Neural Textures & Neural Polygons, Upscaling : To Map & Map - NeuralML-TextileExpansion (c)RS 2025

Neural Textures & Neural Polygons, Upscaling : To Map & Map (c)RS : Data is our Seed

NeuralML-TextileExpansion (c)RS 2025

GPU compression has Neural Textures; We can use Neural Textures for Video,..

Now you imagine that Neural Textures can be used to directly represent the video pixels,..

We can do that! We can present the texture blocks neural textures,..

A big problem with that is that we have a maximum LOD Level of detail...

So let us imagine presenting a texture map array of ATSC, PowerVR Compression , DXT5 & BC,..

Now imagine that our root kernel is the compression block of physical textures & Neural Texture effects where reserved for expanding on the root texture,..

So let's compose the array and see how it looks with automatic upscaling with Neural Textures..

T = Texture Block , B = Basic Image Block 5551 565, 4444, 8888, 1010102 , N = Neural Texture Expansion & N = Neural Polygon Expansion & N = Neural Data Expansion

O = Original Source Texture , P = Higher Resolution Texture Pattern Pack & P = Polygon_P & P = Data Packed Elements

Example low bit Alpha & BW

5551 represents where we have 555 Bit & 1 Alpha, What do we do with 1 BW, Alpha? 75%, 50%, 25% BW, Transparency or a Shader set level!

T, T Expansion with N, N
T, T Expansion with N, N

B, B Expansion with N, N
B, B Expansion with N, N

T > N
B > N

Now we have a base block that we expand,..

Now in texture block expansion we use a standard pack of higher resolution textures,..

We call this variety based expansion, Where we expand the original block with a shaped pattern that expands the basic texture content with variable layers that expand the total texture set..

Now we do the same thing with polygons & use replacement mapping instead of P, Indeed Polygon P & Data P

The principles of compression are preserved & expansion is made of the elements P, N, O, T,..

Indeed Data is our Seed

(c)Rupert S

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Data Extenders : N, T, B

HDR Colour range extenders work with graphs matching pixels in close proximity in the texture that have been enlarged & scaled in DPI...

Expanders work by pre-working as much detail expansion as possible into the pre computed palette expansion,..

Maths extenders work by aligning data with median & gaussian average differentiation,..

Stored in compression caches in RAM or storage,

They extend the produced details without overworking repeating maths.

Data is our Seed : Expansion explained in terms of direct copy commands :

The image is loaded :

T : B , The image is upscaled using Bi-Linear scaling & increased in pixel density, 96DPI to 192DPI to 300 DPI optimal range..

N Greyscale or reduced palette or HDR Colour range extenders,.. micro texture packs are applied to emboss the graphic with some meaning..

Details are matched with pre computed texture packages in cache, They can be computed before game/App runtime is in motion, Before the main work package is run or played.

Application of detail extenders involves exactly matching details with fast loading direct mapping of almost identical higher resolution data..

Mapped on the pixel expansion to fill in details.

Rupert S

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Upscaling neural textures & polygons (c)RS

The requirement to upscale neural textures simply requires a larger frame buffer,..

Since DPI scaling makes sense for most content, We simply double or multiply the details per cm of screen space,..

Since Neural textures & polygons emit higher precision output, We increase DPI per CM of screen space,..

A larger buffer is required for the task so we allocate more ram per cm; A higher DPI,..

We can therefore use a buffer for original content, Parallel data expansion buffers with the required Texture / Polygon mappings .. To the output frame buffer..

For TAA, FSR, DLSS & so on

Multi Frame processing

Frame 1
Frame 2
Frame 3

Input frame Buffer
Expansion buffers
Output frame buffer

Write frame or frames

DSC, HDMI, DP
Screen Presentation

This method assures a lower latency channel to the screen or write buffer in the case of a recorded video.

Rupert S

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Direct attention variable locality (c)RS

DLSS is using multihead attention to obtain multiple samples per frame & thus increase quality..

Over the last couple of years 2025- 5 years multi headed attention has received a lot of usage..

In cancer & disease cases with parallel processing on GPU & CPU the research for images of cancer cells gets more intense... Because it saves lives!

The issue for cancer is that cancer cells are small clusters & large clusters, Tiny clusters can proliferate cancer to other places, Liver to brain or arm for example..

