mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-06 13:47:06 +00:00
1269 lines
49 KiB
Python
1269 lines
49 KiB
Python
import argparse
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import datetime
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import gc
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from importlib.util import find_spec
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import random
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import os
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import re
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import time
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import copy
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from types import ModuleType, SimpleNamespace
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from typing import Tuple, Optional, List, Any, Dict, Union
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import numpy as np
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import torch
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from safetensors.torch import load_file, save_file
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from safetensors import safe_open
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from tqdm import tqdm
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from diffusers.utils.torch_utils import randn_tensor
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from PIL import Image
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from library import hunyuan_image_models, hunyuan_image_text_encoder, hunyuan_image_utils
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from library import hunyuan_image_vae
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from library.hunyuan_image_vae import HunyuanVAE2D
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from library.device_utils import clean_memory_on_device, synchronize_device
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from library.safetensors_utils import mem_eff_save_file
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from networks import lora_hunyuan_image
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lycoris_available = find_spec("lycoris") is not None
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if lycoris_available:
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from lycoris.kohya import create_network_from_weights
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from library.utils import setup_logging
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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class GenerationSettings:
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def __init__(self, device: torch.device, dit_weight_dtype: Optional[torch.dtype] = None):
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self.device = device
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self.dit_weight_dtype = dit_weight_dtype # not used currently because model may be optimized
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def parse_args() -> argparse.Namespace:
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"""parse command line arguments"""
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parser = argparse.ArgumentParser(description="HunyuanImage inference script")
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parser.add_argument("--dit", type=str, default=None, help="DiT directory or path")
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parser.add_argument("--vae", type=str, default=None, help="VAE directory or path")
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parser.add_argument("--text_encoder", type=str, required=True, help="Text Encoder 1 (Qwen2.5-VL) directory or path")
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parser.add_argument("--byt5", type=str, default=None, help="ByT5 Text Encoder 2 directory or path")
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# LoRA
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parser.add_argument("--lora_weight", type=str, nargs="*", required=False, default=None, help="LoRA weight path")
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parser.add_argument("--lora_multiplier", type=float, nargs="*", default=1.0, help="LoRA multiplier")
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parser.add_argument("--include_patterns", type=str, nargs="*", default=None, help="LoRA module include patterns")
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parser.add_argument("--exclude_patterns", type=str, nargs="*", default=None, help="LoRA module exclude patterns")
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parser.add_argument(
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"--save_merged_model",
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type=str,
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default=None,
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help="Save merged model to path. If specified, no inference will be performed.",
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)
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# inference
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parser.add_argument(
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"--guidance_scale", type=float, default=3.5, help="Guidance scale for classifier free guidance. Default is 3.5."
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)
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parser.add_argument(
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"--apg_start_step_ocr",
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type=int,
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default=38,
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help="Starting step for Adaptive Projected Guidance (APG) for image with text. Default is 38. Should be less than infer_steps, usually near the end.",
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)
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parser.add_argument(
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"--apg_start_step_general",
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type=int,
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default=5,
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help="Starting step for Adaptive Projected Guidance (APG) for general image. Default is 5. Should be less than infer_steps, usually near the beginning.",
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)
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parser.add_argument(
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"--guidance_rescale",
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type=float,
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default=0.0,
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help="Guidance rescale factor for steps without APG, 0.0 to 1.0. Default is 0.0 (no rescale).",
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)
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parser.add_argument(
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"--guidance_rescale_apg",
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type=float,
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default=0.0,
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help="Guidance rescale factor for steps with APG, 0.0 to 1.0. Default is 0.0 (no rescale).",
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)
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parser.add_argument("--prompt", type=str, default=None, help="prompt for generation")
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parser.add_argument("--negative_prompt", type=str, default="", help="negative prompt for generation, default is empty string")
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parser.add_argument("--image_size", type=int, nargs=2, default=[2048, 2048], help="image size, height and width")
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parser.add_argument("--infer_steps", type=int, default=50, help="number of inference steps, default is 50")
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parser.add_argument("--save_path", type=str, required=True, help="path to save generated video")
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parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.")
