Move your team from Tensor.Art to SeaArt.
Each person gets a short interview, then a personalized step-by-step guide showing exactly where their saved work, prompts, and projects land in SeaArt — and the few things that need a manual step, walked through. You send one invite; nobody becomes the help desk, and nothing in Tensor.Art is changed or cancelled. Want to see where you’re paying for both first? Run the free audit.
Both platforms host browsable community model libraries where users can discover, use, and publish checkpoints, LoRAs, and embeddings without downloading files. On SeaArt, use the Explore section to filter by style (anime, realism, illustration, sci-fi) and type (checkpoint, LoRA, workflow); favorite models attach to your account. On Tensor.Art, use the model search with tag filtering across its 400,000+ model library; models used in cloud generation do not require a download. When switching platforms, note your favorite or frequently used model names and search for the same model name or architecture on the destination — community models with permissive licenses (e.g., CivitAI-sourced) often exist on both platforms. Creators publishing models can upload to Tensor.Art's marketplace independently; SeaArt's Creator Incentive Program pays revenue share, while Tensor.Art allows paid model uploads, so creator monetization paths differ but both are available.
- Warning: SeaArt hosts 1,000,000+ models vs Tensor.Art's 400,000+ — specialized or low-popularity models on SeaArt may not have a Tensor.Art equivalent; check availability before committing to the switch.
- Warning: Some Tensor.Art platform-exclusive models are available for cloud generation but cannot be downloaded even on Pro plans, limiting portability for users who want local copies.
- Warning: SeaArt's Creator Incentive Program pays revenue share based on usage; Tensor.Art's monetization terms differ. Creators should review Tensor.Art's creator payout policy separately before migrating published models.
Both platforms support uploading an existing image and applying AI-driven edits including style transfer, inpainting, outpainting, background removal, face swap, and upscaling. On SeaArt, upload via the Image-to-Image tab, enter your target prompt, and set the denoising strength to control how far the output strays from the original (lower = closer to source). On Tensor.Art, upload an image in the web UI and choose the equivalent tool (img2img remix, background removal, face swap, or upscale). For inpainting workflows, both platforms use the same brush-mask interface — paint the area to edit and describe the replacement in the prompt. Download your SeaArt source images locally before switching; both platforms operate on uploaded copies and do not sync image libraries between them.
- Warning: Tensor.Art's upscaler caps free-tier output at 1536x1024; SeaArt does not document an equivalent cap. Move to Tensor.Art Pro for large-format upscaling (up to 3840x2160).
- Warning: SeaArt's ControlNet for image-to-image accepts stacked multiple ControlNet units in one pass; Tensor.Art achieves the same via its ComfyUI workflow editor rather than a single-click UI — expect more setup steps for multi-ControlNet edits on Tensor.Art.
- Warning: SeaArt's face swap and outpainting tools consume stamina from the same daily pool as generation; Tensor.Art bills these as separate credit-consuming operations — heavy editing workflows may exhaust credits faster.
Both platforms train LoRAs in-browser on their cloud infrastructure with no local GPU needed. On SeaArt, training is initiated from the Model Training section; upload your image set, configure training parameters, and the completed LoRA saves to your account for use in any generation. On Tensor.Art, the same workflow is available in the browser — upload approximately 20 images at 1024px resolution, start training, and the LoRA is saved to your library when done. Trained LoRA weights are not exportable from either platform to an external file (both lock models to the platform); you will need to retrain on the destination platform using the same source images. Keep your original training image set backed up locally before migrating so you can re-submit it on the new platform.
- Warning: Neither SeaArt nor Tensor.Art allows exporting the raw LoRA weights file (.safetensors), so trained models cannot be transferred between platforms — a full retrain on the destination is required.
- Warning: SeaArt gates training behind VIP tiers and credits purchased via SeaArt Mall; Tensor.Art gates training behind paid credit balance. Confirm you have sufficient credits on the destination before starting.
- Warning: SeaArt offers separate training pipelines for image LoRAs, video LoRAs, and FLUX-specific LoRAs. Tensor.Art's video LoRA support is less documented — verify availability before migrating a video-LoRA workflow.
Both platforms run Stable Diffusion, SDXL, Illustrious, and FLUX checkpoints through a web UI with no local GPU required, so prompts and model choices transfer almost directly. On SeaArt, open the Create tab, paste your prompt and negative prompt, choose your checkpoint, and adjust CFG scale, sampling method, and step count. On Tensor.Art, the same fields appear in the generate panel; select the equivalent checkpoint from the 400,000-model library (search by model name or architecture). Export or screenshot your SeaArt generation settings (model name, CFG, steps, seed) before switching so you can replicate them on Tensor.Art. Seeds are portable between platforms for the same architecture. Free-tier users on Tensor.Art will notice watermarks on output images; upgrade to Pro ($9.90/month) to remove them.
- Warning: SeaArt free users get 150 stamina/day; Tensor.Art free users get 50 credits/day — the Tensor.Art free allowance is tighter for high-step generations (~0.2 credits each).
- Warning: SeaArt hosts 1,000,000+ models; Tensor.Art hosts 400,000+ — some niche SeaArt-exclusive checkpoints will not be present on Tensor.Art.
- Warning: Tensor.Art free tier caps output resolution at 1536x1024; SeaArt does not publish an equivalent hard cap for free users. Switch to Tensor.Art Pro for resolutions up to 3840x2160.
Both platforms offer text-to-video and image-to-video generation in the browser without a local GPU. On SeaArt, navigate to Video Generation, choose Txt2Vid (enter prompt) or Img2Vid (upload reference image plus prompt), select output quality tier (Lite/Depth/Ultra/Sparkle), and optionally specify camera movement or start/end frames. On Tensor.Art, load a Wan 2.1/2.2 video workflow from the ComfyUI workflow editor or pick a dedicated video template; connect an image node or text prompt, set duration and steps, and run. Download your reference images or source clips from SeaArt before migrating — neither platform syncs media libraries. Video tasks are significantly more credit-intensive on both sides; verify your credit balance before starting a batch.
- Warning: SeaArt integrates named third-party video models (Veo, Sora, Kling AI, Seedance 2.0); Tensor.Art's video capability is primarily Wan 2.1/2.2 — output style and motion quality will differ noticeably between equivalent prompts.
- Warning: Tensor.Art Pro includes only 1 dedicated video task slot alongside image slots; heavy video workflows on the free tier may queue or fail due to credit exhaustion.
- Warning: SeaArt supports video LoRA training for personalized video styles; Tensor.Art's video LoRA training is not clearly documented — users who trained video LoRAs on SeaArt should verify support before migrating that workflow.