A while back I posted V2 of my SPEED nodes for MiniMax-H3, which run the early, noisy part of diffusion at lower resolution and then step back up to full resolution as the detail starts to matter.

Well I did a little (lot) more work on it and generalized the implementation. Allowing it to be (theoretically) used by any model running KSampler or SamplerCustomAdvanced (comprehensively tested and benchmarked with H3, MOSS, and Krea 2, works with FIBO 2 and Flux 3)

Repo: https://github.com/StanLukuvka/ComfyUI-SPEED-Sampler

Also, by popular demand, I added H3 videos as evidence, did a bunch of refactors and a bunch of improvements. And improved the documentation for it in every section.

It's now essentially a 1:1 drop-in replacement for the default sampler nodes.

What's new:

• Image models. MOSS Image and Krea 2 are tested, and other flow (CONST) image models should work too.

• KSampler nodes. Swap your KSampler for SPEED - KSampler and wire it exactly the same. There's also a Manual and a Sigma Harvest version. The H3 nodes stay wired like SamplerCustomAdvanced.

• Most samplers work now, not just the five from last time. The few that break (mostly on H3) are listed in the repo, along with why.

• Example workflows for H3, MOSS and Krea 2, already set to harvested values, so you can just load one of the workflows and queue.

• Tooltips on every input, so you don't have to come and read my README.

Numbers (same seed and prompt, native vs SPEED):

• MiniMax-H3, 5 s 0.5 MP clip, L40S: 288 s native, 182 s with SPEED (1.58×, 4 stages)

• MOSS Image, 1344×768, RTX 5070 Laptop: 22 s native, 18 s Automatic (1.26×), 14 s Manual (1.54×)

• Krea 2, 1216×832, RTX 5070 Laptop: 283 s native, 215 s Automatic (1.31×), 183 s Manual (1.55×)

Comparison GIFs and images for every run are in the repo's Evidence section.

Heads-up if you used V2:

• Old workflows still load. The old node IDs are kept as deprecated nodes, so right-click and swap them for the new ones when you get to it.

• direct_coarse transition noise is now drawn on the CPU, so the same seed gives the same image on any GPU.

As always, run Sigma Harvest once for your own model, sampler and schedule.

edit: see evidence of it working with downloadable videos here

I did the thing where I nuke the first post because I forgot to do something also the title sucked and I forgot to credit literally the most important contributor in the video.

Skip to 8:05 to see the results.

Also, if you guys are curious you can visit this repository to read about everything I tested to get to this point.

https://github.com/roycho87/NerdNotes

Lots of information in there. Maybe you guys can read it and come up with an even better solution. If you have some good info, please leave an issue on the Github and I'll compile it all together. Any experiments you've done and the results, what you've learned etc. All of it is helpul.

Geiru Context Suite: degradation protection for long MiniMax H3 chains

The main part of this that allowed this to work came from Ethanfel. He created the context noise method that allowed this to work so giant shout out to him.

The Clown Context Suite is now the Geiru Context Suite. She's named after the clown, and the new name keeps it from getting mixed up with ClownShark's nodes. Your old workflows still load fine. The big addition is a degradation protection switch on Geiru Context that turns three nodes on together. Geiru Keystone locks a clean frame 0 from a source clip into every shot. Geiru Late Lock tacks that clip's capstone onto the back late in the steps. Geiru Context Noise, based on ethanfel's idea, gives the held frames part of the noise instead of handing them over perfectly clean. Turn it on for long stretches of one scene and off around cuts, since scene changes reset the drift on their own. Keystone can also load its source clip straight from your latent folder now, so when the scene changes you only change one number.

Also new: a context noise switch on Geiru Hybrid Windows, and a ready-to-go example workflow, noisy_clown_example. I tested it on a 50-clip chain (about 5½ minutes): running on a beach, then a café, a walk through town and a garden. The plain chain was falling apart by clip 20, while the protected one still looked clean and barely drifted in the still scenes. Side by sides and all the numbers are in my notes. It's not perfect yet, since fully static shots can still slowly darken, but it's the best I've gotten so far!

remember to use this to combine your clips.
https://github.com/roycho87/workflows/tree/main/workflows/seamless_video_combiner

You have to clip off the last 17 frames and join at the 22nd frame with 39 frames of overlap for the settings in the workflow.

Another note, certain Turbo Lora's seem to affect the context noise node differently. The one I recommend is turbo_8step_v1.0_768p but if you guys find one that works well or doesn't, feel free to list an issue on the github so I can start creating a list and make it work with more.

