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", 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
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README.md
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library_name: diffusers
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tags:
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- image-to-video
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library_name: diffusers
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tags:
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- image-to-video
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---
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Working remote so this is taking longer than I'd like.
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Uploading four versions. Minimal testing done as I'm away for work.
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Bob = Made with Bob's upgraded node
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DMD = DMD lora merged in at 1.0 strength. Saves space and should speed up your generation speed.
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If you use the non DMD models you will need to load the DMD lora as you normally would at strength 1.0
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Check 10Eros discord for sigmas. I used 1.2 sigmas and it was fine however.
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10Eros_v1.4_Bob_INT8_Convrot.safetensors
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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.
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10Eros_v1.4_int8_convrot.safetensors
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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.
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Resources:
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https://github.com/BobJohnson24/ComfyUI-INT8-Fast/tree/main
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https://huggingface.co/tech77/int8
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https://github.com/silveroxides/convert_to_quant
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