Move your team from Civitai to SeaArt.

Both platforms host large libraries of community-contributed AI models (checkpoints, LoRAs, embeddings, workflows) that users can browse and apply without downloading files locally. On Civitai, the Models section at civitai.com/models lets you filter by type, base model (SD 1.5, SDXL, Flux), tag, and popularity; downloads are available for local use but may require sign-in or creator-set permissions. On SeaArt, the Explore section provides a library exceeding 1,000,000 models and LoRAs searchable by style (anime, realism, sci-fi, illustration) and type (checkpoint, LoRA, ComfyUI workflow); models are applied directly in-browser without downloading. To migrate a model discovery workflow: use SeaArt's Explore search to find the same model by name or by style/tag equivalents. If a specific model exists on Civitai but not SeaArt, download the file from Civitai (if creator permissions allow) and check whether SeaArt supports user model uploads. Bookmark favorite models on each platform separately; neither exports a favorites list in a cross-compatible format.

Both platforms let users layer additional AI resources on top of a base model to steer style, character, or concept, and both support image-to-image workflows as enhancement passes. On Civitai, you add LoRAs, DoRAs, LyCORIS, or embeddings inside the on-site generator; each resource beyond 10 MB adds Buzz cost, and you can chain Hi-Res Fix, ADetailer face refinement, and upscaling in the same job. On SeaArt, you select LoRAs and models from the 1M+ library when generating, then run inpainting, outpainting, face swap, background removal, or upscaling as follow-on steps in the image-to-image editor. To migrate: identify the equivalent LoRA on SeaArt by searching its model library for the same style or character name; apply it in the SeaArt generation interface the same way. For image-to-image enhancement (e.g. upscaling or face fix), use SeaArt's dedicated tools in the editing panel rather than Civitai's ADetailer pass. Export source images from Civitai (download as standard image files) and re-import into SeaArt's image-to-image panel to continue editing.

Both platforms let users train custom LoRA models from uploaded image datasets in the browser with no local GPU required. On Civitai, training is accessed via Create > Train a model; supported base models span SD 1.5, SDXL, Flux.1/Flux.2, Chroma, Qwen-Image, Z-Image (image LoRAs) and Hunyuan, Wan 2.1, LTX2 (video LoRAs), running on Musubi or AI-Toolkit engines, costing Buzz starting at 500 for SD 1.5/SDXL. On SeaArt, training is initiated from the model training section; separate pipelines exist for image LoRAs, video LoRAs, and Flux-specific LoRAs, and training consumes credits purchased via SeaArt Mall or earned through community tasks. To migrate: prepare the same dataset of training images (SeaArt accepts standard image formats); re-enter the same parameter settings (epoch count, repeat steps, learning rate if exposed) on the new side. Trained LoRAs are saved to your account on each platform separately and cannot be directly exported from one to the other as ready-to-use files in the UI. Publish the trained LoRA to the community library on either side after training if desired.

Both platforms support conditioning image generation on a reference image to preserve composition, pose, or structure. On Civitai, image-to-image and Hi-Res Fix passes are available within the on-site generator; these use an uploaded image as a starting point and adjust denoising strength to control how much of the original is retained. On SeaArt, dedicated ControlNet units are available in the generation interface: users supply a control image and choose a preprocessor (Canny, OpenPose Full, Line Art, Depth, Normal Bae, Segmentation, Tile Resample, MLSD, Scribble HED, HED, Reference Generation); multiple ControlNet units can be stacked. To migrate a reference-guided generation workflow from Civitai to SeaArt: export the reference image from Civitai (download to local machine), upload it into SeaArt's generation panel, select the ControlNet tab, choose the preprocessor that matches your intent (Canny for edge structure, OpenPose Full for human pose, Depth for spatial layout), and set control weight. Prompts and base model selection follow the same process as standard text-to-image migration. Moving from SeaArt to Civitai: use Civitai's image-to-image workflow with the control image as input and tune denoising strength instead of ControlNet preprocessors.

Both platforms offer browser-based text-to-image generation without local GPU hardware. On Civitai, generation is launched from the Create button at civitai.com/generate; you pick a base model (SD 1.5, SDXL, or Flux variants), write a prompt, and the job runs on Civitai's cloud GPU, costing Buzz. On SeaArt, generation lives in the main web app; you choose from 1M+ models (SDXL, SD 3.5, NOOBAI XL, Illustrious series), set advanced parameters (CFG scale, sampling method, seed, CLIP skip), and the job consumes daily stamina. To migrate a prompt workflow: copy prompts and negative prompts verbatim — both tools accept standard SD-style prompts. Identify the equivalent base model on the destination side (e.g., SDXL on Civitai maps to SDXL or NOOBAI XL on SeaArt). Re-enter advanced settings (steps, CFG, resolution) manually; neither platform exports generation configs in a cross-compatible format. Download completed images from either platform to your local machine as standard image files.