Text-to-Image
Diffusers
TensorBoard
lora
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use lzsds/output_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lzsds/output_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lzsds/output_model") prompt = "Please paint in ChenLe's style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-100/optimizer.bin from lzsds/output_model: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/lzsds/output_model/resolve/main/checkpoint-100/optimizer.bin
- Command line
-
hf download hf://lzsds/output_model/checkpoint-100/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/lzsds/output_model/resolve/main/checkpoint-100/optimizer.bin
6.59 MB
- Xet hash:
- a6bc506f0a9cd04072b78a0a4a22daa4717ff3846ca091a2cc29657cf8eebdc5
- Size of remote file:
- 6.59 MB
- SHA256:
- 3b4312b7d27a23a9bd3c30c998ac7d862c6a38a692816330bda89633dafa1e49
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