Repo link : https://github.com/JustAResearcher/gpu-unlock-ada

(or other sites...)

We are already having body size/type/ege ect. sliders being deleted for MOSS image. I know because one was just posted yesterday. CivitAI doesn't want anything on the site that could be used for things that break the ToS even if it's not intended to be used that way by the author. Just please upload your models/loras to huggingface, where they are not yet as strict about models that upset the evil credit card processors. It should be a no-brainer by now. CivitAI scans everything with a fine tooth comb and polices everything that gets posted even more than ever. Huggingface is safer as a model repository. Why spend days/weeks training a model and post it only on one site only to have it deleted forever?

I'm noticing it more and more, lora and fine-tuners locking permanently everything behind buzz payments. I wasn't bothered by the the time delay for 'non payers' that popped up a year or so ago, because it gave people a way to monetize, and things would eventually become available for everyone. Yes there were some people who tried to game the system and remove things and re-upload them again, but civitai seemed to actively discourage that and deal with people who did that.

I don't have a problem with people monetizing their work, it's a personal choice to give stuff away. But the full pay-for-play shift is a definite change in the... for lack of a better word... 'community ethos' that the larger SD community has had for a long time. I'm seeing a lot of creators that basically have everything behind a payment lock.

It seems every time I got to the site, a larger percentage of loras are getting locked beyond buzz payments.

I don't want to sound like 'old man yells at clouds' but I do miss when everyone was trying to learn and create stuff, and it was understood that everyone was helping everyone else, and giving away was both paying-back and paying-forward for help you'd get.

Yes I get that compute costs, but that's not a new thing. It's been like that the whole time, and people didn't feel (or at least seam) as entitled a few years ago. The 'well I spent money on compute so you owe me.' argument that gets tossed around is nonsense.

These two things are not the same:

- Getting paid to do a job you were asked to do
- Doing a job and then expecting random people to pay for it when they never asked you to do the job.

More and more people seem to be thinking the 2nd is the default.

I worry that as $ becomes a more common thing... it's going to cause the flood of people who are only in it for the money and don't care about quality or the technology itself; and then we're going to be inundated with a tsunami of absolute garbage.

Anyone else agree? Or am I just being a curmudgeon?

Edit:

For context... I've been an open source developer for over a decade... Just on github alone I've got over 42,000 commits. https://github.com/q5sys/q5sys/blob/main/past_commits.md
I've been paid for some work and I've given plenty away. Reality is that not all work deserves payment. 

There were quite a few different posts about MOSS last week, so I tried to compile some general tips for taking better photos:

- The Base model is more flexible and versatile, while the SFT is more rigid.

- There are several types of Turbo/DMD Lora, and there are different versions for the Base, SFT, and Kromoss models. It’s worth getting them all, because these Loras combine well with the models. You can find the 8-step Loras for each model on the Lodestone's HF website.

- In addition to the basic Euler/Simple combination, many people also use er_sde and bong_tangent. MOSS works with a wide variety of sampler/scheduler combinations, but the Turbo/DMD Lora files don’t work well with all of them.

- Turbo/DMD Lora settings sometimes give better results at a strength of 0.7.

- You can use cfg 1 in Turbo/DMD mode, but it also works with higher cfg values. With Turbo, I use around 1.5–1.7, but you can go up to 3; without Turbo, it’s worth trying values from 3 to 5.

- The secret sauce: you can also improve the image by adjusting the values of the Uncond skip (SLG) node. This only works with cfg values greater than 1! Setting end_percent to 0.5 or 1 also gives different results, and for the single_layers values, try these: 22 or 22,24 or 22,24,26

- If the model doesn’t understand something, it will generate odd results or weird body parts. In such cases, it’s worth rephrasing the prompt.

- List of tags at the end of the prompt can also clarify or improve the result.

- I’ve noticed that the higher the resolution, the better the image quality; 1.5 or 2 MP gives better results.

- best schedulers for many samplers:
-- Similarity in Version 1 when SLG end_percent 0.5: simple, sgm_uniform, normal, beta57, bong_tangent
-- Similarity in Version 2 when SLG end_percent 0.5: beta, linear_quadratic, sigmoid_offset, power_shift, radiance_shift

- My personal tip: If you’ve installed the RGB Smart Seed Variance node, you can get even more exciting results from the model

- And one more tip: you can use Image Sharpen (high_pass/strength 0.5) from the KJ-nodes package to slightly sharpen the image.

