diff --git a/README.md b/README.md
index eb9bcbac2..c4e55125d 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,21 @@
-[](https://github.com/sponsors/vladmandic)
-
-
-
-
-
+
+
# SD.Next
+**Stable Diffusion implementation with advanced features**
+
+[](https://github.com/sponsors/vladmandic)
+
+
+
+[](https://discord.gg/VjvR2tabEX)
-**Stable Diffusion implementation with modern UI and advanced features**
+### [Docs](README.md#docs) | [Discord](https://discord.gg/VjvR2tabEX) | [Changelog](CHANGELOG.md)
-This project started as a form from [Automatic1111 WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui/) and it grew siginificantly since then, but although it diverged significanly, any substantial features to original work is ported to this repository as well
+
-Individual features are not listed here, instead check [Changelog](CHANGELOG.md) for full list of changes
+This project started as a fork from [Automatic1111 WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui/) and it grew significantly since then, but although it diverged considerably, any substantial features to original work is ported to this repository as well.
+
+Individual features are not listed here, instead check [Changelog](CHANGELOG.md) for full list of changes.
## Platform support
@@ -32,12 +37,13 @@ Individual features are not listed here, instead check [Changelog](CHANGELOG.md)
`webui.bat` or `webui.sh`:
- Platform specific wrapper scripts For Windows, Linux and OSX
- Starts `sdnext.py` in a Python virtual environment (`venv`)
- - Uses `install.py` to handle all actual requirements and dependencies
- - *Note*: Server can run without virtual environment, but it is recommended to use it to avoid library version conflicts with other applications
+ - Uses `install.py` to handle all actual requirements and dependencies
-*Note*: **nVidia/CUDA** and **AMD/ROCm** are auto-detected is present and available, but for any other use case specify required parameter explicitly or wrong packages may be installed as installer will assume CPU-only environment
+> Server can run without virtual environment, but it is recommended to use it to avoid library version conflicts with other applications
-Full startup sequence is logged in `sdnext.log`, so if you encounter any issues, please check it first
+> **nVidia/CUDA** and **AMD/ROCm** are auto-detected is present and available, but for any other use case specify required parameter explicitly or wrong packages may be installed as installer will assume CPU-only environment.
+
+Full startup sequence is logged in `sdnext.log`, so if you encounter any issues, please check it first.
Below is partial list of all available parameters, run `webui --help` for the full list:
@@ -64,14 +70,16 @@ Below is partial list of all available parameters, run `webui --help` for the fu
### **Collab**
- To avoid having this repo rely just on me, I'd love to have additional maintainers with full admin rights. If you're interested, ping me!
-- In addition to general cross-platform code, desire is to have a lead for each of the main platforms
-This should be fully cross-platform, but I would really love to have additional contibutors and/or maintainers to join and help lead the efforts on different platforms
+- In addition to general cross-platform code, desire is to have a lead for each of the main platforms.
+This should be fully cross-platform, but I would really love to have additional contibutors and/or maintainers to join and help lead the efforts on different platforms.
### **Goals**
-The idea behind the fork is to enable latest technologies and advances in text-to-image generation
-*Sometimes this is not the same as "as simple as possible to use"*
-If you are looking an amazing simple-to-use Stable Diffusion tool, I'd suggest [InvokeAI](https://invoke-ai.github.io/InvokeAI/) specifically due to its automated installer and ease of use
+The idea behind the fork is to enable latest technologies and advances in text-to-image generation.
+
+> *Sometimes this is not the same as "as simple as possible to use".*
+
+If you are looking an amazing simple-to-use Stable Diffusion tool, I'd suggest [InvokeAI](https://invoke-ai.github.io/InvokeAI/) specifically due to its automated installer and ease of use.
General goals:
@@ -95,6 +103,7 @@ General goals:
### **Docs**
+- [Wiki](https://github.com/vladmandic/automatic/wiki)
- [ReadMe](README.md)
- [ToDo](TODO.md)
- [Changelog](CHANGELOG.md)
diff --git a/modules/devices.py b/modules/devices.py
index ddd02fb28..dbc7b94a7 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -175,8 +175,10 @@ else:
backend = 'cpu'
if backend == 'ipex':
- #Fix broken function in ipex 1.13.120+xpu
+ #Fix broken functions with ipex
from modules.sd_hijack_utils import CondFunc
+ torch.cuda.empty_cache = torch.xpu.empty_cache
+
#Functions with dtype errors:
CondFunc('torch.nn.modules.GroupNorm.forward',
lambda orig_func, *args, **kwargs: orig_func(args[0], args[1].to(args[0].weight.data.dtype)),
@@ -184,15 +186,20 @@ if backend == 'ipex':
CondFunc('torch.nn.modules.Linear.forward',
lambda orig_func, *args, **kwargs: orig_func(args[0], args[1].to(args[0].weight.data.dtype)),
lambda *args, **kwargs: args[2].dtype != args[1].weight.data.dtype)
+
#Functions that does not work with the XPU:
#UniPC:
CondFunc('torch.linalg.solve',
lambda orig_func, *args, **kwargs: orig_func(args[0].to("cpu"), args[1].to("cpu")).to(get_cuda_device_string()),
lambda *args, **kwargs: args[1].device != torch.device("cpu"))
#SDE Samplers:
- CondFunc('torchsde._brownian.brownian_interval._randn',
- lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=device, generator=torch.xpu.Generator(device).manual_seed(int(seed))),
- lambda _, size, dtype, device, seed: device != torch.device("cpu"))
+ CondFunc('torch.Generator',
+ lambda orig_func, device: torch.xpu.Generator(device),
+ lambda orig_func, device: device != torch.device("cpu") and device != "cpu")
+ #Diffusers Float64 (ARC GPUs doesn't support double or Float64):
+ CondFunc('torch.from_numpy',
+ lambda orig_func, *args, **kwargs: orig_func(args[0].astype('float32')),
+ lambda *args, **kwargs: args[1].dtype == float)
#ControlNet:
CondFunc('torch.batch_norm',
lambda orig_func, *args, **kwargs: orig_func(args[0].to("cpu"),