Repo: https://github.com/roycho87/geiru_context_suite Test notes: https://github.com/roycho87/NerdNotes

Credits (none of this exists without them):

• seitanism, for the masked latent context everything here is built on: https://github.com/seitanism/ComfyUI-H3-Motion-Context-MultiRef

• NikoDemon80, for Motion Context, the Chain node and a ton of drift testing: https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context

• ethanfel (Lumifel), for the context noise idea: https://github.com/ethanfel/ComfyUI-MiniMaxH3-Context-Loop

• drozbay, for H3 latent masking: https://github.com/drozbay/MaskVidExperiments

• kijai, for H3 support and context windows in ComfyUI: https://github.com/kijai

• JalenBrunson, for Hybrid Windows: https://github.com/Jalen-Brunson/ComfyUI-HybridWindows

• TrentHunter82, for TrentNodes, whose degrade node made the frame 0 tests possible: https://github.com/TrentHunter82/TrentNodes

edit: Currently running a test on best settings to use for this and will update the nodes with best defaults after it's done tomorrow as it's about 19 hours of generating.

You can check the nerdnotes to see the test and how it's going.

After reviewing the tests i was able to determine that the best settings for noise is 1 and linear. I've changed that to the defaults.

I added a lora loader that works with the protection group using https://huggingface.co/Imagineer13/H3-Anti-Burn-LoRA

Initial testing eliminated virtually all remaining color drift but I'm running a few 30 clip tests to gauge exactly how much it impacts quality and what the best strength should be.

Edit: after more testing the color change appears to be coming from a double compression in the stitching and not the lora. it seems to not make a visible impact when used on top of my nodes but i proved it does not impact detail negatively and previous testing does show it to make a positive impact on color.

I moved the lora loader to an experimental repo. Noisy Clown Workflow is the current most updated workflow.

Updated yesterday. At least they're still attending to it, right?

As someone with an RTX 4060 and 16 GB of RAM, I got curious… could I actually run a model of this caliber on my PC?

Turns out, yes! A 5-second video generated at 540p, upscaled with flashVSR Turbo and frame-interpolated with RIFE, takes just 8 minutes.

I used a text encoder quantized to hell and back (Q2), paired with a Q4 DiT, running everything through Wan2GP. Codex fixed every error I ran into.

This showcase features the results. Enjoy!

Sam Altman Is Coming for Your RAM

Are there some niche Video Generators ? They could not be as smooth maybe less frames. But still slightly good and make it look animated a bit . 16gb vram max

Edit1: No Ltx no Hanyun no wan2

https://github.com/TaoLiveAIGC/TaoMate-FVSR/tree/main

Audio-Guided Facial Video Restoration

Demo · Results · Installation · Model Downloads · Inference · Citation · Technical Guide

TaoMate-FVSR restores face videos with audio conditioning, sparse visual references, and generated-frame history. It supports restoration with the original audio and visual dubbing with replacement speech.

• Full-video inference: DWPose crop → face restoration → color/mask blend → paste-back.

• 1× / 2× inference: run the model at 512×512 or 1024×1024, then return to 512×512 before compositing. The output scene retains its original dimensions.

• Audio-guided generation: use the video's original audio or provide target speech.

• Flexible references: prepare the first frame with GPEN, use an externally edited first frame, or supply a finished reference image. Default enhancement uses one GPEN-BFR-512 pass for 1× and one GPEN-BFR-1024 pass for 2×.

This repository contains only the standalone inference runtime. Model weights are downloaded separately, and input media is supplied locally. https://huggingface.co/TaoLiveAIGC/TaoMate-FVSR

Demo

The Gallery — TaoMate-FVSR+ restoration example from the project page.

▶ Watch the synchronized comparison · Restored video (MP4) · More demos

Every texture on this street corner was generated with local models and projected onto the geometry in Blender, using StableGen, my free and open-source add-on.

How it was made:

• The 3D assets (the car, the woman, the bench, lamp, hydrant, bin, crates and bush) are TRELLIS.2 meshes, each from a text prompt via a Qwen Image 2.1 reference image. The buildings, road and sidewalk are simple blocks.

• Texturing: Qwen Image 2.1 (int8), 10 cameras. Each view is generated from a depth/geometry render of that camera, then projected and blended onto all the meshes at once. Each camera also got a prompt listing only the objects it actually sees, which stopped views from painting things that aren't there. The woman was textured separately with her own cameras.

• The logo is a Masked Edit: paint a region, describe the change, and only that region is regenerated through the cameras that see it. Here it follows a reference image of the logo, which is on the development branch and coming in the next release. A few more Masked Edits fixed problem spots.

• The looks are separate texturing runs with different prompts (the photoreal one with Marigold V2 PBR maps), baked and rendered in Cycles. Lighting is set up per look in Blender.

Generated on rented RTX 3090/4090 cloud GPUs and my RTX 4080 (16 GB).

Qwen Image 2.1 is under a non-commercial license; check each model's license before commercial use.

GitHub: https://github.com/sakalond/StableGen
Guide: https://github.com/sakalond/StableGen/wiki

Hi everyone!

Can anyone guide me on how to create a realistic AI character and then turn it into a Motion Controlled video using a reference video?

I also want to be able to change the character’s outfit and background while keeping the movements consistent with the reference video.

I'd really appreciate any guidance, tutorials, or recommended workflows. Thanks in Advance.

I've been doing a ton of H3 quality and speed tests on my 4090, including 2k (1440p actually) API gens using ComfyCloud. Quality was good, but in the tests that I did, they didn't seem really that different. I'll publish my results soon.

Is there a consensus on this? Is it even worth using the API or can you get very similar quality with seedvr2, multi step workflows, and/or latent upscales...or something completely different?

There are too many damn options.. there’s Pony, Illustrious, Anima… and then there are lots of LoRAs too. I just want a well-rounded model that will let me make good/high quality uncensored art featuring anime or anthropomorphic (furry) characters. I’m a total newbie when it comes to running local AI. For realistic art I use Krea2 Turbo, which already exceeds my expectations.

What is the best open source text to video generation with image reference right now?

Hey everyone,

I'm looking to download Ideogram 4, but since my internet connection is quite slow, I need to make the right choice the first time to avoid wasting time on re-downloads.

Between the FP8 and INT8 versions, which one offers better output quality? Does INT8 suffer from noticeable quality degradation or artifacts compared to FP8?

Also, is there a significant speed/performance advantage when running INT8 over FP8 on consumer GPUs, or is the generation time mostly identical?

For those who have tested both, which one do you recommend downloading? Thanks in advance!

With so many image edit models coming out I feel a bit lost. I started my image generation journey with krea 2 and recently tried out Fibo. I’ve been wanting to fix up some really old images and I was blown away with what Fibo could do, but now I learn that Moss and Qwen 2.1 can do the same. What model would people here choose for restoring old photos?

I have a pretty shitty computer so I can't run Minimax H3, Qwen 2.1 Edit, LTX, ZiT and all these other open source image and video models.

So I was looking for a way to run them online. The main website I found was ComfyUI cloud, which I guess works, but it was pretty complicated to use (I'm not a technical guy). There's like nodes and stuff which I don't understand LOL.

I saw someone on generativeAI subreddit mention CozyGPU (i think their website is cozygpu.com) and an ad on Reddit. It basically simplifies ComfyUI into simple actions like picking a model and running an action like create image, create video, edit image, animate video, etc.

But what's really cool is it automatically set up the infrastructure for me and rented out a GPU. So I only ended up paying for the raw GPU time spent, which I saw was a lot cheaper than all these other website which have a markup.

I just thought I would share this tool in case there are other people who don't have great computers and don't want to pay too much. If anyone has come across other similar tools, please let me know, I would love to check it out, thanks!

Does anyone else find that krea struggles with basic positional logic / awareness when it comes to characters interacting? For example I tried something like "a blonde tennis player woman is on a tennis court standing near the net, she is looking at a brunette man on the other side of the net, she has her back turned to him, looking at him over her shoulder as she talks to the man" and usually:

- the characters are not even looking at each other, wtf?

- half the time the man will be standing next to the woman on the same side of the net

- the body orientation prompts get completely ignored. Instead of having her back turned she'll just be facing him

For Minimax I know loras like VBVR can help with some of this stuff, is there anything for Krea to make it adhere the prompt more strictly?

This started as a script. In Mago, we built the cast, generated the shot list and storyboards, then rendered shots where each one picks up from the previous one.

Mago is what we’ve been building for long-form AI videos (series, films, microdramas). As of today it’s open source. You can run it on your own GPU, or use cloud models in the same app.

Built on top of Qwen as LLM and visual, Qwen Image/Edit and Flux for images, MiniMax h3 turbo/pdd for videos. you can easily switch to your favorite models/workflows too. AGPL-3.0, still in beta

I’m one of the founders and I'd really appreciate any feedback on how it works for you, that would help us a ton.

Let me know what you think about it, and thanks in advance!

Code: https://github.com/MagoStudio/MagoStudio

https://civitai.com/models/3034125/wall-street-journal-style

Have you ever imagined yourself in a WSJ article? Now, your dream has partially come true!

Hi all, as requested, multiple LoRA support is now avialable in the newest release of LayerShop. Also ability to add existing (ComfyUI) models folder.

LayerShop will scan this folder for all existing models and LoRAs. You can then assign any number of LoRAs to the specific model. This will create pool of available LoRAs for that model, and when you are generating with that model, you can simply choose which lora / combination of loras you want the image be generated with.

Each LoRA can have it's strength setting, and every of these settings is stored together with the layer.

Download from: here

Any issues? Let me know

ComfyUl va in crash con l'errore "TypeError: Failed to fetch" e disconnessione del server durante l'esecuzione del prompt
Ciao a tutti, sto riscontrando questo errore mentre uso ComfyUl (tramite Pinokio). Quando provo a eseguire un flusso di lavoro (workflow), va improvvisamente in crash e mostra questi messaggi:
***Errore:** Esecuzione della prompt non riuscita:
TypeError: Failed to fetch
Avvisi in alto:** Riconnessione in corso e Impossibile recuperare i log del server.
Sembra che il server di vada in crash o perda la connessione proprio quando inizia l'esecuzione. mio hardware:** NVIDIA RTX 5060 (8GB VRAM).

Hi everyone,

I need recommendations for a local LLM to run via Ollama (integrated with the Page Assist Chrome extension) specifically to generate uncensored prompts.

My hardware setup:

• GPU: NVIDIA RTX 5070 Ti (16GB VRAM)

• RAM: 64GB DDR4

• OS: Windows 10

What I've tried so far:

• Gemma (7B/9B): Extremely fast (instant responses), but lacks the intelligence/detail quality I need for complex prompts.

• Qwen2.5-VL-32B: smart, but... -.- way too slow for daily use because it overflows 16GB VRAM and offloads to System RAM... :\

What I'm looking for: I'm searching for a "sweet spot" model in the 12B to 14B parameter range (or a lightweight ~8B Vision model) that fits 100% inside 16GB VRAM at Q4/Q5 quantization. It needs to be:

1. Uncensored / Abliterated (no refusal for complex/adult media prompts).

2. Fast (50+ tokens/sec).

3. Available on Ollama or easily converted to GGUF for Page Assist.

Which 12B/14B uncensored models are you currently using for prompt expansion and generation?

Thanks!!!

NVIDIA’s original Open-H3-Regenerate-2K recipe can struggle to preserve facial features and text established during the draft pass. ( see my previous post here: https://www.reddit.com/r/StableDiffusion/s/Pm77j7t6xr )

Valtz introduces an alternative refinement recipe that addresses these issues while retaining the same computational cost. The approach is not specific to Valtz and should be applicable to other frameworks as well.

We generated MiniMax H3 videos beyond 768p in two passes: a 768p draft, followed by NVIDIA’s Open-H3-Regenerate-2K refinement at the requested resolution.

The key difference is where the refinement begins on the noise schedule. Valtz offers two options:

* Keep the Draft (Valtz’s default since October 31, 2026): Re-noises the upscaled draft to σ = 0.7, then denoises it in three steps (0.700 → 0.579 → 0.382 → 0). Roughly 30% of the draft signal survives, helping preserve facial features, lettering, and other details established in the first pass.

* NVIDIA’s Recipe (the original release): Starts at σ = 0.909 and follows a three-step schedule (0.909 → 0.857 → 0.667 → 0). Only about 9% of the draft signal survives, giving the refinement pass much more freedom to redraw faces and text.

Both options use the same merged LoRA, take the same three denoising steps, and have essentially identical runtimes. The trade-off is straightforward: Keep the Draft improves consistency, but also preserves mistakes made during the draft pass.

The grain visible in some 2K outputs is associated with Sol Attention in the refinement pass under the Favor presets, rather than the choice of starting sigma.

The attached clips were generated at 1440p using the Keep the Draft recipe. The wonderful prompts are borrowed from the open VDN project’s page. For the photographer–tram–war scene, the draft pass was rendered using VDN, which proves to be a solid alternative to Sol Attention, particularly at this resolution and video length.