Text-to-Image
Diffusers
TensorBoard
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Instructions to use rourkepalmer/desma-160-2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rourkepalmer/desma-160-2-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rourkepalmer/desma-160-2-lora") prompt = "A photo of <s0><s1> a close up of a red dahlia flower" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - rourkepalmer/desma-160-2-lora

- Prompt
- A photo of <s0><s1> a close up of a red dahlia flower

- Prompt
- A photo of <s0><s1> a pink dahlia flower on a blurred leafy, flowery background

- Prompt
- A photo of <s0><s1> two pink dahlia flowers on a dark background

- Prompt
- A photo of <s0><s1> a red dahlia flower on a leafy background

- Prompt
- A photo of <s0><s1> a close up of a pink dahlia flower

- Prompt
- A photo of <s0><s1> a close up of two pink dahlia flowers on a leafy, flowery background

- Prompt
- A photo of <s0><s1> a side-on of a pink dahlia flower on a blurred dark background

- Prompt
- A photo of <s0><s1> a pink dahlia flower on a blurred leafy background

- Prompt
- A photo of <s0><s1> a side-on of a yellow dahlia flower with purple tips on a blurred leafy background

- Prompt
- A photo of <s0><s1> a pink dahlia flower on a leafy background

- Prompt
- A photo of <s0><s1> an orange dahlia flower on a black background

- Prompt
- A photo of <s0><s1> orange dahlia flower on a leafy background

- Prompt
- A photo of <s0><s1> a white dahlia flower with purple tips on a dark background

- Prompt
- A photo of <s0><s1> a red and white dahlia flower on a leafy background

- Prompt
- A photo of <s0><s1> a white dahlia flower with pink tips on a blurred leafy background

- Prompt
- A photo of <s0><s1> a white dahlia flower with a pink center on a black background

- Prompt
- A photo of <s0><s1> a orange dahlia flower on green background

- Prompt
- A photo of <s0><s1> a white dahlia flower with purple tips on a gray background

- Prompt
- A photo of <s0><s1> a white dahlia flower on a blurred leafy background

- Prompt
- A photo of <s0><s1> a red dahlia flower on a leafy, flowery background

- Prompt
- A photo of <s0><s1> a pink dahlia flower on a leafy, flowery background
Model description
These are rourkepalmer/desma-160-2-lora 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
desma-160-2-lora.safetensorshere ๐พ.- Place it on your
models/Lorafolder. - On AUTOMATIC1111, load the LoRA by adding
<lora:desma-160-2-lora:1>to your prompt. On ComfyUI just load it as a regular LoRA.
- Place it on your
- Embeddings: download
desma-160-2-lora_emb.safetensorshere ๐พ.- Place it on it on your
embeddingsfolder - Use it by adding
desma-160-2-lora_embto your prompt. For example,A photo of desma-160-2-lora_emb(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 ๐งจ 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('rourkepalmer/desma-160-2-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='rourkepalmer/desma-160-2-lora', filename='desma-160-2-lora_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 โ use <s0><s1> in your prompt
Details
All Files & versions.
The weights were trained using ๐งจ 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.
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Model tree for rourkepalmer/desma-160-2-lora
Base model
stabilityai/stable-diffusion-xl-base-1.0