import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("mm9all/flux2-lora-flux2-lora-3dwvpp-1769616974")
prompt = "a photo of zwx_htvu"
image = pipe(prompt).images[0]Flux2 DreamBooth LoRA - mm9all/flux2-lora-flux2-lora-3dwvpp-1769616974
Model description
These are mm9all/flux2-lora-flux2-lora-3dwvpp-1769616974 DreamBooth LoRA weights for black-forest-labs/FLUX.2-dev.
The weights were trained using DreamBooth with the Flux2 diffusers trainer.
Quant training? BitsandBytes
Trigger words
You should use a photo of zwx_htvu to trigger the image generation.
Download model
Download the *.safetensors LoRA 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.2", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('mm9all/flux2-lora-flux2-lora-3dwvpp-1769616974', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of zwx_htvu').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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]
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Model tree for mm9all/flux2-lora-flux2-lora-3dwvpp-1769616974
Base model
black-forest-labs/FLUX.2-dev