ComfyUI-Detail-Daemon

A port of muerrilla's sd-webui-Detail-Daemon as a node for ComfyUI, to adjust sigmas that generally enhance details, and possibly remove unwanted bokeh or background blurring, particularly with Flux models (but also works with SDXL, SD1.5, and likely other models). If the values are taken too far it results in an oversharpened and/or HDR effect. There are four nodes here. Multiply Sigmas and Lying Sigma Sampler are also included as alternative methods of generally enhancing details.

• Detail Daemon Sampler
• Detail Daemon Graph Sigmas (to graph the sigmas adjustment visually)
• Multiply Sigmas (stateless)
• Lying Sigma Sampler

Note that Detail Daemon and Lying Sigma Sampler nodes work by default with custom sampler nodes such as SamplerCustomAdvanced. If you want to use them with non-custom sampler nodes such as KSampler or KSamplerAdvanced, then you'll need to make a custom sampler preset using the BlehSetSamplerPreset node, so you can select the preset from the list in the sampler node, as discussed here.

Example and testing workflows are also available.

Nodes

Detail Daemon Sampler

Allows sampling with the Detail Daemon schedule adjustment, which keeps the noise levels injected the same while lowering the amount of noise removed at each step, which effectively adds detail. Detail_amounts between 0 and 1.0 work best. See muerrilla's Detail Daemon repo for full explanation of inputs and methodology. Generally speaking, large features are established in earlier steps and small details take shape in later steps. So adjusting the amount in earlier steps will affect bigger shapes, and adjusting it in later steps will influence smaller fine details. The default adjusts mostly in the middle steps.

Parameters (the graphing node below can help visualize these parameters):

• detail_amount: the main value that adjusts the detail in the middle of the generation process. Positive values lower the sigmas, reducing noise removed at each step, which increases detail. For Flux models, you'll probably want between 0.1–1.0 range. For SDXL models, probably less than 0.25. You can also use negative values if you want to decrease detail or simplify the image.
• start: when do you want the adjustment to start, in a percent range from 0–1.0, 0 being the first step, 1.0 being the last step. Recommended: 0.1–0.5
• end: when do you want the adjustment to end, in a percent range from 0–1.0, 0 being the first step, 1.0 being the last step. Recommended: 0.5–0.9
• bias: shifts the detail_amount in the middle steps forward or back in the generation process.
• exponent: changes the curvature of the adjustment. 0 is no curvature, 1 is smoothly curved.
• start_offset: start the detail_amount at a particular value at the beginning of the generation process. Not recommended.
• end_offset: end the detail_amount at a particular value at the end of the generation process.
• fade: reduce the entire adjustment curve by a particular value.
• smooth: (true/false), do you want the adjustment curve to be smooth or not.
• cfg_scale_override: if set to 0 (default), the sampler will automatically determine the CFG scale (if possible). Set to some other value to override (should probably match the CFG used in your workflow).

Advanced Artifact Control (New!)

The Detail Daemon node now includes advanced features to help you push detail_amount for stronger effects while minimizing common artifacts like bright, isolated pixels ("fireflies"). This is achieved through an adaptive schedule calculation and a new artifact_control parameter.

Understanding the Key Parameters:

• detail_amount: This remains your primary control for the overall strength of the detail effect.

  • Internally, this value is first scaled using tanh to prevent extremely large inputs from immediately destabilizing the schedule.
  • Adaptive Exponent: The exponent you provide for the schedule curve now adapts automatically. As you increase detail_amount (making abs(tanh(detail_amount)) larger), the effective exponent used to generate the schedule becomes gentler. This helps to naturally smooth out very sharp schedule transitions that can occur with high detail_amount and aggressive exponents.

• artifact_control (New Parameter):

  • Range: 0.0 to 1.0
  • Default: 0.5
  • Purpose: This parameter directly controls how much smoothing is applied to the final detail schedule by limiting the maximum change (delta) allowed between any two consecutive steps in the schedule.
  • How it works:
    • artifact_control = 0.0: Maximum smoothing. The allowed change between schedule steps is smallest. Use this if you are seeing persistent artifacts even at moderate detail_amount settings, or if you want the smoothest possible schedule.
    • artifact_control = 0.5: Balanced smoothing. This is the default and provides a good starting point.
    • artifact_control = 1.0: Minimum smoothing. The allowed change between schedule steps is largest. Use this if artifacts are not an issue for you, and you want to allow the schedule to have more variance and potentially sharper effects.

