sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: stable-diffusion-webui
Issue: [Feature Request]: Latent diffusion upscaler for the Stable Diffusion autoencoder
Labels: enhancement

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What would your feature do ?

Can we implement it?

Q from Twitter [@RiversHaveWings](https://twitter.com/RiversHaveWings):
I've trained a latent diffusion upscaler for the Stable Diffusion autoencoder (and anything you feel like feeding into it if you can tolerate a little artifacts) in collaboration with 
[@stabilityai](https://twitter.com/StabilityAI)
. Try the Colab written by 
[@nshepperd1](https://twitter.com/nshepperd1)
https://colab.research.google.com/drive/1o1qYJcFeywzCIdkfKJy7cTpgZTCM2EI4


![image](https://user-images.githubusercontent.com/113993746/200418900-0d8f4fc9-e094-4087-aedb-daff1de602ec.png)
![image](https://user-images.githubusercontent.com/113993746/200418930-aef257ff-2118-47b0-9716-73d2f9edaf3d.png)


### Proposed workflow

See in colab

### Additional information

_No response_
Repository: pytorch
Issue: Bernoulli uses legacy contiguous memory format
Labels: triaged,module: memory format

### 🐛 Describe the bug

```
RuntimeError: output 0: meta disagrees with real impl:
aten.bernoulli.default(
  tensor(..., device='meta', size=(2, 3, 4)) stride=(1, 2, 6),
  
) = (
  tensor(..., device='meta', size=(2, 3, 4)) stride=(1, 2, 6)
)
but real stride was (12, 4, 1)
```

after patching in bernoulli metas

```
+@register_meta([aten.bernoulli.default, aten.bernoulli.out])
+@out_wrapper()
+def meta_bernoulli(self, *, generator=None):
+    return torch.empty_like(self)
+
+
 @register_meta(aten.bernoulli_.float)
 def meta_bernoulli_(self, p=0.5, generator=None):
     return self
 
 
+@register_meta(aten.bernoulli.p)
+def meta_bernoulli_p(self, p=0.5, generator=None):
+    return torch.empty_like(self)
+
+
```

python test/test_meta.py -k test_dispatch_symbolic_meta_outplace_all_strides_bernoulli_cuda_float32

cpu passes with these metas

### Versions

master

cc @VitalyFedyunin @jamesr66a
Repository: kubernetes
Issue: dynamic resource allocation: benchmark scheduler plugin
Labels: priority/important-soon,sig/scheduling,sig/node,kind/feature,needs-triage

### What would you like to be added?

Performance optimizations may be possible.

Other ideas for classic DRA:
- randomize node selection with rand.Perm
- remove sorting of node names

For structured parameters (incomplete list):
- cache compiled CEL expressions
- optimize attribute lookup

### Why is this needed?

Better performance.
Repository: pytorch
Issue: The libtorch tests Simplify.{SimplifySymbolicMinMax,SimplifyNestedMax,SimplifyNestedMin} fail on Apple Silicon
Labels: oncall: jit,module: macos,module: arm

### 🐛 Describe the bug

Following [these docs](https://github.com/pytorch/pytorch/blob/v1.13.0/docs/libtorch.rst#building-libtorch-using-python) to build libtorch v1.13.0, then running one of the test binaries:

```sh
gh repo clone pytorch/pytorch && cd pytorch
git checkout v1.13.0
git submodule update --init --recursive
mkdir build_libtorch && cd build_libtorch
python ../tools/build_libtorch.py
build/bin/test_tensorexpr
```

I get these errors:

```
[ RUN      ] Simplify.SimplifySymbolicMinMax
/Users/samueles/github/pytorch/pytorch/test/cpp/tensorexpr/test_simplify.cpp:1997: Failure
Expected: (nullptr) != (node_), actual: (nullptr) vs (nullptr)
[  FAILED  ] Simplify.SimplifySymbolicMinMax (0 ms)
[ RUN      ] Simplify.SimplifyNestedMax
/Users/samueles/github/pytorch/pytorch/test/cpp/tensorexpr/test_simplify.cpp:2020: Failure
Expected: (nullptr) != (max), actual: (nullptr) vs (nullptr)
[  FAILED  ] Simplify.SimplifyNestedMax (0 ms)
[ RUN      ] Simplify.SimplifyNestedMin
/Users/samueles/github/pytorch/pytorch/test/cpp/tensorexpr/test_simplify.cpp:2296: Failure
Expected: (nullptr) != (min), actual: (nullptr) vs (nullptr)
[  FAILED  ] Simplify.SimplifyNestedMin (0 ms)
```

### Versions

PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: macOS 12.6.1 (arm64)
GCC version: Could not collect
Clang version: 14.0.0 (clang-1400.0.29.202)
CMake version: version 3.24.2
Libc version: N/A

Python version: 3.9.7 (default, Sep 8 2021, 10:47:21) [Clang 12.0.5 (clang-1205.0.22.11)] (64-bit runtime)
Python platform: macOS-12.6.1-arm64-arm-64bit
Is CUDA available: N/A
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.2
[pip3] torch==1.12.1
[pip3] torchvision==0.13.1
[conda] Could not collect

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @malfet @albanD @snadampal @milpuz01
Repository: kubernetes
Issue: VolumeBinding's Score plugin in enabled by default in v1beta3 and v1 config
Labels: kind/bug,sig/scheduling,help wanted,needs-triage

### What happened?

Followed up with @alculquicondor, VolumeBinding Score plugin is enabled only when the feature gate `VolumeCapacityPriority ` is enabled:

https://github.com/kubernetes/kubernetes/blob/43a2bb4df4ff3c255337f5ed927b8fd17c38c235/pkg/scheduler/apis/config/v1beta2/default_plugins.go#L115-L119

However, with v1beta3 and v1, VolumeBinding Score plugin is no longer feature gated (although its underlying logic is).

### What did you expect to happen?

We'd pursue disabling a plugin if the feature gate is disabled. Another thread: https://github.com/kubernetes/kubernetes/pull/113275#discussion_r1014327168

### How can we reproduce it (as minimally and precisely as possible)?

Run kube-scheduler using a v1beta3 or v1 config, without enabling `VolumeCapacityPriority` feature gate.

### Anything else we need to know?

_No response_

### Kubernetes version

<details>

```console
$ kubectl version
# paste output here
```

</details>

### Cloud provider

<details>
N/A
</details>


### OS version

<details>

```console
# On Linux:
$ cat /etc/os-release
# paste output here
$ uname -a
# paste output here

# On Windows:
C:\> wmic os get Caption, Version, BuildNumber, OSArchitecture
# paste output here
```

</details>


### Install tools

<details>

</details>


### Container runtime (CRI) and version (if applicable)

<details>

</details>


### Related plugins (CNI, CSI, ...) and versions (if applicable)

<details>

</details>
Repository: godot
Issue: GPUParticles3D trails warning doesn't handle custom shader
Labels: bug,topic:editor,topic:shaders,topic:particles

### Godot version

4.0beta4

### System information

PopOS 22.04, 6.0.3-76060003-generic kernel, Ryzen 6800h, Radeon 6700m, amdgpu, Vulkan

### Issue description

When using GPUParticles3D with custom gdshader, the warning for:

"Trails enabled, but one or more mesh materials are either missing or not set for trails rendering."

Doesn't account for custom gdshaders.

Code that implements the warning:

https://github.com/godotengine/godot/blob/b61fda9d2a6166e41538405ad8ae24f1aed40570/scene/3d/gpu_particles_3d.cpp#L362-L364

Image of the warning showing:

![image](https://user-images.githubusercontent.com/15805689/200434176-449cee30-17d5-407d-af5f-4b27be9efb10.png)



### Steps to reproduce

- Add a GPUParticles3D to scene
- Set Trails to enabled on GPUParticles3D
- Add particle proccess material
- Add supported mesh (RibbonTrail or TubeTrail mesh)
- Add shader material with custom gdshader that has render mode particles trails enabled

https://github.com/Mikeysax/example-project-for-godot-bug-shader-trails-warning-bug

### Minimal reproduction project

https://github.com/Mikeysax/example-project-for-godot-bug-shader-trails-warning-bug
Repository: stable-diffusion-webui
Issue: [Feature Request]: Add ability to merge images ad hoc
Labels: enhancement,extension-request

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What would your feature do ?

Feature allows you to merge two images using BLIP/CLIP interrogate and then blending image latents and conditioning.

