u/tekprodfx16

First of all, quick disclaimer, yes this was vibe coded. No I don't care as long as it works. This is my first publicly released LoRa. Please be nice and remain civil if this doesn't work for you.

Here is the LoRa on Civitai - https://civitai.red/models/2746817/krea2-filter-bypass-fedor?modelVersionId=3089754 with example comparison images (w/lora applied and without lora) that are bloody but ultimately SFW

Claude Explanation of how this works and context

Krea 2 is a diffusion transformer. Ignore the fancy name — think of it as a very long assembly line that turns your prompt into an image. At each station on the line, your text prompt gets combined with the in-progress image to nudge it toward what you asked for.

Between the text and the assembly line there's a small mixing board. It has 12 knobs. Each knob controls how strongly a particular kind of text signal — things like "how sharp is the style," "how anatomically correct," "is this content allowed" — gets fed into the image at each station.

Krea's engineers baked the "safety filter" into two of those knobs, specifically knob 9 and knob 10. When your prompt triggers refusal, those two knobs shove the pipeline toward a generic/censored output. You'd expect a safety filter to be some huge separate moderation model — nope. It's literally 24 bytes of numbers sitting inside the diffusion model itself.

Every "safety bypass LoRA" is just a tiny file that overwrites the positions of some of those knobs. That's literally all they do.

Where the existing files differ:

• skc3vo / z0jglf — twists ALL 12 knobs. But knobs 1–8 and 12 aren't refusal at all, they're style/anatomy priors. When you crank strength high enough to defeat refusal, you also warp faces, skin, and proportions. Hence the "plastic" look at higher strength.
• FilterBypass3 — twists knobs 9, 10, and 11. Knob 11 is a secondary refusal, but it also has a side-effect on how naturally humans render. So at strength 5 you get uncensored output but expressions look stiffer than they should.
• FilterBypass2 — twists only knobs 9 and 10. Actually the cleanest of the community bunch, but for some reason hardly anyone uses it.

The file I made keeps FB2's exact knob-9 and knob-10 values and locks every other knob at zero:

col:    1..8      9        10       11     12
mine:   0.0   -0.5117  -0.8906   0.0     0.0
FB3 :   0.0   -0.5117  -0.8906  -0.6094  0.0
skc3vo: nonzero across all 12 columns

Because knobs 1–8, 11, and 12 are literally zero in my file, no amount of strength can move them. Cranking strength only turns knobs 9 and 10 harder. You get full uncensor with a mathematical guarantee against style/appearance drift — because zero times any number is still zero.

Usage — same as any LoRA. Drop it in your loras folder, load it before your KSampler, start at strength 3–5. If something still refuses at strength 5, that refusal probably rides on knob 11 — swap in FB3 just for that generation.

Credit to u/piero_deckard on this post for the vector-by-vector analysis that revealed which knob does what. This file was trivial to design once someone did the archaeology.

EDIT — what's been composing really well with this file:

Because the file leaves the model's anatomy/style priors completely untouched (they're literally zero in the delta), the rest of your stack gets to operate at its actual trained fidelity. Combinations that have been working particularly well:

• Realism / anatomy LoRAs at their normal recommended strengths. No need to dial them down to compensate for bypass interference. Krea's own training on real-world proportions comes through cleanly, and the LoRA layers on top of that instead of fighting it.
• Character / likeness LoRAs. Faces stay sharp and identifiable instead of getting smoothed toward a generic "AI face" — which is what tends to happen when skc3vo tugs on the style priors at higher strengths.
• Style LoRAs. The specific artistic style (film grain, oil painting, whatever) comes through without the flat/plasticized tendency you get from bypasses that touch the style-prior knobs.
• Detailed anatomical prompting. Descriptive prompts about body proportions, skin texture, age, weight — all land accurately instead of defaulting toward the influencer/mannequin archetype other bypasses drift toward.

The reason (extending the mixing-board analogy from above): every LoRA you stack modifies a different set of knobs deeper in the pipeline. Anatomy LoRAs adjust anatomy knobs, character LoRAs adjust identity knobs, this bypass file only adjusts refusal knobs (9 and 10). No overlap. They stack cleanly without fighting each other. Contrast with skc3vo, which touches all 12 — including some of the same knobs your anatomy LoRA is trying to set — creating a tug-of-war where both effects partially cancel.

Basically the file removes the refusal gate and gets out of the way — letting Krea's own trained knowledge, your prompt, and your LoRA stack do their actual jobs at full fidelity.

Most ComfyUI users tune shift, mu, std, and step count by trial and error — render, eyeball the result, guess what to change, repeat. The sigma schedule itself (the actual noise curve the sampler walks) is usually invisible. RES4LYF has a SigmasPreview node that renders the schedule as a chart, but the chart only helps if you already know how to read it.

So I wired the SigmasPreview's IMAGE output into an Ollama Chat node with a vision-capable model (Gemma 3 12B works great, Qwen2.5-VL is faster), and gave the model a system prompt that turns it into a sampler-tuning critic. After every generation, the LLM looks at the sigma curve and returns a JSON report: a 0-100 quality score for the schedule, a one-sentence observation of the curve shape, predicted output character ("strong composition, slightly soft mid-detail"), and — most importantly — exactly 2 specific knob changes with target values, using the precise knob names from my workflow (Ideogram 4 Scheduler.mu, ModelSamplingAuraFlow.shift, etc.). It even accounts for Ideogram 4's resolution-boost on effective mu at 4 MP+.

What surprised me is how much faster this builds intuition than just generating and squinting at outputs. After ~20 runs the LLM's observations start clicking into patterns ("oh, every time the cliff is under 4 steps, the analyzer flags soft detail — and yeah, my texture is mushy"). The pattern feels general: anywhere you can render a tuning metric to a chart, you can attach a vision LLM as a critic. Loss curves during LoRA training, VAE color histograms, CFG schedules, latent statistics — all valid targets. Multimodal LLMs are genuinely good at reading constrained-grammar charts; it's an underused capability.

Example output:

```json
{
  "observation": "The curve is generally monotonic and well-shaped, exhibiting a strong initial high-sigma plateau due to shift=2.75. The inflection point where major detail emergence begins seems around step 18-20. The final cliff (cleanup) starts robustly around step 36 and finishes cleanly at step 46, reaching very low sigma levels.",
  "compute_distribution": "High Sigma (Composition/Layout): ~35% / Mid Sigma (Textures/Detail): ~45% / Low Sigma (Fine Edges/Cleanup): ~20%. This split is well-balanced for high-resolution imagery.",
  "likely_output_character": "Highly detailed, robustly composed image with excellent textural fidelity and clean edges, benefiting from the final steps having low CFG guidance.",
  "detail_knobs": [
    {
      "knob": "Ideogram 4 Scheduler.mu",
      "current": "0.65",
      "suggested": "0.35",
      "reason": "Lowering mu shifts the entire logSNR distribution toward less noise and more information content earlier in the process. This forces more probability mass into the detail and cleanup regions, significantly boosting fine textural consistency throughout the middle steps."
    },
    {
      "knob": "Ideogram 4 Scheduler.std",
      "current": "1.35",
      "suggested": "1.20",
      "reason": "Reducing std tightens the sampling cliff. While the current value is good, lowering it slightly ensures that the cleanup phase (low sigma) is executed with maximum resolution and minimal randomness, leading to sharper, more robust final crispness in the last ~5 steps."
    }
  ],
  "general_recommendations": [
    "Dual Model CFG Guider.cfg: Increase from 3.0 to 3.2. A slight increase in base guidance will boost overall prompt adherence and sharpness without dramatically increasing the risk of burn, allowing the optimized sigma curve to handle the fine detail injection more gracefully.",
    "Ideogram 4 Scheduler.shift: Consider slightly decreasing from 2.75 to 2.50 if you want a marginally faster transition out of composition into mid-detail (less pure compositional compute)."
  ],
  "quality_score": 100,
  "score_reasoning": "The curve is excellently shaped with a high plateau and strong cliff, receiving the bonus for elegant tuning focused specifically on enhancing detail distribution through μ and σtd."
}
```

heres the way I’m looking at these headsets based on short research over the last 2 days

dream air pros:

- amazing lens quality

- no screen door effect

- light weight

- no visible pixels

- high refresh rate

dream air cons:

- extremely cheap feeling build for $2300k

- vertical and horizontal fov

- head strap is a cheap joke for $2300, its pretty insulting what they’re offering in strap quality at that price point, even $400 goggles have better strap quality

- shitty software that always has 1 or 2 things that never work right

- 2300 slam kit is minimum for optical pass through and no receivers necessary the same thing the PSVR2 gets you for 400 bucks

- extremely cheap quality controllers for 2300 dollar kit that is on par with first gen quest controllers in terms of functionality

- shoddy steam vr compatibility

Meganix2 pros

- great picture quality

- extended fov well past dream air

- good quality build feels expensive because of the more solid build

- great controllers where you can see your hands move

- amazing head strap makes it feel comfortable and solid to wear

- no screen door effect

- no visible pixels

Meganix cons

- smaller sweet spot

- software has issues, more often than not it’s not just simple plug and play, you have to finagle with the set up every time you use the headset

- vr receivers required for functionality

- no optical pass through which is ridiculous for a $2,200 headset

- spotty compatibility with steamvr

I could be wrong on some of these but this is what i recall from some of the reviewers on YouTube and Reddit threads. Is the miniLED headset market this shitty? Are there no headsets that seem to have it all? I want high quality lenses, no visible pixels, high refresh rate, nice build quality, optical passthrough, no receivers required, high quality vr controllers, working reliable software, easy connecting and good steamvr support. for 2k I dont think thats too much to ask for

I consider myself a pretty good counter puncher. I’ve spent well over 2 to 3000 hours playing this game and that’s probably a conservative number. I know how to combo break. I know how to counter. I know how to disrupt combos I am division 18 but routinely beat division 20 players I feel like I have an adequate skill set to be able to disrupt combos usually but I can’t seem to do it in this game for some reason I’m on a non-pro ps5 and I’m wondering if that has something to do with it because I have a pretty good broadband connection so I don’t think that’s the issue but again I just can’t seem to interrupt combos quick enough the input lag is just too much and so all people do is just pile on the punches and I can’t seem to block or interrupt them in this game is anybody else experiencing this?