Instructions to use maxmarcon/klimt-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use maxmarcon/klimt-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("maxmarcon/klimt-diffusion", 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
Epoch 180
Browse files
logs/train_unconditional/events.out.tfevents.1787237863.0c9b25d5248e.1336.0
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9892efd5aa600ca11c14572e624158cdc75f4db3eb2e9d08c33ae31eeaa2c6f4
|
| 3 |
+
size 1696053
|
unet/config.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"_class_name": "UNet2DModel",
|
| 3 |
"_diffusers_version": "0.39.0.dev0",
|
| 4 |
-
"_name_or_path": "ddpm-model-64/checkpoint-
|
| 5 |
"act_fn": "silu",
|
| 6 |
"add_attention": true,
|
| 7 |
"attention_head_dim": 8,
|
|
|
|
| 1 |
{
|
| 2 |
"_class_name": "UNet2DModel",
|
| 3 |
"_diffusers_version": "0.39.0.dev0",
|
| 4 |
+
"_name_or_path": "ddpm-model-64/checkpoint-4000",
|
| 5 |
"act_fn": "silu",
|
| 6 |
"add_attention": true,
|
| 7 |
"attention_head_dim": 8,
|
unet/diffusion_pytorch_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 454741108
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:273c5f8f6bfcb36d6790073a52444e30670bb77ae037ccd978c79bd71c6153e3
|
| 3 |
size 454741108
|