--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - diffusers-training - text-to-image - diffusers - dora - template:sd-lora - edm-training inference: parameters: scheduler: EulerDiscreteScheduler widget: - text: 'a TOK emoji dressed as an easter bunny' output: url: "image_0.png" - text: 'a TOK emoji dressed as an easter bunny' output: url: "image_1.png" - text: 'a TOK emoji dressed as an easter bunny' output: url: "image_2.png" - text: 'a TOK emoji dressed as an easter bunny' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a TOK emoji license: openrail++ --- # SDXL LoRA DreamBooth - linoyts/huggy_dora_edm_v3 ## Model description ### These are linoyts/huggy_dora_edm_v3 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 **[`huggy_dora_edm_v3.safetensors` here 💾](/linoyts/huggy_dora_edm_v3/blob/main/huggy_dora_edm_v3.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('linoyts/huggy_dora_edm_v3', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a TOK emoji dressed as an easter bunny').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 You should use a TOK emoji to trigger the image generation. ## Details All [Files & versions](/linoyts/huggy_dora_edm_v3/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: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.