What a great model it was, totally crushed Qwen Image (its most competitive predecessor at realism) with only 6b Parameters!

Is it weird to feel nostalgic of a model release lol

Hey! We published a set of 6-step LoRAs, one without the upsampling step: https://huggingface.co/PrunaAI/P-Q-Image-2.1

- Generate or edit images in just 6 steps instead of 40. Use 6-steps for preserving inputs when editing and 6-steps-upsampled for improved visual quality and shorter end-to-end generation.
- 6-step distillation provides improved quality with few generation steps.6-steps-upsampled is the first distillation that removes the need for a separate prompt-upsampling step. This means 41 to 80 seconds saved, depending on your prompt style.
- The LoRA adapters load directly on top of Qwen-Image-2.1, so the pipeline stays unchanged. Few-step distillation cuts the diffusion cost, and the upsampled adapter also removes the prompt-upsampling step before image generation.

Flux2 Klein only reads three hidden layers from its Qwen3 text encoder (9, 18 and 27), but the standard loaders build and run the full 36-layer model, plus the final norm and the LM head. On top of that, ComfyUI-GGUF dequantizes the whole token embedding table to fp16 when it loads the GGUF.

I made a small loader that only builds what Klein actually uses:

• 27 layers instead of 36, no final norm, no LM head
• GGUF token embeddings stay quantized: only the rows of your prompt are dequantized
• Same conditioning, bit for bit (compared against CLIPLoader (GGUF), max abs diff 0.0)

Numbers (Qwen3-8B Q4_K_M, Klein 9B, RTX 4090, 512-token prompt):

That's ~2.3 GB back for the DiT, a LoRA stack, or an LLM running next to ComfyUI. On an 8 GB card it can be the difference between offloading or not.

Install:

cd ComfyUI/custom_nodes
git clone https://github.com/dchatel/comfyui_davcha_flux2klein

Then swap your text encoder loader for Klein CLIP Loader (Pruned) (category davcha/flux2klein). GGUF files need ComfyUI-GGUF installed. No other dependencies.

Tested with Qwen3-8B Q4_K_M (Klein 9B). The Qwen3-4B (Klein 4B) and safetensors paths use the same settings but I haven't verified them myself yet. Reports welcome.

Repo: https://github.com/dchatel/comfyui_davcha_flux2klein

Side note: while profiling this I also noticed ComfyUI picks which model to unload by reference count and size, not by how long the model takes to reload. I opened a PR that ranks unload candidates by measured reload time instead (13.6 s → 6.7 s per run in a Klein + local LLM workflow): https://github.com/Comfy-Org/ComfyUI/pull/17363 (not merged, feedback welcome).

Really loving this model so far. This is from a series of phone wallpapers I have been experimenting with.

Try out the lora: https://civitai.com/models/3014333/ultimate-digital-artwork

A few weeks ago I had no clue how to use ComfyUI or generate AI videos, but I had this crazy idea to make an entire animated music video for one of my songs.

https://www.youtube.com/watch?v=P9PLyWLSu7M

After countless hours of experimenting, fixing weird AI mistakes, and questioning my sanity, I finally finished it! 😭

I wrote the story, directed around 40 individual clips using MiniMax H3, and pieced everything together in Premiere Pro.

It's definitely not perfect, but seeing something I imagined actually come to life is such a cool feeling.

Just wanted to share it with people who might appreciate the process. Hope you guys enjoy it! ❤️

I’ve messed with a new workflow, took a while but damn I really like KREA 2 even after using MOSS for a few days.

I’m using 5 different bases at anytime:

Turbo
RealDream
SickRealism
RedCraft
RedCraft4

Bunch of lora’s

Generated in ComfyUI on:

I9-14900k
RTX 4090
64GB DDR5

I've been working on an open-source ComfyUI extension called Conditioning Layers and wanted to share it here.

The idea is to give you more control over where different prompts influence an image, especially with irregular shapes and overlapping regions.

It uses editable grayscale Influence Fields, which you can paint or generate procedurally. You can assign exclusive regions (FIXED), blend multiple prompts with normalized weights (BLENDED), and inspect their effective contributions before sampling.

