Instructions to use cocktailpeanut/makima with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cocktailpeanut/makima 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("cocktailpeanut/makima") prompt = "mkm is playing chess" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
makima
Trained with Fluxgym

- Prompt
- mkm is playing chess

- Prompt
- mkm lying on a beach

- Prompt
- mkm dressed as chef, making ramen

- Prompt
- mkm is wearing a wedding dress
Trigger words
You should use mkm to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
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Model tree for cocktailpeanut/makima
Base model
black-forest-labs/FLUX.1-dev