If you’re running Keras/TF in Jupyter on a local server and another notebook is open which was accessing the GPU, you can also get this error. Just halt and close the other notebook(s). This can occur even if the other notebook isn’t actively running anything.
This is distinct from PyTorch OOM errors, which typically refer to PyTorch’s allocation of GPU RAM and are of the form
OutOfMemoryError: CUDA out of memory. Tried to allocate 734.00 MiB (GPU 0; 7.79 GiB total capacity; 5.20 GiB already allocated; 139.94 MiB free; 6.78 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Because PyTorch manages a subset of GPU RAM for a given job, it can sometimes draw an OOM error even though there’s sufficient available RAM in the GPU (just not enough in Torch’s self-allocation)
These errors can be a bit obscure to troubleshoot, but generally three techniques can be helpful:
- at the head of your notebook, add these lines:
import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:64" - delete objects that are on the GPU as soon as you don’t need them anymore
- reduce things like batch_size in training or testing scenarios
You can monitor GPU RAM simplistically with watch nvidia-smi
Every 2.0s: nvidia-smi numbaCruncha123: Wed May 31 11:30:57 2023
Wed May 31 11:30:57 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.108.03 Driver Version: 510.108.03 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:26:00.0 Off | N/A |
| 37% 33C P2 34W / 175W | 7915MiB / 8192MiB | 3% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 2905 C ...user/z_Venv/NC/bin/python 1641MiB |
| 0 N/A N/A 31511 C ...user/z_Venv/NC/bin/python 6271MiB |
+-----------------------------------------------------------------------------+
This will tell you what’s using RAM across the entire GPU.
Note: if you’ve got a notebook running but don’t see anything here, it’s possible you’re running on the CPU.
I have an MSI RTX 3060 with 12gb VRAM and 48gb system ram ddr4
and I am using Stable Diffusion webui version Automatic1111 version
I haven’t really tried generating any images yet but I really need this tool for upscaling some of my images
I am using R-ESRGAN 4X+Anime6B under Upscaler 1 option and leaving the upscaling 2 option to None (note all stable diffusion settings are not touched)
and I am getting error whenever I am trying to upscale images which surpass the 4000 pixel dimension limit, I even tried to change the setting in Stable Diffusion where it says ‘Width’Height limit for the above option, in pixels’ to 40000 instead (just to remove the 4000 pixel dimension cap) but that didn’t seem to help me alongside all the above mentioned things I tried…
The error code I am getting is — OutOfMemoryError: CUDA out of memory. Tried to allocate 9.20 GiB (GPU 0; 12.00 GiB total capacity; 6.93 GiB already allocated; 2.71 GiB free; 7.24 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
other things to mention are that for the images over 4000 pixel dimension (in width) are taking up more than 1000 tiles to process and after all the tiles are done processing and then it’s time to provide the image is when the error code appears and most importantly is that although my gpu has 12gb of vram I only see barely 7-10gb being used even though I am sure that no other processes are using my gpu and the remaining 2+ gb is always sitting idle
and this one I understand is for bigger images which surpasses 4000 pixel dimension, as I have seen even images with 3912 x 6950 (and remember that the 4000 dimension I am talking about is in width, not the height as I have seen the height doesn’t matter)
but the next issue I am having is that whenever I am trying to upscale like say about 600 images all at once, it is only able to upscale like sometimes only 256 ones or 384 ones and then shows a lot of temp error (I sadly don’t have that error message copied to the clipboard now) which seems like it’s crashing… is it normal or is my gpu overclock unstable and that’s why it’s crashing?
my overclocks are +160 on core clock and +1000 on mem clock which seems to be fine for games.
