Any successful result replication?

Hey guys, I am just wondering if anyone has successfully replicated the 1 image embedding and recreated similar results from 7eu7d7?
Right now I have no luck testing it myself.
Training time for the embedding takes around 2.5 hours on my 3090 GPU for 8000 steps. Some results only resemble a bit of that 1 training image.

Haven't had any great results myself, but that is testing on my own style, which seems to be adjacent to most of the models.
It is odd that you're at 2.5 hours, I am at 40 minutes per 4000 steps.

I've tried with 15 images and 5 images and not had any success with it learning a person.  I think there are far too many variables that are not explained on what they should be set.

The embedding can influence image generation, but fails to replicate my waifu.
Need some more testing probably.

I used 22 images but they are basically one image cropped into different sizes and with some small alternates, for example, with or without a hat. I got decent results using 6 vectors for both positive and negative prompt, learning rate 0.0025, cfg scale 3, reconstruction loss weight 1, negative lr weight 1, custom prompt template that does not use filewords, image size 384x384 and 10000 steps.

I used 22 images but they are basically one image flipped and cropped into different sizes and with some small alternates, for example, with or without a hat. I got decent results using 6 vectors for both positive and negative prompt, learning rate 0.0025, cfg scale 3, reconstruction loss weight 1, negative lr weight 1, custom prompt template that does not use filewords, image size 384x384 and 10000 steps.

I tried without filewords in the template and it heavily copied the backgrounds for the subject to the point that sometimes it would just generate a landscape without any people at all.  That was with 6000 steps or so before I canceled.

I used 22 images but they are basically one image flipped and cropped into different sizes and with some small alternates, for example, with or without a hat. I got decent results using 6 vectors for both positive and negative prompt, learning rate 0.0025, cfg scale 3, reconstruction loss weight 1, negative lr weight 1, custom prompt template that does not use filewords, image size 384x384 and 10000 steps.

I tried without filewords in the template and it heavily copied the backgrounds for the subject to the point that sometimes it would just generate a landscape without any people at all. That was with 6000 steps or so before I canceled.

My image has a simple white background so maybe that's why. Also 7eu7d7 once mentioned that this algorithm works best when clip is set to 1 instead of 2, which most people seems to use nowadays. I've always used clip 2, but maybe you can check that.
I should also mention that even though the negative prompt seems to be able to improve the face quality, it also often leads to mosiac or distorted shapes in background and distorted limbs in my example.

I tried training with a single image, I don't see any obvious visual improvements weirdly.
Every parameter here is the same except for using Anythingv3 for the model

Clip skip 1, anythingv3 vae, no hypernetwork, xformer on
the prompt template file is [name] with purple eyes and purple hair wearing a purple kimono outfit standing in a field of flowers with a purple sword in her hand and a purple butterfly flying around her, by Masaaki Sasamoto
training image is the character Raiden Shogun from Genshin
my preview results every 500 steps: https://imgur.com/a/3qe6Fb3
my loss: https://imgur.com/a/unEBSD2
Attempting to replicate Nahida rn since working on my own has failed various times.

I tried training with a single image, I don't see any obvious visual improvements weirdly.
Every parameter here is the same except for using Anythingv3 for the model  Clip skip 1, anythingv3 vae, no hypernetwork, xformer on, reconstruction on
the prompt template file is [name] with purple eyes and purple hair wearing a purple kimono outfit standing in a field of flowers with a purple sword in her hand and a purple butterfly flying around her, by Masaaki Sasamoto
training image is the character Raiden Shogun from Genshin
my preview results every 500 steps: https://imgur.com/a/3qe6Fb3 my loss: https://imgur.com/a/unEBSD2
Nothing resembles the training image, most notably feature was that the subject is standing but most preview is sitting
Attempting to replicate Nahida rn since working on my own has failed various times.

This is just my experience, but you probably shouldn't include any feature you want the model to learn in the template.

tried it today with myself, got similar results. some generations would just render a landscape and others a lot of identity loss (3/6 vectors, 0.005 lr,  3000 steps)

For those who can generate valid images in logs but fail to replicate in txt2img,
say if your file is called zzzz1234
your prompt is "art by zzzz1234", not just the file name.
You can see this prompt during training.

Haven't had any great results myself, but that is testing on my own style, which seems to be adjacent to most of the models.
It is odd that you're at 2.5 hours, I am at 40 minutes per 4000 steps.
For replication it may also matter that Xformers is enabled for Textual inversion, that may make it impossible to replicate 1-to-1 if it were done.
Edit, Adding comparison images
Original
What it generates

Hello I want to see how good it is but no clue how to use it, could you show me a good result from this prompt from 4-8 samples, or all 4 samples if can:

Text to image:
"up close shot of a tall green yoshi robot in the shape of yoshi standing on the open desert sand and is wearing a long egyptian robe dress, and is holding a cat in front of a smooth gold castle made of gold bricks with multi-colored rubies, 4K HD cinematic dslr photo"

And how to use img2img could you try a few for me if not easy? I wish there was a huggingface for this.

I tried again training with a single image, in an attempt to replicate the results of ani-nahida that 7eu7d7 have shown on README. I used the exact image to train. I named this embedding ani-greengod.
Every parameter is the same except for using Anythingv3 for the model; clip skip 1, anythingv3 vae, no hypernetwork, xformer on, reconstruction on, and also included the suggestion from @zhupeter010903 where I use an empty template
The results definitely don't look like the provided ani-nahida.pt that 7eu7d7 provided.
my preview results for every 500 steps: https://imgur.com/a/LyQqYmM
the results of my replication attempt: https://imgur.com/a/nUxRnjh
Here's a comparison with only using the TI without any other text prompt

Same for me 5000 step and all I got is something that resemble the girl but nothing of the detail like in the readme. If this not gonna work then is quite sad honestly.

