Instructions to use CornLogic/10EROS_1.4_Int8_ConvRot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CornLogic/10EROS_1.4_Int8_ConvRot 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_1.4_Int8_ConvRot", 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
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Verified signatureWorking remote so this is taking longer than I'd like. Uploading four versions. Minimal testing done as I'm away for work. Bob = Made with Bob's upgraded node DMD = DMD lora merged in at 1.0 strength. Saves space and should speed up your generation speed. If you use the non DMD models you will need to load the DMD lora as you normally would at strength 1.0 Check 10Eros discord for sigmas. I used 1.2 sigmas and it was fine however. 10Eros_v1.4_Bob_INT8_Convrot.safetensors 10Eros_v1.4_Bob_DMD_INT8_Convrot.safetensors <- from testing this is the fastest for me on a 3090 and 64GB ram. Most people will want this one. 10Eros_v1.4_int8_convrot.safetensors 10Eros_v1.4_DMD_int8_convrot.safetensors <- this one was expected to be faster but not proving to be. Not sure why. Just testing it for fun. Resources: https://github.com/BobJohnson24/ComfyUI-INT8-Fast/tree/main https://huggingface.co/tech77/int8 https://github.com/silveroxides/convert_to_quantdc2ab63 -
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