min_size", type=int, default=None, help="spatial tile sample min size for VAE, default 256" ) parser.add_argument("--bulk_decode", action="store_true", help="decode all frames at once") parser.add_argument("--blocks_to_swap", type=int, default=0, help="number of blocks to swap in the model") parser.add_argument( "--use_pinned_memory_for_block_swap", action="store_true", help="use pinned memory for block swapping, which may speed up data transfer between CPU and GPU but uses more shared GPU memory on Windows", ) parser.add_argument( "--output_type", type=str, default="video", choices=["video", "images", "latent", "both", "latent_images"], help="output type", ) parser.add_argument("--no_metadata", action="store_true", help="do not save metadata") parser.add_argument("--latent_path", type=str, nargs="*", default=None, help="path to latent for decode. no inference") parser.add_argument( "--lycoris", action="store_true", help=f"use lycoris for inference{'' if lycoris_available else ' (not available)'}" ) setup_parser_compile(parser) # MagCache parser.add_argument( "--magcache_mag_ratios", type=str, default=None, help="Enable MagCache for inference with specified ratios, comma separated values. Example: `1.0,1.06971,1.29073,...`. " + "It is recommended to use same count of ratios as as inference steps." + "Default is None (disabled), if `0` is specified, it will use default ratios for 50 steps.", ) parser.add_argument("--magcache_retention_ratio", type=float, default=0.2, help="MagCache retention ratio, default is 0.2") parser.add_argument("--magcache_threshold", type=float, default=0.24, help="MagCache threshold, default is 0.24") parser.add_argument("--magcache_k", type=int, default=6, help="MagCache k value, default is 6") parser.add_argument("--magcache_calibration", action="store_true", help="Enable MagCache calibration") # New arguments for batch and interactive modes parser.add_argument("--from_file", type=str, default=None, help="Read prompts from a file") parser.add_argument("--interactive", action="store_true", help="Interactive mode: read prompts from console") args = parser.parse_args() # Validate arguments if args.from_file and args.interactive: raise ValueError("Cannot use both --from_file and --interactive at the same time") if args.latent_path is None or len(args.latent_path) == 0: if args.prompt is None and not args.from_file and not args.interactive: raise ValueError("Either --prompt, --from_file or --interactive must be specified") if args.lycoris and not lycoris_available: raise ValueError("install lycoris: https://github.com/KohakuBlueleaf/LyCORIS") return args def parse_prompt_line(line: str) -> Dict[str, Any]: """Parse a prompt line into a dictionary of argument overrides Args: line: Prompt line with options Returns: Dict[str, Any]: Dictionary of argument overrides """ # TODO common function with hv_train_network.line_to_prompt_dict parts = line.split(" --") prompt = parts[0].strip() # Create dictionary of overrides overrides = {"prompt": prompt} # Initialize control_image_path and control_image_mask_path as a list to accommodate multiple paths overrides["control_image_path"] = [] overrides["control_image_mask_path"] = [] for part in parts[1:]: if not part.strip(): continue option_parts = part.split(" ", 1) option = option_parts[0].strip() value = option_parts[1].strip() if len(option_parts) > 1 else "" # Map options to argument names if option == "w": overrides["video_size_width"] = int(value) elif option == "h": overrides["video_size_height"] = int(value) elif option == "f": overrides["video_seconds"] = float(value) elif option == "d": overrides["seed"] = int(value) elif option == "s": overrides["infer_steps"] = int(value) elif option == "g" or option == "l": overrides["guidance_scale"] = float(value) elif option == "fs": overrides["flow_shift"] = float(value) elif option == "i": overrides["image_path"] = value # elif option == "im": # overrides["image_mask_path"] = value # elif option == "cn": # overrides["control_path"] = value elif option == "n": overrides["negative_prompt"] = value elif option == "vs": # video_sections overrides["video_sections"] = int(value) elif option == "ei": # end_image_path overrides["end_image_path"] = value elif option == "ci": # control_image_path overrides["control_image_path"].append(value) elif option == "cim": # control_image_mask_path overrides["control_image_mask_path"].append(value) elif option == "of": # one_frame_inference overrides["one_frame_inference"] = value # magcache elif option == "mcrr": # magcache retention ratio overrides["magcache_retention_ratio"] = float(value) elif option == "mct": # magcache threshold overrides["magcache_threshold"] = float(value) elif option == "mck": # magcache k overrides["magcache_k"] = int(value) # If no control_image_path was provided, remove the empty list if not overrides["control_image_path"]: del overrides["control_image_path"] if not overrides["control_image_mask_path"]: del overrides["control_image_mask_path"] return overrides def apply_overrides(args: argparse.Namespace, overrides: Dict[str, Any]) -> argparse.Namespace: """Apply overrides to args Args: args: Original arguments overrides: Dictionary of overrides Returns: argparse.Namespace: New arguments with overrides applied """ args_copy = copy.deepcopy(args) for key, value in overrides.items(): if key == "video_size_width": args_copy.video_size[1] = value elif key == "video_size_height": args_copy.video_size[0] = value else: setattr(args_copy, key, value) return args_copy def check_inputs(args: argparse.Namespace) -> Tuple[int, int, int]: """Validate video size and length Args: args: command line arguments Returns: Tuple[int, int, float]: (height, width, video_seconds) """ height = args.video_size[0] width = args.video_size[1] video_seconds = args.video_seconds if args.video_sections is not None: video_seconds = (args.video_sections * (args.latent_window_size * 4) + 1) / args.fps if args.one_frame_inference is not None and args.one_frame_auto_resize and args.control_image_path is not None: with Image.open(args.control_image_path[0]) as control_image: width, height = image_video_dataset.BucketSelector.calculate_bucket_resolution( control_image.size, (width, height), architecture=image_video_dataset.ARCHITECTURE_FRAMEPACK ) logger.info(f"Adjusted image size to {width}x{height} based on control image size {control_image.size}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") return height, width, video_seconds # region DiT model def load_dit_model(args: argparse.Namespace, device: torch.device) -> HunyuanVideoTransformer3DModelPackedInference: """load DiT model Args: args: command line arguments device: device to use Returns: HunyuanVideoTransformer3DModelPackedInference: DiT model """ # If LyCORIS is enabled, we will load the model to CPU and then merge LoRA weights (static method) loading_device = "cpu" if args.blocks_to_swap == 0 and not args.lycoris: loading_device = device # load LoRA weights if not args.lycoris and args.lora_weight is not None and len(args.lora_weight) > 0: lora_weights_list = [] for lora_weight in args.lora_weight: logger.info(f"Loading LoRA weight from: {lora_weight}") lora_sd = load_file(lora_weight) # load on CPU, dtype is as is lora_sd = convert_lora_for_framepack(lora_sd) lora_sd = filter_lora_state_dict(lora_sd, args.include_patterns, args.exclude_patterns) lora_weights_list.append(lora_sd) else: lora_weights_list = None # load DiT model logger.info(f"Loading DiT model from: {args.dit}") model: HunyuanVideoTransformer3DModelPackedInference = load_packed_model( device, args.dit, args.attn_mode, loading_device, args.fp8_scaled and not args.lycoris, for_inference=True, lora_weights_list=lora_weights_list, lora_multipliers=args.lora_multiplier, disable_numpy_memmap=args.disable_numpy_memmap, ) # apply RoPE scaling factor if args.rope_scaling_timestep_threshold is not None: logger.info( f"Applying RoPE scaling factor {args.rope_scaling_factor} for timesteps >= {args.rope_scaling_timestep_threshold}" ) model.enable_rope_scaling(args.rope_scaling_timestep_threshold, args.rope_scaling_factor) # magcache initialize_magcache(args, model) if args.lycoris: # merge LoRA weights statically if args.lora_weight is not None and len(args.lora_weight) > 0: # ugly hack to common merge_lora_weights function merge_lora_weights( lora_framepack, model, args.lora_weight, args.lora_multiplier, args.include_patterns, args.exclude_patterns, device, lycoris=True, save_merged_model=args.save_merged_model, converter=convert_lora_for_framepack, ) if args.fp8_scaled: state_dict = model.state_dict() # bf16 