Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
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
File size: 1,306 Bytes
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# AutoPipeline
The `AutoPipeline` is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the `AutoPipeline` automatically retrieves the correct pipeline class from the checkpoint `model_index.json` file.
> [!TIP]
> Check out the [AutoPipeline](../../tutorials/autopipeline) tutorial to learn how to use this API!
## AutoPipelineForText2Image
[[autodoc]] AutoPipelineForText2Image
- all
- from_pretrained
- from_pipe
## AutoPipelineForImage2Image
[[autodoc]] AutoPipelineForImage2Image
- all
- from_pretrained
- from_pipe
## AutoPipelineForInpainting
[[autodoc]] AutoPipelineForInpainting
- all
- from_pretrained
- from_pipe
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