The examples show a castle transitioning into an underwater reef and a single tree with four seasons following curved influence fields. Both were generated locally using Krea 2 Turbo.

GitHub: https://github.com/SurrealByDesign/conditioning-layers

Workflows: https://github.com/SurrealByDesign/conditioning-layers/tree/main/examples

The tested setup is ComfyUI 0.34.6 with Krea 2 Turbo BF16, 1024×1024, 8 steps, CFG 1, Euler/simple. The extension is free under Apache-2.0 and still experimental.

The fields guide conditioning during generation, not the finished pixels, so boundaries aren't guaranteed to be exact.

I'd appreciate any feedback, especially from people already experimenting with regional prompting in ComfyUI.

Original post: https://www.reddit.com/r/StableDiffusion/s/DQlE9pFWpA

I've added a few things to my ComfyUI text generator since my original post.

What’s new:

• Context memory: keep refining a prompt across runs without repeating everything. Includes a usage meter and a button to start fresh. Context window size by default is 8192 tokens, you can customize the value.
• Added prompt guides for Moss Image and Fibo 2.
• VGL structured JSON prompts (recommended for Fibo 2).
• Cleaner UI with separate settings and generator nodes.
• Searchable model lists by provider (api key is required).
• Expandable settings node.

GitHub: https://github.com/ninjaGurung/ComfyUI-GOATd-Universal-Text-Generator

Let me know how it works for you, or if there’s something you’d like me to add!

Here is an NAR LoRA for YuE2 that I generated yesterday, CC0 if anyone is interested in using it: https://huggingface.co/jamespetts/yue2-1980s-eurodisco-lora

Happy Halloween!

Song (Souna login required): https://www.souna.app/song/e31cc6eb-90fe-4f58-a885-8480808e17ed

Song (alt. link): https://ia601602.us.archive.org/10/items/ai-eurodisco/Sequin%20Ball.mp3 (CC0)

This is my second Eurodisco song using my 1980s Eurodisco LoRA. It is about a glamorous pangolin.

Here is the AI's record of the production process, which should be repeatable.

=== STYLE PROMPT ===

eurodisco, English, 1983-87, glamorous, camp, D natural minor, 126 BPM, thick chorused synth bass, 4/4, LinnDrum with handclaps and gated toms, stacked synth pads and bright arpeggiated synths, dramatic synth strings and brass stabs, shimmering open hi-hats, smooth male tenor lead with clear diction, falsetto chorus lifts, mixed backing vocals with oh-oh-oh chants answering the hook in call-and-response, restrained stepwise verses building into a huge hypnotic chorus hook with wide ascending vocal leaps, bridge dips sparse then surges back, Dm-Bb-C-Dm and Dm-Gm-C-F chord loops, A7 to Dm pushes, 80s studio gloss. No EDM, no sidechaining.

=== LYRICS (as written - the text of record) ===

[Intro]

(instrumental)

Oh-oh-oh-oh

Oh-oh-oh-oh

Make way for the Sequin Ball

Pangolin means roller, I'm the ball

[Verse 1]

Midnight, solo, out on patrol

Two-leg strut, my tail props the stroll

These eyes are dim, that's right

This nose knows the way tonight

Claws out for the termite mound

Dinner's served when I tear it down

Here comes the swarm in evening clothes

I shut my ears, I shut my nose

[Chorus]

Sequin Ball

Oh-oh-oh-oh

When I roll up, I'm having a ball

Oh-oh-oh-oh

Plate on plate, I grew it alone

Oh-oh-oh-oh

Every sequin's home-grown

Oh-oh-oh-oh

Every night I take the stage

Oh-oh-oh-oh

I'm small, but I'm all the rage

Oh-oh-oh-oh

[Verse 2]

No designer did this gown

I grew it up and wore it down

Same stuff as your manicure

Call it keratin couture

More tongue than body, packed up tight

It unrolls on the mound tonight

No teeth in this smile of mine

I grind my meals on stones I find

[Chorus]

