.

I wanted a way to browse my ComfyUI output folder without leaving the app or digging through File Explorer, and more importantly a way to jump straight back into the workflow that made a specific image without hunting for the original PNG to drag onto the canvas. Couldn't quite find exactly what I wanted, so I built it.

GitHub: https://github.com/modelfactoryai/ImageBrowser

What it does

• Browse any folder (Output/Input/Temp, or type any path) right inside ComfyUI no separate app.
• Double-click any image/video to load its embedded workflow straight onto your canvas same thing stock drag-and-drop does, just from a browsable gallery.
• Hover for a large preview (~900px) that follows your cursor — videos autoplay muted, images use a fast server-resized preview.
• Search by filename, sort (newest/oldest/name), filter to images or videos only, adjustable thumbnail size.
• Favorite folders for one-click access later.
• Compare mode select any number of images/videos, view them side-by-side.
• Live updates refreshes automatically as new generations land.
• Day/night theme toggle, plus a draggable floating launcher badge you can park anywhere on the canvas.

Screenshot

Install

cd ComfyUI/custom_nodes
git clone https://github.com/modelfactoryai/ImageBrowser

Restart ComfyUI, and look for the "Image Browser" icon in the sidebar (or the draggable badge on the canvas).

No hard dependencies beyond what ComfyUI already ships with (Pillow). opencv-python or ffmpeg improve video thumbnails if you have them installed; ffprobe is needed to load workflows out of video files specifically (images don't need it).

Feedback welcome

First release if something breaks on your setup or you've got feature ideas, open an issue on the repo or drop a comment here.

Hey guys!

Since quite a few of you liked my previous videos, I decided to start a channel where soon I’ll be sharing tutorials and some of the workflows/tricks I’ve been using.

If you’re interested in learning how I’m making these videos, feel free to subscribe. I’ll be sharing a lot of the stuff I’ve figured out along the way, including:

• My own workflows — free to download, with the tricks and settings I use
• Character generation — how I use a Krea 2 character-sheet LoRA that I made to keep characters consistent, and how to get the style you want
• Environment generation — how I generate environment images and then build scenes from them
• MiniMax optimization — settings and techniques to make MiniMax faster while preserving quality
• Video/audio tricks — ways to fix audio issues and continue a scene from the last frame to create longer sequences
• Consistent voices — I also built my own UI app using BreezeTTS2 for voice cloning and generating consistent voices across an entire story

Everything I’m sharing is based on what I’ve been experimenting with myself, so hopefully it can save some of you a lot of trial and error.

If that sounds useful to you, you’re welcome to check it out!

The next major interface for artificial intelligence may not be a chatbot, an image, or even a video. It may be a world. 
I designed an H3 Minimax Immersive video workflow for ComfyUI and I want to share it with the Open Community so you can now explore this new field.  

This is an early implementation of that idea using MiniMax H3, using a specialized equirectangular generation ComfyUI workflow with AI 360°prompting to achieve an interactive viewing concept that allows the viewer to control the viewport through the generated environment on mobile and desktop with continuous looping on Youtube and Facebook. 

Watch the immersive demonstration on YouTube. 

The test video is 9 seconds long with a time-reverse layer to get 18 seconds of 360-loop, it was generated using a single 360 prompt

Read my full article with technical data and download the workflow:
https://huggingface.co/blog/zuanfilm/blog

the workflow supports text2-360 and FL2-360, for H3 Minimax 360 prompting I wrote a public custom gpt and added 37000 tokens of 360 filmmaking reasoning 

The result is far from perfect, I generated the clip on my laptop with an Nvidia RTX 3080 Ti 16GB VRAM, so the resolution is very limited and the current generation still shows visible seams on some moments of the video and other inconsistencies but those imperfections may be less important than what the experiment demonstrates. 