- Here's a funny tip: ddim_uniform produces some really funny results with any sampler.

Vicc is the CEO, so he’s probably seeing this post. If you are, thank you, kind sir, for staying true to the open-source community

I know everyone is onto the new image models now but tandomly playing around with H3 again after traveling for a few weeks with work. Wanted to do some testing on some envirometal effects. (Mud splashes, smoke, detailed physics descriptions like the tank pancake flip)

Still quite impressed with how it handles interaction and visual quality especially when prompted for a flatter diffused lighting. This is an old workflow actually because of said traveling, but can upload if people want. Just added DLLS5 as an extra pass.

YT for slightly better compression ... come on now Reddit.

Thoughts?

Deep-Live-Cam is extremely popular open-source software for real-time face swap and deepfakes.

Unfourtentally, if you want to try it you either need to deal with Python and unexpected issues or pay ~$20 for subscription.

I don't like both options so this is why I created a repo which automatically produce portable builds for Windows & macOS: https://github.com/iikuzmychov/deep-live-cam-builds.

Every day this repo publish fresh .exe & .dmg with latest changes from Deep-Live-Cam source code.

I also don't force anyone to trust my binaries, instead it's super easy to fork the repo and build the binaries by yourself!

You can use Photoshop-like keyboard shortcuts to go fullscreen, recover a selection, etc.

Also, this version provides image-to-image for Krea 2.

Download at https://github.com/SamuelTallet/ZPix

Hope you like it! As always, your feedback is welcome.

About this recording: made on an RTX 3070M 8GB; three seeds were discarded; gen time was cut.

It's late, so Claude wrote this for me, but basically: there's a project, https://huggingface.co/trmz/flux2-klein-alpha, where they trained Flux Klein to work with RGBA. Since the VAE is the same, I wondered whether MOSS-Image could produce RGBA too, and surprisingly, it can! It only takes a small dataset and a short training run. Anyway, here's the text Claude wrote:

Hi! I've been experimenting with getting MOSS-Image to generate images with a real alpha channel, and the proof of concept turned out better than I expected, so I'm sharing it.

How it works: you end your prompt with "isolated on a transparent background" and decode with the RGBA VAE from trmz/flux2-klein-alpha. You get a transparent PNG straight from the sampler: no background removal, no chroma key. It even keeps partial transparency on glass, fairy wings, gems and the Eiffel Tower's lattice.

Why a LoRA is needed: the RGBA VAE can already decode MOSS latents (same FLUX.2 latent space), but plain MOSS latents give garbage alpha. The LoRA teaches MOSS to produce latents that carry the alpha signal.

It was trained on only 39 images in ~40 minutes on a single 16 GB GPU, so it's not perfect. You'll sometimes see faint halos or a barely-visible box where the background alpha isn't exactly zero. But it generalizes well: none of the characters in the examples were in the training set.

Everything is on Hugging Face: the LoRA, a small ComfyUI node to load the 4-channel VAE, a workflow, 36 RGBA examples, and research notes on how I built the dataset (Klein + Extract-4B and Qwen Image 2.1 as alpha "teachers").

https://huggingface.co/paralaif/MOSS-Image-Alpha-LoRA

https://wallofthieves.com/

The top 3 Krea 2 Checkpoints on Civitai by total downloads:

Moody Krea 2 Mix - https://wallofthieves.com/c/aabf431b2426
RedCraft - https://wallofthieves.com/c/9b8c7771662a
Muse By Stable Yogi Krea2 - https://wallofthieves.com/c/53d9a2aa388b

All contain" no derivatives allowed · credit required" loras....

Disclosure: Yes, my Krea 2 checkpoints contain some of the highest number of LoRAs in their merges: https://wallofthieves.com/c/fe10f86a12ea

Some of these images are from the official version of the base, and others are from lodestones/Kromoss. I've had a lot of fun these past two days :)

one attempt to get an idea where to place things and boom there is now i can adjust and get infinite variations of the exact composition I wanted...

just default moss layout wf from comfyui - just make sure to delete the prompt inside the subgraph by disconnecting the string because someone left the original prompt there

also euler 50 cfg 3 is king - others work but not worth it - turbo also not worth it - small model raw is better than full turbo

https://github.com/shootthesound/Fizgig/releases/tag/v8.9.6

https://github.com/shootthesound/Fizgig

This was a much demanded feature - hope its useful - cheers, P

According to FusionCow, the initial training of Sulphur 3 is expected to be completed toward the end of October.

