ComfyUI KSampler Bug: Add_noise Parameter Issue

Dec 20, 2025 by Alex Johnson 48 views

Hey ComfyUI enthusiasts! Today, we're diving into a peculiar bug that's been causing a bit of a stir within the community, specifically concerning the KSampler (Advanced) node and its add_noise parameter. If you've been experimenting with advanced workflows, especially those involving custom noise generation or fine-tuning the diffusion process, you might have encountered a situation where disabling add_noise doesn't quite behave as expected. This isn't just a minor glitch; it can significantly impact workflows that rely on precise control over the initial latent image. Let's break down what's happening, why it matters, and what we can potentially do about it.

Understanding the Expected Behavior of `add_noise`

In the world of Stable Diffusion and ComfyUI, the add_noise parameter within the KSampler (Advanced) node plays a crucial role in the diffusion process. Its primary function is to determine whether noise should be added to the initial latent image. When you're aiming for a specific outcome, especially if you've already generated or prepared a particular latent image with intentional characteristics, you might want to bypass the KSampler's built-in noise addition. The expected behavior when add_noise is disabled is straightforward: the node should take your provided latent_image and use it as is, without introducing any new noise. This allows users to maintain the integrity of their pre-existing latent data, which is vital for workflows like image-to-image transformations, inpainting with specific noise patterns, or using external noise sources. Imagine you've meticulously crafted a latent representation that holds unique artistic intent – you'd want the KSampler to respect that and build upon it directly. This parameter acts as a gatekeeper, ensuring that your carefully prepared latent input isn't inadvertently altered by the default noise generation mechanism.

This expectation is rooted in the fundamental understanding of how diffusion models work. The initial noise is a key component that the model gradually denoises to create an image. However, advanced users often want to control this noise or even start with a latent representation that isn't purely random noise. By disabling add_noise, the intention is to tell the KSampler, "Hey, I've got the starting point right here, don't mess with it. Just use this and proceed with the denoising steps."

This is particularly important for reproducibility and for creating complex, layered diffusion processes. If add_noise were always adding noise, it would be impossible to reliably use pre-generated latents or to test the impact of specific noise profiles. The ability to disable it is, therefore, a critical feature for flexibility and control. It empowers users to move beyond basic text-to-image generation and explore more nuanced creative avenues within ComfyUI. The developers have provided this option for a reason, and its correct functioning is key to unlocking the full potential of the KSampler node for a wide range of applications.

The Unforeseen Issue: Actual Behavior of `add_noise`

Now, let's talk about what's actually happening when you disable the add_noise parameter in the KSampler (Advanced) node. Instead of using the provided latent_image as is, the node appears to be creating a new, empty image. This is a significant departure from the intended functionality and can lead to unexpected and often undesirable results in your ComfyUI workflows. If you're expecting to feed a carefully prepared latent image into the KSampler, only to have it replaced by a blank canvas, it can be incredibly frustrating and disruptive. This bug essentially bypasses the user's input for the initial latent state when add_noise is set to False, which is the exact opposite of what the parameter is designed to achieve.

This behavior suggests that somewhere in the node's logic, when add_noise is disabled, the code responsible for handling the latent_image input is being skipped or overridden. Instead of processing the input latent_image, a new, uninitialized latent tensor is being generated. This completely negates the purpose of providing a custom latent_image in the first place, especially when add_noise is turned off. The link in the provided bug report points to lines within nodes.py in the ComfyUI repository, specifically around lines 1493-1494, which are likely where this logic is implemented. These lines might be incorrectly handling the condition where add_noise is false, leading to the generation of a new latent rather than utilizing the existing one.

For users trying to achieve specific results, this bug can be a major roadblock. For instance, if you're working on an image-to-image workflow and want to slightly modify an existing image by adding a small amount of noise before the diffusion process, this bug prevents you from doing so correctly. Similarly, if you've developed a custom method for generating latent noise with specific statistical properties, you won't be able to use that output effectively with the KSampler when add_noise is disabled. The latent_image input effectively becomes unusable in this scenario, forcing users to either enable add_noise and accept its default behavior or find complex workarounds that might not be optimal.

This issue undermines the precise control that ComfyUI aims to provide. The ability to dictate how noise is handled is fundamental to many advanced generative art techniques. When this core functionality is broken, it limits the creative possibilities and can lead to confusion and wasted effort as users try to figure out why their workflows aren't behaving as expected. It's a critical bug that affects anyone who needs to deviate from the standard text-to-image pipeline and leverage their own latent data.

Steps to Reproduce the Bug

While the bug report states that specific steps to reproduce are not applicable in the traditional sense, the scenario is quite clear and reproducible under specific conditions. The core of the issue lies in the configuration of the KSampler (Advanced) node itself. To encounter this bug, you simply need to set up a workflow where a latent_image is provided as input to the KSampler (Advanced) node, and subsequently, the add_noise parameter within that same node is explicitly disabled (set to False).

Here's a breakdown of how one would typically set up a scenario where this bug manifests:

Provide a latent_image: This could be an output from another node, such as a LoadImage node followed by a VAEEncode node, or it could be generated latent noise from a custom node or even a simple EmptyLatentImage node configured with specific dimensions and batch size. The crucial part is that a tensor representing a latent image is being fed into the latent_image input of the KSampler (Advanced).
Configure the KSampler (Advanced) Node: Within the KSampler (Advanced) node's settings, locate the add_noise parameter. Ensure this parameter is set to False. In many UIs, this is a checkbox or a dropdown where you explicitly select 'disable' or 'false'.
Execute the Workflow: Run the ComfyUI workflow. The expected outcome is that the KSampler will proceed using the provided latent_image without adding any new noise. However, due to the bug, the node will instead generate a new, empty latent tensor, disregarding the input latent_image.