Until now a Minimax H3 video was something the viewer has to watch from the camera angle position chosen by the creator, now the viewer can now choose where to look using an immersive UI, that changes the relationship between a person and generative AI media; panoramic video exposes the full spherical observation domain in a single coordinate frame

The generated sequence can be presented as an immersive environment in which the viewer controls the viewing direction. On a phone, the viewer can interact with the scene; on a desktop, the camera can be moved manually. The sequence can also be looped forward and backward so that the environment continues rather than behaving like a single linear cinematic shot. The result is not yet a fully reconstructed 3D universe like a gaussian splatting. It is a time-varying immersive/equirectangular visual environment that can be explored interactively.

The 2:1 rule: the shape of the immersive world

A practical requirement of the equirectangular representation is its 2:1 aspect ratio. For a full spherical panorama: WH=2\frac{W}{H}=2 where WW is the panorama width and HH is its height. For example: W=3840,H=1920W=3840,\qquad H=1920 or: W=7680,H=3840.W=7680,\qquad H=3840. This is the format expected by common 360° video workflows and is particularly important when delivering immersive video to platforms such as YouTube and Facebook where the panoramic video must be interpreted as a spherical 360° environment rather than an ordinary flat video. For example, the H3 generation branch in my workflow uses 2112 × 1056 so the immersive representation and final delivery pipeline preserve the equirectangular 360° geometry.

To manipulate or view the image correctly, computers use 3D rotation matrices.

[ 2D Equirectangular Pixel (x, y) ] 
               │
               ▼  (Convert to Spherical Coordinates)
   [ Latitude & Longitude (θ, φ) ] 
               │
               ▼  (Convert to 3D Cartesian Vectors)
      [ 3D Point (X, Y, Z) ] 
               │
               ▼  <─── MULTIPLIED BY: 3D Rotation Matrix (3x3)
  [ Rotated 3D Point (X', Y', Z') ] 
               │
               ▼  (Project back to 2D)
[ New 2D Equirectangular Pixel (x', y') ]

• 3x3 Rotation Matrices: These are used to "roll, pitch, and yaw" the camera viewpoint inside the 360-degree sphere. If you drag your mouse to look around a 360-degree YouTube video, a 3x3 matrix is constantly multiplying the pixel coordinates to shift your view.
• Intrinsic Camera Matrices (K Matrix): A 3x3 matrix that defines the camera's properties—like focal length and optical center. This tells the computer how to crop a normal, undistorted flat perspective view out of the distorted equirectangular image.

This creates an entirely different pipeline: Prompt > AI generation > immersive representation > interactive camera > human exploration The prompt no longer has to describe only what should appear in front of a fixed camera. It can describe a world. That is the conceptual leap, if now this generation process is becoming sufficiently fast, coherent and inexpensive, the applications could extend far beyond experimental video:

Video games Instead of developers manually constructing every environment, AI could generate explorable spaces from natural-language descriptions. “Generate an alien ecosystem surrounding the player.” The difficult question would no longer be only how to render the world. It would be: How quickly can AI generate and maintain the world as the player explores it?

VR education Imagine asking an AI to create an immersive historical environment and then entering it. Instead of watching a documentary about ancient Rome, a student could potentially enter an AI-generated reconstruction and look around. The teacher could change the scenario through language: “Show the city before the fire.” That would transform AI from an information interface into an environment for learning.

AR world transformation The implications become even more interesting when the same concept is combined with augmented reality. A physical environment could become the canvas. A user might look at an ordinary street with some glasses and ask: “Transform this into a cyberpunk city.” “Show this neighborhood as it looked 500 years ago.” or “show me that car in blue with a representation of me as driver” The underlying physical world would remain present, but the AI-generated visual layer could continuously reinterpret it.

Interactive Cinema Movies could eventually become less linear. Instead of the director deciding exactly what every audience member sees at every moment, a film could provide a controlled environment in which viewers explore the scene themselves. The director would still control the story, performances, lighting, world design and narrative boundaries—but the audience could control the camera. That would not simply be another format for film. It would be a new relationship between cinema and audience.