Same for me 5000 step and all I got is something that resemble the girl but nothing of the detail like in the readme. If this not gonna work then is quite sad honestly.

Guys...I've gotten it to work GREAT...even for mario video game case and cool aliens by hr giger, giving me similar photos!
Take a look at my results. I have very VERY good ones HOWEVER I THINK all I can post here is Safe For Work photos? YOU gotta see them! Anyway let me know here is just one for show! HMU!
input pic:
https://ibb.co/hZrs0jq
results!:
https://ibb.co/nC85xGp
https://ibb.co/dkkW0qy

Ok now see above comment, I had to edit is lots sorry haha. Let me know you have seen this comment else maybe you saw it before editing...

oh and the link appears in 2nd last cell believe it or not, it brings you to the app!

ani-g | animefull-latest |   | 3, 10 | 1500 | 0.003 | 5
The original image in the github

My closest result to the final output

Stuck in a painting style for all of them.
Following the S*, light blue hair, forest, blue butterfly, cat ears, flowers, dress example

See my message above, my results look good.

One of my best HD 3D ones (3D!!), its img2img from 2D:
https://ibb.co/XXpP9mN

The original instructions are quite rough and you may not use DreamArtist correctly. You can use it following the new instructions.

The original instructions are quite rough and you may not use DreamArtist correctly. You can use it following the new instructions.

Is there any suggested value for the reconstruction loss weight and negative lr weight?

The original instructions are quite rough and you may not use DreamArtist correctly. You can use it following the new instructions.

Is there any suggested value for the reconstruction loss weight and negative lr weight?
Edit: and also for template files?

reconstruction loss weight and negative lr weight can be set to 1.0. In fact, you can get decent results without reconstruction in most cases, adding the reconstruction can be much slower and the improvement is not very huge.
template files better to use the verison without filewords.

@bladedsupernova do you mind sharing your exact parameters in training the embeddings?

I didn't train it to get those great results. I simply used the link I gave is all.

I tried again training with a single image, in an attempt to replicate the results of ani-nahida that 7eu7d7 have shown on README. I used the exact image to train. I named this embedding ani-greengod.
Every parameter is the same except for using Anythingv3 for the model; clip skip 1, anythingv3 vae, no hypernetwork, xformer on, reconstruction on, and also included the suggestion from @zhupeter010903 where I use an empty template
The results definitely don't look like the provided ani-nahida.pt that 7eu7d7 provided.
my preview results for every 500 steps: https://imgur.com/a/LyQqYmM the results of my replication attempt: https://imgur.com/a/nUxRnjh
Here's a comparison with only using the TI without any other text prompt

My ani-nahida and ani-nahida-neg is trained on animefull-latest, but training with anythingv3.0 should also get good results. You can try to refer to the new instructions.

@7eu7d7 someone that trained some really good TI actually suggested anythingv3 doesn't do as good as using a mix model between 0.2anime-full&0.8wd, will try both.
Just wanna make sure, when you said no filewords, I'm assuimg the PTF is empty? Thanks for your great work!

no filewords

Just use style.txt or subject.txt. Train with filewords may excluded the described features.

@7eu7d7
I still have a few questions as I have yet to achieve much that can be called a success.

Is the prefix attached to the Embedding Name in the sample to avoid duplication with other nouns and to give a unique noun?
In the sample ani-nahida, embedding length is set to (3, 6) and cfg scale to 3. Is there a guideline for determining these values?
What are the advantages of enabling the "Train with reconstruction" option?

Please remember me.
Thanks for developing these great features.

no filewords

Just use style.txt or subject.txt. Train with filewords may excluded the described features.

Hi, does this conclusion apply to the original TI?
Because I've seen similar conclusions in https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/1528#discussioncomment-4044422
It's really a very counter-intuitive conclusion, which makes it hard to believe. But through my test I found that it seems to be correct.
Does this mean that almost all tutorials nowadays are wrong about filewords ......?

I had a decent run in replication, however the results doesn't look like the training image of Nahida.
This is the .pt that 7eu7d7 provided

This is the .pt that I generated

I follow the exact instructions that @7eu7d7 suggested, the only thing that is different is probably just the model that was trained on. I speculate this difference might be due to the model I trained based on, so will do it again.
Also, is the training image of Nahida 512x512 or did you throw the uncropped image that is 1417x1417? Currently I am using 1417x1417 that I downloaded from this github.

no filewords

Just use style.txt or subject.txt. Train with filewords may excluded the described features.

Hi, does this conclusion apply to the original TI?
Because I've seen similar conclusions in https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/1528#discussioncomment-4044422
It's really a very counter-intuitive conclusion, which makes it hard to believe. But through my test I found that it seems to be correct.
Does this mean that almost all tutorials nowadays are wrong about filewords ......?

From my understanding, filewords should describe attributes and elements of the corresponding image which you do not want the TI to learn. For instance, I used an image cropped into different sizes as the dataset, and my filewords are simply portrait, cowboy shot, full body, etc. describing the portion of the character visible in the image, which outperforms the plain subject.txt case.

