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1 files changed, 10 insertions, 9 deletions
diff --git a/README.md b/README.md
index 4a393c00..7458f1ae 100644
--- a/README.md
+++ b/README.md
@@ -71,10 +71,10 @@ Run the command to start web ui:
python stable-diffusion-webui/webui.py
```
-If you have a 4GB video card, run the command with `--lowvram` argument:
+If you have a 4GB video card, run the command with either `--lowvram` or `--medvram` argument:
```
-python stable-diffusion-webui/webui.py --lowvram
+python stable-diffusion-webui/webui.py --medvram
```
After a while, you will get a message like this:
@@ -280,17 +280,18 @@ print("Seed was: " + str(processed.seed))
display(processed.images, processed.seed, processed.info)
```
-### `--lowvram`
+### 4GB videocard support
Optimizations for GPUs with low VRAM. This should make it possible to generate 512x512 images on videocards with 4GB memory.
-The original idea of those optimizations is by basujindal: https://github.com/basujindal/stable-diffusion. Model is separated into modules,
-and only one module is kept in GPU memory; when another module needs to run, the previous is removed from GPU memory.
-
-It should be obvious but the nature of those optimizations makes the processing run slower -- about 10 times slower
+`--lowvram` is a reimplementation of optimization idea from by [basujindal](https://github.com/basujindal/stable-diffusion).
+Model is separated into modules, and only one module is kept in GPU memory; when another module needs to run, the previous
+is removed from GPU memory. The nature of this optimization makes the processing run slower -- about 10 times slower
compared to normal operation on my RTX 3090.
-This is an independent implementation that does not require any modification to original Stable Diffusion code, and
-with all code concenrated in one place rather than scattered around the program.
+`--medvram` is another optimization that should reduce VRAM usage significantly by not peocessing conditional and
+unconditional denoising in a same batch.
+
+This implementation of optimization does not require any modification to original Stable Diffusion code.
### Inpainting
In img2img tab, draw a mask over a part of image, and that part will be in-painted.