Zandebar

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  • in reply to: Colab notebooks not loading #16333
    Zandebar
    Participant

      Yeah! I have just signed up (Ngrok) and verified

      Getting this error as per the topic I started as it out of scope for this thread

      CheckpointLoaderSimple
      Error while deserializing header: HeaderTooSmall

      in reply to: Colab notebooks not loading #16331
      Zandebar
      Participant

        Am really having a hard time getting the ComfyUI to load, I’ve now succeeded once after five reloads. Can you offer any help here please.

        in reply to: Colab notebooks not loading #16330
        Zandebar
        Participant

          This now appears to be working, I shutdown the notebook and reloaded it and it came up as it should, so just a glitch I assume.  It loaded in just a couple of mins with the spinning circle, so I’m sorted for now.

          Generally how long should you  wait for it to load before giving up?

          in reply to: Colab notebooks not loading #16329
          Zandebar
          Participant

            Getting Colab Comfy Working for the first time isn’t working for me, I’ve loaded the script in the Colab environment, the Tunnel Password I’ve entered and clicked on the link and it has just been spinning in my browser for 30 mins and using compute time in Colab.

            I’ve loaded the defaults…

            in reply to: Getting Colab Automatic1111 Working for the first time #16327
            Zandebar
            Participant

              Colab pro subscription is what I did have then wasn’t sure what would happen at billing, will have to see. I’ll resubscribe and see I’ll do that now…..

               

              You are subscribed to Colab Pro. Learn more
              Available: 181.51 compute units
              Usage rate: approximately 0 per hour
              You have 0 active sessions.

               

              That seems to have done it, will have to see what happens when it rolls over next month if it just adds it, thats presuming I have any units left.  Now that I’ve learnt what things do in ComfyUI I’ll be experimenting more and getting to know SD when I need more than 8gb of vram.

               

              The confusing bit was I asked Gemini what would happen and it advised that the compute units wouldn’t be added, am good for now.

               

              Thank You for your response..

              in reply to: Getting Colab Automatic1111 Working for the first time #16323
              Zandebar
              Participant

                Hello

                Does anyone know how exactly Colab billing works, as I’ve read that any remaining compute units will rollover and stay valid for 3 months. Then I’ve read that I will receive another 100 compute units at next billing, this part gets a little confusing.  As it appears that any remaining units are not taken in to account,  I’ve only used 18.49 compute units this month leaving 81.51 compute units left.  As per their information at next billing my compute units will goto 100,  whereby not adding 100 compute units to my remaining units totalling 181.51 units.

                I’ve now cancelled Colab so I don’t lose any compute units and my remaining units are still in the account, can anyone expand on this.  As it looks like Google Colab are stealing remaining compute units from any unused units no matter how many compute units are left, at the end of each billing cycle. Just refreshing the total to 100 compute units at the start of each billing cycle, am sure I’ve got this wrong as Google wouldn’t do that, would they. As those remaining units are bought and paid for.

                in reply to: Include rsources needed in course intro #15984
                Zandebar
                Participant

                  Hello All

                  Andrew: I must 2nd David’s post, as I was having problems moving from a local install to Colab where I needed to know which assets were needed for a Colab session.

                   

                  I suggest you have a quick look up of what’s needed for the completion of course 1-4 on each course at the start. As in course X you need these addon’s installed to be able to complete this course, list what’s needed, then you can check these off on the Colab install to ensure its all correct.

                  https://stable-diffusion-art.com/forums/topic/getting-colab-automatic1111-working-for-the-first-time/

                  I Suggested at the start of each Course, maybe a more granular per section of the course, I also have trouble knowing which models you are using for your examples. You mostly sat which ones but there has been a good few time where you haven’t. I just used one in my list to get past that point, I think David is quite right in suggesting what he did.

                  Something like: Assets needed for course and then per section assets needed for section.

                  Being new to Colab and it’s UI the would for me be a great benefit, until I get used to using the web app on an online server.

                   

                  Your response to my suggestion was:

                  Thanks for the suggestions.

                  When I wrote the course, I tried to keep it agnostic to how to use A1111 (local/colab/online service). So I didn’t write much about instructions specific to the Colab notebook. I will add them to lessons that require additional extensions.