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# Flow Matching
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parser.add_argument(
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"--flow_shift",
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type=float,
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default=5.0,
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help="Shift factor for flow matching schedulers. Default is 5.0.",
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)
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parser.add_argument("--fp8", action="store_true", help="use fp8 for DiT model")
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parser.add_argument("--fp8_scaled", action="store_true", help="use scaled fp8 for DiT, only for fp8")
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parser.add_argument("--text_encoder_cpu", action="store_true", help="Inference on CPU for Text Encoders")
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parser.add_argument(
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"--vae_chunk_size",
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type=int,
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default=None, # default is None (no chunking)
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help="Chunk size for VAE decoding to reduce memory usage. Default is None (no chunking). 16 is recommended if enabled"
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" / メモリ使用量を減らすためのVAEデコードのチャンクサイズ。デフォルトはNone（チャンクなし）。有効にする場合は16程度を推奨。",
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)
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parser.add_argument(
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"--device", type=str, default=None, help="device to use for inference. If None, use CUDA if available, otherwise use CPU"
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)
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parser.add_argument(
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"--attn_mode",
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type=str,
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default="torch",
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choices=["flash", "torch", "sageattn", "xformers", "sdpa"], # "sdpa" for backward compatibility
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help="attention mode",
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)
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parser.add_argument("--blocks_to_swap", type=int, default=0, help="number of blocks to swap in the model")
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parser.add_argument(
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"--output_type",
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type=str,
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default="images",
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choices=["images", "latent", "latent_images"],
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help="output type",
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)
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parser.add_argument("--no_metadata", action="store_true", help="do not save metadata")
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parser.add_argument("--latent_path", type=str, nargs="*", default=None, help="path to latent for decode. no inference")
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parser.add_argument(
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"--lycoris", action="store_true", help=f"use lycoris for inference{'' if lycoris_available else ' (not available)'}"
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)
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# arguments for batch and interactive modes
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parser.add_argument("--from_file", type=str, default=None, help="Read prompts from a file")
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parser.add_argument("--interactive", action="store_true", help="Interactive mode: read prompts from console")
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args = parser.parse_args()
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# Validate arguments
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if args.from_file and args.interactive:
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raise ValueError("Cannot use both --from_file and --interactive at the same time")
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if args.latent_path is None or len(args.latent_path) == 0:
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if args.prompt is None and not args.from_file and not args.interactive:
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raise ValueError("Either --prompt, --from_file or --interactive must be specified")
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if args.lycoris and not lycoris_available:
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raise ValueError("install lycoris: https://github.com/KohakuBlueleaf/LyCORIS")
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if args.attn_mode == "sdpa":
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args.attn_mode = "torch" # backward compatibility
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return args
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def parse_prompt_line(line: str) -> Dict[str, Any]:
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"""Parse a prompt line into a dictionary of argument overrides
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Args:
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line: Prompt line with options
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Returns:
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Dict[str, Any]: Dictionary of argument overrides
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"""
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# TODO common function with hv_train_network.line_to_prompt_dict
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parts = line.split(" --")
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prompt = parts[0].strip()
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# Create dictionary of overrides
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overrides = {"prompt": prompt}
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for part in parts[1:]:
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if not part.strip():
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continue
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option_parts = part.split(" ", 1)
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option = option_parts[0].strip()
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value = option_parts[1].strip() if len(option_parts) > 1 else ""
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# Map options to argument names
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if option == "w":
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overrides["image_size_width"] = int(value)
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elif option == "h":
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overrides["image_size_height"] = int(value)
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elif option == "d":
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overrides["seed"] = int(value)
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elif option == "s":
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overrides["infer_steps"] = int(value)
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elif option == "g" or option == "l":
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overrides["guidance_scale"] = float(value)
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elif option == "fs":
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overrides["flow_shift"] = float(value)
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# elif option == "i":
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# overrides["image_path"] = value
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# elif option == "im":
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# overrides["image_mask_path"] = value
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# elif option == "cn":
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# overrides["control_path"] = value
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elif option == "n":
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overrides["negative_prompt"] = value
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# elif option == "ci": # control_image_path
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# overrides["control_image_path"] = value
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return overrides
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def apply_overrides(args: argparse.Namespace, overrides: Dict[str, Any]) -> argparse.Namespace:
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"""Apply overrides to args
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Args:
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args: Original arguments
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overrides: Dictionary of overrides
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Returns:
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argparse.Namespace: New arguments with overrides applied
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"""
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args_copy = copy.deepcopy(args)
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for key, value in overrides.items():
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if key == "image_size_width":
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args_copy.image_size[1] = value
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elif key == "image_size_height":
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args_copy.image_size[0] = value
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else:
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setattr(args_copy, key, value)
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return args_copy
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def check_inputs(args: argparse.Namespace) -> Tuple[int, int]:
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"""Validate video size and length
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Args:
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args: command line arguments
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Returns:
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Tuple[int, int]: (height, width)
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"""
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height = args.image_size[0]
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width = args.image_size[1]
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if height % 32 != 0 or width % 32 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
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return height, width
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# region Model
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def load_dit_model(
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args: argparse.Namespace, device: torch.device, dit_weight_dtype: Optional[torch.dtype] = None
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) -> hunyuan_image_models.HYImageDiffusionTransformer:
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"""load DiT model
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Args:
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args: command line arguments
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device: device to use
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dit_weight_dtype: data type for the model weights. None for as-is
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Returns:
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qwen_image_model.HYImageDiffusionTransformer: DiT model instance
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"""
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# If LyCORIS is enabled, we will load the model to CPU and then merge LoRA weights (static method)
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loading_device = "cpu"
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if args.blocks_to_swap == 0 and not args.lycoris:
