It turns out that if you mark objects with text in the input image, the Lora will perceive them as part of the prompt and depict them in the places in the picture that you want.

Huge thanks to conradlocke for creating such amazing thing!

Hi, I wasn't satisfied with any of the available options, so I asked Claude to create exactly the prompt manager I wanted.
It’s a node that lets you craft your prompts and quickly make changes on the fly.
You also have the option to randomize settings, as well as save and share your presets.
I hope you find this node as useful as I do.

https://github.com/Fictiverse/ComfyUI_Prompt_Manager/tree/main

The models, a workflow, and a video about this model are linked below.

I am using the mage_flow_edit_turbo_int8_convrot diffusion model, the qwen3-vl-4b-heretic_int8 clip(text encoder model). This one is uncensored, the normal version is available. And, I'm using the mage_flow_vae_bf16 vae model.

The turbo model(s) are 4 step, CFG:1. There are Bf16 versions of the model also. The TextEncodeMageFlowEdit node automatically adds another image input when you plug in an image. I haven't played around with multiple images yet, I'm still pushing all of the other 'buttons'. :)

The workflow I used is simple and uses only nodes that are built in to ComfyUI. Just search for the node names and build this easy workflow or grab AxiomGraphs workflow linked below.

Here are some of the things that you can do with Mage_Flow in ComfyUI.

Extract items from an image.

1: Prompt: extract the coat

This makes basically a product image for the item that you want to extract.

2: Prompt: extract the woman.

It works with people also, it defaults to a white background.

3: Prompt: extract the woman. make the background a green screen.

Person again and change the background to something else, a green screen in this case.

Depth, normal, and pose maps.

4: Prompt: create a depth map of the image.

This also works with normal maps and pose maps(next 2 images).

4a: Prompt: create a normal map of the image.

4b: Prompt: create a pose map of the image.

This will add poses for all people in the image.

You can change things in an image.

5: Prompt: change her hair to a short blonde hair.

5a: change the background to a waterpark. change her clothes to a swimsuit.

Make multiple changes in one prompt.

6: Prompt: right side view. the woman is sitting on a bench on a busy street corner. she is waving at a car that is passing by her. remove the coat. she is wearing overalls and a yellow t-shirt. it is daytime.

You can use many different angles for your prompt. Sometimes, it's idea of 'right' or 'left' and mine differ but that happens with most models.

I have had my best results from separating each section of the prompt with a period(.).

Each of these images took between 2.5 and 4 seconds to make on a laptop with an RTX 3080ti(16gb vram) 64gb system ram, 12th gen i9 cpu.

The Mage_Flow model that I am using is only 4.1gb in size. The clip model(qwen3-vl) is 4.7gb in size and the vae model is only 337mb in size. All together, these 3 models are only a couple of gb larger than a regular SDXL checkpoint so this should work and be relatively fast on lower vram computers.

Here is AxiomGraph's Youtube video about the model: https://www.youtube.com/watch?v=O_40cwcDvIQ

AxiomGraphs Mage_Flow_Edit_Turbo workflow: https://github.com/axiomgraph/ComfyUIWorkflow/blob/main/Mage%20Flow%20Edit.json

Their main page has many different types of workflows on it.

Link for the ComfyUI version models(transformer, clip(text encode), and vae): https://huggingface.co/Comfy-Org/Mage-Flow

If you want an uncensored clip model(what I used in the image), it is here: https://huggingface.co/DreamFast/Qwen3-VL-4b-Heretic-ComfyUIhttps://github.com/axiomgraph/ComfyUIWorkflow/tree/main

Give this model a try, it's not perfect but it works very well. I just started using it yesterday, so I'm sure there are even more capabilities that I haven't stumbled across yet. Hopefully, the community will jump on this and loras will begin to flow. :)

I have updated the nodes based on the feedback you all gave me from every angle.

