Multi-Image Generation with Flux 2 Klein

One of Flux 2 Klein's more advanced features is its ability to work with multiple input images simultaneously. This opens up creative possibilities that go beyond simple text-to-image or single-reference workflows. You can blend concepts from different sources, maintain consistency across variations, and create compositions that would be difficult to prompt with text alone.

Multi-image workflows require understanding how Klein processes visual information alongside text prompts. The results can be remarkably powerful when used correctly.

Understanding Multi-Image Input

When you provide multiple images to Klein, each image is:

1. Encoded into latent space via the VAE
2. Combined with other image latents
3. Processed alongside your text conditioning
4. Used to guide the generation toward a blend of all inputs

The model doesn't simply average the images. It extracts semantic concepts, styles, and structural elements from each input and attempts to coherently combine them based on your prompt.

Use Cases for Multi-Image Generation

Style Blending

Combine artistic styles from different reference images.

Example workflow:

• Image 1: Watercolor painting sample
• Image 2: Your subject photo
• Prompt: "Watercolor style portrait, artistic brushstrokes"

Result: Your subject rendered in the watercolor style from the reference.

Character Turnarounds

Create consistent character views from limited references.

Example workflow:

• Image 1: Character front view
• Image 2: Character profile view
• Prompt: "Same character from 3/4 angle view, consistent features"

Result: A new angle maintaining character identity from the references.

Multiple reference images help maintain consistency across different outputs

Pose + Identity Transfer

Combine a pose from one image with identity from another.

Example workflow:

• Image 1: Person whose appearance you want
• Image 2: Pose reference (different person)
• Prompt: "Person from first image in the pose from second image"

Result: The identity transferred into the new pose.

Environment Composition

Place subjects in new environments by combining references.

Example workflow:

• Image 1: Subject/character
• Image 2: Background/environment
• Prompt: "Subject naturally placed in the environment, matching lighting"

Result: Coherent composition with proper integration.

Technical Setup in ComfyUI

Multi-image workflows in ComfyUI require specific node configurations.

Required Nodes

1. Multiple Load Image nodes - One per reference image
2. VAE Encode for each image - Convert to latent space
3. LatentBatch - Combine multiple latents
4. Standard generation nodes - KSampler, etc.

Workflow Structure

Image 1 → VAE Encode → LatentBatch → KSampler
Image 2 → VAE Encode ↗
Image 3 → VAE Encode ↗

Text Prompt → CLIP Encode → KSampler
Model → KSampler
KSampler → VAE Decode → Output

Key Settings

• Batch dimension: Ensure latents are properly combined along the batch dimension
• Prompt importance: Your text prompt guides how references are interpreted
• Weight balancing: Some setups allow adjusting influence of each reference

Best Practices

Image Selection

Choose references that have complementary elements:

• Similar aspect ratios when possible
• Clear, unambiguous content
• Relevant to what you want in the output

Avoid references that:

• Contradict each other conceptually
• Have vastly different styles (unless that's intentional)
• Are low quality or unclear

Prompt Crafting

Your prompt should guide how references are combined:

Good prompt examples:

• "Combine the pose from image A with the face from image B, professional lighting"
• "Apply the artistic style from reference to create a new scene with similar mood"
• "Character from first reference in the environment from second reference"

Avoid vague prompts:

• "Combine these images" (too ambiguous)
• Prompts that don't reference the inputs

Number of References

• 2 images: Most reliable results
• 3 images: Works well with clear prompt guidance
• 4+ images: Results become less predictable

More references means more concepts for the model to balance. Keep it focused unless you specifically want chaotic blending.

Advanced setups can chain multiple references for complex outputs

Limitations and Workarounds

4B vs 9B Capability

The 9B model handles multi-image input more effectively than the 4B. If you're doing significant multi-reference work, the 9B is recommended despite its higher VRAM requirements.

Conflicting Concepts

When references contain contradictory elements, results can be unpredictable. Mitigate by:

• Using clearer prompts that prioritize certain elements
• Reducing to fewer, more compatible references
• Iterating with different combinations

Identity Preservation

Maintaining exact facial identity across multi-image workflows is challenging. For better identity preservation, consider:

• Using dedicated face-swapping tools for final touches
• Providing multiple angles of the same face as references
• Being explicit in prompts about preserving specific features

Practical Examples

Example 1: Style Transfer with Subject

Goal: Apply the style of a famous painting to a modern photo

Setup:

• Reference 1: Van Gogh's Starry Night
• Reference 2: Modern cityscape photo
• Prompt: "Cityscape in Van Gogh's swirling brushstroke style, vibrant night colors, impressionist interpretation"

Example 2: Character Consistency

Goal: Generate a character in a new pose

Setup:

• Reference 1: Character facing camera
• Reference 2: Different person in desired pose
• Prompt: "First character's face and appearance in the second image's pose, same clothing style, consistent lighting"

Example 3: Product Placement

Goal: Show a product in different environments

Setup:

• Reference 1: Product image
• Reference 2: Lifestyle environment
• Prompt: "Product naturally placed in the scene, realistic shadows and reflections, commercial photography style"

Frequently Asked Questions

How many images can I use as references?

Technically 2-4 works best. More references make results less predictable as the model struggles to balance all inputs.

Does the 4B model support multi-image?

Limited support exists but the 9B handles it significantly better. For serious multi-image work, use the 9B.

Can I control which reference has more influence?

Some ComfyUI setups allow weight adjustments, but it requires additional nodes. By default, influences are roughly balanced.

Why do faces change when using multiple references?

Identity preservation across references is inherently difficult. Use explicit prompts about facial features and consider post-processing with face-swap tools.

Can I use multi-image for consistent character generation?

Yes, providing multiple angles of the same character as references helps maintain consistency across new generations.

Do all images need to be the same resolution?

They'll be resized to match, but starting with similar aspect ratios produces better results.

Can I combine photos and artwork as references?

Yes, this is useful for style transfer. The photo provides structure while artwork provides style.

Is multi-image slower than single-image generation?

Slightly, due to additional VAE encoding, but the difference is minimal on capable hardware.

Multi-image generation expands Flux 2 Klein's capabilities beyond simple prompting. Master this technique to unlock complex creative workflows that combine the best elements from multiple sources.

For users wanting easier multi-reference workflows without technical setup, platforms like Apatero offer intuitive interfaces for advanced generation techniques including LoRA training for consistent characters on Pro plans.