Edit — It happened again, and surprisingly only with 30 images selected which are not too big in size and are in need of upscaling, here’s the error code
Error completing request
Arguments: (1, None, [<tempfile._TemporaryFileWrapper object at 0x00000141C2089280>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089FA0>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089550>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089B50>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089BE0>, <tempfile._TemporaryFileWrapper object at 0x00000141C20897F0>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089220>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089C10>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089940>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089310>, <tempfile._TemporaryFileWrapper object at 0x00000141C2089190>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF14F0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1AC0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1F70>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1CA0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF12B0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1940>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF19A0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF11C0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1880>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1250>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1A30>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1A90>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF10D0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1850>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF15E0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1130>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1040>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF16D0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF17F0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1700>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1790>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1070>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF18B0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1BB0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FF1D00>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FD8CD0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FD8A00>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FD87F0>, <tempfile._TemporaryFileWrapper object at 0x00000141C1FD88E0>, <tempfile._TemporaryFileWrapper object at 0x00000141C20848B0>, <tempfile._TemporaryFileWrapper object at 0x00000141C20842E0>, <tempfile._TemporaryFileWrapper object at 0x00000141C2084370>, <tempfile._TemporaryFileWrapper object at 0x00000141C20895E0>, <tempfile._TemporaryFileWrapper object at 0x00000141C2084CD0>], », », True, 0, 4, 512, 512, True, ‘R-ESRGAN 4x+ Anime6B’, ‘None’, 0, 0, 0, 0) {}
Traceback (most recent call last):
File «D:Anime Artwork generation by Aistable-diffusion-webuimodulescall_queue.py», line 57, in f
res = list(func(*args, **kwargs))
File «D:Anime Artwork generation by Aistable-diffusion-webuimodulescall_queue.py», line 37, in f
res = func(*args, **kwargs)
File «D:Anime Artwork generation by Aistable-diffusion-webuimodulespostprocessing.py», line 61, in run_postprocessing
scripts.scripts_postproc.run(pp, args)
File «D:Anime Artwork generation by Aistable-diffusion-webuimodulesscripts_postprocessing.py», line 130, in run
script.process(pp, **process_args)
File «D:Anime Artwork generation by Aistable-diffusion-webuiscriptspostprocessing_upscale.py», line 100, in process
upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
File «D:Anime Artwork generation by Aistable-diffusion-webuiscriptspostprocessing_upscale.py», line 70, in upscale
image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)
File «D:Anime Artwork generation by Aistable-diffusion-webuimodulesupscaler.py», line 63, in upscale
img = self.do_upscale(img, selected_model)
File «D:Anime Artwork generation by Aistable-diffusion-webuimodulesrealesrgan_model.py», line 62, in do_upscale
upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
File «D:Anime Artwork generation by Aistable-diffusion-webuivenvlibsite-packagestorchutils_contextlib.py», line 115, in decorate_context
return func(*args, **kwargs)
File «D:Anime Artwork generation by Aistable-diffusion-webuivenvlibsite-packagesrealesrganutils.py», line 254, in enhance
output = (output_img * 255.0).round().astype(np.uint8)
numpy.core._exceptions._ArrayMemoryError: Unable to allocate 1.12 GiB for an array with shape (12288, 8192, 3) and data type float32
- Home
- Tech
28 Feb 2023 2:40 PM +00:00 UTC
Try these tips and the Stable Diffusion runtime error will be a thing of the past.
Credit: Stability.ai
If the Stable Diffusion runtime error is preventing you from making art, here is what you need to do.
Stable Diffusion is one of the best AI image generators out there. Unlike DALL-E and MidJourney AI, Stable Diffusion is available to the public and anyone with a powerful machine can generate images from texts.
However, Stable Diffusion might sometimes run into memory issues and stop working. If you are experiencing the Stable Diffusion runtime error, try the following tips.
How To Fix Runtime Error: CUDA Out Of Memory In Stable Diffusion
So you are running Stable Diffusion locally on your PC, maybe trying to make some NSFW images and bam! You are hit by the infamous RuntimeError: CUDA out of memory.
The error is accompanied by a long message that basically looks like this. The amount of memory may change but the content is the same.