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loading_device = device
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# load LoRA weights
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if not args.lycoris and args.lora_weight is not None and len(args.lora_weight) > 0:
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lora_weights_list = []
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for lora_weight in args.lora_weight:
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logger.info(f"Loading LoRA weight from: {lora_weight}")
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lora_sd = load_file(lora_weight) # load on CPU, dtype is as is
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# lora_sd = filter_lora_state_dict(lora_sd, args.include_patterns, args.exclude_patterns)
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lora_weights_list.append(lora_sd)
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else:
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lora_weights_list = None
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loading_weight_dtype = dit_weight_dtype
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if args.fp8_scaled and not args.lycoris:
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loading_weight_dtype = None # we will load weights as-is and then optimize to fp8
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model = hunyuan_image_models.load_hunyuan_image_model(
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device,
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args.dit,
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args.attn_mode,
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True, # enable split_attn to trim masked tokens
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loading_device,
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loading_weight_dtype,
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args.fp8_scaled and not args.lycoris,
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lora_weights_list=lora_weights_list,
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lora_multipliers=args.lora_multiplier,
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)
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# merge LoRA weights
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if args.lycoris:
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if args.lora_weight is not None and len(args.lora_weight) > 0:
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merge_lora_weights(lora_hunyuan_image, model, args, device)
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if args.fp8_scaled:
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# load state dict as-is and optimize to fp8
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state_dict = model.state_dict()
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# if no blocks to swap, we can move the weights to GPU after optimization on GPU (omit redundant CPU->GPU copy)
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move_to_device = args.blocks_to_swap == 0 # if blocks_to_swap > 0, we will keep the model on CPU
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state_dict = model.fp8_optimization(state_dict, device, move_to_device, use_scaled_mm=False) # args.fp8_fast)
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info = model.load_state_dict(state_dict, strict=True, assign=True)
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logger.info(f"Loaded FP8 optimized weights: {info}")
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# if we only want to save the model, we can skip the rest
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if args.save_merged_model:
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return None
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if not args.fp8_scaled:
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# simple cast to dit_weight_dtype
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target_dtype = None # load as-is (dit_weight_dtype == dtype of the weights in state_dict)
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target_device = None
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if dit_weight_dtype is not None: # in case of args.fp8 and not args.fp8_scaled
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logger.info(f"Convert model to {dit_weight_dtype}")
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target_dtype = dit_weight_dtype
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if args.blocks_to_swap == 0:
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logger.info(f"Move model to device: {device}")
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target_device = device
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model.to(target_device, target_dtype) # move and cast at the same time. this reduces redundant copy operations
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# if args.compile:
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# compile_backend, compile_mode, compile_dynamic, compile_fullgraph = args.compile_args
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# logger.info(
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# f"Torch Compiling[Backend: {compile_backend}; Mode: {compile_mode}; Dynamic: {compile_dynamic}; Fullgraph: {compile_fullgraph}]"
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# )
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# torch._dynamo.config.cache_size_limit = 32
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# for i in range(len(model.blocks)):
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# model.blocks[i] = torch.compile(
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# model.blocks[i],
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# backend=compile_backend,
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# mode=compile_mode,
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# dynamic=compile_dynamic.lower() in "true",
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# fullgraph=compile_fullgraph.lower() in "true",
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# )
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if args.blocks_to_swap > 0:
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logger.info(f"Enable swap {args.blocks_to_swap} blocks to CPU from device: {device}")
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model.enable_block_swap(args.blocks_to_swap, device, supports_backward=False)
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model.move_to_device_except_swap_blocks(device)
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model.prepare_block_swap_before_forward()
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else:
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# make sure the model is on the right device
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model.to(device)
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model.eval().requires_grad_(False)
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clean_memory_on_device(device)
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return model
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def merge_lora_weights(
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lora_module: ModuleType,
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model: torch.nn.Module,
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lora_weights: List[str],
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lora_multipliers: List[float],
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include_patterns: Optional[List[str]],
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exclude_patterns: Optional[List[str]],
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device: torch.device,
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lycoris: bool = False,
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save_merged_model: Optional[str] = None,
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converter: Optional[callable] = None,
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) -> None:
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"""merge LoRA weights to the model
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Args:
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lora_module: LoRA module, e.g. lora_wan
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model: DiT model
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lora_weights: paths to LoRA weights
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lora_multipliers: multipliers for LoRA weights
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include_patterns: regex patterns to include LoRA modules
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exclude_patterns: regex patterns to exclude LoRA modules
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device: torch.device
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lycoris: use LyCORIS
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save_merged_model: path to save merged model, if specified, no inference will be performed
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converter: Optional[callable] = None
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"""
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if lora_weights is None or len(lora_weights) == 0:
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return
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for i, lora_weight in enumerate(lora_weights):
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if lora_multipliers is not None and len(lora_multipliers) > i:
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lora_multiplier = lora_multipliers[i]
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else:
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lora_multiplier = 1.0
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logger.info(f"Loading LoRA weights from {lora_weight} with multiplier {lora_multiplier}")
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weights_sd = load_file(lora_weight)
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if converter is not None:
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weights_sd = converter(weights_sd)
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# apply include/exclude patterns
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original_key_count = len(weights_sd.keys())
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if include_patterns is not None and len(include_patterns) > i:
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include_pattern = include_patterns[i]
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regex_include = re.compile(include_pattern)
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weights_sd = {k: v for k, v in weights_sd.items() if regex_include.search(k)}
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logger.info(f"Filtered keys with include pattern {include_pattern}: {original_key_count} -> {len(weights_sd.keys())}")
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if exclude_patterns is not None and len(exclude_patterns) > i:
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original_key_count_ex = len(weights_sd.keys())
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exclude_pattern = exclude_patterns[i]
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regex_exclude = re.compile(exclude_pattern)
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weights_sd = {k: v for k, v in weights_sd.items() if not regex_exclude.search(k)}
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logger.info(
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f"Filtered keys with exclude pattern {exclude_pattern}: {original_key_count_ex} -> {len(weights_sd.keys())}"
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)
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if len(weights_sd) != original_key_count:
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remaining_keys = list(set([k.split(".", 1)[0] for k in weights_sd.keys()]))
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remaining_keys.sort()
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logger.info(f"Remaining LoRA modules after filtering: {remaining_keys}")
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if len(weights_sd) == 0:
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logger.warning("No keys left after filtering.")