![frida](https://user-images.githubusercontent.com/110263573/200436039-6e16139f-c10c-4bc5-b645-6b383c683afa.png)

![earring](https://user-images.githubusercontent.com/110263573/200436048-b4dd9f2b-bc4c-49a2-873e-eda0f754708a.png)

![fusion_ha](https://user-images.githubusercontent.com/110263573/200436051-59eabd01-bd99-4e0f-91b9-344a47a61bab.png)

![fusion_ha_ha](https://user-images.githubusercontent.com/110263573/200436056-3236a82f-0100-455b-8ab0-de62cc0ff223.png)


### Proposed workflow

Just follow the demo code here, I prepopulated the prompts but you can use BLIP/CLIP to generate them.

https://gist.github.com/AmericanPresidentJimmyCarter/b4b69daa577936cb72aec4db44d0a2ea

### Additional information

_No response_
Repository: rust
Issue: Allow users to override or filter list of exported symbols passed to the WASM linker
Labels: A-linkage,O-wasm

Currently, the way export visibility is handled in WASM is that rustc manually passes a list of all exported functions to the WASM linker:

https://github.com/rust-lang/rust/blob/d69c33ad4c65e42a7a2d018a5c2ee1c7dbd0fc15/compiler/rustc_codegen_ssa/src/back/linker.rs#L1312-L1314

In almost every mode of compilation for Rust it doesn't matter too much if the exports list covers more than is needed, because there's a linker at the very end dealing with this. Rlibs and staticlibs may end up containing extra symbols, but the end binary will have dead code removed by the final linker. Dynamic libraries end up containing extra symbols which is a disk size issue (not a huge deal), but the dynamic linker's capable of only paging in the necessary bits.

The landscape is very different for WASM, however. In WASM, the final binary is a .wasm file that gets sent over the network, and its size matters. It's impractical to compile large libraries to a single .wasm file and recommend everyone use it since most users will not be using 99% of that wasm file.

There are tools for doing dead code elimination in JS, usually going by the name "tree shaking". Furthermore it's often practical for a WASM user to just list out the symbols they need.

With all of this, it would be useful to be able to customize which symbols get exported as a part of the linker process, rather than updating the code (as different library clients will need different things and it might not always be practical to pepper the library with a hundred client specific cfgs, nor is it an appropriate separation of concerns)

It would be nice if rust had a pair of codegen flags that would:

 - Allow explicitly allowlisting/listing symbols that get passed down to `rust-lld --export`
 - Allow explicitly denylisting symbols that get passed down to `rust-lld --export`, to "filter out" features

cc @sffc
Repository: pytorch
Issue: [FSDP] FSDP produces different gradient norms vs DDP, and w/ grad norm clipping creates different training results
Labels: oncall: distributed,triaged,release notes: distributed (fsdp)

### 🐛 Describe the bug

Running a T5 large on C4 dataset, and using same random seeding, optimizer, lr scheduler, etc, training with DDP vs FSDP NO_SHARD and FULL_SHARD produce different gradient norms and with norm clipping different training outcomes:

![comparison](https://user-images.githubusercontent.com/46302957/200437760-cb9df95e-0aa3-435a-a574-bed5bd4ff14e.png)

The core issue is shown in this logging of the gradient norms:
![comparison_norm](https://user-images.githubusercontent.com/46302957/200437875-f282d44f-b62b-4a25-baf3-a06b2b2ce236.png)


Details on grad norm calculations:
Training was run with l2 norm. 
FSDP uses FSDP clip_grad_norm_()
DDP uses nn.utils.clip_grad_norm_()

code:
~~~
if isinstance(model, FSDP):
                    # grad_norm_before_clip = get_grad_norm_fsdp(model, local_rank)
                    model.clip_grad_norm_(clip_th)
                    grad_norm_after_clip = get_grad_norm_fsdp(model, local_rank, model_sharding_strategy)
                else:
                    # grad_norm_before_clip = get_grad_norm_local(model)
                    nn.utils.clip_grad_norm_(model.parameters(), clip_th)
                    grad_norm_after_clip = get_grad_norm_local(model) 
 ~~~

For logging, this is the code used:
~~~
def get_grad_norm_local(model):
    total_norm = 0.0
    for p in model.parameters():
        local_norm = torch.linalg.vector_norm(p.grad, dtype=p.dtype)
        total_norm += local_norm ** 2
    return total_norm ** 0.5
    
def get_grad_norm_fsdp(model, rank, sharding_strategy=ShardingStrategy.FULL_SHARD):
    local_norm = get_grad_norm_local(model)
    op = torch.distributed.ReduceOp.SUM
    return_norm = local_norm.clone().detach().requires_grad_(False).to(rank)**2
    dist.all_reduce(return_norm, op=op)
    if sharding_strategy == ShardingStrategy.NO_SHARD:
        return_norm = return_norm / world_size
    return return_norm ** 0.5
~~~
 


### Versions

pending...opening issue from partner. 

