Bobs LoRA Loader for ComfyUI

Block-weighted LoRA loading for ComfyUI. Instead of one strength slider for the whole model, you get a slider per conceptual part of it — text encoder, composition, subject, style, detail, texture — so you can keep the parts of a LoRA you want and turn down the parts you don't.

Three nodes, all under the Bobs_Nodes category:

Node	Use it for
Bobs LoRA Loader (FLUX)	FLUX.1 dev/schnell, Chroma and other FLUX variants, with FLUX's named blocks
Bobs LoRA Loader (SDXL)	SDXL and SD1.5/SD2, with the UNet's input/middle/output stages
Bobs LoRA Loader (Universal)	Everything else, and anything new — see supported architectures

Features

• Works on any supported architecture. The Universal loader discovers the model's block layout at runtime rather than reading a hard-coded table, so pruned, distilled and brand-new architectures work without an update.
• Granular block-level control. Tune a LoRA's strength separately on each conceptual part of the diffusion model.
• Presets for common jobs: Character, Style, Concept, Detail & Texture, Fix Hands/Anatomy — plus Custom for the sliders.
• Dialect-proof compatibility. Classification runs on the canonical model key each patch targets, after ComfyUI has translated the LoRA's own naming scheme. Every format ComfyUI can load — kohya lora_unet_*, OneTrainer lora_transformer_*, diffusers transformer.*, LyCORIS, DiffSynth, PEFT — is bucketed correctly, including fused qkv / linear1 patches.
• Per-block report. An info output (and a matching console log) shows the detected architecture and, per block, the weight used, how many tensors were found and how many were actually patched.
• Tells you when the pairing is wrong. Point a node at the wrong model family and it says so instead of silently doing nothing useful.
• Optional CLIP. Leave the clip input unconnected to patch the model only.
• Drop-in standard behaviour. Select Full (Normal LoRA) to get the same result as ComfyUI's built-in LoraLoader.

Installation

1. Navigate to your ComfyUI custom_nodes directory:

   cd ComfyUI/custom_nodes/

2. Clone this repository:

   git clone https://github.com/BobsBlazed/Bobs-Lora-Loader

3. Restart ComfyUI.

Or install Bobs_LoRA_Loader from the ComfyUI Manager / Comfy Registry.

How to Use

1. Right-click → Add Node → Bobs_Nodes, and pick the node matching your base model (see the table above). When in doubt, use Universal — it works on FLUX and SDXL too, just with depth-based block names instead of architecture-specific ones.
2. Connect MODEL and CLIP. CLIP is optional — leave it unconnected to patch the model only.
3. Pick the LoRA from lora_name.
4. Choose a preset, or set it to Custom and drive the sliders yourself.
5. strength is a global multiplier applied on top of every block weight.
6. Hook info up to a preview-text node (or read the console) to see exactly which blocks the LoRA touched.

Presets override the sliders. Anything other than Custom ignores the slider values entirely — it does not blend with them. Only strength still applies on top.

Reading the info output

[UNIVERSAL] mylora.safetensors  (preset: Style)
architecture: QwenImage  transformer_blocks[60]  (total 60)
block                                     weight   found  applied
Text Encoder                                0.20       0        0
Input & Embeddings                          1.00       9        9
Early Blocks (Composition)                  0.10     384      384
Early-Mid Blocks (Subject)                  0.00     384        0
Mid Blocks (Concept & Style)                0.50     384      384
Late-Mid Blocks (Detail)                    1.00     384      384
Late Blocks (Texture)                       1.00     384      384
Output Head                                 1.00       4        4
Other Tensors                               1.00       0        0
TOTAL                                               1933     1549

• architecture — the backbone ComfyUI detected, and the block stacks found in it. The FLUX and SDXL nodes report their architecture here too.
• found — tensors in this LoRA belonging to that block.
• applied — tensors actually patched. found > 0 with applied 0 means the block's weight is 0.00, which is usually what you asked for.
• found 0 means the LoRA contains no weights for that block at all; the console log spells this out per block.
• Other Tensors should normally be 0 or close to it. A large number here means the classifier could not place those tensors — see Troubleshooting.

Block Layout

FLUX

Ranges below are for the canonical FLUX.1 geometry (19 double-stream blocks, 38 single-stream blocks). Other depths are scaled proportionally.

Block	Covers
Text Encoder	CLIP-L / T5 text encoder weights
Text Conditioning	txt_in
Timestep Embedding	time_in
Image Hint	img_in
Guidance Embedding	guidance_in
Vector Embedding	vector_in
Early Downsampling (Composition)	double_blocks.0–3
Mid Downsampling (Subject & Concept)	double_blocks.4–7
Late Downsampling (Refinement)	double_blocks.8–9
Core/Middle Block (Style Focus)	double_blocks.10–18, single_blocks.0–7
Early Upsampling (Initial Style)	single_blocks.8–15
Mid Upsampling (Detail Generation)	single_blocks.16–31
Late Upsampling (Final Textures)	single_blocks.32–37
Final Output Layer (Latent Projection)	final_layer
Other Tensors	anything unmatched (normally empty)

SDXL

Also works for SD1.5 and SD2 — same UNet shape, different depth.

