How to Run Image Diffusion Models Locally with Unsloth
Generate and edit AI images on your own computer using diffusion models without writing code or setting up a complex workflow. With Unsloth, you can run models like MiniMax-H3, Z-Image and FLUX locally with support for GGUFs, FP8, LoRAs, reference images, and more. Start with a text prompt or an existing image, then create, transform, edit, extend, upscale, or fine-tune images using the tools and workflows supported by each model.
Choose from MiniMax-H3, Z-Image, LTX, Qwen-Image, FLUX, SDXL and more.
To create your own LoRA, fine-tune a supported model on your dataset. See Fine-tune an image model to learn more.
⚡ Quickstart
Install or update
The desktop app is the easiest way to get started.
Follow the installer, then open the app from your applications. If you already have it installed, make sure it is up to date before continuing.
Command line
Prefer the terminal? Run the command for your operating system. The same command will update an existing installation.
MacOS, Linux, WSL:
Windows PowerShell:
Launch Unsloth
Once the installation finishes, launch Unsloth:
Open http://localhost:8888 if it does not open automatically.
Enter a prompt
Describe the image you want to generate.
For example:
Top-down shot of a tranquil koi pond in a Japanese garden, orange, white and black koi swimming beneath the clear water, surrounded by moss-covered stepping stones and small wooden bridges, with red maple leaves floating on the surface. Bright midday light, saturated colour, crisp reflections. Whimsical cartoon illustration, 50mm.
You can keep the recommended settings for your first image.
⭐ Features
The available workflows depend on the model you load. Unsupported workflows are hidden automatically.
Create and edit images
Create: Generate an image from a text prompt.
Transform: Use an existing image as the starting point for a new generation.
Inpaint: Select and regenerate part of an image.
Extend: Generate content beyond the original canvas.
Upscale: Increase the resolution of an image.
Reference: Guide a generation with one or more reference images.
Edit: Describe the changes you want to make to an image.
Edit requires a compatible model such as Qwen-Image-Edit or FLUX.1 Kontext.
LoRAs and ControlNet
Load compatible LoRAs to add a character, subject or visual style to your generations. Adjust the LoRA weight to control how strongly it affects the result.
Supported models can also use ControlNet to guide the structure of an image. Adjust its strength to control how closely the result follows the guidance image.
Gallery
Generated images are saved to the local gallery. Open a result to view its prompt and settings, select Recipe to restore its generation setup or download the image.
Supported models and workflows
Generate or transform images
Z-Image, Qwen-Image, FLUX.1, SDXL
Edit using instructions
Qwen-Image-Edit, FLUX.1 Kontext
Use reference images
FLUX.2 klein
More models are available in the model picker. Formats and workflows vary by model, and unsupported workflows are hidden automatically.
Generation settings
The default settings are a good starting point. However, it is possible to adjust settings to get the best result, these are the main parameters you may want to change:
Prompt
Describes the image you want to create. In Edit, this becomes the instruction to follow.
Aspect ratio and resolution
Set the shape and size of the image. Larger images use more memory and take longer.
Steps
Control how long the model spends generating. More steps do not always improve the result.
Guidance
Controls how strongly the prompt guides the result. Some models work best at 0.
Seed
Helps recreate a result. Leave it empty to use a random seed.
Advanced generation settings
These controls change how the model runs on your device. Adjust them to reduce memory usage, improve performance or troubleshoot generation.
Speed
Controls compilation and performance optimizations. Compiled modes may take longer during the first generation.
Precision
Changes the numerical format used to run the model. Lower precision can reduce memory usage. Available options depend on the model and hardware.
Attention
Selects the attention implementation used during generation.
Memory
Balances generation speed against GPU memory usage. Try Low VRAM if a model is close to your memory limit.
Step cache
Reuses some calculations between diffusion steps to improve generation speed.
CPU offload
Moves parts of the model into system memory. This reduces GPU memory usage but may make generation slower.
⚠️ Troubleshooting
Images does not appear
Update to the latest version, then restart the app. See Updating Unsloth for instructions.
A model will not download or load
Some image models are large and take time to download. Check that you have enough free storage and a stable internet connection.
Some models also require you to accept their licence on Hugging Face and add a Hugging Face token before downloading them.
The model runs out of memory
Try a smaller GGUF quantization or a 4-bit version of the model. For GGUF models, start with the size marked recommended.
You can also:
Lower the image resolution.
Keep the batch size at
1.Close other applications using the GPU.
Choose a smaller model.
A TIGHT model may use system memory and run more slowly. A model marked OOM is unlikely to fit.
A workflow is missing
The available workflows depend on the loaded model. Unsupported workflows will not appear.
For example, instruction-based editing requires a model such as Qwen-Image-Edit or FLUX.1 Kontext.
❓ FAQ
Does image generation run locally?
Yes. Once a model is downloaded, image generation runs on your device.
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