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if lycoris:
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lycoris_net, _ = create_network_from_weights(
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multiplier=lora_multiplier,
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file=None,
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weights_sd=weights_sd,
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unet=model,
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text_encoder=None,
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vae=None,
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for_inference=True,
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)
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lycoris_net.merge_to(None, model, weights_sd, dtype=None, device=device)
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else:
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network = lora_module.create_arch_network_from_weights(lora_multiplier, weights_sd, unet=model, for_inference=True)
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network.merge_to(None, model, weights_sd, device=device, non_blocking=True)
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synchronize_device(device)
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logger.info("LoRA weights loaded")
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# save model here before casting to dit_weight_dtype
|
||
if save_merged_model:
|
||
logger.info(f"Saving merged model to {save_merged_model}")
|
||
mem_eff_save_file(model.state_dict(), save_merged_model) # save_file needs a lot of memory
|
||
logger.info("Merged model saved")
|
||
|
||
|
||
# endregion
|
||
|
||
|
||
def decode_latent(vae: HunyuanVAE2D, latent: torch.Tensor, device: torch.device) -> torch.Tensor:
|
||
logger.info(f"Decoding image. Latent shape {latent.shape}, device {device}")
|
||
|
||
vae.to(device)
|
||
with torch.no_grad():
|
||
latent = latent / vae.scaling_factor # scale latent back to original range
|
||
pixels = vae.decode(latent.to(device, dtype=vae.dtype))
|
||
pixels = pixels.to("cpu", dtype=torch.float32) # move to CPU and convert to float32 (bfloat16 is not supported by numpy)
|
||
vae.to("cpu")
|
||
|
||
logger.info(f"Decoded. Pixel shape {pixels.shape}")
|
||
return pixels[0] # remove batch dimension
|
||
|
||
|
||
def prepare_text_inputs(
|
||
args: argparse.Namespace, device: torch.device, shared_models: Optional[Dict] = None
|
||
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
|
||
"""Prepare text-related inputs for T2I: LLM encoding."""
|
||
|
||
# load text encoder: conds_cache holds cached encodings for prompts without padding
|
||
conds_cache = {}
|
||
vl_device = torch.device("cpu") if args.text_encoder_cpu else device
|
||
if shared_models is not None:
|
||
tokenizer_vlm = shared_models.get("tokenizer_vlm")
|
||
text_encoder_vlm = shared_models.get("text_encoder_vlm")
|
||
tokenizer_byt5 = shared_models.get("tokenizer_byt5")
|
||
text_encoder_byt5 = shared_models.get("text_encoder_byt5")
|
||
|
||
if "conds_cache" in shared_models: # Use shared cache if available
|
||
conds_cache = shared_models["conds_cache"]
|
||
|
||
# text_encoder is on device (batched inference) or CPU (interactive inference)
|
||
else: # Load if not in shared_models
|
||
vl_dtype = torch.bfloat16 # Default dtype for Text Encoder
|
||
tokenizer_vlm, text_encoder_vlm = hunyuan_image_text_encoder.load_qwen2_5_vl(
|
||
args.text_encoder, dtype=vl_dtype, device=vl_device, disable_mmap=True
|
||
)
|
||
tokenizer_byt5, text_encoder_byt5 = hunyuan_image_text_encoder.load_byt5(
|
||
args.byt5, dtype=torch.float16, device=vl_device, disable_mmap=True
|
||
)
|
||
|
||
# Store original devices to move back later if they were shared. This does nothing if shared_models is None
|
||
text_encoder_original_device = text_encoder_vlm.device if text_encoder_vlm else None
|
||
|
||
# Ensure text_encoder is not None before proceeding
|
||
if not text_encoder_vlm or not tokenizer_vlm or not tokenizer_byt5 or not text_encoder_byt5:
|
||
