Understanding the text-to-image Pipeline with Comfy
Nodes, Noise, and Know-How: A ComfyUI Crash Course for A1111 Refugees & Curious Creators.
Welcome back, AI art enthusiasts! Today, we’re diving into ComfyUI, the powerful but sometimes intimidating node-based interface for generative AI.
We’ll focus on the KSampler to understand Comfy’s unique data flow and how it is built on top of nodes like the VAE decoder.
If you’re coming from A1111, ComfyUI can feel like switching from a car’s automatic transmission to manual. Let’s get started.
The "Fixed Pipeline" Approach of Automatic1111
A1111 operates like a modular assembly line: It provides predefined pipelines (such as text-to-image or image-to-image generation), where each step can be customized—like swapping out a checkpoint model or adjusting the output resolution. However, the fundamental structure of the pipeline remains fixed, much like how a factory conveyor belt follows a set sequence even when individual components are modified.
At first glance, Automatic1111’s simplicity is appealing—just type a prompt and hit Generate. But as users dive deeper into generative AI, they inevitably hit walls that ComfyUI’s modular design helps break through. Soon you will desire to adapt the pipeline and understand its inner workings.
The "Build your own Pipeline" Approach of ComfyUI
ComfyUI gives you modular components to build custom pipelines (respectively workflows in Comfy jargon). The pipeline is constructed using nodes, connected by wires.
Nodes serve as the fundamental building blocks, each performing a specific function that can be configured via parameters in the user interface. Inputs are received on the left side of a node, while outputs are generated on the right.
The data flows between nodes through connecting wires. When executing a pipeline, processing begins with nodes that have no inputs. These nodes execute first, and their outputs propagate through the wires to downstream nodes until the pipeline completes its execution.
These fully customizable pipelines unlocks unparalleled flexibility. Build “if-else” workflows ("if character’s eyes are blurry, run face refinement"), inspect intermediate results, blend models, so many more; the canvas is your!
A Data Flow for Text-to-Image Generation
Let’s understand one of the pipelines using the Yanoya AI Platform. After starting the ComfyUI playground, select a pipeline via the Workflow / Browse Templates menu:
Several templates will be shown. Please select the “Image Generation”.
You should now see a pipeline as following:
Using A1111, a similar pipeline works in the background — Comfy gives you the freedom and flexibility to zoom in and to understand and adapt the underlying pipeline steps. Pressing the blue Queue button executes the pipeline and the resulting image will be shown.
In my previous post on A1111, we explored key configuration parameters like the seed value. In the upcoming tutorial, we’ll walk through the example above step by step—but first, let’s break down how this pipeline works.
The Stable Diffusion Pipeline Architecture
To truly grasp how this pipeline works, we need to examine its three core components working in harmony:
CLIP (Contrastive Language-Image Pretraining): Understands the prompt and translates it into a world of visual concepts, which can be understood by the KSampler.
KSampler (Kolmogorov Sampler): Executing the image generation: Executes the iterative refinement engine to transform noise into an image.
VAE (Variational Autoencoder): The KSampler doesn’t directly produce the image in pixel space with each pixel representing a color. Instead, it uses the latent space (of smaller resolution), which also act as a 2D canvas, where each pixel consists out of multiple channels. Each channel represents abstract visual features (like edges, textures, shapes). The VAE translates this abstract canvas into a final high-quality image.
The "meaning" of the visual concepts and the channels of the latent space are discovered during the training phase of the checkpoint model. This training determines how the AI will interpret the image features and the latent space, and it produces the three models CLIP, KSampler and VAE to translate to and from the representations used by the humans (textual prompt, pixel space).
From Meow to Model
How would it look like if the diffusion model would be trained only using pairs of cat images and image descriptions?
1. Image Feature Space: Specialized in Feline Visual Concepts
The model would develop a highly specialized understanding of cat-related concepts contained in textual descriptions, such as:
Physical attributes: Ear shapes (pointed, folded), fur patterns (tabby, calico), eye variations (slit, round)
Poses & behavior: Sitting, pouncing, stretching
Breed distinctions: Siamese vs. Maine Coon vs. Sphynx
2. CLIP Model: Text-to-Visual Concept Mapping
The text encoder learns the translation of textual prompts into these visual concepts, including learning of associations (e.g., "feline" = "cat", "kitten" = "young cat").
CLIP becomes like a cat show judge; an expert at describing/classifying cats.
Typical Limitations:
Failure on out-of-distribution prompts (e.g., "a dog" → poor generation or artifacts)
Hallucination risk: Might impose cat-like features (e.g., "ears") on unrelated inputs
3. Latent Space: Compact Representation of Cat Anatomy
The latent space would develop a highly specialized understanding of cat-related visual features contained in cat images, such as:
Compact representation of cat anatomy (4 legs, tail, whiskers)
Efficient encoding of fur textures (short/long, smooth/fluffy)
Spatial relationships (eye above nose, eye placement/symmetry)
4. VAE: Pixel-Level Translation with Constraints
The VAE learns the translation between these visual features and the image space.
The VAE becomes like a cat printer: It converts a compressed blueprint of a cat into a full picture. The latent space is the language to describe the blueprint.
Typical Limitations:
Any input is translated into cat-like pictures, which can produce "cat-like noise" with disjointed features (e.g., floating ears, misplaced eyes)
Background neglect: Blurring or inconsistency, as the training prioritized cat-centric features
5. KSampler
The KSampler operates in the VAE's latent space, iteratively refining noise into a final latent that matches well to the intention of the text prompt (which has been translated into the image feature space).
If you want to build your own pipelines in ComfyUI, this level of understanding is essential to help you choose the right nodes and assemble them into a working workflow.
🚀 Try It Yourself
ComfyUI is complex, but once you get used to creating your own pipelines, you’ll wonder how you lived without it. I will be describing customized pipelines on the blog and publishing them on the Yanoya AI playground. Start experimenting with ComfyUI right now!
Coming soon: A deep dive into tweaking ComfyUI's text-to-image pipeline. We'll start with the basics, then get creative by swapping nodes and testing configurations to truly understand how everything connects.
Got questions? Drop them in the chat.