Instructions to use rimpang/emji with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rimpang/emji 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("rimpang/emji") prompt = "emji person with cigarette" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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("rimpang/emji")
prompt = "emji person with cigarette"
image = pipe(prompt).images[0]emji
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- emji person with cigarette

- Prompt
- emji person with hat and glasses

- Prompt
- emji gather people talk each other
Trigger words
You should use emji 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 rimpang/emji
Base model
black-forest-labs/FLUX.1-dev