aigc-apps-VideoX-Fun/scripts/z_image/README_TRAIN_LORA.md

Z-Image LoRA Fine-Tuning Training Guide

This document provides a complete workflow for Z-Image LoRA fine-tuning training, including environment configuration, data preparation, multiple distributed training strategies, and inference testing.

Note

: Z-Image has two model variants: Z-Image (standard version) and Z-Image-Turbo (fast inference version). This guide uses Z-Image by default. To use Z-Image-Turbo, simply replace the model path accordingly.

---

Table of Contents

• 1. Environment Configuration
• 2. Data Preparation
  • 2.1 Quick Test Dataset
  • 2.2 Dataset Structure
  • 2.3 metadata.json Format
  • 2.4 Relative vs Absolute Path Usage
• 3. LoRA Training
  • 3.1 Download Pretrained Model
  • 3.2 Quick Start (DeepSpeed-Zero-2)
  • 3.3 LoRA-Specific Parameters
  • 3.4 Training Validation
  • 3.5 Training with FSDP
  • 3.6 Other Backends
  • 3.7 Multi-Machine Distributed Training
• 4. Inference Testing
  • 4.1 Inference Parameter Parsing
  • 4.2 Single GPU Inference
  • 4.3 Multi-GPU Parallel Inference
• 5. Additional Resources

---

1. Environment Configuration

Method 1: Using requirements.txt

pip install -r requirements.txt

Method 2: Manual Dependency Installation

pip install Pillow einops safetensors timm tomesd librosa "torch>=2.1.2" torchdiffeq torchsde decord datasets numpy scikit-image
pip install omegaconf SentencePiece imageio[ffmpeg] imageio[pyav] tensorboard beautifulsoup4 ftfy func_timeout onnxruntime
pip install "peft>=0.17.0" "accelerate>=0.25.0" "gradio>=3.41.2" "diffusers>=0.30.1" "transformers>=4.46.2"
pip install yunchang xfuser modelscope openpyxl
pip uninstall opencv-python opencv-contrib-python opencv-python-headless -y
pip install opencv-python-headless
pip install deepspeed==0.17.0 numpy==1.26.4

Method 3: Using Docker

When using Docker, please ensure that the GPU driver and CUDA environment are correctly installed on your machine, then execute the following commands:

# pull image
docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun

# enter image
docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun

---

2. Data Preparation

2.1 Quick Test Dataset

We provide a test dataset containing several training samples.

# Download official demo dataset
modelscope download --dataset PAI/X-Fun-Images-Demo --local_dir ./datasets/X-Fun-Images-Demo

2.2 Dataset Structure

📦 datasets/
├── 📂 my_dataset/
│   ├── 📂 train/
│   │   ├── 📄 image001.jpg
│   │   ├── 📄 image002.png
│   │   └── 📄 ...
│   └── 📄 metadata.json

2.3 metadata.json Format

Relative Path Format (example):

[
  {
    "file_path": "train/image001.jpg",
    "text": "A beautiful sunset over the ocean, golden hour lighting",
    "width": 1024,
    "height": 1024
  },
  {
    "file_path": "train/image002.png",
    "text": "Portrait of a young woman, studio lighting, high quality",
    "width": 1328,
    "height": 1328
  }
]

Absolute Path Format:

[
  {
    "file_path": "/mnt/data/images/sunset.jpg",
    "text": "A beautiful sunset over the ocean",
    "width": 1024,
    "height": 1024
  }
]

Key Fields Description:

• file_path: Image path (relative or absolute)
• text: Image description (English prompt)
• width / height: Image dimensions (recommended to provide for bucket training; if not provided, they will be automatically read during training, which may slow down training when data is stored on slow systems like OSS)
  • You can use scripts/process_json_add_width_and_height.py to add width and height fields to JSON files without these fields, supporting both images and videos
  • Usage: python scripts/process_json_add_width_and_height.py --input_file datasets/X-Fun-Images-Demo/metadata.json --output_file datasets/X-Fun-Images-Demo/metadata_add_width_height.json

2.4 Relative vs Absolute Path Usage

Relative Paths:

If your data uses relative paths, configure the training script as follows:

export DATASET_NAME="datasets/X-Fun-Images-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Images-Demo/metadata_add_width_height.json"

Absolute Paths:

If your data uses absolute paths, configure the training script as follows:

export DATASET_NAME=""
export DATASET_META_NAME="/mnt/data/metadata_add_width_height.json"

💡 Recommendation: If the dataset is small and stored locally, use relative paths. If the dataset is stored on external storage (e.g., NAS, OSS) or shared across multiple machines, use absolute paths.