Multi attention allows multiple identification per search,.. But we need something more..

The large lower resolution scan of the entire frame..

Sub section passes of large arias of the photo to initially identify cancers..

Small aria intense scans of identified cancer cells..

In the case of DLSS & FSR & so on .. This system of ..

Whole frame
Large sections
Small sections

Is called a subset mask, What that does is speed up the process of analysing the frame,..

Subset masking is a clever trick in terms of the brain & thinking,..

We call this system direct attention variable locality,..

We resolve to train in research to pay attention to special details,

A specialised topic is walls,.. We want walls lavished with details if there is something to see!

But we need to know when the wall is not in view any more .. Or we are still processing it,..

What if the wall is coming into view again? Do we know? Do we cache it?

Cache is a major way to do details, We however have to use RAM to store caches, ..

So there is a system motivated priority!

System:

Priority processing & reasoning

Whole frame

Cache

Large sections

Cache

Small sections

Cache

Data output

Rupert S

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Reducing throw-away processing in Image ML (c)RS

The Pixel Enhancement came under review

https://www.tweaktown.com/news/102229/sony-explains-how-it-modified-ps5-pros-gpu-to-enable-pssr-neural-network-ai-upscaling/index.html

https://www.tweaktown.com/news/102225/ps5-pro-gpu-explained-by-architect-mark-cerny-hybrid-with-multi-generational-rdna-tech/index.html

https://www.youtube.com/watch?v=lXMwXJsMfIQ

So they are using tiling, Tiling used by PowerVR is an example of that, Now in the previous text I stated the work group,..

The strategy to use is to examine the whole frame, Now Cerny specifically mentioned lower resolution frames in the centre of the complex CNN,

But we need a full frame analysis of the whole frame, Due to the RAM in the WGU, We have MB over the whole frame, So we approach that frame at lower resolution as suggested by CNN & Cerny...

So the approach is to use Super Sampling Anti Aliasing & Blur at a lower resolving level to both sharpen edges & blur the whole image a tiny bit,.. That reduced the compressed image size,.. not the resolution,..

But reducing details reduces image sizes, But we need edges to analyse & can with a snap parallel process the whole image for details..

With the analytics we can then clip the image into pieces & use ML on the sub groups with the same effect of using work groups,..

We know the whole frame so we can analyse each section with meta data that tells the localised WGU what to process as a list..

Reducing throw-away processing.

We can for example cast global illumination over the full size image, because global illumination is maths over the whole scene,..

Process the GI into the input image for processing, & slice that image into cubes that have full resolution resolved maths..

We can group rays into boxes & therefore prioritise the upscaler to thread the entire cube

We do not have to process small texture cubes with lower resolution maths on the GI & Ray tracing, If the maths is fully furnished,..

This approach is to display the maths in a virtualized pixel render, We can keep the Maths of the polygons behind the data for the rasterized frame,..

Fully furnished maths from the Polygon, Shader Path & RayTracing / GI can be shot into the final resolved image & improve quality

We can also process all 3D content with ML designed to improve the result accuracy, That also goes into the final content..

This offers a faster end view with a fully furnished render.

Rupert S

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# Envisioning Neural Textures for Video Pixel Representation

We are proposing a fusion of classic block‐based GPU compression and learned neural expansions—essentially a hybrid codec where each compressed block carries not only raw bits but also the “seed” for a neural upscaling network.

Below is a structured blueprint to turn that vision into an architecture and pipeline you could prototype.

---

## 1. Core Concepts and Notation

- **O**: Original source block (e.g., low-res compressed texture)

- **T**: Physical Texture Block (compressed with DXT5/BCx, PowerVR ASTC, etc.)

- **B**: Basic Image Block (uncompromised pixel block, e.g., 5-5-5-1, 5-6-5, 4-4-4-4, 8-8-8-8, 10-10-10-2)

- **N**: Neural Expansion Seed

- N_T: Neural Texture Expansion (spatial detail)

- N_P: Neural Polygon Expansion (geometric/detail extrapolation)

- N_D: Neural Data Expansion (metadata, motion vectors, semantic maps)

- **P**: Pattern Pack

- P_T: High-res texture patterns

- P_P: Polygon replacement patterns

- P_D: Data-packed elements

**Data is our Seed**—each block’s raw bits become the conditioning input (seed) to a tiny neural subnetwork that hallucinates higher‐frequency detail.