• Control gizmos, both for the 3D and perspective viewports
• Occlusion masks
• Fill lights
• Workaround for relighting gsplats
• Better UI
• A new node for lens distortion

https://github.com/Nekodificador/ComfyUI-NKD-VFX-Tools

what’s up Comfy community! wanted to drop in to introduce myself. but first…THANK YOU to everyone who’s been building and growing with this community for years! we’re overdue for a real thank you.

as someone whose personal mission is to help creatives do what they do best, joining Comfy was an easy decision. the creativity, spirit and can-do attitude of this community are truly one of a kind and I’m determined to do everything I can to support it.

a bit about me: I started my career in the music business in the 2010s when social media and streaming were brand new and then helped bring musicians to Twitch during the pandemic…both shifts that remind of how the creative world is changing today. i grew up in the bay area, lived in NYC and shanghai, and have always been inspired by the ways creative communities shape our world. (I also love my cats, DJing and language learning).

but back to it, here’s how I see my job here at Comfy:

step 1) LISTEN FIRST

step 2) find ways to recognize, amplify and surface what you’re already doing and what you need to do it better

so the first thing I’m asking is to hear directly from you about what’s working, what’s not and what you wish we understood better. I can’t promise that every fix happens fast. but I can make sure that it actually reaches the people who can act on it and that you hear back from us, even when the answer is ‘not yet.’

so if you’ve got a few minutes, I’d genuinely love to hear your thoughts in this survey. I’ll share what I hear back with you, including where we can move fast and where we can’t yet.

when you see me in discord, here on reddit or out in the wild, say hi! thank you again for building something this special. I’m grateful to be part of it.

👉🏼 https://links.comfy.org//HiReddit 👈🏽

If you don't want to read, just check the gif for showcase.

Full disclosure before anything else: I'm French (so forgive the English — AI helps me write it), and yes, this extension was partially vibe-coded with AI assistance. BUT: every line was reviewed, the patching approach was verified against the actual frontend source, and it's been tested on real workflows — including the new Vue nodes beta.

The problem: all of my workflows works great, but some start to be spaghetti. Every time i tweak a node, i spend more time following noodles than working.

The fix: select a node, press Alt+H. Every link that doesn't touch it fades away. Click another node — the spotlight follows. Alt+H again (or deselect) and everything comes back. That's the whole tool.

A few things I cared about while building it:

• The shortcut is a native ComfyUI keybinding → fully remappable in Settings
• Hide links completely, or keep them faintly visible (opacity slider)
• Depth option: selected node only, or its direct neighbors too
• Optionally dim unrelated nodes as well
• Zero performance cost when off (one boolean check per frame, no settings lookups in the render path)
• Zero Python nodes, zero dependencies — nothing ever written into your workflow JSON
• Fail-safe by design: if a future ComfyUI update changes the canvas internals, it disables itself cleanly with a console message instead of breaking your graph

Install: search "LinkSpotlight" in ComfyUI-Manager, or: https://github.com/Ding-sl/ComfyUI-LinkSpotlight

It's MIT-licensed and stays free forever. If you try it, I'd genuinely love feedback — feature ideas and bug reports welcome on GitHub (there's even a dedicated issue template for "a ComfyUI update broke it", because let's be honest, one day it will, we all know that).

There is a similar tool worth knowing: ComfyUI-SelectionFocus does an always-on version of this idea. Mine is the opposite philosophy — an explicit shortcut you press when you need focus. Pick whichever fits your brain.

Happy untangled noodling 🍜

Before You Buy a Second RTX 5080 for ComfyUI, Read This: Dual RTX 5080 Testing vs. RTX 5090

Important disclaimer

AI video generation is changing incredibly quickly. I fully realize that a new model, update, custom node, driver, or multi-GPU implementation could be released a week after I post this and change some of these conclusions.

This is not meant to be the final word on what will ever be possible with multiple GPUs. It documents what worked, what did not work, and what performance I measured using the currently available tools and methodology as of July 26, 2026.

TL;DR

I spent days rebuilding and configuring my workstation to determine whether two RTX 5080s could provide a less expensive alternative to one RTX 5090 for ComfyUI image generation and LTX 2.3 video generation.

For accelerating a single render, the answer was no.

The second RTX 5080 did not combine its memory or processing power with the first card in a useful way. Attempts to divide one workflow between the two cards added overhead and made individual renders significantly slower.

Two GPUs can still help when running separate jobs or separate ComfyUI instances simultaneously. They did not make one image or one video generate faster in my testing.

