Instructions to use michaelpiro1/train_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use michaelpiro1/train_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("michaelpiro1/train_model", 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 model_index.json
Browse files- model_index.json +1 -13
model_index.json
CHANGED
|
@@ -1,25 +1,13 @@
|
|
| 1 |
{
|
| 2 |
"_class_name": "MusicLDMPipeline",
|
| 3 |
"_diffusers_version": "0.20.0.dev0",
|
| 4 |
-
"feature_extractor": [
|
| 5 |
-
"transformers",
|
| 6 |
-
"ClapFeatureExtractor"
|
| 7 |
-
],
|
| 8 |
"scheduler": [
|
| 9 |
"diffusers",
|
| 10 |
"DDIMScheduler"
|
| 11 |
],
|
| 12 |
-
"text_encoder": [
|
| 13 |
-
"transformers",
|
| 14 |
-
"ClapModel"
|
| 15 |
-
],
|
| 16 |
-
"tokenizer": [
|
| 17 |
-
"transformers",
|
| 18 |
-
"RobertaTokenizerFast"
|
| 19 |
-
],
|
| 20 |
"unet": [
|
| 21 |
"diffusers",
|
| 22 |
-
"
|
| 23 |
],
|
| 24 |
"vae": [
|
| 25 |
"diffusers",
|
|
|
|
| 1 |
{
|
| 2 |
"_class_name": "MusicLDMPipeline",
|
| 3 |
"_diffusers_version": "0.20.0.dev0",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
"scheduler": [
|
| 5 |
"diffusers",
|
| 6 |
"DDIMScheduler"
|
| 7 |
],
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
"unet": [
|
| 9 |
"diffusers",
|
| 10 |
+
"UNet2DModel"
|
| 11 |
],
|
| 12 |
"vae": [
|
| 13 |
"diffusers",
|