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", torch_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
is there fp8 version of this?
#3
by Namaku - opened
is there fp8 version of this?
I'm not sure, I'm just doing int8 variants and then int4 blends. Check in the discord that's your best bet.
I'm interested also in a fp8 version of the new 10Eros v1.5
Which discord are you referring to, if I may ask?
In my tests, no matter what I do usually the int8 quant ends up being a bit faster but giving worse quality output than the original v1.4 fp8 checkpoint by TenStrip for the same settings. I know that in theory int8 convrot quants usually perform closer to the original fp16 reference, but for some reason here it's not the case (maybe it's the new DMD LORA, not sure).