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control_v11f1p_sd15_depth_fp16.safetensors

Welcome to the deep end—literally. control_v11f1p_sd15_depth_fp16.safetensors is the Depth ControlNet model from the SD1.5 series (fp16 version), designed to guide generation in ComfyUI using depth maps as the controlling signal. Think of it as giving your image generation process a sixth sense—for spatial awareness.

🧠 Overview​

This ControlNet model uses depth maps (monochrome representations of distance in 3D space) to inject spatial geometry into Stable Diffusion image generation. It's most effective when you want to preserve or simulate realistic object positioning, perspective, and spatial continuity.

It leverages a depth-predicting encoder (usually Midas or similar) to interpret input images and influence the denoising steps in your pipeline.

🛠️ Filename​

  • control_v11f1p_sd15_depth_fp16.safetensors

🌍 Ideal Use Cases​

  • Photorealistic composition retention (e.g. adding objects to a photo with perspective awareness)
  • Image-to-image refinement using 3D structure hints
  • 3D scene simulation in stills (e.g., architectural renders, interior design mockups)
  • Stylization of existing scenes while keeping spatial realism intact
  • Consistent multi-angle views of a character or object
  • Depth-aware fantasy and sci-fi environments that feel more grounded in physical space

🧩 Compatible Components​

ComponentRecommended Option
CheckpointAny SD1.5-based model
VAEvae-ft-mse-840000-ema-pruned or vae-ft-ema-560000 for cleaner structure
PreprocessorMidas Depth (depth_midas), ZoE-Depth, or similar depth map providers
SchedulerDDIM, DPM++ 2M Karras, or Euler A (with minor variation tolerance)

🔧 Required Workflow Nodes​

Here’s what your ComfyUI graph should include for a basic depth-controlled pipeline:

1. Preprocessor Node​

  • Use depth_midas or zoe_depth_preprocessor.
  • Input: Source image (to derive depth map from).
  • Output: A depth map to feed into the ControlNet.

2. ControlNetLoader Node​

  • Load the ControlNet model: CopyEdit control_v11f1p_sd15_depth_fp16.safetensors
  • Hook it up to the ControlNet node.

3. ControlNetApply Node​

  • Connect depth map output here.
  • Ensure the conditioning strength is adjusted according to desired control (see tips below).

4. Sampler/Generation Node​

  • The ControlNet conditioning will influence the denoising process.
  • Your main prompt still dictates style and detail, but ControlNet ensures spatial consistency.
SettingValue
Control Weight0.7–1.0 for strong structure adherence
Guidance Scale6–9 (typical for SD1.5 workflows)
Steps20–35 depending on model and complexity
Start/End Control0.0 to 1.0 for full influence
Input Image SizeKeep resolution under 768x768 for memory sanity unless you like GPU meltdowns

🧠 Prompting Tips​

  • Prompts should complement the geometry, not fight it. If the depth map shows a sofa in the foreground, don’t prompt for a beach sunset with "no furniture"—unless you're into uncanny valley furniture-shaped sand dunes.
  • Use phrases like:
    • “realistic lighting”
    • “in natural perspective”
    • “high detail, depth-focused”
  • Avoid overly abstract prompts if you're relying on precise geometry.

⚠️ Known Issues & Gotchas​

  • Depth map quality matters. Garbage in, garbage out. Always inspect your preprocessor’s depth output before generating.
  • Misalignment can occur if the prompt implies objects not consistent with depth.
  • Artifacts: If using models trained on anime or stylized data, depth control may get overridden or conflict with exaggerated anatomy.
  • Memory hog: The fp16 version helps, but ControlNet + large VAEs + high-res = bring a fan for your GPU.

✅ Pro Tips​

  • Want soft spatial guidance? Drop control weight to ~0.5.
  • Want photobashed realism with depth consistency? Try mixing this with SoftEdge or Canny ControlNets in parallel using the Multi-ControlNet node.
  • Stack it with LoRAs for pose or clothing control to finesse your outputs into structured masterpieces.

📚 Additional Resources

💬 Summary​

control_v11f1p_sd15_depth_fp16.safetensors is your go-to ControlNet for depth-aware, spatially coherent image generation in ComfyUI. Whether you're preserving photographic realism or nudging your creations into new 3D-aware directions, this ControlNet acts like a polite architect whispering, “Hey… maybe don’t put that chandelier under the couch.”

Pair it wisely, guide it gently, and it will reward you with structure you didn’t even know you needed.