state dict # if no blocks to swap, we can move the weights to GPU after optimization on GPU (omit redundant CPU->GPU copy) move_to_device = args.blocks_to_swap == 0 # if blocks_to_swap > 0, we will keep the model on CPU state_dict = model.fp8_optimization(state_dict, device, move_to_device, use_scaled_mm=False) # args.fp8_fast) info = model.load_state_dict(state_dict, strict=True, assign=True) logger.info(f"Loaded FP8 optimized weights: {info}") # if we only want to save the model, we can skip the rest if args.save_merged_model: return model if not args.fp8_scaled: # simple cast to dit_dtype target_dtype = None # load as-is (dit_weight_dtype == dtype of the weights in state_dict) target_device = None if args.fp8: target_dtype = torch.float8e4m3fn if args.blocks_to_swap == 0: logger.info(f"Move model to device: {device}") target_device = device if target_device is not None and target_dtype is not None: model.to(target_device, target_dtype) # move and cast at the same time. this reduces redundant copy operations if args.blocks_to_swap > 0: logger.info(f"Enable swap {args.blocks_to_swap} blocks to CPU from device: {device}") model.enable_block_swap( args.blocks_to_swap, device, supports_backward=False, use_pinned_memory=args.use_pinned_memory_for_block_swap ) model.move_to_device_except_swap_blocks(device) model.prepare_block_swap_before_forward() else: # make sure the model is on the right device model.to(device) if args.compile: model = model_utils.compile_transformer( args, model, [model.transformer_blocks, model.single_transformer_blocks], disable_linear=args.blocks_to_swap > 0 ) model.eval().requires_grad_(False) clean_memory_on_device(device) return model # endregion def decode_latent( latent_window_size: int, total_latent_sections: int, bulk_decode: bool, vae: AutoencoderKLCausal3D, latent: torch.Tensor, device: torch.device, one_frame_inference_mode: bool = False, ) -> torch.Tensor: logger.info("Decoding video...") if latent.ndim == 4: latent = latent.unsqueeze(0) # add batch dimension vae.to(device) if not bulk_decode and not one_frame_inference_mode: latent_window_size = latent_window_size # default is 9 # total_latent_sections = (args.video_seconds * 30) / (latent_window_size * 4) # total_latent_sections = int(max(round(total_latent_sections), 1)) num_frames = latent_window_size * 4 - 3 latents_to_decode = [] latent_frame_index = 0 for i in range(total_latent_sections - 1, -1, -1): is_last_section = i == total_latent_sections - 1 generated_latent_frames = (num_frames + 3) // 4 + (1 if is_last_section else 0) section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2) section_latent = latent[:, :, latent_frame_index : latent_frame_index + section_latent_frames, :, :] if section_latent.shape[2] > 0: latents_to_decode.append(section_latent) latent_frame_index += generated_latent_frames latents_to_decode = latents_to_decode[::-1] # reverse the order of latents to decode history_pixels = None for latent in tqdm(latents_to_decode): if history_pixels is None: history_pixels = hunyuan.vae_decode(latent, vae).cpu() else: overlapped_frames = latent_window_size * 4 - 3 current_pixels = hunyuan.vae_decode(latent, vae).cpu() history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames) clean_memory_on_device(device) else: # bulk decode logger.info("Bulk decoding or one frame inference") if not one_frame_inference_mode: history_pixels = hunyuan.vae_decode(latent, vae).cpu() # normal else: # one frame inference history_pixels = [hunyuan.vae_decode(latent[:, :, i : i + 1, :, :], vae).cpu() for i in range(latent.shape[2])] history_pixels = torch.cat(history_pixels, dim=2) vae.to("cpu") logger.info(f"Decoded. Pixel shape {history_pixels.shape}") return history_pixels[0] # remove batch dimension def prepare_image_inputs( args: argparse.Namespace, device: torch.device, vae: AutoencoderKLCausal3D, shared_models: Optional[Dict] = None, ) -> Dict[str, Any]: """Prepare image-related inputs for I2V: VAE encoding and image encoder features.""" height, width, video_seconds = check_inputs(args) # prepare image def preprocess_image(image_path: str): image = Image.open(image_path) if image.mode == "RGBA": alpha = image.split()[-1] else: alpha = None image = image.convert("RGB") image_np = np.array(image) # PIL to numpy, HWC image_np = image_video_dataset.resize_image_to_bucket(image_np, (width, height)) image_tensor = torch.from_numpy(image_np).float() / 127.5 - 1.0 # -1 to 1.0, HWC image_tensor = image_tensor.permute(2, 0, 1)[None, :, None] # HWC -> CHW -> NCFHW, N=1, C=3, F=1 return image_tensor, image_np, alpha section_image_paths = parse_section_strings(args.image_path) section_images = {} if section_image_paths: for index, image_path in section_image_paths.items(): img_tensor, img_np, _ = preprocess_image(image_path) section_images[index] = (img_tensor, img_np) else: # image_path should be given, if not, we create a placeholder image (black image) placeholder_img_np = np.zeros((height, width, 3), dtype=np.uint8) # Placeholder placeholder_img_tensor = torch.zeros(1, 3, 1, height, width) section_images[0] = (placeholder_img_tensor, placeholder_img_np) section_image_paths[0] = "placeholder_image" # check end image if args.end_image_path is not None: end_image_tensor, _, _ = preprocess_image(args.end_image_path) else: end_image_tensor = None # check control images if args.control_image_path is not None and len(args.control_image_path) > 0: control_image_tensors = [] control_mask_images = [] for ctrl_image_path in args.control_image_path: control_image_tensor, _, control_mask = preprocess_image(ctrl_image_path) control_image_tensors.append(control_image_tensor) control_mask_images.append(control_mask) else: control_image_tensors = None # Keep as None if not provided control_mask_images = None # load image encoder # VAE is passed as an argument, assume it's on the correct device or handled by caller if shared_models is not None and "feature_extractor" in shared_models and "image_encoder" in shared_models: feature_extractor, image_encoder = shared_models["feature_extractor"], shared_models["image_encoder"] else: feature_extractor, image_encoder = load_image_encoders(args) image_encoder_original_device = image_encoder.device image_encoder.to(device) section_image_encoder_last_hidden_states = {} for index, (img_tensor, img_np) in section_images.items(): with torch.no_grad(): image_encoder_output = hf_clip_vision_encode(img_np, feature_extractor, image_encoder) image_encoder_last_hidden_state = image_encoder_output.last_hidden_state.cpu() section_image_encoder_last_hidden_states[index] = image_encoder_last_hidden_state if not (shared_models and "image_encoder" in shared_models): # if loaded locally del image_encoder, feature_extractor else: # if shared, move back to original device (likely CPU) image_encoder.to(image_encoder_original_device) clean_memory_on_device(device) # VAE encoding logger.info("Encoding image to latent space with VAE") vae_original_device = vae.device vae.to(device) section_start_latents = {} for index, (img_tensor, img_np) in section_images.items(): start_latent = hunyuan.vae_encode(img_tensor.to(device), vae).cpu() # ensure tensor is on device section_start_latents[index] = start_latent end_latent = hunyuan.vae_encode(end_image_tensor.to(device), vae).cpu() if end_image_tensor is not None else None control_latents = None if control_image_tensors is not None: control_latents = [] for ctrl_image_tensor in control_image_tensors: control_latent = hunyuan.vae_encode(ctrl_image_tensor.to(device), vae).cpu() control_latents.append(control_latent) vae.to(vae_original_device) # Move VAE back to its original device clean_memory_on_device(device) arg_c_img = {} for index in section_images.keys(): image_encoder_last_hidden_state = section_image_encoder_last_hidden_states[index] start_latent = section_start_latents[index] arg_c_img_i = { "image_encoder_last_hidden_state": image_encoder_last_hidden_state, "start_latent": start_latent, "image_path": section_image_paths.get(index, "placeholder_image"), } arg_c_img[index] = arg_c_img_i return { "height": height, "width": width, "video_seconds": video_seconds, "context_img": arg_c_img, "end_latent": end_latent, "control_latents": control_latents, "control_mask_images": control_mask_images, } def prepare_text_inputs( args: argparse.Namespace, device: torch.device, shared_models: Optional[Dict] = None, ) -> Dict[str, Any]: """Prepare text-related inputs for I2V: LLM and TextEncoder encoding.""" n_prompt = args.negative_prompt if args.negative_prompt else "" section_prompts = parse_section_strings(args.prompt