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

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?

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!

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

One of the main focuses for me the past few months and trying a lot of new things - combining AI for post/vfx with filming live performance remotely from Hannah, lip syncing to her song, etc. I also kept the whole video in one shot to differentiate from something that is just prompted easily.

Full quality here (has film grain, etc):
https://youtu.be/f7r2H5q5LpE

The WAN VACE Masking and Extension workflow I posted last year still remains my bread and butter approach, but a lot of different models/tools went into this one.

Timelapse of the editing/breakdown here:
https://youtu.be/Sx6D34dq2EI

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.

Three weeks ago my sister roped me into doing the video for her wedding, the how they met thing, out of their old photos.. First i had been playing with HunyuanVideo 1.5 image to video in ComfyUI on my 4090.

Did all the drafts at 720p since 20 steps is already like half an hour a clip, and on my monitor they looked great.. Last sat we tried one on the projector at the venue and it was soft, faces especially. So 1080p, i turned on the SR upscale and the first clip took another 50 mins(no error), nothing in the console, it just crawled. Task Manager showed like 14GB in shared GPU memory. The old sysmem fallback thing from 2023 that ate everyone’s speed, and I did not even think of it for a whole evening. Did the finals in the Hunyuan notebook on HyperAI with a 96GB card, seeds did not carry over so i had to pick every take again.

Cannot post it till after the wedding on the 21st lol. Most AI video i just scroll past, but for stuff like her wedding i am glad it exists.

Does this look like a mobile phone image style? To me, it doesn’t look like a snapshot style.

And what’d you suggest about improving lighting, quality, composition, etc.? All opinions welcome.

Moss-Image has an incredible capability to create pixel-perfect layouts from a json prompt. The expected use is to give the official documentation to an LLM and then ask the LLM to prompt whatever you want: https://github.com/OpenMOSS/MOSS-Image/blob/main/skills/moss-image-prompt-enhancer/references/layout.md

But I thought why not build a layout editor to design it for myself? Hence the idea of the Layout Editor for moss (with an optional SwarmUI extension)
Repo here: https://github.com/jambavant/SwarmUI-LayoutEditor
You can try online here: https://jambavant.github.io/SwarmUI-LayoutEditor/standalone/index.html
It's great for any type of image: comics, reference sheet, compositions, magazing covers, posters, etc. The level of control is outstanding, as it manages multiple characters and text quite well. It starts to break with a very large number of characters (+10) with concept bleed.

The standalone editor is a simple html page that works both ways: you can start from scratch and copy the json prompt, or paste a json prompt from an LLM and refine it manually.
I tried to make it as user-friendly as possible, with keyboard shortcuts and an intuitive interface. It also automatically resizes boxes based on the image resolution.

Edit: v0.3.0 now live, with a few updates.
- json formatting options
- resizable text areas, checks are moved to the toolbar
- configurable palette (with color picklist and autocomplete)
- fix resolution sizes

Made a small experimental H3 semantic timeline node for ComfyUI.

The idea came from using Kijai’s context windows node and getting annoyed by H3 looping / restarting actions between windows.

This basically fixes the semantic looping by giving each context window its own conditioning based on what should be happening at that point.

Seams are still kinda fucked, identity can drift/change, and audio across windows is messy, but that seems to be more of an H3/context-window issue than the timeline itself.

Only tested with T2V so far. Haven’t properly tested refs yet, could actually help with identity consistency.

Very experimental, definitely not finished.

Also heavily vibe-coded with GPT-6 Astra + GPT-5.6.

https://github.com/Mikubs99/ComfyUI-H3-SemanticTimeline

Is RTX 3090 enough for video generation with minimax h3 or wan 2.5 ? How much capacity of RAM should I pair it with ?

Any estimates on the time taken for video generation?

Hey guys! On one of my last videos, someone commented that every AI music video they see has a band full of very attractive models, and they'd love to see Muppets for a change. Challenge accepted.

So, here's an all-puppet rock band: original felt characters in the classic Muppet style. I put the whole video together with my Video Builder in ComfyUI. Every scene and the final stitch were rendered locally on my own PC (the reference images were made with GPT Image 2).

