Flux Fill Complete Guide: Inpainting and Outpainting 2025

Flux Fill is Black Forest Labs' purpose-built model for inpainting and outpainting. Unlike using base Flux for masked generation, Fill was trained specifically for seamless editing—producing results that blend naturally with surrounding content.

Quick Answer: Flux Fill handles both inpainting (replacing masked areas) and outpainting (extending beyond boundaries). It offers superior blending compared to base Flux models and works through dedicated workflows in ComfyUI and via API.

Flux Fill vs Base Flux for Editing

Why use Fill instead of base Flux with masks?

Training Difference: Flux Fill was specifically trained on inpainting tasks. It understands how to analyze surrounding context and generate content that blends seamlessly.

Edge Handling: Fill produces smooth transitions at mask boundaries. Base Flux often shows visible seams or style mismatches.

Prompt Interpretation: Fill interprets prompts relative to the masked area. Base Flux sometimes replaces more than intended.

For serious editing work, Fill produces consistently better results.

Setting Up Flux Fill in ComfyUI

Installation:

cd ComfyUI/custom_nodes
git clone https://github.com/city96/ComfyUI-GGUF
# Or use FluxInpaint nodes

Model Download: Download Flux Fill from HuggingFace or Black Forest Labs. Place in:

ComfyUI/models/diffusion_models/flux-fill.safetensors

Required Components:

• Flux Fill model
• Flux VAE
• CLIP text encoders (same as base Flux)

Basic Inpainting Workflow

Node Setup:

Load Image → Create Mask → Load Flux Fill →
Encode Image → Sample with Mask → Decode → Save

Key Nodes:

1. Load Image: Your source image
2. Mask Editor: Create or load mask (white = replace)
3. VAE Encode Inpaint: Handles masked latent encoding
4. FluxSampler: With conditioning and mask
5. VAE Decode: Output final image

Settings:

• Steps: 20-30 (fewer needed than base Flux)
• Guidance: 30-50 (Flux uses higher guidance)
• Denoise: 1.0 for full replacement, lower for blending

Basic Outpainting Workflow

Outpainting extends images beyond their current boundaries:

Process:

1. Load original image
2. Expand canvas in desired direction
3. Create mask covering the new empty area
4. Run Flux Fill with directional prompt
5. Result fills the expansion naturally

Canvas Expansion Methods:

• Manual: Expand in image editor, save with transparency/white
• ComfyUI: Use Pad Image node to add space
• Custom: ImageMagick or PIL scripting

Prompting for Flux Fill

Fill prompts describe what should appear in the masked area:

Object Removal:

clean background, empty floor, matching wooden texture,
natural lighting from window, consistent shadows

Object Replacement:

modern leather armchair, dark brown, matching room lighting,
casting natural shadow on floor

Outpainting Extension:

continuing forest path, autumn leaves, trees extending
into distance, consistent afternoon lighting

Advanced Techniques

Iterative Refinement

For complex edits:

1. First pass: Rough fill at high denoise
2. Create new mask for problem areas
3. Second pass: Lower denoise for refinement
4. Repeat until satisfied

Context Maximization

Flux Fill uses surrounding context. For better results:

• Provide more context around the mask
• Don't mask too close to image edges
• Include reference elements in visible areas

Multi-Region Editing

Edit multiple areas in one pass:

• Create a single mask with multiple regions
• Prompt describes all replacements
• Works if regions need similar content

For different content per region, process separately.

Outpainting in Steps

For large extensions (2x or more):

1. Extend 25-50% per pass
2. Use previous output as next input
3. Overlap slightly for consistency
4. More passes = better coherence

Flux Fill vs Flux Kontext

Both edit images but differently:

Flux Fill:

• Uses explicit masks
• You control exactly what changes
• Better for precise modifications
• Requires manual mask creation

Flux Kontext:

• Uses text instructions
• AI decides what to modify
• Better for described changes
• No masking needed

Use Fill when you need precise control over what changes. Use Kontext when you can describe the change verbally.

Common Use Cases

Product Photography

Remove unwanted elements, extend backgrounds:

clean white product background, soft gradient,
professional studio lighting, no shadows

Portrait Editing

Fix backgrounds, extend compositions:

neutral bokeh background, soft out of focus elements,
warm lighting, professional portrait style

Real Estate

Remove clutter, extend spaces:

clean empty room, matching flooring, consistent wall color,
natural window light, staged home appearance

Creative Extension

Expand artwork beyond original frame:

continuing art style, matching brush strokes,
color palette extension, seamless continuation

Troubleshooting

Issue: Visible seams at mask boundary Solution: Use larger mask blur, extend mask beyond target

Issue: Style doesn't match Solution: Add style descriptors to prompt, reduce steps slightly

Issue: Wrong content generated Solution: Be more specific in prompt, add negative guidance

Issue: Extension looks repetitive Solution: Add variety prompts, process in smaller chunks

Issue: Colors don't match Solution: Include color descriptions, try color conditioning

Performance Optimization

Flux Fill has similar requirements to base Flux:

• VRAM: 12GB+ recommended
• Speed: ~10-15 seconds per generation
• Resolution: Works up to 2K, optimal at 1K

For faster iteration:

• Use lower steps (15-20) for drafts
• Process at lower resolution first
• Use fp8 quantization on smaller GPUs

Frequently Asked Questions

Can Flux Fill remove watermarks?

It can, but may require multiple passes. The model excels when context helps—watermarks on complex backgrounds need more refinement.

Does it work with video?

Frame-by-frame is possible but lacks temporal consistency. Dedicated video inpainting is better for motion content.

Can I use Flux LoRAs with Fill?

Base Flux LoRAs generally work but may affect fill quality. Test carefully.

What mask format is required?

Standard black/white masks where white = replace. PNG with alpha channel also works.

How does it compare to SD inpainting models?

Flux Fill typically produces higher quality fills with better context understanding. Trade-off is higher VRAM requirements.

Conclusion

Flux Fill represents the current best for AI-powered image editing—combining the quality of Flux with purpose-built inpainting/outpainting capabilities.

For professional editing work, it produces results that genuinely blend with originals. The workflow is straightforward once set up, and the context awareness means fewer iterations for acceptable results.

Master the prompting approach (describe what should appear, not what's being removed) and you'll achieve edits that appear native to the original image.