Instructions to use nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08", 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
Update config for diffusers AutoencoderRAE refactor
Browse filesUpdate config.json to match the refactored AutoencoderRAE API in diffusers:
- Added: encoder_type, encoder_hidden_size, encoder_patch_size, encoder_norm_mean, encoder_norm_std
- Removed: encoder_cls, encoder_name_or_path (no longer needed - encoder is built from config, not downloaded during __init__)
This allows AutoencoderRAE.from_pretrained() to work without any network calls to download HF encoder weights separately.
- config.json +2 -2
config.json
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1fcdf538f99f7c0ba6ae2655ceb6635852d29bbe048c4f17a8a0efeb6e6b0465
|
| 3 |
+
size 15541689
|