---

3. LoRA Training

3.1 Download Pretrained Model

# Create model directory
mkdir -p models/Diffusion_Transformer

# Download Z-Image official weights
modelscope download --model Tongyi-MAI/Z-Image --local_dir models/Diffusion_Transformer/Z-Image

# (Optional) Download Z-Image-Turbo fast inference version
modelscope download --model Tongyi-MAI/Z-Image-Turbo --local_dir models/Diffusion_Transformer/Z-Image-Turbo

3.2 Quick Start (DeepSpeed-Zero-2)

If you have downloaded the data as per 2.1 Quick Test Dataset and the weights as per 3.1 Download Pretrained Model, you can directly copy and run the quick start command.

DeepSpeed-Zero-2 and FSDP are recommended for training. Here we use DeepSpeed-Zero-2 as an example.

The difference between DeepSpeed-Zero-2 and FSDP lies in whether the model weights are sharded. If VRAM is insufficient when using multiple GPUs with DeepSpeed-Zero-2, you can switch to FSDP.

export MODEL_NAME="models/Diffusion_Transformer/Z-Image"
export DATASET_NAME="datasets/X-Fun-Images-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Images-Demo/metadata_add_width_height.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/z_image/train_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --train_batch_size=1 \
  --image_sample_size=1328 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=1e-04 \
  --seed=42 \
  --output_dir="output_dir_z_image_lora" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --enable_bucket \
  --rank=64 \
  --network_alpha=64 \
  --target_name="to_q,to_k,to_v,feed_forward.w1,feed_forward.w2,feed_forward.w3" \
  --use_peft_lora \
  --uniform_sampling

3.3 LoRA-Specific Parameters

LoRA Key Parameter Descriptions:

Parameter	Description	Example Value
--pretrained_model_name_or_path	Path to pretrained model	models/Diffusion_Transformer/Z-Image
--train_data_dir	Training data directory	datasets/X-Fun-Images-Demo/
--train_data_meta	Training data metadata file	datasets/X-Fun-Images-Demo/metadata_add_width_height.json
--train_batch_size	Samples per batch	1
--image_sample_size	Maximum training resolution, auto bucketing	1328
--gradient_accumulation_steps	Gradient accumulation steps (equivalent to larger batch)	1
--dataloader_num_workers	DataLoader subprocesses	8
--num_train_epochs	Number of training epochs	100
--checkpointing_steps	Save checkpoint every N steps	50
--learning_rate	Initial learning rate (recommended for LoRA)	1e-04
--lr_warmup_steps	Learning rate warmup steps	100
--seed	Random seed (for reproducible training)	42
--output_dir	Output directory	output_dir_z_image_lora
--gradient_checkpointing	Enable activation checkpointing	-
--mixed_precision	Mixed precision: fp16/bf16	bf16
--enable_bucket	Enable bucket training: trains entire images grouped by resolution without center cropping	-
--uniform_sampling	Uniform timestep sampling (recommended)	-
--resume_from_checkpoint	Resume training from checkpoint path, use "latest" to auto-select latest	None
--rank	Dimension of LoRA update matrices (higher rank = stronger expressiveness but more VRAM usage)	64
--network_alpha	Scaling factor of LoRA update matrices (typically set to half of rank or same)	64
--target_name	Components/modules to apply LoRA, separated by commas	to_q,to_k,to_v,feed_forward.w1,feed_forward.w2,feed_forward.w3
--use_peft_lora	Use PEFT module for adding LoRA (more VRAM-efficient)	-
--validation_steps	Execute validation every N steps	100
--validation_epochs	Execute validation every N epochs	100
--validation_prompts	Prompts used during validation	"A young woman..."

3.4 Training Validation

You can configure validation parameters to periodically generate test images during training, allowing you to monitor training progress and model quality.

Validation Parameters:

Parameter	Description	Recommended Value
--validation_steps	Execute validation every N steps	100
--validation_epochs	Execute validation every N epochs	100
--validation_prompts	Prompt for validation image generation. Use multiple space-separated prompt strings	Space-separated prompt strings

Example:

  --validation_steps=100 \
  --validation_epochs=100 \
  --validation_prompts="A young woman standing on a sunny coastline, her white dress gently fluttering in the sea breeze."

Notes:

• Validation images will be saved to the output_dir directory
• For multi-prompt validation, use: --validation_prompts "prompt1" "prompt2" "prompt3"

3.5 Training with FSDP

If VRAM is insufficient when using multiple GPUs with DeepSpeed-Zero-2, you can switch to FSDP.

✅ Recommended: FSDP has been thoroughly tested in this repository, with fewer errors and greater stability.

export MODEL_NAME="models/Diffusion_Transformer/Z-Image"
export DATASET_NAME="datasets/X-Fun-Images-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Images-Demo/metadata_add_width_height.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --mixed_precision="bf16" --use_fsdp --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap=ZImageTransformerBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False scripts/z_image/train_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --train_batch_size=1 \
  --image_sample_size=1328 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=1e-04 \
  --seed=42 \
  --output_dir="output_dir_z_image_lora" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --enable_bucket \
  --rank=64 \
  --network_alpha=64 \
  --target_name="to_q,to_k,to_v,feed_forward.w1,feed_forward.w2,feed_forward.w3" \
  --use_peft_lora \
  --uniform_sampling

3.6 Training Without DeepSpeed or FSDP

This approach is not recommended as it lacks VRAM-saving backends and may easily cause out-of-memory errors. This is provided for reference only.

export MODEL_NAME="models/Diffusion_Transformer/Z-Image"
export DATASET_NAME="datasets/X-Fun-Images-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Images-Demo/metadata_add_width_height.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --mixed_precision="bf16" scripts/z_image/train_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --train_batch_size=1 \
  --image_sample_size=1328 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=1e-04 \
  --seed=42 \
  --output_dir="output_dir_z_image_lora" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --enable_bucket \
  --rank=64 \
  --network_alpha=64 \
  --target_name="to_q,to_k,to_v,feed_forward.w1,feed_forward.w2,feed_forward.w3" \
  --use_peft_lora \
  --uniform_sampling

3.7 Multi-Machine Distributed Training

Suitable for: Ultra-large-scale datasets, faster training speed

3.7.1 Environment Configuration

Assuming 2 machines with 8 GPUs each:

Machine 0 (Master):

export MODEL_NAME="models/Diffusion_Transformer/Z-Image"
export DATASET_NAME="datasets/X-Fun-Images-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Images-Demo/metadata_add_width_height.json"
export MASTER_ADDR="192.168.1.100"  # Master machine IP
export MASTER_PORT=10086
export WORLD_SIZE=2                  # Total number of machines
export NUM_PROCESS=16                # Total processes = machines × 8
export RANK=0                        # Current machine rank (0 or 1)
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --mixed_precision="bf16" --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --num_machines=$WORLD_SIZE --num_processes=$NUM_PROCESS --machine_rank=$RANK --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/z_image/train_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --train_batch_size=1 \
  --image_sample_size=1328 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=1e-04 \
  --seed=42 \
  --output_dir="output_dir_z_image_lora" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --enable_bucket \
  --rank=64 \
  --network_alpha=64 \
  --target_name="to_q,to_k,to_v,feed_forward.w1,feed_forward.w2,feed_forward.w3" \
  --use_peft_lora \
  --uniform_sampling

Machine 1 (Worker):

export MODEL_NAME="models/Diffusion_Transformer/Z-Image"
export DATASET_NAME="datasets/X-Fun-Images-Demo/"
export DATASET_META_NAME="datasets/X-Fun-Images-Demo/metadata_add_width_height.json"
export MASTER_ADDR="192.168.1.100"  # Same as Master
export MASTER_PORT=10086
export WORLD_SIZE=2
export NUM_PROCESS=16
export RANK=1  # Note this is 1
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

# Use the same accelerate launch command as Machine 0

3.7.2 Multi-Machine Training Notes

• Network Requirements:

  • RDMA/InfiniBand recommended (high performance)
  • Without RDMA, add environment variables:

    export NCCL_IB_DISABLE=1
    export NCCL_P2P_DISABLE=1
    

• Data Synchronization: All machines must be able to access the same data paths (NFS/shared storage)

---

4. Inference Testing

4.1 Inference Parameter Parsing

Key Parameter Descriptions:

