QwenEdit2509-FlatLogColor is a specialized LoRA designed to bridge the gap between AI image generation and professional creative workflows.
A common challenge for professionals using AI-generated content is that most images are created as "finished" products. They come with fully applied color, contrast, and saturation, much like a JPEG straight out of a consumer camera. This leaves little room for creative manipulation, such as color grading, which is a crucial step in film, photography, and multimedia production.
This model solves that problem. It takes a standard AI-generated image and converts it into a FLAT or LOG color profile.
It's a finetuning of Alibaba Cloud's powerful Qwen-Image-Edit-2509 model, designed for a single, critical purpose: to convert a standard, graded AI image into a FLAT or LOG color profile.
Try out the FlatLogColor Huggingface Space : https://huggingface.co/spaces/tlennon-ie/QwenEdit2509-FlatLogColor
What is a FLAT/LOG Profile?
In professional photography and videography, cameras can capture images in a "raw" or "logarithmic" (LOG) format. Instead of producing a vibrant, high-contrast image ready for immediate sharing, a LOG profile preserves the maximum amount of detail captured by the camera's sensor.
It achieves this by creating an image that appears:
Low-contrast: The difference between light and dark areas is reduced.
Desaturated: The colors are muted and less vibrant.
Detail-rich: Information in the deepest shadows and brightest highlights is retained, not "crushed" to pure black or "clipped" to pure white. See this example from youtuber Evan Schneider
This "flat" image is the perfect digital negative, providing a flexible foundation for colorists and editors to apply their own creative vision and ensure consistency across different shots and sources. This LoRA brings that same professional capability to your AI-generated content.
To demonstrate the effect and scalability of the QwenEdit2509-FlatLogColor LoRA, we will analyze the histograms of an original image and its transformation at two different strengths: 1.0 and 1.25.
RGB/Luminance: The luminance histogram displays a wide tonal distribution, characteristic of a high-contrast image. The individual RGB channels are distinctly separated, with a strong Red channel (mean=162.5) and a less prominent Blue channel (mean=129.8). The data touches the extreme ends of the spectrum, with Red min=1 and max=211, indicating a high risk of information loss through shadow clipping (crushed blacks) and highlight clipping.
HSV: The Saturation histogram (the cyan plot in the HSV graph) shows significant peaks towards the right side, confirming the vibrant, highly saturated colors in the original image.
RGB/Luminance: The effect of the LORA is a clear and deliberate compression of the tonal range towards the mid-tones.
Dynamic Range Preservation: The min values for all channels have been lifted significantly from the zero point (e.g., Red min=4, Blue min=14), which translates to lifting the blacks and preserving shadow detail. The max values have been pulled down (e.g., Red max=209), rolling off the highlights to prevent burnout.
Color Neutralization: The mean value for the Red channel has dropped substantially from 162.5 to 144.5. This quantitatively demonstrates the neutralization of the strong warm/red cast, creating a more balanced starting point.
HSV: The Saturation histogram is the key indicator here. The entire plot has shifted to the left, with its peaks now concentrated in the lower half of the range. This is a perfect visual representation of desaturation. Importantly, the Hue histogram (orange plot) has maintained its fundamental shape and distribution. This is a critical success factor, as it shows the LoRA is reducing saturation without introducing undesirable hue shifts, preserving the image's color integrity.
RGB/Luminance: This histogram demonstrates the scalability of the effect. Compared to the strength 1.0 result, the tonal compression is even more pronounced.
Increased Headroom/Footroom: The min values are lifted further (Red min=7, Blue min=15), and the max values are pulled down more aggressively (Red max=208, Green max=210). This creates an even flatter image with maximum room for grading adjustments.
Further Neutralization: The mean of the Red channel is now 143.8, a slight decrease from the 1.0 strength, showing that a stronger setting continues to refine the color balance towards neutral.
HSV: The Saturation plot is pushed even further to the left than at strength 1.0. This gives the colorist an "ultra-flat" option for scenarios requiring the most extreme color manipulation. The Hue histogram again remains consistent, proving the reliability of the tool even at higher strengths.
This comparative analysis reveals the clear technical advantages of the LoRA for any professional workflow:
Recovered Detail & Expanded Latitude: The LORA's primary function is to pull tonal information away from the clipping points (0 and 255). Detail that would be lost in the original graded image is now preserved and made accessible, giving you far greater flexibility—or latitude—to shape the contrast and exposure in your grade.
Creation of a Neutral Digital Negative: It effectively strips the "opinionated" color science of the base AI model, neutralizing color casts and desaturating the image to a clean, unbiased state. This makes matching AI shots to professional LOG footage or establishing a consistent creative look a much simpler and more precise process.
Scalable and Reliable Control: The difference between the 1.0 and 1.25 strengths shows that you can precisely dial in the amount of "flatness" your project requires. The tool's ability to perform this transformation while maintaining hue integrity makes it a predictable and reliable asset, saving valuable time in post-production.
To apply the effect, incorporate the LoRA into your generation process and use the recommended trigger words in your prompt.
Recommended Weight: A good starting point is a weight of 0.75 to 1.25, but feel free to experiment to achieve the desired level of flatness.
Prompting Strategy:
First, generate your desired image without the LoRA to establish the base content.
Once you have a composition you like, add the LoRA and the trigger word to your prompt to transform the image into the FLAT/LOG profile.
For best results, use the trigger word flatcolor combined with the prompt I provide in the examples.
You can enhance the effect with descriptive terms. For example:
Simple trigger: flatcolor
Descriptive prompt: Desaturate the image and lower the contrast to create a flat, ungraded look similar to a camera's log profile. Preserve details in the highlights and shadows.
This LoRA is designed for professionals and enthusiasts who want to integrate AI-generated imagery into a color-grading workflow. It is ideal for:
Filmmakers & Videographers: To create AI assets that can be seamlessly matched with footage from professional cameras (e.g., S-Log, C-Log, V-Log).
Photographers & Digital Artists: To gain greater creative control over the final look and feel of their images during post-production.
AI Art Enthusiasts: To experiment with professional coloring techniques and achieve a more cinematic aesthetic.
When you use these flat color images as your input image in most video models, the color profile will stay consistent. You can have more control by setting BOTH the start and end frame with FirstFrameLastFrame workflows, but even just as the input image I have found the flat log profile stays consistent throughout , without having to prompt it.
Here is a Wan2.2 example of my QwenEdit2509-FlatLogColor image used as the input for the video scene:
A wide, cinematic shot of a person in a white hazmat suit crouching on gravelly ground at twilight. The scene is illuminated by a single, bright industrial lamp. The person slowly stands up, holding a small evidence bag, and turns their head to look towards the light as the camera slowly pushes in.
As you can see, the log profile stayed intact throughout the scene, which shows the value in having a FLAT or LOG image as the input image for video generations, to allow professionals to color grade it according to their project needs.
This model is a fine-tuning of a large-scale, pre-trained model and may carry some of its inherent biases. The training dataset for this LoRA was focused on transforming color profiles and may not behave identically across all artistic styles or subjects. The output is heavily influenced by the input image and prompt; results may vary depending on the initial image's contrast, saturation, and composition. Users are encouraged to experiment with weights and prompts to achieve their desired outcome.
Disclaimer: This model is intended for artistic and creative purposes. Users are responsible for the content they create and should adhere to ethical guidelines and respect the privacy and dignity of individuals.
You can download the model files in the "Files & Versions" tab.
Qwen/Qwen-Image-Edit-2509