---

## 2. Block Format & Alpha Handling

| Block Type | Bit Layout | Typical Use | Challenges |

| ---------- | ------------ | -------------------------- | ---------------------------------------- |
| 5-5-5-1 | 15 bit color + 1 bit alpha | Simple low-bit blocks | Single-bit alpha: dithering vs. threshold |
| 5-6-5 | 16 bit color | Color-only blocks | No transparency—needs separate mask |
| 4-4-4-4 | 16 bit color + 4 bit alpha | Medium fidelity + alpha | Banding in alpha |
| 8-8-8-8 | 32 bit RGBA | High-fidelity textures | Large storage |
| 10-10-10-2 | 32 bit HDR + 2 bit alpha | HDR content + low-res alpha | Interpreting 2-bit alpha levels |

Alpha-and-BW strategies:

- **Thresholded Mask**: treat 1-bit alpha as binary mask, then feed both mask and color bits into N seeds.

- **Dither-based Transparency**: expand the 1-bit plane by adding noise pattern packs (P_T) so that N_T can refine smooth edges.

- **Shader Control**: reserve a shader flag for blocks with minimal alpha depth, letting the GPU combine multiple expansions (e.g., 75%/50%/25%) via runtime interpolation.

---

## 3. High-Level Pipeline

1. **Encoding Stage**

- Split the frame into blocks → produce T (compressed block) + B (base pixel block if needed).

- Generate or look up P_T, P_P, P_D pattern packs.

- Compute seeds N_T, N_P, N_D via a small encoder network per block.

- Pack `[T, B, P, N]` into your bitstream.

2. **Decoding Stage**

- Decompress T → reconstruct coarse block.

- Feed `[coarse block, P<sub>T</sub>, N<sub>T</sub>]` into a lightweight neural upscaler to generate fine details.

- For geometry overlays, feed `[coarse polygon mesh, P<sub>P</sub>, N<sub>P</sub>]` into a mesh-refinement network.

- Combine data expansions (motion, semantics) via N_D to further refine temporal coherence or dynamic effects.

3. **Level-of-Detail (LOD) Management**

- Define max LOD for each block type—higher LODs use more seeds and larger pattern packs.

- If you hit the LOD ceiling, degrade gracefully by dropping N_T offsets or merging blocks.

---

## 4. Variety-Based Expansion

You coined this “variety based expansion”: essentially a layered approach where each block sprouts multiple neural-generated variants, which the decoder chooses or blends based on context.

- **Layer 0**: Root physical block (T)

- **Layer 1…n**: P_T pattern overlays + N_T detail maps

- **Blending**: weighted sum or GAN-based refinement to avoid visible seams

Same principle applies to polygons (P_P, N_P) and data channels (P_D, N_D).

---

## 5. Next Steps & Questions

1. **Model Architecture**

- Tiny per‐block CNN or MLP for seed encoding/decoding?
- Shared global weights vs. block-specific codebooks?

2. **Hardware Constraints**

- Real‐time decoding on mobile GPUs vs. offline transcoding?
- Memory budgets for pattern packs and networks.




3. **Training Data**

- Source video/textures for pretraining expansions.
- Loss functions: perceptual, adversarial, L1/L2.

What’s your primary target—live streaming, offline rendering, or mixed reality? Knowing that will help fine-tune the network size, bit budgets, and interactive LOD strategy.

RS

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# Neural Textures & Polygons: System Blueprint

## Overview

We are proposing a hybrid compression-expansion pipeline where traditional block-compressed textures (DXT5/BC*/ATSC/PowerVR) serve as the **root data**, and “neural” expansions refill missing detail at runtime..

This breaks down into three core phases:

1. **Preprocessing** – pack physical textures, generate higher-res pattern packs, precompute micro-texture & color-range extenders.

2. **Compression Storage** – store T (Texture Blocks) and B (Basic Image Blocks) in GPU memory, alongside compact Neural Expansion data (N).

3. **Runtime Expansion** – upsample, apply neural detail synthesis, blend alpha/greyscale layers, and map expanded polygons.

