Instructions to use CornLogic/10EROS_Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CornLogic/10EROS_Int4 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_Int4", 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
Create README.md
Browse files
README.md
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---
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library_name: diffusers
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---
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Quality - 10Eros_v1.4_DMD_TFO_Int8-Int8mm_Q 17.5 GB is 50/50 int8/w4a4-int8mm. 20 second gens ok seems to hold up pretty well.
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Balanced -10Eros_v1.4_DMD_TFO_Int8-Int8mm_B 14.9 GB is 20/80 int8/w4a4-int8mm. 20 second gens ok, might be better off sticking to 10-15 seconds
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Full - 10Eros_v1.4_DMD_TFO_w4a4-Int8mm_F 13.2 GB is 0/100 int8/w4a4-int8mm. Does 5 second gens ok, starts to fall apart pretty quickly after that.
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All were calibrated on 1920x1024 481 frames 24fps
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TFO = Transformer Only
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DMD = DMD lora baked in at 1.0 strength
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Made possible by Tsolfils brilliant node https://github.com/tsolful/ComfyUI-INT8-Fast a modification of Bob’s node.
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I spent a while going down a rabbit hole splicing int4/int8/bf16 combos in a modified version of comfy-quants, trying to figure out what works before finding Tsolfils node. I’m pleased I was heading in the right direction but his node is probably much better than what I was going to do.
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Testing has been unscientific and done in fits and bursts so keep that caveat in mind.
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It seems any amount of true w4a4 tensors on their own will taint ltx/eros/sulphur. You get swimming textures on any length/size generation. So that's a no go. But packing them in int8mm seems to fix that problem.
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At first I thought the hard limit was a 50/50 combo of int8 and w4a4 int8 matmul, but once I stopped letting true w4a4 filter into the mix I was able to make a 20/80 mix and 100% w4a4 int8-matmul with no more texture swimming artifacts or vastly diminished at any rate.
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Quality - 10Eros_v1.4_DMD_TFO_Int8-Int8mm_Q.safetensor sits below fp8 in my opinion but that's subjective and I have no LPIPS data to back that up.
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