raise ValueError("Text encoder or tokenizer is 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 Encoder 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_encoder_vlm.to(vl_device) # If text_encoder_cpu is True, this will be CPU
|
||
text_encoder_byt5.to(vl_device)
|
||
|
||
logger.info("Encoding prompt with Text Encoder")
|
||
|
||
prompt = args.prompt
|
||
cache_key = prompt
|
||
if cache_key in conds_cache:
|
||
embed, mask, embed_byt5, mask_byt5, ocr_mask = conds_cache[cache_key]
|
||
else:
|
||
move_models_to_device_if_needed()
|
||
|
||
with torch.no_grad():
|
||
embed, mask = hunyuan_image_text_encoder.get_qwen_prompt_embeds(tokenizer_vlm, text_encoder_vlm, prompt)
|
||
ocr_mask, embed_byt5, mask_byt5 = hunyuan_image_text_encoder.get_glyph_prompt_embeds(
|
||
tokenizer_byt5, text_encoder_byt5, prompt
|
||
)
|
||
embed = embed.cpu()
|
||
mask = mask.cpu()
|
||
embed_byt5 = embed_byt5.cpu()
|
||
mask_byt5 = mask_byt5.cpu()
|
||
|
||
conds_cache[cache_key] = (embed, mask, embed_byt5, mask_byt5, ocr_mask)
|
||
|
||
negative_prompt = args.negative_prompt
|
||
cache_key = negative_prompt
|
||
if cache_key in conds_cache:
|
||
negative_embed, negative_mask, negative_embed_byt5, negative_mask_byt5, negative_ocr_mask = conds_cache[cache_key]
|
||
else:
|
||
move_models_to_device_if_needed()
|
||
|
||
with torch.no_grad():
|
||
negative_embed, negative_mask = hunyuan_image_text_encoder.get_qwen_prompt_embeds(
|
||
tokenizer_vlm, text_encoder_vlm, negative_prompt
|
||
)
|
||
negative_ocr_mask, negative_embed_byt5, negative_mask_byt5 = hunyuan_image_text_encoder.get_glyph_prompt_embeds(
|
||
tokenizer_byt5, text_encoder_byt5, negative_prompt
|
||
)
|
||
negative_embed = negative_embed.cpu()
|
||
negative_mask = negative_mask.cpu()
|
||
negative_embed_byt5 = negative_embed_byt5.cpu()
|
||
negative_mask_byt5 = negative_mask_byt5.cpu()
|
||
|
||
conds_cache[cache_key] = (negative_embed, negative_mask, negative_embed_byt5, negative_mask_byt5, negative_ocr_mask)
|
||
|
||
if not (shared_models and "text_encoder_vlm" in shared_models): # if loaded locally
|
||
# There is a bug text_encoder is not freed from GPU memory when text encoder is fp8
|
||
del tokenizer_vlm, text_encoder_vlm, tokenizer_byt5, text_encoder_byt5
|
||
gc.collect() # This may force Text Encoder to be freed from GPU memory
|
||
else: # if shared, move back to original device (likely CPU)
|
||
if text_encoder_vlm:
|
||
text_encoder_vlm.to(text_encoder_original_device)
|
||
if text_encoder_byt5:
|
||
text_encoder_byt5.to(text_encoder_original_device)
|
||
|
||
clean_memory_on_device(device)
|
||
|
||
arg_c = {"embed": embed, "mask": mask, "embed_byt5": embed_byt5, "mask_byt5": mask_byt5, "ocr_mask": ocr_mask, "prompt": prompt}
|
||
arg_null = {
|
||
"embed": negative_embed,
|
||
"mask": negative_mask,
|
||
"embed_byt5": negative_embed_byt5,
|
||
"mask_byt5": negative_mask_byt5,
|
||
"ocr_mask": negative_ocr_mask,
|
||
"prompt": negative_prompt,
|
||
}
|
||
|
||
return arg_c, arg_null
|
||
|
||
|
||
def generate(
|
||
args: argparse.Namespace,
|
||
gen_settings: GenerationSettings,
|
||
shared_models: Optional[Dict] = None,
|
||
precomputed_text_data: Optional[Dict] = None,
|
||
) -> torch.Tensor:
|
||
"""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: (HunyuanVAE2D model (vae) or None, 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
|
||
|
||
if precomputed_text_data is not None:
|
||
logger.info("Using precomputed text data.")
|
||
context = precomputed_text_data["context"]
|
||
context_null = precomputed_text_data["context_null"]
|
||
|
||
else:
|
||
logger.info("No precomputed data. Preparing image and text inputs.")
|
||
context, context_null = prepare_text_inputs(args, device, shared_models)
|
||
|
||
if shared_models is None or "model" not in shared_models:
|
||
# load DiT model
|
||
model = load_dit_model(args, device, dit_weight_dtype)
|
||
|
||
# if we only want to save the model, we can skip the rest
|
||
if args.save_merged_model:
|
||
return None
|
||
|
||
if shared_models is not None:
|
||
shared_models["model"] = model
|
||
else:
|
||
# use shared model
|
||
logger.info("Using shared DiT model.")
