Seedream v4 Edit Sequential API by ByteDance
Open and Advanced Large-Scale Image Generative Models.
Seedream 4.0 - Le modèle de création visuelle tout-en-un de ByteDance
NOUVEAUTÉLa dernière génération du moteur de création d'images Doubao
Seedream 4.0 est le modèle de création d'images de dernière génération de ByteDance, conçu comme un outil professionnel « tout-en-un » alliant génération et édition. Un seul et même modèle prend en charge la génération texte-vers-image, l'édition d'images et la génération multi-images, rendant votre parcours créatif, de l'inspiration à la réalisation, plus efficace et mieux maîtrisé.
Points forts du modèle
Doté de cinq capacités fondamentales : Édition par instructions précises, Haute préservation des caractéristiques, Compréhension profonde des intentions, Entrée/sortie multi-images et Résolution Ultra HD. Couvrant une diversité de scénarios créatifs, donnant vie à chaque inspiration instantanément avec une qualité exceptionnelle.
Édition par instructions précises
Décrivez simplement vos besoins en langage courant pour effectuer avec précision des opérations d'ajout, de suppression, de modification et de remplacement. Adapté aux applications de design commercial, de création artistique et de divertissement.
Haute préservation des caractéristiques
Compréhension profonde des intentions
Entrée/Sortie multi-images
Saisissez plusieurs images à la fois, prenant en charge des opérations d'édition complexes comme la combinaison, la migration, le remplacement et la dérivation, pour des compositions de haute difficulté
Résolution Ultra HD
Résolution de nouveau améliorée, prenant en charge une sortie ultra-haute définition pour une qualité d'image professionnelle
Cas d'usage
Prompt Examples & Creative Templates
Discover the power of Seedream 4.0 with these carefully crafted prompt examples. Each template showcases specific capabilities and helps you achieve professional results.
Perspective & Composition Control
Transform camera angles, adjust scene distance, and modify aspect ratios with precisionChange the camera angle from eye-level to bird's-eye view, adjust the scene from close-up to medium shot, and convert the image aspect ratio to 16:9. Maintain all original elements and lighting while adapting the composition for the new perspective and format.
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Mathematical Whiteboard Creation
Generate clean whiteboard with precise mathematical formulas and equationsCreate a clean white whiteboard with the following mathematical equations written in clear, professional handwriting: E=mc², √(9)=3, and the quadratic formula (-b±√(b²-4ac))/2a. Use black or dark blue marker style, with proper spacing and mathematical notation.
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Sketch to Reality Transformation
Transform rough sketches into detailed realistic objects - bringing wild imagination to lifeBased on this rough sketch, generate a vintage television set from the 1950s-60s era. Transform the abstract lines and shapes into a realistic, detailed old-style TV with wooden cabinet, rounded screen, control knobs, and period-appropriate design elements. Make the vague concept concrete and lifelike.
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Lossless Detail Enhancement
Maximize original image detail retention, avoiding AI-generated artifacts for truly lossless editingEnhance this image while maximizing the preservation of original details. Avoid any AI-generated 'plastic' or 'oily' artifacts. Maintain authentic textures, natural lighting, and original image characteristics. Focus on clean, lossless enhancement that respects the source material's integrity.
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Creative Font Styling
Transform plain text into artistic, creative typography while maintaining readabilityTransform all the text in this image into creative, artistic fonts. Replace the standard typography with stylized lettering that matches the image's aesthetic - use decorative fonts, calligraphy styles, or artistic text treatments. Maintain the same text content and layout while making the typography more visually appealing and creative.
Capacités fondamentales
Capacités avancées de compréhension du texte et de génération d'images, prenant en charge divers styles artistiques et exigences professionnelles, du concept à l'œuvre finale en une seule étape.
Commandes d'édition basées sur le langage naturel, prenant en charge l'ajout/suppression d'objets, le transfert de style, le remplacement d'arrière-plan et d'autres opérations d'édition plus complexes.
Capacité révolutionnaire d'entrée multi-images, permettant une synthèse d'images complexe, une migration de style et des combinaisons créatives avec un contrôle sans précédent.
Pourquoi choisir Seedream 4.0 ?
Solution tout-en-un
Un seul modèle gère la génération, l'édition et la composition — inutile de jongler entre différents outilsQualité professionnelle
Qualité de sortie de niveau commercial avec un contrôle précis sur chaque détailStyle cohérent
Maintient la cohérence des personnages et du style sur plusieurs générations et modificationsSpécifications Techniques
Découvrez toute la puissance de Seedream 4.0
Rejoignez les créateurs du monde entier et révolutionnez la création de contenu visuel grâce au modèle d'IA d'images intégré le plus avancé de ByteDance.