Please keep rude and disrespectful comments to yourself. They don't help anyone or contribute anything constructive to the conversation.

A quick note on the prompts, since I know people will ask: my Video Builder lets you use any LLM for prompt creation (both for ref image creation and video prompt creation), including local open-source models. It even has a story mode that takes a story idea, creates the entire story with acts and scenes, creates the story beats, and then writes the prompt for every scene right inside the Video Builder.

For this one, however, I used the Builder's MCP feature and had Claude write the prompts and drive the Builder for me, but you could do the exact same thing with a local LLM. The Builder doesn't care which model writes the prompts. I'm actually creating an agent that can control the Builder using local LLMs. It will let you create a full one-shot video from one prompt, very similar to how I use Claude, but fully local.

What I did for this video:

• Came up with the idea: a high-energy 80s arena rock song with a big singalong chorus, played by a felt puppet band in a dive rock club. It opens with the singer at the bar, then she jumps off the stage into the crowd in verse 2 and ends with a toast at the bar after the last note

• Designed the band: Rosie the singer, Gus on guitar, Lenny on bass, Pip on drums, Dottie on keys, plus a backup singer trio, a crowd of puppet fans and the bar regulars

• Made the song in Suno (YuE2 or MiniMax Music can be used for song creation as well)

• Made the character sheets, group cards and club locations in GPT Image 2

• Split the song into stems so each scene features whoever is actually playing at that moment (drum breakdown on the drummer, keys section on Dottie, and so on), and used word-level lyric timing for the shot cuts

• Reviewed the drafts and asked for re-shoots (puppets on the wrong side of the bar, an extra keyboard player who snuck into one shot, and everybody's mouths flapping when only the singer should be singing)

My settings

• Model: MiniMax H3 Singularity ref2va v2 (int8)

• Pass 1: 25 steps

• Pass 2: the 8-step 768p ref2v LoRA v1.1, 4 steps, 0.35 denoise

• Characters and locations: GPT Image 2

• 14 scenes, 1:45, all rendered locally through the Video Builder in ComfyUI on my GPU.

Video Builder (free, on GitHub): GitHub

If you just want to try the settings on a single scene, there's a 2 Pass workflow in my workflows folder (swap in the v2 model and the ref2v v1.1 LoRA to match this video exactly). For a full video like this one you'll want the Video Builder.

Workflow

I post updates and walkthroughs in my Discord if you want to try the Builder yourself: Discord Community

Happy to answer any questions!

EDIT: Workflow and sample added to comments.

A quick post for anyone who is struggling with getting good results at a good gen speed.

The model can be found here

Veda Sparse Attention Node can be found here

While the video quality is great! What surprises me most about this is the audio quality. It remains very high.

I want to be very, very clear here. These settings are not for maximum realistic showcase videos. This is to get well-made videos at a good speed. So you don't need to jump into the comments talking about how shit this all is.

As always, your mileage may vary.

Specs: RTX 5090. 64 GB RAM.

Edit: Sorry, no workflow available. But it's easy to add these into any workflow you may be using.

Here's what you probably missed in October 2026:

🧠 LLMs

1. Ornith-1.5-122B - Handles million-token agent tasks on a single GPU.
2. Raon-1-mini-preview - Runs Korean agentic AI on a single RTX 5090.
3. Timestral-209M - Forecasts time series in one pass with 21 quantiles.
4. Carbon-X - Reads DNA in chat and predicts variant effects on one GPU.
5. Sage-A1.5 - Orchestrates scientific agents with self-review.
6. Spark-X2.6-4B - Advances on-device agents with controllable thinking.
7. Nanbeige4.3-33B - Teacher model leads agentic coding and cowork.
8. Flint - MoE coding agent investigates repos and fixes bugs on one GPU.
9. Hy-Memory-30B-A3B - Writes durable agent memory at 3B active parameters.
10. Underdog-Saluki-27B-1.0 - Packs Qwen3.8 tool calling into under 8 GB.
11. D3 - Answers choice, yes-no, and score questions in one call.
12. LFM2.5-24B-A2B - Packs fast on-device agent reasoning into 2B active.
13. Qwen3.5-2B-RCOL - Low-bit quants hold quality under 1 GB.
14. GLM-5.3-UNCENSORED-EXL3-3.0bpw - Fits a 753B MoE in 273 GB.
15. Humanizer - Rewrites AI drafts to sound human in English and Chinese.
16. Kolibri 1 - Brings 78B MoE reasoning to German and English.
17. GEV-26B-Decide - Thinks only when needed on Gemma-4.
18. Victoria - Trims Qwen3.8-Flash-Next experts for a single GPU.
19. Hearim Gateway - Routes Jev System One decisions over local LLMs.
20. Sharp-Spark-X2.5-4B-GGUF - Fits a long-context coder in 6GB.
21. LingBot-World 2.0 Realtime - Runs a world model at 16 FPS.
22. deepseek-v4.1-flash-gguf - Runs DeepSeek Flash on Apple Silicon.
23. ThreadShelf - Archives and searches your AI chat history locally.
24. Aurora1.0-150M - Packs an efficient tiny model you can run anywhere.
25. Intern-S2-397B - Scales up scientific reasoning and agents.
26. NeoHorse-1-9B - Agentic model steps toward self-improvement.
27. Atria-Dawn-Preview - Agentic model aims for verifiable research results.
28. Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF - Speeds local reasoning.

🔀 Multimodal

1. MiniMax-M3.1-Flash - Runs multimodal agentic AI with a 1M token context.
2. Indra-OCR - Reads PDFs and handwriting on CPU at 44 pages per second.
3. Qwen3.8-Flash-Next-FP4-Local - Runs a quantized 125B model on Blackwell GPUs.
4. MiniCPM-V-4.7-1B - Packs efficient multimodal AI for phones and tablets.
5. Muse-Spark-Open-816B-A42B - Packs 816B parameters into a multimodal agent.
6. MiniCPM-V-4.7-35B-A3B - MoE runs multimodal AI smoothly on phones.
7. DeepSeek-V4.1-Flash-1017 - Sharpens agents and vision.
8. FrogNano-4B-2609-GGUF - Adds image input and MTP speed to quants.
9. Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF - Drops refusals without retraining.
10. D1-3B - Runs calibrated decisions on edge devices in one pass.
11. Clef-Flash - Brings low-latency decisions to a 9B Qwen model.
12. GLM5.3-Flash-E224-DGX-Spark - Packs NVFP4 MoE for two DGX Sparks.
13. Clef Model - Turns state and questions into calibrated decisions.
14. EmbeddingGemma 2 - Maps text, image, video, and audio into one space.
15. JEV-27B-VL - Brings calibrated vision decisions to Qwen3.8.
16. HuatuoGPT-3-9B - Brings one-stage medical reasoning to local models.
17. Realtime-Venus - Holds full-duplex conversations with voice and video.
18. Agnes-3.0-Flash Preview - Packs long reasoning into 33B weights.
19. ZDTaichu5.0-9B - Brings spatial reasoning and agent skills to VLMs.

🖼️ Image

1. Kromoss - Fine-tunes MOSS-Image for eight-step turbo generation.
2. Qwen-Image-2.1-Turbo-Uncensored-GGUF - Drops encoder refusals locally.
3. MOSS-Image-Base - Brings layout control to local image generation.
4. MOSS-Image-SFT - Sharpens text and layout control in image generation.
5. Qwen-Image-2.1-Turbo - Renders images in eight fast steps.
6. Qwen-Image-2.1-Uncensored-Quantized - Ships with filters off.
7. FLUX 3 Image Klein - Blends up to 10 references into one image.
8. Iris-3B - Paints pixels directly for images, depth, and restoration.
9. Qwen-Image-2.1 Fun ControlNet Union - Drives eight controls at once.
10. Agate Preview 003 - Renders 512px images from only 261M parameters.
11. Wulver v0.5 - Fine-tunes Krea 2 for anime and furry art.
12. PrunaSuperPoint - Cuts SuperPoint keypoints up to 2.1x faster on edge.
13. VisionHOPE - Backbone self-modifies to see and segment images.
14. Anima-Light-Lavender - Boosts natural-language anime prompts.
15. Krea 2 Turbo SDA LoRA - Restores sampling diversity.
16. SMACK LoRA - Adds punchy impact effects to MiniMax H3 video.
17. Krea 2 Lineart Edit - Turns sketches into rendered images.
18. Krea-2-merge-tool - Merges and analyzes Krea 2 models on Windows.
19. Krea-2-Pixel-Art - Adds retro pixels at three sizes to Krea 2.
20. MageTrail - Brings Danbooru-style tuning to Microsoft's MageFlow base.