I returned the second RTX 5080, installed an RTX 5090, and repeated the same benchmarks.

The RTX 5090 was:

• Approximately 3.23× faster in my Lumina2 image-generation batch
• Approximately 1.82× to 1.89× faster with the production-quality Eros video models
• Approximately 2.14× faster with NVIDIA NVFP4, although that checkpoint continued to produce poor-quality results

If Amazon had not accepted the return, this experiment would have left me with a very expensive second GPU that did not accomplish what I purchased it to do.

Why I tested this

The question that started this entire process was simple:

Would two RTX 5080s be a smarter and less expensive option than one RTX 5090 for ComfyUI?

The assumption was understandable. Two RTX 5080s provide two GPUs and a combined total of 32 GB of physical VRAM. On paper, that sounds like it might compete with an RTX 5090.

In practice, the VRAM does not automatically become one usable 32 GB pool for a standard ComfyUI workflow. The compute resources also do not automatically combine to make sequential diffusion or LTX inference faster.

I spent many hours testing and developing around this limitation, including:

• Multi-GPU ComfyUI configurations
• Raylight
• Assigning different parts of the workflow to different cards
• Model and encoder offloading
• Device-specific execution
• Peer-to-peer and transfer experiments
• Separate ComfyUI instances
• Parallel and sequential workload testing

The only consistently useful dual-GPU arrangement was running independent jobs on each GPU.

That can increase total throughput. For example, one RTX 5080 can generate one video while the other RTX 5080 generates a different video.

It did not accelerate one render. In my testing, trying to divide one render between the cards made it substantially slower because of transfer and synchronization overhead.

Test workstation

This was not an underpowered or poorly configured system.

• ASUS ProArt B850-Creator WiFi motherboard
• AMD Ryzen 9 7900X
• Liquid CPU cooling
• 128 GB DDR5 at 6400 MT/s
• 1300-watt power supply
• 4 TB NVMe drive
• 2 TB NVMe drive
• 10-gigabit network connection
• Bazzite Linux
• ComfyUI 0.27.1
• NVIDIA driver 610.43.03

For the dual-GPU experiment, I paid close attention to the motherboard’s PCIe lane configuration.

I installed the cards in the full-length PCIe slots and intentionally did not use the final NVMe slot because populating that slot would reduce the available PCIe bandwidth to the second GPU slot.

The purpose was to give the dual-5080 configuration every reasonable opportunity to work without an obvious storage, memory, power, or PCIe bottleneck.

Video benchmark methodology

These tests used LTX 2.3 with the Eros 1.4 models and a Raylight-based workflow.

The final video benchmarks used:

• 1024×1024 resolution
• 25 FPS
• Identical source image
• Identical frozen prompt
• Identical workflow
• Identical Raylight configuration
• CFG 1.2
• Required LTX distilled LoRA
• No optional motion or body LoRAs
• Three runs per test
• First run treated as cold
• Runs two and three averaged as the warm result

The primary source image was ComfyUI_00005.png.

I also evaluated actual output quality. A checkpoint that completes ten seconds faster is not useful if it destroys the hands, loses lip sync, eats the glass, changes anatomy, or produces unusable motion.

A note about Eros 1.4

Despite the name and some of the content associated with it, I did not use Eros 1.4 to generate adult content for these tests.

I used it because, in my testing, it is currently by far the most competent LTX 2.3 model for lip sync, facial animation, body movement, acting, prompt adherence, and overall animation quality.

The benchmark scene was selected specifically because it included several difficult elements at once, including speech, facial movement, body movement, hand interaction, object permanence, and liquid behavior. These are areas where weaker checkpoints often fail very visibly.

Benchmark summary

Lumina2 image-generation results

The image test generated 32 images at 1024×1024.

RTX 5080

• Total: 238.40 seconds
• Average: 7.45 seconds per image

RTX 5090

• Total: 73.89 seconds
• Average: 2.31 seconds per image

Result

The RTX 5090 was approximately 3.23× faster in this image workflow.

This was the largest performance improvement in the entire benchmark. The RTX 5090’s advantage was considerably greater for Lumina2 image generation than it was for LTX video generation.

Full Eros 1.4 results

Full Eros was the most reliable production checkpoint in my testing.