Block	Covers
Text Encoder	CLIP-L / CLIP-G
Input Blocks	input_blocks.*
Middle Block	middle_block.*
Output Blocks	output_blocks.*
Other Tensors	time_embed, label_emb, out.*

Universal

The Universal loader works on a single normalised depth axis. Almost every diffusion backbone is one or more ordered stacks of repeated blocks:

UNet (SD1.5 / SDXL)     input_blocks.N -> middle_block -> output_blocks.N
Dual-stream DiT (FLUX)  double_blocks.N -> single_blocks.N
HiDream                 double_stream_blocks.N -> single_stream_blocks.N
MMDiT (SD3)             joint_blocks.N
AuraFlow                double_layers.N -> single_layers.N
Qwen-Image / LTX-Video  transformer_blocks.N
Wan / Mochi / PixArt    blocks.N
Lumina                  noise_refiner.N -> context_refiner.N -> layers.N

Those stacks are discovered from the loaded model, concatenated in execution order, and every block gets a position from 0 to 1 along the result. That axis is split into five buckets, so the same five sliders mean the same thing on a 19+38-block FLUX, a 60-block Qwen-Image and a 20-stage SDXL UNet.

Block	Covers
Text Encoder	Every text-encoder weight (CLIP / T5 / LLM)
Input & Embeddings	Patch, timestep, guidance and context embedders
Early Blocks (Composition)	First 20% of the stack
Early-Mid Blocks (Subject)	20–40%
Mid Blocks (Concept & Style)	40–60%
Late-Mid Blocks (Detail)	60–80%
Late Blocks (Texture)	Final 20%
Output Head	Final projection back to latent space
Other Tensors	Anything unmatched (normally empty)

Directly verified against SD1.5, SDXL, SD3, FLUX (full and pruned geometry), AuraFlow, PixArt, LTX-Video, Lumina, Qwen-Image and Wan — every state-dict key of each classified, none falling through to Other Tensors.

Architectures such as HiDream, Chroma, Mochi, HunyuanVideo and Cosmos are supported by the same mechanism but were not part of that run, so treat them as expected-to-work rather than confirmed. Because the layout comes from the model rather than a table, an architecture missing from both lists will usually still work — the info output tells you whether the stacks were found.

Use the dedicated FLUX or SDXL loader when you want that architecture's named blocks; use Universal for everything else, or when you want one node whose sliders behave consistently across models.

Why Use Block-Weighted LoRA?

A single LoRA file often contains training for multiple concepts — a character's face, their clothing, and the overall artistic style. A standard LoRA loader applies all of it at one uniform strength.

That can be limiting:

• You might want a character's features but not the stiff, overbaked style it was trained with.
• You might want a LoRA's artistic style but not the character baked into it.
• Two LoRAs might fight each other when both are applied at full strength.

Roughly, earlier blocks carry composition and subject identity while later blocks carry style, detail and texture — so a Character preset keeps the early blocks and drops the late ones, and Style does the reverse. The exact split is in the tables above.

Troubleshooting

The nodes don't appear in the menu. Check the ComfyUI startup console for a traceback mentioning bobs_. The package needs no dependencies beyond ComfyUI itself, so this is usually a partial clone or a stale __pycache__.

WARNING: N% of UNet tensors landed in '<block>'. The node and the model disagree about the architecture — e.g. an SDXL model in the FLUX node. Switch to the node matching your model, or to Universal. The LoRA still applies, but the block sliders won't mean what their names say.

none of its tensors match this model. The LoRA was trained for a different architecture than the loaded model. Nothing is applied and the model passes through untouched.

High Other Tensors count. The classifier placed those tensors nowhere. For the FLUX/SDXL nodes this usually means the wrong node for the model; try Universal. If Universal also shows a high count, that's worth an issue — please include the info output, which names the architecture and stacks it found.

Results differ from the built-in LoraLoader. With Full (Normal LoRA) at the same strength they should match. Any other preset deliberately differs — that's the point of the node.

Development

The classification logic imports nothing from ComfyUI or torch, so the test suite runs on a bare interpreter:

python -m unittest discover -s tests -v

File	Contents
bobs_blocks.py	FLUX and SDXL block tables, presets, classifiers, strength resolution
bobs_universal.py	Runtime stack discovery and the depth-axis classifier
bobs_lora_loader.py	The ComfyUI nodes: key maps, patch routing, reporting
tests/	71 tests, no ComfyUI or torch required

See docs/ARCHITECTURE.md for how classification works and how to add support for a new architecture.

Changelog

See CHANGELOG.md. Latest release: 1.2.1.

License

Apache-2.0