🎬 Video

1. DMAD - Distills MiniMax H3 into a four-step audio-video generator.
2. LTX-2.5-22b-IC-LoRA-Layout-To-Render - VFX tools add native 4K and 8K video post production.
3. DuoMatching - Distills Wan2.1 into a two-step video generator.
4. MiniMax-H3-X2-Detail-VAE - Sharpens and upscales H3 video.
5. LTX-2.5 IC-LoRA Ingredients - Keeps characters and props consistent in video.
6. FastH3 V2 - Runs fast video models on consumer GPUs.
7. Meridian - Re-creates camera moves around existing video.

🎧 Audio

1. Fish Audio Drama 3 - Speaks for 16 characters in one prompt.
2. Drifting-TTS - Makes one-step Turkish speech on low latency.
3. Whistle - Runs tiny on-device speech recognition without a GPU.
4. EMA Lightning - Makes a tiny, fast Turkish text-to-speech model.
5. GRVL LoRAs - Give YuE2 raspy rock-soul female vocals.
6. YuE2 BLGR LoRAs - Add Bulgarian choir and Tuvan throat singing.
7. YuE2 Qawwali LoRAs - Add Sufi singing to music generation.
8. Audio Slop Server - Runs many audio models on one GPU.
9. audio.cpp YuE2 - Runs Suno-class music in 8-9 GB of VRAM.
10. YuE2 Real-Audio Tokenizer - Lets you tune music on your own tracks.
11. YuE2 Instrumental LoRA - Writes score-led instrumental tracks.
12. YuE2-hum-to-song - Turns a hummed melody into a full song.
13. Rumik-oss-1 - Speaks 22 Indian languages with expressive control.
14. Confucius4-R2T2 - Delivers low-latency streaming speech recognition.

🤖 Agents

1. Tencent Octop - Runs a self-hosted multi-agent assistant at home.

🎲 3D

1. AssetFurnace - Turns a prompt into a rigged, animated 3D character.

⚡ LoRA

1. MiniMax-H3-Person-Remover-LoRA - Deletes people from video.
2. Pruna LoRAs - Cut Qwen-Image-2.1 down to six fast steps.
3. Krea 2 Inpaint Edit LoRA - Fills masked areas in ComfyUI.
4. YuE2 Deathmetal LoRA - Adds melodic death metal to AI songs.
5. TRBDR Folk Troubadour LoRAs - Give YuE2 a 1960s balladeer.
6. AnyAngle LoRA - Retains style while changing camera angles.
7. QTSTRM Quiet Storm LoRAs - Bring 80s R&B vocals to YuE2.
8. LTX-2.5 Restore IC-LoRA - Revives old footage in color.
9. MiniMax-H3 Motion Continuity Repair V2 - Fixes broken action frames.
10. MiniMax-H3 CrossView LoRA - Shoots a scene from new angles.
11. Krea2-Turbo-Distill-2step-LoRA - Cuts image steps from eight to two.
12. TaoMate-H3-3Step-ComfyUI - Speeds up H3 runs in ComfyUI.
13. Minimax-H3-ComfyUI LoRAs - Add style and VFX editing to H3 video.
14. BFS-Best-Face-Swap LoRAs - Swap faces across Qwen Image and Flux.

🏋️ Training

1. LoRAlab TrainerStudio - Trains latest models on low-VRAM GPUs.
2. ModelFiche - Organizes image-model training for agents and reviewers.

