https://preview.redd.it/zkjiyb5mc60d1.jpg?width=1280&format=pjpg&auto=webp&s=ce0ab67b29dce94e4893ea1ee4c7fbd096be53ff
I’ve had my Arturo Pro SC for a couple of weeks now and thought it worth sharing a review, Note that I am a VR racer, and predominantly race iRacing these days, so my use case my be more specific than some. I reckon you have all seen reviews from Dan Suzuki and co on YouTube, so I won’t re-tread on old ground, rather I’ll talk about how it feels after two weeks of exclusively using it in iRacing, and what features really appeal to me, particularly as a VR racer.
Build: exceptional. It really feels amazing in the hands. I race without gloves and initially found the grips to feel a bit too grippy/tacy/sticky but that feeling as disappeared after using for some time, I assume natural hand oils polishing the grip material over time. The shifters and clutches also feel very very nice, very smooth, and just clicky enough without too much noise. The buttons have a lovely positive feel to them, and there is no binding in the plastic surround as Dan noticed in his early release.
Features: Whilst it has lovely lights, but lacks the battery life and fancy telemetry integration of it’s USB wired counter-part, it’s a non-issue as a VR racer - I run with the lights off because I can’t see them, so 800 hours per charge is plenty, and no different to my previous wheel (SRB BB Ultra + Sportline Imola rim). What works exceptionally well for VR are the rotary switches, rotary encoders, and buttons. There’s just so many configurations that it ticks everything you could need as a VR racer. I know Dan wasn’t feeling the Rotary encoders, but with a bit of thought, they are superb for VR. Here’s how I have mine setup.
I have my most commonly used controls assigned to buttons (WipeViper, PTT, VR Centre, Mute Driver, Limiter, Toggle TCR, Sorry, Thanks, toggle tire change, toggle auto fuel, Pit In/Out, Damage Report, Tow, and Exit). On three encoders I have ABS, TC, and BB. The fourth encoder is linked to the shift function on the right rotary. The left rotary is set to the black box.
The rotaries are what really make this wheel great in VR. With the left rotary in the default rotary mode, 12 o-clock is relative, and one click in either direction takes me to standings or lap times.Rotating to 3, 6, or 9 o’clock (easy muscle memory) takes me to the mirroffb box, fuel, and in-car adjustments, with clicks to either side of those to related boxes. It gets me very quick and easy access to my most used black boxes.
But it is the right rotary in shift mode that is the most clever. In shift mode, the rotary changes what the blue rotary encoder does, which allows me to tailor depending on the activity or car I am driving. But it also still sends out a pulse when you turn it. You can use that pulse in conjunction with AutoHotKey and JoyToKey to trigger a wav file (I used Narakeet with the Beatrice voice to create sound samples for 12 key functions - “Weight Jacker”, “Front ARB”, “Rear ARB”, “Active Reset”, etc). So I turn the right rotary and I get an audible confirmation of the setting I have enabled (great when in VR) but you can have it do something similar with dashboards in SimHub where it displays the selected setting (Beautiful Dashboard for example). So if I am in Test Drive mode, I turn the right rotary to 12 o’clock, now rotate up and it sets an active reset point, rotate down and it sends me back to the reset point. I’m in Indycar, on ovals, I set it to 1 o’clock, now it controls my Weight Jacker, and if need be, I can flick it to 2 o’clock and access my rear ARB. Maybe I jump into VR and iRacing has decided to move some of my overlays around again, even though they are custom per car - 5 o’clock, up toggles the virtual mirror, down toggles edit UI so I can drag the overlays around without fumbling for my keyboard with my VR headset on, 6’oclock lets me toggle FPS and Driving controls on (the latter useful in NASCAR). I’ve included a screen grab of my spreadsheet controls layout. But the shift mode on the right toggle lets me access EVERY control I need in VR without a keyboard, and gives me audible notification of the selected rotary position (it doesn’t spam every wav either, as it only pulses once you stop turning, plus AHK cancels the currently playing wav if it triggers another).
I’ve had a really great time using this with the Vee, MX5, Radical, IndyCar, GT4, and even NASCAR (yes I know). It feels brilliant, and the controls are super. Because there are so many buttons, especially with the shift mode, every single car has the same config, so I don’t need to rebind keys if I decide to change my layout a little, and I just change the right rotary depending on car, or if I am in Test Drive. The thumb encoders are quite firm, and the edges are not quite sharp, but borderline sharp, but work well. I’d prefer them to be a tad more rounded on the edges, and slightly lighter in action, but I’m splitting hairs. The other thing is the stock layout of the “dual shifters” feels wrong. By default the main paddle shifters are closer to the wheel, and the upper “trigger” shifters are further away. I found that when shifting my fingers would brush off the triggers every shift, not pressing them, but it was annoying. The shifters them selves were too close for me to be comfortable, and I would feel constantly rub the trigger button with my finger tips when going for the shifter. The triggers themselves felt too far back making them harder to press. But the simple fix was swapping the spacer and screws on the triggers with those on the shifters, pushing the paddle shifters further away and bringing the triggers closer. Triggers are much easier to reach, paddle shifters feel more comfy, and because they are further back, no matter where I put my fingers they never brush the trigger. I don’t know why they aren’t like that out of the box. I also found shifting the clutch levers in a notch (there are two mounting positions) made the reach on those much better too.
Summary: It’s the best wheel I have used for VR across my time with SC and Fanatec. If I were a triples racer, I’d probably go for the USB version for the telemetry integration, but for VR and wireless convenience the SC version is great. It works surprisingly well in NASCAR too. In fact, it’s a great all rounder, outside of rally. Expensive, but I don’t regret it. Looks great too, even with the lights off.
https://preview.redd.it/o1r8fd5mc60d1.jpg?width=960&format=pjpg&auto=webp&s=51fb6d6981842ad129ea71043d622bc5294c1ea2
https://preview.redd.it/gk7ntb5mc60d1.jpg?width=960&format=pjpg&auto=webp&s=dbb0af06dd0d44334657e6ef6665e78c9a38b9c7
Upper trigger shifter now closer, Paddle further away
My Keybinds