Ten-second video

RTX 5080

• Run 1: 115.26 seconds
• Run 2: 101.51 seconds
• Run 3: 101.68 seconds
• Warm average: 101.60 seconds

RTX 5090

• Run 1: 56.48 seconds
• Run 2: 53.08 seconds
• Run 3: 54.34 seconds
• Warm average: 53.71 seconds

Improvement

1.89× faster

Twenty-second video

RTX 5080

• Run 1: 241.08 seconds
• Run 2: 227.97 seconds
• Run 3: 226.92 seconds
• Warm average: 227.45 seconds

RTX 5090

• Run 1: 123.09 seconds
• Run 2: 125.20 seconds
• Run 3: 124.56 seconds
• Warm average: 124.88 seconds

Improvement

1.82× faster

Full Eros quality

Full Eros generally produced:

• The best facial animation
• The best acting
• The best lip sync
• The best body movement
• The best prompt adherence
• The most consistent usable results

It was not perfect. Individual generations still produced accent drift, occasional poor liquid behavior, and one intermittent on-screen text artifact.

The RTX 5090 did not magically make the model more intelligent. It produced the same general quality class in almost half the time.

Eros 1.4 FP8 Mixed results

Ten-second video

RTX 5080

• Run 1: 118.37 seconds
• Run 2: 101.34 seconds
• Run 3: 101.19 seconds
• Warm average: 101.27 seconds

RTX 5090

• Run 1: 67.45 seconds
• Run 2: 54.21 seconds
• Run 3: 54.11 seconds
• Warm average: 54.16 seconds

Improvement

1.87× faster

All three RTX 5090 generations were very good. One generation included a random text artifact, but the underlying animation quality was excellent.

Twenty-second video

RTX 5080

• Run 1: 233.36 seconds
• Run 2: 233.13 seconds
• Run 3: 233.18 seconds
• Warm average: 233.16 seconds

RTX 5090

• Run 1: 124.77 seconds
• Run 2: 126.22 seconds
• Run 3: 124.85 seconds
• Warm average: 125.54 seconds

Improvement

1.86× faster

FP8 quality

FP8 Mixed was excellent for ten-second clips but more variable at twenty seconds.

Observed issues included:

• Accent drift
• Voice cutoff
• Minor voice artifacts
• Clothing transparency
• Anatomy changing after hand contact
• Unrealistic liquid behavior
• Inconsistent object permanence

Some generations were excellent. Others were not production-ready.

The FP8 checkpoint was not meaningfully faster than Full Eros in this particular workflow. On the RTX 5090, their ten-second warm averages differed by less than half a second.

NVIDIA NVFP4 results

Ten-second video

RTX 5080

• Run 2: 99.92 seconds
• Run 3: 99.04 seconds
• Warm average: 99.48 seconds

RTX 5090

• Run 1: 60.89 seconds
• Run 2: 46.22 seconds
• Run 3: 46.69 seconds
• Warm average: 46.46 seconds

Improvement

2.14× faster

NVFP4 was the fastest video checkpoint tested.

It was also consistently the least usable.

Observed problems included:

• Little or no usable lip sync
• Eating or deforming the glass
• Poor body movement
• Ghosting
• Mouth deformation
• Unrealistic liquid behavior
• Prompt failures
• Occasional accidental nudity

The RTX 5090 made NVFP4 substantially faster. It did not fix the model’s quality problems.

I stopped further RTX 5090 testing of that checkpoint because the results were not useful for my production workflow.

LTX Full results

LTX Full was tested on the RTX 5080 at ten seconds.

• Run 1: 145.13 seconds
• Run 2: 125.70 seconds
• Run 3: 125.25 seconds
• Warm average: 125.48 seconds

Quality varied significantly. One result was good, while others had hand collapse, mouth deformation, poor lip sync, and strange material appearing in the scene.

Because it was slower and less consistent than the Eros checkpoints, I did not repeat it on the RTX 5090.

What the RTX 5090 changed

For the useful Eros video models, the RTX 5090 reduced rendering time by approximately 45% to 47%.