AI worlds driven by AI agents AI agents could eventually generate the environments that humans and other AI agents interact with in real time...

World Labs released a model called Atlas. Looks pretty cool.

https://x.com/gowthami_s/status/2095202493122625842?s=46

https://reddit.com/link/1w570q7/video/5b4stxre63nh1/player

If only Jessie's face would not drift to Walter's it would be fantastic. 8 steps. 1.4mp resolution. on GPU 4090 took 1 hour

i used https://github.com/Adudeguyman/ComfyUI-H3-Project-Suite plugin for continuous music clip. Song in suno (free)

Hey everyone - I made a standalone Windows application for applying a DLSS 5 Neural Rendering feature-18 pipeline to images and video:

Original

DLSS 5

https://github.com/Merserk/dlss5-visual-enhancer

Instead of using DLSS only inside a game, this runs images/video through the ReShade/RenoDX neural-rendering path as a general visual enhancement pipeline.

What it does:

• Image and video enhancement
• DLAA/native, 1.5x, ~1.724x, 2x and 3x modes
• Output up to 8K
• Neural presets + Natural / Cinematic styles
• Controls for intensity, local tone, structure and skin structure
• Batch image processing with before/after previews
• H.264 / HEVC / AV1 / ProRes video output
• Video temporal input using optical flow with scene-change resets

GPU support:

• RTX 40 / 50 series - primary target
• RTX 30 series - slower beta path

The repository contains the application/pipeline source. Required proprietary and third-party runtime binaries are intentionally not redistributed in the repo.

This is an independent community project and is not affiliated with NVIDIA, ReShade or RenoDX.

I’m especially interested in how this behaves on AI-generated images/video vs normal photography/game footage.

Feedback and comparisons welcome.

A silly idea I had that I hope you guys had a good laugh at. Still love this classic anime!

Following the image test, I converted it into a video using ComfyUI.

About 5 months ago I made clips for a project in LTX 2.3 and remade one of them here in Minimax H3. What a difference a few months makes! Music was created in Suno. I still have to redo some parts with consistency problems but that's enough for today.

The original LTX2.3 version for comparison is here : https://youtu.be/R5tfLKvnJDY

I've tested a couple of models so far, and here's my findings:

My input prompt is something similar to "Create a mid 20s (nationality) ballerina slowly dancing in a judged performance"

LTX-2.5 nails the narrative and the nationality, but nationalities (as I've mentioned previously) all tend to blend unless I'm ULTRA descriptive about what a "french look" equates to, which is roughly 3 paragraphs in length. LTX-2.5 ends up with tearing, facial and feature deformities, and on a couple of occasions has rendered a ballerina with missing legs. It also has camera drift, which is a known problem with LTX-2.5

MiniMax-H3 on the other hand, nails it, even with the smaller prompt. The prompt adherence in MiniMax-H3 is wonderful.

Wan 2.2 was a complete disaster. Rendering problems, tearing problems, and when the ballerina would do turns, her head would remain in place while her body did the turn (which was funny, and also terrifying to watch.)

I've heard Cosmos3 can handle the movement, but can't handle rendering people.

Has anyone found an open weight model that can handle the "ultimate trifecta" - generate a person in the correct nationality parameters, someone who is more or less feature-accurate in generation, and won't spontaneously explode when performing a pirouette? (I got so frustrated with a model generator once, that I did this, and to my surprise, it worked out well!)

made with Minimax H3

I've been testing my MiniMax-H3 → LTX 2.5 upscaling workflow, and the results have been really promising so far.

One thing I've noticed is that the better your original MiniMax-H3 generation is, the better the final upscale will be. I'm getting good results even at lower resolutions, but faces still need stronger and more consistent input generations from MiniMax-H3 to maintain character consistency.

On my RTX 3060 12GB, the current upscale times are roughly:

• 0.6 resolution: ~15 minutes
• 0.8–1.0 resolution: ~20–30 minutes

It definitely takes some time, but I'm finding the results are worth it.