|
||
model: hunyuan_image_models.HYImageDiffusionTransformer = shared_models["model"]
|
||
model.move_to_device_except_swap_blocks(device) # Handles block swap correctly
|
||
model.prepare_block_swap_before_forward()
|
||
|
||
return generate_body(args, model, context, context_null, device, seed)
|
||
|
||
|
||
def generate_body(
|
||
args: Union[argparse.Namespace, SimpleNamespace],
|
||
model: hunyuan_image_models.HYImageDiffusionTransformer,
|
||
context: Dict[str, Any],
|
||
context_null: Optional[Dict[str, Any]],
|
||
device: torch.device,
|
||
seed: int,
|
||
) -> torch.Tensor:
|
||
|
||
# set random generator
|
||
seed_g = torch.Generator(device="cpu")
|
||
seed_g.manual_seed(seed)
|
||
|
||
height, width = check_inputs(args)
|
||
logger.info(f"Image size: {height}x{width} (HxW), infer_steps: {args.infer_steps}")
|
||
|
||
# image generation ######
|
||
|
||
logger.info(f"Prompt: {context['prompt']}")
|
||
|
||
embed = context["embed"].to(device, dtype=torch.bfloat16)
|
||
mask = context["mask"].to(device, dtype=torch.bfloat16)
|
||
embed_byt5 = context["embed_byt5"].to(device, dtype=torch.bfloat16)
|
||
mask_byt5 = context["mask_byt5"].to(device, dtype=torch.bfloat16)
|
||
ocr_mask = context["ocr_mask"] # list of bool
|
||
|
||
if context_null is None:
|
||
context_null = context # dummy for unconditional
|
||
|
||
negative_embed = context_null["embed"].to(device, dtype=torch.bfloat16)
|
||
negative_mask = context_null["mask"].to(device, dtype=torch.bfloat16)
|
||
negative_embed_byt5 = context_null["embed_byt5"].to(device, dtype=torch.bfloat16)
|
||
negative_mask_byt5 = context_null["mask_byt5"].to(device, dtype=torch.bfloat16)
|
||
# negative_ocr_mask = context_null["ocr_mask"] # list of bool
|
||
|
||
# Prepare latent variables
|
||
num_channels_latents = model.in_channels
|
||
shape = (1, num_channels_latents, height // hunyuan_image_vae.VAE_SCALE_FACTOR, width // hunyuan_image_vae.VAE_SCALE_FACTOR)
|
||
latents = randn_tensor(shape, generator=seed_g, device=device, dtype=torch.bfloat16)
|
||
|
||
logger.info(
|
||
f"Embed: {embed.shape}, embed byt5: {embed_byt5.shape}, negative_embed: {negative_embed.shape}, negative embed byt5: {negative_embed_byt5.shape}, latents: {latents.shape}"
|
||
)
|
||
|
||
# Prepare timesteps
|
||
timesteps, sigmas = hunyuan_image_utils.get_timesteps_sigmas(args.infer_steps, args.flow_shift, device)
|
||
|
||
# Prepare Guider
|
||
cfg_guider_ocr = hunyuan_image_utils.AdaptiveProjectedGuidance(
|
||
guidance_scale=10.0,
|
||
eta=0.0,
|
||
adaptive_projected_guidance_rescale=10.0,
|
||
adaptive_projected_guidance_momentum=-0.5,
|
||
guidance_rescale=args.guidance_rescale_apg,
|
||
)
|
||
cfg_guider_general = hunyuan_image_utils.AdaptiveProjectedGuidance(
|
||
guidance_scale=10.0,
|
||
eta=0.0,
|
||
adaptive_projected_guidance_rescale=10.0,
|
||
adaptive_projected_guidance_momentum=-0.5,
|
||
guidance_rescale=args.guidance_rescale_apg,
|
||
)
|
||
|
||
# Denoising loop
|
||
do_cfg = args.guidance_scale != 1.0
|
||
# print(f"embed shape: {embed.shape}, mean: {embed.mean()}, std: {embed.std()}")
|
||
# print(f"embed_byt5 shape: {embed_byt5.shape}, mean: {embed_byt5.mean()}, std: {embed_byt5.std()}")
|
||
# print(f"negative_embed shape: {negative_embed.shape}, mean: {negative_embed.mean()}, std: {negative_embed.std()}")
|
||
# print(f"negative_embed_byt5 shape: {negative_embed_byt5.shape}, mean: {negative_embed_byt5.mean()}, std: {negative_embed_byt5.std()}")
|
||
# print(f"latents shape: {latents.shape}, mean: {latents.mean()}, std: {latents.std()}")
|
||
# print(f"mask shape: {mask.shape}, sum: {mask.sum()}")
|
||
# print(f"mask_byt5 shape: {mask_byt5.shape}, sum: {mask_byt5.sum()}")
|
||
# print(f"negative_mask shape: {negative_mask.shape}, sum: {negative_mask.sum()}")
|
||
# print(f"negative_mask_byt5 shape: {negative_mask_byt5.shape}, sum: {negative_mask_byt5.sum()}")
|
||
|
||
autocast_enabled = args.fp8
|
||
|
||
with tqdm(total=len(timesteps), desc="Denoising steps") as pbar:
|
||
for i, t in enumerate(timesteps):
|
||
t_expand = t.expand(latents.shape[0]).to(torch.int64)
|
||
|
||
with torch.no_grad(), torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=autocast_enabled):
