Instructions to use Aminrabi/diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aminrabi/diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aminrabi/diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Outputs
All models outputs are subclasses of [~utils.BaseOutput], data structures containing all the information returned by the model. The outputs can also be used as tuples or dictionaries.
For example:
from diffusers import DDIMPipeline
pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
outputs = pipeline()
The outputs object is a [~pipelines.ImagePipelineOutput] which means it has an image attribute.
You can access each attribute as you normally would or with a keyword lookup, and if that attribute is not returned by the model, you will get None:
outputs.images
outputs["images"]
When considering the outputs object as a tuple, it only considers the attributes that don't have None values.
For instance, retrieving an image by indexing into it returns the tuple (outputs.images):
outputs[:1]
To check a specific pipeline or model output, refer to its corresponding API documentation.
BaseOutput
[[autodoc]] utils.BaseOutput - to_tuple
ImagePipelineOutput
[[autodoc]] pipelines.ImagePipelineOutput
FlaxImagePipelineOutput
[[autodoc]] pipelines.pipeline_flax_utils.FlaxImagePipelineOutput
AudioPipelineOutput
[[autodoc]] pipelines.AudioPipelineOutput
ImageTextPipelineOutput
[[autodoc]] ImageTextPipelineOutput