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Full Finetuning for LTX possibily extended to other models. (#192)

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* Full Finetuning for LTX possibily extended to other models.

* Change name of the flag

* Used disable grad for component on lora fine tuning enabled

* Suggestions Addressed
Renamed to SFT
Added 2 other models.
Testing required.

* Switching to Full FineTuning

* Run linter.

* parse subfolder when needed.

* tackle saving and loading hooks.

* tackle validation.

* fix subfolder bug.

* remove __class__.

* refactor

* remove unnecessary changes

* handle saving of final model weights correctly

* remove unnecessary changes

* LTX uses a default frame rate of 24 FPS
We need to modify the output validation framerate to match that value.
Add Framerate args.
Add Update video output and inference frame rate

* There was a results_args mapping that needed to be modified.

* update

* update README

* Update README.md

* update docs

* add training configuration in cogvideox

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
This commit is contained in:
ArEnSc
2025-01-13 13:25:39 -05:00
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parent a3898075d1
commit e5df80cc36
17 changed files with 365 additions and 131 deletions
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docs/training/ltx_video.md
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@@ -2,7 +2,7 @@
## Training
Provided you have a dataset:
For LoRA training, specify `--training_type lora`. For full finetuning, specify `--training_type full-finetune`.
```bash
#!/bin/bash
@@ -88,6 +88,8 @@ echo -ne "-------------------- Finished executing script --------------------\n\
## Memory Usage
### LoRA
LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, `49x512x768` resolution, **without precomputation**:
```
@@ -140,6 +142,31 @@ Training configuration: {
Note: requires about `17.5` GB of VRAM with precomputation. If validation is not performed, the memory usage is reduced to `11` GB.
### Full Finetuning
```
Training configuration: {
"trainable parameters": 1923385472,
"total samples": 1,
"train epochs": 10,
"train steps": 10,
"batches per device": 1,
"total batches observed per epoch": 1,
"train batch size": 1,
"gradient accumulation steps": 1
}
```
| stage | memory_allocated | max_memory_reserved |
|:-----------------------------:|:----------------:|:-------------------:|
| after precomputing conditions | 8.89 | 8.937 |
| after precomputing latents | 9.701 | 11.615 |
| before training start | 3.583 | 4.025 |
| after epoch 1 | 10.769 | 20.357 |
| before validation start | 10.769 | 20.357 |
| after validation end | 10.769 | 28.332 |
| after training end | 10.769 | 12.904 |
## Inference
Assuming your LoRA is saved and pushed to the HF Hub, and named `my-awesome-name/my-awesome-lora`, we can now use the finetuned model for inference: