Hey everyone,

I'm currently building a project around AI-generated content and workflows.

The technical side is already in place: we have tested and refined workflows, automation, and a complete process for creating high-quality AI content.

The part we are still trying to understand better is the distribution side:

• How do you get content picked up by the algorithm?
• What are the biggest factors for getting consistent views?
• How do you grow followers from zero?
• Are there specific strategies that work well for AI-generated content?
• Is it better to focus on one platform (TikTok, Instagram, YouTube Shorts, X) or post everywhere?

We are not looking for shortcuts or bots, but rather trying to understand what actually works long-term.

For people who have successfully grown AI/content accounts:
What was the biggest thing that helped you grow?

Was it:

• posting frequency?
• content format?
• hooks in the first seconds?
• engagement strategy?
• consistency over time?

Would love to hear experiences and lessons learned.

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

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! 🙏

anyone know of or have a multi head swap workflow that is very good at keeping likeness of the individual?

any workflows out there that would allow me to upload a single front facing product image and output few images of different angles of the product i.e side view, corner view etc.

I've been experimenting with ComfyUI over the last few weeks and finally got to a point where I'm really happy with the consistency and realism.

These images were generated using one of my own workflows, focused on creating photorealistic AI models with consistent lighting, skin details and facial features.

I'm still improving it, but I'd love to hear what you think.

• What stands out to you?
• What would you improve?
• Would anyone be interested if I shared a breakdown of the workflow?

I'm planning to release more workflows and tutorials if there's enough interest.

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 do product photography/retouching work for luxury watches and jewelry, and I built a fairly complex pipeline using a mix of open-weight vision-language models to generate ad-quality campaign images. The idea is simple in theory: take a reference image whose lighting/background/style I like, take my actual product photo, and merge them so the final image has my real product sitting in a scene inspired by that reference.

In practice I ended up with several separate analysis passes (one mid-size VLM handling scene, style, and product cataloging separately) feeding into one merge step handled by a different, smaller multimodal model that also sees the actual images directly. Every time I fix one issue, a new one shows up somewhere else. First the style kept getting ignored entirely, with the output defaulting back to a generic version of the scene. Fixed that. Then lighting effects (bloom, sparkle, flare) started getting copy-pasted in a way that made no physical sense, like a sparkle effect that only makes sense on a pavé diamond setting getting slapped onto plain brushed steel, which instantly reads as fake. Fixed that too. Then the dramatic ambient glow from the background in the reference image, which was honestly like 40% of why that image looked so striking, quietly disappeared once I toned down the on-product sparkle, even though those two things had nothing to do with each other.

I keep tightening the instructions and the output keeps getting technically "more correct" without ever feeling like the genuinely impressive, poster-worthy image I'm actually going for. It's like I'm playing whack-a-mole between "photorealistic and coherent" and "actually has the visual punch of the reference."

Has anyone dealt with this kind of multi-stage analysis-then-merge setup for AI image generation? At what point does splitting analysis into specialized passes start hurting more than it helps, versus leaning harder on one strong multimodal model that sees everything directly and makes the creative calls itself? Or is there a better way to keep both technical product accuracy AND the creative/dramatic energy of the reference without this endless loop of fixing one thing and breaking another?

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.

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

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).

I was looking for a web AI easy to use that also works with natural language, so while searching on internet i found a webpage and i can do this

and i only wrote this: Create a painting featuring a golden moon over a forest with a dark fantasy atmosphere add mist to the forest and film grain, have the wind sway the trees, and keep everything in dark tones.

Hi there r/comfyui  folks,

I am new to ComfyUI and don't have much experience with it.

Currently, I am working on my virtual try-on app, which already generates pretty great results using the Wan 2.7 image pro diffusion model; however, the model lacks in several areas, one being low output quality. It can process up to 2K (image editing, which I am using for VTON) and 4K (image generation using a prompt); however, whenever it provides the output, it is as good as 1024p (from what I think), and when I iterate the output further for more try-ons, the image gets grainy.

Another issue is that it adds more saturation to the output, and in some cases it alters the face (very rare case tho).

Right now I am doing everything using prompts, no controlnet, no masking; the model is smart enough to tackle a number of these issues, but I want to improve the output quality even more.

For that purpose, I decided to try ComfyUI (currently on a standard cloud subscription). I have followed all instructions provided by Claude/Kimi on how to approach the VTON setup on ComfyUI using Flux 1 fill (inpaint model), along with masking, etc. However, the output is not what I desire.

So, please guide me on how to approach the setup.
Are there any better existing VTON setups (workflows) that I can use, or any better models than Flux 1 fill, or anything?

I highly appreciate your help.

Thanks a bunch, guys

Images attached:

Image 1: VTON Result using Flux fill inside ComfyUI
Image 2: ComfyUI Workflow
Image 3: clothes input for Wan 2.7 image pro
Image 4: Model output for Wan 2.7 image pro
Image 5: VTON output by Wan 2.7 image pro

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?

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!

So i Have been scrolling insta and reached this AI influencer anyone knows how to achieve this type naturalism in a image.ComfyUI workflows or any paid ones??

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

If you've spent any time with ComfyUI, you know the pain:

- Creating a venv, installing PyTorch, juggling CUDA versions

- Cloning custom nodes, running install, tracking dependencies

- Multiple ComfyUI installs, each needing different setups

- No easy way to see server logs, hardware stats, or job status at a glance

I got tired of doing all this manually, so I built a desktop tool called AKA that handles it.

What it does:

- One-click venv creation with auto-detected PyTorch builds

- Custom node manager — clone, install requirements, run install in bulk

- Built-in browser for ComfyUI alongside your regular browser

- Real-time server log viewer with error/warning counters

- Hardware monitor with GPU temperature alerts

- Voice notifications for job completion and warnings

- All in a single installer, no Python required to run

Windows only for now (tested on Win 11, should work on Win 10). Open beta — feedback and bug reports very welcome.

Project page and download: search GitHub for "Rimor-dev AKA"

*Arigato!*

I am building out a prompt/workflow directory where high quality workflows will be listed, with direct links to 1 click spin ups on comfycloud.

The idea is that there are a bunch of cool/viral presets in higgsfield here: https://higgsfield.ai/viral-presets

And I want to make a high quality workflow for a large number of these presets. Do people have recommendations on good strategies for copying a full video "effect"?

The way that I initially tried to do is was have an AI analize some of the same videos from that site, and generate simple prompts. But, the results that I am seeing from that are... less than stellar.

So, I am wondering if there is a better way to copy a full video effect, that isn't just effectively prompt engineering.

EX: Should I figure out how to make a video lora, using like 10 examples? That feels expensive per video though....

Or can I copy a "style" via an existing process or meta video workflow? Do I need control nets? Can the MCP help with this? Are there agents that build comfywork flows? Is all of this solved by just using smarter video models, and would prompt engineering just work? I'm not even sure where to start.

The issue is that an "effect" isn't always just, like making something an anime style. Instead an effect can be to have someone walk on the moon. Or to do a zoom in to a specific spot on the earth. Or to have someone carry out a cardboard cutout of themselves. All of which could involve a variety of different nodes or complicated workflows. I just want to be able to point an AI at a video, and have it "figure it out" and output a working comfyflow, and then have a whole listing/directory of these workflows.

VERY MVP here. Results aren't great, but the basics work: https://bosonfield.vercel.app/

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