adroc_thurston
Diamond Member
- Jul 2, 2023
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132p internal upscaled to 800p with FSR 3.1 to fit the other GEMM bits.Just need to aim for 240p internal res with 10 fps target.![]()
132p internal upscaled to 800p with FSR 3.1 to fit the other GEMM bits.Just need to aim for 240p internal res with 10 fps target.![]()
upgrade the screen to a crt will all be good.132p internal upscaled to 800p with FSR 3.1 to fit the other GEMM bits.
FSR 4 int8 (for rdna 3 & 2) is better than xess is the conclusionHWUB tests FSR4 Int8 (from the source code slip up):
It's not quite identical to the standard FSR4 FP8 mode, but it's VERY close. It is slower than FSR3, so you may have to drop to lower quality modes, but even it's performance mode is better than FSR3 or XESS quality.
Definitely use it, and AMD should definitely release it officially.
FSR4 is good especially the FP8 version.FSR 4 int8 (for rdna 3 & 2) is better than xess is the conclusion
Minor pixel quality wins of xess loses to overall stability of FSR 4 int8
I would appreciate that for science. No other reason to do it. Being as all 3 vendors now have upscalers that work best with their own proprietary hardware, the relevant comparison is transformer vs FP8 vs XMX. To date: they consensus rank in that order in almost every game tested.XESS XMX version as well.
Would like to see an IQ comparison with XESS XMX version as well.
Base PS5
- Doesn't support int 8, because it is RDNA 1 ?? What ???
Makes no sense. If the performance hit is significant then users would be highly incentivized to upgrade, FSR4 on RDNA2 would be the perfect commercial for RDNA4+. Moreover, the press coverage would not reflect badly on AMD, it would actually praise AMD for releasing it and then tell consumers to buy RDNA4+ instead.AMD would like RDNA 2 users to "upgrade" to RDNA 4
Makes no sense. If the performance hit is significant then users would be highly incentivized to upgrade, FSR4 on RDNA2 would be the perfect commercial for RDNA4+. Moreover, the press coverage would not reflect badly on AMD, it would actually praise AMD for releasing it and then tell consumers to buy RDNA4+ instead.
The only reason to not release it for RDNA2 is having the engineering teams spread too thin, which is likely the case considering AMD wants FSR improvements yesterday.
those parts got KIA'd my friendthen they should have provided a viable upgrade path for all ranges.
those parts got KIA'd my friend
Wolfgang has it working in 14 of 18 games tested on RDNA 2&3. https://www.computerbase.de/artikel/grafikkarten/fsr-4-rdna-2-rdna-3.94512/
His conclusion -
"AMD finewine" is likely a combination of AMD being much less aggressive with game devolopment, which leads to them being able to squeeze a bit more performance with drivers than Nvidia (which has almost max performance in most new games) and more hardware (mostly vram) than the comparable NVIDIA cards, which allows them to brute force future games.It seems AMD not ready to make that commitment/leap to software. Maybe they are not convinced of the power/Magic of software ??
It seems AMD not ready to make that commitment/leap to software. Maybe they are not convinced of the power/Magic of software ??
Control variates are a variance-reduction technique for Monte Carlo integration. The principle involves approximating the integrand by a function that can be analytically integrated, and integrating using the Monte Carlo method only the residual difference between the integrand and the approximation, to obtain an unbiased estimate. Neural networks are universal approximators that could potentially be used as a control variate. However, the challenge lies in the analytic integration, which is not possible in general. In this manuscript, we study one of the simplest neural network models, the multilayered perceptron (MLP) with continuous piecewise linear activation functions, and its possible analytic integration. We propose an integration method based on integration domain subdivision, employing techniques from computational geometry to solve this problem in 2D. We demonstrate that an MLP can be used as a control variate in combination with our integration method, showing applications in the light transport simulation.
Despite the latest advances in generative neural techniques for producing photorealistic images, they lack generation of multi-bounce, high-frequency lighting effect like caustics. In this work, we tackle the problem of generating cardioid-shaped reflective caustics using diffusion-based generative models. We approach this problem as conditional image generation using a diffusion-based model conditioned with multiple images of geometric, material and illumination information as well as light property. We introduce a framework to fine-tune a pre-trained diffusion model and present results with visually plausible caustics.
Deep learning-based denoising and upscaling techniques have emerged to enhance framerates for real-time rendering. A single neural network for joint denoising and upscaling offers the advantage of sharing parameters in the feature space, enabling efficient prediction of filter weights for both. However, it is still ongoing research to devise an efficient feature extraction neural network that uses different characteristics in inputs for the two combined problems. We propose a multi-branch, multi-scale feature extraction network for joint neural denoising and upscaling. The proposed multi-branch U-Net architecture is lightweight and effectively accounts for different characteristics in noisy color and noise-free aliased auxiliary buffers. Our technique produces superior quality denoising in a target resolution (4K), given noisy 1spp Monte Carlo renderings and auxiliary buffers in a low resolution (1080p), compared to the state-of-the-art methods.
