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Machine Learning Diagram Maker: build any machine learning diagram fast

Transformer and CNN architectures, training and inference pipelines, diffusion and generative-model schematics, RAG and agent system diagrams, distributed-training topologies — describe the model with text to figure and LabFig renders a clean, conference-grade machine learning diagram in seconds. Reproducing a baseline from a paper? Match its layout with reference to figure, then fine-tune every block on the vector canvas. This machine learning diagram maker is built for ML researchers, AI engineers, and CS grad students who would rather tune models than wrestle with boxes and arrows in PowerPoint. Every block, label, and arrow in the machine learning diagram stays editable afterward.

Make an ML diagram See examples

†Free credits to start — no credit card required

A transformer architecture machine learning diagram with encoder stack and attention detail, generated by LabFig

A convolutional neural network image-classification pipeline machine learning diagram, generated by LabFig

Transformer · NeurIPS

Model architecturesTraining pipelines

RAG & agent systems

Workbench

Make a machine learning diagram

Describe a model architecture, training pipeline, or AI system — or pick an example below — and render a clean, conference-ready machine learning diagram right here.

LabFig · Workbench

AI Scientific Figure Workbench

Pick a mode, describe the figure, get a journal-grade draft in seconds. Export to SVG / PDF.

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Cost 2 credits·Remaining 0 credits

Your figure will appear here

Pick a mode · Describe the figure · Generate

Examples

Start from a machine learning diagram example

Click any machine learning diagram example to load it into the workbench above, then tweak the wording and generate.

How it works

From a model description to a conference machine learning diagram in three steps

Describe the architecture in plain language, let LabFig lay out the blocks and data flow of your machine learning diagram, and polish the details — all in one place, no design software.

Describe the figure you need in plain language

1Step 1

Describe the architecture

Name the blocks and how data flows through them — input embedding and positional encoding, multi-head attention, residual connections and layer norm, then a softmax head; or an encoder-decoder, a U-Net, a CNN backbone, or a multi-stage training pipeline.

LabFig generates a publication-quality draft figure

2Step 2

Generate a draft

LabFig reads the ML semantics in your description and renders a clean, conference-grade machine learning diagram with the layers, skip connections, tensor-shape annotations, and labeled arrows already arranged the way a reviewer expects to read them.

Refine on the vector canvas and export to SVG, PDF or PNG

3Step 3

Refine and export

Rename a layer, recolor a stage, or reroute a residual arrow in your machine learning diagram on the vector canvas, then export SVG, PDF, or 300dpi PNG straight into your NeurIPS, ICML, or CVPR submission, slide deck, or thesis.

Why LabFig for ML

A machine learning diagram that speaks the language of the model.

A machine learning diagram maker that understands attention blocks, skip connections, and data pipelines — and outputs an editable, submission-ready machine learning diagram instead of flat AI pictures.

Model architectures

Transformers, CNNs, and beyond, laid out cleanly

Describe a transformer with multi-head attention and feed-forward blocks, a ResNet or U-Net backbone, or a multi-encoder fusion model, and LabFig arranges the layers, residual paths, attention insets, and tensor-shape labels into the kind of machine learning diagram you see in NeurIPS and ICLR papers.

TransformersCNN backbonesEncoder-decoderAttention insets

Pipelines & systems

From training loops to RAG and agent stacks

Need a left-to-right training-and-inference pipeline, a diffusion forward-and-reverse schematic, a contrastive pretraining setup, or a RAG retriever-plus-LLM and tool-using agent diagram? LabFig drafts the system-level and distributed-training machine learning diagram ML engineers rely on, with the data flow and component boundaries intact.

Training pipelinesDiffusion schematicsRAG & agentsDistributed training

Editable output

Editable vectors, not flat pixels

Every layer name, tensor-shape annotation, and arrowhead stays editable after generation. Fix a dimension label, swap a palette to match your figure set, or select one block and regenerate it — no need to redraw the whole architecture for a reviewer's comment.

SVGPDFPNG · 300dpiVector canvas

Figure templates

Start from a machine learning diagram template

Pick a machine learning diagram starting point — model architectures, ML pipelines, generative-AI schematics, AI system stacks, or distributed systems-design diagrams — and load it straight into the workbench to make it yours.

Machine learning diagram FAQ

Common questions about making a machine learning diagram and neural network diagram with LabFig.

Any machine learning diagram you need: model architecture figures (transformers, CNNs, ResNets, U-Nets, encoder-decoders), training and inference pipelines, diffusion and other generative-model schematics, RAG and tool-using agent system diagrams, distributed-training and data-parallel topologies, ablation and benchmark overview figures, and end-to-end ML system architectures. If it appears as a schematic in an ML paper or system design doc, you can describe it and generate it.

Yes — this is the most common use. Name the blocks and the data flow (for example 'input embedding plus positional encoding, a stack of multi-head self-attention and feed-forward layers with residual connections and layer norm, ending in a softmax head, with an attention-map inset') and LabFig arranges the layers, skip connections, and labeled arrows into a clean machine learning diagram you can refine on the canvas. For a worked example, read how authors make AI figures for machine learning papers.

LabFig produces a schematic-level machine learning diagram that follows common publishing conventions for resolution, typography, and color, and exports SVG, PDF, and 300dpi PNG — so it sits cleanly alongside the architecture and pipeline figures in NeurIPS, ICML, ICLR, CVPR, and ACL papers. For exact tensor shapes or precise equations, generate the layout first and fine-tune the specific labels and symbols on the vector canvas, then check your venue's figure guidelines before submission.

Yes. The output is editable vector art, not a flat image. You can rename any layer or block, correct a tensor-shape or dimension annotation, recolor a stage of the pipeline, reroute a residual or skip connection, or select a single block and regenerate just that part with a new instruction — so responding to a reviewer takes seconds, not a redraw.

That's exactly who it's built for. You write the architecture or pipeline in plain language and LabFig handles the layout, alignment, and conference-grade styling. ML researchers, AI engineers, and CS grad students use it to turn a whiteboard sketch of a model into a clean machine learning diagram for a paper, slide deck, or thesis chapter without learning Illustrator, TikZ, or diagrams.net. Working on embodied AI or control? The robotics figure maker shares the same block-and-arrow engine for autonomy stacks and control loops.

Make your next machine learning diagram

Describe an architecture, pipeline, or system and get a conference-ready machine learning diagram in minutes — free while you explore.

Make an ML diagram Text to Figure

Prefer to start from a sentence? Try Text to Figure