Instructions to use CornLogic/10EROS-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CornLogic/10EROS-INT8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CornLogic/10EROS-INT8", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Feature Request & Feedback: VAE/Text Encoder Compatibility for 10Eros v1.5 (INT8)
Hi CornLogic,
First of all, thank you so much for your incredible work on the 10Eros models. The performance of v1.5 (INT8_DMD_TFO) is impressive—being able to generate videos so quickly without needing a distilled LoRA is truly amazing!
I would like to share some feedback and a polite request regarding the node setup in ComfyUI when using v1.5.
Currently, when using v1.5_INT8 in workflows (such as the 10S pipeline), we run into a couple of workflow hurdles:
LTXAVTextEncoderLoader Compatibility:
Because v1.5 uses INT8 quantization / TFO patches, specifying v1.5_INT8 under the ckpt_name field in LTXAVTextEncoderLoader throws an error (AttributeError: 'Linear' object has no attribute 'weight'). To bypass this, we currently have to load 10Eros v1.4 (FP8) in the Text Encoder Loader just to pass the layer structure, while using v1.5_INT8 in the main CheckpointLoaderSimple. This forced "dual-checkpoint loading" feels a bit awkward and redundant.
VAE Integration:
Since v1.5 does not bundle internal Video/Audio VAE weights, workflows fail unless users manually route separate Load VAE nodes.
Request / Suggestion:
If possible, would it be feasible to release a repacked or updated variant of v1.5 that:
Allows LTXAVTextEncoderLoader to read its structural tensor weights natively (or includes/exposes the necessary text encoder structural metadata), eliminating the need to keep v1.4 FP8 on disk.
(Optionally) includes the standard Video and Audio VAEs packed directly within the checkpoint, similar to v1.4, so that standard CheckpointLoader VAE outputs work seamlessly without manual re-routing.
Having a version of v1.5 that works smoothly out-of-the-box with both Video/Audio VAE and the LTXAV Text Encoder Loader—just like v1.4 did—would make this fantastic model even more seamless and user-friendly.
Thank you very much for considering this request, and thank you again for your dedication to the community!
Best regards,
Hi, I'll see if I can get to it. Full disclosure I am working remotely so honestly you could have yourself up and running in five minutes if you just rewire your workflow a little. Waiting for me to get to this and then uploading over a slow connection will take far longer.
Make a backup of your workflow before you do so if you are unsure of wiring you can switch between tabs to double check.
all vae's, text projection are found here: https://huggingface.co/Kijai/LTX2.3_comfy
it includes a picture showing you what nodes to use on the model card. If that helps.
Thank you for the quick reply.
That was very helpful, as I wasn't sure which text projections file was the right one.
It does work with the minor workflow adjustments you suggested, but with the non-DMD version 1.4 checkpoint file, I was able to use that same checkpoint file for both the VAE Loader and Dual Clip Loader; I found that setup a bit easier to understand.
Hi.
BF16 and FP8 models are usually packaged as full checkpoints all-in-one including the VAEs, text encoders, (and in the case of LTX2.3 and derivatives the vocoder).
This is done because all that additional material is usually stored in BF16 format for maximum quality, and usually those are never quantized to lower precisions, because the loss of quality is big and the space and memory savings are low. Usually only the Transformer tensors are quantized.
Now, Comfyui can load mixed precision BF16/FP8 checkpoints natively, but I’m not sure it can load mixed INT8+otherQ files (and same with other more exotic quantizations like GGUF)
To read a model that includes a INT8 quantized transformer and a full quality BF16 VAE and encoders packaged together you might need to use a specialized loader node.
Edited: since July 2026 Comfyui does support loading mixed int8/bf16 checkpoints
It’s much easier to give the transformer only when publishing a quantization other than FP8, and then load the VAEs and text encoder independently
Here you have a screenshot of how you can replace the regular full 10Eros checkpoint loading nodes with CornLogic int8 quantization instead and independent aux files:
https://huggingface.co/TenStrip/LTX2.3-10Eros/discussions/63#6a6d278757d2c16b7a491f17
I'm in a rainforest right now so finding a ssd was interesting but its uploaded
re what LokkenJP said:
That's incorrect. A bf16/int8 checkpoint works fine. You might get a clip text encoder not in checkpoint error but it then goes on to complete with no issues. You can make one in convert to quant or you can just pack together the individual components together with a python script as long as you don't trip over a few gotcha's along the way like audio vae/vocoder or not porting across the metadata from all the pieces. You'll get a bit identical safetensor checkpoint.
=== diff ===
A: I:/ComfyUI SF/models/checkpoints/LTX 2.3/10Eros_v1.5_DMD_int8_convrot.safetensors
B: I:/ComfyUI SF/models/checkpoints/LTX 2.3/10Eros_v1.5_int8_DMD_chpnt.safetensors
Common tensors: 8411
Only in A: 0
Only in B: 0
Shape/dtype mismatches: 0
Headers are identical (names, shapes and dtypes all match).
slot tensors size prefix how
model 6908 23.31 GB (none) inferred from reference (48/48 sampled keys matched)
text-projection 4 2.15 GB (none) inferred from reference (4/4 sampled keys matched)
vae 170 1.35 GB vae. inferred from reference (48/48 sampled keys matched)
audio-vae 1329 347.77 MB (none) inferred from reference (48/48 sampled keys matched)
total 8411 27.16 GB
You’re right. I’ve just checked and indeed since 1 July update about a month ago with the 0.27 convrot update, the standard Load Checkpoint node of comfyui also supports mixed int8/bf16 safetensors files, much in the same way as the mixed floating point loading was added last year.
So my info was outdated. Sorry about that 👍🏻👍🏻👍🏻
I’ve edited my post accordingly