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Thrcle
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Dual_Inv_Edit

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Diffusers library
# Gated model: Login with a HF token with gated access permission
hf auth login
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]

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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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