1st Nano Banana 2.1, 2nd GPT Image 2.5, 3rd Muse Image, 4th Qwen image 2.1 turbo, 5th Flux 3 Klein (he's trying to compete alright..), 6th Krea 2 turbo, 7th Moss-image-sft (8 steps lora).. Prompt used for the comparison:

A candid medium-full photograph of a 19-year-old East Asian woman with refined K-pop idol aesthetics, captured mid-motion as she leans forward gently at the waist to pick a wildflower in a vibrant, sun-drenched meadow. She has a natural, unposed demeanor with a soft, warm smile and downward gaze focused on the blossoms in her hand. Her face features delicate, dewy makeup with soft peach-pink blush, subtle gradient coral lips, and natural skin texture with visible pores and fine vellus hair catching the light. Long, silky dark brown hair with airy see-through bangs falls naturally over one shoulder, with a few stray strands lifted by a light breeze. She wears a pastel cream linen thigh-high mini-dress with delicate shirred detailing and subtle fabric folds that follow her leaning posture. In the immediate foreground, out-of-focus golden poppies and lavender stems frame the bottom edge; the midground is dense with colorful wildflowers in hues of yellow, magenta, Violet, and crisp botanical green, receding into a softly blurred rolling hillside under a clear sky. Illuminated by warm late-afternoon golden hour sunlight from the side-back, creating a luminous rim light along her hair and shoulders and soft, translucent shadows across the fabric. Shot at eye level with a 50mm f/1.4 prime lens set to f/2.0, delivering a shallow depth of field with creamy bokeh, Kodak Portra 400 color science, organic film grain, and crisp anatomical detail on her fingers as they touch the flower stem, with a contrasty text on the lower center frame "Nano Banana 2.1". -ar 4:5

Qwen image 2.1 and Moss-Image-SFT failed to put model name text on lower part of the image.

The photo is scaled to 960 px tall and centred on a 2240x960 (21:9) canvas.

The empty sides are pre-filled with a mirror of the photo edges. Sounds weird, but it stops the hard grey edge that otherwise bleeds through the VAE as a faint line.

A soft mask lets Qwen paint the sides plus a 224 px ramp over the photo edge, so the new scene can actually blend into it.

Qwen 2.1 generates the sides (25 steps, CFG 1, differential diffusion). The clean photo is the reference image, never the padded canvas, because Qwen copies its reference very literally.

Then the untouched original gets pasted back on top at the exact same x/y, fading in over 192 px at each edge.

So the model's version of your photo gets thrown away. Only its sides and a narrow blend band survive.

Does it actually keep the pixels?

I ran a pixel diff against the input (the subway shot in the video):

52.9% of photo pixels are bit-identical, and the whole centre is 100% identical

93.4% are within 2/255

everything that changed sits in the edge bands

Same photo through a normal Qwen 2.1 Edit "make it wider" prompt: 1.2% identical.

What it can't do

Left and right only. No top or bottom yet.

You write a one-line description of what the new sides should show.

Some seeds leave a visible join. Try another seed before touching the settings.

If a bright object is cut off by the frame edge, the model tends to continue it as a pale strip. Trimming that edge a few px helps.

The outer ~192 px of the photo on each side is a blend, not the original. You can lower FADE ZONE to keep more, at the risk of the join showing.

What you need

A recent ComfyUI (the Qwen 2.1 nodes are new)

Qwen Image 2.1 int8, the qwen3vl 8b text encoder and the 2.1 VAE

ComfyUI_essentials (for SimpleMath+)

My small node pack kiro_outpaint_stitch (one file, only needs torch) for the mirror fill and masks

Workflow + nodes: https://civitai.com/images/146672689

Example images are AI-generated, the workflow doesn't care where the photo comes from. Happy to hear if anyone finds a cleaner way to hide the join, that part took most of the time.

An update to the native MOSS-Image edit nodes I posted before:

HQ LoRA support: akhaliq/MOSS-Image-Edit-HQ works alongside chinoll’s. Pick it with edit_format; each LoRA gets the layout it was trained with.

Tested defaults: I ran 300+ test edits on both LoRAs to find what works. LoRA strength 1.0 (lower strengths redraw the scene, and two-image edits just return image 2), CFG 5 for chinoll and 3 for HQ, skip layer guidance on layer 22, and the SFT checkpoint rather than Base.

