Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use CompVis/stable-diffusion-v1-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CompVis/stable-diffusion-v1-4 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") prompt = "A high tech solarpunk utopia in the Amazon rainforest" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
36ae9ff
1
Parent(s): f99eae7
Update README.md
Browse files
README.md
CHANGED
|
@@ -24,8 +24,11 @@ extra_gated_fields:
|
|
| 24 |
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
|
| 25 |
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with D🧨iffusers blog](https://huggingface.co/blog/stable_diffusion).
|
| 26 |
|
| 27 |
-
The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-
|
| 28 |
-
checkpoint and subsequently fine-tuned on
|
|
|
|
|
|
|
|
|
|
| 29 |
|
| 30 |
## Model Details
|
| 31 |
- **Developed by:** Robin Rombach, Patrick Esser
|
|
|
|
| 24 |
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
|
| 25 |
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with D🧨iffusers blog](https://huggingface.co/blog/stable_diffusion).
|
| 26 |
|
| 27 |
+
The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
|
| 28 |
+
checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
|
| 32 |
|
| 33 |
## Model Details
|
| 34 |
- **Developed by:** Robin Rombach, Patrick Esser
|