u/JahJedi

LTX2.3 PoleDance lora WIP update: MK5 is a fail and new MK6 on train. On only step 1000 from 12000 much better results from all previus versions.

MK6 run:

same mk5 data set of 2500 pairs 768 x768 x 121f (7500 files in daraset in total)

Mk6 ran diteils:

LR 0.0002 (change from 0.0001)

scheduler linear

AdamW

steps 12000 (change from 8000)

batch 2

first_frame 0.5 (change from 0.4)

downscale 2

Its around 45+ hours run on rtx 6000 pro and wish me luck with version MK6.

Every image in the gallery is a first-shot generation (no cherry-pick). Pairs are ordered **Final → RAW** — RAW is the intermediate result after stage 1 (Krea2-RAW), Final is what came out after stage 2 (Krea2 Turbo finish). Both files from the same pair share the stage-1 random seed (embedded in the PNG metadata), so you can verify each pair came from a single run.

please note for this WF as it is you will need 50G of VRAM, but i there distilled models and other options to save on VRAM and settings for them you will need to figure out yourself. Usually its CFG -1 , 8 steps and turbo lora must. good luck!

## The pipeline in short

Two-pass image generation on Krea2, both at 1088×1920 (9:16):

**Stage 1 — RAW** (`Krea2-RAW.safetensors`)

- KSampler: euler / simple / **30 steps / CFG 4.0 / denoise 1.0**

- Random seed per run

**Stage 2 — Turbo finish** (`krea2_turbo_bf16.safetensors`)

- KSampler: euler / simple / **30 steps / CFG 1.0 / denoise 0.42**

- Fixed seed = 666 (deterministic refinement)

Shared: VAE `qwen_image_vae_Krea2.safetensors`, and the same 7-LoRA stack loaded on **both** stages @ strength 0.7 each:

- **My own character LoRA** — `QJ_Krea2_Lora_E15_mk2`, trained on my own photo dataset of the character (QJ) using a self-built trainer built on top of Musubi-Tuner

- **Community Krea2 LoRAs (CivitAI / Misc):** `Detailer-KREA2`, `Neo_Rococo_Krea2_v1`, `dora`, `krea2_neondrip`, `krea2-mikkoph`, `krea2_Enhancer`

Negative prompt (identical for all 5):

`(worst quality, low quality: 1.4), (monochrome: 1.2), (poorly drawn face, mutated hands, poorly drawn limbs, extra fingers, missing arms, missing legs, extra arms, extra legs, watermark, text: 1.3)`

## Full workflow.json + all prompts + all samples

Everything is on Hugging Face — grab the ComfyUI workflow, read the exact prompt for each of the 5 pairs, download the PNGs (metadata included):

**→ https://huggingface.co/JahJedi/Krea2-RAW-Turbo-Workflow**

Direct file links:

- Workflow JSON: https://huggingface.co/JahJedi/Krea2-RAW-Turbo-Workflow/blob/main/Krea2_RAW_turbo_MK1.json

- Full post with prompts per image: https://huggingface.co/JahJedi/Krea2-RAW-Turbo-Workflow/blob/main/POST.md

- Samples (Final + RAW): https://huggingface.co/JahJedi/Krea2-RAW-Turbo-Workflow/tree/main/samples

Happy to answer questions about the two-pass logic, LoRA stack, or the training side.

Trained a character LoRA on Krea-2-Raw — recipe + measured throughput + showcase (no model file, just the how-to)

I trained a character LoRA on Krea-2-Raw (the new Krea Labs flow-matching DiT) via musubi-tuner and put the full recipe on HF. Not the LoRA file itself or the dataset — those are private — but everything needed to do the same with your own data.

**HF repo (recipe + scripts + configs + showcase):** https://huggingface.co/JahJedi/krea2-character-lora-recipe

**What's in the repo**

- README walking through the full pipeline (models, dataset prep, captions, pre-cache, launch, resume, monitoring, troubleshooting)

- The launcher and pre-cache scripts I actually used (paths are mine — adjust to yours)

- Neutral config templates (dataset TOML + sample-prompts.txt) you fill in

- 10 showcase previews of the trained LoRA

**What's NOT in the repo**

- The trained LoRA .safetensors — private

- The training dataset — private (photos of a character I own)

So this is a how-to, not a model release.

