Move your team from Tensor.Art to Civitai.
Each person gets a short interview, then a personalized step-by-step guide showing exactly where their saved work, prompts, and projects land in Civitai — 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 train custom LoRA models from an image dataset entirely in the browser on cloud GPUs — no local Python, VRAM, or scripts needed. To migrate a training workflow: export your training image dataset from wherever it's stored (images are not held by either platform after training completes), then upload the same image set into the destination platform's training UI. Civitai's trainer is reached via Create > Train a model; Tensor.Art's training tool is in the model section of the dashboard. Both recommend ~20 high-quality training images at 1024px resolution for a subject LoRA. On Civitai, cost is in Buzz (starting at 500 Buzz for SD 1.5/SDXL runs); on Tensor.Art, training consumes credits and is available on paid tiers. Both platforms support SDXL and FLUX base models for training. Trained LoRAs on either platform can be kept private or published to the community.
- Warning: Civitai supports a wider range of training base models including video LoRA bases (Hunyuan, Wan 2.1, LTX2) and multiple Flux.2 variants; Tensor.Art's browser training is primarily targeted at image LoRAs and may not offer equivalent video LoRA training.
- Warning: Training pricing structures differ: Civitai charges Buzz with cost visible before submission; Tensor.Art charges credits but exact cost per training run is not publicly documented in the same pre-submission preview format.
- Warning: Trained LoRA files (.safetensors) may need re-uploading to the destination platform to use them — neither platform cross-publishes trained models.
Both platforms offer browser-based text-to-image generation on cloud GPUs with no local hardware required. To move from Civitai to Tensor.Art: open tensor.art, select a checkpoint model (SDXL, FLUX, or Pony are common equivalents to Civitai's model options), paste your prompt, and run. Civitai uses 'Buzz' credits (daily free allotment); Tensor.Art uses a credit system (~0.2 credits per basic generation, 50 free daily on free tier). Save your prompt text from Civitai's generation history before switching — both platforms display past prompts but do not export them as a batch. On Tensor.Art, watermarks are added on the free tier and removed on Pro ($9.90/mo); Civitai's Draft Mode outputs lower-resolution watermarked previews before a full paid generation.
- Warning: Civitai supports Flux.1 and Flux.2 variants; Tensor.Art supports FLUX but specific Flux.2 fine-tunes may not have direct equivalents in its library.
- Warning: Tensor.Art free tier caps output resolution at 1536x1024; Civitai free Buzz allows SDXL at 1024x1024 — switching on free tiers may produce differently-sized outputs.
- Warning: Civitai's 'Draft Mode' (very low Buzz cost) has no equivalent in Tensor.Art; test generations on Tensor.Art cost the same ~0.2 credits as production runs.
Both platforms expose HTTP APIs that let developers programmatically trigger image generation and browse the model catalog from external apps. To migrate an integration: generate a Tensor.Art API key instantly from the user dashboard (tensor.art after login, no waitlist), then replace the Civitai API base URL (https://civitai.com/api/v1/) with the Tensor.Art API endpoint and swap the Authorization header from a Civitai Bearer token to a Tensor.Art key. Civitai's API is primarily a read/browse API (list models, images, creators) with generation triggered through the site UI or a separate Civitai Runner workflow; Tensor.Art's API directly supports submitting generation tasks and polling for results, making it a better fit for automated generation pipelines. Update any paginated model-listing calls — Civitai uses nextCursor/nextPage pagination; check Tensor.Art's docs (tensor.art after login) for its own pagination scheme.
- Warning: Civitai's public REST API is well-documented at developer.civitai.com; Tensor.Art's API documentation is behind login at tensor.art with no publicly indexed reference page, making pre-integration evaluation harder.
- Warning: Civitai offers read access to community model metadata (tags, ratings, version history) via unauthenticated endpoints; Tensor.Art's API is generation-focused and may not expose equivalent model metadata browsing endpoints.
- Warning: Tensor.Art's API billing uses the same credit economy as the web UI; Civitai's API model downloads are subject to creator-set permissions and may require additional token scopes not needed for generation.
Both platforms offer post-generation enhancement workflows including upscaling, face fixing, and image-to-image remixing on cloud GPUs. On Civitai, enhancements are added as resources inside the on-site generator: you stack LoRAs/DoRAs on a base model and apply Hi-Res Fix, ADetailer (face/eye refinement), or upscaling (1.5x–4x Foolhardy Remacri) within the same generation job. On Tensor.Art, enhancements are available as post-generation one-click tools or via the ComfyUI workflow editor — upscale up to 3840x2160 on Pro, background removal, face swap, and image-to-image are all in the web UI. To migrate: recreate your prompt and model selection on Tensor.Art, then use the enhancement tools in the post-generation panel or select a community workflow template (e.g. 'High-Definition with hand fix') that bundles the same upscale and face-fix pipeline Civitai's ADetailer + Hi-Res Fix provides.
- Warning: Civitai's LoRA stacking inside generation (layering multiple LoRAs on a single run) is a native feature; on Tensor.Art, multi-LoRA stacking depends on the ComfyUI workflow editor, which has a steeper learning curve than Civitai's simpler resource picker.
- Warning: Civitai charges extra Buzz per additional resource over 10 MB; Tensor.Art's enhancement tools charge more credits (e.g. ~0.5 credits for a workflow with face/hand fixing vs. 0.2 credits for basic generation) — costs are not directly comparable.
- Warning: Tensor.Art's upscaling caps at 3840x2160 on Pro; Civitai's maximum effective upscale depends on model and Buzz budget, so output resolution ceilings may differ.
Both platforms host large community libraries of checkpoints, LoRAs, and embeddings you can browse and apply to cloud generations. To migrate a saved model list from Civitai to Tensor.Art: note the model names and base model type (SD 1.5, SDXL, FLUX) from your Civitai favorites, then search tensor.art for matching model names or equivalent community uploads. Many popular Civitai checkpoint and LoRA titles are re-uploaded on Tensor.Art; use the same search terms in the Tensor.Art model browser (tensor.art/models). For models not present on Tensor.Art, you can upload them manually via the Tensor.Art upload flow if you have the downloaded .safetensors file. Civitai's catalog has tens of thousands of resources; Tensor.Art lists 400,000+ models, so coverage is broad but may differ in niche fine-tunes.
- Warning: Tensor.Art marks some models as 'exclusive platform models' — these can be used in cloud generation but cannot be downloaded, preventing local backup or transfer to other platforms.
- Warning: Civitai allows model creators to set download permissions and may require account sign-in or local runner setup to download; Tensor.Art also requires login for most downloads.
- Warning: Model versioning schemes differ: Civitai tracks discrete version IDs per model; Tensor.Art does not expose the same version history, so the exact version of a model may be unclear.