AcademiaSD / AcademiaSD_LoRAlab-TrainerStudio

# AcademiaSD LoRAlab Trainer Studio 2.01
https://github.com/user-attachments/assets/8f3b6d3f-dbd5-45a4-8ae5-028e241c449a
 Train LoRAs for the latest image and video models on consumer NVIDIA GPUs (and AMD, experimental) — from 8 GB of VRAM. 
 One installation, one Python environment, one launcher. Every trainer in one place. 
> 🧪 **Aviso:** se ha añadido soporte a muchos modelos de forma casi simultánea. Es posible que haya bugs, ajustes pendientes o que las configuraciones de entrenamiento y de previsualización por defecto no sean las adecuadas. Por favor, escribe en Issues contando los problemas que encuentres o los ajustes con los que has conseguido mejores resultados. ¡Gracias!
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> Las previsualizaciones no tienen por qué tener la calidad del LoRA final: sirven para seguir la evolución del entrenamiento y el parecido del personaje, objeto o estilo. La calidad final del LoRA compruébala en su interfaz (ComfyUI, Forge...), después de ajustar la fuerza del LoRA y con los ajustes de calidad del modelo que uses.
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> 🧪 **Notice:** support for many models has been added almost at the same time. There may be bugs or rough edges, or the default training and preview settings may not be the right ones. Please open an issue with the problems you find or the settings that gave you better results. Thank you!
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> The previews do not have to reach the quality of the final LoRA: they are there to follow the training and the likeness of the character, object or style. Judge the final quality in the matching interface (ComfyUI, Forge...), after adjusting the LoRA strength and with the quality settings of the model you use.
**AcademiaSD LoRAlab Trainer Studio** brings together all the AcademiaSD LoRAlab trainers. Each model is loaded in **4-bit NF4**, the text encoder and the VAE run only once in a **pre-cache** stage, and 100 % of the GPU goes to training. Every trainer has the same web interface: dataset manager with an automatic captioner, live previews, exact-step resume and one-click export to ComfyUI. It also works from another device on your network: upload the dataset and download the LoRA from the browser.
New trainers are added **here**. One `Update_LoRAlab-TrainerStudio.bat` brings them to your existing installation, next to your models and projects — no new repository, no new environment.
| Trainer | Trains | Minimum VRAM |
| :--- | :--- | :--- |
| **Qwen-Image 2.1** | Image LoRAs (characters, objects, styles) **and edit LoRAs** (before → after) | 8 GB |
| **Qwen-Image 2512 / Edit 2511** | Image LoRAs on Qwen-Image-2512 **and edit LoRAs** on Qwen-Image-Edit-2511 (before → after); 20B in NF4 with block swap | **~4 GB** (512², measured) / 8 GB (edit at 1024²) |
| **Krea 2** | Image LoRAs (characters, objects, styles) for Krea 2 Raw and Turbo | 8 GB |
| **Boogu Image Edit 0.1** | Edit LoRAs (before → after) and image LoRAs on Boogu-Image-0.1-Edit | **10 GB** (512², ~8 GB measured) |
| **Z-Image** | Image LoRAs (characters, objects, styles) for Z-Image a