Flux DreamBooth LoRA - rangwani-harsh/3d-icon-Flux-LoRA

Prompt

a <s0><s1> icon of an astronaut riding a horse, in the style of <s0><s1>

Prompt

a <s0><s1> icon of an astronaut riding a horse, in the style of <s0><s1>

Prompt

a <s0><s1> icon of an astronaut riding a horse, in the style of <s0><s1>

Prompt

a <s0><s1> icon of an astronaut riding a horse, in the style of <s0><s1>

Model description

These are rangwani-harsh/3d-icon-Flux-LoRA DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.

The weights were trained using DreamBooth with the Flux diffusers trainer.

Was LoRA for the text encoder enabled? False.

Pivotal tuning was enabled: True.

Trigger words

To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:

to trigger concept `TOK` → use `<s0><s1>` in your prompt 

Download model

Download the *.safetensors LoRA in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
    from safetensors.torch import load_file
            
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')

embedding_path = hf_hub_download(repo_id='rangwani-harsh/3d-icon-Flux-LoRA', filename='3d-icon-Flux-LoRA_emb.safetensors', repo_type="model")
    state_dict = load_file(embedding_path)
    pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
            
image = pipeline('a <s0><s1> icon of an astronaut riding a horse, in the style of <s0><s1>').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]