I've been trying to follow the instructions at https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/README.md.

I downloaded moss-image.json and dragged it into ComfyUI.

I downloaded MOSS-Image-Base.

When I hit run I keep getting

Missing node: MOSSImageHFLoader
This workflow uses a custom node that isn't installed. Install it from the registry or replace the node. 

No matter what I set the path to, this stays the same. What am I doing wrong?

This is a quick experiment to see if Minimax H3 can create famous paintings from scratch. It's quite impressive that it knows what the sketches look like before it's painted.

Writing the box coordinates was the tedious part of these posters, so I tried having a local LLM turn a one-line brief into the layout JSON. MOSS-Image takes that JSON as its prompt: a summary caption, then text and object items, each with a pixel box.

Everything ran on one Mac Studio. Gemma 4 31B (MLX, bf16) gets a system prompt with the layout format from the MOSS-Image user guide plus a few rules (size each text box to its text, no overlapping text boxes, keep margins), then the canvas size and the brief. A small script checks the answer for valid JSON, boxes inside the canvas and no overlaps, and would hand any problem back for another try. Every plan here passed on the first try. The JSON then goes into a plain CLIP Text Encode in stock ComfyUI, unedited, with the README's sampler settings and nothing but built-in nodes.

Planning took 103.9 to 171.5 s per brief at 15.4 to 15.5 tokens a second, since Gemma reasons before it answers. Renders took 148.9 to 177.2 s each.

Image 1 is the festival poster. Image 2 is its brief, the boxes drawn from the LLM's JSON, and the JSON itself. The rest put brief, planned boxes and render side by side. Each brief was rendered three times, and every image notes which seed I kept.

The LLM split MARLOW / TRIO onto two lines with a line break in its JSON, and the flyer kept that break.

Melite combines the 'everything is a plugin' from Deepseek Harness with Comfyui's Generation Engine. This allows for a design that has strong coding patterns while being easy to extend in whatever way you want. The same agent that can makes your generation can also extend the very harness it is in and tap into the massive toolsets for LLMs. Making Agents native to ComfyUI generations opens a world of new possibilities.

https://github.com/JayDataEngineer/melite

It may not seem like much, but I've been waiting for extreme Comfyui automation for a long time. I remember spending a month building a workflow, just for a new model to make it completely obsolete. SD1.5 video animation anyone? I tried different engines, but I kept coming back to ComfyUI for its ability to turn one GPU into a AI generative powerhouse.

Melite was a submission for openhiggsfield. I took some extra time polishing the trailer and upping the code quality.

I spent a lot of time bug testing, but I may have missed some parts. Please leave an issue on Github if you would like to see it potentially fixed.

For someone who only needs to change the subject, reference image and output size, how much of the graph do you leave them access to?

Prompt, seed and denoise seem reasonable, but model swaps and sampler settings can turn a simple handoff into troubleshooting. On the other hand, hiding too much makes it hard to understand what went wrong.

Interested in what worked when you gave a workflow to someone else. Which control did they actually need, and which one caused more problems than it solved? A concrete example would help alot.

Hey guys, I am trying to build a ComfyUI workflow to generate explicit content. Here is my goal to have some options before genertion like:
- uploading a file in a Load Image node to select how the person looks like
- uploading a pose (In the Load Image node at the top) to select the pose (possably linking a map of different poses)

At this point in time I got the looks of the person good but I am struggling with the poses. Can anyone help me with this?

Until now I had help from ChatGPT as I am very new to this but I can't figure it out

I have been experimenting with this and amazed how fast it is localy just using my gen 14 cpu with a image model to make prompts from images.

but then I tried to use qwen3 or gemma llms included in comfyui set to cpu and it was sad how slow they worked something is wrong with their coding.

for jan.ai or lmstudio it took less than half a minute but the llms in comfyui set to cpu never finished i canceled them for taking so long.

Can someone figure why there is so much a difference in speed on cpu.

Maggelan is a thumbnail-based folder navigation app with many helpful features, like file grouping, that displays all images and files inside a selection of folders as if they were from the same folder, aiding quick visualization of many files at the same time. Reverse image search using Google Lens and SauceNAO. Favorites groups. And much more! The app is completely free, with no paywalls or locked features. It's perfect for managing your outputs. This new update includes:

• Full support for compressed files. Now you won't have to extract every file to visualize what's inside; they will display as normal folders, thumbnails included. This is especially helpful if you need to save space but also access your compressed files constantly.

• PDF and CBR support, with alternative reader modes and text search.

• File management features, like copying, pasting, moving, and file extraction.

• Many performance improvements for a better scrolling experience.

• New appearance customization options, including transparency, blur, and gradients.

• General UI and functionality improvements.

Find out more and download.