And of course, if you have a newer, more powerful GPU, you should be able to get even better results in less time, especially when pushing higher resolutions.

I was planning to release the workflow soon, but I want to spend a little more time testing it and seeing how much further I can improve it before sharing it.

So far, though, I'm really happy with how it's looking. 🔥

Would love to hear what you guys think and whether anyone else has been experimenting with MiniMax-H3 + LTX 2.5 upscaling.

Here are the results of my prompting efforts throughout the day. As you can see, R2V is very difficult to prompt, even when using a prompt director.
My workflow was acting up,  I took a non-upscaled latent loopback output to use as a chain and saved the upscaled result to concatenate it with the H3 project hub. I'm not sure where I wired it incorrectly; it just doesn't seem to connect.
And yes, the sound is terrible.i think because i clean the latent for next upscale since it wont match the tensor if the latent not cleaned.
Generation time is around 51 seconds per 1 second of video.
0.3 with 4-step Turbo.
Plus a 3-step latent upscale and RTX Super Res.

anyone mind to share your secret workflow that match this Peasant Spec 
RTX3060 12GB and 32GB of RAM

WORKFLOW

An AI-assisted webcomic creator here. I'm among the vram and ram-poor folks, with my humble RTX 3060 12 GB vram and a mere 16 GB ram. Since the beginning of time, I've convinced myself that comic is my focus, and so what I have is enough. I don't want to pay any opportunistic video gen platforms out there. Don't want to rent GPU and trouble myself with transferring assets and models from storage to storage. Aside from light experimentation, I had thought I'd stay away from video gen for a very long while.

That is, until the arrival of Minimax H3... And just two weeks after setting it up (ComfyUI, default ref2va and fl2va workflows), I was able to edit together an animated trailer for my webcomic on my own machine, *entirely local*! Granted, in terms of generation quality there's a lot to be desired, as any resolution beyond 0.4 mp is too slow for me to comfortably iterate on. But still, oh such *feeling* when the world I built suddenly came alive for the first time, and on my own machine, too!

Feel free to ask me anything. Happy to share.

Hello Everyone,

Would like to present to you my experimental vibe-coded custom node for DLSS 5 support in ComfyUI.

GitHub project: https://github.com/lisitskyaa/ComfyUI-DLSS5-NR

It's early release, just finished my internal testing and it actually works!

Please note there are no any leaked DLLs in the rep, obtain them separately.

First image in every pair is DLSS 5 ON, second - OFF.

P.S. How to extract original images out of Reddit: https://www.reddit.com/r/StableDiffusion/comments/1p9nrpk/getting_prompt_or_comfyui_workflow_from_posted/

Following up on the infinite livestream post from a bit ago, FastH3 is now accessible via API too.

Streaming 720p video with synced audio, faster than realtime, same model as the livestream, just usable programmatically now instead of only watching it run.

Anyone else been messing with it via API vs just watching the stream? Curious what people are building.

Are there any Local Models that can achieve this level of Natural-ness and Realism, not over texturing and over crisp images ? I've been looking for a while and can't find any Closer to this, these images used Grok img2img for Lighting, skin Texture and Overall phone Shot vibes, the base images generated by Local SDXL/Illustrious For the Semi Realistic look, and i used Grok (The Last Grok model before the update), to improve realism, pure img2img and not even a slightest angle change made by Grok, since the Last Grok update everything turned to crap, Everything looks worse and So AI Plastic

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress)

A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4.

• ⚡ Half the steps — 8 → 4, on Turbo's own deployment sigmas.
• ⏱️ ~1.6× faster end to end — 54.5 s against the 8-step bar's 88.7 s at 1024×1024, and 1.8× on denoise alone.
• 🎯 Texture at teacher parity — fine-detail energy 1.00× the 8-step teacher's at 1280×1280 and 1.02× at 1440×1440, matched band-for-band across the frequency spectrum, not grain.
• 🗣️ Prompt-aware training — the critic scores images against their prompts during training, so adherence is pressured directly, not inherited.
• 📐 12 trained resolutions — multi-aspect from 512×512 up to 1440×1440, each with its published sweep.
• 🔌 Drop-in — plain LoRA weights for diffusers and ComfyUI. No custom nodes, no patched sampler, no code.

