SDXL LoRA DreamBooth - computational-mama/bike-doodles

Prompt

A drawing of <s0><s1>, a drawing of a bike, road bike, green color, racing handle, vintage bike, fenders

Prompt

A drawing of <s0><s1>, a drawing of a bike, city bike with fenders, green color

Prompt

A drawing of <s0><s1>, a drawing of a bike, city bike with a light, pink color

Prompt

A drawing of <s0><s1>, a drawing of a bike, city bike with lights, pink color

Prompt

A drawing of <s0><s1>, a drawing of a bike, foldable bike, black color, small wheels

Prompt

A drawing of <s0><s1>, a drawing of a bike, city bike with carrier, black color, Next Bike, bike sharing, fenders

Prompt

A drawing of <s0><s1>, a drawing of a bike, city bike with carrier, blue color, bike basket

Prompt

A drawing of <s0><s1>, a drawing of a bike, city bike with carrier, blue color, bike basket

Model description

These are computational-mama/bike-doodles 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 bike-doodles.safetensors here 💾.
  • Place it on your models/Lora folder.
  • On AUTOMATIC1111, load the LoRA by adding <lora:bike-doodles:1> to your prompt. On ComfyUI just load it as a regular LoRA.
• Embeddings: download bike-doodles_emb.safetensors here 💾.
  • Place it on it on your embeddings folder
  • Use it by adding bike-doodles_emb to your prompt. For example, A photo of bike-doodles_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('computational-mama/bike-doodles', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='computational-mama/bike-doodles', filename='bike-doodles_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)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('A photo of <s0><s1>').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> 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.