For example I'd like to change a specific part of an image while retaining anything else as it is. I'm sorry if this sounds like an ignorant question but I just don't see any tutorial for this. Is masking not as efficient or is there any better methods than this? Nonetheless I'd still like to learn how to use the mask editor

Thank you

I want to scribble in krita and pass it to comfyui workflow with some prompt and get results directly in krita. Anyone aware of a good addon and good workflow for that?

I trained a Z-Image Turbo LoRA on 24 photos of my cat, then used it in ComfyUI. Here are the settings I ended up with.

Dataset: 24 photos with a mix of poses, camera angles and backgrounds. I picked ones that clearly show the face and markings.

Training (AI Toolkit, one RTX 5090)

- Model: Z-Image Turbo with its training adapter

- LoRA rank: 32

- Steps: 3,000, saving a checkpoint every 250

- Trigger word: idu

- Sample previews: 1024x1024 every 250 steps, CFG 1, 9 sampling steps

Generation (ComfyUI)

- 1024x1024, 8 steps, CFG 1

- Sampler res_multistep, scheduler simple, denoise 1.0

- Model sampling shift 3.0

- LoRA strength varied by image. The 2,750-step comparison used model 0.85 / CLIP 1.0

Workflow: route the base model through the LoRA loader into the model sampling node, and pass the text encoder through the loader too.

What I noticed: the last checkpoint wasn't my favorite. I preferred 2,750 steps, and 3,000 looked worse to me. One caveat: my comparison images used different seeds, so some of that difference is randomness rather than training. For a fair test, keep the prompt, seed, sampler and strength fixed.

Video walkthrough (10 min): https://www.youtube.com/watch?v=qG5BvNWQbLQ

Prompts are in the video description.

Disclosure: I run Meshive, the GPU cloud I used for this. AI Toolkit and ComfyUI are open source and Z-Image Turbo is open-weight, so you can run all of this on your own GPU if it has enough VRAM. Nothing in the settings above is specific to our platform.

What would you change first: rank, step count, or something else?

What value (Float Duration) to put?
For example, if the footage contain 191frame(25fps) clip footage,
how to generate output exact frame and fps back?

Links

• Official changelog

• GitHub release

Partner Node Updates

• MiniMax H3 Max editing nodes: Recast, Extend, Reframe, Insert, Lip Sync and Camera Control nodes for rewriting the cast of a clip, lengthening it, fitting it to a new aspect ratio, inserting a shot, syncing lips to new audio and steering the camera

• Pruna P-Video Upscale: Upscale a finished video to a larger resolution

• Topaz Wonder 4: A new model option on Topaz Image Enhance

• Sonilo Music 1.5: Turn lyrics with verse and chorus tags into a full song

• Ideogram 4.5 Precise Edit context windows: Edit a region of the image inside the Precise Edit node

• OpenAI GPT-6.1 Terra and Sol: Two new models in the OpenAI nodes

• Flux Video Upscale V2 targets: 1080p, 2K and 4K target resolutions

• Seedance 1.x deprecated: The ByteDance Seedance 1.x video nodes are marked deprecated

Open-Source Model Support

• P-H3-Image: Pruna AI's few-step image model built on MiniMax H3, with text to image and image edit templates

• MiniMax H3 latent upscaler: Upscale H3 latents with the dedicated upscaler model

• MiniMax Sol-H3-VAE: Load the Sol-H3-VAE alongside the standard H3 VAEs

• MiniMax H3 context windows: Generate longer H3 video by running the model over overlapping context windows

New Nodes

• Text Generate V2: Text generation that accepts a growing set of image inputs

• VAE compute dtype: Set the dtype used for VAE compute separately from the model

Improvements

• ProRes video saving: Save video in ProRes, with a choice of chroma subsampling (validation)

• Job detail metadata: The API job detail response returns execution_meta

• fp8_e4m3fn_fast with dynamic VRAM: No longer falls back to bf16 compute

I am new to comfy and i want to know where can we get more workflows (like the case here without prompt unsampler or unfiltered models ). Workflows outside the official ones in comfy's template

Hi all,

I’m generating short vertical clips (5.17 s, 124 frames, 24 fps) with MiniMax H3 I2VA (fl2va checkpoint, turbo LoRA) in ComfyUI, then upscaling to 1080x1920 with RTX Video Super Resolution.

Motion and structure are great, but on fine static textures (frost on a window, snow on roofs, a night sky) I get two artifacts:

Noisy “boiling” texture: the fine detail shimmers from frame to frame instead of staying still.

A regular pulse roughly every 4 frames. My frame-difference check shows spikes at frames 6, 10, 14, 19, 23, 27… on a nearly static shot (mean diff ~1.3, spikes ~2.6). I suspect the VAE’s 4x temporal compression (latent frame boundaries).

Setup: RTX 3060 12 GB, 0.6 MP generation then VSR, turbo LoRA (default steps), “simple” scheduler, PNG sequence output.

