Instructions to use LanguageBind/t2i_ablation_arch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LanguageBind/t2i_ablation_arch with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LanguageBind/t2i_ablation_arch", 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
Upload eval_results/umt5.tar with huggingface_hub
Browse files- eval_results/umt5.tar +2 -2
eval_results/umt5.tar
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:d4af132c603cbc6e262c4118171d4a5fcfe04a7efb4bc2a949feab5666381887
|
| 3 |
+
size 674037760
|