WebUI Forge is a popular alternative (‘fork’) to the classical Automatic1111 platform. In this guide, I describe a complete routine for super high-resolution upscaling of AI-generated images with incremental adding of detail into their content, using any of the two features of Forge, SD Upscale and MultiDiffusion integrated.
Due to reddit limitations, the included 16:9 demo image of a fantasy landscape is a scaled down to 8K version of the full-size upscaled image produced with this routine from an original 2K picture generated with Leonardo.ai. Here’s the link to the full-size 15360x8640 image (a 58 MB file).
A lighter image of 16 MB size (87% jpeg compression) is available here.
The source 2k image is included second in this posting.
The folder with the complete selection of demo images from the project prepared for this post is available here.
Routine prerequisites: Forge webUI running locally on a PC equipped with a capable GPU (RTX 4070 Ti Super with 16 GB, in my setup), or on a leased one in the cloud (RunDiffusiion, salad, runpod.io, vast.ai, sailflow.ai and the like). The author used the WebUI Forge version available within the StabilityMatrix package.
The approach
In the image upscaling business, the temptation is to upscale the image to the target resolution in one go, if possible: if the tool supports a 4x upscale, sure, let’s use that! 8x, even better! It is however a very flawed approach, all you will get is a ruined image (or at best something far from what you wanted the result to be), and running times much longer than they should be. With this routine, I promote an incremental approach, where you increase the resolution 2 times at most with each upscale step, keeping the runtimes short to allow for more creative experimenting. The underlying idea is that you want to be in charge of the process and not rely on the magic of the AI too much, or on some dense, rigid workflow. For that reason, I am much against upscaling in batches or in whichever automatic fashion. Each image is unique and requires an individual approach; and especially so, for anything to be called a work of art.
The steps in detail
Each step in this routine is done in 2-3 substeps.
Substep 1: pre-upscaling of the image. During AI-assisted upscaling with SD Upscale (SDU) or MultiDiffusion integrated (MD), the actual resizing of the image is always done by an upscaler model, not by an internal routine of the extension or the script (I will call them ‘methods’ from here on). Typically, the user simply chooses the Scale By factor, selects one of the available in the UI models, like UltraSharp, foolhardy_Remacri etc, sets all the other relevant upscaling parameters and clicks on Generate. In contrast, in this routine the ‘raw’ upscale operation is necessarily a separate substep; it’s done explicitly in Forge’s Extras, or using a standalone upscaler such as the highly recommended freeware upscayl, before proceeding to generate. This allows you to exercise a fuller control over the overall look of the eventual upscaled output, the grain or the texture, and also to prevent an unsolicited color shift. If you don’t separate this part, it will be impossible for you to determine in what degree the upscaler model influenced the eventual output. Forge comes with plenty of top-class models already built in, and you can always look for a specialized one fine tuned for the type of images you need to have upscaled (here’s the site that hosts practically every sort). For more details on the subject, see this recent discussion.
This approach of separation not only gives you more control of the output, but also significantly saves on the total computing time: once you have a raw-upscaled image with qualities closest to the way you want the upscaled image to look ultimately, the pre-upscaling operation won’t have to be performed at the beginning of every subsequent run of the method (and chances are that you are going to do a lot of trial runs at each upscaling step). Conversely, if the pre-upscaled image has a texture with a particular strong (synthetic) grain, or any artifacts of its own (which does happen occasionally), it might be difficult to alter that look within the adding detail run (substep 2), so choose wisely! (FWIW, the models used for upscaling the demo image for this routine were 4xLSDIRplusC, HAT-L_SRx4_ImageNet-pretrain and 4xHFA2k, they seemed to be suited better for the type of synthetic landscape photos used in this project than the others I tried; but that’s only my personal preference. In contrast, 4x_NMKD-Siax_200K proved unsuitable for this project, as this model tends to add too much noise in the upscaled output, making it appear unnaturally sharp.)
Note: the pre-upscaling factor of 2x at each step is only a default, for an even finer control, you might want to reduce it to 1.5 or even lower.
Timing. Pre-upscaling is usually done very quickly, it takes about 20-30 seconds for an average model to 2x upscale a 4K image on my RTX 4070 Ti Super. 2x upscaling of an 8K image might take from 1.5 or 2 minutes (4xHFA2k) to anything between 4 and 12 minutes using a slower model like SwinIR or HAT-L_SRx4.
Substep 2: adding detail to the pre-upscaled image with the chosen SD method and checkpoint. At this step, you use the pre-upscaled image as the source in the chosen Forge img2img method and run the process with the scale factor of 1. Depending on the Denoising strength parameter, the checkpoint used, the prompt etc, this generation substep will add a variable amount of detail into the rendered image (see more on the ways to control the amount and contents in ‘Forge webUI parameters’ below). For the demo image used in this guide, the details added have been of this variety: birds flying in the sky, features of the spire-shaped towers on the left and on the right, waterfall shapes, houses, flower petals, figures of people at a distance, sometimes a bicycle or a horse, and even a tiny village on the cliff that was formed during subsequent upscale steps. By experimenting on each step, you arrive at a rendering that contains newly added detail in the quantity and appearance which is most appealing to you. Once gotten that, you use the rendered image as the source for the next upscale step. Amazingly, as I witnessed more than once in my project, and as opposed to a more traditional img2img process, such an iterative process wherein an image is generated and then used as the upscale source again and again, will not cause any degradation of the subsequent output (when generated with the right set of parameters of course). The detail added on a detail-enriching step will usually be kept and further developed at the next one, given a carefully tuned set of parameters, and the sharpness will be retained without any visible softening of the texture.
Keep in mind though that processing time increases with each upscale step and the increased resolution, so it makes sense to do the most of the creative experimenting in the middle of the routine, at the level of 4K and 8K. Generating at the last 16K level should be done just to keep the detail already introduced, not to add any substantially new one (unless you have a super-fast GPU, of course).
Timing. Forge runs very, very fast when img2img-processing, with either of 1.5 or SDXL versions of checkpoints, processing a 4k image in about 1 minute, for either method. It takes between 2 and 3 minutes to process a 8K image, and between 8 and 18 minutes for a 16K image, with all the right parameters set. (Automatic, in comparison, takes anything from 1 to 3 hours to do the same with a SD 1.5 checkpoint, and the quality of the output is much harder to maintain.)
Substep 3 (optional): refining and fixing artifacts. You should be mindful of possible artifacts (small defects and off-color patterns), and particularly of visible tiles and seams that tend to appear in the generated image when you fail to moderate the process by means of lowering the denoise parameter and/or using the ControlNet Tile mode (more on this in the next part). Probability of the tiles becoming visible is also dependent on the image contents: images with a light blue sky or a smooth gradient of any kind are particularly vulnerable, as demonstrated by the example images in the demo folder. I learned the hard way that, except in a few cases, it’s practically impossible to get rid of the seams by any post-processing. Generally, you will have to discard an image with tiles and seams too prominent. Speaking of visible tiles and seams, I noticed that the MD method is more prone to that issue than the SDU one, while not being any faster in processing, so I recommend using the latter for most use cases.
That said, the artifacts such as visible tiles and seams, as well as minor blemishes, can be made less pronounced (if not completely removed) by running the substep 2 again with the just-rendered image as the input one, with a different checkpoint, the Denoising strength or CFG parameter adjusted. This substep is only needed if the upscale step is the last one in the sequence; otherwise, the substep 2 on the next level will most likely do this job. Also to consider: activating the Refiner option at the above substep, to be used with a secondary checkpoint (see below).
Check out the demo folder for the most striking examples of tiles, seams and other artifacts.
Forge webUI parameters
Stable Diffusion checkpoint
The choice of checkpoint is a major factor influencing the detail that will be added to the image during the generation process. Different checkpoints react differently to the input material, some hallucinate more readily with the same source than the others. The checkpoints used to produce the demo image in this guide were: albedobaseXL_v21, juggernautXL_v9Rundiffusionphoto2, sleipnirSDXLTurbo_v125, leosamsHelloworldXL50GPT4V, for the SDXL version, and juggernaut_reborn, photon_v1 and absolutereality_v181, for the v1.5 one. Since upscaling, as described in this guide, is a multi-pass process in which the checkpoint is freely changed at each step, all of them contributed to the final result to varying degrees.
Check out the demo folder for the most striking examples of hallucinations I encountered during this project.
LoRa
Specialized LoRas can also be used to influence the type and the amount of the detail added to the image, or the style it is rendered in (this is most likely how Magnific, Leonardo U-Upscaler and others of the kind support various styles available in their UI.) No LoRas have been used in this project, however.

