Instructions to use fwwrsd/ohwx-epoch1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fwwrsd/ohwx-epoch1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SG161222/RealVisXL_V5.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("fwwrsd/ohwx-epoch1") prompt = "ohwx" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 868 Bytes
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license: apache-2.0
language:
- en
tags:
- text-to-image
- lora
- safetensors
- stable-diffusion-xl
- sdxl
- diffusion-pipe
- identity
base_model: SG161222/RealVisXL_V5.0
base_model_relation: adapter
library_name: diffusers
pipeline_tag: text-to-image
instance_prompt: "ohwx"
---
# ohwx — Epoch 1/3
SDXL identity LoRA checkpoint (epoch 1 of 3).
## Trigger Word
Use **`ohwx`** in your prompt.
## Download
```
https://huggingface.co/fwwrsd/ohwx-epoch1/resolve/main/ohwx_epoch1.safetensors
```
## Training Details
| Parameter | Value |
|-----------|-------|
| Base Model | [SG161222/RealVisXL_V5.0](https://huggingface.co/SG161222/RealVisXL_V5.0) |
| Epoch | 1 / 3 |
| LoRA Rank | 16 |
| Learning Rate | 0.0001 |
| Resolution | 1024px |
| Training Media | 12 |
---
*Trained with [NanoBanana LoRA Bot](https://t.me/LoraDatasetBot) on RunPod* |