Text-to-Image
Diffusers
Safetensors
FluxPipeline
diffusers-training
flux
flux-diffusers
template:sd-lora
Instructions to use yangmjie/trained-flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yangmjie/trained-flux with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yangmjie/trained-flux", dtype=torch.bfloat16, device_map="cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Flux [dev] DreamBooth - yangmjie/trained-flux

- Prompt
- A photo of sks dog in a bucket

- Prompt
- A photo of sks dog in a bucket

- Prompt
- A photo of sks dog in a bucket

- Prompt
- A photo of sks dog in a bucket
Model description
These are yangmjie/trained-flux DreamBooth weights for black-forest-labs/FLUX.1-dev.
The weights were trained using DreamBooth with the Flux diffusers trainer.
Was the text encoder fine-tuned? False.
Trigger words
You should use a photo of sks dog to trigger the image generation.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('yangmjie/trained-flux', torch_dtype=torch.bfloat16).to('cuda')
image = pipeline('A photo of sks dog in a bucket').images[0]
License
Please adhere to the licensing terms as described here.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
- Downloads last month
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Model tree for yangmjie/trained-flux
Base model
black-forest-labs/FLUX.1-dev