
Seedream V3 | Text to Image
Seedream 3.0 is a dual-language (Chinese and English) model optimized for generating images from text prompts.
Avg Run Time: 30.000s
Model Slug: seedream-v3-text-to-image
Category: Text to Image
Input
Output
Example Result
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Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Overview
Seedream 3.0 is a dual-language (Chinese and English) text-to-image AI model developed by ByteDance, designed to generate high-quality images from natural language prompts. It is part of the Seedream series, which has gained attention for its strong performance in both creative and professional image generation tasks. Seedream 3.0 is notable for its balanced capabilities across prompt comprehension, structural accuracy, and aesthetic quality, making it suitable for a wide range of applications.
The model leverages advanced deep learning architectures to interpret complex prompts and produce visually compelling, contextually accurate images. It supports both Chinese and English input, with particularly high accuracy in rendering Chinese text within images. Seedream 3.0 stands out for its robust handling of visual details, textures, and full-body actions, as well as its adaptability to various artistic and functional design scenarios. Its performance has been recognized in independent benchmarks, where it often ranks at or near the top among leading text-to-image models.
Technical Specifications
- Architecture: Advanced diffusion-based architecture (specifics not publicly disclosed)
- Parameters: Not officially disclosed
- Resolution: Supports up to 2K image generation (higher resolutions available in later versions)
- Input/Output formats: Accepts natural language prompts in Chinese and English; outputs standard image formats such as PNG and JPEG
- Performance metrics:
- Achieves top ELO and MOS scores in expert and public evaluations for prompt following, structural accuracy, and aesthetic quality
- 94% accuracy in Chinese text rendering within images
- Generation speed averages around 3 seconds per 2K image (improved in later versions)
Key Considerations
- Seedream 3.0 excels in both creative and functional design tasks, making it versatile for different user needs
- For best results, prompts should be clear and descriptive, leveraging the model’s strong language understanding
- The model is optimized for both Chinese and English, but may perform best with prompts that avoid ambiguous or highly idiomatic language
- Quality and speed are balanced; higher resolution or more complex prompts may increase generation time
- Prompt engineering is important: specifying desired styles, elements, and relationships improves output fidelity
- Avoid overloading prompts with conflicting instructions, as this can reduce image coherence
Tips & Tricks
- Use detailed, context-rich prompts to guide the model toward your intended result (e.g., "A futuristic cityscape at sunset, with flying cars and neon lights")
- For text rendering within images, specify the exact text and its placement for higher accuracy, especially in Chinese
- Experiment with iterative prompt refinement: start broad, then add details or constraints based on initial outputs
- To achieve specific artistic styles, include style descriptors (e.g., "in the style of traditional Chinese ink painting" or "photorealistic portrait")
- For action or interaction scenes, clearly describe the relationships and actions between subjects (e.g., "two children playing with a dog in a park, both smiling")
- Adjust prompt complexity based on desired output speed; simpler prompts yield faster results
Capabilities
- Generates high-quality images from both Chinese and English text prompts
- Excels in visual detail, texture rendering, and full-body or hand action depiction
- Strong at following complex prompts and maintaining structural accuracy in generated scenes
- High accuracy in rendering Chinese text within images (94% accuracy reported)
- Balanced performance across art, entertainment, functional design, and aesthetic scenarios
- Adaptable to a wide range of creative and professional use cases
What Can I Use It For?
- Professional design tasks such as advertising visuals, product concept art, and marketing materials
- Creative projects including digital art, illustration, and storyboarding for comics or animation
- Business applications like rapid prototyping of visual ideas, presentation graphics, and branding assets
- Personal projects such as custom wallpapers, social media content, and hobbyist art
- Industry-specific uses in entertainment, film pre-visualization, and educational content creation
Things to Be Aware Of
- Some experimental features or behaviors may be present, as noted in community discussions
- Users have reported occasional inconsistencies in highly complex or ambiguous prompts
- Performance is generally strong, but resource requirements can increase with higher resolutions or batch processing
- Consistency across multiple images is good, but not perfect—character or style drift may occur in series generation
- Positive feedback highlights the model’s balanced output quality, versatility, and strong Chinese language support
- Some users note that while aesthetic quality is high, semantic or structural accuracy may lag behind top-tier models in certain technical scenarios
- Negative feedback patterns include occasional "AI feeling" in images and rare failures in prompt comprehension for edge cases
Limitations
- The model’s architecture and parameter count are not publicly disclosed, limiting transparency for technical users
- May not be optimal for tasks requiring ultra-high resolution (native 4K and above) or advanced multi-modal input, which are supported in later versions
- Occasional inconsistencies in prompt following or image coherence for highly complex or ambiguous instructions
Pricing Type: Dynamic
Dynamic pricing based on input conditions
Pricing Rules
Parameter | Rule Type | Base Price |
---|---|---|
num_images | Per Unit Example: num_images: 1 × $0.03 = $0.03 | $0.03 |
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