---
base_model: stabilityai/stable-diffusion-xl-base-1.0
license: cc-by-nc-4.0
tags:
- text-to-image
- lora
- diffusers
- stable-diffusion-xl
library_name: diffusers
pipeline_tag: text-to-image
---

# ImageGen — SDXL LoRA (general-purpose)

LoRA fine-tune of `stabilityai/stable-diffusion-xl-base-1.0` on a broad, filtered aesthetic dataset
for improved prompt adherence and image quality over stock SDXL.

## Training status
- **Step:** 6,200
- **Epoch:** 2.00
- **Validation loss:** 0.1364
- **LoRA:** rank 32, alpha 64, targets `to_q, to_k, to_v, to_out.0`
- **Precision:** bf16, gradient checkpointing on

## Checkpoints
- `latest/` — most recent adapter weights
- `best/`   — lowest validation-loss adapter weights

![validation grid](best/val_grid_step_6200.png)

## Dataset sources
- `Spawning/PD12M` (target ~30,000)
- `common-canvas/commoncatalog-cc-by` (target ~20,000)
- `laion/laion2B-en-aesthetic` (target ~23,000)
- `laion/laion-art` (target ~7,500)
- `poloclub/diffusiondb` (target ~12,500)
- `kakaobrain/coyo-700m` (target ~5,500)

> License note: trained on research datasets of scraped image-text pairs.
> Released under `cc-by-nc-4.0` (non-commercial). Verify each source
> dataset's terms before any downstream commercial use.

## Usage
```python
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("Dikshan1234/ImageGen", subfolder="best")
image = pipe("a cozy cabin in a snowy forest, golden hour").images[0]
```