Prompt
Compared to other SD-based solutions, the prompt plays a much lesser role in this routine. In fact, you can use the same prompt for each upscaling project, something like “masterpiece, best quality, highres”, and that will do it. As a rule, no specific terms or object names should be used in the prompt. The reason for that is that the large-size upscaling is always a tile-based process, wherein each tile is generated independently of others, so, if your prompt includes some specific object you want to have generated in the output, there is a high chance that that object will appear everywhere in the picture, or at least at every spot where the checkpoint ‘thinks’ it is appropriate. This holds especially for the MD-based upscaling with 1.5 checkpoints. For SDU under Forge using SDXL ones, it is less extreme, but I would still recommend avoiding specifics in the prompt. In this project, I went no further with the prompt than using “majestic fantasy vista, cinematic, high contrast, highly detailed”. Just inserting “mountainous” adjective before “vista” would cause mountains rendered in the dark parts of the sky and other strange features appearing in the picture, without much added realism.
In any event, due to the nature of the Stable Diffusion process, it is not possible to control what exactly the checkpoint will inject, and where, even with the best crafted prompt. You can, however, try restricting it from injecting something you don’t want it to, by including various synonymous terms in the negative prompt.
Sampling method (‘sampler’)
In this project, I consistently used two classical samplers, DPM++ 2M Karras, and, a bit less often, DPM++ 3M SDE Exponential, they seemed to be the fastest of the bunch and delivered desired quality with a relatively low step count of 20-22. Some other samplers, like Euler and HEUN, proved to be too eager to hallucinate bizarre detail into the image (with the same step count), so I avoided them; some others produced completely damaged output or were unacceptably slow.
Sampling steps
With a sampler chosen as above, the step count of 20-22 was sufficient most of the times for a good output quality. Occasionally, I would raise the count to 25 or 30, or even 50, but could never notice much difference in the output. Increasing the step count does make the generation take longer though, in almost linear fashion.

CFG Scale
The CFG parameter has a major influence on the output, augmenting the hallucination as you raise it. With that, however, also rises the chance of tiles and seams and other artifacts appearing, so it’s a good idea to be conservative with this value. As a rule, I would use a CFG no higher than 8 for a regular detail-adding generation, and restrict the value to 5 at the last (16K) step, to avoid undesired detail injection, while keeping the overall sharpness level. With the MD method and 1.5 checkpoints specifically, raising this value to extreme levels such as 13-15 and setting tile dimensions just above 100 pixels will cause a profuse injection of the sharpest detail possible into the output, which can often produce a stunning effect, but is generally hard to control (not to mention additional artifacts).
Denoising strength
The denoise parameter which is at the heart of this ‘creative’, img2img-based upscaling routine is the single most influential factor; you should use it within a pretty narrow range. The CFG, the sampler, the step count, the checkpoint and of course the prompt all play their role in the process, but the denoise value leads the way. Experimenting with it, you nudge the img2img generation to add a desired (relatively small) amount of detail, and no more. If you don’t restrict the value of this parameter, your image will contain a wild (‘insane’) amount of detail, particularly when upscaling with the MD method, which tends to insert in the image, depending on the checkpoint, all kinds of stuff (NSFW one including) at any spot imaginable. That is often accompanied by tiles appearing in the output image and other artifacts - which means, again, wasted time and effort. But visually, it can be great fun of course.
Recommended values (based on this project): between 0.28 (basic detail level, low hallucination) and 0.38 (new detail is prominent, checkpoint-dependent hallucinations across the image). The lowest level of 0.28-0.30, or lower, is recommended to use at the last, 16K step. See also ControlNet integrated below on how to dampen the effect of the denoise parameter and keep the output faithful to the original image.
Resize to, Width and Height / Resize by, Scale
This must be the most mystifying part of the entire Automatic/Forge interface. After all this time, I am still figuring out, shall we say, the subtleties of its logic.
The sliders labeled ‘Width’ and ‘Height’ play a different role under img2img-based upscaling than in a regular image generation. When upscaling with Forge’s SDU, with these sliders you don’t set the resolution you want the image to be upscaled to (it won’t allow values higher than 2048 anyway), but rather the tile dimensions used for upscaling/refining, see Tile configuration below. When running SDU script under Forge, make sure that the Resize to (NOT Resize by) dialog box is in foreground before clicking on Generate, or else it will take very long to process the image at 8K, and forever, at 16K. The image’s target dimensions are defined in SDU via its internal Scale Factor parameter.
In contrast, when upscaling with Forge’s MD, before clicking on Generate you need to ensure that the Resize by dialog box is in foreground, with the Scale factor set properly, or else it will just generate an image of whatever dimensions set by the sliders in Resize To, but luckily, in just a few seconds. For the purposes of this routine, the Scale can be set to 1.
And that is the simplest part of the puzzle, all kinds of things can go wrong if you set something in the UI that Forge developers didn’t really anticipate. To avoid excessively long runtimes and other pitfalls, follow the guidelines below when upscaling.
Tile configuration
Image tiles are the core part of the design of HR upscaling in Stable Diffusion, I believe it’s the only effective means to process large size images without running out of GPU memory (VRAM) very quickly.
In SDU, you set tile dimensions, as suggested above, in the Resize To dialog box. For SDXL checkpoints, I used the 1024x1024 dimensions, as well as 768x768 (the tiles don’t have to be aspect ratio-shaped), and for 1.5 checkpoints, the standard 512x512 dimensions, or 768x768, which worked equally well.
An important parameter in both methods used in the routine is Tile overlap, it defines the pixel width and height of the overlapping area of adjacent tiles. Making it as large as reasonably possible helps to tame the visibility of tile seams, by the price of slower computation. In my experience, an overlap of size 64 pixels for SDXL would suffice; smaller sizes could make sense for 1.5 checkpoints and in cases when visibility of tiles is not an issue. In any case, it’s a good idea to use the default value first.

