Instructions to use Lonuhbow/beth3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lonuhbow/beth3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Lonuhbow/beth3") prompt = "A cinematic, wide shot of Beth3 as a futuristic cybernetic explorer standing on the neon-lit crystalline plains of a distant purple planet." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Lonuhbow/beth3: direct link, hf CLI and curl.
- Browser
- Download file 2.5 kB
-
https://huggingface.co/Lonuhbow/beth3/resolve/main/README.md
- Command line
-
hf download hf://Lonuhbow/beth3/README.md
-
curl -L -o README.md https://huggingface.co/Lonuhbow/beth3/resolve/main/README.md
2.5 kB
| base_model: krea/Krea-2-Raw | |
| tags: | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| - krea2 | |
| - template:sd-lora | |
| license: apache-2.0 | |
| instance_prompt: "Beth3" | |
| widget: | |
| - text: "A cinematic, wide shot of Beth3 as a futuristic cybernetic explorer standing on the neon-lit crystalline plains of a distant purple planet." | |
| output: | |
| url: sample_0.png | |
| - text: "A cozy, sun-drenched oil painting of Beth3 reading an ancient leather book in a dusty Victorian library filled with floating candles." | |
| output: | |
| url: sample_1.png | |
| - text: "A high-fashion editorial photo of Beth3 wearing an avant-garde gown made of liquid gold, posing amidst a surreal desert of white sand." | |
| output: | |
| url: sample_2.png | |
| # Krea 2 LoRA — Lonuhbow/beth3 | |
| <Gallery /> | |
| A DreamBooth-LoRA for **Krea 2**, trained on **Krea 2 RAW** and shown on **Krea 2 Turbo**. The samples below were generated with this LoRA on Turbo (8 steps). | |
| ## Trigger | |
| Use the token `Beth3` to invoke the concept. | |
| ## Samples | |
|  | |
| > *"A cinematic, wide shot of Beth3 as a futuristic cybernetic explorer standing on the neon-lit crystalline plains of a distant purple planet."* | |
|  | |
| > *"A cozy, sun-drenched oil painting of Beth3 reading an ancient leather book in a dusty Victorian library filled with floating candles."* | |
|  | |
| > *"A high-fashion editorial photo of Beth3 wearing an avant-garde gown made of liquid gold, posing amidst a surreal desert of white sand."* | |
| ## Training snapshots | |
| Directly usable LoRA weights saved during training: | |
| - [Step 200](./snapshots/step-200.safetensors) | |
| - [Step 400](./snapshots/step-400.safetensors) | |
| - [Step 600](./snapshots/step-600.safetensors) | |
| - [Step 800](./snapshots/step-800.safetensors) | |
| - [Step 1000](./snapshots/step-1000.safetensors) | |
| - [Step 1200](./snapshots/step-1200.safetensors) | |
| - [Step 1400](./snapshots/step-1400.safetensors) | |
| - [Step 1600](./snapshots/step-1600.safetensors) | |
| - [Step 1800](./snapshots/step-1800.safetensors) | |
| - [Step 2000](./snapshots/step-2000.safetensors) | |
| ## Use it with diffusers | |
| ```py | |
| import torch | |
| from diffusers import Krea2Pipeline | |
| pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda") | |
| pipe.load_lora_weights("Lonuhbow/beth3") | |
| image = pipe("A cinematic, wide shot of Beth3 as a futuristic cybernetic explorer standing on the neon-lit crystalline plains of a distant purple planet.", num_inference_steps=8, guidance_scale=0.0).images[0] | |
| image.save("output.png") | |
| ``` | |