Ok so it turns out the model that was used to train and evaluate matters a lot.
I trained a new embedding based on animefull-latest, called ani-dendro-nai
This is the result when I use animefull-latest with the ani-dendro-nai embedding:

This is the result when I use anythingv3.0 with the ani-dendro-nai embedding:

From what I observed, embedding is better to train on animefull-latest rather than anythingv3.0, but when inferencing the embedding, anythingv3.0 will give u a much better result.
Still can't replicate the very high quality Nahida that 7eu7d7 did though. But can confirm that choosing model to train the embeddings for TI probably applies for dreamartist too.

I think we need some more information to exactly replicate @7eu7d7's results (I'm trying to get the Nahida one right at the moment):

Clip skip: 1 or 2?
Prompt template file: The tutorial has style.txt, but that doesn't seem right.
Reconstruction loss: The tutorial has it on, but IIRC when the first examples were done there was no reconstruction loss in the repo.
Initialization text: girl?

no filewords

Just use style.txt or subject.txt. Train with filewords may excluded the described features.

Hi, does this conclusion apply to the original TI?
Because I've seen similar conclusions in https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/1528#discussioncomment-4044422
It's really a very counter-intuitive conclusion, which makes it hard to believe. But through my test I found that it seems to be correct.
Does this mean that almost all tutorials nowadays are wrong about filewords ......?

Yes and yes.
It might not apply to hypernetworks, perhaps? I'm not sure why, though.

Did a training run with EMA (positive) at 0.97 from the latest commits and there's a bit of editability.
ani-nahida:

ani-nahida, light blue hair:

Didn't have much luck with other changes, though.
VAE is off for these previews.
I'm gonna do a run with EMA (negative) at 0.97 too later.

Still can't replicate the very high quality Nahida that 7eu7d7 did though. But can confirm that choosing model to train the embeddings for TI probably applies for dreamartist too.

The Nahida images I provide is actually training on animefull-latest and inference on anything 3.0. This does work better.

I think we need some more information to exactly replicate @7eu7d7's results (I'm trying to get the Nahida one right at the moment):

I trained with CLIP=1 on animefull-latest and did not load VAE. The inference is on anything 3.0.
The new version add the EMA training and setting it to 0.97 or 0.95 may make training more stable.

tried many times using tutorials setting but not even close, please explain detailer, how does your dataset looks like and everything around vae, clip skip, xformer and did you edit anything in style.txt or just the vanilla one? thanks @7eu7d7

I trained with CLIP=1 on animefull-latest and did not load VAE with xformers disabled. The inference is on anything 3.0. There is no changes in style.txt.

I am still struggling to exactly replicate what @7eu7d7 did, but this is what I have so far, for people that want to get some decently high quality results out of DreamArtist. There are a lot of little things that you need to get right in order to get decent results, and the training takes HOURS. So remember to do it correctly or else you'll make mistakes like me where I had to retrain a lot of times becuase I had the wrong model or had vae on.
(@7eu7d7 大佬 please correct me and teach me the ways to replicate your result)
Here is my most recent and the most successful replication & comparisons

ani-nahida is what 7eu7d7 provided
ani-dendro-afl-sty is my replication attempt, but with Prompt Template File (PTF) as the default Style.txt
ani-dendro-afl-sbj is my also replication attempt, but with PTF as the default Subject.txt

ani-dendro-afl-sty & ani-dendro-afl-sbj are trained with the following:

based on animefull-latest (afl) (hash: e6e8e1fc)
no xformers, clipskip 1, no vae, no hypernetwork
Everything is the same here:
reconstruction ON
both positive and negative EMA 0.95, 25 steps
training image is the nahida.jpg 512x512
max steps 8000 (I'll get back on this later)
8000 steps runtime around 2 hours on RTX3090
initialization text: girl

The parameters I used when evaluating the embeddings:

I used anythingv3 (hash: 6569e224)
anythingv3 VAE, clipskip 1, no hypernetwork, no xformers
Have ani-dendro-afl-sty  or ani-dendro-afl-sbj in the text prompt and ani-dendro-afl-sty-neg or ani-dendro-afl-sbj-neg in the negative text prompt correspondingly
some basic negative text prompt (eg. bad anatomy, etc) and positive text prompt (eg. masterpiece, etc)
cfg 9, steps 3, euler a, 512x512

The PTF made some visible differences, but I couldn't come to any concrete conclusion as to what is different and why.
Here is a more detailed comparison:

Subject.txt
Style.txt

It is also interesting that my replication has probably the best visual at around 3500~4000 steps. I have no idea why.
It is also important to have the CORRECT corresponding -neg TI in the negative text prompt, as it will make the result slightly more consistent and coherent.

without -neg
with -neg

Wrong corresponding -neg will make your image look like this, which is kind of sharpened or thick lined in some cases:

Remember to use VAE WHILE inferencing, not while training. As it'll make your result image look more vibrant

without VAE
with VAE

please correct me and teach me the ways to replicate your result

Excellent experimental investigation!
I actually training nahida without reconstruction, and EMA is also set to 1.0 thus turning it off.
Due to the randomness of latent noise and prompt selection during training, it may be difficult to run the exact same result twice. Even the same random seeds, the results may be different on different devices. However, multiple experiments should be able to reproduce similar results.

I've had some successes with style training so far, but only if I leave out the -neg embedding in the negative prompt. I seem to get better and more expected results just using the positive embedding. (I tried to uncheck creation of the negative embedding entirely but it errors out when it can't find a negative embedding on training, line 370 of cptuning.py I believe)

please correct me and teach me the ways to replicate your result

Excellent experimental investigation!
I actually training nahida without reconstruction, and EMA is also set to 1.0 thus turning it off. Due to the randomness of latent noise and prompt selection during training, it may be difficult to run the exact same result twice. Even the same random seeds, the results may be different on different devices. However, multiple experiments should be able to reproduce similar results.

