| | --- |
| | tags: |
| | - stable-diffusion-xl |
| | - stable-diffusion-xl-diffusers |
| | - diffusers-training |
| | - text-to-image |
| | - diffusers |
| | - lora |
| | - template:sd-lora |
| | base_model: stabilityai/stable-diffusion-xl-base-1.0 |
| | instance_prompt: a <s0><s1> pack of pop tarts |
| | license: openrail++ |
| | --- |
| | |
| | # SDXL LoRA DreamBooth - linoyts/poptart_lora_v1 |
| |
|
| | <Gallery /> |
| |
|
| | ## 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 💾](/linoyts/poptart_lora_v1/blob/main/poptart_lora_v1.safetensors)**. |
| | - 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](https://comfyanonymous.github.io/ComfyUI_examples/lora/). |
| | - *Embeddings*: download **[`poptart_lora_v1_emb.safetensors` here 💾](/linoyts/poptart_lora_v1/blob/main/poptart_lora_v1_emb.safetensors)**. |
| | - Place it on it on your `embeddings` folder |
| | - Use it by adding `poptart_lora_v1_emb` to your prompt. For example, `a poptart_lora_v1_emb pack of pop tarts` |
| | (you need both the LoRA and the embeddings as they were trained together for this LoRA) |
| | |
| | |
| | ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) |
| |
|
| | ```py |
| | 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](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) |
| |
|
| | ## 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 |
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|
| |
|
| | ## Details |
| | All [Files & versions](/linoyts/poptart_lora_v1/tree/main). |
| |
|
| | The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). |
| |
|
| | 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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