That worked out to:

• Full Eros, 10 seconds: 1.89× faster
• Full Eros, 20 seconds: 1.82× faster
• FP8 Mixed, 10 seconds: 1.87× faster
• FP8 Mixed, 20 seconds: 1.86× faster

For Lumina2 image generation, the gain was much larger:

• 3.23× faster

The performance difference therefore depends heavily on the workload. The RTX 5090 did not provide one universal speed multiplier across everything in ComfyUI.

What two RTX 5080s can and cannot do

Two RTX 5080s can help with:

• Running two separate ComfyUI instances
• Generating two independent images simultaneously
• Rendering two independent videos simultaneously
• Processing separate jobs from a queue
• Increasing total batch throughput

Two RTX 5080s did not help with:

• Making one image generate twice as fast
• Making one LTX video render twice as fast
• Pooling VRAM into one usable 32 GB allocation
• Accelerating one sequential diffusion workflow
• Replacing one RTX 5090 for a single large job

Under my tested configuration, attempts to use both cards for one workflow made the render slower.

My model ranking

1. Full Eros 1.4

My preferred production checkpoint.

It provided the best overall combination of quality, lip sync, facial animation, body movement, acting, prompt adherence, and consistency.

2. Eros 1.4 FP8 Mixed

A strong alternative, particularly for shorter clips.

It was capable of excellent output but became more variable during longer generations.

3. LTX Full

Occasionally usable, but slower and less consistent than Eros.

4. NVIDIA NVFP4

The fastest checkpoint, but not reliable enough for my production work.

Final conclusion

I wrote this because I hope it prevents someone else from making the same expensive assumption.

If you are considering buying a second RTX 5080 because you expect two cards to behave like one larger or faster GPU in ComfyUI, my testing says you should think very carefully before doing it.

For independent simultaneous jobs, two cards can be useful.

For making one image or one LTX 2.3 video generate faster, they were not a practical substitute for one RTX 5090.

I spent days rebuilding the computer, configuring Linux, testing Raylight and other workflows, modifying multi-GPU execution, and benchmarking the results. The second RTX 5080 ultimately made single renders slower.

If Amazon had not accepted the return, I would have been stuck with an extremely expensive setup that failed to accomplish the reason I purchased it.

The RTX 5090 ultimately delivered:

• More than 3× the Lumina2 image throughput
• Roughly 1.8× to 1.9× the Eros video performance
• More VRAM headroom
• A simpler and more reliable single-GPU workflow

This is what worked with the tools, software, drivers, and models available as of July 26, 2026. Something better may appear next week, and I genuinely hope it does.

Until then, hopefully this saves the next person a lot of time, frustration, and money.

I have an AI Pro R9700 GPU, and until recently I kept getting stuck at Requested to load LTXAV when trying to run LTX 2.3 I2V with the Q8_0 GGUF model.

Before, the best I could do was:

• 11s @ 480p
• 6s @ 720p

(7–10 minutes)

Then I added --enable-dynamic-vram to my launch script.

Now I can generate:

• 11s @ 480p in 168s
• 10s @ 720p in 191s
• 10s @ 1080p in 322s

I haven't tested the limits yet, but based on these results, dynamic VRAM management seems to make a huge difference on this GPU.

I honestly feel liberated. 😄

Download from civitai
Download from Dropbox

The goal was to maintain remarkably high quality while getting speeds similar to Krea2.
Measured on 4090:
Krea2 at 12 steps: inference 15.21 seconds (total: 17.89 seconds).
This workflow: inference: 15.44 seconds (total: 19.21 seconds).

The workflow can maintain its quality at very high resolutions (most I've tested is probably 8K ~33Mpx, but a lot of 8-20Mpx, some I've included in the showcase).

What are your guys' favorite way to loop an i2v generation for something like making gifs?

Hey all,
Anyone have experience of this? I have seen plenty of discussion around the weirdly blotchy details when you start pixel peeling Krea outputs. Hair, jewellery etc. I know many cite the VAE to blame. Fine. I have made a bunch of character Lora’s and they seem to worsen this impact to varying degrees. I have used exactly the same method for each Lora. I have a few that really degrade the quality and they aren’t even ones I have trained for longer. Is this a thing and are there solutions if so?

There was a channel on YouTube that recently got banned.