if args.prompt else " ") # Ensure prompt is not None # load text encoder: conds_cache holds cached encodings for prompts without padding conds_cache = {} if shared_models is not None: tokenizer1, text_encoder1 = shared_models.get("tokenizer1"), shared_models.get("text_encoder1") tokenizer2, text_encoder2 = shared_models.get("tokenizer2"), shared_models.get("text_encoder2") if "conds_cache" in shared_models: # Use shared cache if available conds_cache = shared_models["conds_cache"] # text_encoder1 and text_encoder2 are on device (batched inference) or CPU (interactive inference) else: # Load if not in shared_models tokenizer1, text_encoder1 = load_text_encoder1(args, args.fp8_llm, device) # Load to GPU tokenizer2, text_encoder2 = load_text_encoder2(args) # Load to CPU text_encoder2.to(device) # Move text_encoder2 to the same device as text_encoder1 # Store original devices to move back later if they were shared. This does nothing if shared_models is None text_encoder1_original_device = text_encoder1.device if text_encoder1 else None text_encoder2_original_device = text_encoder2.device if text_encoder2 else None logger.info("Encoding prompt with Text Encoders") llama_vecs = {} llama_attention_masks = {} clip_l_poolers = {} # Ensure text_encoder1 and text_encoder2 are not None before proceeding if not text_encoder1 or not text_encoder2 or not tokenizer1 or not tokenizer2: raise ValueError("Text encoders or tokenizers are not loaded properly.") # Define a function to move models to device if needed # This is to avoid moving models if not needed, especially in interactive mode model_is_moved = False def move_models_to_device_if_needed(): nonlocal model_is_moved nonlocal shared_models if model_is_moved: return model_is_moved = True logger.info(f"Moving DiT and Text Encoders to appropriate device: {device} or CPU") if shared_models and "model" in shared_models: # DiT model is shared if args.blocks_to_swap > 0: logger.info("Waiting for 5 seconds to finish block swap") time.sleep(5) model = shared_models["model"] model.to("cpu") clean_memory_on_device(device) # clean memory on device before moving models text_encoder1.to(device) text_encoder2.to(device) with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad(): for index, prompt in section_prompts.items(): if prompt in conds_cache: llama_vec, clip_l_pooler = conds_cache[prompt] else: move_models_to_device_if_needed() llama_vec, clip_l_pooler = hunyuan.encode_prompt_conds( prompt, text_encoder1, text_encoder2, tokenizer1, tokenizer2, custom_system_prompt=args.custom_system_prompt ) llama_vec = llama_vec.cpu() clip_l_pooler = clip_l_pooler.cpu() conds_cache[prompt] = (llama_vec, clip_l_pooler) llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512) llama_vecs[index] = llama_vec llama_attention_masks[index] = llama_attention_mask clip_l_poolers[index] = clip_l_pooler if args.guidance_scale == 1.0: # llama_vecs[0] should always exist because prompt is guaranteed to be non-empty first_llama_vec = llama_vecs.get(0) # this is cropped or padded, but it's okay for null context first_clip_l_pooler = clip_l_poolers.get(0) llama_vec_n, clip_l_pooler_n = torch.zeros_like(first_llama_vec), torch.zeros_like(first_clip_l_pooler) else: with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad(): if n_prompt in conds_cache: llama_vec_n, clip_l_pooler_n = conds_cache[n_prompt] else: move_models_to_device_if_needed() llama_vec_n, clip_l_pooler_n = hunyuan.encode_prompt_conds( n_prompt, text_encoder1, text_encoder2, tokenizer1, tokenizer2, custom_system_prompt=args.custom_system_prompt ) llama_vec_n = llama_vec_n.cpu() clip_l_pooler_n = clip_l_pooler_n.cpu() conds_cache[n_prompt] = (llama_vec_n, clip_l_pooler_n) llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512) if not (shared_models and "text_encoder1" in shared_models): # if loaded locally del tokenizer1, text_encoder1, tokenizer2, text_encoder2 gc.collect() # transformer==4.54.1 seems to need this to free memory else: # if shared, move back to original device (likely CPU) if text_encoder1: text_encoder1.to(text_encoder1_original_device) if text_encoder2: text_encoder2.to(text_encoder2_original_device) clean_memory_on_device(device) arg_c = {} for index in llama_vecs.keys(): llama_vec = llama_vecs[index] llama_attention_mask = llama_attention_masks[index] clip_l_pooler = clip_l_poolers[index] arg_c_i = { "llama_vec": llama_vec, "llama_attention_mask": llama_attention_mask, "clip_l_pooler": clip_l_pooler, "prompt": section_prompts[index], } arg_c[index] = arg_c_i arg_null = { "llama_vec": llama_vec_n, "llama_attention_mask": llama_attention_mask_n, "clip_l_pooler": clip_l_pooler_n, } return { "context": arg_c, "context_null": arg_null, } def prepare_i2v_inputs( args: argparse.Namespace, device: torch.device, vae: AutoencoderKLCausal3D, # VAE is now explicitly passed shared_models: Optional[Dict] = None, ) -> Tuple[int, int, float, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[dict, dict]]: """Prepare inputs for I2V by calling image and text preparation functions.""" image_data = prepare_image_inputs(args, device, vae, shared_models) text_data = prepare_text_inputs(args, device, shared_models) return ( image_data["height"], image_data["width"], image_data["video_seconds"], text_data["context"], text_data["context_null"], image_data["context_img"], image_data["end_latent"], image_data["control_latents"], image_data["control_mask_images"], ) # def setup_scheduler(args: argparse.Namespace, config, device: torch.device) -> Tuple[Any, torch.Tensor]: # """setup scheduler for sampling # Args: # args: command line arguments # config: model configuration # device: device to use # Returns: # Tuple[Any, torch.Tensor]: (scheduler, timesteps) # """ # if args.sample_solver == "unipc": # scheduler = FlowUniPCMultistepScheduler(num_train_timesteps=config.num_train_timesteps, shift=1, use_dynamic_shifting=False) # scheduler.set_timesteps(args.infer_steps, device=device, shift=args.flow_shift) # timesteps = scheduler.timesteps # elif args.sample_solver == "dpm++": # scheduler = FlowDPMSolverMultistepScheduler( # num_train_timesteps=config.num_train_timesteps, shift=1, use_dynamic_shifting=False # ) # sampling_sigmas = get_sampling_sigmas(args.infer_steps, args.flow_shift) # timesteps, _ = retrieve_timesteps(scheduler, device=device, sigmas=sampling_sigmas) # elif args.sample_solver == "vanilla": # scheduler = FlowMatchDiscreteScheduler(num_train_timesteps=config.num_train_timesteps, shift=args.flow_shift) # scheduler.set_timesteps(args.infer_steps, device=device) # timesteps = scheduler.timesteps # # FlowMatchDiscreteScheduler does not support generator argument in step method # org_step = scheduler.step # def step_wrapper( # model_output: torch.Tensor, # timestep: Union[int, torch.Tensor], # sample: torch.Tensor, # return_dict: bool = True, # generator=None, # ): # return org_step(model_output, timestep, sample, return_dict=return_dict) # scheduler.step = step_wrapper # else: # raise NotImplementedError("Unsupported solver.") # return scheduler, timesteps def convert_lora_for_framepack(lora_sd: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: # Check the format of the LoRA file keys = list(lora_sd.keys()) if keys[0].startswith("lora_unet_"): # logging.info(f"Musubi Tuner LoRA detected") pass else: transformer_prefixes = ["diffusion_model", "transformer"] # to ignore Text Encoder modules lora_suffix = None prefix = None for key in keys: if lora_suffix is None and "lora_A" in key: lora_suffix = "lora_A" if prefix is None: pfx = key.split(".")[0] if pfx in transformer_prefixes: prefix = pfx if lora_suffix is not None and prefix is not None: break if lora_suffix == "lora_A" and prefix is not None: logging.info("Diffusion-pipe (?) LoRA detected, converting to the default LoRA format") lora_sd = convert_lora_from_diffusion_pipe_or_something(lora_sd, "lora_unet_") else: logging.info("LoRA file format not recognized. Using it as-is.") # Check LoRA is for FramePack or for HunyuanVideo is_hunyuan = False for key in lora_sd.keys(): if "double_blocks" in key or "single_blocks" in key: is_hunyuan = True break if is_hunyuan: logging.info("HunyuanVideo LoRA detected, converting to FramePack format") lora_sd = convert_hunyuan_to_framepack(lora_sd) return lora_sd def convert_lora_from_diffusion_pipe_or_something(lora_sd: dict[str, torch.Tensor], prefix: str) -> dict[str, torch.Tensor]: """ Convert LoRA weights to the format used by the diffusion pipeline to Musubi Tuner. Copy from Musubi Tuner repo. """ # convert from diffusers(?) to default LoRA # Diffusers format: {"diffusion_model.module.name.lora_A.weight": weight, "diffusion_model.module.name.lora_B.weight": weight, ...