|
||
noise_pred = model(latents, t_expand, embed, mask, embed_byt5, mask_byt5)
|
||
|
||
if do_cfg:
|
||
with torch.no_grad(), torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=autocast_enabled):
|
||
uncond_noise_pred = model(
|
||
latents, t_expand, negative_embed, negative_mask, negative_embed_byt5, negative_mask_byt5
|
||
)
|
||
noise_pred = hunyuan_image_utils.apply_classifier_free_guidance(
|
||
noise_pred,
|
||
uncond_noise_pred,
|
||
ocr_mask[0],
|
||
args.guidance_scale,
|
||
i,
|
||
apg_start_step_ocr=args.apg_start_step_ocr,
|
||
apg_start_step_general=args.apg_start_step_general,
|
||
cfg_guider_ocr=cfg_guider_ocr,
|
||
cfg_guider_general=cfg_guider_general,
|
||
guidance_rescale=args.guidance_rescale,
|
||
)
|
||
|
||
# ensure latents dtype is consistent
|
||
latents = hunyuan_image_utils.step(latents, noise_pred, sigmas, i).to(latents.dtype)
|
||
|
||
pbar.update()
|
||
|
||
return latents
|
||
|
||
|
||
def get_time_flag():
|
||
return datetime.datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S-%f")[:-3]
|
||
|
||
|
||
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
|
||
|
||
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}",
|
||
"infer_steps": f"{args.infer_steps}",
|
||
# "embedded_cfg_scale": f"{args.embedded_cfg_scale}",
|
||
"guidance_scale": f"{args.guidance_scale}",
|
||
}
|
||
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_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}"
|
||
|
||
x = torch.clamp(sample, -1.0, 1.0)
|
||
x = ((x + 1.0) * 127.5).to(torch.uint8).cpu().numpy()
|
||
x = x.transpose(1, 2, 0) # C, H, W -> H, W, C
|
||
|
||
image = Image.fromarray(x)
|
||
image.save(os.path.join(save_path, f"{image_name}.png"))
|
||
|
||
logger.info(f"Sample images saved to: {save_path}/{image_name}")
|
||
|
||
return f"{save_path}/{image_name}"
|
||
|
||
|
||
def save_output(
|
||
args: argparse.Namespace,
|
||
vae: HunyuanVAE2D,
|
||
latent: torch.Tensor,
|
||
device: torch.device,
|
||
original_base_name: Optional[str] = None,
|
||
) -> None:
|
||
"""save output
|
||
|
||
Args:
|
||
args: command line arguments
|
||
vae: VAE model
|
||
latent: latent tensor
|
||
device: device to use
|
||
original_base_name: original base name (if latents are loaded from files)
|
||
"""
|
||
height, width = latent.shape[-2], latent.shape[-1] # BCTHW
|
||
height *= hunyuan_image_vae.VAE_SCALE_FACTOR
|
||
width *= hunyuan_image_vae.VAE_SCALE_FACTOR
|
||
# print(f"Saving output. Latent shape {latent.shape}; pixel shape {height}x{width}")
|
||
if args.output_type == "latent" 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
|
||
|
||
if latent.ndim == 2: # S,C. For packed latents from other inference scripts
|
||
latent = latent.unsqueeze(0)
|
||
height, width = check_inputs(args) # Get height/width from args
|
||
latent = latent.view(
|
||
1, vae.latent_channels, height // hunyuan_image_vae.VAE_SCALE_FACTOR, width // hunyuan_image_vae.VAE_SCALE_FACTOR
|
||
)
|
||
|
||
image = decode_latent(vae, latent, device)
|
||
|
||
if args.output_type == "images" or args.output_type == "latent_images":
|
||
# save images
|
||
if original_base_name is None:
|
||
original_name = ""
|
||
else:
|
||
original_name = f"_{original_base_name}"
|
||
save_images(image, 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
|
||
vl_dtype = torch.bfloat16 # Default dtype for Text Encoder
|
||
tokenizer_vlm, text_encoder_vlm = hunyuan_image_text_encoder.load_qwen2_5_vl(
|
||
args.text_encoder, dtype=vl_dtype, device="cpu", disable_mmap=True
|
||
)
|
||
tokenizer_byt5, text_encoder_byt5 = hunyuan_image_text_encoder.load_byt5(
|
||
args.byt5, dtype=torch.float16, device="cpu", disable_mmap=True
|
||
)
|
||
shared_models["tokenizer_vlm"] = tokenizer_vlm
|
||
shared_models["text_encoder_vlm"] = text_encoder_vlm
|
||
shared_models["tokenizer_byt5"] = tokenizer_byt5
|
||
shared_models["text_encoder_byt5"] = text_encoder_byt5
|
||
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)
|
||
dit_weight_dtype = gen_settings.dit_weight_dtype
|
||
device = gen_settings.device
|
||
|
||
# 1. Prepare VAE
|
||
logger.info("Loading VAE for batch generation...")
|
||
vae_for_batch = hunyuan_image_vae.load_vae(args.vae, device="cpu", disable_mmap=True, chunk_size=args.vae_chunk_size)
|
||
vae_for_batch.eval()
|
||
|
||
all_prompt_args_list = [apply_overrides(args, pd) for pd in prompts_data] # Create all arg instances first
|
||
for prompt_args in all_prompt_args_list:
|
||
check_inputs(prompt_args) # Validate each prompt's height/width
|
||
|
||
# 2. Precompute Text Data (Text Encoder)
|
||
logger.info("Loading Text Encoder for batch text preprocessing...")
|
||
|
||
# Text Encoder loaded to CPU by load_text_encoder
|
||
vl_dtype = torch.bfloat16 # Default dtype for Text Encoder
|
||
tokenizer_vlm, text_encoder_vlm_batch = hunyuan_image_text_encoder.load_qwen2_5_vl(
|
||
args.text_encoder, dtype=vl_dtype, device="cpu", disable_mmap=True
|
||
)
|
||
tokenizer_byt5, text_encoder_byt5_batch = hunyuan_image_text_encoder.load_byt5(
|
||
args.byt5, dtype=torch.float16, device="cpu", disable_mmap=True
|
||
)
|
||
|
||
# Text Encoder to device for this phase
|
||
vl_device = torch.device("cpu") if args.text_encoder_cpu else device
|
||
text_encoder_vlm_batch.to(vl_device) # Moved into prepare_text_inputs logic
|
||
text_encoder_byt5_batch.to(vl_device)
|
||
|
||
all_precomputed_text_data = []
|
||
conds_cache_batch = {}
|
||
|
||
logger.info("Preprocessing text and LLM/TextEncoder encoding for all prompts...")
|
||
temp_shared_models_txt = {
|
||
"tokenizer_vlm": tokenizer_vlm,
|
||
"text_encoder_vlm": text_encoder_vlm_batch, # on GPU if not text_encoder_cpu
|
||
"tokenizer_byt5": tokenizer_byt5,
|
||
"text_encoder_byt5": text_encoder_byt5_batch, # on GPU if not text_encoder_cpu
|
||
"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
|
||
context, context_null = prepare_text_inputs(prompt_args_item, device, temp_shared_models_txt)
|
||
text_data = {"context": context, "context_null": context_null}
|
||
all_precomputed_text_data.append(text_data)
|
||
|
||
# Models should be removed from device after prepare_text_inputs
|
||
del tokenizer_batch, text_encoder_batch, temp_shared_models_txt, conds_cache_batch
|
||
gc.collect() # Force cleanup of Text Encoder from GPU 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, dit_weight_dtype) # Load directly to target device if possible
|
||
|
||
if first_prompt_args.save_merged_model:
|
||
logger.info("Merged DiT model saved. Skipping generation.")
|
||
|
||
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_text_data = all_precomputed_text_data[i]
|
||
height, width = check_inputs(prompt_args_item) # Get height/width for each prompt
|
||
|
||
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 Text Encoders.
|
||
# It will use the DiT model from shared_models_for_generate.
|
||
latent = generate(prompt_args_item, gen_settings, shared_models_for_generate, 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", "latent_images"]:
|
||
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
|
||
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 "latent_images", we already saved latent above.
|
||
# so we skip saving latent here.
|
||
if 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
|
||
|
||
vae = hunyuan_image_vae.load_vae(args.vae, device="cpu", disable_mmap=True, chunk_size=args.vae_chunk_size)
|
||
vae.eval()
|
||
|
||
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 encoders.
|
||
latent = generate(prompt_args, gen_settings, shared_models)
|
||
|
||
# # If not one_frame_inference, move DiT model to CPU after generation
|
||
# 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
|
||
|
||
# Save latent and video
|
||
# returned_vae from generate will be used for decoding here.
|
||
save_output(prompt_args, vae, latent, 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 = torch.bfloat16 # 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.image_size = [height, width]
|
||
|
||
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 = hunyuan_image_vae.load_vae(args.vae, device=device, disable_mmap=True, chunk_size=args.vae_chunk_size)
|
||
vae.eval()
|
||
save_output(args, vae, latent, device, original_base_names[i])
|
||
|
||
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 (Text Encoders, DiT).
|
||
latent = generate(args, gen_settings)
|
||
# print(f"Generated latent shape: {latent.shape}")
|
||
# if args.save_merged_model:
|
||
# return
|
||
|
||
clean_memory_on_device(device)
|
||
|
||
# Save latent and video
|
||
vae = hunyuan_image_vae.load_vae(args.vae, device="cpu", disable_mmap=True, chunk_size=args.vae_chunk_size)
|
||
vae.eval()
|
||
save_output(args, vae, latent, device)
|
||
|
||
logger.info("Done!")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|