Practical Workflow for Tuning:

1. Start with detail_amount: Begin by setting the detail_amount to a level where you start seeing the desired detail enhancement, even if some minor artifacts appear. Use the exponent parameter as you normally would to shape the general curve.
2. Introduce artifact_control:
   • If you observe bright pixels or fireflies, try decreasing artifact_control from its default of 0.5. Values like 0.25 or even 0.0 can provide stronger smoothing and artifact reduction.
   • If the image feels too "smoothed out" or the detail effect is too dampened by a low artifact_control value, try increasing it (e.g., towards 0.75 or 1.0). This gives the schedule more freedom but might reintroduce artifacts if detail_amount is very high.
3. Balance detail_amount and artifact_control:
   • The goal is to find a balance. You might be able to use a higher detail_amount than before if you also set an appropriate artifact_control value.
   • For example, if a high detail_amount (e.g., 0.8) previously caused too many artifacts, you might now be able to use that same detail_amount but with artifact_control set to 0.25 to mitigate those artifacts.
4. Iterate: Adjust detail_amount and artifact_control (and potentially your main exponent if the general curve shape needs tweaking) until you achieve the best trade-off between detail enhancement and artifact suppression for your specific image and style.

Example Scenario:

• You set detail_amount to 0.7 and exponent to 2.0. You see good detail, but also some distracting bright pixels.
• Action: Try setting artifact_control to 0.3.
• Result: The bright pixels are significantly reduced, and the main detail effect is largely preserved. If the detail is still too aggressive, you might slightly lower detail_amount or further reduce artifact_control. If the image became too soft, you could try artifact_control at 0.4 or 0.5.

Remember that the visual impact of these settings can also depend on the sampler, CFG scale, and the content of your image. Experimentation is key!

Detail Daemon Graph Sigmas

Allows graphing adjusted sigmas to visually see the effects of different parameters on a graphed curve. This had to be a separate node from the Detail Daemon Sampler node in order to function properly. Just set the values the same as that node, or set inputs on separate primitive nodes that input into both the Detail Daemon Sampler and this Graph Sigmas node. You'll need to run the queue in order to see the graph on the node. Please note: this node doesn't actually change the sigmas used when generating, it only graphs them.

Multiply Sigmas

Simple node to multiply all sigmas (noise levels) by the supplied factor. It multiplies both the noise levels added and denoised by the factor, which somehow adds detail with a factor less than 1. It is stateless, meaning it calculates the sigmas fresh on every queue (other multiply sigmas nodes seem to calculate on prior run sigmas).

Parameters:

• factor: the amount that you want to multiply the sigma (noise level) by at each step. So, for example, if the first step has a sigma of 1, then using a factor of 0.95 would make this sigma 0.95. If a step has a sigma of 0.7, then a factor of 0.95 would make it 0.665. You probably want to keep this factor between 0.95–0.99. Lower values increase detail, but might also increasingly change the composition of the image, or introduce noisy grain. Setting it to 1.0 effectively disables the node.
• start: when do you want the adjustment to start, in a percent range from 0–1.0, 0 being the first step, 1.0 being the last step.
• end: when do you want the adjustment to end, in a percent range from 0–1.0, 0 being the first step, 1.0 being the last step.

Lying Sigma Sampler

A simpler version of Detail Daemon Sampler, with only amount adjustment and start and end values.

Parameters:

• dishonesty_factor: similar to detail_amount in the Detail Daemon node, this adjusts the amount of detail. It is on a different scale though, for example, -0.05 dishonesty_factor is the equivalent of 0.5 in detail_amount of Detail Daemon (or 0.95 of Multiply Sigmas). Negative values adjust the sigmas down, increasing detail. You probably want to stay between -0.1 and -0.01. Positive values would increase the sigmas, decreasing detail.
• start_percent: when do you want the adjustment to start, in a percent range from 0–1.0, 0 being the first step, 1.0 being the last step. Recommended: 0.1–0.5
• end_percent: when do you want the adjustment to end, in a percent range from 0–1.0, 0 being the first step, 1.0 being the last step. Recommended: 0.5–0.9

Example and testing workflows

• Flux: the Comparing Detailers.json workflow will allow you to compare all these various detailer nodes on the same prompt and seed.
• Flux img2img: the Flux img2img-DetailDaemon.json is an example of using Detail Daemon in a Flux img2img workflow.
• Flux upscale: the Flux upscale-DetailDaemon.json is an example of using Detail Daemon in a Flux upscale workflow.
• Flux inpainting: the Flux inpainting-DetailDaemon.json is an example of using Detail Daemon in a Flux inpainting workflow.
• SDXL: the SDXL txt2img-DetailDaemon.json is an example of using Detail Daemon in a SDXL workflow.

Credits

• Detail Daemon concept and schedule generation function from muerrilla: https://github.com/muerrilla/sd-webui-detail-daemon/
• ComfyUI sampler implementation and schedule interpolation, as well as Lying Sigma Sampler, by https://github.com/blepping/
• Multiply Sigmas node based on the one included here: https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler