docs: add docs for low vram mode

docs/configuration.md

@@ -83,6 +83,10 @@ A subset of settings may be specified using CLI args:
 - `--root`: specify the root directory
 - `--config`: override the default `invokeai.yaml` file location
 
+### Low-VRAM Mode
+
+See the [Low-VRAM mode docs][low-vram] for details on enabling this feature.
+
 ### All Settings
 
 Following the table are additional explanations for certain settings.
@@ -185,3 +189,4 @@ The `log_format` option provides several alternative formats:
 
 [basic guide to yaml files]: https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/
 [Model Marketplace API Keys]: #model-marketplace-api-keys
+[low-vram]: ./features/low-vram.md

docs/features/low-vram.md

@@ -0,0 +1,108 @@
+---
+title: Low-VRAM mode
+---
+
+As of v5.6.0, Invoke has a low-VRAM mode. It works on systems with dedicated GPUs (Nvidia GPUs on Windows/Linux and AMD GPUs on Linux).
+
+This allows you to generate even if your GPU doesn't have enough VRAM to hold full models. Most users should be able to run even the beefiest models - like the ~24GB unquantised FLUX dev model.
+
+## Enabling Low-VRAM mode
+
+To enable Low-VRAM mode, add this line to your `invokeai.yaml` configuration file, then restart Invoke:
+
+```yaml
+enable_partial_loading: true
+```
+
+For most users, this is all that is needed, but it is possible to fine-tune the settings for best performance or if you still get out-of-memory errors (OOMs).
+
+!!! tip "How to find `invokeai.yaml`"
+
+    The `invokeai.yaml` configuration file lives in your install directory. To access it, run the **Invoke Community Edition** launcher and click the install location. This will open your install directory in a file explorer window.
+
+    You'll see `invokeai.yaml` there and can edit it with any text editor. After making changes, restart Invoke.
+
+    If you don't see `invokeai.yaml`, launch Invoke once. It will create the file on its first startup.
+
+## Details and fine-tuning
+
+Low-VRAM mode involves 3 features, each of with can be configured or fine-tuned:
+
+- Partial model loading
+- Dynamic RAM and VRAM cache sizes
+- Working memory
+
+Read on to learn about these features and understand how to fine-tune them for your system and use-cases.
+
+### Partial model loading
+
+Invoke's partial model loading works by streaming model "layers" between RAM and VRAM as they are needed.
+
+When an operation needs layers that are not in VRAM, but there isn't enough room to load them, inactive layers are offloaded to RAM to make room.
+
+#### Enabling partial model loading
+
+As described above, you can enable partial model loading by adding this line to `invokeai.yaml`:
+
+```yaml
+enable_partial_loading: true
+```
+
+### Dynamic RAM and VRAM cache sizes
+
+Loading models from disk is slow and can be a major bottleneck for performance. Invoke uses two model caches - RAM and VRAM - to reduce loading from disk to a minimum.
+
+By default, Invoke manages these caches' sizes dynamically for best performance.
+
+#### Fine-tuning cache sizes
+
+Prior to v5.6.0, the cache sizes were static, and for best performance, many users needed to manually fine-tune the `ram` and `vram` settings in `invokeai.yaml`.
+
+As of v5.6.0, the caches are dynamically sized. The `ram` and `vram` settings are no longer used, and new settings are added to configure the cache.
+
+**Most users will not need to fine-tune the cache sizes.**
+
+But, if your GPU has enough VRAM to hold models fully, you might get a perf boost by manually setting the cache sizes in `invokeai.yaml`:
+
+```yaml
+# Set the RAM cache size to as large as possible, leaving a few GB free for the rest of your system and Invoke.
+# For example, if your system has 32GB RAM, 28GB is a good value.
+max_cache_ram_gb: 28
+# Set the VRAM cache size to be just large enough to hold the models you want to use.
+# For example, on a 24GB GPU that will be running unquantized FLUX without any auxiliary models,
+# 18GB is a good value.
+max_cache_vram_gb: 18
+```
+
+!!! tip "Max safe value for `max_cache_vram_gb`"
+
+    To determine the max safe value for `max_cache_vram_gb`, subtract `device_working_mem_gb` from your GPU's VRAM. As described below, the default for `device_working_mem_gb` is 3GB.
+
+    For example, if you have a 12GB GPU, the max safe value for `max_cache_vram_gb` is `12GB - 3GB = 9GB`.
+
+    If you had increased `device_working_mem_gb` to 4GB, then the max safe value for `max_cache_vram_gb` is `12GB - 4GB = 8GB`.
+
+### Working memory
+
+Invoke cannot use _all_ of your VRAM for model caching and loading. It requires some VRAM to use as working memory for various operations.
+
+Invoke reserves 3GB VRAM as working memory by default, which is enough for most use-cases. However, it is possible to fine-tune this setting if you still get OOMs.
+
+#### Fine-tuning working memory
+
+You can increase the working memory size in `invokeai.yaml` to prevent OOMs:
+
+```yaml
+# The default is 3GB - bump it up to 4GB to prevent OOMs.
+device_working_mem_gb: 4
+```
+
+!!! tip "Operations may request more working memory"
+
+    For some operations, we can determine VRAM requirements in advance and allocate additional working memory to prevent OOMs.
+
+    VAE decoding is one such operation. This operation converts the generation process's output into an image. For large image outputs, this might use more than the default working memory size of 3GB.
+
+    During this decoding step, Invoke calculates how much VRAM will be required to decode and requests that much VRAM from the model manager. If the amount exceeds the working memory size, the model manager will offload cached model layers from VRAM until there's enough VRAM to decode.
+
+    Once decoding completes, the model manager "reclaims" the extra VRAM allocated as working memory for future model loading operations.

mkdocs.yml

@@ -137,6 +137,7 @@ nav:
       - Invocation API: 'nodes/invocation-api.md'
   - Configuration: 'configuration.md'
   - Features:
+      - Low VRAM mode: 'features/low-vram.md'
       - Database: 'features/database.md'
       - New to InvokeAI?: 'help/gettingStartedWithAI.md'
   - Contributing: