sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: stable-diffusion-webui
Issue: [Bug]: The interface layout becomes very messy as it scales, and the issue is so severe that it even frustrated me during screen recording.
Labels: cannot-reproduce,bug-report

### Is there an existing issue for this?

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

### What happened?


https://github.com/AUTOMATIC1111/stable-diffusion-webui/assets/131615326/bcd514be-283f-4fd8-b005-8b7bb524b719



### Steps to reproduce the problem

1. Go to .... 
2. Press ....
3. ...


### What should have happened?

This shouldn't happen with scaling.

### Sysinfo

[sysinfo-2023-09-01-03-23.txt](https://github.com/AUTOMATIC1111/stable-diffusion-webui/files/12494036/sysinfo-2023-09-01-03-23.txt)


### What browsers do you use to access the UI ?

Microsoft Edge

### Console logs

```Shell
Tile 46/70
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        Tile 70/70
```


### Additional information

_No response_
Repository: stable-diffusion-webui
Issue: [1.6.0] XL model change too slow in ubuntu server, Can I keep this model in memery forever?
Labels: bug-report

### Is there an existing issue for this?

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

### What happened?

loading xl model cost too long time

### Steps to reproduce the problem

1、load a SD1.5 model
2、change to a XL model. 

Can I always keep a special model in memery?

### What should have happened?

loading XL model cost long time. (SSD)

### Sysinfo

It take 3 mins to load sdxl

### What browsers do you use to access the UI ?

Google Chrome

### Console logs

```Shell
Applying attention optimization: xformers... done.
Model loaded in 21.8s (load weights from disk: 2.6s, create model: 0.6s, apply weights to model: 14.7s, apply half(): 0.1s, apply dtype to VAE: 1.7s, move model to device: 0.2s, calculate empty prompt: 1.7s).
【--------------- cost in this place-----------------】
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:17<00:00,  1.15it/s]
Total progress: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:15<00:00,  1.30it/s]
Total progress: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:15<00:00,  1.55it/s]
```


### Additional information

Can I always keep a special model in memery?
Repository: stable-diffusion-webui
Issue: [Bug]: Switching models back and forth leads to different images
Labels: bug

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

A known [issue](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/12619) which I think was briefly fixed appears to be still be broken in 1.6 while using Loras and switching XL models. May have to do with the new model cache settings I've used:

Maximum number of checkpoints loaded at the same time = 2
Only keep one model on device = true
Checkpoints to cache in RAM (obsolete) = 0
VAE Checkpoints to cache in RAM = 0
Number of Lora networks to keep cached in memory = 0
Model cache size (requires restart) = 0

### Steps to reproduce the problem

1. XL model loaded while using a lora, generate
2. Switch to another XL model, generate
3. Switch back to first model, gen with all same settings and see different result
4. May need to switch twice in step 2 before going back to first model to see the new result. May vary depending on the number of max checkpoints loaded settings

### What should have happened?

Same result after switching models

### Sysinfo

Linux Ubuntu 32GB RAM 6800XT
Version 1.6

### What browsers do you use to access the UI ?

Mozilla Firefox

### Console logs

```Shell
No errors
```


### Additional information

_No response_
Repository: PowerToys
Issue: Disabling the CTRL+ESC key combination does not work when pressing CTRL+*any_key*+ESC
Labels: Idea-Enhancement,Product-Keyboard Shortcut Manager,Needs-Triage,Needs-Team-Response

### Microsoft PowerToys version

0.73.0

### Installation method

Microsoft Store

### Running as admin

Yes

### Area(s) with issue?

Keyboard Manager

### Steps to reproduce

Add a combination of CTRL+ESC keys with the Disable action in the "Reassigning keyboard shortcuts" section

![1](https://github.com/microsoft/PowerToys/assets/48589947/1a3fa160-b03a-4fe0-83ee-3d571cef0f5e)
[PowerToysReport_2023-09-01-10-42-27.zip](https://github.com/microsoft/PowerToys/files/12494741/PowerToysReport_2023-09-01-10-42-27.zip)


### ✔️ Expected Behavior

Pressing CTRL+*any_key*+ESC, where any_key is any key on the keyboard (for example, W - CTRL+W+ESC) will not open the start menu

### ❌ Actual Behavior

Pressing CTRL+*any_key*+ESC, where any_key is any key on the keyboard (for example, W - CTRL+W+ESC) opens the start menu

### Other Software

_No response_
Repository: pytorch
Issue: Generalize weight prepacking during quantized model deserialization
Labels: oncall: quantization,low priority,triaged

### 🚀 The feature, motivation and pitch

## Motivation

PyTorch only can serialize&deserialize parameters in quantized model for official supported QEngine now. There's no chance to extend it to achieve this goal for out-of-tree QEngine based on current code structure.  `torch.save/torch.load` would fail with a ScriptModule on QuantizedXPU backend.

Serializing quantized module (e.g. `ConvPackedParamsBase, LinearPrepackParamsBase`) depends on specific `prepack&unpack` implementation with regard to each QEngine.  We want to propose a mechanism that allows out-of-tree QEngines register their `prepack` implementation in PyTorch to finish the overall serialization process.

## Current behaviour
 Current pickling method call `prepack` of different backend based on compilation marco and global context `QEngine`. For each `QEngine`, code of using prepacking function to deserialize weight need to be added in https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/quantized/cpu/fbgemm_utils.cpp#L481.  Like, `PackedLinearWeightsQnnp::prepack`, `PackedLinearWeightsOnednn::prepack` `PackedLinearWeight::prepack`

 There's no chance to extend it to out-of-tree QEngines.
 
Also, it is inconvenient for official supported qengine. For any new qengine added, it is required to add the code for deserialization, and maintainers need to know the some details of pickling method.

## Plan

Considering the `prepack` function has same signature for each `QEgnine`, we suggest to generalize the process of calling prepacking function. The prepacking function could be found at runtime based on the global context `QEngine`. Following is our proposal:

A dictionary is maintained by the library, where the key for each entry is `QEngine` and the value is the pointer to prepacking function of such engine. Each engine only need register the prepack function to the dictionary. When deserializing weight file, the `__getstate__` method can use `QEngine` as key to find the proper pointer to prepacking function. 

For maintainers of each official supported `QEngine`, only registering the prepacking function is needed (through `register_prepack_fn`).  No need to add code in https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/quantized/cpu/fbgemm_utils.cpp#L481 anymore.

For maintainers of out-of-tree `QEngine`, maintainers can register prepacking function within their library.  Then function pointer is inserted to the map after registration and be found at runtime. No modifications is needed in PyTorch.  (Maintainers still need add enum `QEngine` in PyTorch.）

![quantsave0](https://github.com/pytorch/pytorch/assets/12457857/8c86a538-0b53-4640-9cb9-41ac83cfd08a)


 @ezyang  @arthuryuan1987  @gujinghui 

### Alternatives

_No response_

### Additional context

_No response_

cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel
Repository: stable-diffusion-webui
Issue: [Bug]: LDSR upscaler breaks prompt token counters
Labels: bug

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

Title. Although this doesn't cause inability to use the rest of the application, it does prevent token counters being used until the webui is restarted.

### Steps to reproduce the problem

1. Enter a prompt, note that token counters work correctly
2. Generate an image with hires fix, with the LDSR upscaler
3. After image is generated, modify the prompt, and observe the error thrown in the console and the UI

### What should have happened?

The LDSR upscaler should not break the behavior of the prompt token counters.

### Sysinfo

[sysinfo.txt](https://github.com/AUTOMATIC1111/stable-diffusion-webui/files/12482405/sysinfo-2023-08-31-05-08.txt)

### What browsers do you use to access the UI ?

Mozilla Firefox

### Console logs

```Shell
File "Q:\AI\venv\a1111\lib\site-packages\gradio\routes.py", line 488, in run_predict
    output = await app.get_blocks().process_api(
  File "Q:\AI\venv\a1111\lib\site-packages\gradio\blocks.py", line 1431, in process_api
    result = await self.call_function(
  File "Q:\AI\venv\a1111\lib\site-packages\gradio\blocks.py", line 1103, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "Q:\AI\venv\a1111\lib\site-packages\anyio\to_thread.py", line 31, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "Q:\AI\venv\a1111\lib\site-packages\anyio\_backends\_asyncio.py", line 937, in run_sync_in_worker_thread
    return await future
  File "Q:\AI\venv\a1111\lib\site-packages\anyio\_backends\_asyncio.py", line 867, in run
    result = context.run(func, *args)
  File "Q:\AI\venv\a1111\lib\site-packages\gradio\utils.py", line 707, in wrapper
    response = f(*args, **kwargs)
  File "Q:\AI\git\stable-diffusion-webui\modules\call_queue.py", line 13, in f
    res = func(*args, **kwargs)
  File "Q:\AI\git\stable-diffusion-webui\modules\ui.py", line 168, in update_token_counter
    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
  File "Q:\AI\git\stable-diffusion-webui\modules\ui.py", line 168, in <listcomp>
    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
  File "Q:\AI\git\stable-diffusion-webui\modules\sd_hijack.py", line 301, in get_prompt_lengths
    _, token_count = self.clip.process_texts([text])
  File "Q:\AI\venv\a1111\lib\site-packages\torch\nn\modules\module.py", line 1614, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'Identity' object has no attribute 'process_texts'
```


### Additional information

https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/9466 was similar to this issue but it was glossed over because OP was actually running with `--ui-debug-mode`. That is not the case here however.
Repository: rust
Issue: Memory usage of re-exported types is high
Labels: T-rustdoc,I-compilemem,C-bug,A-cross-crate-reexports,S-needs-repro

Hi!

I am working on crates for the FHIR specification and that involves having a LOT of types defined. This drives up memory usage at compilation to the point it uses 12 GB.
I put all the types into one crate and made another crate `fhir-sdk` that adds functionality on top. This crate is not super big, it mostly re-exports the `fhir-model` types and adds some client functionality.
So the weird part is that `fhir-sdk` uses slightly more memory than `fhir-model`, even though it is just re-exporting the types. I assume it just needs this to compile the API surface of the library? Or is this a bug?

Additional info: https://github.com/rust-lang/docs.rs/issues/2205#issuecomment-1702333591
Repository: axios
Issue: use proxy with keep alive agent
Labels: priority::medium

### Describe the bug

i got problems while using proxy with agentkeepalive at the same time
here is my code:
```javascript
const { default: axios } = require("axios");
const Agent = require("agentkeepalive").HttpsAgent;

const agentkeepalive = new Agent({
  timeout: 60000,
  freeSocketTimeout: 30000,
});
const request = axios.create({
  httpsAgent: agentkeepalive,
});
request
    .get(
      "https://api.myip.com", 
        proxy: {
          host: "192.168.1.4",
          port: 8888, 
          protocol: "http",
        },
      }
    )
    .then(( ) => { 
      console.log(agentkeepalive.getCurrentStatus()); 
    })
``` 
agentkeepalive current status log
```javascript
{
  createSocketCount: 0,
  createSocketErrorCount: 0,
  closeSocketCount: 0,
  errorSocketCount: 0,
  timeoutSocketCount: 0,
  requestCount: 0,
  freeSockets: {},
  sockets: {},
  requests: {}
}
``` 
 

### To Reproduce

_No response_

### Code snippet

_No response_

### Expected behavior

if agentkeepalive work, it should be:
```javascript
{
  createSocketCount: 1,
  createSocketErrorCount: 0,
  closeSocketCount: 0,
  errorSocketCount: 0,
  timeoutSocketCount: 0,
  requestCount: 1,
  freeSockets: { 'api.myip.com:443:::::::::::::::::::::': 1 },
  sockets: {},
  requests: {}
}
``` 

### Axios Version

1.5.0

### Adapter Version

_No response_

### Browser

_No response_

### Browser Version

_No response_

### Node.js Version

v18.15.0

### OS

window 11

### Additional Library Versions

_No response_

### Additional context/Screenshots

_No response_
Repository: pytorch
Issue: enhance documentation around the developer build
Labels: triaged,topic: docs,easy,docathon-h1-2024

### 📚 The doc issue

At the following or similar to the source build docs
```
To resolve the issue of your local folder not being picked up first in the Python path when building PyTorch, you can try the following steps:

Remove the previously installed package:
Since you have already run python setup.py develop, you need to undo that step first. To do this, go to the root directory of the PyTorch project and run:
python setup.py develop --uninstall
This will remove the package installed with develop mode.
Install the package in editable mode again:
Navigate to the root directory of your PyTorch project and run:
pip install -e .
This will install the package in editable mode, which will create a link to your local code, ensuring that your local folder is picked up first in the Python path.
```

I can implement this 




### Suggest a potential alternative/fix

_No response_
Repository: transformers
Issue: Support for the custom kernel for Mask2Former and OneFormer
Labels: Feature request

> And @shivalikasingh95 sure but let's add support for the custom kernel in a separate PR for Mask2Former and OneFormer. We can set it to False by default, and add a boolean attribute for those models.

_Originally posted by @NielsRogge in https://github.com/huggingface/transformers/pull/20993#discussion_r1062224249_

Hello, could I know if there's any plan to add support for the custom kernel for Mask2Former and OneFormer as mentioned there, thanks!
Repository: pytorch
Issue: torch.einsum() computes different results on cpu and cuda on A100 GPU.
Labels: module: cuda,triaged,module: linear algebra

### 🐛 Describe the bug

**Dear Developers:**

Currently, `torch.einsum()` is commonly used to compute the attention scores of query and key matrix. However, we found this operator computes different results on CPU and CUDA on a **A100 GPU**. And V100 doesn't have this problem.

Below is the reproduced code, where `n` is the sequence length, `h` is the number of heads, `d` is the hidden_size per head. We fixed `h, d = 64, 96`, and tested the results of different `n`.

```python
import torch

for n in [1, 10, 50, 64, 100, 200, 500]:
    h, d = 64, 96

    qq = torch.ones((1, n, h, d), dtype=torch.float32)  # use ones tensor
    kk = torch.ones((1, n, h, d), dtype=torch.float32)
    vv = torch.ones((1, n, h, d), dtype=torch.float32)

    kk /= torch.sqrt(torch.tensor(d))

    scores1 = torch.einsum(
        "bthd,bshd->bhts", qq.cuda(), kk.cuda()
    ).cpu()  # cuda
    scores2 = torch.einsum("bthd,bshd->bhts", qq, kk)  # cpu
    print(n, (scores1 - scores2).abs().max())   # compute the max different abs value.
```

A100 results, when `n>=64`, this problem arises.
```bash
1 tensor(7.6294e-06)
10 tensor(0.)
50 tensor(0.)
64 tensor(0.0011)
100 tensor(0.0011)
200 tensor(0.0011)
500 tensor(0.0011)
```

V100 results, almost no error.
```bash
1 tensor(7.6294e-06)
10 tensor(7.6294e-06)
50 tensor(7.6294e-06)
64 tensor(0.)
100 tensor(7.6294e-06)
200 tensor(7.6294e-06)
500 tensor(7.6294e-06)
```

### Versions

Python Envs

```
Versions of relevant libraries:
[pip3] numpy==1.22.2
[pip3] pytorch-quantization==2.1.2
[pip3] pytorch-triton==2.1.0+e6216047b8
[pip3] torch==2.1.0.dev20230812+cu121
[pip3] torch-tensorrt==1.5.0.dev0
[pip3] torchdata==0.7.0a0
[pip3] torchtext==0.16.0a0
[pip3] torchvision==0.16.0a0
[pip3] triton==2.0.0
[conda] numpy                     1.24.4                   pypi_0    pypi
```

cc @ptrblck @jianyuh @nikitaved @pearu @mruberry @walterddr @IvanYashchuk @xwang233 @Lezcano
Repository: opencv
Issue: Error "Invalid SOS parameters for sequential JPEG" when use opencv-python
Labels: wontfix,category: 3rdparty

### System Information

I used this python script below to check image from my directory
// example for python user
OpenCV python version: 4.7.0.72
Operating System / Platform: ArchLinux
Python version: 3.9.16
```py
for roots, dirs, files in os.walk(images_dir):
    for file in files:
        img = cv.imread(images_dir + os.sep + file)
```

### Detailed description

I receive error message like this: Invalid SOS parameters for sequential JPEG

### Steps to reproduce

![ArcoLinux_2023-09-01_16-37-36](https://github.com/opencv/opencv/assets/52648432/a5b0e005-b6f8-4524-8543-9e6b28fd5843)


### Issue submission checklist

- [X] I report the issue, it's not a question
- [X] I checked the problem with documentation, FAQ, open issues, forum.opencv.org, Stack Overflow, etc and have not found any solution
- [X] I updated to the latest OpenCV version and the issue is still there
- [X] There is reproducer code and related data files (videos, images, onnx, etc)
Repository: vscode
Issue: Sticky Scrolling Makes Scrolling Stutter
Labels: debt,polish,papercut :drop_of_blood:,editor-sticky-scroll

Scrolling through a file with sticky scrolling enabled:

![Image](https://github.com/microsoft/vscode/assets/2931520/8fa98249-a92f-410a-8fe0-12f7dd3c2795)

without:

![Image](https://github.com/microsoft/vscode/assets/2931520/9b43aedc-73bb-44b3-9191-617cb71a055e)

It looks like there are more dropped frames when sticky scrolling is enabled.


A rendering frame looks like this with sticky scrolling disabled:

![Image](https://github.com/microsoft/vscode/assets/2931520/21acad73-f803-40a7-a38f-e8d205d4e3ad)

And like this with sticky scrolling enabled:

![Image](https://github.com/microsoft/vscode/assets/2931520/034e8160-a9f8-4721-9fe8-1572c7dbdbd4)


(`u` seems to be `StickyScrollWidget._renderRootNode`)

I think the slow `Recalculate Style` comes from reading the layout in line 339:

https://github.com/microsoft/vscode/blob/6b49d155ca9f2547e9e8ddfdb324db47da97411b/src/vs/editor/contrib/stickyScroll/browser/stickyScrollWidget.ts#L336-L344

Can you cache this somehow? I don't know why reading this property causes this additional layout (because I thought the browser would do this layout anyway). Maybe Alex can also help here.
Repository: kubernetes
Issue: Inability to receive WatchBookmarks from the etcd storage implementation
Labels: kind/bug,sig/api-machinery,triage/accepted

### What happened?

In general, clients can request `WatchBookmark` events by specifying the `allowWatchBookmarks` query parameter. However, the server is not obliged to deliver any `WatchBookmark` events, as described in https://github.com/kubernetes/enhancements/tree/master/keps/sig-api-machinery/956-watch-bookmark#proposal

The motivation for this approach is that the frequency of `WatchBookmark` events from a storage system like etcd can be very high. As the number of watch requests increases, so does the server load.

In practice, watch requests destined for the watch cache will receive the events, while those destined for the etcd storage implementation will not.

I would like all storage implementations to be compatible and provide the same set of capabilities. This would make life easier for developers and end users, as the behaviour of the system would be consistent regardless of the actual storage used to handle a request.

### What did you expect to happen?

I would like all storage implementations to be compatible and provide the same set of capabilities. This would make life easier for developers and end users, as the behaviour of the system would be consistent regardless of the actual storage used to handle a request.

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

Today when you do `k get --raw '/api/v1/namespaces/default/events?watch=true&allowWatchBookmarks=true'` against a cluster built from the `master` branch you won't get any bookmark events.

### Anything else we need to know?

_No response_

### Kubernetes version

<details>

```console
$ kubectl version
# paste output here
N/A
```

</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: vscode
Issue: Editor Guides seem to cause performance issues
Labels: bug,editor-indent-guides,bracket-pair-guides
Repository: PowerToys
Issue: Optimize PowerRename keyboard navigation
Labels: Idea-Enhancement,Product-PowerRename,Needs-Triage

### Description of the new feature / enhancement

Back in the old PowerRename UI the keyboard navigation was quite easy but in the new UI it is not. I often used it in combination with clipboard history to rename a chunk of files without any mouse interaction needed. Doing this in the new UI needs a lot more keystrokes and it is not that intuitive.

Example old UI:
1. Copy {Replace String}
2. Copy {Search String}
3. Ctrl+A to mark all files in a folder 
4. Context menu button
5. w top open PowerRename
6. Ctrl+V to paste {Search String}
7. Tab
8. Win+V to select {Replace String}
9. Ctrl+V to paste {Replace String}
10. Enter and Done

Example new UI:
1. Copy {Replace String}
2. Copy {Search String}
3. Ctrl+A to mark all files in a folder 
4. Context menu button
5. w top open PowerRename
**6. Tab three times**
7. Ctrl+V to paste {Search String}
**8. Tab four times**
9. Win+V to select {Replace String}
10. Ctrl+V to paste {Replace String}
**11. Tab thirteen times**
12. Enter and Done

### Scenario when this would be used?

Every time I and keyboard heavy users will use PowerRename

### Supporting information

Every time I and keyboard heavy users PowerRename