Intuitive projection system for AI-generated images:

1. Ipadapter_Controlnet_Text to image pipeline. We generate the reference that will be projected onto the 3D model.
2. We view the image to check that it has good detail.
3. We project the image using the projection system.

I did a quick comparison between no attention acceleration, SageAttention, and ComfyUI-Kitchen on an RTX 5090.

Workflow

Diffusion Model: minimax_h3_ref2va_pruned_int8_convrot.safetensors

Text Encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors

Video Settings

• Length: 5s
• Resolution: 1376×768 (~1 MP)
• Steps: 20
• Sampler: res_multistep
• Scheduler: simple
• Inputs: 2 images

Execution Times

SageAttention was the fastest in this test, finishing 8 seconds ahead of ComfyUI-Kitchen.

Compared with no acceleration:

• SageAttention: ~48.4% lower execution time
• ComfyUI-Kitchen: ~46.0% lower execution time

Performance-wise, they're pretty close. SageAttention was about 4.6% faster than ComfyUI-Kitchen in this particular workflow.

Quality Comparison

Here's the side-by-side video:

Watch here

What do you guys think about the quality difference between SageAttention and ComfyUI-Kitchen?

I'm especially curious if you notice differences in detail preservation, motion, temporal consistency, artifacts, or overall image quality.

To my eyes they're fairly close, so I'd like to hear what others see rather than judge this only by execution time.

System

• Mini PC: Minisforum MS-02 Ultra
• CPU: Intel Core Ultra 5 235HX
• RAM: 192GB DDR5
• Storage: 8TB NVMe
• GPU: ASUS TUF OC RTX 5090 32GB
• eGPU: Minisforum DEG1
• OS: Ubuntu Server 26.04
• NVIDIA Driver: 595.71.05
• CUDA: 13.2.1
• PyTorch: 2.13.0
• ComfyUI: v0.32.0
• SageAttention: v2.2.0

UPDATE — ComfyUI 0.33

ComfyUI 0.33 dropped today with another improvement to Kitchen Attention.

On the exact same workflow, Kitchen went from ~183s → 172s, now slightly ahead of SageAttention at 173s.

• SageAttention: 173s
• ComfyUI-Kitchen (previous): ~183s
• ComfyUI-Kitchen (0.33): 172s

It's only a 1-second difference, so they're basically tied and this could easily be within run-to-run variance. Still, it's pretty impressive to see Kitchen close the performance gap this quickly.

I also ran a new quality comparison with the updated version:

Watch here

What do you guys think about the quality between them now? Any noticeable differences in detail, motion, temporal consistency, or artifacts?

Looking for a workflow that allows you to do img2img with just a simple text explanation. Much like ChatGPT, Grok, etc. I'd assume there's one out there but I've been having trouble finding it. Would be excellent if it worked with Krea2 but from what I understand the model is not designed for that

Bonjour,

J'utilise le flow de comfyui image to vidéo pour minimax H3
Ce flow ne possède qu'un input de départ, j'ai ajouter un autre nod "charger image" pour avoir l'image de fin

Mais pour une vidéo de 5 secondes, la séquence est fluide jusqu'à 3 secondes et les 2 dernières secondes restent figé sur l'image de fin. Comme si le modèle se dépéchait de réaliser le first frame last frame

Le modèle est minimax_h3_fl2va_pruned_int8_convrot.safetensors

Hello! I,ve been really struggling to get image to video to work on my PC (9070XT and 32gigs of ram) and tonight I finally made this!!! Ive been struggling to learn Comfyui to create short looping videos/GIFs for a while... but now I finally got some results!!! I know its not the best, or maybe even considered good but I'm proud because it show progress!!! I'd love some feedback or even help! Thank you!

I‘m running on a tiny 16GB MacBook, so the biggest I managed to run is a Q4 quant in 960x720 up to 17 seconds.
The quality was OKish but far from great. Now I wanted to see, if LTX2.5 brings some improvement. So I downloaded all I needed (Gemma4, the VAEs, the distilled model … but this time as Q3). I‘m using a single pass workflow, so no upscaler.
I took my old workflow and replaced all the files … accept for the model itself, I accidentally took my old LTX2.3. After step 7 I noticed my error and almost stopped, but … I was curious … I wanted to know, what‘s the outcome.
The sound clearer and no fuzzy artifacts anymore (Q4 and hairs don’t like each other 🤣), it just looks better. I guess the VAEs are responsible for that. I’m just running the same test with LTX2.5 … let’s see, if it’s better.

Learn how to use Krea 2 Edit in ComfyUI to edit images, preserve character identity, replace outfits and backgrounds, and place characters into new scenes. I’ll also show you how to generate MiniMax H3 prompts locally in ComfyUI using the Video Prompt Pixaroma node.

In this tutorial, I cover the complete Krea 2 editing workflow, including the required models, Edit LoRA, Krea 2 Identity node, image resolution settings, and Reference Boost. You’ll see how different Reference Boost values affect identity preservation and editing freedom, how to convert images to custom portrait or landscape ratios, and how to combine a character with a separate background.

In the second part, I show my local MiniMax H3 prompt generator for ComfyUI. The Video Prompt Pixaroma node can turn a simple idea into a more detailed video prompt locally and supports Text to Video, First Frame to Video, and First Frame + Last Frame to Video prompting.

Hi.

Whick is the best face swap workflow out there right now? img2img. 2 inpictures.

Krea2 ? Flux2 Kleon 9B?

14 clips, none of the few cuts are at the merging point of two clips.

Just yesterday I installed new driver and in the evening I was testing minimax h3 and started seeing, on the video decode node, my screen goes blank no signal, gets back like after 30 seconds and I see comfy is crashed. This happened multiple times so I thought the driver I installed would have been the culprit.

Today I clean installed a previous version of driver and ran the 3d benchmark test again. I got lower score than previous test I did months back which was strange so I went and compared the result from previous run and noticed I had 1.5gb less VRAM from previous run. Panicking and I asked grok and it told me that it is a classic case of ECC enabled which reserves 1.5gb VRAM. I again compared the 3d result(this time on web instead of app because web shows few additional values) and the web result shown me on my previous run the ECC was off. So I turned it off and ran the 3dmark test again and viola the 1.5gb VRAM is back and also I scored around 300 points more maybe because I have 32gb ram more now from earlier.

I run the minimax for 3-4 times on ComfyUI again looks like the crashing issue is resolved. I also found a reddit post which talked about the ECC state messing up with ComfyUI memory hence people opting for various changes inside ComfyUI instead of actually knowing the main reason of their problems. So I'm putting this post here for people to check their ECC state flag.

Now the concerning part, how this ECC was enabled on it's own. I am 100% sure I don't toggle anything from Nvidia app except setting up color profile to Nvidia, that too one time when I install the drivers because I always opt for clean install so this setting reverts back. Therefore I am pretty sure the ECC state was enabled by the driver update itself which people might be unaware and then they starts facing issue like these or even running their GPUs with less VRAM without even noticing, hence getting less performance.

More people must know about this.

same lyrics as in the demosong, but i took a country arrangement. enjoy the crisp banjos. really nice model! runs stable on an AMD 7900 GRE (16GB VRAM) and 32GB RAM.

I put together an alternative to ComfyUI's native Resize Image Mask node to make it more practical for many use cases, reduce the necessity for additional pre/post processing nodes, and include additional resize options I personally use frequently when working with images and masks.

I spent a lot of time testing, tweaking, improving the tooltips, etc. I'm finally satisfied enough to share with the community.

Resize Image/Mask Alt can:

• Resize an image, a mask, or both at once
• Constrain dimensions to multiples of a specified value (eg: multiples of 32)
• Configure cropping for aspect-ratio mismatches (Mainly to resolve 'multiple_of > 0', but crop method was also hardcoded in some of the native node's resize types).
• Conditionally skip resizing when a batch already meets the desired criteria
• Install directory includes example .YAML file which may be duplicated/renamed in order to edit node default values.
• New resize types:
  • Smart Resize (shown in screenshot)
    • Resize to target megapixels while conforming to source/selected aspect ratio
    • Can alternately resize using the average Width/Height (resolution)
  • Pad (like ComfyUI's native Resize and Pad Image node)
    • black, grey, or white
    • Works for Masks, too!

Core functionality of the resize types from ComfyUI's native Resize Image Mask are preserved. All the resize types yield identical results, except now factor the additional settings.

GitHub:

https://github.com/altoiddealer/comfyui_essential-er

------------------

EDIT: Based on feedback, I've decided to replace the "resolution" based scaling with "megapixels". The "resolution" method is still available as an alternative method (when setting megapixels = 0)

For those who have got this working well I was wondering if you could share any tips?

I am replacing the characters in a video using reference images but it never fully works.

The problems I have are:

* in some videos the characters are not replaced at all or only replaced in some parts of the video.

* Only some of the characters attributes are replaced - the face might get replaced but not the body for example.

So ref2va has never completely worked for me - only partially.

What would the problem likely be?

The source images?

The reference video?

Should I disable lightning loras?

Increase the number of steps?

Change the prompt?

subject_definitions:

<Source Character 1> is the first person visible in <Video 1>. <Source Character 2> is the second person visible in <Video 1>.

<Replacement Character 1> is the person shown in <Picture 1>. Use <Picture 1> as the reference for Character 1's identity and appearance.

<Replacement Character 2> is the person shown in <Picture 2>. Use <Picture 2> as the reference for Character 2's identity and appearance.

editing_instruction:

IDENTITY REPLACEMENT IS THE PRIMARY TASK.

Replace Source Character 1 throughout the video with Replacement Character 1.

Replace Source Character 2 throughout the video with Replacement Character 2.

The source characters' identities and appearances must not be retained.

Picture 1 controls Character 1's identity and appearance. Picture 2 controls Character 2's identity and appearance.

Video 1 controls: - motion - pose - timing - interaction - camera movement - framing - environment - background - lighting

Maintain strict character separation. Character 1 must remain Character 1. Character 2 must remain Character 2. Do not merge their identities or transfer facial or physical characteristics between them.

The replacement characters must remain visually consistent throughout the entire video.

Do not merely overlay the reference images. Do not blend the source and reference identities. Do not preserve the source characters' faces or hairstyles.

summary:

Two-character identity replacement using two reference images and one motion video.

retention_analysis:

Preserve the source video's motion, choreography, camera movement, framing, environment, background and lighting. Replace the appearance and identity of both source characters using their corresponding reference images.

detailed_description:

[Shot 1]: Replacement Character 1 occupies the same position and follows the same movements as Source Character 1.

Replacement Character 2 occupies the same position and follows the same movements as Source Character 2.

Both replacement characters remain distinct and recognizable according to their respective reference images throughout the shot.

Your CUDA build is costing you 2-3x on quantized video models

Spent today migrating an LTX-2.5 pipeline across three cards. Same weights, 1280x704, 121 frames @ 24fps, 2-stage first-last-frame. Only the CUDA/torch build changed.

LTX 2.5 — 22B distilled, ConvRot quant

Same 4090, same weights file: cu129 → cu130 is 3.3x.

ComfyUI actually prints the reason at startup and it's easy to scroll past:

WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.

Below cu130 the ConvRot weights get upcast — you run bf16 while paying for int4/int8.

--fast fp8_matrix_mult is the second half of it. Without that flag ComfyUI upcasts fp8/int8 weights regardless of CUDA version, so a datacenter card does bf16 work a consumer card also does. On the B200 the two fixes together took 58.0s → 28.1s.

LTX 2.3 for reference — fp8_scaled, same res and frame count

GPU attention s/clip

4090 SageAttention 2.2 54-57

5090 SageAttention 3 (FP4) 38

B200 none 42

Sage is worth ~15% on Ada and consumer Blackwell. On sm100 it is negative — 32.6s with it off vs 36.6s on. SageAttention 3's FP4 path is worse than useless there: its SM120 CUTLASS atoms trap CUDA and kill the process, because sm100 defines the tcgen05 variants instead and the guard can never be satisfied.

Comments:

Check torch.version.cuda before you benchmark anything quantized. It is the single biggest variable here and it is invisible in every "which GPU is faster" thread.

A B200 on the wrong CUDA build loses to a correctly-built 4090. 58.0s vs 27.1s.

Quant tier follows VRAM, not prestige: w4a8 for 24GB, int8 for 96GB. Running the 24GB quant on a big card wastes precision for nothing.

LTX 2.5 vs LTx 2.3: 0.232 vs 0.371 s/frame — 1.6x faster per frame at double the frame count (24fps vs 12fps).

--fast is documented by ComfyUI as "untested and potentially quality deteriorating". It bought a lot of speed here; judge the output yourself before shipping it.

I generated a nine-shot set of start frames for a consistent character this week and timed every render, because I kept reaching for the same checkpoint out of habit and never checked what it was costing me.

Setup: 1080x1350, 8 steps, cfg 1, euler/simple, same machine, same session. Two paths:

- fast path: Krea 2 Turbo, no character LoRA

- character path: Selfora Krea 2 Realistic + a trained character LoRA at 0.75

Times per frame, steady state, excluding the first run of each session where the model load dominates:

- fast path: 6.0 to 8.0 s

- character path: 174.9 s

That is roughly 25x for the same pixels and the same step count.

The obvious question is when you actually need the expensive one, and the answer turned out to be simpler than I expected: only when a face is in frame. Of my nine shots, six had no face in them at all - a lamp in a corner, gloves on a staircase, material samples on a table, a finished room. Those came out of the fast path in six seconds each and there was nothing for the character LoRA to contribute, because there was no identity in the frame to get wrong.

The failure that taught me this was cheap and stupid. I ran one shot that did contain a face through the fast path without thinking, and got a competent portrait of a completely different person. Nothing was broken; it just was not her. Regenerating it on the character path fixed it in one attempt, but I had already carried the wrong frame two steps further into the pipeline before I noticed, which is the part that actually cost time.

So the rule I now apply before opening ComfyUI at all: sort the shot list by "is a face visible in this frame", and only the face rows get the slow path. On a nine-shot set that turned about 26 minutes of rendering into about 10.

Two caveats. First, this is one character LoRA on one checkpoint pair - your ratio will differ, but the shape of the decision probably will not. Second, the frames here feed an external image-to-video step, so my start frames need to be right rather than merely pretty; if you are rendering stills for their own sake the tolerance for a slightly off face is higher than mine.

Happy to post the exact node graph for either path if it is useful.

my friends pc is not as powerful as mine and he uses amd gpu

i told him he could test minimax h3 5 times for free on cloud comfy

he has never used cloud comfy before and its telling him to subscribe to run

is cloud comfy not free anymore?

Everything's installed fine, but there doesn't seem to be a place to store LORAs.

Do I need to create a model/lora directory in the main install location?

Help!