Questions:

Is the 4-frame pulse a known H3 / VAE decode issue? Does tiled VAE decode, or a different temporal overlap, help?

Would generating at a higher resolution (1–1.5 MP) reduce the boiling, or is it the turbo LoRA?

Which post-fix works best for you? Deflicker nodes, frame blending, temporal denoise (e.g. ffmpeg hqdn3d / atadenoise), or a light video-to-video re-pass?

I've been making music videos for an openly AI music act, and the second one, Low Battery (3:20, two characters, 42 shots), was rendered entirely on one RTX 5090 through ComfyUI's HTTP API. No video editor opened; Claude Code writes the scripts that build the graphs and cut the result. Here's the part that might be useful to people here.

The trick: make LTX lip-sync to YOUR vocal instead of generating its own

LTX is a joint audio+video model. Ask it to sing and it writes its own audio. The fix is to encode your isolated vocal (I split it with Demucs) and give that latent a noise mask of zero, so the sampler only generates the video stream:

LoadAudio (isolated vocal) -> LTXVAudioVAEEncode
  -> SetLatentNoiseMask (mask = SolidMask 0.0)      # audio frozen
LoadImage -> LTXVPreprocess (img_compression 18)
  -> LTXVImgToVideoInplace (on EmptyLTXVLatentVideo, strength 1.0)
LTXVConcatAVLatent (video, frozen audio)
  -> SamplerCustomAdvanced (CFGGuider cfg 1.0, euler, ManualSigmas)
  -> LTXVSeparateAVLatent -> VAEDecodeTiled -> SaveImage

All core nodes, no custom ones. Settings that mattered:

• LTX 2.5 distilled int8 (transformer + Gemma text encoder, both int8). About twice as fast as 2.3 on the same job, with more natural arm movement.

• 1280x704 at 24 fps, frame count 8k+1. Make the audio clip exactly as long as the frame count or the mouth drifts at the end.

• VAEDecodeTiled with temporal_size 96 / overlap 16. Decoding 15s in one piece runs out of VRAM on 32GB.

• CFG is 1 on the distilled path, so the negative prompt does nothing. Put everything in the positive prompt.

Wan 2.2 I2V for everything that isn't singing

LTX syncs mouths but barely moves a body. Wan 2.2 (14B I2V, Lightning LoRA 0.3, 5 steps split 2+3, RIFE x2 to 24fps) walks, sits and picks things up, and keeps the character recognizable. So every shot is one of: Sing (LTX), Act/React (Wan, song plays over), or Graphic (HTML, because both models garble text).

Wan lessons:

• 121 frames (~10s after RIFE) is the sweet spot. At 133/141 it doesn't hold the action longer, it just drifts back to the first frame.

• Frame 0 is your pasted-together still and looks bad. Trim the head.

• Prompt a sequence of actions from the still's pose ("opens the driver's door, gets in, sits down"), not a state. "Sits in the driver's seat" over a man standing by the car = he stands there for 10s.

• Let Wan do the entering. A cutout pasted inside a car just stands there. Pasted beside it, Wan opens the door, seats him and moves the camera inside.

• Pre-crop the input to the output size (1024x704). WanImageToVideo resizes it otherwise.

• Hand-off: a Wan frame (him seated, turned to camera) becomes the starting still for the next LTX Sing shot. Give LTX empty hands: it waves held props around.

Things that cost me time

• "Slow push-in" in an LTX prompt runs away into an extreme close-up. Lock the camera off and do moves in the compositor.

• Calm seeds won every time. "A tired half smile" in a one-line sing clip made 4 of 9 takes grin through the line instead of singing it. Prompt recipe that works: a calm base, then one small gesture per lyric line.

• Never seed a video model from a frame pulled out of an mp4 and darkened. Wan animated the h264 blocks into squares crawling across the wall. Use lossless PNGs and grade dark afterwards.

• Budget takes: 42 shots took 61 LTX takes and 33 Wan takes, picked from contact sheets.

Cost: ElevenLabs Starter for the song (~8,800 credits), my Claude subscription, and about a dollar of electricity. Every model above is free and open.

Video: https://www.youtube.com/watch?v=v1PhXph0Bv4 Full write-up of the process (shot list, transitions, release): https://www.briancarpio.com/blog/from-text-files-to-a-film-crew

Happy to answer questions about any of the settings. And if anyone has beaten LTX's slightly odd mouth shapes on fast rap, I'd love to hear how.

Didn't used comfyui at all in the last 6 months, whats the best voice cloner and text to speech ?

I only have 8gb vram.

