SDXL LoRA DreamBooth - rikhoffbauer2/naomi-makkelie-seaweed-painting-style-4

Prompt

a painting of seaweed underwater that look similar to green vines and leaves in the style of <s0><s1><s2><s3>

Prompt

a painting of seaweed painted using organic lines of green and black lines on a black background in the style of <s0><s1><s2><s3>

Prompt

an abstract painting (acrylic on canvas) of seaweed at the bottom of the ocean, the painting also features a yellow border in the style of <s0><s1><s2><s3>

Model description

These are rikhoffbauer2/naomi-makkelie-seaweed-painting-style-4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

Download model

Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

• LoRA: download naomi-makkelie-seaweed-painting-style-4.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:naomi-makkelie-seaweed-painting-style-4:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download naomi-makkelie-seaweed-painting-style-4_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding naomi-makkelie-seaweed-painting-style-4_emb to your prompt. For example, in the style of naomi-makkelie-seaweed-painting-style-4_emb (you need both the LoRA and the embeddings as they were trained together for this LoRA)

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('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('rikhoffbauer2/naomi-makkelie-seaweed-painting-style-4', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='rikhoffbauer2/naomi-makkelie-seaweed-painting-style-4', filename='naomi-makkelie-seaweed-painting-style-4_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('in the style of <s0><s1><s2><s3>').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

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><s2><s3> in your prompt

Details

All Files & versions.

The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script.

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.