Stable Diffusion XL LoRA Trainer

Fine-tune Stable Diffusion XL models with LoRA adapters using the diffusers library and Kohya-ss training scripts. Manages dataset preparation, training configuration, and checkpoint merging for custom image generation.

Installation

Use the upstream install or setup path that matches your environment:

• git clone https://github.com/Stability-AI/generative-models.git
• pip install hatch
• pip install "numpy>=1.17" "PyWavelets>=1.1.1" "opencv-python>=4.1.0.25"
• pip install --no-deps invisible-watermark

Requirements and caveats from upstream:

• python scripts/sampling/simple_video_sample_4d2.py --input_path assets/sv4d_videos/camel.gif --output_folder outputs (after downloading sv4d2.safetensors from HuggingFace...
• Run inference: python scripts/sampling/simple_video_sample_4d2.py --input_path <path/to/video>
• Run inference: python scripts/sampling/simple_video_sample_4d2.py --model_path checkpoints/sv4d2_8views.safetensors --input_path assets/sv4d_videos/chest.gif --output_folder outputs

Basic usage or getting-started notes:

• To run SV4D 2.0 on a single input video of 21 frames:

• Low VRAM environment : To run on GPUs with low VRAM, try setting --encoding_t=1 (of frames encoded at a time) and --decoding_t=1 (of frames decoded at a time) or lower video resolution like --img_size=512.

• The 5x8 model takes 5 frames of input at a time. But the inference scripts for both model take 21-frame video as input by default (same as SV3D and SV4D), we run the model autoregressively until we generate 21 frames.

• Source: https://github.com/Stability-AI/generative-models

• Extracted from upstream docs: https://raw.githubusercontent.com/Stability-AI/generative-models/HEAD/README.md

Source

• Agent Skill Exchange