ComfyUI Nodes and Models for Architecture

Which files does the graph load, and which node reads each one?

Which of those families belongs on a given job is already argued on the model routing page.

Comfy's Flux ControlNet tutorial writes the official routes as two different diffusion loads. In the full Canny workflow, Load Diffusion Model reads flux1-canny-dev.safetensors. In the Depth LoRA workflow, Load Diffusion Model reads flux1-dev.safetensors and LoraLoaderModelOnly reads flux1-depth-dev-lora.safetensors. The same page tells you to repeat the full-model workflow with FLUX.1 Depth, and the LoRA workflow with FLUX.1 Canny LoRA. Flux ControlNet tutorial

Both of those workflows load the text encoders together. DualCLIPLoader takes t5xxl_fp16.safetensors in clip_name1 and clip_l.safetensors in clip_name2. Load VAE takes ae.safetensors. The tutorial's folder list puts the encoders in models/text_encoders/, the VAE in models/vae/, the diffusion file in models/diffusion_models/, and the LoRA in models/loras/. Flux ControlNet tutorial

The examples page publishes flux1-canny-dev-lora.safetensors and flux1-depth-dev-lora.safetensors for models/loras/, and the full models for models/diffusion_models/. For lower memory it offers t5xxl_fp8_e4m3fn_scaled.safetensors, a T5 file, in place of the fp16 T5. It recommends the fp16 T5 when the computer has more than 32 GB of RAM. That figure is RAM, not a statement about which device runs the encoder. The encoder download lists clip_l.safetensors under that full name. Flux examples flux_text_encoders

The single-file checkpoint is a different install. flux1-dev-fp8.safetensors, published by Comfy-Org, goes in models/checkpoints/ and loads with Load Checkpoint. Set CFG to 1.0. The page says fp8 degrades the quality a bit, and that the full 16-bit weights are preferable when the machine can hold them. Flux examples flux1-dev-fp8.safetensors

ae.safetensors belongs in models/vae/. The examples page links the copy on Comfy-Org/Lumina_Image_2.0_Repackaged. The tutorial's list links an ae.safetensors on the FLUX.1 schnell repo. ae.safetensors Flux ControlNet tutorial

FLUX.1 [dev] is a 12 billion parameter rectified flow transformer, trained with guidance distillation. FLUX.1-dev model card

Load, encode, constrain, sample, save: that order is the starter graph. A custom node is an install. Dependency budget

The split-file rows follow the tutorial's nodes and folders. The LoRA filenames, the fp8 T5 substitute, and the single-file checkpoint row follow the examples page. Flux ControlNet tutorial Flux examples

Where do the depth and Canny files go, and why are they not named control_flux_depth?

Black Forest Labs ships structural conditioning as four weights: a full Canny model, a Canny LoRA, a full Depth model, and a Depth LoRA. The lab's description is narrow. The method uses a Canny edge map or a depth map, and it is particularly effective for retexturing. The Hugging Face repos named there are black-forest-labs/FLUX.1-Canny-dev, black-forest-labs/FLUX.1-Depth-dev, and the two LoRA repos beside them. Structural conditioning

Comfy's filenames are flux1-canny-dev.safetensors and flux1-depth-dev.safetensors in models/diffusion_models/, and flux1-canny-dev-lora.safetensors and flux1-depth-dev-lora.safetensors in models/loras/. Nothing on that examples page is called control_flux_depth.safetensors. Flux examples

The depth card says the model generates an image from a text description while following the structure of a given input image, and that it too is a 12 billion parameter rectified flow transformer. FLUX.1-Depth-dev

A flat render with no exported pass is the case this recovery page takes. No source 3D model, only that flat image, is the estimated-map case. Depth without the model Sorting a line drawing, a viewport, or a finished image is the triage. Input triage Matching a control to one defect comes after the file family is right. Mask, edge, or depth

The four-pass article's own headings are structure and composition, light and material response, semantic-region inpainting, then upscale. Vegetation and glazing sit inside the inpainting pass. Four passes

What is a preprocessor, and why is it not the model?

comfyui_controlnet_aux makes hint images. The README says the repo only supports preprocessors that make those hints, and that the all-in-one preprocessor cannot set a preprocessor's own thresholds. ControlNet aux README

