Flux.2 Klein 9B LCS Consistency LoRA 20260415 - Maximum Color Stability Without Sacrificing Editing Capability

Hi everyone,

Following up on my previous Flux.2 Klein 4B Consistency LoRA release, I'm excited to share a major update: the **Flux.2 Klein 9B LCS Consistency LoRA (20260415)**. This version brings significant improvements in color stability and editing flexibility, specifically trained for the Flux.2 Klein 9B model.

In my earlier 4B release, I mentioned that a 9B-compatible version would depend on community interest — and the response was overwhelming. So I went back to training, and this time I focused on solving one of the hardest problems in consistency editing: **maximum color stability without sacrificing editing capability**.

🔍 What's New in the 9B Version:

**Maximum Color Stability:**

* **Latent Color Subspace (LCS) Alignment:** A new training approach that aligns the latent color subspace, ensuring the model maintains color consistency at a fundamental level while preserving far more editing headroom than traditional methods.
* **Latent2Lab Conversion:** Colors are now mapped through a Lab color space conversion during training, resulting in perceptually more accurate and consistent color reproduction across edits.
* **Helios Frame Perturbation:** A novel data augmentation technique that introduces controlled perturbations during training, making the model significantly more robust to input variations and noise.

**Minimal Editing Capability Degradation:**

One of the biggest trade-offs with existing consistency LoRAs is that they tend to lock down the image too aggressively, making it nearly impossible to make meaningful edits. This LoRA is designed differently.

* **Weight at 1.0 — No Tuning Required:** Unlike other consistency LoRAs where you need to carefully dial in weights (0.3–0.7) to balance consistency vs. editability, the LCS Consistency LoRA is designed to work at **full strength (1.0)** right out of the box. No more tedious weight adjustments.
* **High Compatibility:** Works alongside other LoRAs without conflicts. Stack it with your favorite style or detail LoRAs and it plays nicely.

⚠️ IMPORTANT COMPATIBILITY NOTE:

**Model Requirement:** This LoRA is trained EXCLUSIVELY for **Flux.2 Klein 9B Base**. But it could use with turbo lora to achieve 4 steps editing.

**Not Compatible with Flux.2 Klein 4B:** Due to architectural differences between the 4B and 9B models, this LoRA will not work correctly on Flux.2 Klein 4B. If you're using the 4B model, please use the original 4B Consistency LoRA instead.

🛠 Usage Guide:

**Base Model:** Flux.2 Klein 9B Base

**Recommended Strength:** 1.0

**Workflow:** Designed to work seamlessly within ComfyUI. Integrates easily into standard pipelines without requiring complex custom nodes.

🚀 Summary of Improvements Over 4B Version:

|Feature|4B LoRA|9B LCS LoRA|
|:-|:-|:-|
|Color Stability|Good|Maximum (LCS + Latent2Lab)|
|Recommended Weight|0.5 – 0.75|**1.0**|
|Weight Tuning Needed|Yes|No|
|LoRA Compatibility|Moderate|High|
|Editing Flexibility|Moderate|High|

All test images are derived from real-world inputs to demonstrate the model's capacity for consistent reproduction with editing flexibility. I'd love to hear your feedback — especially on how well it handles color consistency across different editing scenarios!
Difference between Klein 4B and Klein 9B is sooo big
https://redd.it/1sow758
@rStableDiffusion
What does LTX actually do with ingested audio?

When you load audio and feed it into LTX's audio latent, it's not like it uses that actual audio in terms of its own generated audio output...

Instead it seems to be 'influenced' by the audio. But that influence seems to vary substantially and be quite weak in general - for example it won't use the accent of the voice fed in

So what does it actually do with the audio? In an ideal world, we'd be able to configure how much it drifts from the audio fed in

https://redd.it/1soxytu
@rStableDiffusion
EditAnything IC-LoRA - LTX-2.3

This model was trained on 8,000 video pairs, and training is still ongoing for a few thousand more steps. It is still experimental, not trained with a fully professional production target, and the model may be updated unexpectedly as new checkpoints.

The current goal is not final polished production quality, but to explore:

edit-anything behavior
prompt-following
inference tradeoffs
synthetic dataset building, especially for style data

The model was trained around four main prompt patterns:

Add
Add a/an [subject/object] with [clear visual attributes], [precise location in the scene].

Remove
Remove the [subject/object] [location or identifying description].

Replace
Replace the [original subject/object] [location] with a/an [new subject/object] with [clear visual attributes].

Convert / Style
Convert the video into a [style name] style.

Workflow URL: `https://huggingface.co/Alissonerdx/LTX-LoRAs/blob/main/workflows/ltx23_edit_anything_v1.json`

Model URL: ltx23\_edit\_anything\_global\_rank128\_v1\_9000steps\_adamw.safetensors · Alissonerdx/LTX-LoRAs at main

Or
CivitAI URL: EditAnything - v1.0 | LTX Video LoRA | Civitai

One important thing during inference is CFG.

A good starting point is testing a distilled setup with CFG = 1. If the edit feels too weak or the model is not following the prompt well enough, increasing CFG can be the key. In some cases, increasing the distill LoRA strength to around 1.2 can also help.

The workflow is also not fully optimized yet. It still needs more testing to find the best combination of:

CFG
LoRA strength
number of steps
model combinations

It may also be interesting to combine this model with other models and see what kinds of results emerge.

If you can test it, please share your findings. Feedback on prompt behavior, edit strength, consistency, style transfer, and failure cases would be very helpful while training is still in progress.

Add a small, brown dog dancing in the foreground next to the woman.

Convert the entire video to an anime style with vibrant colors and exaggerated character expressions.

Remove the blue car in the background of the scene.

Add a wide, genuine smile to the person's face.

Replace the person's clothing with a dark blue hoodie and gray sweatpants.



https://redd.it/1sp03jq
@rStableDiffusion
Tired of paid templates in comfyui

https://preview.redd.it/50fopk3xs0wg1.png?width=1299&format=png&auto=webp&s=f1df7211bf04aea251620876405451baf75834e5

Am I the only one tired of seeing this? To be honest, I don’t usually browse templates in fact, it’s been a while since I last opened ComfyUI, about four months. I wanted to see what’s new, but now it seems bloated with paid API templates. The filter also appears to be broken, so I can’t sort anything properly either.
I think they should put 2 simple filters with API/LOCAL

https://redd.it/1spao5u
@rStableDiffusion
Let's talk - When do we think the next real breakthrough open source image model will drop?

I've been running flux.2 klein and z-image turbo as my daily drivers for a while now and they still feel like the last big jumps for local setups. ernie image dropped recently and its solid in some areas but not that big a difference compared to Z-Image and other models. GPT image 2 is as good if not better than NBP so it feels like other companies are starting to catch up on the closed side.

just wondering what everyone thinks... when is the next breakthrough open source image model likely to land? one that actually feels like a solid step up in quality and coherence, maybe getting closer to what nano banana pro can do.

Also what do you guys think about the current open source image gen situation overall? are you happy with where things are at or feeling a bit stalled? what models are you mainly using these days?



https://redd.it/1spdpq9
@rStableDiffusion