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
Repository: pytorch
Issue: Cloud-based rendezvous backend / distributed store?
Labels: oncall: distributed

We're submitting elastic PyTorch runs on top of Azure Machine Learning

The two in-built rendezvous backends are c10d and etcd. We were wondering if you considered a rendezvous backend based on a cloud storage provider?

Both c10d and etcd require a stable endpoint / dedicated compute. (c10d requires a stable master node in the training cluster, and etcd requires a stable etcd server running on dedicated compute.) We're trying to train only on top of low-priority Spot VMs, which can enter / leave the training at any time.

To unblock ourselves, we implemented `AzureBlobRendezvousBackend` on top of `torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend` and `AzureBlobStore` on top of `torch.distributed.Store`. Using Azure Blob as the key-value store, we didn't need an endpoint hosted by separate dedicated compute.

We were wondering if you've considered this approach before?

(Also, in the same spirit, in addition to `torch.distributed.FileStore`, have you considered something like `FolderStore`, which would be like `FileStore`, but where each key-value pair is stored in a separate file? If this had been present, we could have avoided implementing `AzureBlobStore`. Instead, we could have configured the nodes to use a common file system and then used `FolderStore`. (The common file system could have been a mounted Blob store, a shared NFS, etc.))


cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
Repository: godot
Issue: Distorted audio running Android export on Acer Chromebooks
Labels: bug,platform:android,topic:porting,needs testing,topic:audio

### Godot version

3.5.1.stable.official

### System information

Acer Chromebook CB5-132T

### Issue description

Audio playback is very distorted sounding when running an Android apk exported with Godot 3.5.1 on an Acer Chromebook CB5-132T.  Our users have also reported distorted sound on Acer Chromebooks CB3-431 and CB3-532

The problem did not reproduce on a Dell Chromebook 5190

Audio also sounds fine when exported with Godot 3.4.5.   I was unable to test on 4.0 due to another error (Unable to setup the Godot Engine! Aborting.)

I also tested converting the ogg to wav and it still sounded bad.


### Steps to reproduce

1. Create New Project
2. Create User Interface Scene
3. Add AudioStreamPlayer
4. Add a ogg file to project (I used House In a Forest Loop.ogg from https://docs.godotengine.org/en/stable/_downloads/923b18d4e18125cf494ee5f7efba7e03/dodge_assets.zip)
5. Drag ogg to AudioStreamPlayer
6. Check on Playing and Autoplay
7. Export Android APK
8. Install APK on Acer Chromebook and run

### Minimal reproduction project

[AndroidAudioTest.zip](https://github.com/godotengine/godot/files/9956654/AndroidAudioTest.zip)

Screen recording with audo from chromebook:
[ChromebookDistortedAudio.webm](https://user-images.githubusercontent.com/361560/200448255-6e632a3c-458d-489e-bbc1-67b8a23e85de.webm)
Repository: PowerToys
Issue: command window here,  admin
Labels: Idea-New PowerToy,Product-File Explorer,Needs-Triage

### Description of the new feature / enhancement

Requesting command window here, elevated, admin

There also needs to be a category menu within PowerToys to enable/disable options, with the ability to change hotkey from shift+LMB or user-defined.

