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docs(i18n): add hints for SDNQ quantization settings

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CalamitousFelicitousness
2026-06-07 01:03:16 +01:00
parent c4d8576063
commit 5105e0ad87
1 changed files with 23 additions and 19 deletions
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ui/locale/locale_en.json
+23 -19
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@@ -393,8 +393,8 @@
{"id":"","label":"Disable accelerate","localized":"","hint":"","ui":"settings_sd"},
{"id":"","label":"Disable T5 text encoder","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"Dynamic loss threshold","localized":"","hint":"Target per-layer quantization error (normalized MSE) for dynamic quantization. Each layer starts at the base <b><i>Quantization type</i></b> and steps up to higher precision until its error falls at or below this value.<br>Lower values keep more layers at higher precision for a larger, more accurate model; higher values let more layers stay at the base type for a smaller one.<br><br>Only takes effect when <b><i>Use Dynamic quantization</i></b> is enabled.<br><br><b>-1</b> auto-selects a threshold from the base type, about <b>1e-4</b> for 8-bit or <b>1e-2</b> for 4-bit.<br><b>0</b> accepts only layers that quantize losslessly, which on a normal model leaves almost everything at full precision.<br><br>Default is <b>-1</b>.","ui":"settings_quantization"},
{"id":"","label":"Dequantize using torch.compile","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Dequantize using full precision","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Dequantize using torch.compile","localized":"","hint":"Compiles the dequantization step with <i>torch.compile</i> for faster inference. Requires <i>Triton</i>.<br><br>Changing this needs a full restart to take effect.<br><br>Enabled by default when Triton is available.","ui":"settings_quantization"},
{"id":"","label":"Dequantize using full precision","localized":"","hint":"Uses <b>FP32</b> for the dequantization step for better numerical accuracy, at a small speed cost.<br><br>Enabled by default.","ui":"settings_quantization"},
{"id":"","label":"Disabled","localized":"","hint":"","ui":"settings_cuda"},
{"id":"","label":"Dynamic Attention BMM","localized":"","hint":"Performs attention computation in steps instead of all at once. Slower inference times, but greatly reduced memory usage","ui":"settings_cuda"},
{"id":"","label":"Dynamic attention","localized":"","hint":"Adjusts attention computation dynamically per step. Saves VRAM but slows generation.","ui":"settings_cuda"},
@@ -645,7 +645,7 @@
{"id":"","label":"Google Cloud project ID","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"Google Cloud location ID","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"group","localized":"","hint":"","ui":"settings_offload"},
{"id":"","label":"Group size","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Group size","localized":"","hint":"Number of weight elements that share one quantization scale. Smaller groups improve accuracy at a small size and speed cost; larger groups are leaner but coarser.<br><br><b>0</b> auto-selects a group size from the <b><i>Quantization type</i></b>; with <b><i>Use quantized MatMul</i></b> enabled at 6-bit or wider weights, it falls back to one scale per row. <b>-1</b> uses one scale per row (no grouping).<br><br>Default is <b>0</b>.","ui":"settings_quantization"},
{"id":"","label":"GC threshold","localized":"","hint":"","ui":"settings_backends"},
{"id":"","label":"Grid max rows count","localized":"","hint":"","ui":"settings_saving-images"},
{"id":"","label":"Grid max columns count","localized":"","hint":"","ui":"settings_saving-images"},
@@ -662,6 +662,7 @@
{"id":"","label":"Huggingface","localized":"","hint":"Settings related huggingface access"},
{"id":"","label":"HiDream","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"HyperTile","localized":"","hint":"","ui":"settings_advanced"},
{"id":"","label":"Hadamard group size","localized":"","hint":"Number of elements that share one Hadamard rotation group when <b><i>Use Hadamard rotations</i></b> is enabled.<br><br>Applies only when <b><i>Use Hadamard rotations</i></b> is enabled.<br><br>Default is <b>128</b>.","ui":"settings_quantization"},
{"id":"","label":"HiDiffusion","localized":"","hint":"HiDiffusion allows creation of high-resolution images using your standard models without duplicates/distortions and improved performance","ui":"settings_advanced"},
{"id":"","label":"Height","localized":"","hint":"Target height of the output image in pixels.<br>For generation, this sets the resolution the model produces. For resize and upscale operations, this is the height the input is fitted to.<br><br>Should be a multiple of 8 for <i>SD1.x</i> and <i>SDXL</i> latents; newer architectures (<i>Flux</i>, <i>SD3</i>, video models) may require higher multiples (16, 32, or 64). Values that don't match are automatically floored to the nearest valid multiple for the loaded model.","ui":"txt2img"},
{"id":"","label":"HiRes steps","localized":"","hint":"Number of sampling steps for upscaled picture. If 0, uses same as for original","ui":"txt2img"},
@@ -942,8 +943,8 @@
{"id":"","label":"Modules to always offload","localized":"","hint":"","ui":"settings_offload"},
{"id":"","label":"Modules to never offload","localized":"","hint":"","ui":"settings_offload"},
{"id":"","label":"Model types not to quantize","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Modules to not convert","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Modules dtype dict","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Modules to not convert","localized":"","hint":"Names of modules to leave unquantized (kept in original precision), separated by spaces, commas, or semicolons.<br>Useful for layers that are sensitive to quantization, such as gate or projection layers. Example: <code>proj_out, x_embedder</code>.<br><br>Some models already exclude sensitive modules by default; entries here extend that built-in list rather than replacing it.<br><br>Default is empty.","ui":"settings_quantization"},
{"id":"","label":"Modules dtype dict","localized":"","hint":"Advanced: JSON mapping a quantization type to a list of module names, to quantize specific modules at a different type than the global <b><i>Quantization type</i></b>. Example: <code>{\"uint4\": [\"proj_out\"]}</code>.<br><br>Some models already assign certain modules a specific type by default; entries here merge with those built-in mappings rather than replacing them.<br><br>Default is empty.","ui":"settings_quantization"},
{"id":"","label":"Math","localized":"","hint":"","ui":"settings_cuda"},
{"id":"","label":"Memory limit","localized":"","hint":"","ui":"settings_backends"},
{"id":"","label":"migraphx","localized":"","hint":"","ui":"settings_compile"},
@@ -1150,13 +1151,15 @@
{"id":"","label":"Qwen layered","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"Quicksettings","localized":"","hint":"","ui":"settings_ui"},
{"id":"","label":"Qwen layered number of layers","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"Quantization mode","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantization type","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantized MatMul type","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantization type for Text Encoders","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantized MatMul type for Text Encoders","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantize convolutional layers","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantize using GPU","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantization enabled","localized":"","hint":"Selects which model components SDNQ quantizes: <b>Model</b> (the diffusion transformer or UNet), <b>TE</b> (pipeline text encoders), <b>LLM</b> (standalone LLM/VLM tools like captioning and prompt enhance), <b>Control</b> (ControlNet), or <b>VAE</b>.<br>Quantization lowers VRAM use and can speed up inference at some accuracy cost.<br><br>An empty selection disables SDNQ.<br><br>Default is empty.","ui":"settings_quantization"},
{"id":"","label":"Quantization mode","localized":"","hint":"When SDNQ quantizes the model.<br>- <b>auto</b>: quantizes during load when possible, otherwise after load<br>- <b>pre</b>: quantizes layer by layer as the model loads, lowering peak memory<br>- <b>post</b>: quantizes after the full model is loaded<br><br>Default is <b>auto</b>.","ui":"settings_quantization"},
{"id":"","label":"Quantization type","localized":"","hint":"Weight data type SDNQ quantizes to. Lower bit widths (e.g. <b>uint4</b>) shrink the model and can speed up inference; higher bit widths (e.g. <b>int8</b>) keep more accuracy.<br>Integer types are widely supported; float types (<b>float8_e4m3fn</b>, etc.) only accelerate on newer GPUs.<br><br>Applies to the components selected in <b><i>Quantization enabled</i></b>.<br><br>Default is <b>int8</b>.","ui":"settings_quantization"},
{"id":"","label":"Quantized MatMul type","localized":"","hint":"Compute data type for quantized matrix multiplication, used when <b><i>Use quantized MatMul</i></b> is enabled.<br><b>auto</b> picks <b>int8</b> for integer weights or <b>float8_e4m3fn</b> / <b>float16</b> for float weights.<br><br>Default is <b>auto</b>.","ui":"settings_quantization"},
{"id":"","label":"Quantization type for Text Encoders","localized":"","hint":"Weight data type for text encoder quantization, separate from the main <b><i>Quantization type</i></b>.<br><b>Same as model</b> uses the main <b><i>Quantization type</i></b>.<br><br>Applies to both <b>TE</b> (pipeline text encoders) and <b>LLM</b> (standalone tools like captioning and prompt enhance) when selected in <b><i>Quantization enabled</i></b>.<br><br>Default is <b>Same as model</b>.","ui":"settings_quantization"},
{"id":"","label":"Quantized MatMul type for Text Encoders","localized":"","hint":"Quantized matrix multiplication type for text encoders and standalone LLM tools (captioning, prompt enhance), separate from the main <b><i>Quantized MatMul type</i></b>.<br><b>Same as model</b> uses the main setting.<br><br>Default is <b>Same as model</b>.","ui":"settings_quantization"},
{"id":"","label":"Quantize convolutional layers","localized":"","hint":"Also quantizes convolutional layers, such as those in UNet models like <i>SDXL</i>.<br>Saves more memory, but convolutions are often more sensitive to quantization.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Quantize embedding layers","localized":"","hint":"Also quantizes embedding layers in text models.<br>Saves extra memory at some risk to quality.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Quantize using GPU","localized":"","hint":"Runs the quantization computation on the GPU instead of the CPU, which is much faster but uses VRAM during model load.<br>Disabling it keeps the computation on the CPU when load-time VRAM is limited.<br><br>Enabled by default.","ui":"settings_quantization"},
{"id":"","label":"Quantization weights type","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quantization activations type","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Quicksettings list","localized":"","hint":"List of setting names, separated by commas, for settings that should go to the quick access bar at the top instead the setting tab","ui":"settings_ui"}
@@ -1372,9 +1375,9 @@
{"id":"","label":"Search Changelog","localized":"","hint":"","ui":"system_tab_changelog"},
{"id":"","label":"Stage boundary ratio","localized":"","hint":"","ui":"settings_model_options"},
{"id":"","label":"sequential","localized":"","hint":"","ui":"settings_offload"},
{"id":"","label":"SVD rank size","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"SVD steps","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Shuffle weights in post mode","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"SVD rank size","localized":"","hint":"Rank of the low-rank correction added by <b><i>Use SVD quantization</i></b>. Higher ranks recover more accuracy but add parameters and compute.<br><br>Applies only when <b><i>Use SVD quantization</i></b> is enabled.<br><br>Default is <b>32</b>.","ui":"settings_quantization"},
{"id":"","label":"SVD steps","localized":"","hint":"Number of iterations used to estimate the low-rank correction for <b><i>Use SVD quantization</i></b>. More steps refine the estimate at the cost of longer quantization.<br><br>Applies only when <b><i>Use SVD quantization</i></b> is enabled.<br><br>Default is <b>8</b>.","ui":"settings_quantization"},
{"id":"","label":"Shuffle weights in post mode","localized":"","hint":"In <b>post</b> quantization mode, processes model components through the GPU one at a time, moving each off the GPU before the next, to limit peak VRAM during quantization.<br><br>Applies only when <b><i>Quantization mode</i></b> is <b>post</b>.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"SDXL: Use weighted pooled embeds","localized":"","hint":"","ui":"settings_text_encoder"},
{"id":"","label":"Sana: Use complex human instructions","localized":"","hint":"","ui":"settings_text_encoder"},
{"id":"","label":"Scaled-Dot-Product","localized":"","hint":"Memory optimization. Non-Deterministic unless SDP memory attention is disabled.","ui":"settings_cuda"},
@@ -1557,10 +1560,11 @@
{"id":"","label":"Username","localized":"","hint":"","ui":"component-8823"},
{"id":"","label":"UNET model","localized":"","hint":"","ui":"settings_sd"},
{"id":"","label":"Use torch streams","localized":"","hint":"","ui":"settings_offload"},
{"id":"","label":"Use SVD quantization","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Use Dynamic quantization","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Use quantized MatMul","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Use quantized MatMul with conv","localized":"","hint":"","ui":"settings_quantization"},
{"id":"","label":"Use SVD quantization","localized":"","hint":"Adds a low-rank (SVDQuant) correction on top of SDNQ to recover accuracy lost at low bit widths, at the cost of extra size and compute.<br>Tuned by <b><i>SVD rank size</i></b> and <b><i>SVD steps</i></b>.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Use Dynamic quantization","localized":"","hint":"Picks a per-layer weight type automatically instead of one type everywhere. Each layer starts at the <b><i>Quantization type</i></b> (the minimum) and steps up to higher precision until its error meets the <b><i>Dynamic loss threshold</i></b>.<br>Protects error-sensitive layers at the cost of a larger model and slower load.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Use Hadamard rotations","localized":"","hint":"Applies a Hadamard rotation before quantizing to spread out weight outliers, which can improve accuracy at low bit widths.<br>Group size is set by <b><i>Hadamard group size</i></b>.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Use quantized MatMul","localized":"","hint":"Runs matrix multiplications in <b>int8</b> or <b>fp8</b> instead of <b>bf16</b> / <b>fp16</b>, which can speed up inference on supported hardware.<br>Compute type is set by <b><i>Quantized MatMul type</i></b>.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Use quantized MatMul with conv","localized":"","hint":"Runs quantized matrix multiplication on convolutional layers, such as those in UNets like <i>SDXL</i>. This is the convolution counterpart of <b><i>Use quantized MatMul</i></b> and is controlled independently of it.<br><br>Only affects conv layers that are quantized, so it needs <b><i>Quantize convolutional layers</i></b>.<br><br>Disabled by default.","ui":"settings_quantization"},
{"id":"","label":"Use line break as prompt segment marker","localized":"","hint":"","ui":"settings_text_encoder"},
{"id":"","label":"Use zeros for prompt padding","localized":"","hint":"Force full zero tensor when prompt is empty to remove any residual noise","ui":"settings_text_encoder"},
{"id":"","label":"Upcast sampling","localized":"","hint":"Usually produces similar results to --no-half with better performance while using less memory","ui":"settings_cuda"},