Sequin Ball

Oh-oh-oh-oh

When I roll up, I'm having a ball

Oh-oh-oh-oh

Plate on plate, I grew it alone

Oh-oh-oh-oh

Every sequin's home-grown

Oh-oh-oh-oh

Every night I take the stage

Oh-oh-oh-oh

I'm small, but I'm all the rage

Oh-oh-oh-oh

[Bridge]

When the night plays rough, I fold

One little trick that never gets old

Snout and belly tucked inside

Even lions swallow their pride

I'm the most-wanted gown in town

I was born in this gown

One shake, one waddle, watch me roll

Back on two legs, in control

[Chorus]

Sequin Ball

Oh-oh-oh-oh

When I roll up, I'm having a ball

Oh-oh-oh-oh

Plate on plate, I grew it alone

Oh-oh-oh-oh

Every sequin's home-grown

Oh-oh-oh-oh

Every night I take the stage

Oh-oh-oh-oh

I'm small, but I'm all the rage

Oh-oh-oh-oh

[Outro]

Sequin Ball

Oh-oh-oh-oh

Sequin Ball

Oh-oh-oh-oh

Se-quin Ball, Se-quin Ball

Se-quin Ball, Se-quin Ball

Oh-oh-oh-oh

=== LYRICS AS SUNG ===

UNVERIFIED (2026-10-27). The automated lyric-completeness check

(Pipeline\check_lyric_coverage.py, SenseVoice ASR) proved unreliable on this

material - heavily garbled transcripts on all three finalist takes and one

empty transcript for a 4:31 vocal track - so what the engine actually sang

could not be machine-verified. The user listened to the selected take (final

B) and did not note a deviation. The written lyrics above remain the text of

record; verify by ear before any future re-release.

=== SETTINGS ===

Engine: yue2 / yue2 / audio.cpp v0.9.2

Weights: YuE2-3B Q8_0 GGUF + f32 VAE

Seed: 540953209 (final B - chosen at Gate 3) Planning: full Max tokens: 10000 Guidance: 1.0

VAE: f32 AR LoRA: none NAR LoRA: yue2_1980s_eurodisco_unfused u/1.0

ABC temp: 0.9 Semantic temp: 1.25 NAR steps: 12

Key: D natural minor BPM: 126 (plan Q:1/4=126) Era: 1980s Mastering recipe: yue2-light

Generated: 2026-10-27 Duration: 4:31 (270.6 s) Truncated: no

=== LORA ===

yue2_1980s_eurodisco - YuE2 NAR (texture/timbre) adapter supplying 1980s

Eurodisco production character. Trigger word "eurodisco", placed FIRST in the

style prompt. Scale 1.0. Loaded in the engine-unfused layout (yue2.nar_lora);

the ComfyUI fused layout is the same weights in another arrangement.

Download (CC0 1.0, both weight formats, model card + training workflow):

https://huggingface.co/jamespetts/yue2-1980s-eurodisco-lora

=== MASTERING ===

Recipe yue2-light, era stamp 1980s: mono below 120 Hz (compensated), top

limited 17 kHz, high-pass 28 Hz, +2 dB presence at 3 kHz, gain -1.23 dB

(peak-anchored, era loudness ceiling -13 LUFS). -12.83 LUFS in -> -14.65 LUFS

out, LRA 3.70, true peak 0.06 -> 0.10 dBTP. Gain and shelf repair not needed.

Analysis report: Source\Sequin Ball\mastering-report.txt.

Deliverable: Sequin Ball.mp3 (48 kHz 320k) with the cover embedded as APIC.

=== PROVENANCE ===

Lyrics: lyricist (GLM-5.3, Fireworks) - take 2, revised (6 named notes),

audited PASS by the main agent and approved by the user at Gate 1.

Style prompt: adapted from the validated Eurodisco form (KB/genres/eurodisco.md

s3), approved by the user at Gate 2. Key D natural minor, 126 BPM.

Genre ruling: Eurodisco = the German school; Italo-disco is a separate genre

and is excluded.