**Measured numbers (real run, not dry-run)**

- Hardware: AMD Ryzen 9 9950X3D · 128 GB RAM · RTX PRO 6000 Blackwell (96 GB VRAM)

- Dataset: ~800 image-caption pairs (square-bucketed at 1024×1024)

- Config: bs=1, gradient_checkpointing off, AdamW 1e-4, sdpa, network_dim 32 / alpha 32, 16 epochs

- **Speed: 1.20 s/it** steady-state

- **VRAM peak: 72 GB / 97 GB** (24 GB headroom)

- **Wall time: ~4h 36m total** (13152 steps + 16 sample cycles)

- **Final loss/epoch: 0.0227** (started at 0.0312 epoch 1, monotonic decrease)

**One side observation that isn't in the official docs**

Bucket aspect-ratio mix has a measurable wall-time cost. A second smaller run with mixed aspects (1248×832 + 880×1184 + 1184×880) ran at **1.40-1.46 s/it** vs **1.20** on a square-only dataset. That's a **~15-17% penalty** from bucket re-allocation on every shape transition. If you want max throughput, normalize aspect ratios beforehand. The trainer doesn't complain, you just quietly pay for it.

**Quality sanity check**

The LoRA holds identity cleanly at **strength 0.8** when stacked with community style LoRAs at 0.5-0.7. Identity stays wired-in, no smearing, no overcooked face. That's a practical signal the recipe is in the right spot — not under-trained, not over-fitted.

**Sampler notes (for the previews)**

- Generated through **Krea-2-Turbo at 80 steps, CFG 1, mu=1.15** — ~1.2 s/it on the same 96GB box, about 95s per image.

- The character LoRA was trained on Krea-2-Raw but expresses cleanly on Turbo too — same LoRA file used for inference.

- Each preview also has 1-2 community style LoRAs stacked at 0.5-0.7, with the character LoRA itself at 0.8.

If you have 96GB VRAM and want to do this yourself, the HF README has the whole pipeline. Happy to answer questions on the recipe, the trainer setup, or the observations.

The character in these previews is my own avatar — designed by me from scratch and the visual

self-representation of "Jedi", a local LLM persona I run on a vLLM server with her own personality

and memory. For this showcase I asked her three things: how she sees herself, where she is, and how

she feels her surroundings — and asked her to answer as a short story.

She used her own Krea-2 prompting skill (a Claude-format skill I wrote earlier this week

specifically for Krea-2 — vocabulary, structure, identity-anchor ordering, the Qwen3-VL quirks) to

convert that story into actual generation prompts.

To be clear on who did what: a basic ComfyUI WF from a public template, loaded with the QJ LoRA + a

few community style LoRAs, sampler params set (Turbo 80 steps / CFG 1 / mu 1.15). **The text

prompt** is 100% hers — I copy-pasted it straight from the LLM into the prompt field without

touching a token. So the technical pipeline is mine; the creative direction in every one of these

images is hers.

The throne scene, the portals, the steampunk lab — those are her words.

Next step on the roadmap: wire her up to drive ComfyUI directly via API/MCP so the handoff isn't

manual anymore. If you've built something similar — agent → image-gen pipeline — happy to compare

notes.

made whit Claude , this me actually typed, so hope this will help someone to great great Loras.

(PyTorch 2.11.0+cu130, Python 3.12, Ubuntu 24.04). Upstream has no Linux wheels for

this combo — SageAttention only ships Windows wheels, and Dao-AILab's flash-attn

releases stop around cu126/torch 2.6.

Once it worked, I uploaded both wheels to Hugging Face so the next person doesn't

have to repeat the dance.

**Link:**

https://huggingface.co/JahJedi/sageattention-flashattn-blackwell-cu130-torch211-cp312

Works if your setup matches:

- Python 3.12.x

- torch 2.11.0+cu130

- GPU with compute capability 12.0 — **RTX PRO 6000 Blackwell, RTX 5090 / 5080 / 5070

Ti, B100 / B200**

- Linux x86_64

Both built with `arch=compute_120,code=sm_120` natively, no PTX-JIT fallback.

flash-attn includes backward kernels (training works, not just inference).