The MD method, which includes its own set of sliders to define the tile configuration, has an additional parameter, Tile Batch Size. It defines how many tiles are held in memory simultaneously and processed in one basic operation; 8 is the maximum using which will supposedly achieve the highest speed of upscaling, lowering it will decrease the amount of VRAM used by this process. In MD under Automatic, setting this parameter lower than the default (to 4, 5 or 6) is an essential means to avoid running out of CUDA memory; in Forge, it’s of a lesser importance, since that system has its own, by all indications much more efficient memory management.
ControlNet integrated
To keep the upscaled/refined output as faithful to the source image as possible, ControlNet Tile resample mode is used. When this mode is activated, the effect of the Denoising strength parameter is dampened, which gives you a higher degree of freedom to play with the parameter without the associated risk of tiles and seams appearing, but at the price of longer (about 10-30%) processing times. In my experience, with the Controlnet Tile resample switched on under SDU, the Denoising strength could be set as high as 0.4, with no or little visible artifacts appearing in the output image.
To engage this option, enable ControlNet Unit 0 in the Forge img2img UI, check Pixel Perfect, select Tile in the Control Type combo, select Tile resample in the preprocessor dropdown box and the corresponding model in the next box, which is usually control_v11f1e_sd15_tile when using a 1.5 checkpoint and ttplanetSDXLControlnet_v10Fp16 when using a SDXL one (the only one that worked for me, might require explicit downloading and installing to Forge). Next, set Control weight to a value between 0.5 and 0.7 inclusive (this relaxes the ControlNet Tile fidelity, which we need for the purposes of detail-adding), and leave the rest of the ControlNet settings at the default.
Note that the MD + ControlNet mode combination, as I found, doesn’t really work under Forge: when both are selected, the process upscale starts quickly but then stagnates without any visible progress, for hours. (In contrast, the same combination works just fine under SDU, for both 1.5 and SDXL flavors.)
Other parameters in Forge’s img2img interface
Clip skip. Leave at the default. Changing this might make no impact whatsoever, I have never checked.
Resize mode. Leave at Just resize.
Refiner. This is an interesting option worth experimenting with. It allows the user to select a secondary checkpoint whose output will be mixed with that of the primary one, at a selected point of processing specified by the Switch at parameter (reasonable values between 0.6 and 0.85). Unfortunately, while potentially useful from the creative perspective, this option is too computationally costly, slowing down runtimes to anything from 2 (for SD 1.5) to 8 times (for SDXL checkpoints, which are larger). This happens due to constant checkpoint loading and unloading (a rather time-consuming operation) performed for every tile being processed.
Batch count / Batch size. Leaving these at 1 would be a practical choice, to avoid wasting your computing time - unless you want, say, to experiment with the Denoising strength at its higher values when upscaling at a low to medium image resolution.
Seed. Usually left at -1 to allow random variation of the output.
MultiDiffusion integrated. Enable this control when you want to upscale with this particular extension, as opposed to the SD upscale script. The choice of the specific Method between MultiDiffusion and Mixture of Diffusers does not affect the output that much (in my experience anyway), I understand it was retained in Forge for backward compatibility with MD under Automatic. An important related option: see Never OOM integrated below.
Never OOM integrated: when selecting MD extension for upscaling, it is necessary to synchronously enable this extension as well, and check the box labeled Enabled for VAE (always tiled), and not the other one above it. If this is not done, upscaling of a large sized image will last indefinitely long. In contrast, upscaling with SDU necessitates unchecking of that option and leaving Never OOM inactive, for exactly the same reason.
Script. Select SD upscale form the dropdown box when upscaling with SDU. In this case, MultiDiffusion integrated must be deactivated, or else neither of the two options will work properly. Also, when selecting SDU, make sure to deactivate Never OOM, as mentioned above. The Upscaler choice is up to your experimentation (see Substep 1 at the top), but for the purposes of this routine it is normally set to None.
All the other integrated extensions present in Forge’s interface are best to leave inactive.
Forge vs Automatic1111, SD 1.5 vs SDXL and the fate of Tiled Diffusion
Based on my experience in this upscaling project, a few general conclusions can be made. For the purposes of the project, Forge WebUI proved a much better choice, with either of the two methods: it runs significantly faster and its output is much less prone to visible tiling, seams and other artifacts than when using equivalent tools under Automatic. The MD implementation in Forge, however, is much incomplete, with important features, such as Noise Inversion, slow modes of Tiled VAE encoding / decoding and others left out, as compared to the Automatic version of MD (where it is called Tiled Diffusion btw). In my view, this drawback of Forge is largely compensated not only by faster runtimes but also by much more robust, stable performance, and most importantly much better tile management that practically solves the problem of visible tiles and seams (you won’t even find an option like Seams fix in Forge, it’s done behind the scenes and done exceptionally well), not to mention it allows to run highres upscaling on GPUs with 8 GB VRAM.
What’s more, support for v1.5 checkpoints in Froge feels somewhat incomplete, at least speaking of the MD/TD implementation. All in all, however, I feel that the MD/TD method is no longer relevant for HR image upscaling, seeing the drastic improvement of the SD upscale script. From my upscaling perspective, unless Automatic will be merged with Forge to take advantage of the improvements in the latter, the former remains relevant only for MD/TD-based upscaling with 1.5 checkpoints. It doesn’t help either that development of TD/MD has remained dormant for the last two months. (Which is sad, since SDXL support in MD, native or under Forge, has not been developed beyond only a nominal one - it doesn’t really work well with checkpoints of that version, as I found in the course of my project.)
Although, to be fair, an even more worrying picture holds for Forge development, which hasn’t seen any update since February.
The quality of SDXL-based generation as compared to that of v1.5 is a matter of a separate discussion; due to space constraints, I won’t go into that here. I will just add that personally I find SDXL-based upscaling superior to v1.5 one, for most use cases.
The folder with the complete selection of demo images from the project prepared for this post is available here.
https://preview.redd.it/3y1nwkod110d1.jpg?width=7680&format=pjpg&auto=webp&s=5c12e51fa2d44bb8336a7ca8c02094b349a61799

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Ever wondered how Originality AI stacks up against Winston AI? Dive into this showdown of cutting-edge generative AI technologies and machine learning algorithms that are revolutionizing the way we interact with artificial intelligence. Which one will emerge as the superior solution for your needs, considering the pros and cons, privacy, pricing plans, and machine learning algorithms? Let's unravel the mysteries and unveil the truth behind these innovative AI platforms.
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Key Takeaways

• Understanding the importance of AI content detection can help businesses maintain originality and protect their intellectual property.
• Winston AI offers advanced capabilities in detecting and preventing plagiarism, making it a valuable tool for content creators and organizations.
• To assess Winston AI's performance effectively, consider factors such as accuracy, speed, time, and user-friendly interface for seamless integration into existing workflows.
• Comparing Winston AI with competitors can provide insights into unique features, pricing options, and overall suitability for specific business needs.
• Implementing Winston AI can streamline content creation processes, enhance quality control, and safeguard against copyright violations.
• Leveraging AI tools like Winston AI can empower businesses to uphold originality standards, boost efficiency, and stay ahead in the competitive digital landscape.

Overview of AI Content Detection

Importance

AI content detection plays a crucial role in maintaining the integrity of digital content, text, and data. It ensures detection accuracy and helps identify plagiarized content swiftly, safeguarding against intellectual property theft.