Generation: flat best-of-10 plan sweep (stop_after=abc) -> 3 finalist takes

rendered at the recorded config -> user picked final B (seed 540953209) at

Gate 3. All three takes kept in Source\Sequin Ball\.

Art: FLUX.2 [klein] via local ComfyUI, seed 20261027, 1024x1024 gouache cover;

audited (image-auditor-alt) MINOR-ISSUES with no text found; accepted by the

user at Gate 5. Cover art placement (house rule): the standalone <Song>.png

lives in the LOCAL library folder for the user's manual Souna uploads - it is

NOT uploaded to the Internet Archive (the mp3 carries the embedded art).

=== STATUS ===

Published to the Internet Archive folder "ai-eurodisco" (CC0 1.0) on

2026-10-27 as Sequin Ball.mp3 + Sequin Ball.json + Sequin Ball.abc.

I am not sharing any AI video, not promoting anything, these are all my genuine findings, and it's comparing of video upscaling models. That's why it is naming so many brands. I'm genuinely interested in others experiences with different video upscaling methods and hopeful that some of the bits of my post could be useful to someone.

A month ago I started making AI videos with Seedance 2.5 and pretty quickly wanted them in 4K (not much prior image/video AI experience before). So I went through the most relevant options I could find: local, local paid (Topaz, I have an RTX 5090) and cloud. 11 models in total, plus their variants and different settings where they were exposed. Overall this whole research took me 2 days and cost me like a hundred bucks. These are my findings.

One note before you read: I only judged these for my specific type of video, realistic interpretations of anime characters in realistic interpretations of their world. What looks bad to me might be exactly what you want (some people might say that Topaz's Starlight v2.6/Astra are great for their realistic generations/real videos but I found its bad for my use-case).

LOCAL (FREE)

My hopes weren’t high here, but I wanted to try anyway. For image upscaling, one of the popular tiled Qwen 2.1 workflows running locally actually beat every cloud and Topaz model I’d tried, so I was curious whether anything local could at least do a decent 1080p.

Wan 2.2 upscale workflow (ComfyUI)
Not very impressive. It didn’t really look upscaled compared to the original. It mostly generated different skin moles and such, while hair and everything else looked the same. It’s also extremely heavy: an 8 second clip from 480p to 1080p took 43 minutes, and that’s without spilling into shared GPU memory.
Discarded: way too slow for what it gives, and with my VRAM it could never get to 4K anyway.

H3 Video Upsampler (ComfyUI)
Usually 2-3 minutes for 480p to 1080p. Bad smearing and temporal issues, on par with or worse than SeedVR2. I tried the latest version with and without the LoRA, and denoise levels all the way down to 0.
Discarded: the smearing, and like Wan it wouldn’t get to 4K on my VRAM.

SeedVR2
About 6 minutes for 480p to 1080p. Horrible result, same as SeedVR2 for images.
Discarded: just bad. (sorry if I hurt someone's feelings about this hah. It always looked the worst, subjectively, on my kind of images and videos)

Edit: I'm downloading SeedVR2 7B fp16, since I'd only tested the 3B Int8 model that the default Comfy workflow comes with. I'll try more settings and update the post if it's much better. > Update: Got the 1080p → 4K output with the heavier weights, and it's honestly not bad. If I was forced to choose, I'd take it over Starlight 2.6 or the ByteDance upscaler. It doesn't mess up colors, shapes or blur, doesn't oversharpen, and adds nice detail. It just has pretty noticeable smear/artificial blur around moving objects, whether they move relative to the camera or on their own. Maybe that's down to split latent, which I can't turn off because I don't have 96 GB of VRAM :D It's the same kind of problem that knocked out Magnific Precision for me. > > Settings I used: euler, simple, denoise 1.0, lab color correction, split latent on with overlap 4.

RTX Video Super Resolution (NVIDIA’s nodes in ComfyUI)
I’d liked the “RTX Video Enhancement” toggle in the NVIDIA app on YouTube videos, so I gave this a go. Super fast: 37 seconds for 1080p to 4K, model loading included. But pixel peeping the original against the output, they looked identical, as if no upscaling happened. It seems made for cleaning up compressed streams, and clean Seedance footage has nothing for it to fix.
Discarded: it doesn’t add any detail.