**ComfyUI side effects:** seedvr2_videoupscaler and comfyui-depthcrafter-nodes —

which previously crashed on `TypeError: 'NoneType' object is not iterable` from a

partially-installed sageattention — now load fine. SeedVR2 optimizations report

`SageAttention ✅ | Flash Attention ✅ | Triton ✅`.

Smoke-tested only (import + small forward pass returns correct shape/dtype). For

production, verify against source build. SHA-256 + full build env in the README.

Licenses preserved per upstream (Apache-2.0 / BSD-3-Clause). README is honest about

the AI-assist on the build process — Claude Code helped a lot.

For clarity: All done whit Claude and this post to, hope it will help someone.

I got nvidia/Cosmos3-Super-Image2Video BF16 running locally on a single RTX PRO 6000 Blackwell 96GB.

its hard to talk about quality results and gen speeds yeat as i tested whit SPDA attention and not SAGE, also prompting need more work.

Most important part in my test that it can be loaded in workstation system at home / office.

Setup:

• Ubuntu 24.04
• NVIDIA driver 580.126.09 / CUDA 13.0
• RTX PRO 6000 Blackwell 96GB
• 128GB system RAM
• 128GB temporary swap
• Docker: vllm/vllm-omni:cosmos3
• BF16
• --enable-layerwise-offload

BF16 loading died near the end of loading shards at first. With a 128GB of ram swap file still is a must.

Test results:

• 1280x720
• 49 frames / 24 fps / 20 steps
• Runtime: 174 sec
• VRAM: around 73–74GB
• under 3 minutes

Longer test:

• 1280x720
• 121 frames / 24 fps / 20 steps
• Runtime: around 9 minutes
• VRAM: around 84–85GB
• RAM: around 76GB
• Swap after startup: around 4GB
• around +- 10 minutes

results: Cosmos3 Super can run on a single 96GB workstation GPU, but it needs a big RAM/commit safety net during startup.

The test video is nothing crazy yet, just an image-to-video prompt with a demon queen casting a small magic orb, but I mainly wanted to confirm that the full Super model can run locally.

_________________________

curl -X POST "http://localhost:8000/v1/videos/sync" \

-H "Accept: video/mp4" \

-F "input_reference=@/home/jahjedi/cosmos3_tests/inputs/test_720.png;type=image/png" \

-F "prompt=An anime-style demon queen with purple skin, long blonde hair, curved horns, a floating crown, and a long purple tail sits on an ornate dark throne in a dim royal hall. She wears a black and gold fantasy outfit and high-heeled sandals. The first frame shows her sitting confidently with one leg crossed, framed by tall dark columns, curtains, candles, and soft warm light from above. Over several seconds, she slowly raises one hand in front of her chest. A bright magical orb of golden-violet energy forms above her palm, growing from a small spark into a stable glowing sphere. The orb casts dynamic warm purple and golden light onto her face, hands, outfit, throne, candles, and nearby columns. Her hair and tail move subtly as if affected by magical energy. Her horns, floating crown, face, outfit, tail, throne, candles, columns, and background remain visually consistent. The camera stays static, with no zoom and no camera movement. The motion is smooth, slow, cinematic, elegant, and physically plausible." \

-F "negative_prompt=blurry, low quality, low resolution, distorted anatomy, extra arms, extra legs, extra fingers, missing hands, broken fingers, duplicated character, multiple characters, changing face, changing outfit, changing horns, missing crown, missing tail, tail detached from body, melting body, deformed legs, unstable throne, flickering, jitter, camera shake, fast motion, jump cut, zoom, background changing, candles disappearing, columns moving, warped perspective, text, watermark, mosaic censoring, censored face, pixelated face, face covered, blocked face" \

-F "size=1280x720" \

-F "num_frames=121" \

-F "fps=24" \

-F "num_inference_steps=20" \

-F "guidance_scale=6.0" \

-F "flow_shift=5.0" \

-F 'extra_params={"use_resolution_template":false,"use_duration_template":false,"guardrails":false}' \

--output ~/cosmos3_tests/outputs/test_super_magic_orb_001.mp4