---

## Key Components

- T: Block-compressed textures (e.g. BC1/5, ATSC)

- B: Low-bit formats (5551, 4444, 8888, 1010102)

- N: Neural expansions (textures, polygons, data patterns)

- P: Pattern packs (high-res micro-textures, polygon replacements, data tables)

- O: Original source textures

---

## Data Flow & Pipeline

1. **Asset Preparation**

- Extract root blocks (T, B) from O.
- Generate P: packs of 2×–8× higher-res patches and polygon-shapes.
- Train small neural nets to map T→P and B→P (e.g. autoencoders, CNN upsamplers).

2. **Compression & Packaging**

- Store T/B in standard GPU compressed formats.
- Store learned N weights (or lookup tables) alongside P in GPU-resident caches.

3. **Runtime Loading**

- Load T/B and N into VRAM.

- For each frame or region-of-interest:

* Upscale T/B via fast interpolation (bi-linear / bi-cubic).

* Query N to generate micro-texture detail or HDR/color expansions.

* Blend alpha channels using presets (75%, 50%, 25%) or dynamic shader thresholds.

* Swap or refine polygon meshes via Polygon_P replacements.

4. **Shader-Level Composition**

- Compose expanded texture layers in a single pass using:

* Base T/B sample

* Neural detail mask (grayscale or Gaussian-weighted)

* Combined with P pattern overlays

- Output final pixel

---

## Alpha & BW Handling

- **Single-bit alpha (5551)**

- Map “1” to fully opaque, Partially opaque based on engine configs or run a shader threshold based on local neural confidence.

- For BW channels, interpret as detail masks 50% (mid-grey) yields smooth transitions,..

- &or run a shader threshold based on local neural confidence.

- **Variable opacity**

- Precompute alpha gradients in P to avoid step artefacts.

- Use a tiny neural model to predict per-pixel alpha offsets (e.g. fine hair, foliage edges).

---

## LOD & Level-Of-Detail

- Max LOD hits fixed root block.
- **LOD Strategies**

- Progressive neural upscaling: chain multiple small models (2×, 4×, 8×).
- On-demand P loading: load only needed high-res patches per camera distance.
- Cache eviction based on screen-space pixel error.

---

## Challenges & Open Questions

- **Performance budget**: balancing shader cost vs. memory bandwidth.
- **Model size**: embedding N-texture nets in limited VRAM.
- **Training data**: generating representative P packs for varied scenes.
- **Synchronization**: ensuring polygon- and texture-expansions align seamlessly.

---

## Next Steps & Exploration

- Prototype a minimal proof of concept in a game engine (Unity/Unreal).
- Train a micro-CNN for 4× texture detail from DXT1 blocks.
- Benchmark shader pass times vs. traditional mipmapping.
- Explore neural video codecs (e.g. D-NeRF) to directly drive per-frame expansions.

---

RS

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A Vision for Next-Generation Graphics: Neural Textures, Polygons, and the Power of "Data as a Seed"

A novel approach to graphics rendering, termed "NeuralML-TextileExpansion,"..

Envisions a future where traditional data compression techniques are seamlessly interwoven with the power of neural networks to create highly detailed and dynamic visual experiences.

This concept, attributed to Rupert S (c)RS 2025, proposes a paradigm shift where compressed data acts as a "seed," which is then expanded upon by neural networks to generate rich textures, complex polygons, and intricate data structures in real-time.

At the core of this vision lies the integration of "Neural Textures" with established GPU compression formats such as ATSC, PowerVR, DXT5, and various Block Compression (BC) standards..

The fundamental idea is to leverage the efficiency of these traditional methods for the base representation of a texture, the "root kernel." This compressed block would then be intelligently upscaled and enhanced by a neural network, a process referred to as "Neural Texture Expansion."

This method addresses a significant limitation of current texture mapping techniques: the maximum Level of Detail (LOD)..

By using a compact base texture and applying neural expansion, the system could theoretically generate near-infinite detail, adapting the texture resolution to the viewer's proximity and the capabilities of the hardware..

The proposed system would utilize "variety based expansion," where a standard pack of higher-resolution texture patterns is used by the neural network to inform the expansion of the original block, adding layers of variable and rich detail.