Originality AI

Originality AI specializes in detecting AI-generated content, ensuring that materials are authentic and not machine-produced. This tool is essential for verifying the originality of AI text, providing a reliable solution for content creators.
https://preview.redd.it/xcikpjv21uzc1.png?width=823&format=png&auto=webp&s=68deca7cb90fcdd83844579559bac436dd20b59e
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Winston AI

Winston AI, on the other hand, focuses on plagiarism detection, identifying copied or unoriginal content across various sources. By leveraging advanced algorithms, Winston AI offers users a comprehensive AI detector tool to maintain the authenticity of their work.

Key Features and Benefits

• Both Originality AI and Winston AI offer free AI detectors, making them accessible to a wide range of users.
• These AI detector tools provide quick and accurate results, saving time for content creators.
• Users can rely on these tools to ensure that their content is original, thereby enhancing their credibility and reputation online.

Deep Dive into Winston AI

Features

Winston AI offers a range of features, including plagiarism detection, project creation, file uploading, and OCR technology. Users can easily upload files in various formats and access detailed reports on content authenticity.

Accuracy Comparison

Despite Winston AI's claims of high accuracy in detecting plagiarized content, actual test results have shown discrepancies. Users have reported instances where the tool failed to identify copied material accurately, raising concerns about its reliability.

Pricing Model and Limitations

Winston AI's pricing model is structured differently from competitors like Originality AI. While it may offer competitive rates for basic services, there are limitations in terms of advanced features, accuracy, ai detection tools, generative ai, and human that place it behind other industry players.

Evaluating Winston AI Performance

Detection Accuracy

Winston AI showcases high accuracy in detecting AI-generated content, ensuring reliable results for users. Its advanced algorithms excel in identifying manipulated information effectively.

Comparison Test Results

In a recent comparison test with Originality AI, Winston AI outperformed by demonstrating superior detection rates. Users benefit from its precision in flagging potential instances of AI-generated text.
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Document Upload Feature

Winston AI offers a seamless document upload feature, enhancing user experience for academic purposes. This functionality streamlines the process of analyzing content for authenticity, plagiarism, and AI detection tools.

Visual Indicators

The inclusion of visual indicators by Winston AI to highlight the presence of AI content simplifies the identification process for users. These clear signals aid in distinguishing between human-written and AI-generated text seamlessly.

Comparing Winston with Competitors

Accuracy Score

Winston AI has shown remarkable accuracy in detecting plagiarized content, boasting a high accuracy score compared to its competitors. It excels in providing precise results.

Features Comparison

When comparing features, Winston AI offers a wide array of tools and technology that cater to the needs of various industries. Its detailed analysis sets it apart from other AI content detection tools.

Pricing and User Experience

One of the key advantages of Winston AI is its competitive pricing structure, making it an attractive option for businesses of all sizes. Users appreciate its user-friendly interface and seamless workflow.

Unique Selling Points

Winston AI stands out from competitors like Originality AI due to its superior confidence levels in detecting plagiarism. Its AI ability to handle diverse text samples effectively adds to its appeal.

• Pros:
  • High accuracy score
  • Competitive pricing
  • User-friendly interface
• Cons:
  • Limited in some advanced features

Closing Thoughts

In wrapping up this exploration of AI content detection and the comparison between Winston AI and its competitors, you now have a clearer understanding of the innovative solutions available. By delving into Winston AI's capabilities and performance metrics, you can see how it stands out in the realm of originality detection. As you consider incorporating such technology into your workflow, remember to weigh the benefits against your specific needs to make an informed decision. The world of AI continues to evolve rapidly, offering exciting tools like Winston AI that can revolutionize how you approach content creation.
Explore further, test different tools, and stay updated on the latest advancements to ensure you leverage the best resources for your projects and goals. Your journey towards enhancing content originality starts with informed choices and continuous learning. Keep exploring, keep creating, and keep innovating!
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Frequently Asked Questions

What is the importance of originality in AI content detection?

Originality in AI content detection ensures accurate identification of unique content, preventing plagiarism and ensuring authenticity. It enhances trust in the integrity of information shared online.

How does Winston AI stand out in content detection technology?

Winston AI excels due to its advanced algorithms that analyze text, images, and videos comprehensively. Its deep learning capabilities enable accurate identification of original content, setting it apart from competitors.

How is Winston AI's performance evaluated in content detection tasks?

Winston AI's performance is assessed based on criteria such as accuracy, speed, scalability, and adaptability. Through rigorous testing and comparison with benchmarks, its efficiency in detecting original content is determined.

What are the key benefits of using Winston AI over other competitors?

Winston AI offers superior accuracy in identifying original content, providing reliable results for users. Its user-friendly interface, quick response time, and robust security features make it a top choice for content detection needs.

Can Winston AI be used across various industries for content detection purposes?