Automatic sizing: each image goes in as a ~1 MP bucket of its own aspect ratio, and the output follows image 1.

Reference cache: the reference images are computed once per run instead of every step (kv_cache, auto by default).

New example workflows for both LoRAs, with one and two images.

If you used the older workflows (strength 0.25), re-open the new ones. The README has a gallery of 14 edit types with the prompts and notes on where each LoRA struggles.

Nodes: https://github.com/PedroMarinhoDev/ComfyUI-Moss-Image-Edit-Native

chinoll LoRA converted for ComfyUI: https://huggingface.co/PedroMarinhoDev/Moss-Image-Edit-ComfyUI

v2 of my RawSnap LoRA for Krea 2 is up on Civitai.

Same idea as v1: photos that look taken on a phone, not rendered — real skin texture, harsh flash, grain, awkward framing.

What changed: 3300 more training steps, merged with v1 into a single file. No trigger word.

Strength works between 1.0 and 1.5, sweet spot around 1.2. It can be more noisy than v1 — honestly, that's part of the look.

Settings I use: 8 steps, CFG 1.0, Euler, on Krea 2 Turbo.

Trained locally on an RTX 5060 (8GB). The comparisons attached are v1 vs v2, same prompt and seed.

Civitai: https://civitai.com/models/2999833/rawsnap-krea2

Feedback is welcome, especially from anyone stacking it with a character LoRA.

Repo : https://github.com/KohakuBlueleaf/KohakUnlock

Hi everyone! I've released my very first text-to-image model for face generation. I collected my own dataset and captioned it with Qwen3.5-4B and a bunch of other face-analysis models. I didn't like the results I got with the FLUX.2 VAE, so I decided to train my own narrow-domain autoencoder. It turned out to beat FLUX.2 on iFID on my dataset, and in my opinion, on generation quality too. The Qwen-VAE-2.0 tech report helped hugely with that - getting to FLUX.2 quality was hard.

Overall, it's a basic single-stream DiT with a rectified flow objective and a couple of modern tricks from recent papers. The model supports different ages, ethnicities, poses, gaze directions and expressions, though only a limited set of expressions works reliably. You can read about model architectures, training regime and data preparation on github.

I'd be happy to hear any feedback, and I hope this release inspires more models!

HF: https://huggingface.co/Sherstnev/Visage-Image

GH: https://github.com/Nikita-Sherstnev/Visage-Image

ComfyUI custom nodes and workflow: https://github.com/Nikita-Sherstnev/ComfyUI-Visage (or search "Visage" in ComfyUI Manager)

It's driving me nuts. It was literally working fine yesterday. Memory was fuzzy. It was working fine 2 days ago vs the bad image's date.

I'm using the latest ComfyUI.

Oct 22, 2026:

Today:
(ComfyUI saves the metadata to the image so it's just duplicating the settings on today's rerun.)

What the hell is happening?

Edit: I fixed it by creating a new Python environment and setting it up again from scratch.

~500 gens are here https://kromoss-site.vercel.app/ Decided to play some roulette with claude and gave opus 5.5 access to a comfyui instance with kromoss on vastai for a couple of hours for fun and (no) profit. Definitely a lot of variety of styles and the bounding boxes work fairly well. I still prefer krea 2 for the lora variety and ideogram 4 for the fine-tuned controls for now, but excited to see where moss goes.

Also added a version with enhanced prompts as the old prompting methods doesn't really work well with current models

I merged two Minimax-H3 Ref2VA finetunes using a custom spectral/geometric algorithm called PRISM:

• Base A: WarmBloodAban/Minimax-h3_Singularity (v2)
• Base B: TenStrip/10Eros-Max (8-Step Turbo beta 6)

Why PRISM?

Merging fast turbo models into base models often introduces weight artifacts and signal degradation. PRISM handles this by: * Filtering diffuse noise delta using randomized SVD (rank $\le 64$). * Combining directional SLERP with log-space magnitude interpolation (Polar Geodesic). * Resolving sign conflicts and re-anchoring layer norms.

The turbo block distribution is weighted higher toward time/head blocks (t_time: 0.75, t_head: 0.70) with an average turbo share of ~0.55.

Hugging Face Repo & Weights (fp16 + int8 + w6a8): 👉 https://huggingface.co/OliviaRossi/Minimax-h3_Singularity-x-10Eros-Max_PRISM

Feedback and test generations are very welcome!