I dont know Im trying a concept art picture in SD 1.4 and it's getting nowhere...
Settings (for 1 image):
name: test1
init: angel
vec and neg: 3-6, overwrite.
Txtimg: masterpiece, best quality, test1, angel
Neg: test1-neg, lowers, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps 30 Euler A and Batch size 4 with 512x512
Lr: 0.003 (also tried 0.0025 <- Looks even worse)
HNLr: 0.00001
Reconstruction
Scale: 5 (changed to 3 <- Looking worse)
Style.txt
10000 steps max (tried 20000, nothing)
Emas 0.95-0,95 25 stepsx 2
Can you explain me the red style one setting or the dog one? Just to make sure Im doing this right

I'm attempting to train on the Gradio app, see screenshot below, but I don't understand where do I get my directory from, very lost here really.
https://ibb.co/8XzTZRB
Also no neg prompt embed on tab1 at left either.
Also if I click Create Embed, is that it and I jsut have to wait a few hours to train on 1 input pic i want variations of? Then I use the tag word I made to trigger her up in pics?

I'm attempting to train on the Gradio app, see screenshot below, but I don't understand where do I get my directory from, very lost here really.
https://ibb.co/8XzTZRB
Also no neg prompt embed on tab1 at left either. Nor reconstruction bar to set to max.
Also if I click Create Embed, is that it and I just have to wait a few hours to train on 1 input pic i want variations of? Then I use the tag word I made to trigger her up in pics?

Are you running the latest version of Auto?

Are you running the latest version of Auto?

I'm using this, version 54:
https://www.kaggle.com/code/miolovers1/stable-diffusion-automatic1111

Been trying to get this to work all week, and I've gotten nothing in all that time that's remotely usable. I've tried single images, I've tried multiple images, I've tried images with cleaned backgrounds, and images without them; images without any filewords to guide it, and images with filewords describing what's in it, and the usual textual inversion practice of filewords describing what's not part of the desired subject. I've tried to learn a style or a character. EMA set to 1, or EMA set to 0.97. Lots of tokens or no tokens. Xformers off, full precision, no VAE, multiple different models, reconstruction on or off.
Every single result has been basically random. It finds some initial loss value, and just bounces around that the entire time. I've seen training with a stable loss of 0.08, but the output has nothing in common with the subject. It's baffling.


Preserve request chaining when duplicating or importing workspaces

Expected Behavior
Let's say you have a folder A of chained requests.
/A
  - r1
  - r2 (depends on r1 response)

When you duplicate that folder (creating new folder B), the chained links should update properly:
/A
  - r1
  - r2 (depends on r1 response)
/B
  - r1
  - r2 (depends on B/r1 response)

Actual Behavior
/A
  - r1
  - r2 (depends on r1 response)

When you duplicate that folder (creating new folder B), the chained links don't update properly:
/A
  - r1
  - r2 (depends on r1 response)
/B
  - r1
  - r2 (depends on A/r1 response) <---- should depend on B/r1 response)

Reproduction Steps
Create a folder with 2 requests.
The second request should have a variable that depends on the first response body.
Duplicate it.
Is there an existing issue for this?

[X] I have searched the issue tracker for this problem.

Additional Information
No response
Insomnia Version
2022.7.5
What operating system are you using?
macOS
Operating System Version
12.6
Installation method
Download from Insomnia site
Last Known Working Insomnia version
No response

This problem also affects environmental variables
/A
  - r1
  - r2 (depends on r1 response)
  - env (depends on r2 response id)
/B
  - r1
  - r2 (depends on A/r1 response) <---- should depend on B/r1 response)
  - env (depends on A/r2 response id)

The ids are globally unique, would it be possible to change this for each document?
In document A
r1 has the id x
In document B r1 has the id y.
A possible solution would be for the requests and properties of the document to be unique only within it.
With this we can have more than one request with the same ID, but not in the same document

Any updates here?
Issue 5942 referenced above is still a pain when needing to share collections (export/import).

I have the same issue but noticed something strange.
If I export a collection and import it as a new collection on the same Insomnia instance I exported it, the reference IDs are maintained. However when I send the export file to a coworker and they import the collection the reference IDs are reassigned and the tags break.

@Stratum-Jeremy
When you export a collection that you export yourself, the IDs exist in the local database and therefore work perfectly.
Test export, delete and import. This way the error will occur

This bug is blocking to work with team, every member tries to fix same problem sometimes success sometimes failure. We choose to change tool.

This issue has been reported multiple times and there is a PR for it. Can someone give an update what is required for this to proceed?

Yes, this is a big problem for collaborative work. We will change the tool if this is not fixed soon.

Started trying to share Collections with team mates today and hit this issue, is there an ETA on resolution?
Really kills the usefulness of the app if a junior can't import an API Collection to see how an API works.

Any chance to have this thing fixed? It's impossible to share collections

Any updates here? It's impossible to share collections

Switching software, this won't work.

Any updates on this? Request chains still break after exporting and importing in the most recent version (v9.3.3), making team collaboration impossible.
It appears that the referenced request IDs remain intact after export, but new request IDs are generated for some reason, and the references are not updated, which breaks the entire collection of chained requests.
Can someone seriously address this? Insomnia is a great tool, but this fundamentally destroys team collaboration since we cannot share collections properly.

a temporary solution is to build the version of this PR #6521. Doesn't completely fix it, but it works

Any ETA for this one? Sharing Insomnia collections consistently across teams is impossible.

