# ComfyUI GPU Resident Loader

A ComfyUI custom-node pack that targets **time-to-VRAM** and **sticky GPU residency**, not just lower peak host RAM.

It does two things:

1. **Installs startup-time loader and residency patches** before any workflow nodes run.
2. Ships **KJ-compatible loader nodes** for diffusion models and checkpoints, plus preload/pin/evict/report nodes for manual residency control.

## Why this exists

Stock ComfyUI makes separate decisions for:

- where a model **lives after load**, and
- where checkpoint tensors are **materialized first**.

Those are not the same thing.

This repo targets the second problem directly for `.safetensors` by steering eligible loads toward direct GPU ingest, then targets the first problem by overriding offload policy and by teaching `free_memory()` to respect sticky entries until the VRAM budget is actually exceeded.

## What is included

### Startup patcher

Installed automatically from `__init__.py` when the custom node loads.

Current patch surface:

- `comfy.utils.load_torch_file`
- `comfy.model_management.free_memory`
- `comfy.model_management.load_models_gpu`
- `comfy.model_management.unet_offload_device`
- `comfy.model_management.text_encoder_offload_device`
- `comfy.model_management.vae_offload_device`
- `comfy.model_management.text_encoder_device`
- `comfy.model_management.vae_device`
- `comfy.model_management.unet_inital_load_device`
- `comfy.sd.load_checkpoint_guess_config`
- `comfy.sd.load_diffusion_model`
- `comfy.sd.load_clip`
- `comfy.clip_vision.load`
- `comfy.controlnet.load_controlnet`
- `comfy.diffusers_load.load_diffusers`

### Loader nodes

- **Diffusion Model Loader Resident**
- **Checkpoint Loader Resident**
- **Diffusion Model Selector Resident**

`Diffusion Model Loader Resident` mirrors the relevant KJ diffusion-loader feature surface:

- weight dtype override
- compute dtype override
- cublas-ops toggle
- SageAttention override
- fp16 accumulation toggle
- optional extra-state-dict merge

### Residency nodes

- **Set Global Residency Policy**
- **Registry Snapshot**
- **Pin Model/CLIP/VAE Residency**
- **Preload Model/CLIP/VAE To GPU**
- **Evict Model/CLIP/VAE From GPU**
- **Report Model/CLIP/VAE Residency**

## Policies

The startup patcher exposes four policies:

- `legacy` — leave ingest/offload behavior close to stock ComfyUI.
- `balanced` — keep the registry and diagnostics, but do not aggressively steer ingest to GPU.
- `prefer_gpu` — prefer GPU ingest and GPU offload devices, but do not auto-pin tracked objects.
- `sticky_gpu` — prefer GPU ingest, prefer GPU offload devices, and auto-mark tracked loader outputs sticky.

Default selection order:

1. `COMFYUI_GPU_RESIDENT_POLICY` environment variable, if set.
2. `sticky_gpu` when `--gpu_only` is active.
3. `sticky_gpu` when `--highvram` is active.
4. otherwise `prefer_gpu`.

## Important scope limits

### Best path: `.safetensors`

This repo is optimized around `.safetensors`.

Direct GPU ingest is attempted for `.safetensors` loads. If the direct path fails, the patcher falls back to CPU read + GPU copy and records that fallback in the registry.

### `.ckpt` / `.pt` remain CPU-first under PyTorch

Those formats still go through `torch.load()`. The repo tracks that path and can still keep the resulting model hot in VRAM, but it does **not** claim true direct-to-GPU checkpoint ingest for pickle-based formats.

Use the included conversion helper to migrate hot models to `.safetensors`.

### Cross-process persistence is out of scope

This repo does **not** keep VRAM contents alive after ComfyUI or WSL exits. CUDA memory lifetime is process/context scoped. Achieving persistence across process shutdown requires a long-lived keeper process or server that owns the CUDA context.

## Installation

Clone into `custom_nodes`:

```bash
git clone https://github.com/xmarre/ComfyUI-GPU-Resident-Loader ComfyUI/custom_nodes/ComfyUI-GPU-Resident-Loader
```

Install dependencies inside the same Python environment ComfyUI uses:

```bash
pip install -r ComfyUI/custom_nodes/ComfyUI-GPU-Resident-Loader/requirements.txt
```

Optional SageAttention dependencies are **not** installed by default. Install those separately if you plan to use the SageAttention loader modes.

## Basic usage

### For direct diffusion-model loading

Use **Diffusion Model Loader Resident**.

Recommended on a large VRAM machine:

- policy: `sticky_gpu`
- model format: `.safetensors`
- preload with **Preload Model To GPU**
- inspect with **Report Model Residency** or **Registry Snapshot**

### For full checkpoints

Use **Checkpoint Loader Resident**.

That tracks and binds the resulting diffusion model, CLIP, and VAE independently so they appear in the registry snapshot.

### For manual residency control

- use **Pin ... Residency** to mark a tracked object sticky or evictable
- use **Preload ... To GPU** to fully materialize it in VRAM immediately
- use **Evict ... From GPU** to unload it from the current loaded-model set

## Observability

Every tracked load stores:

- source path
- last load method
- requested device
- actual device
- sticky flag
- current loaded bytes
- total bytes
- current/offload/load device

That data is surfaced through the report nodes and the registry snapshot node.

## Conversion helper

`scripts/convert_checkpoint_to_safetensors.py` is included for one-time conversion of hot `.ckpt` / `.pt` files into `.safetensors`.

Example:

```bash
python ComfyUI/custom_nodes/ComfyUI-GPU-Resident-Loader/scripts/convert_checkpoint_to_safetensors.py \
  --input /path/to/model.ckpt \
  --output /path/to/model.safetensors
```

## License

GPL-3.0-or-later.

This repo intentionally stays GPL-compatible because it adapts behavior from GPL-licensed ComfyUI and mirrors feature behavior from the GPL-3.0-licensed KJNodes diffusion loader.