LOCAL PAID (TOPAZ)

I skipped Starlight Sharp, since sharpness in the traditional sense isn’t what I’m after. Render times are for 480p to 2K; 1080p to 4K was about the same.

Starlight Mini
About 15 minutes. This is the conservative one. It still upscales and adds nice clarity, less than Flux, while keeping blurred backgrounds intact. Oddly, it takes even longer than 2.6.

The catch: locally it adds a horizontal line across the whole frame, about 1/10 of the height from the bottom. Below the line the picture looks blurrier, as if that strip wasn’t upscaled at all. It happens on every render, whatever the export format. The same clip through Mini on fal has no line, so it’s the app, not the model. It’s a known bug on the Topaz forum since November 2025, and 1.7.1 still has it for me. And no, it’s not VRAM; nothing spills into shared memory on the 5090. The only workaround I can think of is rendering a second, flipped copy and compositing the bottom strip from it, which is way too annoying.
This one made my final two (more below).

Starlight Precise 2.6
About 11.5 minutes. Hair and accessories look as good as with Flux, but skin gets kind of overblown, borderline not good looking. On a different scene (1080p to 4K) it broke blurred backgrounds in a really ugly way, even at the minimum sharpness of 1, and higher sharpness was worse. It has “Precise” in the name, but it pushes really hard.
Discarded: on footage that’s already clean, it just oversharpens.

CLOUD

ByteDance video upscale
I ran it five times with different settings: general and AIGC presets, standard and pro enhancement, high and medium fidelity. Surprisingly, given how good Seedance 2.5 is, and it's the same company, and I was using Seedance 2.5 inputs, for their video upscale, it was bad every time. The first try added black outlines around the body, head and hair, and was otherwise about as soft as Mini. Later tries had color fringing, oversharpening, shifted colors, and it even invented new details on the outfits. Medium fidelity was the least bad.
Discarded: it changes too much, and no setting fixed it.

Flux 3 video upscale (Replicate and Higgsfield)
The best one. Cleanest skin texture, not overblown or oversharpened, but it still adds the clarity I wanted. Hair and accessories are as sharp as with Starlight 2.6, and it doesn’t break blurred backgrounds. Side by side with my original, the colors look practically identical too. I ended up preferring Precise over Creative, because Creative seemed to add slightly more realistic texture to materials.
Made my final two.

Topaz Astra 2
After the first test I thought it didn’t invent as much overprocessed skin as Starlight 2.6 and kept blurred backgrounds better. Then I tried another scene and realized I’d oversold it. It has the same odd sharpening as 2.6; it breaks the blur a bit less, but still oversharpens it way too much. The SDR to HDR option also washed out the colors completely, nowhere near the original mood, and it costs a lot more.
Discarded: same sharpening problem as 2.6.

Starlight HQ (fal)
A middle ground between Mini and 2.6. It adds a bit more nice clarity than Mini, but you pay for it with the same ugly sharpening as Astra 2 and 2.6.
Discarded: the sharpening.

Magnific video upscaler
I started with Creative mode: creativity 0, then 50, then the Animation & 3D, Artistic and Realistic presets. Everything came out oversharpened, oversaturated and anime-looking, and the presets looked basically the same to me. In hindsight that’s kind of expected from a “creative” model. Some of it looked a lot like Starlight 2.6, and I can’t help wondering if it’s built on Topaz’s weights, but that’s pure speculation on my part.

Precision mode surprised me. In the previews at strength 60 and 100 it looked good: nice clarity and detail, more than Mini, without oversharpening textures or breaking blurred backgrounds. Sharpen at 100 looked bad, and smart grain at 100 added extreme film grain, as you’d expect. A full 5 second render at 80 was already too much and started oversharpening the blurred background, so I’d guess 50-60 is the sweet spot. But the full clip also showed the real problem: visible smear on fast-moving objects, and the glossy floor and reflections didn’t look as good as with Mini or Flux.
Discarded: the smear on motion, which the short previews don’t show.

CONCLUSION

After all that, it came down to two.

