r/comfyui_elite

Hey everyone,

I’m currently doing my internship and working on setting up an AI-assisted product photography pipeline for high-end furniture. I really want to deliver the best of the best and blow my boss away with what ComfyUI can do, so I’m reaching out to see if anyone has a battle-tested workflow or advice for this specific setup.

Here is the exact pipeline I’m aiming to build:

1. Exact Geometry Retention: Starting with 4 Blender renders/passes of the furniture piece so the 3D form, proportions, and edges are 100% accurate (ControlNet Depth + Canny/Lineart).
2. Multi-Texture Ingestion via Image Reference: Injecting real photo references for specific materials (e.g., a specific high-res wood grain for the body, leather texture for details/handles). I assume masked IP-Adapter or regional conditioning is the way to go here?
3. Custom Studio Setting & Lighting: Placing it in a minimalist neutral grey studio setting, but with our own signature dramatic light and shadow style (considering IC-Light, specific ControlNets, or Style LoRAs).
4. Ultra-High-Res Output: Multi-pass rendering / ultimate upscaler to get crisp micro-textures without plastic AI artifacts.

My questions for the experts here:

• Does anyone have a .json workflow template or a similar node architecture they’d be willing to share as a starting point?
• What’s the current gold standard stack for this? (Flux vs. SDXL for texture fidelity + IC-Light vs. regional IP-Adapter?)
• Any crucial custom nodes / techniques I shouldn’t overlook for keeping industrial product accuracy intact?

Any workflow links, node suggestions, or tips would be massively appreciated. Thank you in advance for helping me out!

Hello everyone

Want to get faster MiniMax H3 video generation without sacrificing quality? In this tutorial, I’m testing the new H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes to find the best combination for speed and quality. The goal is to push MiniMax H3 as far as possible while cutting generation times by up to 2×, then upscale the results with LTX Upscaler to reach a stunning 2432 × 1344 (2K-class) resolution. By combining both H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes I generated video at 0.8 megapixel using "**RTX3060 6GB 16GB RAM "**and I got

 13 minutes vs 41 minutes at 8 steps

 27 minutes vs 52 minutes at 20 steps

LTX 2.3 Upscaler 11 minutes to get 2432 × 1344 resolution

Workflow link

https://civitai.com/articles/34028/comfyui-tutorial-minimax-h3-4-steps-lora-upscaling-2x-faster-generation-best-settings-for-2k-ai

First testing of the LTX 2.5 with a 6GB VRAM I’ve created a low-VRAM workflow that supports both Text-to-Video and Image-to-Video, optimized specifically for GPUs with 6GB of VRAM. The goal is to make LTX 2.5 more accessible to users who don’t have high-end GPUs, while keeping the workflow simple and easy to use. If you’re interested in testing LTX 2.5 on a 6GB GPU, check out the workflow and let me know how it performs on your setup!

Video Resolution : 1344x768 for 7 seconds video
Generated Time : 10 min

The model seems very fast the motions are better, lipsync and sound too, however the quality in minimax is better to me

Workflow link

https://civitai.com/articles/33897/comfyui-tutorial-first-test-of-ltx-2-5-new-model-better-than-minimax-h3

I am a B.Tech graduate searching for job while in the meantime I would love to earn through free lancing. Do people still pay for image generation bcoz yk LLMs like chat gpt and Gemini are doing pretty good job generating images so I want to know is this still worth learning so I can earn through this ? And also as my domain is AI&DS , so I can add these works to my portfolio right?

Also, can someone guide me?.I am new to comfy so I am experimenting with everything.If there is any thing you guys feel like a beginner should do.Tell me in the comments.

I am learning comfy using chatgpt . Do you guys have any better free alternatives?

Having a ton of fun, try it!

Super super fast…but not quite as good as minimax. Have you tried it yet?

ComfyUI is still the better tool when I’m exploring a look, testing ControlNets, dialing in an IPAdapter workflow, or building something I’ll reuse. But client work is different. Last week, a client changed the art direction at 11pm: same product, but four new compositions, a different background palette, and usable files by 9am.

My Comfy workflow was tuned for the original brief, specific LoRAs, masks, ControlNet settings, upscaling pass, and output logic. Changing the concept meant touching half the graph and rechecking every stage. It was powerful, but it wasn’t fast.

So now I use ComfyUI for R&D and repeatable production pipelines. For last-minute client variants, I use hosted tools where I can generate, iterate, and hand off without rebuilding the machine underneath it.

Anyone else split their workflow this way, or am I committing heresy?

Hey all, just wanted to drop an update that the latest version of comfyui-mobile-frontend (not an official Comfy org project btw) just dropped here: https://github.com/cosmicbuffalo/comfyui-mobile-frontend/releases/tag/v3.1.0

This one is packed with UX upgrades, including new workflow editing features, easier output management with favorite/reject and bulk processing, and improved video playback handling, among plenty of other quality of life improvements!

Also for anyone currently using this mobile frontend on an iPhone, I've got a slick new iOS app in the works too, if you're interested in what a native app can bring to your mobile ComfyUI experience, feel free to check it out here: https://cueforge.dev

https://preview.redd.it/3cyfiw8c18ih1.jpg?width=1200&format=pjpg&auto=webp&s=e82061904b9537a1d2d5c7654fd7b42c8a654192

Explanation after the images.

Before

After

Before

After

Before

After

FLUX.2 [klein] (and reference-latent edit models in general) have a resolution limit per call. If you want to add real detail to something (sharpen fabric texture, hair, stitching) you can do it working with it in pieces. The obvious way to do that turned out to be full of dead ends, so here's what I learned.

What it does: splits the image into overlapping tiles, regenerates each one at the model's native resolution, and blends them back into one image.

My first attempt did tiling the"proper" way: the MultiDiffusion/Mixture-of-Diffusers trick, where you slice the latent and blend the per-step noise predictions. That works great on convolutional UNets (SD1.5/SDXL), because a convolution is local, it doesn't care where in the canvas a patch sits.

FLUX is a transformer with absolute position embeddings (RoPE), not a UNet. Hand it a raw slice of a bigger latent and it has no idea it's a fragment, it just sees "a small complete image" and redraws the entire subject inside every tile. Every tile becomes a full (wrong-scale) copy of the whole scene.

I found that FLUX's RoPE positions can be shifted per-call via transformer_options so I tried telling each tile where it really sits in the canvas. Didn't help. Turns out FLUX applies that same shift to the tile and to any attached reference latent, so the relative offset between them (the only thing that matters for attention) never changes. Patching the model's forward pass to shift only the tile and not the reference removed the duplication, but the model still composed each slice as a standalone image, it was never trained to generate fragments, so proportions came out wrong regardless.

What actually worked: don't fight the model's training. Tile in pixel space. Every call is a complete image at a resolution it knows how to handle and solve everything else (continuity, color, blending) outside the model:

  - each tile is cropped from the canvas of already-generated neighbours, so it continues real pixels instead of guessing that region blind

  - per-tile color matching back to the source, so tiles don't drift in exposure/tint

  - blend weights derived from the actual per-side overlap, not the requested one (if the fade is narrower than what two tiles really share, you get a flat 50/50 band in the middle.

One node, no manual ReferenceLatent/EmptyLatent/KSampler wiring. You just give it a model, plain CLIPTextEncode conditioning, a VAE and an image.

GitHub: https://github.com/GianlucaMancuso/ComfyUI-TiledUpscale

Also on the ComfyUI Registry, search "TiledUpscale" in Manager.

Happy to answer questions, and if anyone knows a cleaner way to condition a transformer edit model on true image fragments, I'd genuinely like to hear it.

Hello everyone,

I’m looking for a high-quality workflow that can replace the character/face in an existing video with my AI model while keeping the original video as identical as possible.

My main goal is to preserve the original body, movements, expressions, pose, camera motion, background, lighting, composition, and overall video quality, while changing only the character’s identity/face.

I’m specifically looking for a professional workflow that can produce highly realistic results with strong identity consistency, natural skin texture, accurate facial details, and minimal artifacts or quality loss.

Ideally, the final output should look like the original footage was actually recorded with the AI model rather than appearing like an obvious face swap.

If anyone has a reliable ComfyUI workflow, custom setup, or a paid workflow/course that can achieve this level of quality, please feel free to contact me.

Body:

Made this because I kept losing track of which parameter tweaks actually gave me the good result. You know the drill — nudging the sampler, swapping a LoRA weight, tweaking the prompt fifteen times, and then twenty minutes later going "wait, which one of these was the good one again?"

So I built a node that just saves your whole workflow as a JSON file every single time you hit queue. No setup, no remembering to hit "save" manually — you drop it in your graph once and it quietly keeps a snapshot of every run in the background.

A few things it does that I found genuinely useful once I started using it daily:

- If you point it at your SaveImage or VideoHelperSuite output, it names the JSON after your actual output file, so the workflow and the render sit right next to each other with matching names
- You can dump them into date folders automatically if you don't want one giant flat pile
- It never overwrites — if two runs would collide it just appends _1, _2, etc.
- You can also tag variants with a postfix if you're A/B testing something (like _seedA / _seedB)

Honestly wasn't planning on publishing this, it started as a personal utility, but figured other people probably run into the same "which run was the good one" problem so might as well share it.

Install:
comfy node install comfyui-export-workflow-node

or just search "Export Workflow Node" in ComfyUI Manager.

Repo's here if you want to poke at the code or file an issue: https://github.com/kotakcloud/ComfyUI-Export-Workflow-Node

Let me know if it breaks on your setup or if there's a filename pattern you wish it supported, happy to add stuff.

Anyone using comfy cloud? I haven't been able to log in since yesterday, I have even tried in a different system. no word from anyone yet.

Flux 3 isn’t worth it imo. MiniMax H3 is truly amazing.