metadata
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- diffusers-training
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: a <s0><s1> pack of pop tarts in the flavor of ramen
output:
url: image (126).png
- text: a <s0><s1> pack of pop tarts in the flavor of popcorn
output:
url: image (127).png
- text: a <s0><s1> pack of pop tarts in the flavor of greek salad
output:
url: image (128).png
- text: a <s0><s1> pack of pop tarts in the flavor of sushi
output:
url: image (129).png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a <s0><s1> pack of poptarts
license: openrail++
SDXL LoRA DreamBooth - linoyts/poptart_lora_v1
![](https://huggingface.co/linoyts/poptart_lora_v1/resolve/main/image%20(126).png)
- Prompt
- a <s0><s1> pack of pop tarts in the flavor of ramen
![](https://huggingface.co/linoyts/poptart_lora_v1/resolve/main/image%20(127).png)
- Prompt
- a <s0><s1> pack of pop tarts in the flavor of popcorn
![](https://huggingface.co/linoyts/poptart_lora_v1/resolve/main/image%20(128).png)
- Prompt
- a <s0><s1> pack of pop tarts in the flavor of greek salad
![](https://huggingface.co/linoyts/poptart_lora_v1/resolve/main/image%20(129).png)
- Prompt
- a <s0><s1> pack of pop tarts in the flavor of sushi
Model description
These are linoyts/poptart_lora_v1 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
poptart_lora_v1.safetensors
here 💾.- Place it on your
models/Lora
folder. - On AUTOMATIC1111, load the LoRA by adding
<lora:poptart_lora_v1:1>
to your prompt. On ComfyUI just load it as a regular LoRA.
- Place it on your
- Embeddings: download
poptart_lora_v1_emb.safetensors
here 💾.- Place it on it on your
embeddings
folder - Use it by adding
poptart_lora_v1
to your prompt. For example,a poptart_lora_v1 emoji
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
- Place it on it on your
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('linoyts/poptart_lora_v1', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/poptart_lora_v1', filename='poptart_lora_v1_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 <s0><s1> pack of pop tarts in the flavor of pickels').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.