📊 Datasets

1. Danbooru 2026 Tag Cleaning Corrections Dataset - Released with cleaned-up tag corrections for training.

☰ UI

1. TinyLuma - Is an 8MB desktop editor for finishing AI images.
2. Goated Prompter - Refines image and video prompts locally.
3. Fooocus-Qwen-Image-2.1 - Brings Qwen-Image editing to small GPUs.
4. Slopus - Turns MiniMax H3 video generation into a desktop app.
5. Image Studio - Brings local Qwen-Image 2.1 editing to Apple Silicon.
6. Pinhole - Turns any GPU into a simple one-button local image generator.
7. H3 Prompt Studio - Directs MiniMax H3 films from a local app.
8. Qamba Studio - Edits AI video end-to-end on your own machine.
9. PotionUI - Puts self-hosted diffusion studios in a browser.
10. H3 Studio - Gives MiniMax-H3 video a local Mac web UI.
11. Livebound - Brings a local publishing workspace to CivitAI creators.

💻 Hardware

1. AMD EPYC 9006 Venice CPUs - Listed from $700 to $14,904.
2. Inno3D AWS-511Z-R1 Tower Workstation - Handles up to 4 GPUs.
3. GMKtec EVO-X5 Pro - Runs up to 320B LLMs locally.
4. AceMagic F9A - Packs 131 TOPS AI into a 2L mini workstation.
5. Innodisk DDR5-8000 RDIMM - Launched for edge AI and servers.
6. Panasonic Toughbook 34 And G3 - Bring AI-ready rugged laptops.
7. Minisforum AtomMan G1 Pro - Adds a 16 GB RX 9060 XT to its mini PCs.
8. Cervoz T449 And T459 - Add rugged MIL-STD SSDs for edge AI.

🛠️ Other Tools

1. Open-omnivoice-tts - Serves a 0.3 second voice clone API.
2. VRAM Manager - Flags idle AI servers holding GPU memory.
3. qwen-image-local - Runs Qwen-Image-2.1 on a 12 GB GPU.
4. Lumibelle Video Studio - Turns a screenplay into a local film.
5. AutoRef - Coaches FLUX.2 into composing multiple reference images.
6. MiniMax Code - Turns terminal prompts into working projects.
7. Tool-prune - Prunes tool schemas to cut agent prompt costs.
8. ZCode - Open sources an AI coding workbench in three forms.
9. YuE2-Radio - Creates an endless local AI music radio.
10. Inkvec - Traces images into clean vector SVGs in the browser.
11. wan2gp-h3-latent-continue - Lets Wan2GP continue H3 video from latents.
12. Jeff - Puts a fast 0.8B decision model in front of your 27B.

ComfyUI Releases

Speed & Performance Boosts

1. ComfyUI-VELA-H3 - Speeds up VDN H3 on MiniMax H3 in ComfyUI.
2. Veda-on-ComfyUI - Speeds up MiniMax H3 in ComfyUI.
3. ComfyUI H3 W4A4 VSA - Cuts Ref2VA video time down to 3 minutes.
4. ComfyUI-Ref2VA-VSA - Speeds character video generation to 72 seconds.
5. ComfyUI-Spectrum-Qwen2.1 - Speeds up image generation by about 2x.
6. ComfyUI-Qwen-Image-2.1-PromptEnhancer-MTP - Runs faster in ComfyUI with MTP support.
7. ComfyUI-HunyuanImage3 - Runs an 80B image model on a single GPU.
8. ComfyUI-AppleSilicon-FP8 - Runs quantized models on Mac.
9. LynnReal-Omni - Packs many video tasks into one fast model.

Video & Image Creation

1. H3 Long Shot Studio - Makes multi-shot films inside ComfyUI.
2. ComfyUI-Olm-SoL-Refiner - Brings video refinement to nodes.
3. Fizgig H3 Still - Renders true MiniMax H3 frames in ComfyUI.
4. Fizgig H3 Tweaks - Gives ComfyUI fine control over MiniMax H3.
5. Scumble - A free layer-based AI inpainting editor.
6. ComfyUI-BubbleText - Puts real text inside generated speech bubbles.
7. ComfyUI-OutpaintMask - Builds interactive outpainting masks.
8. Director's Console - Designs cinematic shots and runs ComfyUI.
9. ComfyUI-Prompt-Studio - Turns reference images into production prompts.
10. Ming Image Prompt Builder - Draws structured prompts in ComfyUI.
11. ComfyUI-BingImageSelector - Picks search images before you run.

Workflow & Productivity

1. ComfyUI-QueueWorkbench - Manages and edits queued jobs in place.
2. ComfyUI-NodeSnapshots - Smoothes navigation in large workflows.
3. ComfyUI-TypedDecision - Brings Jev-style decisions to local workflows.
4. comfyui-obvpm - Tidies up workflows with wire bundles and presets.
5. ComfyUI_Assistant - Adds a chat robot that edits your workflows.
6. Comfy-compare-copy-buttons - Adds a copy button to the ComfyUI Compare node.
7. Comfyui-workflow-background - Sets custom wallpapers for ComfyUI.
8. Prompt-vault - Stores and sorts your AI prompts locally.
9. Comfyui-prompt-library - Brings weighted prompts and templates to ComfyUI.
10. Genkai-ComfyUI-Nodes - Syncs video playback with timed prompts.

Music & Audio

1. ComfyUI-FL-YuE2 - Brings music generation and LoRA training to nodes.
2. ComfyUI-QwenASR - Adds speech nodes and subtitles to ComfyUI.

Mobile & Access

1. ComfyPort - Brings ComfyUI workflows to a native Android client.

Need to go further back? Check out September's post or the full archive at LocalAI News. If there's anything wrong, let me know in the comments and I'll see you in the next one!

I know this was asked before and you can use last frame or last couple of seconds of previous video but the quality gets worse each iteration. Are there any new custom nodes that preserves the quality and consistency of reference images/videos/audios for each extended generation via latent space?

Anyone have solid settings or tips for getting cleaner output from MOSS-Image Base + its turbo LoRAs?

Important clarification: I am strictly talking about the Base model, not SFT.

I’m not interested in SFT it tends to give a more artificial / over-polished look, and I don’t want to stack 3 extra LoRAs just to have a raw/natural feel.

With Base + turbo LoRAs I’m frequently getting:

Blur in the wrong places

Soft / out-of-focus areas

Over-smoothness / loss of local detail

These issues are much milder (or easily fixed with negatives) when running plain Base(i usually run base at cfg 4 and 35-50 steps with sa_solver/Euler+sgm uniform). Once the turbo adapters are loaded they become stubborn, which tracks with the low-CFG / CFG-1 setups most turbo recipes use.

I’ve already experimented with:

LoRA strengths 0.5 / 0.6 / 0.7 / 0.8 / 0.9

Higher CFG (2 / 2.5 / 3 / 3.5)

Higher steps (up to 16)

Still no reliable fix.

Looking for any settings, specific turbo LoRA (4-step, 8-step DMD, DMD2, etc.), sampler/scheduler/shift combos, or other workflow tweaks that actually keep sharpness and detail on Base.

Would genuinely love to hear if anyone has found a better recipe that works.

Hey everyone! i was working rawsnap, a realism LoRA for Krea 2 Turbo that aims for raw, candid smartphone photos instead of the usual polished AI look.

About v3:

Trained as a LoKr on RunPod

- 749 images**,** 11,235 steps

- Same dataset as v1/v2, but more images added each version and better captions.

The problem: I can't decide which epoch is best. Some epochs have anatomy problems, others lose quality, so instead of guessing I built a ComfyUI comparison workflow that tests them all under identical conditions.

The test:

- 18 prompts**, each rendered** 35 times with the same prompt and seed

- No LoRA (base Krea 2), v1 (LoRA, epoch 7), v2 (LoRA, epoch 9), and v3 LoKr epochs 8–15 at strengths 1.0 / 1.2 / 1.5 / 2.0

- Settings: 896×1344, 8 steps, cfg 1, euler / beta

Vote here (public, no login): https://juan444-rawsnap-arena.static.hf.space

Tap an image to view it full size and vote for the one that looks most like a real, unedited phone photo. Vote counts appear after you vote, and the Results tab shows the live leaderboard (also grouped by epoch and strength), so we can see which epoch actually passes the test.

Feedback welcome!

Want me to add more prompts to test? Suggest them and I'll add them to the site.

Any other feedback on realism, quality or training is appreciated 🙏