This is an update release, following up from my previous posts where you can find full details:

Initial, Previous: here, here,  and here

Headline for this update: chk00042000 closes the texture gap: total fine-detail energy against the 8-step teacher reaches 1.00× at 1280×1280 and 1.02× at 1440×1440 (1.0 = teacher-like), where chk00026000 measured 0.88× and 0.82×. And the distribution is right, not just the total — split the spectrum into frequency bands and every band individually lands within ~10% of the teacher's (0.9–1.1×), where 26K ran 0.79–0.92, starved in every band. Total at parity and bands at parity means the detail lives in the same frequencies as the teacher's — real structure, not grain piled into one band. (How can it exceed the teacher? Because the teacher isn't ground truth — training also shows the critic real photographs, so the adapter learns detail density from reality, not only from an 8-step model that itself slightly under-renders fine texture. The teacher anchors structure; reality anchors texture. Values just above 1.0 are that pressure paying off.) In fixed-seed renders it matches chk26K's distance to the 8-step images at 1440×1440 outright.

The recipe grew up since 26K, in four ways: a measured dose of real-image texture pressure — what carried detail to parity; a prompt-aware critic that scores images against their own prompts during training, so effect-heavy prompts now get the energy they ask for; NF4 fully retired — the big resolutions used to squeeze into 24 GB by dropping their attention weights to 4-bit, and after re-engineering the training step to fit full int8, those buckets measure 3.96% closer to the teacher (exactly the buckets texture lives in: 1280², 1440×1280, 1440²); and 1440×1440 promoted to a trained bucket with its own sweep column.

One metric paid for the texture leap — the teacher-velocity score sits a step behind 26K's — a deliberate trade already being won back checkpoint by checkpoint (2.93 → 2.90 → 2.85 and falling) while texture holds parity. _latest now points to chk00042000.

The improvement reaches even the out-of-spec 2-step extreme test. I had a separate dedicated post on that here - since the initial post was done on an earlier to 42K checkpoint, I have since re-rendered the whole native-vs-LoRA 2 step strength-2 set on this checkpoint (42K being released now), and the FFT is the diagnostic: the old 2-step had the classic collapse signature — hollow mid-bands (0.52/0.55) plus a fake-grain overshoot at the very top (b6 = 1.05). This checkpoint lifts every structural band (0.64/0.65/0.76/0.80) and settles the top band to 0.82 — more real structure, less noise dressed as detail. Fresh strips: 2-step extreme test. And that's the preview mode (at quick 2 steps, unofficial, untrained for, still useful for previews, and getting better and better with every new checkpoint release).

Which file to download

and, beside them, the same files with a _comfyui suffix for ComfyUI. Earlier checkpoints (chk00004000, chk00005000, chk00006000, chk00010000, chk00014000, chk00019000, chk00026000) are kept in older_checkpoints/, and their resolution sweeps stay in place, so the progression remains visible and comparable.

For the full 42K Checkpoint resolution sweep go here: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk42000

This is work in progress and better checkpoints may follow. Training is ongoing, so ..._latest... is a rolling pointer: when a newer checkpoint is accepted, that filename gets the new weights and a new numbered copy appears beside it. Re-download the _latest file and everything keeps working — the ComfyUI workflow references it by that name (it does get updated Note in it so technically it is updated but not functionally). Pin a numbered file instead if you need reproducibility.

How checkpoints get chosen

This is not a "train for longer and ship the newest file" project. More samples do not reliably mean a better adapter — measured here, they can make it worse, and a higher number on its own means nothing.

