Reve | Edit
Reve’s edit model allows you to refine or completely reimagine an existing image through natural language prompts, keeping full control over style and composition.
Avg Run Time: 25.000s
Model Slug: reve-edit
Category: Image to Image
Input
Enter an URL or choose a file from your computer.
Click to upload or drag and drop
(Max 50MB)
Output
Example Result
Preview and download your result.
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
Reve-edit is an advanced AI image generator and editor developed to provide a comprehensive suite for digital art creation, image generation, and context-aware editing. Launched in 2025, Reve-edit stands out for its multifaceted capabilities, blending image creation, remixing, and editing into a single, intuitive system. The model is powered by proprietary architectures such as Halfmoon AI and PhotonNet V2, which deliver high-quality, photorealistic outputs with strong prompt adherence and detail preservation.
Key features include natural language editing, allowing users to modify images using plain text commands, multi-image fusion for combining elements from different sources, and a drag-and-drop graphical editor for pixel-level control. Reve-edit’s unique strengths lie in its deep semantic understanding, spatial intelligence, and ability to maintain consistency across complex compositions. Its API integration further enables seamless adoption into professional workflows, making it suitable for creators, designers, and businesses seeking scalable, high-performance image generation and editing.
Technical Specifications
- Architecture: Proprietary Halfmoon AI model, PhotonNet V2, Prompt Compiler™ 2.0
- Parameters: Not publicly disclosed
- Resolution: Supports standard aspect ratios (1:1, 16:9, 9:16) and high resolutions suitable for print, web, and social media
- Input/Output formats: Accepts text prompts, reference images; outputs in common image formats (PNG, JPG)
- Performance metrics: Recognized for best-in-class prompt comprehension, aesthetic quality, and typography; benchmarks highlight superior realism, spatial coherence, and text rendering
Key Considerations
- Reve-edit excels with detailed, context-rich prompts; leveraging its semantic understanding improves output fidelity
- For optimal results, use multiple reference images to guide composition, lighting, and style
- The drag-and-drop editor enables direct manipulation of image elements, reducing reliance on prompt engineering
- Quality and speed are balanced; high-resolution outputs may require longer processing times
- Avoid overly vague prompts, as specificity enhances both realism and adherence to instructions
- Iterative refinement is recommended for complex edits or multi-stage compositions
Tips & Tricks
- Use clear, descriptive prompts specifying objects, actions, and desired styles for best results
- Combine several reference images to control specific aspects such as environment, pose, or color palette
- For text embedding, utilize the Dynamic TextFusion™ feature to ensure accurate placement and perspective
- Employ the drag-and-drop editor for pixel-level adjustments, especially when fine-tuning object positions or details
- Refine outputs iteratively: generate initial images, edit with natural language commands, and adjust references as needed
- When editing, specify lighting direction and material properties to maintain realism and consistency
Capabilities
- Generates photorealistic, anime, cinematic, and illustrative images with high fidelity
- Edits images using natural language, enabling intuitive modifications without technical expertise
- Fuses multiple images into cohesive composites, blending styles and elements seamlessly
- Maintains spatial and visual consistency across complex scenes, including multi-panel layouts and product designs
- Embeds text accurately within images, preserving perspective and alignment
- Offers batch generation and high-resolution outputs for professional use
What Can I Use It For?
- Professional graphic design and branding, including logo creation and marketing visuals
- Product visualization and iterative design for e-commerce and advertising
- Storybook and comic layout generation, maintaining character and scene consistency
- Architectural rendering with accurate elevations, sections, and annotations
- Creative art projects, such as anime illustrations, cinematic scenes, and digital paintings
- Social media content creation, including high-resolution posts and promotional materials
- Personal projects, such as custom portraits, memes, and hobbyist artwork
- Industry-specific applications like fashion design, interior visualization, and technical documentation
Things to Be Aware Of
- Some features, such as the drag-and-drop editor, are in beta and may exhibit experimental behaviors
- Users report occasional quirks with prompt interpretation, especially for highly abstract or ambiguous instructions
- High-resolution and batch generation require significant computational resources; performance may vary based on complexity
- Consistency across multi-image generations is a noted strength, but edge cases can arise with unusual compositions
- Positive feedback highlights ease of use, realism, and prompt adherence; users appreciate the intuitive editing workflow
- Common concerns include occasional slowdowns during peak usage and limitations in extremely niche artistic styles
- The free tier offers daily image generations, but exact limits are not fully disclosed
Limitations
- Proprietary architecture details and parameter counts are not publicly available, limiting transparency for technical benchmarking
- May not be optimal for highly specialized artistic styles or scientific imaging requiring domain-specific accuracy
- Processing times can increase with high-resolution outputs or complex multi-image fusions
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