Yes, Winston AI caters to diverse industries such as publishing, academia, journalism, and digital marketing. Its versatility in detecting original content makes it a valuable tool for organizations seeking to maintain authenticity and credibility.
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About Her Voice A conversation on Mariah Carey with author and critic Andrew Chan BY DANIELLE AMIR JACKSON DECEMBER 21, 2023
Photo by Raph_PH via Flickr. Artistic rendering by Oxford American. Courtesy Wikimedia Commons This exclusive feature is an online extension of the OA’s annual music issue. Order the Ballads Issue and companion CD here.
Singing is “the most enigmatic of performing arts,” the author, editor, critic, and self-professed “diva lover” Andrew Chan writes. It’s a simple matter of air and anatomy: breath moves through closed vocal folds which then vibrate and resound throughout the throat, chest, head, or sinuses. But when we listen intently, transcendence is available to us. Raised hairs on the upper arm, a tingle on the back of the neck. The irrepressible urge to tap one’s toes. Transcendence is something we can feel–a physical sensation that unleashes the emotions and connects us to the divine. That’s why a host of spiritual traditions embrace the human voice as a conduit for worship, and in secular music, many of the most popular traditions–r&b and its variants, country, even rap—foreground some sort of vocal virtuosity. A skilled vocalist can “seduce us, haunt us, heal us regardless of the text they’re delivering or even the culture that surrounds them,” Chan writes.
In his first book, published just this past fall, Chan highlights the thirty-plus year career of Mariah Carey, whose five-octave vocal range; agile, multisyllabic melisma; and well-honed aptitude for catchy hooks and witty wordplay turned her into one of the most successful pop singer-songwriters of all time. Carey has earned five Grammys and nineteen number ones on the Billboard pop chart—the highest of any act besides the Beatles, surpassing Elvis. Two of her fifteen full-length albums are certified diamond, with sales of ten million or more in the United States alone. Why Mariah Carey Matters, part of the University of Texas Press’s Music Matters series, is the first book-length critical assessment of the artist’s wide-ranging career.
Chan makes the case that from the beginning, Carey’s vocal dexterity and range set her apart—her mastery at blending piercing whistle tones, fluttery, feminine whispers, muscular belts, and “leathery low” notes, often within the same song. “There’s something irrational, bizarre, and hazardous-sounding about the way Mariah hopscotches over and across vocal registers without warning or transition,” Chan writes. She also blended and mixed styles of singing, infusing both big, sentimental ballads and buoyant, weightless bops alike with gospel fervor; in the ’90s, alongside artists like Mary J. Blige and Jodeci, she contributed to the creation and commercial dominance of “hip-hop soul.” In her house remixes, often painstakingly re-recorded versions of her mainstream pop hits, she frequently scatted and improvised in the tradition of Ella Fitzgerald or Sarah Vaughan. Equally impressive, and critical in understanding Carey, Chan says, is her “artistry outside the vocal booth.” She wrote or co-wrote all of her most enduring hits, including “Vision of Love,” “We Belong Together,” and “All I Want for Christmas Is You.” She’s produced herself and other artists, and is one of few women nominated for the Grammy Award for Producer of the Year (Non-Classical). It was an early honor, from 1992, for work on her second LP, Emotions.
Chan is one of my favorite writers and an important voice in contemporary music and film criticism. He’s vivid in his assessment of Carey’s musical gifts. He layers in details of his own upbringing to help us understand why certain songs and singers turned him into a student of the art. I love the way he brings the reader along with him—we’re watching and listening together as Carey delivers her gospel-drenched rendition of “America the Beautiful” on the NBA Finals in 1990, hearing her sing the climactic sea-ahhh as she “evokes rolling vistas and open water.” He acknowledges the blemishes on Carey’s career and the unpredictability of her voice, which he insists is not a recent phenomenon. He situates Carey in refreshing context: with Black singers of the ’80s who influenced her sound, and with other female songwriter-producers like Patrice Rushen, Teena Marie, and Angela Winbush, who don’t often receive credit for their prowess behind the boards.
“So much of the culture and money created during this era is the product of Black female creative energy,” writes Danyel Smith, another of my favorite music writers, in Shine Bright, her sweeping history of Black women in American pop. She’s talking about the middle of the twentieth century, when recordings like the Dixie Cups’ “Chapel of Love” achieved mammoth success that the performers—who came up with the arrangement we all know and love—were not credited for. Carey has received commercial rewards, and, as of late, critical adoration from outlets such as Pitchfork and Rolling Stone.
But Chan suggests we still haven’t absorbed the magnitude of Carey’s genius, that our cultural blinders have hindered our ability to understand the breadth of her labor and mastery. Carey’s upbringing as a biracial daughter of a white mom who raised her largely on her own; her sense of not fully belonging among Black or white people; her insistence on femininity in an industry that privileges masculine presentation when it doles out points for credibility. She used it all in her art—especially in her ballads. Over a long and wide-ranging conversation, Chan and I discussed Carey’s melancholy, artistic lineage, the feeling of singing, r&b, gospel, and transcendence.
Courtesy University of Texas Press Danielle Amir Jackson: Can we start with your background? I know you grew up in some American suburbs and in Malaysia. When did you begin to pay so much attention to Mariah Carey?
Andrew Chan: I moved around quite a bit as a kid. I was born in Minneapolis, in a great music city, but I didn’t live there long. My family moved to Tampa, Florida and then to Malaysia. After moving back to the States, I lived in Atlanta, Georgia and Charlotte, North Carolina—the metropolitan New South.
In the nineties.
In the nineties. I moved to Atlanta… I think in ’97. I remember Butterfly had just come out. And I remember Usher was number one on the charts with “You Make Me Wanna…” Living in Atlanta and Charlotte in the nineties, I was one of the few Chinese Americans in school. For much of middle school and early high school, half of my friends were Black. So, there was a lot of exposure to the music that they were listening to. Hip-hop and r&b were becoming mainstream and dominating the charts. Having friends who were Black exposed me to more than just what was crossing over.
I also felt connected emotionally to Malaysian culture. My parents exposed me to some of the great Asian divas of the eighties and nineties. Mandarin and Cantonese pop were important for me until, maybe, first grade. So, I was listening to people like Anita Mui, Priscilla Chan, and Teresa Teng and was completely obsessed with them before I had much knowledge of American pop music. Even then my ear was attuned to how different they sounded. Anita Mui had this beautiful contralto voice. Teresa Teng was more of a mezzo soprano. And they had different vocal approaches. Even if I didn’t have the language to analyze that or express that at that age, I was really drawn to the variety of women’s singing. That fascination carried over to the period when I started becoming obsessed with American pop music and American divas, mainly through Whitney and Mariah. When I heard “I Will Always Love You” and the whole Bodyguard era, I’d never heard something like that before. That drew me to the soul tradition of American singing.
I don’t often hear people discuss Carey in the lineage of great American interpreters of ballads like Ella Fitzgerald or Frank Sinatra, and I really appreciate that it’s the note you lead with in your book—which parallels the way that Carey started her career. The OA’s annual music issue is a dive into ballads and the elasticity of the form. What’s special about ballads? Why might an artist like Carey launch her career with ballads?
Even though she became frustrated with Tommy Mottola molding her into an adult contemporary ballad singer, the demo was full of ballads. She co-wrote all those songs. She found different ways of making the ballad fresh and interesting for herself.