                   

                  I see where you are coming from, but that doesn’t help when you do a course over multiple days and need to get setup each time for that course. The local install is less important as you set things up along the way, so on that front I’ve not had an issue. The issue raised is Colab whereby you have to setup the installation each time you start the service. So multiple days (on course) or having to restart Colab as David pointed out to add assets which you didn’t think you needed at start, doesn’t help the student.

                  Am I right in saying that the amount of assets which get loaded up into your Colab session affects your compute credits. So needlessly having to restart a session to add assets, has a counter affect on your compute credits?

                  I’ve had Colab now for 2-3 weeks and I’ve avoided using Colab vs local install, because I don’t know which assets to add in the Colab session for the course. The local install just picks up where you last left off in that section of the course, but Colab doesn’t do that. Out of my 100 compute credits I’ve only used 15 and that was just playing around with the Colab setup.

                   

                  In my view David is quite CORRECT  in pointing this out.

                  in reply to: Getting Colab Automatic1111 Working for the first time #15905
                  Zandebar
                  Participant

                    Am I right in saying that in one of the A1111 courses you said its better to upscale x2 in HiRes then take it to the Extras tab.

                    I was just surprised that my RTX 2070 handled 4x and Colab didn’t, surely its a config issue….

                    in reply to: Local Install vs GPU / Render Farms (online GPU) #15904
                    Zandebar
                    Participant

                      I’ve decided that I’m going to hang on upgrading the GPU for when Nvidia launch the RTX 50 Series, and see how they perform. Given the stats it should be impressive, only time will tell,  also the tech issues they’ve had in production. I’m just worried that it may leak into an Intel type issue with the hardware, 12 months after release should be enough time to work this out. Then your 12 months behind, I was kind of hoping to pick up a 4090 in my budget after the launch but that’s looking unlikely as they’ve reduced available stock. Which is keeping the price the same, I’ve been looking in the Black Friday sales and prices remain the same. Which is a bit of a surprize given that the launch of the 50 series is well known, they’ve handled that well to maintain the price.

                       

                      It’s looking most likely that I’ll end up with a RTX ??80 series of some description with 16GB, either 40 0r maybe 50 series, I may spring the extra cash and get the 5080 when it comes out. After the cards have been reviewed and then I’ll work out which way I’m going to jump.

                      That’s why I’m really interested on the limitations and when I’ll need to use a  GPU server farm, maybe a better way to go in the long run. Reading around; the FLUX models can completely fill up the VRAM on consumer cards so I’m considering maybe it’s GPU server farms only.  I do need a performance lift with my present hardware GPU, it’s a matter of working out the pro’s and con’s.

                       

                      I still don’t know where I’m heading with SD I have to work that out first.

                       

                       

                      in reply to: 30 Days Using Stable Diffusion Art with Scholar Monthly #15902
                      Zandebar
                      Participant

                        No Problem!!

                        Your doing something great with this website and I can see that you have put a lot of time and effort into making this educational site what it is.  It’s also a great time saver for me, as it’s all laid out nicely and easy to understand no effort is needed on my side. Which is why its worth the fee and you deserve to be financially compensated for your efforts and the tools you use to get everything up and running.

                         

                        Combine that with a day job and you have true PASSION! 😉

                        • This reply was modified 2 months, 1 week ago by Zandebar.
                        in reply to: Local Install vs GPU / Render Farms (online GPU) #15892
                        Zandebar
                        Participant

                          That does add an extra layer of complication to what I’m trying to work out.

                          It looks like I’m going to need some time to get my head around the GPU issue and what it can or can’t do at a certain VRAM.

                          I just need to work out lets say, @ 16Gb when would I need to use an online GPU service to render a certain model, surely I should be able to look at a model and say OK that model won’t work on this local GPU. Therefore,  if I want to use that model I’ll need to use an online GPU service. With that explanation it’s not so straight forward and its just a matter of when the GPU will crash and give you an error. Surely that’s not the case is it? As you should be able to apply some logic somewhere.

                          in reply to: Getting Colab Automatic1111 Working for the first time #15891
                          Zandebar
                          Participant

                            Please see above post as I couldn’t edit it to add this.

                             

                            OK!

                             

                            I’ve just repeated what I did in Colab on my local machine which has a RTX 2070 super 8GB VRAM card installed. Which completed the task and produced an image, rendered in correctly but never the less completed the task.

