---

license: gemma

base_model:

• google/gemma-3-12b-it

tags:

• gguf
• quantized
• text-encoder
• gemma
• joyai-echo
• ltx-video
• comfyui

---

Gemma-3-12B-it clean encoder GGUFs for LTX-2.3 + JoyAI-Echo (Q8_0 + Q4_0)

> Nodes, workflow, issues and releases: https://github.com/jlucasmcrell/joyai-echo-multishot-patch

> All models: https://huggingface.co/joeygambino

> A word on expectations: the multishot workflow this feeds is powerful but

> not one-click - first renders usually take some tuning to your VRAM, system

> RAM and model build. The workflow repo's

> INSTRUCTIONS.md covers setup and known failure modes, and I am happy to

> troubleshoot: open a discussion and I will answer.

**A one-hop Q8_0 quantization of the TRUE base google/gemma-3-12b-it, in the

loader format the JoyAI-Echo ComfyUI node pack expects.** This is the exact

text encoder the LTX-2.3 / JoyAI-Echo DiT family was trained against — nothing

finetuned, nothing re-quantized.

Files

| file | size | what it is |

|---|---|---|

| gemma3-12b-BASE-clean-Q8_0.gguf | 12.45 GiB | reference tier, ~0.55% RMS error vs bf16 |

| gemma3-12b-BASE-clean-Q4_0.gguf | 7.31 GiB | half-size tier, embedding table held at Q8_0 |

Both are one-hop quantizations of the same clean bf16 base, in the same loader

format. Start with Q8_0; take Q4_0 if those 5 GiB are what fit your card.

Why this exists

The commonly circulated gemma3-12b-joyecho-Q8_0.gguf is not base Gemma —

its architecture string is a different, modified model, and it was built from

an fp8 checkpoint and then re-quantized (double quantization; it carries

.comfy_quant marker tensors from the fp8 stage). In my renders it produced

duplicated subjects and identity corruption. The DiT reads base Gemma's

embedding space; any encoder drift degrades output structurally, not subtly.

This file is built directly from the clean bf16 release in a single hop:

measured ~0.55% RMS error on every spot-checked tensor, including the

embedding table. Functionally the base encoder, at half the size.

Works with any LTX-2.x DiT you load through the pack — base LTX-2.3 dev,

distilled, distilled-1.1, JoyAI-Echo, and every merge or finetune of them. The

whole family was trained against base Gemma-3-12B-it; the encoder is shared,

so this one file serves them all. (The pre-LTX-2 family, LTXV 0.9.x, uses a

different encoder and is not covered.)

The practical payoff

The pack's GGUF encoder path keeps linear weights packed and memory-mapped

— resident footprint is roughly 2.5 GB versus ~12 GB for the fp8 path.

That is the difference between the encoder crowding a 24 GB rig's RAM and it

barely registering. Conditioning is also cached by the pack after first

encode, so the encoder cost is paid once per prompt set.

Compatibility — read this

• Loads in: the JoyAI-Echo GGUF node pack (gemma_file dropdown). Drop the

file in ComfyUI/models/text_encoders/ and pick it. Tokenizer bytes are

embedded; sidecar configs resolve from the pack.

• Does NOT load in: llama.cpp, Ollama, LM Studio, or any llama.cpp-based

runtime. This file preserves HuggingFace tensor names (the pack's loader

contract) instead of llama.cpp's naming. It is an encoder for a video

pipeline, not a chat model.

Verification

All 48x7 text-tower linears present and name-verified against the loader's

matching logic; embeddings and norms stored exactly; per-tensor dequant

spot-checks at 0.54-0.58% RMS error vs bf16; tokenizer (spiece_model) bytes

embedded byte-for-byte.

About the Q4_0 tier

Built on community request. Same recipe and same loader contract as the Q8_0,

with one deliberate exception: model.embed_tokens.weight is held at Q8_0

while the 473 layer linears go Q4_0.

That embedding table is ~1B parameters (262144 x 3840) and is the most

quantization-sensitive tensor in the model. llama.cpp's own K-quants protect

token_embd for the same reason. Here the embeddings are the conditioning

signal the DiT cross-attends to, so pushing them to 4 bits moves prompt

semantics rather than merely losing precision. Protecting it costs ~0.5 GiB and

is the difference between a usable tier and a subtly wrong one.

Measured against bf16: embeddings 0.575% RMS error (protected), layer

linears 8.7-9.0%. For reference, the same 4-bit step on the DiT measured

9.9% error and 5.6 degrees of angular deviation in layer output direction.

Stated honestly: this file is verified structurally and per-tensor

(architecture string, all 1066 tensor names and shapes identical to the Q8_0,

all 48x7 text-tower linears present, tokenizer bytes embedded, dequant

round-trip within the limits above). It has not been validated by rendering.

That is deliberate rather than lazy - comparing renders cannot validate a

quantization, because a weight perturbation this size walks the sampler to a

different valid sample, so any PSNR or eyeball difference measures trajectory

divergence rather than quality. If you use it and see prompt adherence slip

relative to Q8_0, please open a discussion; that is the failure mode to watch.

License

Gemma is provided under and subject to the

Gemma Terms of Use. This repository

redistributes a quantized derivative of google/gemma-3-12b-it; the Gemma

Terms of Use and its Prohibited Use Policy apply to this file and any use of

it. Not affiliated with Google, Lightricks, JD, or RealRebelAI.