} # default LoRA format: {"prefix_module_name.lora_down.weight": weight, "prefix_module_name.lora_up.weight": weight, ...} # note: Diffusers has no alpha, so alpha is set to rank new_weights_sd = {} lora_dims = {} for key, weight in lora_sd.items(): diffusers_prefix, key_body = key.split(".", 1) if diffusers_prefix != "diffusion_model" and diffusers_prefix != "transformer": print(f"unexpected key: {key} in diffusers format") continue new_key = f"{prefix}{key_body}".replace(".", "_").replace("_lora_A_", ".lora_down.").replace("_lora_B_", ".lora_up.") new_weights_sd[new_key] = weight lora_name = new_key.split(".")[0] # before first dot if lora_name not in lora_dims and "lora_down" in new_key: lora_dims[lora_name] = weight.shape[0] # add alpha with rank for lora_name, dim in lora_dims.items(): new_weights_sd[f"{lora_name}.alpha"] = torch.tensor(dim) return new_weights_sd def convert_hunyuan_to_framepack(lora_sd: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: """ Convert HunyuanVideo LoRA weights to FramePack format. """ new_lora_sd = {} for key, weight in lora_sd.items(): if "double_blocks" in key: key = key.replace("double_blocks", "transformer_blocks") key = key.replace("img_mod_linear", "norm1_linear") key = key.replace("img_attn_qkv", "attn_to_QKV") # split later key = key.replace("img_attn_proj", "attn_to_out_0") key = key.replace("img_mlp_fc1", "ff_net_0_proj") key = key.replace("img_mlp_fc2", "ff_net_2") key = key.replace("txt_mod_linear", "norm1_context_linear") key = key.replace("txt_attn_qkv", "attn_add_QKV_proj") # split later key = key.replace("txt_attn_proj", "attn_to_add_out") key = key.replace("txt_mlp_fc1", "ff_context_net_0_proj") key = key.replace("txt_mlp_fc2", "ff_context_net_2") elif "single_blocks" in key: key = key.replace("single_blocks", "single_transformer_blocks") key = key.replace("linear1", "attn_to_QKVM") # split later key = key.replace("linear2", "proj_out") key = key.replace("modulation_linear", "norm_linear") else: print(f"Unsupported module name: {key}, only double_blocks and single_blocks are supported") continue if "QKVM" in key: # split QKVM into Q, K, V, M key_q = key.replace("QKVM", "q") key_k = key.replace("QKVM", "k") key_v = key.replace("QKVM", "v") key_m = key.replace("attn_to_QKVM", "proj_mlp") if "_down" in key or "alpha" in key: # copy QKVM weight or alpha to Q, K, V, M assert "alpha" in key or weight.size(1) == 3072, f"QKVM weight size mismatch: {key}. {weight.size()}" new_lora_sd[key_q] = weight new_lora_sd[key_k] = weight new_lora_sd[key_v] = weight new_lora_sd[key_m] = weight elif "_up" in key: # split QKVM weight into Q, K, V, M assert weight.size(0) == 21504, f"QKVM weight size mismatch: {key}. {weight.size()}" new_lora_sd[key_q] = weight[:3072] new_lora_sd[key_k] = weight[3072 : 3072 * 2] new_lora_sd[key_v] = weight[3072 * 2 : 3072 * 3] new_lora_sd[key_m] = weight[3072 * 3 :] # 21504 - 3072 * 3 = 12288 else: print(f"Unsupported module name: {key}") continue elif "QKV" in key: # split QKV into Q, K, V key_q = key.replace("QKV", "q") key_k = key.replace("QKV", "k") key_v = key.replace("QKV", "v") if "_down" in key or "alpha" in key: # copy QKV weight or alpha to Q, K, V assert "alpha" in key or weight.size(1) == 3072, f"QKV weight size mismatch: {key}. {weight.size()}" new_lora_sd[key_q] = weight new_lora_sd[key_k] = weight new_lora_sd[key_v] = weight elif "_up" in key: # split QKV weight into Q, K, V assert weight.size(0) == 3072 * 3, f"QKV weight size mismatch: {key}. {weight.size()}" new_lora_sd[key_q] = weight[:3072] new_lora_sd[key_k] = weight[3072 : 3072 * 2] new_lora_sd[key_v] = weight[3072 * 2 :] else: print(f"Unsupported module name: {key}") continue else: # no split needed new_lora_sd[key] = weight return new_lora_sd def initialize_magcache(args: argparse.Namespace, model: HunyuanVideoTransformer3DModelPackedInference) -> None: if args.magcache_mag_ratios is None and not args.magcache_calibration: return # parse mag_ratios mag_ratios = None # calibration mode if args.magcache_mag_ratios is not None: mag_ratios = [float(ratio) for ratio in args.magcache_mag_ratios.split(",")] if len(mag_ratios) == 1 and mag_ratios[0] == 0: # use default mag_ratios mag_ratios = None logger.info( f"Initializing MagCache with mag_ratios: {mag_ratios}, retention_ratio: {args.magcache_retention_ratio}, " f"magcache_thresh: {args.magcache_threshold}, K: {args.magcache_k}, calibration: {args.magcache_calibration}" ) model.initialize_magcache( enable=True, retention_ratio=args.magcache_retention_ratio, mag_ratios=mag_ratios, magcache_thresh=args.magcache_threshold, K=args.magcache_k, calibration=args.magcache_calibration, ) def preprocess_magcache(args: argparse.Namespace, model: HunyuanVideoTransformer3DModelPackedInference) -> None: if args.magcache_mag_ratios is None and not args.magcache_calibration: return model.reset_magcache(args.infer_steps) def postprocess_magcache(args: argparse.Namespace, model: HunyuanVideoTransformer3DModelPackedInference) -> None: if args.magcache_mag_ratios is None and not args.magcache_calibration: return if not args.magcache_calibration: return # print mag ratios norm_ratio, norm_std, cos_dis = model.get_calibration_data() logger.info("MagCache calibration data:") logger.info(f" - norm_ratio: {norm_ratio}") logger.info(f" - norm_std: {norm_std}") logger.info(f" - cos_dis: {cos_dis}") logger.info("Copy and paste following values to --magcache_mag_ratios argument to use them:") print(",".join([f"{ratio:.5f}" for ratio in [1] + norm_ratio])) def generate( args: argparse.Namespace, gen_settings: GenerationSettings, shared_models: Optional[Dict] = None, precomputed_image_data: Optional[Dict] = None, precomputed_text_data: Optional[Dict] = None, ) -> tuple[Optional[AutoencoderKLCausal3D], torch.Tensor]: # VAE can be Optional """main function for generation Args: args: command line arguments shared_models: dictionary containing pre-loaded models (mainly for DiT) precomputed_image_data: Optional dictionary with precomputed image data precomputed_text_data: Optional dictionary with precomputed text data Returns: tuple: (AutoencoderKLCausal3D model (vae) or None, torch.Tensor generated latent) """ device, dit_weight_dtype = (gen_settings.device, gen_settings.dit_weight_dtype) vae_instance_for_return = None # prepare seed seed = args.seed if args.seed is not None else random.randint(0, 2**32 - 1) args.seed = seed # set seed to args for saving if precomputed_image_data is not None and precomputed_text_data is not None: logger.info("Using precomputed image and text data.") height = precomputed_image_data["height"] width = precomputed_image_data["width"] video_seconds = precomputed_image_data["video_seconds"] context_img = precomputed_image_data["context_img"] end_latent = precomputed_image_data["end_latent"] control_latents = precomputed_image_data["control_latents"] control_mask_images = precomputed_image_data["control_mask_images"] context = precomputed_text_data["context"] context_null = precomputed_text_data["context_null"] # VAE is not loaded here if data is precomputed; decoding VAE is handled by caller (e.g., process_batch_prompts) # vae_instance_for_return remains None else: # Load VAE if not precomputed (for single/interactive mode) # shared_models for single/interactive might contain text/image encoders, but not VAE after `load_shared_models` change. # So, VAE will be loaded here for single/interactive. logger.info("No precomputed data. Preparing image and text inputs.") if shared_models and "vae" in shared_models: # Should not happen with new load_shared_models vae_instance_for_return = shared_models["vae"] else: vae_instance_for_return = load_vae( args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, args.vae_tiling, device ) height, width, video_seconds, context, context_null, context_img, end_latent, control_latents, control_mask_images = ( prepare_i2v_inputs(args, device, vae_instance_for_return, shared_models) # Pass VAE ) if shared_models is None or "model" not in shared_models: model = load_dit_model(args, device) if args.save_merged_model: # If we only want to save the model, we can skip the rest return model, None if shared_models is not None: shared_models["model"] = model else: # use shared model model: HunyuanVideoTransformer3DModelPackedInference = shared_models["model"] model.move_to_device_except_swap_blocks(device) # Handles block swap correctly model.prepare_block_swap_before_forward() # sampling latent_window_size = args.latent_window_size # default is 9 # ex: (5s * 