The ballad has always meant different things across time. If you were to compare Sinatra, singing an old jazz standard ballad like “Angel Eyes” or “In the Wee Small Hours of the Morning,” what does that have in common with Mariah Carey’s “Can’t Let Go?” They’re slow. They’re about passionate love. This does a couple of things for a singer: It gives you space to really milk every note and moment; the listener is drawn into the space of the ballad and is invited to listen very closely in a way that you just aren’t if you’re competing with an up-tempo beat behind you or if you’re singing fast. The feat is more about rhythm than it is about holding out long notes. The ballad accentuates the tone of the singer’s voice. It creates an intimate connection with the listener. It also puts the singer at risk of being uncool because ballads are kind of forbidden. And that is why we love them. They can be uncool. They almost feel like something that we shouldn’t admit we listen to or respect because they, especially the sad ones allow us to wallow, which we’re not supposed to do if we’re grownups and we want to be serious and mature. We’re not supposed to sink into our feelings of longing and despair. But this is one of the places in our culture where we get access to that intensity of emotion, and the slowness of the music mimics the infatuated person’s inability to let go of love or inability to stop thinking about the beloved.
Mariah is an unabashedly sentimental singer, and that’s why it took so long for her to garner any kind of critical respect. She is in that tradition of musical wallowers. She loves her heartache. She loves to long and pine. She’s a bit of a masochist.
Many interesting people are.
Yeah. Ballads can be transportive to sing. The tempos are slower; you can really get your mouth around the words and feel each one of them. Because the song isn’t whizzing by at a crazy pace, you can build to a satisfying climax. You can go from low to high in this drawn-out, dramatic way. That shows the full capabilities of your voice.
When you say ballads are transportive, are you talking about a transcendent experience? The Holy Ghost?
A little bit. It’s to the point where you’re moving with your own performance, which is why singers sometimes get choked up when they’re singing their ballads, because it is such a vulnerable place to be. In karaoke, which most people don’t take seriously, if I’m singing a particular song and I’m really feeling it, I can get so lost in it.
“She loves her heartache. She loves to long and pine. She’s a bit of a masochist.”
ANDREW CHAN
I like what you said about ballads being almost contraband. I remember when people realized Beyoncé was starting the Renaissance tour with slow songs. It seemed almost like an anachronism.
Yeah, for her big house record. She’s a great ballad girl too. In terms of them being contraband, back in the Maoist era in China, love ballads were banned because they were seen as counterrevolutionary. If you were part of the revolution, you wouldn’t indulge in these individualistic displays of your own personal emotions. I do get into that a little bit in the book where I even had a moment in my teenage years where I was just like, These are pathetic. They’re a distraction from the real business of politics and liberation and revolution, you know?
We include a song by Fannie Lou Hamer on our compilation accompanying the issue. You made me think of Elaine Brown, who was chair of the Black Panther party and recorded songs and some of them are balladlike. They’re propagandist, one-note songs.
There is the political ballad too. I think there’s something about love ballads where it’s like surrendering and succumbing to feelings of longing, loss, yearning, desire. Of course, there’s misogyny involved in that too, because these are “feminized” emotions. Ideas about feminine hysteria are built into this hyperbolic style of singing as well. People forget that Whitney was booed and disrespected for much of her career. It’s funny that she and Mariah had a reappraisal where they’re legends now, but at the beginning of their careers, they were criticized for over-singing and being excessive.
I wonder why people didn’t say that about Luther Vandross. He’s super indulgent.
He’s so indulgent. “A House is Not a Home” or “Superstar”—those songs are seven minutes long or something. He had some pop crossover appeal, but he never hit it as big as Whitney and Mariah. But also, there’s a bit of misogyny in that, the difference between women doing it and men doing it. I mean, Al Green is a show-off. They’re all show-offs.
Let’s talk about the eighties. You say that “Can’t Let Go,” is a revision of “Make It Last Forever” by Keith Sweat and Jacci McGhee and compare Carey’s work as a songwriter-singer-producer to Teena Marie and Angela Winbush. And you go into quite a bit of depth into all her references and homages in Glitter: Indeep, Zapp, Cherrelle. I’m having a moment right now—perhaps I’m where Mariah was back in ’99 and 2000—but I’m so obsessed with the sounds and sights of the Black ’80s. Miki Howard, whom you also mention, has been heavy on my mind, alongside Anita Baker, Patrice Rushen, Regina Belle. In your opinion, what was special about that era in music, particularly in Black pop, and how was it connected to Carey’s debut?
I didn’t come into writing this book as an expert in eighties Black music. That is one of the areas where I felt a bit insecure because I felt I knew sixties and seventies r&b and nineties onward in terms of r&b, but for some reason the eighties were an area that I hadn’t explored sufficiently. I knew the major names and their works, but it is a decade that, when it comes to Black popular music, it’s so defined by one-hit wonders. Aside from the Whitneys and the Michael and Janet Jacksons and Lionel Richies, there weren’t a lot of a long-lasting careers that crossed over to non-Black audiences in a major way. Sometimes, DeBarge would have a pop hit, but for most of their significant catalog, mostly Black listeners were listening. I had to do a lot of catching up to get those sounds into my ears and really hear how they influenced Mariah. I think part of it is because eighties r&b is less canonized than the seventies and nineties. Even the nineties have experienced this resurgence of critical interest, but the eighties are almost like a blip. Part of it is where it came in the history of popular music—after the demise of disco, which really was a shaming of Black music by the white rock establishment. I’m sure it’s more complex than that, but that was certainly a dimension to that whole culture war. In the eighties, you have r&b coming out of the ashes of disco and utilizing the electronic elements that disco had been criticized or seen as superficial for. You get a lot of experimentation like Zapp—so kooky and goofy. The use of the talk box to manipulate vocals. You get club music, like Cherrelle, a sort of post-disco dance music, people having a lot of fun. Just like really deep grooves that went on for like six minutes. Gap Band, all that kind of stuff.
There’s the kind of fun side of eighties r&b, but then on the other side you have this luxuriousness, the plush textures of Quiet Storm, which began in the seventies, but really came into its own commercially in the eighties with people like Luther, Anita Baker—who sort of took the slow-roasted, slow-jam, boudoir sound of Isaac Hayes and Al Green and Smokey Robinson—and pushed it to a whole new level. Even when they were singing at the tops of their lungs, it was still smooth.
I hesitate to just generalize all eighties r&b, but I see those as the two parallel tracks. I think they both deeply informed Mariah’s aesthetic. I think Aretha is a huge influence on pretty much all r&b women singers. I think Mariah would cite her as the ultimate female influence, but I think when it comes to sonics, the luxuriousness, the Quiet Storm sound is so evident in songs like “Underneath the Stars” and “Fourth of July.” Those are what you would think of as Quiet-Storm Mariah, but you [also] hear it in the stuff that’s more hip-hop like “The Roof.” The way she’s stacking her vocals, the way she’s creating texture with her voice. It’s very Luther. The way she is manipulating her voice, the way she’s showing it off but not for its own sake, but to create an environment that you sort of wrap yourself in. When I think of Luther showcases like “Superstar” or “Forever, for Always, for Love,” it’s very much like some kind of texture that you can wrap yourself.
This is quite different from the approach of the belters of the sixties and seventies, like Aretha or even Gladys or Chaka, powerful singers who really prioritized the belt. Mariah is a phenomenal belter—one of the greatest. Where she really distinguishes herself from other divas of her time is the subtler parts of her voice. I think a lot of that is influenced by Quiet Storm. When it comes to the zanier side of eighties r&b, you hear it in her sense of humor, her effervescence, especially as she became more of a jokester lyrically in her later years. You can sort of hear the lyrical experimentation and the kind of devil-may-care attitude of eighties Black music.
One of my favorite live performances of Carey’s is where she sings “Somewhere Over the Rainbow” and “If Only You Knew,” her Patti Labelle homage. I love that era in her voice where there is that level of rasp.
That performance—it’s very eighties Patti. “If Only You Knew” is so eighties. I think Mariah’s samples, too, are so interesting and root her in the time of her youth. She’s such a radio-head, the way she talks about listening to the radio in her memoir and her devotion to soaking up all those sounds. That was before streaming, where you really had to be glued to the radio. I don’t know if she had MTV back in the day, but the radio was the thing. And she wasn’t just listening to r&b. She was listening to Pat Benatar. The range of her musical references is so fascinating.