                            My RTX 2070 super 8GB shouldn’t be able to out perform a 40GB VRAM A100 the Colab used out of the box settings. Which leads me to think that this is a config issue rather than a card issue. I’m a little bit lost for word here, so I need to know what’s going on here.

                             

                            Settings used on the RTX 2070 super 8GB to complete the image.

                            AS-YoungV2, futuristic, Full shot of a young woman, portrait of beautiful woman, solo, pliant, Side Part, Mint Green hair, wearing a red Quantum Dot Reindeer Antler Headpiece outfit, wires, christmas onyx, green neon lights, cyberpunkai, in a Hydroponic mistletoe gardens in futuristic homes with a Robotically animated Christmas displays in public spaces. ambience, shot with a Mamiya Leaf, Fujifilm XF 16mm f/2.8 R WR lens, ISO 6400, f/1.2, Fujifilm Superia X-Tra 400, , (high detailed skin:1.2), 8k uhd, dsir, soft lighting, high quality, film grain,
                            Negative prompt: BadDream, disfigured, ugly, bad, immature, cartoon, anime, 3d, painting, b&w, 2d, 3d, illustration, sketch, nfsw, nude
                            Steps: 30, Sampler: DPM++ 2M, Schedule type: Karras, CFG scale: 7, Seed: 4093592187, Size: 512×768, Model hash: 879db523c3, Model: dreamshaper_8, Denoising strength: 0.7, Hires upscale: 4, Hires upscaler: Latent, Version: v1.10.1
                            Time taken: 8 min. 45.3 sec.

                            in reply to: Getting Colab Automatic1111 Working for the first time #15889
                            Zandebar
                            Participant

                              Hello

                              I’m just trying to work out my parameters on Colab and I thought OOOH VRAM 40GB, lets ramp it up and see what it can do. OK, I broke something any chance you can explain how far you can take these setting, I thought a upscale shouldn’t tax the GPU too much.

                               

                              I set the notebook running with:

                              Colab A100

                              System RAM
                              1.6 / 83.5 GB

                              GPU RAM
                              0.0 / 40.0 GB

                              Disk
                              53.8 / 235.7 GB

                               

                              From: Stable Diffusion – Level 3 > End-to-end workflow: ControlNet > Generate txt2img with ControlNet

                               

                              Model: dreamshaper_8

                               

                              Prompt:
                              AS-YoungV2, futuristic, Full shot of a young woman, portrait of beautiful woman, solo, pliant, Side Part, Mint Green hair, wearing a red Quantum Dot Reindeer Antler Headpiece outfit, wires, christmas onyx, green neon lights, cyberpunkai, in a Hydroponic mistletoe gardens in futuristic homes with a Robotically animated Christmas displays in public spaces. ambience, shot with a Mamiya Leaf, Fujifilm XF 16mm f/2.8 R WR lens, ISO 6400, f/1.2, Fujifilm Superia X-Tra 400, , (high detailed skin:1.2), 8k uhd, dsir, soft lighting, high quality, film grain,

                               

                              Negative Prompt:
                              BadDream, disfigured, ugly, bad, immature, cartoon, anime, 3d, painting, b&w, 2d, 3d, illustration, sketch, nfsw, nude

                               

                              Sampling method: DPM++ 2M Karras
                              Steps: 30
                              Refiner: Not used
                              Hires fix: Did on some
                              CFG scale: 7
                              Seed: -1
                              Size: 512 x 768

                               

                              This works set to Batch size 4 at these settings, good so far its working.

                              Then I hit the Hires. fix:

                              Defaults: Upscaler- Latent > Denoising strength – 0.7

                               

                              This worked @ x2 from 512×768 to 1024 x 1536 – Batch size 1

                               

                              Then got an error @ x4  from 512×768 to 2048 x 3072 – Batch size 1

                               

                              OutOfMemoryError: CUDA out of memory. Tried to allocate 36.00 GiB. GPU 0 has a total capacity of 39.56 GiB of which 35.46 GiB is free. Process 73993 has 4.10 GiB memory in use. Of the allocated memory 3.46 GiB is allocated by PyTorch, and 127.14 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

                              Time taken: 50.9 sec.

                               

                              I can see what’s going on here from the error message; as the render ran out of memory now I need to understand boundaries in Colab and my expectations of this system. Am just concerned when it comes to combining certain features, I need to understand the limits and the process of items coming in and out of vRAM.