Is this issue intentionally left open to make users switch to paid plans? It's nearly two years since this ticket was opened

this is definitely not a priority for them.
But no thought was given to exactly how to correct it.
The software would have to make a change that requires a lot of effort to resolve. I believe that what we can do is try to think and better document a possible solution for this case to move forward.

Yes, it's really bad to have such a major flaw in the software.
I'm currently trying out Bruno (haven't had much time yet to compare the feature set though) as an alternative since this is such a painful bug.

Shh... We don't talk about Bruno.

Nothing yet.. How is it possible...


feat(testing): Add image comparison testing

Add image comparison testing with screener.io

woah. that was easy.


⚠ AGE-Vicmap_Transport-MS has degraded performance

In 04f0563, AGE-Vicmap_Transport-MS (https://vicmap.land.vic.gov.au/hosting/rest/services/vicmap/Vicmap_Transport/MapServer/0/query?where=1%3D1&resultRecordCount=1) experienced degraded performance:

HTTP code: 200
Response time: 174 ms

Resolved: AGE-Vicmap_Transport-MS performance has improved in 3ee3304 after 23 minutes.


Make available pass args and kwargs to Screen setup_keyboard method

The task needs further discussion, so closing so far.


IPv6 support

~IPv6 is badly broken in Scala Native 0.4.x, but is already fixed in the 0.5.x. Our options are to:~

~roll our own bindings instead of using scalanative.posixlib~
~wait it out until 0.5.x~

Update: seems that IPv6 was broken only in Javalib, the actual Posix bindings should be working. This makes this issue actionable 🚀

IPv6 is badly broken in Scala Native 0.4.x, but is already fixed in the 0.5.x.

Arman,
To help the epollcat along, I will check when I fixed what and refresh my
memory of what is in what soup pot.
To the best of my memory, posixlib IPv6 should be working well.
I seem to remember that there were some missing UDP (Datagram)
methods in the 0.4.x stream.
When I added them, I specifically created unit-test Udp6*Test.scala
files.  I am not entirely test-driven-design, but I have found that at least
a first draft of the Tests pays off and focuses my implementation.
If you experience any IPv6 related problems with posixlib IPv6
methods/routines please let me know soonest & loudest.
What is broken in the 0.4.x stream is the javalib IPv6 support.
In 0.4.x (and probably 3.x and back to 2.x) there are bits and
pieces which lead one to think that IPv6 is supported, but
there are show-stopper "end-of-implementation" issues.
It took me 2+ weeks or so to get it working in my sandbox.
The shells on the board move around:  if your concern is indeed javalib, let me know.
I will let it sit on the back of my brain to think if there is anything epollcat for SN can do
to workaround SN 0.4.n javalib IPv6 breakage.

@LeeTibbert thanks for that helpful comment. It seems I may have misunderstood what the support/limitations of IPv6 in SN 0.4.x are.
Note that epollcat is directly interfacing with posixlib and is not using Javalib. So if we can make ipv6 work here on 0.4.x that would be fantastic!
I was under the impression that fixing IPv6 in Javalib required breaking changes to posixlib since you targeted that work at 0.5.x. Which would lead me to think that it is somehow broken in posixlib as well. But I'd be happy to be wrong.
I may have stumbled on some IPv6 issues while developing epollcat, I will look through the commit history and see if I can refresh my memory.

So if we can make ipv6 work here on 0.4.x that would be fantastic!
I think that it is there & silently doing its job, without complaint.  It is always the Good Child
who get passed over for the Prodigal.

Later:  Now some of it comes back to me. I think there was an IPv6 related problem with
two UDP methods. I can check the Repository change log to figure out exactly which ones.
I think they were the simple sendmsg()/readmsg() pair.  I discovered the bug in my
testing but never heard a field report.
I am learning epollcat on the fly and am still in the early stages.  I suspect that users
of epollcat can pass in any kind of socket descriptor from a socket previously created
by the caller.  In that case, the breakage would be users using both IPv6 and UDP.
Before we call the fire department, let me check the commit history.

was under the impression that fixing IPv6 in Javalib required breaking changes to posixlib

I just went back to the primary source for grounding, the pending IPv6 javalib PR.
There was small change to a posixlib file socket.c. That was to a Windows only code path.
The 'breaking change' to provide IPv6 in 0.4.n is basically a useful fiction:

I really do not want to be forcing that big of a change on people when
they upgrade a minor version.  Especially when the only "opt-out" is
to downgrade the version.  I know that I as an application developer or
end user would be livid or beyond if that happened and I had problems:
like a Friday-before-holiday hard broken application.  I try to live my
life in Joy and not Fear, but Fear brings some good lessons.

I am pretty confident with the 0.5.0 javalib IPv6 changes but I would
like to see them get some clock, or better yet, calendar time being
used in the Wild.

I may have stumbled on some IPv6 issues while developing epollcat, I will look through the commit history
and see if I can refresh my memory.

I try to find issues before they ship. Each new environment reveals a new class of bugs,
no matter what the software.  So, you find them, I fix them (if possible) tag-team.

channels, very timely, this morning's discussion.
To the best of my knowledge & belief, SN has no channel support
for any java.net class; not (TCP or streaming) Socket & ServerSocket.
The Datagram class is entirely un-implemented. It is a dream rapidly
receding in my rear-view mirror.
Pardon my over-extended brain and lack of epoll knowledge.  How can
a (application developer) user currently ever get a network related channel
to pass to epoll?
It sounds like the (broken on IPv6) javalib routines are making the network calls,
probably by calling into posixlib.  It is the javalib layered where the 0.4.n breakage
happens.