I’d love to discuss Carey’s gospel moments. You spend a great deal of time on her rendition of Dottie Peoples’ “Jesus, Oh What a Wonderful Child” and note that while Carey didn’t grow up in the Black church, she joined one as an adult. What’s Mariah’s connection to the gospel of the ’90s? I’m thinking of artists like BeBe and CeCe Winans or Commissioned?
I love gospel music, but I would never claim to know it. I love gospel music because that’s where r&b comes from. R&b is my portal into gospel music. It remains the source of so much great singing, even today. Le’Andria Johnson is one of my favorite singers alive. In terms of Mariah and gospel, I think it is so interesting to me that she didn’t grow up in a Black church and yet was so committed to singing in a gospel style, even from the beginning. There may not be songs that feel explicitly gospel on the debut album, but you do have moments. “There’s Got to Be a Way” has a gospel choir that feels kind of in the style of BeBe and CeCe Winans. That pop, commercial gospel that was happening in the late eighties and nineties—the kind of gospel that you would hear in Sister Act 2. Then she employs background singers like Kelly Price and Melonie Daniels—virtuosos of that sound.
In the book, you note that Kelly Price had been trained by Mattie Moss Clark.
Yes, I found that in a video of Kelly Price. She talked about doing some kind of workshop with Mattie Moss Clark when she was younger. [Carey’s] commitment to surrounding herself with not just skilled r&b background vocalists, who could do a commercial sound, but vocalists like Kelly Price and Melonie Daniels, who could bring a church sound, specifically a COGIC sound to her music is completely fascinating to me. The Clark Sisters were playing on r&b radio back in the seventies. Gospel had been having these kinds of crossover moments, but Mariah’s knowledge of the music surpasses just knowing “Oh, Happy Day” or “You Brought the Sunshine.” She was listening to Vanessa Bell Armstrong. From the very first album in interviews, she is citing Vanessa Bell Armstrong and the Clark Sisters as influences.
I have to think that in her teens, she had been exposed to gospel music. I’m fascinated that she came to the music and absorbed its influence without having a longstanding background in the Black church. I bring this up, not so much as a point about appropriation, but more as another example of Mariah being someone obsessed with records and listening to music and soaking up any influence she could find, whether it was Journey—when she covers “Open Arms”—or gospel or hip-hop or what have you.
To go back to gospel and “Jesus, Oh What a Wonderful Child,” she has moments where she wears her gospel influence on her sleeve even before that. “Anytime You Need a Friend” was one of the most significant gospel moments; she’s singing with a choir behind her and doing a lot of riffing and running and belting in the way of the great COGIC singers. “Jesus, Oh What a Wonderful Child” is significant because it sounds live. I read somewhere that it was recorded live in a church. The vamp is unlike anything that had come in her discography before. It is a gesture toward a kind of gospel authenticity. It’s no longer just gospel-pop. It’s going there and trying to recreate the spirit and the atmosphere and the feeling of a live gospel setting.
I’m interested in her study of gospel as an example of her being a constant and abiding student of different forms of Black music. I love her later gospel songs like “Fly like a Bird,” “I Wish You Well,” and “Heavenly” where she combines a James Cleveland song with a Mary Mary song. There is a song called “I Understand” that’s one of those multi-megastar performances. There’s Rance Allen, Kim Burrell, and Mariah does just whistle at the very end.
Do you think Mariah is fundamentally an r&b artist?
We first have to acknowledge that genres are constructs. These terms have historical origins that are usually rooted in marketing and promotion. Most people track [r&b] to the 1940s. It replaced race music as the designation or the category for whatever African Americans listened to that was popular music. It’s a shifting signifier. The idea that there is a commonality between the music of Ray Charles and Lavern Baker and Fats Domino and Mariah and SZA—all these artists sound so different. I think there is something a little bit unhelpful about these genre markers.
That being said, constructs take on their own reality for people who engage with them. For Mariah, and her listeners who gravitate to the r&b side of her catalog, r&b represents something. It’s as different as the music has become over the decades. There are still certain stylistic and sonic continuities. It’s very improvisational. There is melisma, runs. In classical music, you perform it as its notated. Melisma defies notation. You can sing so many notes so fast that you can’t really even transcribe it. It’s rooted in gospel. It’s rooted in a certain passion for delivery, a centrality of the voice and individual expression. An idea about struggle and transcendence, because it’s rooted in the Black experience and an acknowledgement that life is sometimes totally unbearable, and music is a vehicle to help you get over, to get through. People who gravitate to r&b are connecting with that.
Of course, not every r&b song is about that. But even in a slow jam, you can hear that whining, that struggle, that tension. You hear all these elements in Mariah’s discography. For her, r&b became, at a certain point in her life, a way of expressing her Black identity, which had been dismissed or misrepresented or misunderstood. She was constantly asked about her race in interviews, constantly having to remind people of what she had said from the very beginning, that her father was Black and Venezuelan, and her mother was Irish American. Embracing r&b as her heritage was an important part of her owning her identity as a Black woman. R&b is so interesting as a cultural and political marker, because now we’re in an age where white artists like Justin Bieber or Justin Timberlake, or whoever, say that they’re r&b. I’m less interested in saying, “This person’s not r&b; this person is,” and more interested in what is it that makes people so desperate to align themselves with this genre. I think it’s the historical lineage—the gravity of the heritage. It’s the connection to the idea of soul, which is a spiritual idea.
I’m not sure if any artist can be definitively anything when it comes to genre. But I think certainly Mariah perceives herself as an r&b artist and has conducted her artistic life in a way that shows that she’s committed to a certain ideal of what r&b is—passionate, soulful singing; a connection to music as a form of spirituality.
“Even in a slow jam, you can hear that whining, that struggle, that tension.”
ANDREW CHAN
You have this part of the book where you’re talking about her covers of power rock anthems. You don’t say that she’s reappropriating, but you say she’s showing how permeable rock and r&b boundaries are. They have a shared origin, and they come together in her choices of what to cover and what to sing and how to sing them and her arrangements.
For sure. If you think about Foreigner’s “I Want to Know What Love Is” that she covers, that’s an instance of a white band bringing gospel influence into a rock song. These boundaries are always permeable. Rock at one point was called r&b when it was sung by Black artists. What she demonstrates with her music is the variety within r&b and that the music is not a monolith. She’s giving you quiet storm. She’s giving you girl-group songs. She’s giving you New Jack Swing. She’s giving you hip-hop soul. She’s giving you power ballads. She’s giving you deep soul, in the tradition of Aretha with “Mine Again.” She is committed to a vision of herself as an r&b artist, but for her it is many things.
All the things you were saying about the struggle and resilience r&b signifies—I think that’s also reflective of the queerness that many sense in a lot of Mariah’s songs.
Absolutely. One song I want to write about is “Ain’t No Way.” Carolyn Franklin wrote that. I don’t know if we know definitively if she was queer, but I think all the history kind of shows that she was. There’s definitely a [queer] reading of that song. You have Luther as a queer artist and Sylvester, so many of the pioneers of the r&b. Little Richard. It makes sense because gospel was pioneered by queer people. Otherness and survival, the longing for transcendence is something so baked into the music. That’s certainly what I was responding to as a young closeted gay child, who’s experiencing racial otherness in the American South as well. Obviously, my experience is very different from Mariah’s, but I think there’s a longing to transcend the arbitrariness of what oppresses us through sound.
And she does transcend and break through.
She achieves it. What is beautiful about a Mariah Carey ballad is that she takes you into the depths of despair, sorrow, but through the sheer beauty and power and mastery of her voice, she is carrying us over. No matter how sorrowful or despairing it gets—and some of them really are quite dark and fatalistic—there’s something about the voice. The voice can be the vehicle that carries you over.