metadata
tags:
- music
- video
All of the necessary models to run tarmomatic local edition with ComfyUI.
You also need ComfyUI-GGUF custom nodes.
Place all of the models in their respective folders in the ComfyUI models-folder.
Installation
- Go to your ComfyUI directory
- Download with HF CLI (or git):
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.
- Folder:
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.
- Folder:
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
- Folder:
qwen_image_vae.safetensors- Folder:
models/vae
- Folder:
Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors- Folder:
models/loras
- Folder:
Qwen-Image-Edit-2509-Q4_K_M.gguf- Folder:
models/unet
- Folder:
LTX Models
Used for image-to-video generation (Workflow: ltxv_image_to_video.json).
t5xxl_fp8_e4m3fn_scaled.safetensors- Folder:
models/text_encoders
- Folder:
ltxv-2b-0.9.8-distilled-fp8.safetensors- Folder:
models/checkpoints
- Folder:
Wan Models
Used for video generation (Workflow: video_wan2_2_5B_ti2v-GGUF.json).
umt5_xxl_fp8_e4m3fn_scaled.safetensors- Folder:
models/text_encoders
- Folder:
wan2.2_vae.safetensors- Folder:
models/vae
- Folder:
Wan2.2-TI2V-5B-Q5_K_M.gguf- Folder:
models/unet
- Folder:
FAQ
Why does Flux need both a GGUF and a Checkpoint?
The workflow uses a "hybrid" loading strategy:
- Checkpoint (
flux1-schnell-fp8.safetensors): Loads the CLIP (text understanding) and VAE (image decoding) components. - 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.