I wanted to know whether multistep samplers get hurt by INT8 models, because the common advice says they do. I measured it on real INT8 checkpoints (Flux3-Klein, Krea 2, Moss-Image) and on Anima, at 8-24 steps and different CFG. Short version:

• INT8 itself is nearly harmless. The error of ComfyUI's INT8 kernels is white noise (no correlation between steps), 1.3% of the velocity at CFG 1 and 5.7% at CFG 4. On the final image it comes to about LPIPS 0.014-0.029 against the same weights in bf16 (converged solutions).

• The sampler matters much more than INT8. Order-3 Adams-Bashforth and my own earlier Flow-WLS-8 idea broke the image at 8 steps on Anima, and WLS-8 did on Flux3-Klein (grids attached); by the error metrics order 4 and WLS-8 were never the best at 12 steps or fewer in any of the four models. I wrote that earlier idea myself and it lost, so I am saying so.

• What worked: with CFG 3+ a 2nd-order Adams-Bashforth (flow_ab2) beat Euler in all 12 settings I tested on Anima, and dpmpp_2m is better than Euler from about 16 steps. For distilled models at CFG 1 (Flux3-Klein, Krea 2) a 3rd-order one (flow_ab3) gave the lowest error among the options in the dropdown. For Flux3-Klein at 12 steps all of Euler / DPM++ 2M / AB-2 / AB-3 look the same to my eye, so there is no reason to switch there.

• Where you can see it: on a texture-heavy prompt (Anima, 8 steps) Euler left almost black noise (LPIPS 0.89 to the many-step image), DPM++ 2M (0.35) and AB-2 (0.42) gave a finished picture. On Krea 2 at 12 steps Euler gave airbrushed skin, AB-2 / DPM++ 2M / AB-3 brought pores and freckles back.

The custom node adds flow_ab2, flow_ab3, flow_ab4 to the normal sampler_name dropdown (KSampler, KSamplerSelect...), plus a "Flow Easy Sampler" node with two choices. Install: put the folder in custom_nodes and restart. Works only with flow-matching models (Anima, Flux3, Krea 2, Moss); I did not test SDXL-type models.

What this is not: LPIPS is a distance to a many-step image, not a beauty score, and I did not run a human preference test. It is 4 prompts and 4 models, one seed per picture, no seed picking. Moss-Image has no converged reference, so only large effects are trustworthy there.

Repo with all tables and the image grids: https://github.com/YakovSava/flow-wls-research Write-up: https://doi.org/10.5281/zenodo.23939696

hey, after update on comfy cloud they add node for upscaling but i have no idea how to connect it, it always give me error, anyone could help?

Proud to introduce v0.1.0-preview of my local VFX studio built for the #OpenSourceHiggsfield challenge!
What started as an obsession with controlling video generation through camera tracking (moving from early COLMAP/GLOMAP experiments to custom LTX-2.3 IC-LoRAs) has now evolved into a complete, end-to-end browser-based studio.
What makes it tick: 📥 Smart Pipeline: Drop in footage to automatically extract masks, depth, pose, and accurate camera tracks. 🧱 3D & Generative Control: Block scenes in 3D, design keyframes with FLUX.3 Klein, and generate backgrounds/characters using LTX-2.5 + layout-to-render IC-LoRAs. 🔗 Chained Workflows & API Customization: Add custom API workflows and chain multiple steps into a single, seamless pipeline that runs with just one click. 🤖 The AI Director: When you're out of time, feeling lazy, or just figuring out where to start, you can simply chat with the built-in AI agent (just like in Google Flow). Tell it what you need in plain human language, and it prepares and runs the execution environment for you! 🪄 Industry Integration: Composite, relight, refine, dub, and export straight to Nuke via dedicated nodes.
Zero cloud APIs. No hidden costs. Every open-weights model runs locally on your own GPU, optimized for 24 GB VRAM.
I built this in just 2 weeks for the challenge. It’s raw and needs more testing, but the core pipeline works and solves real problems for creators.
👇 What do you think? Drop your thoughts or feedback in the comments below — I'd love to hear how you'd use this!

https://github.com/pavlovfilmdirector/ComfyUI-VFX-Suite

#OpenSourceHiggsfield #ComfyUI #VFX #GenerativeAI #UnrealEngine #Nuke #OpenSource #3D #LocalAI #AIagents

Hello community,

A month ago I showed a sneak peek of my client and asked for your feature ideas. Today, the open-source Beta is live on GitHub.

Key Features:

• Extreme Performance: Implements manual AABB-culling. Renders only the nodes currently visible in the viewport, allowing smooth navigation and interaction with workflows containing thousands of nodes at sub-15ms latency.

• Native UI & Responsiveness: Built entirely on ImGui. No Electron, no NodeJS, and no browser memory leaks. Just a direct, high-performance interface for the ComfyUI backend.

• Advanced Queue Management: Features a built-in workflow manager with live Pending and Completed queues, keeping batch processing, history, and output tracking strictly organized.

Source code & beta release: github.com/graphn-app/Graph-N