Flux Depth and Canny ControlNet: Complete Guide 2025

Flux-Depth and Flux-Canny provide structural guidance for Flux generations—letting you control composition while Flux handles the creative details. These tools bridge the gap between pure text-to-image and precise control.

Quick Answer: Flux-Depth uses depth maps to guide 3D structure and object placement. Flux-Canny uses edge detection to preserve outlines and shapes. Both are official Black Forest Labs extensions that work with base Flux models.

Understanding Depth vs Canny Control

Depth Control:

• Uses grayscale depth maps (white = close, black = far)
• Preserves spatial relationships and perspective
• Best for: Scene composition, maintaining object distances, 3D layout

Canny Edge Control:

• Uses edge detection output (white lines on black)
• Preserves outlines and shape boundaries
• Best for: Maintaining shapes, architectural lines, precise boundaries

Setting Up Flux ControlNet in ComfyUI

Step 1: Install Required Nodes

cd ComfyUI/custom_nodes
git clone https://github.com/Acly/comfyui-inpaint-nodes
# Or use ComfyUI Manager to find Flux ControlNet nodes

Step 2: Download Control Models

From HuggingFace:

• flux-canny-controlnet.safetensors
• flux-depth-controlnet.safetensors

Place in:

ComfyUI/models/controlnet/

Step 3: Download Preprocessors

For generating control images:

• Depth: MiDaS or Zoe depth estimators
• Canny: OpenCV canny edge detector

Basic Depth Control Workflow

Generating Depth Maps:

1. Load source image
2. Apply depth estimation (MiDaS recommended)
3. Output grayscale depth map
4. Feed into Flux-Depth

Workflow Structure:

Load Image → Depth Estimator → Flux-Depth ControlNet →
Base Flux → Sample → Decode → Save

Key Settings:

• Control Strength: 0.5-0.8 (higher = stricter depth matching)
• End Percent: 0.8-1.0 (when to stop guidance)

Basic Canny Control Workflow

Generating Edge Maps:

1. Load source image
2. Apply Canny edge detection
3. Adjust threshold for detail level
4. Feed into Flux-Canny

Threshold Settings:

• Low threshold: 100-150 (more edges, more detail)
• High threshold: 200-250 (fewer edges, major outlines only)

Workflow Structure:

Load Image → Canny Preprocessor → Flux-Canny ControlNet →
Base Flux → Sample → Decode → Save

Key Settings:

• Control Strength: 0.4-0.7 (canny often needs lower strength)
• Lower strength allows more creative interpretation

Combining Depth and Canny

Maximum control comes from combining both:

Dual Control Workflow:

Source Image
    ├→ Depth Preprocessor → Flux-Depth
    └→ Canny Preprocessor → Flux-Canny
                               ↓
                     Combined Conditioning
                               ↓
                         Flux Sample

Strength Balancing: When combining, reduce individual strengths:

• Depth: 0.4-0.5
• Canny: 0.3-0.4
• Total should be around 0.7-0.9

Too much combined control restricts creativity excessively.

Optimal Settings by Use Case

Architectural Rendering:

• Canny: 0.7 (preserve building lines)
• Depth: 0.5 (maintain perspective)
• Prompt: Include architectural style

Portrait Style Transfer:

• Depth: 0.6 (preserve face structure)
• Canny: 0.3 (soft outline guidance)
• Lower canny prevents rigidity

Illustration from Photo:

• Depth: 0.7 (maintain composition)
• Canny: 0.2-0.4 (depending on desired precision)
• Prompt: Specify art style clearly

Product Variations:

• Canny: 0.8 (keep product shape)
• Depth: 0.4 (maintain product prominence)
• High canny for shape accuracy

Creating Control Images Manually

Sometimes preprocessing isn't enough. Create custom control images:

For Depth:

• Paint grayscale in any image editor
• White = foreground, Black = background
• Use gradients for smooth depth transitions

For Canny:

• Draw white lines on black background
• Only include lines you want respected
• Cleaner input = more predictable output

Advanced Techniques

Partial Control

Apply control to only part of the image:

1. Create partial control image (black where no control)
2. Flux only uses guidance where signal exists
3. Creative freedom in masked areas

Control Scheduling

Change control strength over steps:

• Start: High strength (establish structure)
• Middle: Reduce strength (allow details)
• End: Minimal strength (pure refinement)

Resolution Matching

Control images must match generation resolution:

• Upscale/downscale control images as needed
• Maintain aspect ratio
• Bilinear interpolation for smooth scaling

Troubleshooting

Issue: Output doesn't match control image Solution: Increase control strength, check resolution match

Issue: Output looks too rigid/artificial Solution: Lower control strength, especially canny

Issue: Depth is inverted Solution: Invert your depth map (some estimators reverse convention)

Issue: Canny creates artifacts Solution: Lower threshold, use cleaner edge maps, reduce strength

Issue: Combined controls conflict Solution: Lower both strengths, ensure maps are consistent with each other

Flux ControlNet vs SDXL ControlNet

Flux Advantages:

• Higher quality base model
• Better prompt following
• More natural integration

SDXL Advantages:

• More control types available
• Larger ecosystem
• Lower VRAM requirements
• More community examples

For most users, Flux ControlNet produces better results when depth or canny is sufficient.

Performance Considerations

ControlNet adds compute overhead:

VRAM Impact:

• Each control adds ~1-2GB
• Combining depth + canny needs 14GB+ total
• Use fp8 T5 encoder for headroom

Speed Impact:

• ~20% slower than base Flux
• Preprocessing adds initial overhead
• Caching control images helps batch workflows

Frequently Asked Questions

Can I use custom depth maps from 3D software?

Yes! Rendered depth passes from Blender, Maya, etc. work great. Normalize values to 0-255 range.

Do I need specific preprocessors?

MiDaS for depth and OpenCV Canny are recommended but not required. Any proper depth/edge output works.

Can I control specific objects only?

Create control images with only the objects you want controlled. Black areas are ignored.

What's the difference from Flux-Redux?

Redux uses image conditioning (style transfer). Depth/Canny use structural conditioning. Different purposes.

How does control interact with prompts?

Prompts handle content/style; control handles structure. They work together, not in opposition.

Conclusion

Flux-Depth and Flux-Canny fill a critical gap—providing structural guidance while preserving Flux's generative quality. For architectural visualization, consistent character poses, or any application needing spatial control, these tools are essential.

Start with single controls (depth OR canny) at moderate strength, then combine as needed. The balance between control and creative freedom is what produces the best results.