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README.md

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+# RunPod setup for kyrael + sorelith Krea2 LoRA training
+
+## What's already done
+- Both datasets are uploaded as private HF dataset repos:
+  - `JBARU/kyrael-dataset`
+  - `JBARU/sorelith-dataset`
+- No captions yet -- `setup_pod.sh` captions them on the pod (Qwen2.5-VL-7B, fast on a real GPU).
+
+## Files in this folder
+- `setup_pod.sh` -- full bootstrap: installs musubi-tuner, downloads models, downloads datasets, captions, caches, trains both LoRAs
+- `caption_dataset.py` -- auto-captioning script (called by setup_pod.sh)
+- `dataset_kyrael.toml` / `dataset_sorelith.toml` -- musubi-tuner dataset configs
+
+## Steps you need to do yourself (I can't do these for you)
+
+### 1. Accept gated model access
+Before anything else, visit https://huggingface.co/krea/Krea-2-Raw while logged in
+and accept/request access (it auto-approves). Skip this and the download in step 4
+of setup_pod.sh will fail.
+
+### 2. Create a RunPod account + pod
+1. Sign up at https://runpod.io (this needs your own payment method -- I can't do this part)
+2. Go to **Pods** -> **Deploy**
+3. Pick a GPU: **RTX 4090 (24GB)** is the recommended sweet spot for this job (~$0.34-0.69/hr)
+4. Pick a template with PyTorch + CUDA pre-installed (e.g. "RunPod PyTorch 2.x")
+5. Deploy the pod, wait for it to start
+6. Open its **web terminal** (or connect via SSH if you set up a key)
+
+### 3. Get the scripts onto the pod
+Once you have the pod's terminal open:
+```bash
+mkdir -p /workspace
+cd /workspace
+```
+Then either:
+- **Easiest**: use the RunPod web UI's file upload to drop `caption_dataset.py`,
+  `dataset_kyrael.toml`, and `dataset_sorelith.toml` into `/workspace/`
+- Or paste their contents directly using `cat > filename.py << 'EOF' ... EOF` in the terminal
+
+### 4. Run the bootstrap
+Upload `setup_pod.sh` the same way, then:
+```bash
+chmod +x setup_pod.sh
+./setup_pod.sh
+```
+It'll pause at `hf auth login` for you to paste your token interactively --
+same rule as before, paste it only when prompted, never on the command line.
+
+### 5. When it's done
+Trained LoRAs land in `/workspace/output/kyrael/kyrael_lora.safetensors` and
+`/workspace/output/sorelith/sorelith_lora.safetensors`. Push them back to HF
+(so you can grab them locally) with:
+```bash
+hf upload <your-username>/kyrael-lora /workspace/output/kyrael --repo-type model
+hf upload <your-username>/sorelith-lora /workspace/output/sorelith --repo-type model
+```
+Then locally: `hf download <your-username>/kyrael-lora --local-dir D:\ComfyModels\loras\kyrael`
+
+### 6. Don't forget to stop the pod
+RunPod bills by the hour while running -- stop/terminate it once training's
+done so you're not paying for idle GPU time.
+
+## Vaelith run: what changed after kyrael
+
+- **Network volume: use at least 100GB**, not 50GB. Kyrael's pod hit disk-full
+  twice at 50GB -- base models alone (~33GB) plus the Qwen2.5-VL-7B captioning
+  model's cache (~16GB, easy to forget about) leave almost no margin at 50GB.
+- **`num_repeats` dropped from 10 to 3** in `dataset_vaelith.toml`. Kyrael's
+  LoRA came out overtrained/rigid (locked pose, completely resistant to style
+  LoRA blending even at 5.0 weight) -- traced back to 34 images x 10 repeats x
+  16 epochs = 5,440 total training exposures on a small dataset. Lower repeats
+  should fix this without hurting identity retention.
+- **`setup_pod_vaelith.sh` cleans up the captioning model cache immediately**
+  after captioning finishes (`rm -rf /workspace/.cache`), instead of leaving
+  it sitting there until disk fills up mid-training like last time.
+
+## If something fails partway through
+Each numbered section in `setup_pod.sh` is independent enough to re-run on its
+own -- if training crashes on kyrael, you don't need to redo the downloads or
+sorelith's caching, just re-run that one training command.