Parameter	Description	Example Value
GPU_memory_mode	VRAM management mode, see table below for options	model_cpu_offload
ulysses_degree	Head dimension parallelism degree, set to 1 for single GPU	1
ring_degree	Sequence dimension parallelism degree, set to 1 for single GPU	1
fsdp_dit	Use FSDP for Transformer during multi-GPU inference to save VRAM	False
fsdp_text_encoder	Use FSDP for text encoder during multi-GPU inference	False
compile_dit	Compile Transformer for faster inference (effective at fixed resolution)	False
model_name	Model path	models/Diffusion_Transformer/Z-Image
sampler_name	Sampler type: Flow, Flow_Unipc, Flow_DPM++	Flow
transformer_path	Path to load trained Transformer weights	None
vae_path	Path to load trained VAE weights	None
lora_path	LoRA weights path	None
sample_size	Generated image resolution [height, width]	[1728, 992]
weight_dtype	Model weight precision, use torch.float16 for GPUs without bf16 support	torch.bfloat16
prompt	Positive prompt describing the generation content	"A young woman..."
negative_prompt	Negative prompt for content to avoid	"low resolution, low quality..."
guidance_scale	Guidance strength, recommended 0.0 for Turbo model	4.0 / 0.0
seed	Random seed for reproducible results	43
num_inference_steps	Number of inference steps, can be greatly reduced for Turbo model	25 / 9
lora_weight	LoRA weight strength	0.55
save_path	Path to save generated images	samples/z-image-t2i

VRAM Management Mode Description:

Mode	Description	VRAM Usage
model_full_load	Load entire model to GPU	Highest
model_full_load_and_qfloat8	Full load + FP8 quantization	High
model_cpu_offload	Offload model to CPU after use	Medium
model_cpu_offload_and_qfloat8	CPU offload + FP8 quantization	Medium-Low
model_group_offload	Layer groups switch between CPU/CUDA	Low
sequential_cpu_offload	Sequential layer offload (slowest)	Lowest

4.2 Single GPU Inference

Z-Image (Standard Version)

Run the following command for single GPU inference:

python examples/z_image/predict_t2i.py

Edit examples/z_image/predict_t2i.py according to your needs. For first-time inference, focus on these parameters. For other parameters, refer to the inference parameter parsing above.

# Choose based on GPU VRAM
GPU_memory_mode = "model_cpu_offload"
# Based on actual model path
model_name = "models/Diffusion_Transformer/Z-Image"  
# LoRA weights path, e.g., "output_dir_z_image_lora/checkpoint-xxx/lora_weights.safetensors"
lora_path = None
# LoRA weight strength
lora_weight = 0.55
# Write based on generation content
prompt = "A young woman standing on a sunny coastline, her white dress gently fluttering in the sea breeze."  
# ...

Z-Image-Turbo (Fast Version)

Run the following command for single GPU inference:

python examples/z_image/predict_turbo_t2i.py

Edit examples/z_image/predict_turbo_t2i.py according to your needs. For first-time inference, focus on these parameters. For other parameters, refer to the inference parameter parsing above.

# Choose based on GPU VRAM
GPU_memory_mode = "model_cpu_offload"
# Based on actual model path
model_name = "models/Diffusion_Transformer/Z-Image-Turbo"  
# LoRA weights path, e.g., "output_dir_z_image_lora/checkpoint-xxx/lora_weights.safetensors"
lora_path = None
# LoRA weight strength
lora_weight = 0.55
# Write based on generation content
prompt = "A young woman standing on a sunny coastline, her white dress gently fluttering in the sea breeze."  
# ...

4.3 Multi-GPU Parallel Inference

Suitable for: High-resolution generation, accelerated inference

Install Parallel Inference Dependencies

pip install xfuser==0.4.2 yunchang==0.6.2

Configure Parallel Strategy

Edit examples/z_image/predict_t2i.py:

# Ensure ulysses_degree × ring_degree = number of GPUs
# For example, using 2 GPUs:
ulysses_degree = 2  # Head dimension parallelization
ring_degree = 1     # Sequence dimension parallelization

Configuration Principles:

• ulysses_degree must evenly divide the model's number of heads
• ring_degree splits on sequence dimension, affecting communication overhead; avoid using it when heads can be divided

Example Configurations:

GPU Count	ulysses_degree	ring_degree	Description
1	1	1	Single GPU
4	4	1	Head parallelization
8	8	1	Head parallelization
8	4	2	Hybrid parallelization

Run Multi-GPU Inference

torchrun --nproc-per-node=2 examples/z_image/predict_t2i.py

5. Additional Resources

• Official GitHub: https://github.com/aigc-apps/VideoX-Fun
• GRPO LoRA RL Fine-Tuning: README_TRAIN_GRPO_LORA.md