ControlNet, IP-Adapter, LoRA — Fine-Grained Control

The control stack

A bare diffusion model reads a text prompt and generates something plausible. Production creative work needs more: a specific pose, a specific character, a specific style. Three adapter families — ControlNet, IP-Adapter, and LoRA — cover 95% of professional use cases. They compose cleanly.

ControlNet — structural conditioning

ControlNet (Zhang et al., 2023) adds structural guidance to a diffusion model via an auxiliary network. You pass a conditioning image (edge map, depth map, pose skeleton, normal map, segmentation) and the model respects that structure while the text prompt fills in appearance. It's the foundation of 'put THIS character in THAT pose' and 'keep the composition, change the style.'

• Canny edges — preserve line structure of a sketch.
• Depth — preserve 3D layout of a reference photo.
• OpenPose — specify exact human body pose.
• Scribble — rough doodle controls composition.
• Segmentation — color-coded regions define what goes where.
• Tile — upscale with seamless detail.

IP-Adapter — image prompting

IP-Adapter (Ye et al., 2023) lets you prompt with an IMAGE, not just text. Feed it a reference image; the diffusion model's output borrows the reference's subject, style, or composition (depending on the variant). Crucial for character consistency across a comic, style matching across a brand system, and face-preserving portraits.

LoRA — lightweight fine-tuning

LoRA (Low-Rank Adaptation, Hu et al., 2021) adds a small set of trainable matrices to the diffusion model's attention layers. You can fine-tune a few MB of weights on as few as 10-30 images to teach the model a new character, object, artist style, or concept. Swap LoRAs at inference time — 'same base Flux, three different brand styles' is a one-line change.

Stacking adapters

The professional pipeline typically stacks: base model (Flux Dev) + character LoRA + style LoRA + ControlNet pose + IP-Adapter for facial consistency. Each layer adds constraint. The art is knowing when you're over-constraining (outputs look muddy, burnt) vs. under-constraining (outputs drift).

# ComfyUI / Diffusers-style pseudocode stacking adapters on Flux from diffusers import FluxPipeline, ControlNetModel import torch pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16 ).to("cuda") # Load a character LoRA (trained on 15 images of our mascot) pipe.load_lora_weights("./loras/mascot-flux-lora.safetensors", adapter_name="mascot") # Load a brand-style LoRA pipe.load_lora_weights("./loras/brand-style-lora.safetensors", adapter_name="brand_style") pipe.set_adapters(["mascot", "brand_style"], adapter_weights=[1.0, 0.7]) # Pose control from an OpenPose reference controlnet = ControlNetModel.from_pretrained("XLabs-AI/flux-controlnet-pose") # IP-Adapter for facial consistency with hero shot pipe.load_ip_adapter("XLabs-AI/flux-ip-adapter", weight=0.6) image = pipe( prompt="The mascot standing confidently in a neon-lit lab, cinematic", control_image=pose_reference, # OpenPose skeleton ip_adapter_image=hero_face, # Reference face num_inference_steps=28, guidance_scale=3.5, ).images[0]Production-style adapter stacking on Flux Dev in Diffusers.

Training a LoRA yourself

1. Collect 15-30 images of your concept. Varied angles, consistent subject.
2. Caption each image precisely; use a unique token (e.g., 'TDRLMSCT' — a made-up word) for the concept.
3. Train with kohya_ss, Replicate FLUX trainer, or Fal trainer. Typical time: 30 min on an H100, ~$3-10.
4. Validate: generate 10 outputs with and without the LoRA. Check concept is captured without overfitting.
5. Version. Tag the LoRA with training date, base model, and trigger token.