1. Command Window Here
2. Command Window Here (elevated)
3. Powershell Here
4. Powershell Here (elevated)
5. Powershell ISE Here
6. Powershell ISE here (elevated)



### Scenario when this would be used?

power user, admin

### Supporting information

PowerToys has a "command window here" with shift+LMB, but no elevated admin command prompt or prompt for Powershell ISE elevated admin
Repository: PowerToys
Issue: PowerToys Run support user define keyword and shortcut key to open app 
Labels: Needs-Triage

### Description of the new feature / enhancement

Like the Executor we can define any keyword and shortcut key to open 
![image](https://user-images.githubusercontent.com/11538969/200467229-071c1d40-0a9d-4b84-a72a-7d043f3cb85f.png)


### Scenario when this would be used?

Need do some action quickly 

### Supporting information

_No response_
Repository: flutter
Issue: [Impeller] Setup an Android playgrounds unit-tests harness.
Labels: engine,P3,e: impeller,team-engine,triaged-engine

To quickly ensure that the entire test suite runs on Android as well as run one-of experiments with Android specific tools like ARM Mali Streamline or AGI, it is desirable to have an playgrounds harness that runs on Android. This could be backend by NativeActivity and package all relevant assets for running the test suite on Android.
Repository: flutter
Issue: Crash when switching screens on Android folding phone
Labels: c: crash,platform-android,engine,e: OS-version specific,has reproducible steps,P2,c: fatal crash,found in release: 3.3,found in release: 3.4,team-android,triaged-android

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## Steps to Reproduce

<!-- Please include full steps to reproduce so that we can reproduce the problem. -->

1. Creating a new clean project
2. run android emulator。`7.6  Fold-in with outer display API 32`
3.  Toggle folding 


Demo

![image info](https://i.imgur.com/7zkKEaB.gif)



<details>
  <summary>flutter doctor</summary>

```
[✓] Flutter (Channel stable, 3.3.7, on macOS 12.5 21G72 darwin-arm (Rosetta), locale zh-Hant-TW)
[✓] Android toolchain - develop for Android devices (Android SDK version 32.0.0)
[✓] Xcode - develop for iOS and macOS (Xcode 13.2.1)
[✓] Chrome - develop for the web
[✓] Android Studio (version 2020.3)
[✓] VS Code (version 1.71.1)
[✓] Connected device (7 available)
[✓] HTTP Host Availability
```



</details>
Repository: TypeScript
Issue: Use ESM for our executables (tsc, tsserver, typingsInstaller)
Labels: Infrastructure

Our libraries are all UMD-ish, because they can be used in Node or in the browser. I didn't change this for the module transform.

However, most of our executables are CJS, as they are only intended to be run within Node. This includes tsc, typingsInstaller, and watchGuard. An exception is tsserver, which contains web code for vscode.dev.

tsc and typingsInstaller both share a lot of code; if they only run on Node anyway, we could potentially raise our minimum Node version to node 12, and ship them as ESM via esbuild's split ESM bundling. If we get vscode.dev off of tsserver (and onto tsserverlibrary), then all three can share code.

In my testing, this reduces our package size by an additional 7 MB, which is a great win.

See also #27891, #32949.
Repository: TypeScript
Issue: Convert ts.Debug, Parser, etc from namespaces into modules
Labels: Infrastructure

The module transform turned all of our top-level namespaces into modules.

However, some notable exceptions are `ts.Debug` and the parser (and some other minor examples; search for `^namespace `).

`Debug` is used nearly everywhere in the code, but calls to the namespace cannot be scope lifted by esbuild. Converting `debug.ts` into something reexported like `export * as Debug from "./debug.ts"` would work the same, but all calls would get lifted and become direct, which may have a performance win like the rest of the scope lifting did.

The parser has fewer calls across the namespace barrier, however it contains some duplicated allocators and this may aid in converting the parser into a self-contained tree-shakable entity for #34617.
Repository: TypeScript
Issue: Remove internal uses of "namespace barrel" modules, untangle repo
Labels: Infrastructure

In #51387, I introduced the concept of the "namespace barrel" modules, located at `**/_namespaces/*.ts`.

While this does help us transition to modules, internally, this is a gross hack and makes it hard for us to start splitting the codebase up, export just the parser, etc.

We should be trying to remove internal uses of these files and use direct imports, leaving these files for our public API surface only.
Repository: godot
Issue: Inline exposed Resources are <empty> in Remote view
Labels: bug,topic:gdscript,topic:editor,usability

### Godot version

4.0.beta4

### System information

Windows 10

### Issue description

If you expose a Resource with a class_name, then set the value to `New ClassName`, creating an inline version, then when you play and look in the Remote tab, the property says <empty> instead of showing the currently used object.

### Steps to reproduce

- Create a class with a class_name that extends Resource
- Create a separate class that exposes a property of the class type above
- In the inspector choose `New ClassName`
- Play and view the property in the Remote tab

### Minimal reproduction project

_No response_