Shiimiish

hi,

I got the whisper_stt extension running in Oobabooga and it (kinda) works. However it seems really, really bad in understanding my speech and recognition has been spotty at best.

I saw some youtube tutorials where it seemed to have no problem in understanding - even when spoken to in quite a bit of an accent - and in my own experience it performs knowhere near as good as shown there.

So - are there things I can do to improve its performance? Or may the yt tutorials have been edited to give a wrong impression and spotty performance is what to expect?

I'm very happy with the silero_tts and if I can get the speech-to-text to work at the same level, I'd be a happy camper already

Edit: It seems to be a memory problem. I can select several models in the extension interface - tiny, small, base, medium, ... If I choose the tiny or small model, it does work but with the poor results I mentioned above. If I select the medium model I get an OOM error (something like: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 11.99 GiB total capacity; 11.14 GiB already allocated; 0 bytes free; 11.22 GiB reserved in total by PyTorch) It looks to me as if the language model reserves the whole of my VRAM (which is 12GB) and doesn't leave any for the extension - is there a way to tweak it?

Edit 2:

Ok so, if I use a smaller language model (like a 6B model) it seems to be working perfectly fine with the medium whisper model ... so it is probably a memory issue. I have already tried to start with the command flag "--gpu-memory 5/8/10" which doesn't seem to do anything. Are there other ways of memory management?

Hi there, do any of you know of an in-depth guide/tutorial to ComfyUI? I'm looking for something that goes beyond the general setup and talks about the different nodes, where in the workflow they should be placed and so on. Basically a resource that shows me how to build advanced workflows without too much trial and error.

I've been using ComfyUI, but I feel like I'm doing it blindfold and I don't know what screws there are to adjust.

After a bumpy start (see my other thread about it), I start to feel a bit comfortable with SDXL to the point that I probably wont look back at the 1.5 models. This wizard-hat wearing cat was generated in A1111 with:

"a cute kitty cat wearing a wizard hat, candy rays beaming out of the cat ears, (a swirling galaxy of candy pops background:0.7), 1980's style digital art, hyperrealistic, paintbrush, shallow depth of field, bokeh, spotlight on face, cinematic lighting " Negative (from a standard style I use): "(bad anatomy:1.1), (high contrast:1.3), watermark, text, inscription, signature, canvas frame, (over saturated:1.2), (glossy:1.1), cartoon, 3d, ((disfigured)), ((bad art)), ((b&w)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck))), Photoshop, video game, ugly, tiling, poorly drawn hands, 3d render

Generated at 1024x1024 without refiner.

There's a few things to be aware of, when working with SDXL in A1111 that I found:

• make sure you upgraded A1111 to version 1.5.1 (do a "git pull" in the install directory)
• I needed to add "--medvram" to my command line arguments, otherwise I'd get out of memory errors (12 GB VRAM)
• make sure you have your VAE as "automatic" or using the SDXL VAE (can be downloaded from huggingface). Older VAE's wont work
• older LoRa don't work and you will get errors
• there is a noise offset LoRa for SDXL (sd_xl_offset_example-lora_1.0) which does work, but I don't see too much difference in the images. With LoRa they are a tiny bit crisper. However, this LoRa doesn't work with the Refiner model (you will get errors)

And the biggest one for me:

• don't use arbitrary image proportions and stick to the ones posted here: https://platform.stability.ai/docs/features/api-parameters This was the biggest mistake I made initially. By using other image sizes I'd get super wonky images and very unsatisfying results. I stick to the recommended dimensions and now my images are much, much better.

A word to the refiner model: as of now I don't see big quality improvements if I go with the refiner model in img2img @about 0.1 - 0.25 denoising. I think I will play around more with this at higher denoising strength and see what I can get out of it.

Anyway, I think the SDXL is a huge improvement and I start getting really exciting results already Cheers :)

Hi,

I'm looking into hosting a blog site for myself - nothing fancy, just a site where I can publish some of my thoughts and ideas. Maybe I also want a section to publish images. So, basically something lean and mostly text only.

What's the easiest way to set this up for myself?

Hi,

I'm a bit struggling to get good results with SDXL and I'm wondering if I do something wrong ... I tried A1111 and ComfyUI and have been underwhelmed in both cases. I can get bland looking boring images out of it, which seem to be ok from a technical point of view (like they seem to be correctly generated, without weird artifacts or something like that). However, whenever I try to get something more elaborate my prompting leeds to nowhere. Like I can get "a cat" and it will generate a picture of a cat. But if I try to get "a cat wearing a wizard hat floating in a mesmerizing galaxy of candy pops" - these kind of prompts seem to quickly break the final image. I'm not talking about tailored models and LoRa here, but I seem to be able to do much more interesting stuff with the Deliberate 2.0 model than with SDXL.

