README.md

---
tags:
- music
- video
---

All of the necessary models to run **[tarmomatic](https://codeberg.org/jaahas/tarmomatic)** local edition with ComfyUI.
You also need [ComfyUI-GGUF custom nodes](https://github.com/city96/ComfyUI-GGUF).
Place all of the models in their respective folders in the ComfyUI `models`-folder.


## Installation

1. Go to your ComfyUI directory
2. Download with HF CLI (or git):
  ```bash
  curl -LsSf https://hf.co/cli/install.sh | bash
  hf download jaahas/tarmomatic --local-dir models
  ```

## All Models List

| Model Filename | Required ComfyUI Folder | Used In |
|---|---|---|
| `flux1-schnell-fp8.safetensors` | `models/checkpoints` | Flux |
| `flux1-schnell-Q4_K_M.gguf` | `models/unet` | Flux |
| `qwen_2.5_vl_7b_fp8_scaled.safetensors` | `models/text_encoders` | Qwen Image Edit |
| `qwen_image_vae.safetensors` | `models/vae` | Qwen Image Edit |
| `Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors` | `models/loras` | Qwen Image Edit |
| `Qwen-Image-Edit-2509-Q4_K_M.gguf` | `models/unet` | Qwen Image Edit |
| `t5xxl_fp8_e4m3fn_scaled.safetensors` | `models/text_encoders` | LTX Video |
| `ltxv-2b-0.9.8-distilled-fp8.safetensors` | `models/checkpoints` | LTX Video |
| `umt5_xxl_fp8_e4m3fn_scaled.safetensors` | `models/text_encoders` | Wan Video |
| `wan2.2_vae.safetensors` | `models/vae` | Wan Video |
| `Wan2.2-TI2V-5B-Q5_K_M.gguf` | `models/unet` | Wan Video |

---

## Benchmarks (RTX 5090, cold start)

| Model | Speed |
|---|---|
| Flux Schnell Q4_K_M (1024x1024) | 28s |
| Qwen Image Edit 2509 Q4_K_M Lightning (1 image, 1024x1024) | 112s |
| Wan 2.2 TI2V 5B Q5_K_M (10s, 720p) | 460s |
| Wan 2.2 TI2V 5B Q5_K_M (10s, 720p, optimised) | 148s |
| LTXV 2b 0.9.8 distilled fp8 (10s, 512p) | 47s |
| TBA | --- |
| Wan 2.2 I2V A14B Q5_K_M Lightning (10s, 720p) | 1074s |
| Wan 2.2 I2V A14B Q5_K_M Lightning (10s, 480p) | 296s |
| Eigen Banana Qwen Image Edit 2509 Q4_K_M (1 image, 1024x1024) | 151s |


---

## Flux Models
Used for general image generation (Workflow: `flux_schnell-GGUF.json`).

- **`flux1-schnell-fp8.safetensors`**
  - Folder: `models/checkpoints`
  - **Note:** This provides the CLIP (Text Encoder) and VAE for the workflow.
- **`flux1-schnell-Q4_K_M.gguf`**
  - Folder: `models/unet`
  - **Note:** This provides the actual diffusion model (UNet) in a compressed (quantized) format for better performance.

## Qwen Image Models
Used for image editing and synthesis (Workflows: `image_qwen_image_edit_2509-GGUF-*.json`).

- **`qwen_2.5_vl_7b_fp8_scaled.safetensors`**
  - Folder: `models/text_encoders`
- **`qwen_image_vae.safetensors`**
  - Folder: `models/vae`
- **`Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors`**
  - Folder: `models/loras`
- **`Qwen-Image-Edit-2509-Q4_K_M.gguf`**
  - Folder: `models/unet`

## LTX Models
Used for image-to-video generation (Workflow: `ltxv_image_to_video.json`).

- **`t5xxl_fp8_e4m3fn_scaled.safetensors`**
  - Folder: `models/text_encoders`
- **`ltxv-2b-0.9.8-distilled-fp8.safetensors`**
  - Folder: `models/checkpoints`

## Wan Models
Used for video generation (Workflow: `video_wan2_2_5B_ti2v-GGUF.json`).

- **`umt5_xxl_fp8_e4m3fn_scaled.safetensors`**
  - Folder: `models/text_encoders`
- **`wan2.2_vae.safetensors`**
  - Folder: `models/vae`
- **`Wan2.2-TI2V-5B-Q5_K_M.gguf`**
  - Folder: `models/unet`

---

## FAQ

### Why does Flux need both a GGUF and a Checkpoint?
The workflow uses a "hybrid" loading strategy:
1.  **Checkpoint (`flux1-schnell-fp8.safetensors`):** Loads the **CLIP** (text understanding) and **VAE** (image decoding) components.
2.  **GGUF (`flux1-schnell-Q4_K_M.gguf`):** Loads the **UNet** (image generation core).
This setup allows you to use a highly compressed, fast UNet (GGUF) while still getting the necessary support components from the standard checkpoint.