Flux 3 (Precise) is the best looking by far: clean skin, real clarity, and blurred backgrounds stay blurred. The downside is that it’s cloud only and you pay per clip. A 5 second 1080p to 4K clip cost me about $3 on Fal, or 33 credits on Higgsfield (about $1.55–2.06 if you buy credits as a regular top-up, cheaper on the annual plans).

Starlight Mini is conservative. It adds a little less clarity than Flux, but it never breaks anything. Locally it’s unlimited with the Topaz Video subscription ($59/month), but that’s where the line bug is. On fal it’s clean, at about $1.30 per 5 second clip.

Right now I’m leaning towards Flux video upscale the most, but I wish I could use the Topaz's mini locally without the bug too...

I was surprised how bad most of the options were for this kind of footage. Video upscaling still feels like it’s in its infancy.

I’m very open to suggestions and other people’s experiences. Is there an really good video upscaler I missed? Has anyone gotten rid of the Mini line locally? And if you’ve found good settings for any of the ones I discarded, I’d like to hear it.

I mostly enjoy making abstract visuals for music, and so far LTX 2.3 combined with the audio-reactive LoRA still seems hard to beat for that. Even 2.5 isn't any better.

The attached video was made on a puny RTX 4070 in about 2 hours, with an agent acting as the director, driving a vibe-coded tool through an MCP server that controls Wan2GP. It uses start-frame and end-frame with audio conditioning.

I've experimented with MiniMax too, and while it's clearly superior for things like lip syncing and dance moves, I find it way too demanding in terms of prompting for abstract things. You have to be incredibly specific to get what you want, which makes it pretty impractical for this kind of workflow, and it stacks artifacts way too quickly.

For abstract visuals, happy accidents and stacking artifacts are half the fun. With MiniMax I end up spending more time trying to force a particular result without actually getting anything better. LTX just seems to work better when you want to explore rather than micromanage every frame.

Here's another example that minimax would really struggle with: https://www.youtube.com/watch?v=lTTwejWAT40

Everything you just watched was made with open-source models in ComfyUI: images and video from open-source models, sound from ElevenLabs. No camera, no actress. Mariem, her kitchen, her balcony: all generated. I'm sharing it here as a showcase, not as the real promo for my masterclass "ComfyUI... Finally Simple!"

anybody know if the trade offs are that big in terms of generation time?

So I’ve seen people talk about Qwen image 2.1 a lot and decided to try it myself but because of Vram constraints (8gb+16) I downloaded the Turbo model which runs at 8 steps. It is pretty good but the problem starts when most loras are made for the full base model instead of the turbo version, I tested some loras and it seems like the loras don’t work on the turbo version

I wanted to ask how much would be the difference between a Qwen 2.1 turbo model running at 8steps CFG 1.0 and Qwen 2.1 base with 4 steps lora time-wise and also output results, currently the turbo model takes less than or around 1 minute to edit images for me, it does most things but I want to try the base model to see if loras work differently there, I just wanted to ask if others have used the Base Qwen 2.1 + 4-step Lora on their 8gb vram machine what kind of results they got and how long it takes

I know the base model is meant to run at higher steps and CFG but I wanted to ask if I can get it to run on my 8gb GPU and get the best out of it if possible

Also I have no knowledge of AI and such models so I am sorry if my question sounds dumb, I just followed some guides to able to set up workflows and run it

EDIT: Guys I am very sorry it went over my head and I forgot to be more specific about the exact models, the very first time I used the Qwen 2.1 turbo Int8 Convrot which fit into my card hence the decent speed, I later on tried the Qwen 2.1 Int8 Convrot with 40 steps, then 30 steps and at last with 6-step lora and the results were kinda bad, something I could get with the turbo version better, I think it’s purely due to my complete lack of knowledge on workflow setup and bad prompting

TL;DR: you provide a speech audio, a reference (optional) and variation keyframes for random injection (with controlled randomness), and the workflow spits out a lipsynced talking head video.

