Z-image Online Training - First Training FREE!
Z- image Online Training - First Time FREE! & 50% OFFBy anchoring in the SFT stage, Z-Image achieves a superior balance between visual fidelity, generative diversity, and precise instruction following.It's your go-to choice due to its rapid training speeds and minimal dataset requirements.🔥 Limited Time OfferTo celebrate our launch and empower creators, we are offering50% OFF on all online trainingyour FIRST session is on us!Note: We will rebate up to 1,000 credits to your account upon the successful completion of your first training.Don’t miss out!📌 Go to Training 👉 https://tensor.art/trainTraining Tutorial1. Online Training WorkbenchIn the online training workbench, select Custom as the model type, then choose z-image as the base foundation model for training.2. Training SamplesSelect high-quality samples according to the training task. The recommended number of samples is approximately 30–300, and the more, the better.In our internal testing, the Base model uses a relatively high proportion of photorealistic style data during the SFT (Supervised Fine-Tuning) stage, which results in excellent image quality for realistic styles.3. Data Processing & LabelingThe workbench provides multiple advanced multimodal models for Labeling, including the latest Gemini-3-Flash model.You can use the default caption prompt for labeling, or customize your own prompt. After annotation is completed, add a trigger word and a training sample preview prompt. For example, jzxdda_style can be used as a trigger token — it carries no inherent semantic meaning and is used solely to activate the learned style.4. Start TrainingClick the “Start Training” button to launch the training task.The training details page will display the estimated remaining time, which typically ranges from 10 minutes to 1 hour, depending on the number of training samples and training steps.During training, you can monitor real-time loss curves and accuracy changes. The task can be stopped early at an appropriate epoch to prevent overfitting.5. Testing & PublishingClick “Publish” to create a project and deploy the trained model.
