Install OpenTrainer for NVidia 3080 Ti

gistfile1.txt

Good — this is the right moment to reset. Your current env is in “dependency whack-a-mole mode,” so a clean stack will actually save time.

Below is a **known-stable Windows 11 + RTX 3080 Ti + OneTrainer SDXL LoRA setup** that avoids all the version traps you just hit (torch/torchvision/xformers/numpy/diffusers mismatches).

---

# 🧨 CLEAN INSTALL SCRIPT (Windows 11 / SDXL / OneTrainer)

## ⚠️ What this does

* Creates a fresh conda env
* Pins stable PyTorch stack (cu121)
* Installs OneTrainer dependencies correctly
* Avoids NumPy 2 / broken torchvision / broken xformers
* Prevents diffusers conflicts

---

# 🚀 STEP 1 — Create fresh environment

```bash
conda create -n onetrainer_clean python=3.10 -y
conda activate onetrainer_clean
```

---

# 🚀 STEP 2 — Upgrade pip tools

```bash
python -m pip install --upgrade pip setuptools wheel
```

---

# 🚀 STEP 3 — Install stable PyTorch stack (IMPORTANT)

This is the **correct SDXL baseline for RTX 3080 Ti**

```bash
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121
```

---

# 🚀 STEP 4 — Install core ML dependencies (PINNED)

```bash
pip install numpy==1.26.4
pip install transformers==4.44.2
pip install accelerate==0.33.0
pip install safetensors
pip install pillow
pip install tqdm
```

---

# 🚀 STEP 5 — Install xformers (MATCHED)

```bash
pip install xformers==0.0.23.post1
```

---

# 🚀 STEP 6 — Install diffusers (IMPORTANT: compatible range)

```bash
pip install diffusers==0.30.3
```

(Stable SDXL-compatible version, avoids your `quantizers.gguf` mismatch issues)

---

# 🚀 STEP 7 — Install HuggingFace tooling

```bash
pip install huggingface_hub
```

---

# 🚀 STEP 8 — Clone OneTrainer cleanly

```bash
cd D:\Documents\dev
git clone https://github.com/Nerogar/OneTrainer.git
cd OneTrainer
```

---

# 🚀 STEP 9 — Install OneTrainer requirements

```bash
pip install -r requirements.txt
```

---

# 🚀 STEP 10 — Verify system before running

```bash
python -c "import torch; print(torch.__version__)"
python -c "import torchvision; print(torchvision.__version__)"
python -c "import diffusers; print(diffusers.__version__)"
```

Expected:

```txt
torch 2.1.2
torchvision 0.16.2
diffusers 0.30.3
```

---

# 🚀 STEP 11 — Launch OneTrainer

```bash
python scripts/train_ui.py
```

---

# 🧠 What this fixes (your exact errors)

### ✔ Fixes torchvision errors

* `register_fake`
* `nms missing`
* `GroupName missing`

### ✔ Fixes NumPy crash

* `_ARRAY_API not found`

### ✔ Fixes diffusers import crash

* `diffusers.quantizers.gguf`

### ✔ Fixes xformers mismatch

* compiled extension mismatch errors

### ✔ Prevents future breakage

* no bleeding-edge torch builds
* no NumPy 2.x
* no mixed pip + bundled diffusers

---

# ⚠️ IMPORTANT RULES (don’t skip these)

After this install:

### ❌ NEVER do:

```bash
pip install torch --upgrade
pip install numpy --upgrade
pip install diffusers --upgrade
```

### ❌ NEVER mix:

* pip diffusers + OneTrainer src diffusers
* torch 2.4+ with old xformers
* NumPy 2.x

---

# 💡 Optional (recommended for your 3080 Ti)

Add VRAM optimizations:

```bash
pip install bitsandbytes
```

and in training config:

* enable gradient checkpointing
* use batch size 1–2
* use 1024 SDXL resolution with bucketing

---

# 🚀 If you want next step

Once this is working, I can help you:

* tune SDXL LoRA settings for **maximum style fidelity**
* fix captioning pipeline (your earlier script)
* optimize for **16GB VRAM stability**
* or build a ComfyUI inference pipeline for your trained LoRA

Just tell me 👍