Instructions to use NadaGh/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NadaGh/working with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NadaGh/working", dtype=torch.bfloat16, device_map="cuda") prompt = "tst chair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA
LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet ([UNet2DConditionModel], for example) or a Transformer ([SD3Transformer2DModel], for example). There are several classes for loading LoRA weights:
- [
StableDiffusionLoraLoaderMixin] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model. - [
StableDiffusionXLLoraLoaderMixin] is a Stable Diffusion (SDXL) version of the [StableDiffusionLoraLoaderMixin] class for loading and saving LoRA weights. It can only be used with the SDXL model. - [
SD3LoraLoaderMixin] provides similar functions for Stable Diffusion 3. - [
AmusedLoraLoaderMixin] is for the [AmusedPipeline]. - [
LoraBaseMixin] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
To learn more about how to load LoRA weights, see the LoRA loading guide.
StableDiffusionLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
StableDiffusionXLLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin
SD3LoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.SD3LoraLoaderMixin
AmusedLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
LoraBaseMixin
[[autodoc]] loaders.lora_base.LoraBaseMixin