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Local Image Generation Setup (Built-in Engine)

DREAMIO's built-in engine allows you to run image generation models directly on your own hardware. This guide will walk you through the setup process and explain the various settings available.

Step 1: Download an Image Model

To generate images locally, you'll first need to download a model file. These models are typically in formats like .safetensors or .ckpt.

  • Where to find models: A popular resource for image models is Civitai. You can also find them on Hugging Face.
  • Choosing a model: Look for models that match the art style you want to create. Pay attention to the model's base version (e.g., SD1.5, SDXL) as this can affect performance and compatibility.
  • Organize your files: It's a good practice to create a dedicated folder on your computer to store your image models.

Step 2: Configure DREAMIO

Once you have a model, you need to tell DREAMIO where to find it and how to use it.

  1. Launch DREAMIO and navigate to Image Generation Settings.
  2. Set the Provider to Local.

This will reveal the following settings:

Core Settings

  • Models Path: Click to select the folder on your computer where you saved your image model files.
  • Model: Once the path is set, choose the specific model file (e.g., your-model.safetensors) from this dropdown menu.
  • Keep in Memory: Keep the model loaded in your computer's RAM/VRAM for faster generation. This consumes significant resources, so only enable it if you have enough memory.

Generation Parameters

These settings control the quality and style of the final image.

  • Dimensions: The resolution of the generated image (e.g., 512x512, 1024x1024). Higher resolutions require more VRAM.
  • Sample Steps: The number of iterations the AI takes to generate an image. More steps can improve quality but take longer.
  • Sampler: The algorithm used to generate the image. Different samplers can produce stylistically different results.
  • Cfg Scale: How strictly the AI should follow your visual description. Lower values give the AI more creative freedom, while higher values make it adhere more closely to the prompt.
  • Scheduler: The noise scheduler used during image generation.

Advanced Settings

These settings are for users who want more fine-grained control over the generation process.

  • Backend: The processing engine to use. Select CUDA for NVIDIA GPUs, Vulkan for AMD/Intel GPUs, or CPU to use your main processor (slowest).
  • Quantization: The data format for the model's weights (e.g., Q8_0, F16, F32).
  • TAESD Path/Model: You can specify a "tiny autoencoder" to speed up the final stage of image decoding. This is an optional optimization.
  • LoRA Path/LoRA: Settings for LoRA (Low-Rank Adaptation) models. These are small files that can be used to modify the style or add specific concepts to your main model.
  • VAE Tiling: A memory-saving option that can help prevent "out of memory" errors on GPUs with less VRAM.
  • UNET/CLIP/VAE Paths & Models: For ultimate control, you can specify individual file paths for the core components of the diffusion pipeline: the UNET, CLIP model, and VAE. In most cases, these are bundled within the main model file and don't need to be set manually.

Alternative: Using ComfyUI

For users who want maximum flexibility and control over the image generation process, DREAMIO also supports integration with ComfyUI.

ComfyUI is a powerful and modular node-based interface for Stable Diffusion. It allows you to build complex workflows, chain models together, and experiment with advanced techniques that go beyond the capabilities of the built-in engine.

If you are an advanced user or want to leverage a specific ComfyUI workflow, you can connect DREAMIO to your running ComfyUI instance.

Learn how to set up ComfyUI »