                               

                              Here I think I should go, OK it fell over on x4 so do it x2 and then take it to the Extras tab and upscale it from there. I get the logic but it’s more about my expectations of the system and how not to brake it with too much VRAM requests.

                              • This reply was modified 2 months, 1 week ago by Zandebar.
                              in reply to: 24gb VRAM vs Architecture series #15873
                              Zandebar
                              Participant

                                It’s kind of where I’m at, with the move to cloud computing and Nvidia moving away from desktop products due to miniaturisation (mini pc’s, tablets, laptops). I feel that am going to get screwed, as what I can afford to buy a GPU at with the budget I have, will always be entry level. I’ll be always playing catchup, I don’t know yet if the 90% of the rubbish / trash I’ll produce will matter with a cloud GPU. That’s what I’m trying to work out, plus apart from generative ai, I have no barrier with my current old GPU (rtx2070). I know that  I can’t afford a GPU that has 24gb VRAM locally for my present budget (they might drop in price),  to get the benefit for all the modules / workflows. With that,  VRAM going to always go up in size as  modules / workflows get bigger, its better to work smart and know your limitations.  Seeking out that better financial option for that creative data file, where craving for, plus am looking to go down that rabbit hole of video.

                                 

                                I’m not sure where this journey is going to take me and there’s going to be surprizes along the way, who knows where I’ll end up.

                                 

                                Its just about getting that experience in generative ai and being able to make smart discissions along the way.

                                • This reply was modified 2 months, 1 week ago by Zandebar.
                                in reply to: Getting Colab Automatic1111 Working for the first time #15871
                                Zandebar
                                Participant

                                  Hi Andrew

                                   

                                  Hope you are keeping well..

                                   

                                  I’ve got Colab working now with A1111, sorry for the delay in getting back to you.

                                  It seems that the notebook couldn’t connect to your Google Drive. Do you have a Google account? If so, it should ask you to grant permission to access your Google Drive. This is necessary for saving images and accessing models.

                                   

                                  Yes.. I have a Google account.

                                   

                                  I too saw that Google drive couldn’t connect to my Google drive space from the error message and your exactly right when it came to permissions.  I was rushing as I didn’t have a lot of time and was quickly trying to set it up and have a look.  I didn’t read the script line for line, so I wasn’t going to hit the select all button without knowing what it was doing in the background (I just selected a few). As you know in computing you grant the least amount of permissions needed to get the job done, Linux approach not the MS one ;-).

                                   

                                  After reading the script I was more confident to select the ones needed to get the required access to the drive, I left two out.

                                   

                                  I suggest you put an image in the instructions for the least amount of permissions needed to connect to  Google Drive to enable A1111 to function in Colab with a Google Drive connection.

                                   

                                  Also:

                                  I’ve done part of the courses 1-3 (part of 3)  in A1111 on a local install, now am trying Colab am now working out what add-ons I need to complete the rest of the course.

                                   

                                  I suggest you have a quick look up of what’s needed for the completion of course 1-4 on each course at the start. As in course X you need these addon’s installed to be able to complete this course, list what’s needed, then you can check these off on the Colab install to ensure its all correct.

                                   

                                  There are parts in the instructions where you gloss over parts of it without going into the reasoning behind it, such as ngrok I’m coming to this for the first time and don’t know the terminology and the reasons why this would be better. A little more explanation in that area is needed and would improve the experience.

                                   

                                  Is the notebook a one time thing or does the setup reside on Google drive for later use, I can see the name of the notebook there. So I’m thinking if I click on it, it may load up the last setup I entered. I’ve just clicked on it and its just a link to Colab, but that would be useful in my opinion to be able to click on a pre-made setup which is stored in your Google drive. Just a thought…

                                   

                                  This is a little hazy; but you’ve done a great job of Lets Get You Started, perhaps consider adding a what you need to know about Colab course and enable the user to become a power user of Colab when it comes to generative ai on the Colab platform.

                                   

                                  After a quick look and a play on Colab, I think I will get more milage out of a online GPU than a local one, but that’s my bias at this stage, it may change over time. It’s important to learn the basics first and build that foundation first before jumping to any conclusion. It’s always fun discussing what’s out there and looking at other options.

                                   

                                  All the best

                                   

                                  Zandebar

                                   

                                  • This reply was modified 2 months, 1 week ago by Zandebar.
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