Instructions to use SyFeee/LTX-2.3-SyFe-Union-Control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX-2
How to use SyFeee/LTX-2.3-SyFe-Union-Control with LTX-2:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download SyFeee/LTX-2.3-SyFe-Union-Control --local-dir models/LTX-2.3-SyFe-Union-Control hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Video-to-video with the IC-LoRA (runs on the distilled base model) uv run python -m ltx_pipelines.ic_lora \ --distilled-checkpoint-path path/to/distilled_checkpoint.safetensors \ --spatial-upsampler-path path/to/spatial_upsampler.safetensors \ --gemma-root models/gemma-3-12b \ --lora models/LTX-2.3-SyFe-Union-Control/<weights>.safetensors 1.0 \ --video-conditioning reference.mp4 1.0 \ --prompt "your prompt here" \ --output-path output.mp4 - Notebooks
- Google Colab
- Kaggle
Add files using upload-large-folder tool
Browse files
README.md
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---
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base_model: Lightricks/LTX-2.3
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library_name: ltx
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license: other
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license_name: ltx-2-community-license
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license_link: https://github.com/Lightricks/LTX-2/blob/main/LICENSE
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pipeline_tag: video-to-video
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tags:
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- ltx-video
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- ltx-2.3
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- lora
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- ic-lora
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- canny
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- depth
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- pose
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- control-video
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---
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# SyFe LTX-2.3 Union-Control LoRA
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SyFe-trained full-attention IC-LoRA combining Canny, depth, and pose control for LTX-2.3 22B-dev.
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## Checkpoint
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`union_control_bal` was trained for 5,000 steps at rank 128 on a balanced 19,740-row control corpus: 6,580 Canny, 6,580 depth, and 6,580 pose examples. Training used `video_to_video` conditioning at 1280x704 with reference scale 1.0.
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The final checkpoint and exact training configuration are under `runs/union_control_bal/`.
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## Status
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Training completed, but the final checkpoint has not received a complete production-quality validation pass. Treat this as a research checkpoint and evaluate each control mode independently before deployment. This is not the downloaded Lightricks Union-Control adapter and does not replace the separately published SyFe pose, depth, and Canny repositories.
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Use is subject to the LTX-2 community license.
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SHA256SUMS
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87a6731d273e14430e03041b8a240ba7bdfbf6415df3380e2df48ad81c196423 runs/union_control_bal/checkpoints/lora_weights_step_05000.safetensors
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runs/union_control_bal/checkpoints/lora_weights_step_05000.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:87a6731d273e14430e03041b8a240ba7bdfbf6415df3380e2df48ad81c196423
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size 1711612856
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runs/union_control_bal/training_config.yaml
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acceleration:
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load_text_encoder_in_8bit: false
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mixed_precision_mode: bf16
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quantization: null
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checkpoints:
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interval: 500
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keep_last_n: 4
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no_resume: false
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precision: bfloat16
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save_training_state: minimal
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data:
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num_dataloader_workers: 2
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preprocessed_data_root: /data0/syfe/lora_output/union_control_bal/preprocessed
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flow_matching:
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timestep_sampling_mode: shifted_logit_normal
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timestep_sampling_params: {}
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hub:
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hub_model_id: null
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push_to_hub: false
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lora:
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alpha: 128
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dropout: 0.0
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rank: 128
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target_modules:
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- to_k
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- to_q
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- to_v
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- to_out.0
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model:
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load_checkpoint: null
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model_path: /data0/pretrain_models/for_video/Lightricks/LTX-2.3/ltx-2.3-22b-dev.safetensors
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text_encoder_path: /data0/pretrain_models/for_text/google/gemma-3-12b-it-qat-q4_0-unquantized
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training_mode: lora
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optimization:
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batch_size: 1
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enable_gradient_checkpointing: true
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gradient_accumulation_steps: 1
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learning_rate: 0.0001
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max_grad_norm: 1.0
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optimizer_type: adamw
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scheduler_params: {}
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scheduler_type: linear
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steps: 5000
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output_dir: /data0/syfe/lora_output/union_control_bal
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seed: 42
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training_strategy:
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first_frame_conditioning_p: 0.1
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name: video_to_video
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reference_latents_dir: reference_latents
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validation:
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frame_rate: 24.0
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generate_audio: false
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guidance_scale: 4.0
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images: null
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include_reference_in_output: false
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inference_steps: 20
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interval: 500
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negative_prompt: no CGI, no animation, no illustration, blurry, distorted, wrong
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pose, extra limbs
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prompts:
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- "\u4E00\u4F4D\u4E2D\u5F0F\u53E4\u88C5\u4EBA\u7269\u5728\u5EAD\u9662\u4E2D\uFF0C\
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\u52A8\u4F5C\u4E0E\u59FF\u6001\u4E25\u683C\u9075\u5FAA\u53C2\u8003\u63A7\u5236\
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\u56FE\u3002\u771F\u4EBA\u7535\u5F71\u611F\uFF0C\u5199\u5B9E\u4E2D\u5F0F\u53E4\
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\u88C5\u5267"
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- "\u4E24\u4F4D\u4EBA\u7269\u5BF9\u8BDD\u4E92\u52A8\uFF0C\u6784\u56FE\u4E0E\u59FF\
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\u6001\u4E25\u683C\u9075\u5FAA\u53C2\u8003\u63A7\u5236\u56FE\u3002\u771F\u4EBA\
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\u7535\u5F71\u611F\uFF0C\u5199\u5B9E\u4E2D\u5F0F\u53E4\u88C5\u5267"
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reference_downscale_factor: 1
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reference_videos:
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- /data0/syfe/lora_output/union_control_v1/val_refs/val_control_a.mp4
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- /data0/syfe/lora_output/union_control_v1/val_refs/val_control_b.mp4
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seed: 42
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skip_initial_validation: true
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stg_blocks:
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- 29
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stg_mode: stg_v
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stg_scale: 1.0
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video_dims: !!python/tuple
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- 1280
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- 704
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- 145
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videos_per_prompt: 1
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wandb:
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enabled: false
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entity: null
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log_validation_videos: true
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project: ltx-2-trainer
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tags:
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- ltx2
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- lora
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