Its table has a column headed ControlNet/T2I-Adapter. Canny Edge lists control_v11p_sd15_canny, Scribble Lines lists control_v11p_sd15_scribble, and M-LSD Lines lists control_v11p_sd15_mlsd. MiDaS Depth Map and Zoe Depth Map list control_v11f1p_sd15_depth. Depth Anything, Zoe Depth Anything, and Depth Anything V2 link to a checkpoint in the Depth-Anything repository instead. The column records those listings. It does not state that every listed name loads on every checkpoint. ControlNet aux README

M-LSD finds line segments. M-LSD paper Depth Anything V2 and ZoeDepth are estimators with their own code. Depth Anything V2 ZoeDepth ControlNet, the method, is a network trained to take a hint image as a condition. ControlNet paper

A preprocessor writes a hint image from the input. Compatible conditioning weights are a separate model file. An estimator's own weights are the model it runs, and the hint image is the picture it writes out. For edges, the line-extractor row is Canny Edge. Comfy's Flux tutorial calls FLUX.1 Canny and FLUX.1 Depth Black Forest Labs tools, keeps community ControlNets separate, and names this aux pack as one way to make the hint. Its examples start from an image that is already processed. Flux ControlNet tutorial

How far that image may move is the denoise page. Denoise

Can an SDXL file or an SD1.5 file drive a Flux checkpoint?

Comfy puts community Flux ControlNets in models/controlnet. InstantX Canny is renamed instantx_flux_canny.safetensors for the example. Shakker-Labs Depth, Shakker-Labs Union Pro, and the XLabs collection sit in that same folder. The examples page treats them as their own downloads, separate from the Black Forest Labs full models. Flux examples

XLabs depth v3 was trained at 1024, and its card says the weights fall under the FLUX.1 [dev] Non-Commercial License. XLabs flux-controlnet-depth-v3

ip-adapter-plus_sdxl_vit-h is an IP-Adapter for SDXL 1.0. That plus model uses patch embeddings from OpenCLIP-ViT-H-14. The weights are the safetensors file under sdxl_models on that repo. The Comfy nodes that load it are a separate pack, ComfyUI_IPAdapter_plus. IP-Adapter model card ip-adapter-plus_sdxl_vit-h.safetensors ComfyUI_IPAdapter_plus

Flux's own image prompt is Redux. The vision file is sigclip_vision_patch14_384.safetensors in models/clip_vision/, and the style model is flux1-redux-dev.safetensors in models/style_models/. The card is black-forest-labs/FLUX.1-Redux-dev. Flux examples FLUX.1-Redux-dev

What license applies when the weights download?

The Flux README table is the index. FLUX.1 [dev], Fill, Canny, Depth, both structural LoRAs, Redux, and Kontext [dev] are under the FLUX.1 [dev] Non-Commercial License. FLUX.1 [schnell] and the autoencoder weights are Apache-2.0 on that same table. Flux README

The dev model card has two sentences that stay in the same breath. Generated outputs can be used for personal, scientific, and commercial purposes as described in the non-commercial license. And the model falls under the FLUX.1 [dev] Non-Commercial License. "As described in" is the clause that keeps the output sentence from being read as a commercial license for the weights. FLUX.1-dev model card

A paid license for the weights is a separate offer. BFL licensing

Which file is only an upscaler?

Ultimate SD Upscale is a custom node. It runs image-to-image in tiles so the diffusion step stays near the resolution the model was trained for. It is a node, and it does not stand in for flux1-dev.safetensors. Ultimate SD Upscale

On that node's own doc, upscale_by defaults to 2.0. UltimateSDUpscale node doc

4x-UltraSharp is a different object. The OpenModelDB listing identifies it as a 4x ESRGAN by Kim2091, under CC-BY-NC-SA-4.0. The non-commercial term is why the file is a poor default when the image is paid work. 4x-UltraSharp on OpenModelDB

Why do people ask this instead of "which workflow"?

A thread on r/comfyui offered a sketch-to-image JSON aimed at architects, built from stock nodes, and it named Juggernaut SDXL as the checkpoint. A commenter wrote that there were an awful lot of Juggernaut SDXL models, and asked which one the workflow wanted. This page does not crown a Juggernaut file. The thread

A workflow JSON can name a file. The bytes stay outside it. Read the graph before you install what it asks for. Read before you run

The folder is the argument. A Flux checkpoint with an SD1.5 scribble model beside it is two products sharing a hard drive.