@LeeTibbert you may find it informative to look at the test suite:
https://github.com/armanbilge/epollcat/blob/91a0401f49a94ce8c31849a71482aa54949cfa73/tests/shared/src/test/scala/epollcat/tcp/TcpSuite.scala

Pardon my over-extended brain and lack of epoll knowledge. How can
a (application developer) user currently ever get a network related channel
to pass to epoll?

That's the point: the application developer doesn't pass anything to epoll, indeed they don't even need to know that's how it's working under the hood. They just use the Javalib as normal and the EpollRuntime.global as their ExecutionContext. Everything just works.
If you want to look further, I also have the fs2-io SocketSuite passing with the implementations from epollcat.
https://github.com/typelevel/fs2/blob/3d25a6a1723d160afba2a961fc0cfe17aca8cbe9/io/shared/src/test/scala/fs2/io/net/tcp/SocketSuite.scala

OK, TcpSuite.scala give me something concrete to look at.
I checked and postman-echo.com only has IPv4 addresses (and
three of them).  So this test should work until postman.com
becomes contemporary.
The discussion below is for people in the Wild who may attempt
to use epollcat with IPv6 addresses (like me).
I am still digesting it. From my reading I would expect this to be hard broken with IPv6 on
both 0.4.7 and 0.5.0-SNAPSHOT-2022-09-02.
My IPv6 PR has not been merged.  I would hope and be gratified if this worked with that PR.
I can take another look at the example program to see if that would be easier to hack to
use a known IPv6 address.   My favorite is https://www.kame.net/ (should see dancing/swimming
kame).   Now parsing the html returned might take some work.  Probably first start off checking if
a connection happens. Second pass, parse one header.
I think it is worth waiting, say two weeks, until the IPv6 PR merges (or is rejected).
That would give us 0.5.0-SNAPSHOT as opposed to 0.5.0-OfCourseItWorksInDevosEnvironment.

I think it is well worthwhile to amend the base note to say that javalib IPv6 is broken and not
the posixlib currently mentioned.

@LeeTibbert I'm afraid I'm still confused :)
epollcat doesn't use SN Javalib for any IPv6 support. It uses only posixlib. If I understand you correctly, posixlib IPv6 is not broken. So is this statement still correct?

From my reading I would expect this to be hard broken with IPv6 on both 0.4.7

@LeeTibbert and I had a productive chat about this issue. They are going to look into adding IPv6 support to epollcat 🎉

First IPv6 Scala Native 0.4.5 result:
  + server-client ping-pong
[info] Passed: Total 1, Failed 0, Errors 0, Passed 1

Looks indistinguishable from Java IPv6 and ScalaJVM IPv4.
I had to use specialist tools to prove to myself that it was  IPv6.
Long way to go, but looks like there is a way through.

Amazing!! Thanks for the update!!

I am concerned that this issue is becoming more urgent. I suspect it is related to:

https://github.com/armanbilge/epollcat/issues/43
https://github.com/typelevel/fs2/pull/2975#issuecomment-1242964149

As a marker and to summarize a number of discussions in Issue #43 and elsewhere,
I am switching to evolving my IPv6 rapid-prototyping code into a Work in Progress (WIP) PR.
That will give a concrete platform for working on the next evolution of issues.

:rocket:


请教关于gcanvas的一些问题

1.因为工程是非标准的weexpack目录，所以直接将gcanvas的module和component放在工程中，然后build.gradle添加了1.2.3的依赖，发现绘制图片的时候总是会报空指针的错，升级了1.2.5之后，问题解决了。请问是之前是bug吗？
2.gwebgl.js中

这个util是不存在的吧，应该是gutil？这点请指教。

@kfeagle 这个说明无法解答我的问题呀

@AugHua<EMAIL_ADDRESS>我们进一步讨论

请问gcanvas每个界面只能放一个吗

This PR/issue doesn't received response since Weex migrated to Apache two years ago and this repository is no longer active yet.
Feel free to ask question in new repository and thanks for your contribution.

This issue is outdated for a long time and will be closed now. You can create a new one if you still have questions.

This issue is outdated for a long time and will be closed now. You can create a new one if you still have questions.


Fixing miscellaneous acronym capitalization issues

breaks source code compat with beta5 as follows:

VirtualMachine:

withoutVmAgent() renamed as withoutVMAgent()
withWinRm() renamed as withWinRM()

VirtualMachineScaleSet:

withVmAgent() renamed as withVMAgent()
withoutVmAgent() renamed as withoutVMAgent()
withWinRm() renamed as withWinRM()

Can one of the admins verify this patch?


Youtube Watch Later Playlist only opens first video

When loading the "Watch Later" Youtube playlist, instead of opening a playlist only the first video of said playlist is opened.

Hi, @brunomptorres
The Watch Later playlist cannot be embedded.
But you can create a public playlist and move all your videos from Watch Later playlist to it, like this:


Validate chatcompletion to avoid unexpected bugs

Fixes #527
This PR addresses the incorrect handling of error responses from the API when a rate limit is exceeded. The changes include:

Adding a check for the presence of a timestamp in the API response and raising an appropriate error if it is missing.
Updating the _process_response method in pydantic_ai/models/openai.py to handle the absence of a timestamp.
Adding new tests in tests/models/test_openai.py to ensure proper error handling when the timestamp is missing from the response.

These changes ensure that the appropriate error message is surfaced when a rate limit is exceeded, instead of throwing a date-time parsing exception.

