Image-to-Image
Diffusers
StableDiffusionImageVariationPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use lambda/sd-image-variations-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lambda/sd-image-variations-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lambda/sd-image-variations-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Update README.md - Correct Link in Model Card - Line 110
#17
by averad - opened
README.md
CHANGED
|
@@ -107,7 +107,7 @@ This model is fine tuned from Stable Diffusion v1-3 where the text encoder has b
|
|
| 107 |
- **Learning rate:** warmup to 1e-5 for 5,000 steps and then kept constant
|
| 108 |
|
| 109 |
|
| 110 |
-
Training was done using a [modified version of the original Stable Diffusion training code](
|
| 111 |
|
| 112 |
|
| 113 |
# Uses
|
|
|
|
| 107 |
- **Learning rate:** warmup to 1e-5 for 5,000 steps and then kept constant
|
| 108 |
|
| 109 |
|
| 110 |
+
Training was done using a [modified version of the original Stable Diffusion training code](https://github.com/justinpinkney/stable-diffusion).
|
| 111 |
|
| 112 |
|
| 113 |
# Uses
|