Instructions to use Thrcle/Dual_Inv_Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Thrcle/Dual_Inv_Edit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Thrcle/Dual_Inv_Edit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Dual Inv Edit
This repository contains the trained weights for the Dual Inversion Editing project.
Model Description
- transformer_fwd: Forward transformer LoRA weights (pytorch_lora_weights.safetensors)
- transformer_bwd: Backward transformer LoRA weights (pytorch_lora_weights.safetensors)
Training Configuration
- Learning rate: 1e-5
- Batch size: 80
- CFG scale: 5.0
- Cycle forward: 1.0
- Cycle backward: 1.0
- Lambda regularization: 0.0
- Eta: 0.9
- K: 8
Usage
from huggingface_hub import hf_hub_download
# Download forward LoRA weights
lora_fwd_path = hf_hub_download(repo_id="Thrcle/Dual_Inv_Edit", filename="transformer_fwd/pytorch_lora_weights.safetensors")
# Download backward LoRA weights
lora_bwd_path = hf_hub_download(repo_id="Thrcle/Dual_Inv_Edit", filename="transformer_bwd/pytorch_lora_weights.safetensors")
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