Instructions to use juixy/harsh7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use juixy/harsh7 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("juixy/harsh7") prompt = "harsh7" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Harsh7
About this LoRA
This is a LoRA for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on Replicate using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use harsh7 to trigger the image generation.
Run this LoRA with an API using Replicate
import replicate
input = {
"prompt": "harsh7",
"lora_weights": "https://huggingface.co/juixy/harsh7/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
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.float16).to('cuda')
pipeline.load_lora_weights('juixy/harsh7', weight_name='lora.safetensors')
image = pipeline('harsh7').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Training details
- Steps: 786
- Learning rate: 0.0004
- LoRA rank: 11
Contribute your own examples
You can use the community tab to add images that show off what you’ve made with this LoRA.
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Model tree for juixy/harsh7
Base model
black-forest-labs/FLUX.1-dev