Instructions to use MCG-NJU/DMM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MCG-NJU/DMM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MCG-NJU/DMM", 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
Add library name and license
Browse filesThis PR adds the appropriate `library_name` and `license` to the model card.
README.md
CHANGED
|
@@ -1,6 +1,8 @@
|
|
| 1 |
---
|
| 2 |
tags:
|
| 3 |
- text-to-image
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
|
| 6 |
# DMM: Building a Versatile Image Generation Model via Distillation-Based Model Merging
|
|
|
|
| 1 |
---
|
| 2 |
tags:
|
| 3 |
- text-to-image
|
| 4 |
+
library_name: diffusers
|
| 5 |
+
license: apache-2.0
|
| 6 |
---
|
| 7 |
|
| 8 |
# DMM: Building a Versatile Image Generation Model via Distillation-Based Model Merging
|