import argparse from datetime import datetime import gc import json import random import os import re import time import math import copy from typing import Tuple, Optional, List, Union, Any, Dict import torch from safetensors.torch import load_file, save_file from safetensors import safe_open from PIL import Image import cv2 import numpy as np import torchvision.transforms.functional as TF from transformers import LlamaModel from tqdm import tqdm from networks import lora_framepack from hunyuan_model.autoencoder_kl_causal_3d import AutoencoderKLCausal3D from frame_pack import hunyuan from frame_pack.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked, load_packed_model from frame_pack.utils import crop_or_pad_yield_mask, resize_and_center_crop, soft_append_bcthw from frame_pack.bucket_tools import find_nearest_bucket from frame_pack.clip_vision import hf_clip_vision_encode from frame_pack.k_diffusion_hunyuan import sample_hunyuan from dataset import image_video_dataset try: from lycoris.kohya import create_network_from_weights except: pass from utils.device_utils import clean_memory_on_device from hv_generate_video import save_images_grid, save_videos_grid, synchronize_device from wan_generate_video import merge_lora_weights from frame_pack.framepack_utils import load_vae, load_text_encoder1, load_text_encoder2, load_image_encoders from dataset.image_video_dataset import load_video import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) 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="Wan 2.1 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("--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( "--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 depends on task.", # ) 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_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( "--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="use lycoris for inference") # parser.add_argument("--compile", action="store_true", help="Enable torch.compile") # parser.add_argument( # "--compile_args", # nargs=4, # metavar=("BACKEND", "MODE", "DYNAMIC", "FULLGRAPH"), # default=["inductor", "max-autotune-no-cudagraphs", "False", "False"], # help="Torch.compile settings", # ) # 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") 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 # 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 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) -> HunyuanVideoTransformer3DModelPacked: """load DiT model Args: args: command line arguments device: device to use dit_dtype: data type for the model dit_weight_dtype: data type for the model weights. None for as-is Returns: HunyuanVideoTransformer3DModelPacked: DiT model """ loading_device = "cpu" if args.blocks_to_swap == 0 and not args.fp8_scaled and args.lora_weight is None: loading_device = device # do not fp8 optimize because we will merge LoRA weights model = load_packed_model(device, args.dit, args.attn_mode, loading_device) # 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) return model def optimize_model(model: HunyuanVideoTransformer3DModelPacked, args: argparse.Namespace, device: torch.device) -> None: """optimize the model (FP8 conversion, device move etc.) Args: model: dit model args: command line arguments device: device to use """ if args.fp8_scaled: # load state dict as-is and optimize to fp8 state_dict = model.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 args.blocks_to_swap == 0: model.to(device) # make sure all parameters are on the right device (e.g. RoPE etc.) else: # 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.compile: # compile_backend, compile_mode, compile_dynamic, compile_fullgraph = args.compile_args # logger.info( # f"Torch Compiling[Backend: {compile_backend}; Mode: {compile_mode}; Dynamic: {compile_dynamic}; Fullgraph: {compile_fullgraph}]" # ) # torch._dynamo.config.cache_size_limit = 32 # for i in range(len(model.blocks)): # model.blocks[i] = torch.compile( # model.blocks[i], # backend=compile_backend, # mode=compile_mode, # dynamic=compile_dynamic.lower() in "true", # fullgraph=compile_fullgraph.lower() in "true", # ) 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) 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) model.eval().requires_grad_(False) clean_memory_on_device(device) # 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(f"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(f"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_i2v_inputs( args: argparse.Namespace, device: torch.device, vae: AutoencoderKLCausal3D, shared_models: Optional[Dict] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[dict, dict]]: """Prepare inputs for I2V Args: args: command line arguments config: model configuration device: device to use vae: VAE model, used for image encoding shared_models: dictionary containing pre-loaded models Returns: Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[dict, dict]]: (noise, context, context_null, y, (arg_c, arg_null)) """ height, width, video_seconds = check_inputs(args) # define parsing function def parse_section_strings(input_string: str) -> dict[int, str]: 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 # assert 0 in section_prompts, "Section prompts must contain section 0" if 0 not in section_strings: # use smallest section index. prefer positive index over negative index # if all section indices are negative, use the smallest negative index 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 # 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 = {} for index, image_path in section_image_paths.items(): img_tensor, img_np, _ = preprocess_image(image_path) section_images[index] = (img_tensor, img_np) # 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 end 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 control_mask_images = None # configure negative prompt n_prompt = args.negative_prompt if args.negative_prompt else "" # parse section prompts section_prompts = parse_section_strings(args.prompt) # load text encoder if shared_models is not None: tokenizer1, text_encoder1 = shared_models["tokenizer1"], shared_models["text_encoder1"] tokenizer2, text_encoder2 = shared_models["tokenizer2"], shared_models["text_encoder2"] text_encoder1.to(device) else: tokenizer1, text_encoder1 = load_text_encoder1(args, args.fp8_llm, device) tokenizer2, text_encoder2 = load_text_encoder2(args) text_encoder2.to(device) logger.info(f"Encoding prompt") llama_vecs = {} llama_attention_masks = {} clip_l_poolers = {} with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad(): for index, prompt in section_prompts.items(): 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() 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_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vecs[0]), torch.zeros_like(clip_l_poolers[0]) else: with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad(): 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() llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512) # free text encoder and clean memory if shared_models is not None: # if shared models are used, do not free them but move to CPU text_encoder1.to("cpu") text_encoder2.to("cpu") del tokenizer1, text_encoder1, tokenizer2, text_encoder2 # do not free shared models clean_memory_on_device(device) # load image encoder if shared_models is not None: feature_extractor, image_encoder = shared_models["feature_extractor"], shared_models["image_encoder"] else: feature_extractor, image_encoder = load_image_encoders(args) image_encoder.to(device) # encode image with image encoder 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 # free image encoder and clean memory if shared_models is not None: image_encoder.to("cpu") del image_encoder, feature_extractor clean_memory_on_device(device) # VAE encoding logger.info(f"Encoding image to latent space") vae.to(device) section_start_latents = {} for index, (img_tensor, img_np) in section_images.items(): start_latent = hunyuan.vae_encode(img_tensor, vae).cpu() section_start_latents[index] = start_latent end_latent = hunyuan.vae_encode(end_image_tensor, 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, vae).cpu() control_latents.append(control_latent) vae.to("cpu") # move VAE to CPU to save memory clean_memory_on_device(device) # prepare model input arguments arg_c = {} arg_null = {} 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], # for debugging } 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, } 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[index], } arg_c_img[index] = arg_c_img_i return height, width, video_seconds, arg_c, arg_null, arg_c_img, end_latent, control_latents, 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(f"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(f"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 generate( args: argparse.Namespace, gen_settings: GenerationSettings, shared_models: Optional[Dict] = None ) -> tuple[AutoencoderKLCausal3D, torch.Tensor]: """main function for generation Args: args: command line arguments shared_models: dictionary containing pre-loaded models Returns: tuple: (AutoencoderKLCausal3D model (vae), torch.Tensor generated latent) """ device, dit_weight_dtype = (gen_settings.device, gen_settings.dit_weight_dtype) # 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 # Check if we have shared models if shared_models is not None: # Use shared models and encoded data vae = shared_models.get("vae") height, width, video_seconds, context, context_null, context_img, end_latent, control_latents, control_mask_images = ( prepare_i2v_inputs(args, device, vae, shared_models) ) else: # prepare inputs without shared models vae = load_vae(args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, device) height, width, video_seconds, context, context_null, context_img, end_latent, control_latents, control_mask_images = ( prepare_i2v_inputs(args, device, vae) ) if shared_models is None or "model" not in shared_models: # load DiT model model = load_dit_model(args, device) # merge LoRA weights 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, device, convert_lora_for_framepack) # if we only want to save the model, we can skip the rest if args.save_merged_model: return None, None # optimize model: fp8 conversion, block swap etc. optimize_model(model, args, device) if shared_models is not None: shared_models["model"] = model else: # use shared model model: HunyuanVideoTransformer3DModelPacked = shared_models["model"] model.move_to_device_except_swap_blocks(device) 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(f"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) 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=3.0, 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, ) # 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 if shared_models is None: del model # free memory synchronize_device(device) else: # move model to CPU to save memory model.to("cpu") # wait for 5 seconds until block swap is done if 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, real_history_latents def generate_with_one_frame_inference( args: argparse.Namespace, model: HunyuanVideoTransformer3DModelPacked, 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(f"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(f"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(f"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(f"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) 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=3.0, 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, ) 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 = datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S") 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 = datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S") 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 = datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S") 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, 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 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. Args: args: Base command line arguments Returns: Dict: Dictionary of shared models """ shared_models = {} tokenizer1, text_encoder1 = load_text_encoder1(args, args.fp8_llm, "cpu") tokenizer2, text_encoder2 = load_text_encoder2(args) feature_extractor, image_encoder = load_image_encoders(args) vae = load_vae(args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, "cpu") 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 shared_models["vae"] = vae return shared_models def process_batch_prompts(prompts_data: List[Dict], args: argparse.Namespace) -> None: """Process multiple prompts with model reuse Args: prompts_data: List of prompt data dictionaries args: Base command line arguments """ if not prompts_data: logger.warning("No valid prompts found") return # 1. Load configuration gen_settings = get_generation_settings(args) device = gen_settings.device # 2. Load models to CPU in advance except for VAE and DiT shared_models = load_shared_models(args) # 3. Generate for each prompt all_latents = [] all_prompt_args = [] with torch.no_grad(): for prompt_data in prompts_data: prompt = prompt_data["prompt"] prompt_args = apply_overrides(args, prompt_data) logger.info(f"Processing prompt: {prompt}") try: vae, latent = generate(prompt_args, gen_settings, shared_models) # Save latent if needed if args.output_type == "latent" or args.output_type == "both" or args.output_type == "latent_images": height, width = latent.shape[-2], latent.shape[-1] # BCTHW height *= 8 width *= 8 save_latent(latent, prompt_args, height, width) all_latents.append(latent) all_prompt_args.append(prompt_args) except Exception as e: logger.error(f"Error processing prompt: {prompt}. Error: {e}") continue # 4. Free models if "model" in shared_models: del shared_models["model"] del shared_models["tokenizer1"] del shared_models["text_encoder1"] del shared_models["tokenizer2"] del shared_models["text_encoder2"] del shared_models["feature_extractor"] del shared_models["image_encoder"] clean_memory_on_device(device) synchronize_device(device) # 5. Decode latents if needed if args.output_type != "latent": logger.info("Decoding latents to videos/images") vae.to(device) for i, (latent, prompt_args) in enumerate(zip(all_latents, all_prompt_args)): logger.info(f"Decoding output {i+1}/{len(all_latents)}") # avoid saving latents again (ugly hack) if prompt_args.output_type == "both": prompt_args.output_type = "video" elif prompt_args.output_type == "latent_images": prompt_args.output_type = "images" save_output(prompt_args, vae, latent[0], 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) print("Interactive mode. Enter prompts (Ctrl+D or Ctrl+Z (Windows) to exit):") try: while True: try: line = input("> ") if not line.strip(): continue # Parse prompt prompt_data = parse_prompt_line(line) prompt_args = apply_overrides(args, prompt_data) # Generate latent vae, latent = generate(prompt_args, gen_settings, shared_models) # Save latent and video save_output(prompt_args, 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() # 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: latents = load_file(latent_path)["latent"] 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, 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) vae, latent = generate(args, gen_settings) # print(f"Generated latent shape: {latent.shape}") if args.save_merged_model: return # Save latent and video save_output(args, vae, latent[0], device) logger.info("Done!") if __name__ == "__main__": main()