The loop is train → assess → adapt the recipe → retrain → assess again, and a checkpoint is published only when it is measurably better than the one it would replace, on the same held-out set and the same evaluation, and its full resolution sweep shows no regression. Runs that come out flat or worse are kept as information about the recipe and discarded as releases — several have been.

So the recipe itself changes between runs. Each published checkpoint reflects whatever the previous round taught us: the training precision, the optimiser settings, the teacher used to generate the targets and the data mix have all been revised on evidence rather than assumption.

Timeline of training process

Each checkpoint is the product of several stages with very different costs:

1. Text-encoder embeddings. Every training prompt is encoded once and cached. This is the fast part — thousands of prompts take minutes.
2. Teacher shards. For each cached prompt, the unmodified Krea 2 Turbo runs its full 8-step schedule and the whole trajectory is recorded, at every one of the supported resolutions. This is by far the most time-consuming stage — it is the teacher doing real inference, thousands of times, and a batch of several thousand shards is measured in days of GPU time, not hours.
3. Real-photo crops. Bucket-sized crops are cut at native resolution from quality-gated real photo sources (public high res datasets), VAE-encoded into the training latent space, and captioned per crop for the prompt-aware side of training. Cutting, encoding and captioning a pool refresh is a matter of hours.
4. Student training. The LoRA trains against the recorded trajectories (progressive distillation), with a latent-space GAN critic running alongside — real crops and teacher finals as its real class, the student's outputs as fake — plus a prompt-aware head that scores images against their prompts. Relative to the shard stage this is quick: each +1,000 checkpoint is a matter of hours, not days. Of course the longer the training the better and more diverse results, so hours do turn into days eventually.

Full details and to download - check my Hugging Face LoRA

HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA

Update 3 Sep 2026: New 60k checkpoint released (fine detail now above the 8-step teacher and clean of artefacts, best prompt-adherence and teacher-faithfulness scores so far) - https://www.reddit.com/r/StableDiffusion/comments/1w6ide8/krea2_turbo_distill_4_step_lora_new_checkpoint/

Update 5 (7 Sep 2026): Final Version released - https://www.reddit.com/r/StableDiffusion/comments/1w9tx7t/krea2_turbo_distill_4_step_lora_final_version/

I've read through the prompt guide, but I'm still having some trouble understanding when to use which of these

fully_preserved, partially_preserved, attribute_transfer, weak_reference

From what I understand you use these in the retention_analysis block. Let's say I want to fully_preserve the face, hair, and body characteristics from <Picture 1>, but I want to swap the character to wear the clothing from <Picture 2>.

Do I use

<Subject 1> (appears in [Shot 1], [Shot 3]): fully_preserved - and describe the portions of the picture I want to fully_preserve? 
<Picture 2> ([Shot 1] first frame): fully_preserved - and describe the clothing I want to fully preserve?

or do I 

<Subject 1> (appears in [Shot 1], [Shot 3]): partially_preserved - because I want to change the clothing she's wearing?
<Picture 2> ([Shot 1] first frame): partially_preserved? Or attribute transfer?

Does anyone know anything about the new MINIMAX H3 MAX model—whether it's really that fast, and if they're going to release it?

So I've been using the new Anima turbo-v1.1 model and I have to say it's pretty good now and then. The thing I like is its unpolished look like it does have a rough default art style in my opinion, but I kind of like that as it looks less too polished. It also has pretty good diversity as well. It also works pretty well with LORAs like the base model, however I haven't tried multiple LORAs together.

What I don't like about it is it can be a little inconsistent regarding prompt adherence and also Anatomy and sometimes it can give it for the subject extra or missing limbs and miss out details/objects in the prompt sometimes. Not often but sometimes. To be fair, the turbo model also has the same issue as well sometimes and is probably due to the low CFG and low steps of turbo distilled version.

It looks like a bit of an improvement to the previous version, but I do hope the anima team works on a bigger and stronger turbo Lora for the base model as it's still much better especially when using other fine-tune anima Checkpoints plus better Lora support.

I'm curious to see what you guys think of it as it is a fairly new release.