Seedream 4: A next-generation multimodal image generation system developed by ByteDance Seed
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Seedream 4 |
| Developed by | ByteDance Seed Team |
| Release Date | September 9, 2025 |
| Model Type | Multimodal Image Generation |
| Related Links | Official Website, Technical Report (arXiv), GitHub Organization (ByteDance-Seed) |
Introduction
Seedream 4 is a powerful, efficient, and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single, integrated framework. Engineered for scalability and efficiency, the model introduces a novel diffusion transformer (DiT) architecture combined with a powerful Variational Autoencoder (VAE). This design enables the fast generation of native high-resolution images up to 4K, while significantly reducing computational requirements compared to its predecessors.
The primary goal of Seedream 4 is to extend traditional T2I systems into a more interactive and multidimensional creative tool. It is designed to handle complex tasks involving precise image editing, in-context reasoning, and multi-image referencing, pushing the boundaries of generative AI for both creative and professional applications.
Key Features & Innovations
Seedream 4 introduces several key advancements in image generation technology:
- Unified Multimodal Architecture: It integrates T2I generation, image editing, and multi-image composition into a single model, allowing for seamless transitions between different creative workflows.
- Efficient and Scalable Design: The model features a highly efficient DiT backbone and a high-compression VAE, achieving over 10x inference acceleration compared to Seedream 3.0 while delivering superior performance. This architecture is hardware-friendly and easily scalable.
- Ultra-Fast, High-Resolution Output: Seedream 4 can generate native high-resolution images (from 1K to 4K) in as little as 1.4 to 1.8 seconds for a 2K image, greatly enhancing user interaction and production efficiency.
- Advanced Multimodal Capabilities: The model excels at complex tasks such as precise, instruction-based image editing, in-context reasoning, and generating new images by blending elements from multiple reference images.
- Professional and Knowledge-Based Content Generation: Beyond artistic imagery, Seedream 4 can generate structured and knowledge-based content, including charts, mathematical formulas, and professional design materials, bridging the gap between creative expression and practical application.
- Advanced Training and Acceleration: The model is pre-trained on billions of text-image pairs and utilizes a multi-stage post-training process (CT, SFT, RLHF) to enhance its capabilities. Inference is accelerated through a combination of adversarial distillation, quantization, and speculative decoding.
Model Architecture & Technical Details
Seedream 4's architecture is a significant leap forward, focusing on efficiency and power. The core components are a diffusion transformer (DiT) and a Variational Autoencoder (VAE).
- Pre-training Data: Billions of text-image pairs, including a specialized pipeline for knowledge-related data like instructional images and formulas.
- Training Strategy: A multi-stage approach, starting at a 512x512 resolution and fine-tuning at higher resolutions up to 4K.
- Post-training: A joint multi-task process involving Continuing Training (CT), Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF) to enhance instruction following and alignment.
- Inference Acceleration: A holistic system combining an adversarial learning framework, hardware-aware quantization (adaptive 4/8-bit), and speculative decoding.
Intended Use & Applications
Seedream 4 is designed for a wide range of creative and professional applications, moving beyond simple image generation to become a comprehensive visual content creation tool.
- Creative Content Generation: Creating high-quality, artistic images, illustrations, and concept art from text prompts.
- Advanced Image Editing: Performing complex edits on existing images using natural language instructions, such as adding or removing objects, changing styles, and modifying backgrounds.
- Design and Marketing: Generating professional design materials, product mockups, and marketing visuals with precise control over text and branding elements.
- Educational and Technical Content: Creating structured, knowledge-based visuals like diagrams, charts, and mathematical formulas for educational or technical documentation.
- Multi-Image Composition: Blending elements from multiple source images to create new compositions, such as virtual try-ons for fashion or combining characters with new scenes.
Performance
Seedream 4 has demonstrated state-of-the-art performance on both internal and public benchmarks as of September 18, often outperforming other leading models in text-to-image and image editing tasks.
MagicBench (Internal Benchmark)
| Task | Performance Summary |
|---|---|
| Text-to-Image | Achieved high scores in prompt following, aesthetics, and text-rendering. |
| Single-Image Editing | Showed a good balance between prompt following and alignment with the source image. |