@sydney-runkle that might be a sane default for the timestamp, but as mentioned in the issue, the bug arises from the fact that it's not a chat completion but instead an error response. As a result, the other fields will be undefined.

instead an error response

We should then handle this differently, designing a code path that makes the error explicit rather than hiding this in a timestamp related error.

instead an error response

We should then handle this differently, designing a code path that makes the error explicit rather than hiding this in a timestamp related error.

agreed.


Write abi type for binary extension types

Binary extension types weren't working when sending transactions to actions that used them.

This implementation is partial complete and requires more work to be implemented.
Please follow this issue to check the special cases where its allowed or not:
https://github.com/EOSIO/eos/issues/5600
Also check as example eosjs implementation: https://github.com/EOSIO/eosjs/blob/master/src/eosjs-serialize.ts


Extend api capabilities

Extend the existing api capabilities

locations: add api implementation
connections: add missing parameters
stationboard: add missing parameters
urlli/urlencode: Add "True" flag for correct formatting of lists (ex. via parameter via[]=foo1&via[]=foo2)

No existing functionality should be compromised, only new things have been added.
I will likely extend the homeassistant integration as well.

Have not yet worked with black linter. Should be fine now ;)

@fabaff Would it be possible to have a new version with this PR? I would like to extend the home assistant integration as well, but first I need a proper release of this library. :)

Sorry to ping again :) @fabaff


Use unaligned load/stores.

There is no penalty for unaligned load/stores on x86 post Nehalem (November 11, 2008).

Closing since development seems to happen on rust-lang repo. https://github.com/rust-lang/hashbrown/pull/74


Separated status codes for different levels

This is a wished feature, not a bug.
It would be nice to have status codes separated by level. For example status::Success::OK or status::Client::NotFound.
The "everything in one place" is fine for normal codes, but as you read some code and you stumble upon something like "Not exteded" or "Upgrade required" or "Precondition failed" it's really hard to remember if they are 4xx (Client errors) or 5xx (Server errors).
I tried to take a look at the code to prepare a PR, but they are macro-generated and that's beyond me, for now...

In terms of readability, I can see how something like this is improved:
let code = StatusCode::METHOD_NOT_ALLOWED; // this is 405
let code = StatusCode::NOT_IMPLEMENTED; // this is 501

// better?
let code = http::status::client_error::METHOD_NOT_ALLOWED;
let code = http::status::server_error::NOT_IMPLEMENTED;

Is this a useful enough improvement?

Yes, that's exactly what I meant.


Making Identical Memory and Hot Cue Generation Possible

Why
This PR includes all the changes I needed to make to enable a two pass workflow generating both memory and hot cues as suggested in #10.
Currently, existing generated memory cues as well as hot cues are removed on every run, and cues of both types are considered when finding close cues before adding a new cue. These issues make it impossible to generate identical memory and hot cues. This PR changes both of these things to enable this workflow.
What is changed

Only remove cues of the relevant kind before generating new cues.
Only consider cues of the relevant kind as close cues.
Update tests to reflect that memory cues are not considered close to candidate hot cues.
NuGet dependencies and updates required to get the project to build and run on my machine with a fresh install of VS. (Note I have never developed in .NET before, so these changes may not have been necessary.)

Side effects

To remove all generated cues, you would now need to run CueGen.Console -r and CueGen.Console -r --hc.

Thanks!


output the TransportState.CongestionControlState

The field from the protobuf is currently not plotted anywhere.

So, the issue here is that in past we had CC algorithms that put a lot of text into that box.  If we display all of it, the box would become too big (it doesn't line-wrap), get out of bounds and make everyone unhappy.  Could you add some logic that truncates it after certain point?

Sorry for the late reply. I just introduced a limit of 80 characters:

Maybe we should respect \ns in the string, that would allow us to display a lot more information without overflowing the window.

Thanks a lot, Marten!


[Question] Does yabai auto focus to previously focused window after killing a window?

Issue

I have 2 windows open in display 1, and 1 window open in display 2.
Current focused window is in display 1 while the previously focused window is in display 2
After killing the currently focused window, the focus got switched to the previously focused window in display 2.

Desired behavior

After killing the currently focused window, the focus should be switched to the previously focused window in the same space, if there is none, the focus should still stay at the same space.

What I have tried

I have found related issues and many of the suggestions were to include lines below so that yabai will switch the focus under the cursor.

yabai -m signal --add event=window_destroyed action="yabai -m query --windows --window &> /dev/null || yabai -m window --focus mouse"
yabai -m signal --add event=application_terminated action="yabai -m query --windows --window &> /dev/null || yabai -m window --focus mouse"

This works fine if I have the config mouse_follow_focus set to off but doing this introduces other unwanted behavior such as when I have 3 windows opened in the same space, the cursor will always stay on the first opened window
Hence, the focus always gets switched to that particular window after killing any of the other windows.  In this case, I still want the focus to be switched to the previously focused window in the same space (if any).

Question

So I am wondering is this the default behavior where yabai always switch the focus to the previously focused window after the current window is killed
Any suggestions on how I can configure it so that I can achieve my desired behavior (if this is possible)?

Also, a big thank you to everyone contributing to yabai 🙏, I have enjoyed it very much on my first Macbook. I have been using i3 on a Linux machine before this, and I was managed to configure yabai to mimic 90% of my workflow.