30fps) / (9 * 4) = 4.16 -> 4 sections, 60s -> 1800 / 36 = 50 sections total_latent_sections = (video_seconds * 30) / (latent_window_size * 4) total_latent_sections = int(max(round(total_latent_sections), 1)) # set random generator seed_g = torch.Generator(device="cpu") seed_g.manual_seed(seed) num_frames = latent_window_size * 4 - 3 logger.info( f"Video size: {height}x{width}@{video_seconds} (HxW@seconds), fps: {args.fps}, num sections: {total_latent_sections}, " f"infer_steps: {args.infer_steps}, frames per generation: {num_frames}" ) # video generation ###### f1_mode = args.f1 one_frame_inference = None if args.one_frame_inference is not None: one_frame_inference = set() for mode in args.one_frame_inference.split(","): one_frame_inference.add(mode.strip()) if one_frame_inference is not None: real_history_latents = generate_with_one_frame_inference( args, model, context, context_null, context_img, control_latents, control_mask_images, latent_window_size, height, width, device, seed_g, one_frame_inference, ) else: # prepare history latents history_latents = torch.zeros((1, 16, 1 + 2 + 16, height // 8, width // 8), dtype=torch.float32) if end_latent is not None and not f1_mode: logger.info(f"Use end image(s): {args.end_image_path}") history_latents[:, :, :1] = end_latent.to(history_latents) # prepare clean latents and indices if not f1_mode: # Inverted Anti-drifting total_generated_latent_frames = 0 latent_paddings = reversed(range(total_latent_sections)) if total_latent_sections > 4 and one_frame_inference is None: # In theory the latent_paddings should follow the above sequence, but it seems that duplicating some # items looks better than expanding it when total_latent_sections > 4 # One can try to remove below trick and just # use `latent_paddings = list(reversed(range(total_latent_sections)))` to compare # 4 sections: 3, 2, 1, 0. 50 sections: 3, 2, 2, ... 2, 1, 0 latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0] if args.latent_paddings is not None: # parse user defined latent paddings user_latent_paddings = [int(x) for x in args.latent_paddings.split(",")] if len(user_latent_paddings) < total_latent_sections: print( f"User defined latent paddings length {len(user_latent_paddings)} does not match total sections {total_latent_sections}." ) print("Use default paddings instead for unspecified sections.") latent_paddings[: len(user_latent_paddings)] = user_latent_paddings elif len(user_latent_paddings) > total_latent_sections: print( f"User defined latent paddings length {len(user_latent_paddings)} is greater than total sections {total_latent_sections}." ) print(f"Use only first {total_latent_sections} paddings instead.") latent_paddings = user_latent_paddings[:total_latent_sections] else: latent_paddings = user_latent_paddings else: start_latent = context_img[0]["start_latent"] history_latents = torch.cat([history_latents, start_latent], dim=2) total_generated_latent_frames = 1 # a bit hacky, but we employ the same logic as in official code latent_paddings = [0] * total_latent_sections # dummy paddings for F1 mode latent_paddings = list(latent_paddings) # make sure it's a list for loop_index in range(total_latent_sections): latent_padding = latent_paddings[loop_index] if not f1_mode: # Inverted Anti-drifting section_index_reverse = loop_index # 0, 1, 2, 3 section_index = total_latent_sections - 1 - section_index_reverse # 3, 2, 1, 0 section_index_from_last = -(section_index_reverse + 1) # -1, -2, -3, -4 is_last_section = section_index == 0 is_first_section = section_index_reverse == 0 latent_padding_size = latent_padding * latent_window_size logger.info(f"latent_padding_size = {latent_padding_size}, is_last_section = {is_last_section}") else: section_index = loop_index # 0, 1, 2, 3 section_index_from_last = section_index - total_latent_sections # -4, -3, -2, -1 is_last_section = loop_index == total_latent_sections - 1 is_first_section = loop_index == 0 latent_padding_size = 0 # dummy padding for F1 mode # select start latent if section_index_from_last in context_img: image_index = section_index_from_last elif section_index in context_img: image_index = section_index else: image_index = 0 start_latent = context_img[image_index]["start_latent"] image_path = context_img[image_index]["image_path"] if image_index != 0: # use section image other than section 0 logger.info( f"Apply experimental section image, latent_padding_size = {latent_padding_size}, image_path = {image_path}" ) if not f1_mode: # Inverted Anti-drifting indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0) ( clean_latent_indices_pre, blank_indices, latent_indices, clean_latent_indices_post, clean_latent_2x_indices, clean_latent_4x_indices, ) = indices.split([1, latent_padding_size, latent_window_size, 1, 2, 16], dim=1) clean_latent_indices = torch.cat([clean_latent_indices_pre, clean_latent_indices_post], dim=1) clean_latents_pre = start_latent.to(history_latents) clean_latents_post, clean_latents_2x, clean_latents_4x = history_latents[:, :, : 1 + 2 + 16, :, :].split( [1, 2, 16], dim=2 ) clean_latents = torch.cat([clean_latents_pre, clean_latents_post], dim=2) else: # F1 mode indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0) ( clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices, ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1) clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1) clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -sum([16, 2, 1]) :, :, :].split( [16, 2, 1], dim=2 ) clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2) # if use_teacache: # transformer.initialize_teacache(enable_teacache=True, num_steps=steps) # else: # transformer.initialize_teacache(enable_teacache=False) # prepare conditioning inputs if section_index_from_last in context: prompt_index = section_index_from_last elif section_index in context: prompt_index = section_index else: prompt_index = 0 context_for_index = context[prompt_index] # if args.section_prompts is not None: logger.info(f"Section {section_index}: {context_for_index['prompt']}") llama_vec = context_for_index["llama_vec"].to(device, dtype=torch.bfloat16) llama_attention_mask = context_for_index["llama_attention_mask"].to(device) clip_l_pooler = context_for_index["clip_l_pooler"].to(device, dtype=torch.bfloat16) image_encoder_last_hidden_state = context_img[image_index]["image_encoder_last_hidden_state"].to( device, dtype=torch.bfloat16 ) llama_vec_n = context_null["llama_vec"].to(device, dtype=torch.bfloat16) llama_attention_mask_n = context_null["llama_attention_mask"].to(device) clip_l_pooler_n = context_null["clip_l_pooler"].to(device, dtype=torch.bfloat16) preprocess_magcache(args, model) generated_latents = sample_hunyuan( transformer=model, sampler=args.sample_solver, width=width, height=height, frames=num_frames, real_guidance_scale=args.guidance_scale, distilled_guidance_scale=args.embedded_cfg_scale, guidance_rescale=args.guidance_rescale, shift=args.flow_shift, num_inference_steps=args.infer_steps, generator=seed_g, prompt_embeds=llama_vec, prompt_embeds_mask=llama_attention_mask, prompt_poolers=clip_l_pooler, negative_prompt_embeds=llama_vec_n, negative_prompt_embeds_mask=llama_attention_mask_n, negative_prompt_poolers=clip_l_pooler_n, device=device, dtype=torch.bfloat16, image_embeddings=image_encoder_last_hidden_state, latent_indices=latent_indices, clean_latents=clean_latents, clean_latent_indices=clean_latent_indices, clean_latents_2x=clean_latents_2x, clean_latent_2x_indices=clean_latent_2x_indices, clean_latents_4x=clean_latents_4x, clean_latent_4x_indices=clean_latent_4x_indices, ) postprocess_magcache(args, model) # concatenate generated latents total_generated_latent_frames += int(generated_latents.shape[2]) if not f1_mode: # Inverted Anti-drifting: prepend generated latents to history latents if is_last_section: generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2) total_generated_latent_frames += 1 history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2) real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :] else: # F1 mode: append generated latents to history latents history_latents = torch.cat([history_latents, generated_latents.to(history_latents)], dim=2) real_history_latents = history_latents[:, :, -total_generated_latent_frames:, :, :] logger.info(f"Generated. Latent shape {real_history_latents.shape}") # # TODO support saving intermediate video # clean_memory_on_device(device) # vae.to(device) # if history_pixels is None: # history_pixels = hunyuan.vae_decode(real_history_latents, vae).cpu() # else: # section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2) # overlapped_frames = latent_window_size * 4 - 3 # current_pixels = hunyuan.vae_decode(real_history_latents[:, :, :section_latent_frames], vae).cpu() # history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames) # vae.to("cpu") # # if not is_last_section: # # # save intermediate video # # save_video(history_pixels[0], args, total_generated_latent_frames) # print(f"Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}") # Only clean up shared models if they were created within this function wait_for_clean_memory = False if not (shared_models and "model" in shared_models) and "model" in locals(): # if model was loaded locally del model synchronize_device(device) wait_for_clean_memory = True # wait for 5 seconds until block swap is done if wait_for_clean_memory and args.blocks_to_swap > 0: logger.info("Waiting for 5 seconds to finish block swap") time.sleep(5) gc.collect() clean_memory_on_device(device) return vae_instance_for_return, real_history_latents def generate_with_one_frame_inference( args: argparse.Namespace, model: HunyuanVideoTransformer3DModelPackedInference, context: Dict[int, Dict[str, torch.Tensor]], context_null: Dict[str, torch.Tensor], context_img: Dict[int, Dict[str, torch.Tensor]], control_latents: Optional[List[torch.Tensor]], control_mask_images: Optional[List[Optional[Image.Image]]], latent_window_size: int, height: int, width: int, device: torch.device, seed_g: torch.Generator, one_frame_inference: set[str], ) -> torch.Tensor: # one frame inference sample_num_frames = 1 latent_indices = torch.zeros((1, 1), dtype=torch.int64) # 1x1 latent index for target image latent_indices[:, 0] = latent_window_size # last of latent_window def get_latent_mask(mask_image: Image.Image) -> torch.Tensor: if mask_image.mode != "L": mask_image = mask_image.convert("L") mask_image = mask_image.resize((width // 8, height // 8), Image.LANCZOS) mask_image = np.array(mask_image) # PIL to numpy, HWC mask_image = torch.from_numpy(mask_image).float() / 255.0 # 0 to 1.0, HWC mask_image = mask_image.squeeze(-1) # HWC -> HW mask_image = mask_image.unsqueeze(0).unsqueeze(0).unsqueeze(0) # HW -> 111HW (BCFHW) mask_image = mask_image.to(torch.float32) return mask_image if control_latents is None or len(control_latents) == 0: logger.info("No control images provided for one frame inference. Use zero latents for control images.") control_latents = [torch.zeros(1, 16, 1, height // 8, width // 8, dtype=torch.float32)] if "no_post" not in one_frame_inference: # add zero latents as clean latents post control_latents.append(torch.zeros((1, 16, 1, height // 8, width // 8), dtype=torch.float32)) logger.info("Add zero latents as clean latents post for one frame inference.") # kisekaeichi and 1f-mc: both are using control images, but indices are different clean_latents = torch.cat(control_latents, dim=2) # (1, 16, num_control_images, H//8, W//8) clean_latent_indices = torch.zeros((1, len(control_latents)), dtype=torch.int64) if "no_post" not in one_frame_inference: clean_latent_indices[:, -1] = 1 + latent_window_size # default index for clean latents post for i in range(len(control_latents)): mask_image = None if args.control_image_mask_path is not None and i < len(args.control_image_mask_path): mask_image = get_latent_mask(Image.open(args.control_image_mask_path[i])) logger.info( f"Apply mask for clean latents 1x for {i + 1}: {args.control_image_mask_path[i]}, shape: {mask_image.shape}" ) elif control_mask_images is not None and i < len(control_mask_images) and control_mask_images[i] is not None: mask_image = get_latent_mask(control_mask_images[i]) logger.info(f"Apply mask for clean latents 1x for {i + 1} with alpha channel: {mask_image.shape}") if mask_image is not None: clean_latents[:, :, i : i + 1, :, :] = clean_latents[:, :, i : i + 1, :, :] * mask_image for one_frame_param in one_frame_inference: if one_frame_param.startswith("target_index="): target_index = int(one_frame_param.split("=")[1]) latent_indices[:, 0] = target_index logger.info(f"Set index for target: {target_index}") elif one_frame_param.startswith("control_index="): control_indices = one_frame_param.split("=")[1].split(";") i = 0 while i < len(control_indices) and i < clean_latent_indices.shape[1]: control_index = int(control_indices[i]) clean_latent_indices[:, i] = control_index i += 1 logger.info(f"Set index for clean latent 1x: {control_indices}") # "default" option does nothing, so we can skip it if "default" in one_frame_inference: pass if "no_2x" in one_frame_inference: clean_latents_2x = None clean_latent_2x_indices = None logger.info("No clean_latents_2x") else: clean_latents_2x = torch.zeros((1, 16, 2, height // 8, width // 8), dtype=torch.float32) index = 1 + latent_window_size + 1 clean_latent_2x_indices = torch.arange(index, index + 2).unsqueeze(0) # 2 if "no_4x" in one_frame_inference: clean_latents_4x = None clean_latent_4x_indices = None logger.info("No clean_latents_4x") else: clean_latents_4x = torch.zeros((1, 16, 16, height // 8, width // 8), dtype=torch.float32) index = 1 + latent_window_size + 1 + 2 clean_latent_4x_indices = torch.arange(index, index + 16).unsqueeze(0) # 16 logger.info( f"One frame inference. clean_latent: {clean_latents.shape} latent_indices: {latent_indices}, clean_latent_indices: {clean_latent_indices}, num_frames: {sample_num_frames}" ) # prepare conditioning inputs prompt_index = 0 image_index = 0 context_for_index = context[prompt_index] logger.info(f"Prompt: {context_for_index['prompt']}") llama_vec = context_for_index["llama_vec"].to(device, dtype=torch.bfloat16) llama_attention_mask = context_for_index["llama_attention_mask"].to(device) clip_l_pooler = context_for_index["clip_l_pooler"].to(device, dtype=torch.bfloat16) image_encoder_last_hidden_state = context_img[image_index]["image_encoder_last_hidden_state"].to(device, dtype=torch.bfloat16) llama_vec_n = context_null["llama_vec"].to(device, dtype=torch.bfloat16) llama_attention_mask_n = context_null["llama_attention_mask"].to(device) clip_l_pooler_n = context_null["clip_l_pooler"].to(device, dtype=torch.bfloat16) preprocess_magcache(args, model) generated_latents = sample_hunyuan( transformer=model, sampler=args.sample_solver, width=width, height=height, frames=1, real_guidance_scale=args.guidance_scale, distilled_guidance_scale=args.embedded_cfg_scale, guidance_rescale=args.guidance_rescale, shift=args.flow_shift, num_inference_steps=args.infer_steps, generator=seed_g, prompt_embeds=llama_vec, prompt_embeds_mask=llama_attention_mask, prompt_poolers=clip_l_pooler, negative_prompt_embeds=llama_vec_n, negative_prompt_embeds_mask=llama_attention_mask_n, negative_prompt_poolers=clip_l_pooler_n, device=device, dtype=torch.bfloat16, image_embeddings=image_encoder_last_hidden_state, latent_indices=latent_indices, clean_latents=clean_latents, clean_latent_indices=clean_latent_indices, clean_latents_2x=clean_latents_2x, clean_latent_2x_indices=clean_latent_2x_indices, clean_latents_4x=clean_latents_4x, clean_latent_4x_indices=clean_latent_4x_indices, ) postprocess_magcache(args, model) real_history_latents = generated_latents.to(clean_latents) return real_history_latents def save_latent(latent: torch.Tensor, args: argparse.Namespace, height: int, width: int) -> str: """Save latent to file Args: latent: Latent tensor args: command line arguments height: height of frame width: width of frame Returns: str: Path to saved latent file """ save_path = args.save_path os.makedirs(save_path, exist_ok=True) time_flag = get_time_flag() seed = args.seed video_seconds = args.video_seconds latent_path = f"{save_path}/{time_flag}_{seed}_latent.safetensors" if args.no_metadata: metadata = None else: metadata = { "seeds": f"{seed}", "prompt": f"{args.prompt}", "height": f"{height}", "width": f"{width}", "video_seconds": f"{video_seconds}", "infer_steps": f"{args.infer_steps}", "guidance_scale": f"{args.guidance_scale}", "latent_window_size": f"{args.latent_window_size}", "embedded_cfg_scale": f"{args.embedded_cfg_scale}", "guidance_rescale": f"{args.guidance_rescale}", "sample_solver": f"{args.sample_solver}", "latent_window_size": f"{args.latent_window_size}", "fps": f"{args.fps}", } if args.negative_prompt is not None: metadata["negative_prompt"] = f"{args.negative_prompt}" sd = {"latent": latent.contiguous()} save_file(sd, latent_path, metadata=metadata) logger.info(f"Latent saved to: {latent_path}") return latent_path def save_video( video: torch.Tensor, args: argparse.Namespace, original_base_name: Optional[str] = None, latent_frames: Optional[int] = None ) -> str: """Save video to file Args: video: Video tensor