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sdxl-drums
sr5434/sdxl-drums
sdxl-drums is a machine learning model from sr5434. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
An SDXL v1.0 LoRA for generating drum spectorgrams. Sample code:
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Updated Jan 9, 2024
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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Jan 8, 2024
- Updated
- Jan 9, 2024
- SHA
- d2a4f486
Model card
From the Hugging Face model README
An SDXL v1.0 LoRA for generating drum spectorgrams. Sample code:
from diffusers import DiffusionPipeline, AutoencoderKL
import torch
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, vae=vae).to("cuda")
pipe.load_lora_weights("sr5434/sdxl-drums", adapter_name="drums")
img = pipe("0-to-5-seconds of drumming in latin brazilian baiao with 95 beat rythm, version 145, 4-4")
I trained it for 800 training steps. The batch size per device(1 T4) was 10 and there were 12 gradient accumulation steps, so the total batch size was 120. I used the 8 bit adam optimizer. The learning rate was 1e-04 with a cosine schedule and 15 warmup steps.
- Library
- peft
- License
- mit
- Languages
- en
- Created
- Jan 8, 2024
- Updated
- Jan 9, 2024