The ambition of this framework extends beyond textures. The concept of "Neural Polygon Expansion" suggests a similar methodology for geometric data..

Instead of storing vast amounts of vertex information, a base polygonal structure could be expanded upon by a neural network using "replacement mapping."..

This could involve dynamically generating intricate geometric details or even swapping out low-resolution models for high-resolution counterparts based on predefined patterns ("Polygon P") and packed data elements ("Data P").

This layered approach, where T (Texture Block) and B (Basic Image Block) are expanded by N (Neural Expansion), creates a powerful and efficient pipeline:

T > N: A base texture block is expanded by a neural network.

B > N: A basic image block, potentially in various bit formats like 5551, 565, or 8888, is neurally enhanced.

The example of a "5551" format, representing 5 bits for each colour channel and 1 bit for alpha or black and white, highlights the potential for nuanced control..

This single bit could determine levels of transparency or be interpreted by a shader to apply specific effects, demonstrating the granular control envisioned within this system.

Ultimately, "NeuralML-TextileExpansion" proposes a holistic ecosystem where the principles of compression are not just preserved but become the very foundation for dynamic and intelligent content generation..

By treating data as a "seed," this forward-looking concept aims to unlock new potentials in real-time rendering, paving the way for more immersive and visually stunning digital worlds.

RS

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The Future of Visuals: Deconstructing the "NeuralML-TextileExpansion" Vision

The proposed "NeuralML-TextileExpansion," attributed to Rupert S (c)RS 2025,..

Presents a forward-thinking architecture for generating and rendering visual data..

This vision, rooted in the principle that "Data is our Seed," outlines a hybrid system that marries the efficiency of traditional GPU texture compression with the generative power of neural networks...

By deconstructing this blueprint, we can illuminate its potential to revolutionize real-time rendering for applications ranging from live streaming and offline rendering to mixed reality.

A Hybrid Codec: Where Tradition Meets Neural Innovation

At its heart, the proposal describes a sophisticated hybrid codec..

Instead of relying solely on either traditional block-based compression (like DXT5, BCn, ASTC) or end-to-end neural rendering, this system uses a two-pronged approach.

1. The "Root Kernel": A Foundation of Efficiency

The process begins with a "physical texture block" (T) or a "basic image block" (B)..

These are the familiar, highly efficient compressed data formats that GPUs are optimized to handle..

This "root kernel" provides a robust, low-resolution foundation for the final image..

The use of various bit layouts, from the simple 5-5-5-1 to the high-fidelity 8-8-8-8 and HDR-capable 10-10-10-2, allows for a flexible trade-off between data size and base quality.

A key innovation here is the nuanced handling of limited data, such as a single alpha bit in a 5-5-5-1 block..

Instead of a simple on/off transparency, the system could employ dithering, a thresholded mask, or even pass control to a shader for dynamic interpretation, enabling sophisticated effects from minimal data.

2. The "Neural Expansion": Hallucinating Detail

This is where the magic happens; The compressed block (T or B) acts as a "seed" (N) for a lightweight, specialized neural network..

This network, conditioned by the seed, doesn't just upscale the image; it "hallucinates" high-frequency details, effectively generating a much richer visual from a small amount of source data.

This expansion isn't a one-size-fits-all process..

The architecture proposes distinct neural expansion seeds for different data types:

N_T (Neural Texture Expansion): Focuses on generating intricate spatial detail in textures.

N_P (Neural Polygon Expansion): Extrapolates geometric detail, potentially turning a simple mesh into a complex one through "replacement mapping." This aligns with recent research in neural mesh simplification and generation.

N_D (Neural Data Expansion): A powerful concept for expanding metadata, such as motion vectors for improved temporal coherence in video, or semantic maps that could inform the rendering process with a deeper understanding of the scene.

"Variety-Based Expansion" and the Role of Pattern Packs

A crucial element of this architecture is the concept of "variety-based expansion," facilitated by "Pattern Packs" (P)..

These are pre-defined libraries of high-resolution textures (P_T), polygon replacement patterns (P_P), or data-packed elements (P_D).

During the decoding stage, the neural network doesn't generate details from a vacuum..

It uses the pattern packs as a reference, guided by the neural seed (N)..

This layered approach, starting with the root block and progressively adding detail through pattern overlays and neural refinement,..

