Instructions to use uwcc/OrangeOrigami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use uwcc/OrangeOrigami with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("uwcc/OrangeOrigami") prompt = "A seal plays with a ball on the beach, [trigger] style." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
OrangeOrigami
Model trained with AI Toolkit by Ostris

- Prompt
- A seal plays with a ball on the beach, [trigger] style.

- Prompt
- A church in a field on a sunny day, [trigger] style.

- Prompt
- A clown at the circus rides on a zebra, [trigger] style.
Trigger words
You should use ORANGEORIGAMI to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('uwcc/OrangeOrigami', weight_name='OrangeOrigami')
image = pipeline('A seal plays with a ball on the beach, [trigger] style.').images[0]
image.save("my_image.png")
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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Model tree for uwcc/OrangeOrigami
Base model
black-forest-labs/FLUX.1-dev