Update

I have found this related issue
Using a similar approach as the user with some tweaking that I want to focus on the display that the current window is killed at.
The display id is saved to a tmp file on events like window_focused and application_front_switched
Then on events like window_destroyed and application_terminated I will read from the tmp file to get back the display id where the killed window is on.

function record_display_id {
  display_id=$(yabai -m query --windows --window | jq '.display')
  ruby $HOME/display_focus.rb write $display_id
}

function focus_current_display {
  yabai -m display --focus $(ruby $HOME/display_focus.rb read)
}

yabai -m signal --add event=window_focused action="${functions[record_display_id]}"
yabai -m signal --add event=application_front_switched action="${functions[record_display_id]}"
yabai -m signal --add event=window_destroyed action="${functions[focus_current_display]}"
yabai -m signal --add event=application_terminated action="${functions[focus_current_display]}"

Then everything works as my expected behavior.
Anyone has a better approach to handle this flow? I don't think this is a very efficient approach as I am writing to a file every time I switch the focus.
I think this is a really hacky implementation, will appreciate if anyone have any better approach, if not I will be closing this issue soon as I have got what I wanted with the implemetation above.

Anyone has a better approach to handle this flow? I don't think this is a very efficient approach as I am writing to a file every time I switch the focus.

How often do you switch focus? It's usually so rare that it's not time-critical at all, so writing to a file is not an issue.
The way you tackled this works fine I think—I can't come up with a better way to implement this unless you want to touch the code itself.

Thanks for the reply @dominiklohmann.

With my current setup, I have 2 displays with 5 desktops on each display, so I will say I switch focus quite often. But I have been using the yabai config above for almost a day now, and I have not noticed any performance problem. So I think that is good enough for now.
I will definitely love to dive into the code and understand the implementation, hopefully, one day I could contribute to yabai as well.
Actually, I have been digging into the codebase yesterday trying to see what I can tweak to achieve what I wanted. But yeah, I am not very familiar with C and how yabai works. I will appreciate it if you could give me some heads up like which file I should be looking at or even any suggestions.
I will close this issue for now and thanks for the reply again!


Clear all tabs

Sometimes the number of tabs can get a bit out of hand. It would be nice to be able to press one button to close all tabs in one go.

Have you tried adding a #ztrace instead of a #trace to the "first form of the flow".
https://github.com/jpmonettas/flow-storm-debugger#working-at-the-repl
With that the flow will always have flow-id=0, it will only generate one tab, and replace it every time you run your code again.

Oh, didn't know that, that's useful!
There are still cases where you would want to not do that, I think. I have a case where a #trace is hit a number of times, let's say 5, and I need to inspect more than one of them. Then I run the test 2  or 3 times more, forgetting to clean up. This is the point where closing all those tabs becomes painful, and I often just choose to close the debugger and restart.

Yeah I agree that being able to close all tabs is a nice feature to have.
I'm planning to add keyboard support pretty soon and we can add something like hitting x clears all flows to start with.

This is implemented in 0.3.7, please see https://github.com/jpmonettas/flow-storm-debugger#keyboard-commands


Convert tests to pytest style

Sorry for large pr. I should have split up in separate tests modules and helpers in a pre pr before converting to pytest style.
My previous pr on pytest warnings must be merged before this can be merged.

@feffe can you please fix the conflicts so we can move forward?
Nice job 👍

I have rebased branch. Npw it is ready to merge


Landing page: Rework R string

Following up on #2060

@eitsupi I found out that this also works

Wow, that looks great, thanks!


All tables from PSQL database are expanded by default

Issue Type

[ ] Bug
[x] Enhancement
[ ] Feature Request
[ ] Question
[ ] Other

Prerequisites (For bugfixes)

[x] Are you running the latest version?
[ ] Did you check the logs?
[ ] Did you check the Setup?

Description
Thank you for updating sqltools. The last release is great,
I noticed that before the release all tables were colapsed by default in side bar whereas now, all tables are expanded.
Is-there a short cut to collapsed all the table at once ?
Thank you for your job !

Version: 0.15.1
SGDB: PostgresSQL...

It would be great to have a parameter to set this feature on or off, like  "sqltools.tablesStartExpanded": false,
@buzztic : You can select the first table, and pressing space will collapse it. Using with the down arrow you can collapse quickly all tables (but someone may think you're crazy if you have more than 50 tables to collapse every mornings :stuck_out_tongue_winking_eye:)

thank you @PapsOu

I like the idea of an parameter as @PapsOu suggested.
Would you like to make a PR?
I can release it right away.

@mtxr Do you have planned a release soon ?

My intention is too release this this weekend, but I'm very busy with some work deliveries. I'm sorry.
The next release will have lots of enhancements, as you can check on master-webpack branch, but I have to finish some stuff there first, also test with all sgdb's.


Add a flag to remove the accordion/dropdown.

Name of component or pattern:
MultiAddSelect
Brief description
is it possible to add a flag to remove the accordion/dropdown. There isn't any additional information to display can we remove the accordion.  Can you support by Addfing a flag to disable it.
Details
Link to Design Link to example or existing component
https://carbon-for-ibm-products.netlify.app/?path=/docs/ibm-products-patterns-add-select-multiaddselect-canary--default
Links to other materials
e.g. sketch files, icons, images, prototypes
Design Maintainer(s)

Owner/main maintainer(s)
Second/backup maintainer(s)

Tasks
Before starting work on this epic, please review and complete the following.

[ ] Initial review of design/existing code

Working in Carbon for IBM Products package

[ ] Have you reviewed our
contribution guidelines?
[ ] Have you noted our
code guidelines?
[ ] Did you use the CLI to scaffold your new component?

Hi David @davidmenendez. As discussed, can you take a look into this.