It's important to note that the bug doesn't necessarily require a complex workflow. Even a minimal setup with just a source for latent_image and the KSampler (Advanced) configured as described above is sufficient to observe the incorrect behavior. The absence of detailed steps in the original report might stem from the fact that the issue is directly tied to the internal logic of the node when specific parameters are set, rather than a sequence of user interactions with external data.

This makes the bug particularly insidious because it doesn't require any special conditions beyond a standard configuration that many users might employ for various reasons. If you're testing different noise schedules or trying to debug a workflow where you suspect noise addition might be an issue, you might inadvertently trigger this bug by disabling add_noise without realizing the consequences. The lack of explicit error messages in the console or UI further compounds the problem, leaving users to troubleshoot a faulty foundation.

The Impact on Workflows and Potential Workarounds

This bug, while seemingly niche, has a significant ripple effect across various advanced ComfyUI workflows. The primary casualty is any process that relies on the precise control of the initial latent state. As discussed, when add_noise is disabled, the KSampler is expected to treat the input latent_image as the definitive starting point. However, the bug causes it to disregard this input and create a new, empty latent. This directly impacts several key use cases:

Image-to-Image (img2img) Workflows: In traditional img2img, an input image is encoded into a latent space, often with some noise added to allow the diffusion model to 'reimagine' the image. If you want more control, you might want to disable add_noise to use a specific latent representation derived from your input image, perhaps after some manipulation. This bug prevents that, essentially resetting the process.
Latent-Based Editing: Users who generate latent codes externally, perhaps through custom scripts or other AI models, and want to use ComfyUI's KSampler for the denoising process are blocked. The ability to inject a pre-defined latent is crucial for these advanced techniques.
Custom Noise Injection: If a user has a specific noise generation method – perhaps for artistic effect or to introduce particular textures – they intend to feed this custom noise as the latent_image input with add_noise disabled. The bug renders this impossible, forcing the use of the KSampler's default noise addition.
Debugging and Testing: When troubleshooting diffusion models, isolating variables is key. The add_noise parameter is a critical control. If disabling it doesn't work as expected, it becomes difficult to accurately test the impact of other parameters or the base model itself without the confounding factor of unexpected noise behavior.

Potential Workarounds:

Given that the direct functionality is broken, users are forced to explore workarounds. These are generally less elegant and can add complexity:

Alternative Noise Addition: Instead of relying on the KSampler's add_noise parameter, users can manually add noise before feeding the latent_image into the KSampler. This could involve using a separate node (if one exists or is developed) that adds noise to a latent, or potentially writing a small Python script to manipulate the latent tensor directly before passing it to ComfyUI. This bypasses the problematic parameter entirely.
Using a Different Sampler Node: It's possible that other sampler nodes within ComfyUI, or nodes from custom extensions, might handle the add_noise parameter correctly. Users might need to investigate and switch to an alternative node that offers similar functionality but doesn't suffer from this specific bug.
*Workaround for add_noise=True: If the goal was simply to start the diffusion process, and the user needs noise but wants it controlled, they might have to accept add_noise=True and ensure the latent_image input itself has the desired characteristics, while still being aware that additional noise might be introduced by the node. This is not ideal for fine control.

These workarounds highlight the inconvenience caused by the bug. They require users to invest extra time and effort into finding solutions that should ideally be handled natively by the KSampler node. The community is actively discussing this issue, and hopefully, a fix will be implemented in a future update to restore the intended functionality.

Community and Future Fixes

The discovery of this bug within the KSampler (Advanced) node's add_noise parameter has understandably generated discussion within the ComfyUI community. Many users rely on the precise control offered by ComfyUI for their creative endeavors, and issues like this can halt complex workflows. The report itself, originating from a user who has tested extensively (including disabling custom nodes, a crucial troubleshooting step), underscores the problem's persistence within the core functionality.

Discussions often revolve around the implications for specific workflows, as detailed above, and the search for reliable workarounds. Users share their experiences, confirming that the behavior deviates from expectations and highlighting the impact on their generative art projects. This collaborative effort is vital in pinpointing the exact nature of the bug and ensuring it's addressed.

The link provided to the ComfyUI GitHub repository (nodes.py lines 1493-1494) is a critical piece of evidence. It suggests that the issue lies within the core implementation of the node. Developers and contributors can use this information to pinpoint the problematic code section responsible for incorrectly handling the add_noise=False condition. Identifying the exact lines of code is the first major step toward resolution.

Looking Ahead:

Community Reports: The continued reporting and discussion of this bug on platforms like GitHub, Discord, or forums are essential. This keeps the issue visible to the ComfyUI development team and maintainers.
Code Analysis: Developers will likely analyze the specified lines in nodes.py to understand why a new latent is being created instead of using the input when add_noise is false. This could involve checking conditional logic, variable assignments, and function calls related to latent image handling.
Patching and Updates: Once the root cause is identified, a fix will be implemented. This will involve modifying the code to ensure that when add_noise is False, the latent_image input is used directly as intended. The corrected code will then be included in a future release or patch of ComfyUI.
Testing: After a potential fix is deployed, community members and developers will need to re-test the add_noise parameter rigorously to confirm that the bug is resolved and that no new issues have been introduced.

While bugs are an inevitable part of software development, the open-source nature of ComfyUI and the active community mean that issues are often addressed systematically. Users experiencing this problem are encouraged to keep an eye on the official ComfyUI GitHub repository for updates and to contribute to the discussion if they have further insights.

For those interested in the inner workings of diffusion models and tools like ComfyUI, exploring resources on Generative Art with AI can provide a deeper understanding of the concepts involved. Additionally, checking the ComfyUI GitHub Repository directly for ongoing issues and discussions is the best way to stay informed about fixes and updates.