So, what's your experience so far? Does the community need to catch up first and do work on custom models, LoRa, and so on to really get thinks cooking? Or do I need to learn better how to work with XL? I was actually looking forward to have a "bland" and hopefully rather unbiased model to work with where not every prompt desperately trys to become a hot anime girl, but I'm struggling to get interesting images for now.

For reference, I updated my A1111 installation with "git pull" (which seems to have worked, as I now have a SDXL tab in my settings) and downloaded the 1.0 model, refiner and VAE from huggingface. I can generate text2imgage in A1111 with the base model, however I can't seem to get the img2img with the refiner model to work ... On ComfyUI I found a premade workflow that uses the base model first and the refiner from the latent and which seems to work just fine technically, but also seems to require a different approach to prompting than I'm used to.

Hi there, I'm curious to know other people's approach in working with Stable Diffusion. I'm just a hobbyist myself and work on creating images to illustrate the fictional worlds I'm building for fun.

However, I find that getting very specific images (that are still visually pleasing) is really difficult.

So, how do you approach it? Are you trying to "force" your imagined picture out by making use of control net, inpainting and img2img? I find that this approach usually leeds to the exact image composition I'm after but will yield completely ugly pictures. Even after hours of inpainting the best I can get to is "sorta ok'ish", surely far away from "stunning". I played around with control net for dozens of hours already, experimenting with multi-control, weighting, control net only in parts of the image, different starting and ending steps, ... but it's only kinda getting there.

Now, opposed to that, a few prompts can generate really stunning images, but they will usually only vaguely resemble what I had in mind (if it's anything else than a person in a generic pose). Composing an image by only prompts is by no means easier/faster than the more direct approach mentioned above. And I seem to always arrive at a point where the "prompt breaks". Don't know how to describe this, but in my experience when I'm getting too specific in prompting, the resulting image will suddenly become ugly (like architecture that is too closely described in the prompt having all wrong angles suddenly).

So, how to you approach image generation? Do you give a few prompts and see what SD can spit out with that? Taking delight in the unexpected results and explore visual styles more than specific image compositions? Or are you trying to be stubborn like me and want to use it as a tool for illustrating imagination - which at the latter it doesn't seem nearly as good at as at the former.

Hi there, On my router/modem I cannot change the DNS entries, thus just using Adguard/PiHole for DNS blocking ads doesn't work. Would a seperate Router circumvent this problem? Could I set up Adguard (or PiHole) on a Raspberry and use it as a DNS server for my home network?

The plan would be to use my ISP-provided router just as a modem to connect to the internet. Then us a second router to provide my home network, where also Adguard/PiHole can do their thing.

Would this setup work and how would I need to configure it?

Hi there, I've seen a few videos on yt showing it off and it looks incredibly powerful in finetuning the outputs of SD. It also looks dauntingly complicated to learn how to use it effectively.

For those of you, who played around with it - do you think it gives better results than A1111? Is it indeed better in finetuning? How steep was the learning curve for you?

I'm trying to figure out if I'd want to put in hours to learn how to use it. If it improves my ability to get out exactly the images I want, I'll go for it. If it does what A1111 does, just dressed up differently I'll sit it out :)

Hi there, I was intrigued by the idea of self-hosting my social media accounts, but was more or less a complete noob with all things hosting. However, with the help of the community here (and quite a few hours spent on it) I finally have a working setup! Mastodon, Matrix, Lemmy, Nextcloud all self-hosted behind Nginx Proxy Manager.

Google can find a lot of answers, but sometimes some really specific input is needed - which you guys have provided over the last couple of weeks - so I just wanna say thank you for that!

So, I think I ironed out a lot of things to get my selfhosted setup running, but it seems that Nginx Proxy Manager is causing me troubles. When I restart my server, the container with NPM restarts as expected but I can't log into the web ui (the website comes up, but when I try to log in nothing happens) and it also doesn't provide the expected proxy functionalities. I'm not sure what happens - any advice would be welcome. right now my only workaround is to delete the container and make it from scratch, but this also means making all proxy hosts + certificates from scratch as well ...

I finally managed to selfhost Lemmy and Matrix, now it is time to also get a selfhosted Mastodon instance up. A few questions before I start:

I did some research into the topic and it seems that Mastodon doesn't like to run behind an existing reverse proxy and there are quite a few tweaks necessary to get it running - can someone confirm this? Or is this something easily set up?

I'm currently leaning to run it on a dedicated VPS (due to the issue above and also because it seems to need quite a bit of disk space) - this opens up to do a non-docker installation and follow the official install path. Do you think this will make it easier to keep it updated to new releases in the future?

If going with a docker install there seem to be quite a few problems with updating (at least a lot of threads discussing failed update procedures sprung up when I googles "mastodon docker update") - can someone confirm? Are there easy to follow guides for a docker based update routine?

Right now it seems the easiest would be to run on a dedicated server, follow the native installation procedure and use the templates provided for nginx, certbot, .... thoughts?