Instructions to use anaVeUa/lasha-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anaVeUa/lasha-ai 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("anaVeUa/lasha-ai") prompt = "A person in a bustling cafe la$ha" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
lasha-ai
Model trained with AI Toolkit by Ostris

- Prompt
- A person in a bustling cafe la$ha

- Prompt
- A realistic portrait of a woman, wearing a casual outfit, standing outdoors with a blurred background, soft natural lighting, smiling gently, la$ha
Trigger words
You should use la$ha 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('anaVeUa/lasha-ai', weight_name='lasha-ai.safetensors')
image = pipeline('A person in a bustling cafe la$ha').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 anaVeUa/lasha-ai
Base model
black-forest-labs/FLUX.1-dev