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🚨 Issue: LoRA Not Updating Custom Fine-Tuned SDXL Model in SPO Training
I am training a model in two stages:
1️⃣ Stage 1 (Step-Aware Preference Model Training)
- Trained on LAION dataset, outputs
sdxl_step-aware_preference_model.bin
.
2️⃣ Stage 2 (SPO Training)
- Uses:
- Base Model = Fine-tuned SDXL.
- Reference Model =
sdxl_step-aware_preference_model.bin
.
- Outputs LoRA adapters.
Issue 1: Model Weights Size Mismatch
- My first-stage trained model (
final_ckpts.bin
) has two 5GB FP32 weight shards (total 10GB). - The Hugging Face repo (
sdxl_step-aware_preference_model.bin
) is only 5GB in FP16. - How is the Hugging Face model so much smaller?
Issue 2: LoRA Not Updating Fine-Tuned Model
- When I apply LoRA to my fine-tuned SDXL, it says "no modules changed".
- But when I apply it to Hugging Face’s SDXL, it works.
- Why does it not work on my fine-tuned model?
Issue 3: UNet Source in Hugging Face Repo
- The Hugging Face repo contains a
unet
file, but I cannot find where it came from. - Could you clarify how it was generated?
Reproducibility Steps
- Train Stage 1 model → Get
final_ckpts.bin
(10GB). - Convert to FP16 → Expected size should be 5GB, but mismatch with Hugging Face repo.
- Train Stage 2 using
sdxl_step-aware_preference_model.bin
. - Apply LoRA to my fine-tuned SDXL → No changes.
- Apply LoRA to Hugging Face SDXL → Works fine.
Expected Behavior
- LoRA should update the fine-tuned SDXL model correctly.
- Model size should match expectations.
- Source of the UNet in Hugging Face repo should be clear.
Additional Context
Any insights on these issues would be helpful. Thanks!
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