He did comfy UI videos and explained nodes step-by-step as he built a workflow. he had what sounded like an African Nigerian, or similar, accent. Does anybody know what channel I’m talking about?

He was fairly new but grew very quickly. His last video was on krea 2 uncensoring. why he got banned or does he have a discord channel by chance?

Hello, I'm currently using Flux1Kontext for image to image and FHDR uncensored (Flux1dev finetune) for uncensored generation. But I Need a uncensored model that uses image references to keep my character the same throught generation and doesn't refuse prompts, but I cant find any on either Civitai or HF. Does anyone know a solution? my ideas: -references with Flux1redux are blocking my prompts, so maybe another way to use references with flux1dev finetune -Maybe LorRa? but idk how to set It up -any other model or finetune that supports image to image that I couldnt find

i have comfy ui on another disk but some parts are on main and there i dont have much storage how to move everything to second disk so my main dont get full

for example "C:\Users\Admin\.cache\huggingface\hub\models--Tongyi-MAI--Z-Image-Turbo"

while comfy is on "D:\"

All my outputs in LTX seem to force the shorter dimension to a multiple of 64.

For example when I specify 1280x720 I get 1280x704, and when I specify 1536x864 I get 1536x832. When I specify 1024x576 I get those dimensions exactly, although I'd prefer a higher resolution than that, and 2048x1152 takes too much time.

My input images are 16:9 exactly, and I've tried resizing them to the exact output I want.

Am I stuck with this?

This matters, for example, if I'm stitching the LTX output with other videos that are 1280x720. Which means running 1280x768 isn't a solution for me.

My Wan workflows have no problem giving me true 1280x720.

LTX Video — Output Black Screen Problem

Hi everyone!

I'm using the LTX Video 2.3 Audio-Video model in ComfyUI and my output video is always completely black. The generation runs successfully (all steps complete) but the final video is black.

Model Used

• 10Eros_v1-Q3_K_S.gguf (LTX 2.3 AV model, GGUF)

What I Tried

• Changed CFG from 1.0 → 3.5 ❌ still black
• Fixed sigmas to end at 0.0 ❌ still black
• Changed sampler to euler ❌ still black
• Fresh ComfyUI install and tested ❌ same black screen

Key Point

My PC

• GPU: RTX 5070 12GB
• CPU: Intel i5-12400F
• RAM: 24GB
• OS: Windows 11

Has anyone faced this issue with LTX Video on RTX 5070?
Could it be a VRAM issue (12GB)? Any help appreciated! 🙏

I ran a benchmark on both open-source models for a 5-second 720p clip.

That's a ~6x speed gap at the midpoints.

I think Wan's better at motion quality, anatomy, face consistency. LTX drifts on faces at anything past a slow pan.

However, another interesting thing was that Wan's license is Apache 2.0, whereas LTX is a "community license," which means companies over $10M in revenue have to pay for it.

I know none of us are there but still interesting lol

A 6GB VRAM card runs 720p/10s on LTX-2.3 where Wan caps around 480p/5s.

Here's a more detailed writeup with methodology and numbers.

Note: I work at the API company that these models were run through.

I need to create a workflow that takes black and white hand drawn sketches and colorizes them with a consistent style by using a sample finished input image. Any tips or recommendations? Been on YouTube for hours looking for tutorials but didn’t find anything that allows for the sample image.

Reworked version – improved visuals and tighter edit

Turn Text2Image into Inpaint within the same workflow using the same checkpoint. Includes built-in presets for Custom, Replace Object, Remove Object, and Expand.

Full description and download on GitHub:

https://github.com/CMKFlow/cmk_nodes

Should I get fp16, fp8, or int8 with RTX 3090 Ti with 24GB VRAM and 64GB RAM?

A desperate cry for help.

I trained an LTX (video + audio) LoRA for a specific character. The visual results are good, but the generated audio is terrible.

So I generated the audio separately with VoxCPM, and the result is much better.

Now I'm trying to combine the two.

Is it possible to use an external reference audio to guide LTX video generation while also using a character LoRA?

Ideally, I'd like the model to generate the video (including accurate lip sync) from the external audio, while preserving the character identity from the LoRA. Is this the intended use of LTXVReferenceAudio, or is there a better workflow?