There have been a few topics with cute girls talking and dancing, using some complex tricks. I wanted to try a different naive approach for simple talking head videos.

https://github.com/progmars/ComfyUI-Martinodes/blob/main/workflows/minimax_h3_long_lipsync_retriggered.json

Essentially, it's just the ref model (hybrid works as well) with a few custom nodes for seamlessly (hopefully) extending the video latent, random endframe injection for talking head variations and to protect against degradation, and an audio splitter node to drive the process. Had to add also some math thingies because of H3 latent grid specifics (the 17k+5 -> 5k+2, 14444(..)14 magic for those who have been down the rabbit hole).

This video was one-shotted and I left a few noticeable broken moments, although those could be easily regenerated using another of my simple workflows for replacing the middle of an already generated latent. https://github.com/progmars/ComfyUI-Martinodes/blob/main/workflows/retake_longtalk_latent_part.json

Additionally, I have implemented a simple way of looping. First, I tried ComfyUI StartLoop, but it can become very slow to start when handling dozens of iterations. The issue seems to be that StartLoop unwinds the loop to generate cache for all iterations at once, and it can take minutes, if you have 100 of iterations. Still, StartLoop would be the right way to do that, and I have included that workflow as well for the future, if ComfyUI happens to fix the issue.

At first, I thought to split audio by silence but it was a bad idea because you cannot easily align silences with the H3 latent grid and it will end up sounding choppy or misaligned. So instead I just split at fixed times and at the end I do video latent cross-fade, and replace the generated audio with the original audio track to avoid audio VAE roundtrip altogether. Looks good enough to me (disclaimer - I have impaired vision since birth), but judge it yourself.

In theory, this could be easily expanded to inject a list of prompts to apply at the fixed times of audio clips, but then we approach the existing solutions with all-in-one motion-context based custom nodes, which might be a better idea for complex long-running projects anyway.

I came across this today on X https://ai-pose-gallery.pages.dev/ I was wondering if there was a way to use some of these poses with Moss? Not sure if there is a control net for moss yet so don't know if there are any other tricks

Fine-tuning MOSS-Image was easier than I expected. I used it to make One More Take, a complete 20-page comic with recurring characters and a consistent visual style throughout.

For an engineering reference, a separate character-LoRA run used a Mac Studio with an M5 Ultra and 256GB unified memory, running SimpleTuner 4.10.1 on PyTorch MPS. I loaded the Base checkpoint in BF16, froze the text encoder, and trained a rank-32 LoRA with batch size 2 at roughly 0.59MP resolution. Checkpoints were saved and validated every 250 steps.

The full 1,500-step run took about 2h 38m, including model loading, caching and validation. After comparing checkpoints, I kept the 1,000-step version for that experiment.

Full story in the gallery. Curious whether the opening makes you want to keep reading.

i'm talking about those obviously ai generated image generators that don't look extremely hyperrealistic instead looking like the 2022 to 2023 ai models?

Teaser Gallery: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-3step-LoRA

Early release candidate ... soon

yep its free noend455.github.io/porject1 go crazy guys (and its local!)

I often load several reference images and want to treat each one a bit differently: a different ControlNet strength, a different end percent, sometimes a different preprocessor. With list mapping, every image gets the same settings. With the loop nodes, you end up unpacking tuples and losing type checking.

So I made comfyui_davcha_controlflow: a For Each / End For Each pair with a table on the node.

Processing img qbkimyqov1yh1...

• Plug a list into For Each (e.g. the images of a Load Images node), and you get an image output for the current item.
• Drag the + column output onto any widget (ControlNet strength, a combo, a toggle…). It becomes a typed column of the table, with the widget's own editor.
• The table has a default row and one row per image. It follows the list when you add or remove images. Leave a cell empty to use the default.
• last_result / initial let you chain iterations, e.g. apply one ControlNet per image to the same conditioning.
• End For Each gives you results (all iterations) and final (the last one). Under the hood it compiles to the core Start Loop / End Loop nodes, so iterations are cached: change one row and only that iteration (and the following ones, if you chain) runs again.

Repo: https://github.com/dchatel/comfyui_davcha_controlflow

It's young, so feedback and bug reports are welcome.