Allows for a high degree of artistic control and can prevent the common artefacts seen in purely generative models.

The pipeline can be summarized as follows:

Encoding:

A source frame is divided into blocks.

Each block is compressed into a T or B format.

Corresponding pattern packs (P) are selected or generated.

A small encoder network computes the neural seeds (N).

The final bitstream contains a compact package of [T, B, P, N].

Decoding:

The T block is decompressed to form a coarse base.

A lightweight neural upscaler uses the coarse block, P_T, and N_T to generate the final detailed texture.

Similarly, a mesh-refinement network uses P_P and N_P to enhance geometry.

N_D is used to apply dynamic effects or improve temporal consistency.

Addressing Key Challenges and Charting the Path Forward

This ambitious proposal intelligently anticipates several key challenges and opens up exciting avenues for future development.

Level-of-Detail (LOD) Management: The system inherently supports dynamic LOD by design..

Higher LODs would utilize more complex neural seeds and larger pattern packs, while lower LODs could gracefully degrade by simplifying or omitting the neural expansion, falling back to the base compressed block.

Model Architecture: The choice between tiny per-block neural networks (MLPs or CNNs) and shared global weights with block-specific codebooks is a critical design decision..

Per-block networks offer maximum specialization but could increase overhead, while shared weights are more efficient but might lack the fine-grained control..

A hybrid approach could offer the best of both worlds.

Hardware Constraints: The feasibility of real-time decoding, especially on mobile GPUs, is a primary concern..

The design's emphasis on lightweight neural networks is crucial..

For less powerful hardware, an "inference on load" approach, where textures are neurally expanded and then transcoded to a standard block-compressed format, is a practical alternative.

Training Data: A rich and diverse dataset of high-resolution textures, videos, and 3D models would be essential for pre-training the neural expansion models..

The choice of loss functions—balancing perceptual quality (what looks good to the human eye), adversarial losses (for realism), and traditional pixel-level losses (L1/L2) would be critical in achieving the desired visual fidelity.

The Primary Target: A Deciding Factor

The optimal implementation of the "NeuralML-TextileExpansion" hinges on its primary application:

Live Streaming: Would prioritize extremely fast decoding and temporal stability, likely favoring simpler neural models and efficient data expansion for motion vectors (N_D).

Offline Rendering: Could afford more complex and computationally expensive neural networks to achieve the highest possible visual quality.

Mixed Reality: Would demand a balance between real-time performance, low latency, and the ability to seamlessly blend neurally generated content with the real world, making efficient LOD management paramount.

In conclusion, the "NeuralML-TextileExpansion" framework presents a compelling and well-structured vision for the future of graphics..

By leveraging the strengths of both established compression techniques and the rapidly advancing field of neural rendering, it offers a plausible and powerful path toward creating richer, more detailed, and more dynamic virtual worlds.

RS

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https://is.gd/TV_GPU25_6D4

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Build for Linux & Python & Security configurations

https://is.gd/DictionarySortJS

Windows Python Accelerators

https://is.gd/UpscaleWinDL

https://is.gd/OpenStreamingCodecs

https://is.gd/UpscalerUSB_ROM

https://is.gd/SPIRV_HIPcuda

https://is.gd/HPC_HIP_CUDA

Reference

https://is.gd/SVG_DualBlend https://is.gd/MediaSecurity https://is.gd/JIT_RDMA

https://is.gd/PackedBit https://is.gd/BayerDitherPackBitDOT

https://is.gd/QuantizedFRC https://is.gd/BlendModes https://is.gd/TPM_VM_Sec

https://is.gd/IntegerMathsML https://is.gd/ML_Opt https://is.gd/OPC_ML_Opt

https://is.gd/OPC_ML_QuBit https://is.gd/QuBit_GPU https://is.gd/NUMA_Thread

On the subject of how deep a personality of 4Bit, 8Bit, 16Bit is reference:

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

https://science.n-helix.com/2022/10/ml.html

https://science.n-helix.com/2025/07/neural.html

https://science.n-helix.com/2025/07/layertexture.html

https://science.n-helix.com/2025/07/textureconsume.html

Upscaling thoughts Godzilla 4K
https://youtu.be/3c-jU3Ynpkg