Instructions to use alperiox/weapons-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alperiox/weapons-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("alperiox/weapons-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
End of training
Browse files- README.md +1 -1
- image_0.png +0 -0
- image_1.png +0 -0
- pytorch_lora_weights.safetensors +1 -1
README.md
CHANGED
|
@@ -12,7 +12,7 @@ inference: true
|
|
| 12 |
---
|
| 13 |
|
| 14 |
# LoRA text2image fine-tuning - alperiox/weapons-lora
|
| 15 |
-
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the alperiox/
|
| 16 |
|
| 17 |

|
| 18 |

|
|
|
|
| 12 |
---
|
| 13 |
|
| 14 |
# LoRA text2image fine-tuning - alperiox/weapons-lora
|
| 15 |
+
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the alperiox/cctv_captions dataset. You can find some example images in the following.
|
| 16 |
|
| 17 |

|
| 18 |

|
image_0.png
CHANGED
|
|
image_1.png
CHANGED
|
|
pytorch_lora_weights.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3225672
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:24c70635b84999957bfc066bacb7e803eaad159f3b8b0b77fdd19b5bf6aab217
|
| 3 |
size 3225672
|