args: command line arguments original_base_name: Original base name (if latents are loaded from files) Returns: str: Path to saved video file """ save_path = args.save_path os.makedirs(save_path, exist_ok=True) time_flag = get_time_flag() seed = args.seed original_name = "" if original_base_name is None else f"_{original_base_name}" latent_frames = "" if latent_frames is None else f"_{latent_frames}" video_path = f"{save_path}/{time_flag}_{seed}{original_name}{latent_frames}.mp4" video = video.unsqueeze(0) save_videos_grid(video, video_path, fps=args.fps, rescale=True) logger.info(f"Video saved to: {video_path}") return video_path def save_images(sample: torch.Tensor, args: argparse.Namespace, original_base_name: Optional[str] = None) -> str: """Save images to directory Args: sample: Video tensor args: command line arguments original_base_name: Original base name (if latents are loaded from files) Returns: str: Path to saved images directory """ save_path = args.save_path os.makedirs(save_path, exist_ok=True) time_flag = get_time_flag() seed = args.seed original_name = "" if original_base_name is None else f"_{original_base_name}" image_name = f"{time_flag}_{seed}{original_name}" sample = sample.unsqueeze(0) one_frame_mode = args.one_frame_inference is not None save_images_grid(sample, save_path, image_name, rescale=True, create_subdir=not one_frame_mode) logger.info(f"Sample images saved to: {save_path}/{image_name}") return f"{save_path}/{image_name}" def save_output( args: argparse.Namespace, vae: AutoencoderKLCausal3D, # Expect a VAE instance for decoding latent: torch.Tensor, device: torch.device, original_base_names: Optional[List[str]] = None, ) -> None: """save output Args: args: command line arguments vae: VAE model latent: latent tensor device: device to use original_base_names: original base names (if latents are loaded from files) """ height, width = latent.shape[-2], latent.shape[-1] # BCTHW height *= 8 width *= 8 # print(f"Saving output. Latent shape {latent.shape}; pixel shape {height}x{width}") if args.output_type == "latent" or args.output_type == "both" or args.output_type == "latent_images": # save latent save_latent(latent, args, height, width) if args.output_type == "latent": return if vae is None: logger.error("VAE is None, cannot decode latents for saving video/images.") return total_latent_sections = (args.video_seconds * 30) / (args.latent_window_size * 4) total_latent_sections = int(max(round(total_latent_sections), 1)) video = decode_latent( args.latent_window_size, total_latent_sections, args.bulk_decode, vae, latent, device, args.one_frame_inference is not None ) if args.output_type == "video" or args.output_type == "both": # save video original_name = "" if original_base_names is None else f"_{original_base_names[0]}" save_video(video, args, original_name) elif args.output_type == "images" or args.output_type == "latent_images": # save images original_name = "" if original_base_names is None else f"_{original_base_names[0]}" save_images(video, args, original_name) def preprocess_prompts_for_batch(prompt_lines: List[str], base_args: argparse.Namespace) -> List[Dict]: """Process multiple prompts for batch mode Args: prompt_lines: List of prompt lines base_args: Base command line arguments Returns: List[Dict]: List of prompt data dictionaries """ prompts_data = [] for line in prompt_lines: line = line.strip() if not line or line.startswith("#"): # Skip empty lines and comments continue # Parse prompt line and create override dictionary prompt_data = parse_prompt_line(line) logger.info(f"Parsed prompt data: {prompt_data}") prompts_data.append(prompt_data) return prompts_data def load_shared_models(args: argparse.Namespace) -> Dict: """Load shared models for batch processing or interactive mode. Models are loaded to CPU to save memory. VAE is NOT loaded here. DiT model is also NOT loaded here, handled by process_batch_prompts or generate. Args: args: Base command line arguments Returns: Dict: Dictionary of shared models (text/image encoders) """ shared_models = {} # Load text encoders to CPU tokenizer1, text_encoder1 = load_text_encoder1(args, args.fp8_llm, "cpu") tokenizer2, text_encoder2 = load_text_encoder2(args) # Assumes it loads to CPU or handles device internally # Load image encoders to CPU feature_extractor, image_encoder = load_image_encoders(args) # Assumes it loads to CPU or handles device internally shared_models["tokenizer1"] = tokenizer1 shared_models["text_encoder1"] = text_encoder1 shared_models["tokenizer2"] = tokenizer2 shared_models["text_encoder2"] = text_encoder2 shared_models["feature_extractor"] = feature_extractor shared_models["image_encoder"] = image_encoder return shared_models def process_batch_prompts(prompts_data: List[Dict], args: argparse.Namespace) -> None: """Process multiple prompts with model reuse and batched precomputation Args: prompts_data: List of prompt data dictionaries args: Base command line arguments """ if not prompts_data: logger.warning("No valid prompts found") return gen_settings = get_generation_settings(args) device = gen_settings.device # 1. Precompute Image Data (VAE and Image Encoders) logger.info("Loading VAE and Image Encoders for batch image preprocessing...") vae_for_batch = load_vae(args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, args.vae_tiling, "cpu") feature_extractor_batch, image_encoder_batch = load_image_encoders(args) # Assume loads to CPU all_precomputed_image_data = [] all_prompt_args_list = [apply_overrides(args, pd) for pd in prompts_data] # Create all arg instances first logger.info("Preprocessing images and VAE encoding for all prompts...") # VAE and Image Encoder to device for this phase, because we do not want to offload them to CPU vae_for_batch.to(device) image_encoder_batch.to(device) # Pass models via a temporary shared_models dict for prepare_image_inputs # This ensures prepare_image_inputs can use them if it expects them in shared_models # Or it can load them if this dict is empty (though here we provide them) temp_shared_models_img = {"feature_extractor": feature_extractor_batch, "image_encoder": image_encoder_batch} for i, prompt_args_item in enumerate(all_prompt_args_list): logger.info(f"Image preprocessing for prompt {i + 1}/{len(all_prompt_args_list)}: {prompt_args_item.prompt}") # prepare_image_inputs will move vae/image_encoder to device temporarily image_data = prepare_image_inputs(prompt_args_item, device, vae_for_batch, temp_shared_models_img) all_precomputed_image_data.append(image_data) # Models should be back on GPU because prepare_image_inputs moved them to the original device del feature_extractor_batch, image_encoder_batch, temp_shared_models_img vae_for_batch.to("cpu") # Move VAE back to CPU clean_memory_on_device(device) # 2. Precompute Text Data (Text Encoders) logger.info("Loading Text Encoders for batch text preprocessing...") # Text Encoders loaded to CPU by load_text_encoder1/2 tokenizer1_batch, text_encoder1_batch = load_text_encoder1(args, args.fp8_llm, device) tokenizer2_batch, text_encoder2_batch = load_text_encoder2(args) # Text Encoders to device for this phase text_encoder2_batch.to(device) # Moved into prepare_text_inputs logic all_precomputed_text_data = [] conds_cache_batch = {} logger.info("Preprocessing text and LLM/TextEncoder encoding for all prompts...") temp_shared_models_txt = { "tokenizer1": tokenizer1_batch, "text_encoder1": text_encoder1_batch, # on GPU "tokenizer2": tokenizer2_batch, "text_encoder2": text_encoder2_batch, # on GPU "conds_cache": conds_cache_batch, } for i, prompt_args_item in enumerate(all_prompt_args_list): logger.info(f"Text preprocessing for prompt {i + 1}/{len(all_prompt_args_list)}: {prompt_args_item.prompt}") # prepare_text_inputs will move text_encoders to device temporarily text_data = prepare_text_inputs(prompt_args_item, device, temp_shared_models_txt) all_precomputed_text_data.append(text_data) # Models should be removed from device after prepare_text_inputs del tokenizer1_batch, text_encoder1_batch, tokenizer2_batch, text_encoder2_batch, temp_shared_models_txt, conds_cache_batch gc.collect() # transformer==4.54.1 seems to need this to free memory clean_memory_on_device(device) # 3. Load DiT Model once logger.info("Loading DiT model for batch generation...") # Use args from the first prompt for DiT loading (LoRA etc. should be consistent for a batch) first_prompt_args = all_prompt_args_list[0] dit_model = load_dit_model(first_prompt_args, device) # Load directly to target device if possible if first_prompt_args.save_merged_model: logger.info("Merged DiT model