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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
## 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]
```
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