Instructions to use IronP/kontext_ocean_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use IronP/kontext_ocean_v1 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("IronP/kontext_ocean_v1") prompt = "hand-painted oil painting, matte painted surface, ocean, beach, organic imperfect edges, texture, soft palette, contours, long brush strokes, readable facial features, stable likeness, preserve original composition" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Kontext Ocean v1

- Prompt
- hand-painted oil painting, matte painted surface, ocean, beach, organic imperfect edges, texture, soft palette, contours, long brush strokes, readable facial features, stable likeness, preserve original composition
- Negative Prompt
- digital photo, photorealistic, CGI, plastic skin, digital painting, overly smooth, sharp vector edges, polished commercial finish, face distortion
Model description
Purpose: painterly/ocean style transfer for image edit workflows
Trigger words
You should use dagopaint to trigger the image generation.
You should use hy to trigger the image generation.
You should use ocean to trigger the image generation.
Download model
Download them in the Files & versions tab.
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Model tree for IronP/kontext_ocean_v1
Base model
black-forest-labs/FLUX.1-dev