Just trained character lora for Z-Turbo.

https://redd.it/1p94jq1
@rStableDiffusion

Get more variation across seeds with Z Image Turbo

https://redd.it/1p94z1y
@rStableDiffusion

Lora Training for Z Image Turbo on 12gb VRAM

Shoutout to Ostris for getting Z Image supported for lora training so quickly.

[https://github.com/ostris/ai-toolkit](https://github.com/ostris/ai-toolkit)

[https://huggingface.co/ostris/zimage\_turbo\_training\_adapter](https://huggingface.co/ostris/zimage_turbo_training_adapter)

Wanted to share that it looks like you will be able to train this with GPU's with 12gb VRAM. Currently running it on his run pod template.

[https://console.runpod.io/hub/template/ai-toolkit-ostris-ui-official?id=0fqzfjy6f3](https://console.runpod.io/hub/template/ai-toolkit-ostris-ui-official?id=0fqzfjy6f3)

`MODEL OPTIONS`

* `Low VRAM: ON`
* `LAYER OFFLOADING: OFF`


`QUANTIZATION`

* `Transformer: float8 (default)`
* `Text Encoder: float8 (default)`

`TARGET`

* `Target Type: LoRA`
* `Linear Rank: 32`

`SAVE`

* `Data Type: BF16`
* `Save Every: 500`
* `Max Step Saves to Keep: 4`

`TRAINING`

* `Batch Size: 1`
* `Gradient Accumulation: 1`
* `Steps: 3000`
* `Optimizer: AdamW8Bit`
* `Learning Rate: 0.0001`
* `Weight Decay: 0.0001`
* `Timestep Type: Sigmoid`
* `Timestep Bias: Balanced`
* `EMA (Exponential Moving Average):`
* `Use EMA: OFF`
* `Text Encoder Optimizations:`
* `Unload TE: OFF`
* `Cache Text Embeddings: ON`
* `Regularization:`
* `Differential Output Preservation: OFF`
* `Blank Prompt Preservation: OFF`



17 Image data set - Resolution settings 512, 768,1024 (ON)

RTX 5090

1.30s/it, lr: 1.0e-04 loss: 3.742e-01]





Halfway through my training and it's already looking fantastic. Estimating about 1.5hrs to train 3000 steps with samples and saves.

CivitAI is about to be flooded with LORAs. Give this dude some money: [https://www.patreon.com/ostris](https://www.patreon.com/ostris)

https://redd.it/1p957k2
@rStableDiffusion