---
library_name: peft
base_model: Qwen/Qwen-Image-2512
tags:
  - lora
  - text-to-image
  - diffusion
  - witcher3
  - cartography
datasets:
  - PNW-GM/witcher3_velen
license: apache-2.0
---

# qwen-image-witcher3-velen-lora

LoRA adapter for **Qwen/Qwen-Image-2512** trained on aerial cartographic terrain
captures from *The Witcher 3: Wild Hunt*'s Velen region. The adapter steers the
base model toward stylized top-down cartography: muted earth tones, dense
forests, river networks, marsh textures, and the distinctive flat-shaded hills
of the Velen overworld.

## Training config

- **Rank**: `r=128`, `lora_alpha=128`, `bias=none`, `init=gaussian`
- **Target modules**: attention proj (`to_q/k/v`, `add_q/k/v_proj`,
  `to_out.0`, `to_add_out`) + MLP/modulation
  (`img_mlp.net.2`, `img_mod.1`, `txt_mlp.net.2`, `txt_mod.1`)
- **Schedule**: 5 epochs over the full `witcher3_velen` dataset
  (~10k samples after repetitions); final checkpoint at **step 2120**
- **Text-to-image**: prompt embeddings via the Qwen2.5-VL processor path
  (`pipe._get_qwen_prompt_embeds`)
- **Inference**: DiffSynth `FlowMatchScheduler` (vendored) with `shift=3.0`,
  20 steps, `guidance_scale=1.0`

## Dataset

[`PNW-GM/witcher3_velen`](https://huggingface.co/datasets/PNW-GM/witcher3_velen)

## Usage

```python
import torch
from diffusers import QwenImagePipeline

pipe = QwenImagePipeline.from_pretrained(
    "Qwen/Qwen-Image-2512", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("PNW-GM/qwen-image-witcher3-velen-lora")

# Vendored DiffSynth FlowMatchScheduler with shift=3.0 is recommended.
# See the training repo for the exact scheduler swap.

image = pipe(
    prompt="aerial cartographic view of a marshland village in the Velen style",
    num_inference_steps=20,
    guidance_scale=1.0,
).images[0]
image.save("velen.png")
```

## What it produces

Top-down, slightly oblique aerial views with the Velen palette: ochre and olive
ground, dense pine canopies, winding rivers, swamps with reed flecks, and
scattered ruined settlements. Best results when prompts describe terrain rather
than characters or close-up scenes.