1. Load CLIP Vison
Decode the image to form descriptions (prompts), and then convert them into conditional inputs for the sampler. Based on the decoded descriptions (prompts), generate new similar images. Multiple nodes can be used together. Suitable for transforming concepts, abstract things, used in combination with Clip Vision Encode.
2. Load CLIP
The Load CLIP node can be used to load a specific CLIP model, CLIP models are used to encode text prompts that guide the diffusion process.
*Conditional diffusion models are trained using a specific CLIP model, using a different model than the one which it was trained with is unlikely to result in good images. The Load Checkpoint node automatically loads the correct CLIP model.
3. unCLIP Checkpoint Loader
The unCLIP Checkpoint Loader node can be used to load a diffusion model specifically made to work with unCLIP. unCLIP Diffusion models are used to denoise latents conditioned not only on the provided text prompt, but also on provided images. This node will also provide the appropriate VAE and CLIP and CLIP vision models.
*even though this node can be used to load all diffusion models, not all diffusion models are compatible with unCLIP.
4. load controlnet model
The Load ControlNet Model node can be used to load a ControlNet model, Used in conjunction with Apply ControlNet.
5. Load LoRA
6. Load VAE
7. Load Upscale Model
8. Load Checkpoint
9. Load Style Model
The Load Style Model node can be used to load a Style model. Style models can be used to provide a diffusion model a visual hint as to what kind of style the denoised latent should be in.
* Only T2IAdaptor style models are currently supported.
10. Hypernetwork Loader
The Hypernetwork Loader node can be used to load a hypernetwork. Similar to LoRAs, they are used to modify the diffusion model, to alter the way in which latents are denoised. Typical use-cases include adding to the model the ability to generate in certain styles, or better generate certain subjects or actions. One can even chain multiple hypernetworks together to further modify the model.