Instructions to use TorchRik/ImageReFL_HPS_SD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TorchRik/ImageReFL_HPS_SD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TorchRik/ImageReFL_HPS_SD", 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 combined_stable_diffusion.py with huggingface_hub
Browse files
combined_stable_diffusion.py
CHANGED
|
@@ -26,14 +26,7 @@ class CombinedStableDiffusion(
|
|
| 26 |
text_encoder: CLIPTokenizer,
|
| 27 |
) -> None:
|
| 28 |
|
| 29 |
-
super().__init__()
|
| 30 |
-
|
| 31 |
-
print(tokenizer)
|
| 32 |
-
print(text_encoder)
|
| 33 |
-
print(original_unet)
|
| 34 |
-
print(fine_tuned_unet)
|
| 35 |
-
print(scheduler)
|
| 36 |
-
print(vae)
|
| 37 |
|
| 38 |
self.register_modules(
|
| 39 |
tokenizer=tokenizer,
|
|
|
|
| 26 |
text_encoder: CLIPTokenizer,
|
| 27 |
) -> None:
|
| 28 |
|
| 29 |
+
super().__init__()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 30 |
|
| 31 |
self.register_modules(
|
| 32 |
tokenizer=tokenizer,
|