saved. Skipping generation.") del dit_model clean_memory_on_device(device) return shared_models_for_generate = {"model": dit_model} # Pass DiT via shared_models all_latents = [] logger.info("Generating latents for all prompts...") with torch.no_grad(): for i, prompt_args_item in enumerate(all_prompt_args_list): current_image_data = all_precomputed_image_data[i] current_text_data = all_precomputed_text_data[i] logger.info(f"Generating latent for prompt {i + 1}/{len(all_prompt_args_list)}: {prompt_args_item.prompt}") try: # generate is called with precomputed data, so it won't load VAE/Text/Image encoders. # It will use the DiT model from shared_models_for_generate. # The VAE instance returned by generate will be None here. _, latent = generate( prompt_args_item, gen_settings, shared_models_for_generate, current_image_data, current_text_data ) if latent is None and prompt_args_item.save_merged_model: # Should be caught earlier continue # Save latent if needed (using data from precomputed_image_data for H/W) if prompt_args_item.output_type in ["latent", "both", "latent_images"]: height = current_image_data["height"] width = current_image_data["width"] save_latent(latent, prompt_args_item, height, width) all_latents.append(latent) except Exception as e: logger.error(f"Error generating latent for prompt: {prompt_args_item.prompt}. Error: {e}", exc_info=True) all_latents.append(None) # Add placeholder for failed generations continue # Free DiT model logger.info("Releasing DiT model from memory...") if args.blocks_to_swap > 0: logger.info("Waiting for 5 seconds to finish block swap") time.sleep(5) del shared_models_for_generate["model"] del dit_model gc.collect() clean_memory_on_device(device) synchronize_device(device) # Ensure memory is freed before loading VAE for decoding # 4. Decode latents and save outputs (using vae_for_batch) if args.output_type != "latent": logger.info("Decoding latents to videos/images using batched VAE...") vae_for_batch.to(device) # Move VAE to device for decoding for i, latent in enumerate(all_latents): if latent is None: # Skip failed generations logger.warning(f"Skipping decoding for prompt {i + 1} due to previous error.") continue current_args = all_prompt_args_list[i] logger.info(f"Decoding output {i + 1}/{len(all_latents)} for prompt: {current_args.prompt}") # if args.output_type is "both" or "latent_images", we already saved latent above. # so we skip saving latent here. if current_args.output_type == "both": current_args.output_type = "video" elif current_args.output_type == "latent_images": current_args.output_type = "images" # save_output expects latent to be [BCTHW] or [CTHW]. generate returns [BCTHW] (batch size 1). # latent[0] is correct if generate returns it with batch dim. # The latent from generate is (1, C, T, H, W) save_output(current_args, vae_for_batch, latent[0], device) # Pass vae_for_batch vae_for_batch.to("cpu") # Move VAE back to CPU del vae_for_batch clean_memory_on_device(device) def process_interactive(args: argparse.Namespace) -> None: """Process prompts in interactive mode Args: args: Base command line arguments """ gen_settings = get_generation_settings(args) device = gen_settings.device shared_models = load_shared_models(args) shared_models["conds_cache"] = {} # Initialize empty cache for interactive mode print("Interactive mode. Enter prompts (Ctrl+D or Ctrl+Z (Windows) to exit):") try: import prompt_toolkit except ImportError: logger.warning("prompt_toolkit not found. Using basic input instead.") prompt_toolkit = None if prompt_toolkit: session = prompt_toolkit.PromptSession() def input_line(prompt: str) -> str: return session.prompt(prompt) else: def input_line(prompt: str) -> str: return input(prompt) try: while True: try: line = input_line("> ") if not line.strip(): continue if len(line.strip()) == 1 and line.strip() in ["\x04", "\x1a"]: # Ctrl+D or Ctrl+Z with prompt_toolkit raise EOFError # Exit on Ctrl+D or Ctrl+Z # Parse prompt prompt_data = parse_prompt_line(line) prompt_args = apply_overrides(args, prompt_data) # Generate latent # For interactive, precomputed data is None. shared_models contains text/image encoders. # generate will load VAE internally. returned_vae, latent = generate(prompt_args, gen_settings, shared_models) # If not one_frame_inference, move DiT model to CPU after generation # if not prompt_args.one_frame_inference: if prompt_args.blocks_to_swap > 0: logger.info("Waiting for 5 seconds to finish block swap") time.sleep(5) model = shared_models.get("model") model.to("cpu") # Move DiT model to CPU after generation clean_memory_on_device(device) synchronize_device(device) # Ensure memory is freed before loading VAE for decoding # Save latent and video # returned_vae from generate will be used for decoding here. save_output(prompt_args, returned_vae, latent[0], device) except KeyboardInterrupt: print("\nInterrupted. Continue (Ctrl+D or Ctrl+Z (Windows) to exit)") continue except EOFError: print("\nExiting interactive mode") def get_generation_settings(args: argparse.Namespace) -> GenerationSettings: device = torch.device(args.device) dit_weight_dtype = None # default if args.fp8_scaled: dit_weight_dtype = None # various precision weights, so don't cast to specific dtype elif args.fp8: dit_weight_dtype = torch.float8_e4m3fn logger.info(f"Using device: {device}, DiT weight weight precision: {dit_weight_dtype}") gen_settings = GenerationSettings(device=device, dit_weight_dtype=dit_weight_dtype) return gen_settings def main(): # Parse arguments args = parse_args() assert (not args.save_merged_model) or (not args.fp8_scaled), "Save merged model is not compatible with fp8_scaled" # Check if latents are provided latents_mode = args.latent_path is not None and len(args.latent_path) > 0 # Set device device = args.device if args.device is not None else "cuda" if torch.cuda.is_available() else "cpu" device = torch.device(device) logger.info(f"Using device: {device}") args.device = device if latents_mode: # Original latent decode mode original_base_names = [] latents_list = [] seeds = [] # assert len(args.latent_path) == 1, "Only one latent path is supported for now" for latent_path in args.latent_path: original_base_names.append(os.path.splitext(os.path.basename(latent_path))[0]) seed = 0 if os.path.splitext(latent_path)[1] != ".safetensors": latents = torch.load(latent_path, map_location="cpu") else: state_dict = load_file(latent_path) if "latent" in state_dict: latents = state_dict["latent"] else: for key in state_dict: if key.startswith("latent") and state_dict[key].ndim >= 4: latents = state_dict[key] logger.warning(f"'latent' not found in state_dict. Using '{key}' instead.") break else: raise KeyError(f"'latent' not found in state_dict keys: {list(state_dict.keys())}") with safe_open(latent_path, framework="pt") as f: metadata = f.metadata() if metadata is None: metadata = {} logger.info(f"Loaded metadata: {metadata}") if "seeds" in metadata: seed = int(metadata["seeds"]) if "height" in metadata and "width" in metadata: height = int(metadata["height"]) width = int(metadata["width"]) args.video_size = [height, width] if "video_seconds" in metadata: args.video_seconds = float(metadata["video_seconds"]) seeds.append(seed) logger.info(f"Loaded latent from {latent_path}. Shape: {latents.shape}") if latents.ndim == 5: # [BCTHW] latents = latents.squeeze(0) # [CTHW] latents_list.append(latents) # latent = torch.stack(latents_list, dim=0) # [N, ...], must be same shape for i, latent in enumerate(latents_list): args.seed = seeds[i] vae = load_vae(args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, args.vae_tiling, device) save_output(args, vae, latent, device, original_base_names) elif args.from_file: # Batch mode from file # Read prompts from file with open(args.from_file, "r", encoding="utf-8") as f: prompt_lines = f.readlines() # Process prompts prompts_data = preprocess_prompts_for_batch(prompt_lines, args) process_batch_prompts(prompts_data, args) elif args.interactive: # Interactive mode process_interactive(args) else: # Single prompt mode (original behavior) # Generate latent gen_settings = get_generation_settings(args) # For single mode, precomputed data is None, shared_models is None. # generate will load all necessary models (VAE, Text/Image Encoders, DiT). returned_vae, latent = generate(args, gen_settings) # print(f"Generated latent shape: {latent.shape}") if args.save_merged_model: return # Save latent and video # returned_vae from generate will be used for decoding here. save_output(args, returned_vae, latent[0], device) logger.info("Done!") if __name__ == "__main__": main()