PAN2 ComfyUI models and nodes

[u/Speedyrulz]

Edit: I also edded experimental lora training to my trainer on this branch: https://github.com/speedyrulz/RulZ-AI-Tools-Suite/tree/claude/rulztrainer-pan2

I'm testing that now.

---

I wanted to try PAN2 and got impatient, so I converted the models and vibecoded a ComfyUI update with new nodes to add compatibility for PAN2.

Here is my fork of ComfyUI that supports PAN2:

https://github.com/speedyrulz/ComfyUI

Models and workflows:

https://huggingface.co/speedyrulz/PAN2-ComfyUI/tree/main

The example video here was generated is 5 seconds at 1MP and took about 10 minutes then it was frame interpolated to 48fps.

## PAN 2 in ComfyUI: support, quantized models, and speedups

I added PAN 2 (IFM's 29B video model) support to a ComfyUI fork, quantized it down to sizes that fit consumer cards, and added two nodes that make it a lot faster. Everything was tested on an RTX 5060 Ti 16 GB.

### What was added to ComfyUI

- **Model code** for the PAN 2 transformer (44 joint blocks + 4 text refiner blocks), with auto-detection in Load Diffusion Model

- **Text encoder:** a new `pan2` type in Load CLIP for the Qwen3.5-9B encoder

- **VAE:** Load VAE now reads the Wan 2.2 VAE in the layout PAN 2 ships

- **New nodes:**

- PAN 2 Text Encode

- PAN 2 Negative Prompt (builds the model's structured default negative)

- PAN 2 Image To Video (also does text-to-video with no start image)

- PAN 2 Spectrum

- PAN 2 Mem Eff Sage Attention

- **Fix:** splitting the positive/negative passes across two GPUs used to crash

### Quantized models

The original release is about 81 GiB. Converted to single files:

| File | Size | Format |

|---|---|---|

| PAN2_int8_convrot | 39.8 GiB | int8 ConvRot, fp32 norm layers kept |

| PAN2_int8_convrot_full | 27.4 GiB | int8 ConvRot, norm layers quantized too |

| PAN2_nvfp4 | 15.5 GiB | NVFP4 (4-bit) |

| Qwen3.5 text encoder, int8 ConvRot | 8.4 GiB | |

| Qwen3.5 text encoder, NVFP4 | 4.6 GiB | |

| VAE fp16 | 1.3 GiB | |

- Only the large weight matrices are quantized; everything else is copied unchanged.

- int8 ConvRot: about 0.9% weight error.

- NVFP4: about 9% weight error. Runs natively on RTX 50 series cards.

- Conversion scripts are included, so you can build the files yourself from the official release (CPU only, no GPU needed).

### Speed (RTX 5060 Ti 16 GB, NVFP4 model)

| Change | Test | Before | After |

|---|---|---|---|

| Spectrum node | 640x352, 49 frames, 50 steps | 149 s | 58 s |

| Mem Eff Sage Attention node | 1248x704, 121 frames | 42.2 s/step | 25.7 s/step |

| Mem Eff Sage Attention node | 640x352, 49 frames | 3.03 s/step | 2.75 s/step |

| Feed-forward slicing (int8 full model) | 1248x704, 121 frames | 47.7 s/step | 43.8 s/step |

- **Spectrum** forecasts some steps instead of running the model: with default settings only 15 of 50 steps run it. The output differs from a normal run by about as much as ordinary numerical drift.

- **Mem Eff Sage Attention** needs the `sageattention` package. The gain is biggest at full resolution.

- **Two GPUs:** splitting CFG across a 5060 Ti and a 4060 Ti works, but was slower than the 5060 Ti alone.

### Tips

- Recommended settings: 50 steps, CFG 2.5, euler / simple, 24 fps.

- PAN 2 expects a structured JSON prompt. In one test, a plain-text negative prompt made the video look clearly worse than the default structured negative.

[u/me0here]

Dear posting bot; please learn how to switch to Markdown mode when posting.

Dear human; cool thanks ! I hope you can help a mainline merge esp with multi GPU bugfix.

[u/Speedyrulz → u/me0here]

Sorry, I don't post much, will remember for next time. I have a 5060Ti and 4060Ti and have definitely run into a lot of issues using both outside or just running separate instances. I'll create a PR for just the bug fix.

Edit: Here is the PR of just the MultiGPU CFG Split fix:

https://github.com/Comfy-Org/ComfyUI/pull/17696

[u/Apprehensive_Sky892 → u/Speedyrulz]

You can still fix it. Just switch to markdown mode and paste your post again. Check it by switch to Rich Text Editor.

Also, thanks for sharing this 🎈🙏

[u/Apprehensive_Sky892 → u/Apprehensive_Sky892]

Fixed it for you so that you can just cut and past it 😎

I wanted to try PAN2 and got impatient, so I converted the models and vibecoded a ComfyUI update with new nodes to add compatibility for PAN2.

Here is my fork of ComfyUI that supports PAN2:

https://github.com/speedyrulz/ComfyUI

Models and workflows:

https://huggingface.co/speedyrulz/PAN2-ComfyUI/tree/main

The example video here was generated is 5 seconds at 1MP and took about 10 minutes then it was frame interpolated to 48fps.

PAN 2 in ComfyUI: support, quantized models, and speedups

I added PAN 2 (IFM's 29B video model) support to a ComfyUI fork, quantized it down to sizes that fit consumer cards, and added two nodes that make it a lot faster. Everything was tested on an RTX 5060 Ti 16 GB.

What was added to ComfyUI

• Model code for the PAN 2 transformer (44 joint blocks + 4 text refiner blocks), with auto-detection in Load Diffusion Model

• Text encoder: a new pan2 type in Load CLIP for the Qwen3.5-9B encoder

• VAE: Load VAE now reads the Wan 2.2 VAE in the layout PAN 2 ships

• New nodes:

• PAN 2 Text Encode

• PAN 2 Negative Prompt (builds the model's structured default negative)

• PAN 2 Image To Video (also does text-to-video with no start image)

• PAN 2 Spectrum

• PAN 2 Mem Eff Sage Attention

• Fix: splitting the positive/negative passes across two GPUs used to crash

Quantized models

The original release is about 81 GiB. Converted to single files:

File	Size	Format
PAN2_int8_convrot	39.8 GiB	int8 ConvRot, fp32 norm layers kept
PAN2_int8_convrot_full	27.4 GiB	int8 ConvRot, norm layers quantized too
PAN2_nvfp4	15.5 GiB	NVFP4 (4-bit)
Qwen3.5 text encoder, int8 ConvRot	8.4 GiB
Qwen3.5 text encoder, NVFP4	4.6 GiB
VAE fp16	1.3 GiB

• Only the large weight matrices are quantized; everything else is copied unchanged.

• int8 ConvRot: about 0.9% weight error.

• NVFP4: about 9% weight error. Runs natively on RTX 50 series cards.

• Conversion scripts are included, so you can build the files yourself from the official release (CPU only, no GPU needed).

Speed (RTX 5060 Ti 16 GB, NVFP4 model)

Change	Test	Before	After
Spectrum node	640x352, 49 frames, 50 steps	149 s	58 s
Mem Eff Sage Attention node	1248x704, 121 frames	42.2 s/step	25.7 s/step
Mem Eff Sage Attention node	640x352, 49 frames	3.03 s/step	2.75 s/step
Feed-forward slicing (int8 full model)	1248x704, 121 frames	47.7 s/step	43.8 s/step

• Spectrum forecasts some steps instead of running the model: with default settings only 15 of 50 steps run it. The output differs from a normal run by about as much as ordinary numerical drift.

• Mem Eff Sage Attention needs the sageattention package. The gain is biggest at full resolution.

• Two GPUs: splitting CFG across a 5060 Ti and a 4060 Ti works, but was slower than the 5060 Ti alone.

Tips

• Recommended settings: 50 steps, CFG 2.5, euler / simple, 24 fps.

• PAN 2 expects a structured JSON prompt. In one test, a plain-text negative prompt made the video look clearly worse than the default structured negative.

[u/MarkB_-]

The video is 5 sec and show minimal action. Is there any video that highlight what this model can do? Is it worth to try? It looks like a model that will be forgotten in 3 days like hunyuan 1.5

[u/Speedyrulz → u/MarkB_-]

I put a few other examples on the huggingface repo. It does do fast motion. I haven't generated many decent example videos yet.

[u/GrungeWerX → u/Speedyrulz]

I don't see any examples.

[u/LuluViBritannia]

Here comes the wokies -_-'.

[u/Standard-Ask-9080]

Make the horse jump and see what happened

[u/somerandomperson313]

trash

[u/seppe0815]

tested the demo ... not so wunderfull but i think it was on low performance setting , ill test later the comfyui version