<h2 id="sdxl-loralycoris-works-best-on-its-original-training-model.-you-may-need-to-reduce-weight-when-using-it-with-other-models-or-additional-loras.lycoris-combination-recommendation:-try-using-four-of-my-lycoris-models-with-the-following-weights:-0.4-0.3-0.2-0.2-(or-any-combination-with-a-total-weight-around-1.0).-4ndvegqb1">SDXL LoRA/LyCORIS works best on its original training model. You may need to reduce weight when using it with other models or additional LoRAs. LyCORIS Combination Recommendation: Try using four of my LyCORIS models with the following weights: 0.4, 0.3, 0.2, 0.2 (or any combination with a total weight around 1.0).</h2><hr />I will release multiple versions for a few popular models. Feel free to request for artist or model.Version naming convention: MajorVersion.MinorVersion [BaseModel]MajorVesion: For big updates that can apply to any style LoRA. For example, training parameter update.MinorVersion: For small updates that only apply to this LoRA. For example, epoch selection, removing a certain image from dataset.BaseModel: SDXL LoRA/LyCORIS works best on the model that it was trained on.

[SDXL] Artist Style: nero

neroXL_il_lokr_V6311P.safetensors

v6.311P[Illustrious v0.1]

v8.340 [Illustrious 0.1]

SDXL LoRA/LyCORIS works best on its original training model. You may need to reduce weight when using it with other models or additional LoRAs. LyCORIS Combination Recommendation: Try using four of my LyCORIS models with the following weights: 0.4, 0.3, 0.2, 0.2 (or any combination with a total weight around 1.0).I will release multiple versions for a few popular models. Feel free to request for artist or model.Version naming convention: MajorVersion.MinorVersion [BaseModel]MajorVesion: For big updates that can apply to any style LoRA. For example, training parameter update.MinorVersion: For small updates that only apply to this LoRA. For example, epoch selection, removing a certain image from dataset.BaseModel: SDXL LoRA/LyCORIS works best on the model that it was trained on.

Files with SHA256: 8c65d1f2178ca060388ea72493cb08509b900fd57700378246f3bccaefcdb1e7