RuntimeError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 6.00 GiB total capacity; 5.16 GiB already allocated; 0 bytes free; 5.30 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
It appears you have run out of GPU memory. It is worth mentioning that you need at least 4 GB VRAM in order to run Stable Diffusion. If you have 4 GB or more of VRAM, below are some fixes that you can try.
- Restarting the PC worked for some people.
- Reduce the resolution. Start with 256 x 256 resolution. Just change the -W 256 -H 256 part in the command.
- Try this fork as it requires a lot less VRAM according to many Reddit users.
If the issue persists, don’t worry. We have some additional troubleshooting tips for you to try. Keep reading!
Other Troubleshooting Tips
So you have tried all the simple and quick fixes but the runtime error seems to have no intention to leave you, huh? No worries! Let’s dive into relatively more complex steps. Here you go.
- As mentioned in the error message, run the following command first: PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.6, max_split_size_mb:128. Then run the image generation command with: —n_samples 1.
- Call the optimized python script. Use the following command: python optimizedSD/optimized_txt2img.py —prompt «a drawing of a cat on a log» —n_iter 5 —n_samples 1 —H 512 —W 512 —precision full
- You can also try removing the safety checks aka NSFW filters, which take up 2GB of VRAM. Just replace scripts/txt2img.py with this:
https://github.com/JustinGuese/stable-diffusor-docker-text2image/blob/master/txt2img.py
Hopefully, one of the suggestions will work for you and you will be able to generate images again. Now that the Stable Diffusion runtime error is fixed, have a look at how to access Stable Diffusion using Google Colab.
My model reports “cuda runtime error(2): out of memory”¶
As the error message suggests, you have run out of memory on your
GPU. Since we often deal with large amounts of data in PyTorch,
small mistakes can rapidly cause your program to use up all of your
GPU; fortunately, the fixes in these cases are often simple.
Here are a few common things to check:
Don’t accumulate history across your training loop.
By default, computations involving variables that require gradients
will keep history. This means that you should avoid using such
variables in computations which will live beyond your training loops,
e.g., when tracking statistics. Instead, you should detach the variable
or access its underlying data.
Sometimes, it can be non-obvious when differentiable variables can
occur. Consider the following training loop (abridged from source):
total_loss = 0 for i in range(10000): optimizer.zero_grad() output = model(input) loss = criterion(output) loss.backward() optimizer.step() total_loss += loss
Here, total_loss is accumulating history across your training loop, since
loss is a differentiable variable with autograd history. You can fix this by
writing total_loss += float(loss) instead.
Other instances of this problem:
1.
Don’t hold onto tensors and variables you don’t need.
If you assign a Tensor or Variable to a local, Python will not
deallocate until the local goes out of scope. You can free
this reference by using del x. Similarly, if you assign
a Tensor or Variable to a member variable of an object, it will
not deallocate until the object goes out of scope. You will
get the best memory usage if you don’t hold onto temporaries
you don’t need.
The scopes of locals can be larger than you expect. For example:
for i in range(5): intermediate = f(input[i]) result += g(intermediate) output = h(result) return output
Here, intermediate remains live even while h is executing,
because its scope extrudes past the end of the loop. To free it
earlier, you should del intermediate when you are done with it.
Avoid running RNNs on sequences that are too large.
The amount of memory required to backpropagate through an RNN scales
linearly with the length of the RNN input; thus, you will run out of memory
if you try to feed an RNN a sequence that is too long.
The technical term for this phenomenon is backpropagation through time,
and there are plenty of references for how to implement truncated
BPTT, including in the word language model example; truncation is handled by the
repackage function as described in
this forum post.
Don’t use linear layers that are too large.
A linear layer nn.Linear(m, n) uses O(nm)O(nm) memory: that is to say,
the memory requirements of the weights
scales quadratically with the number of features. It is very easy
to blow through your memory
this way (and remember that you will need at least twice the size of the
weights, since you also need to store the gradients.)
Consider checkpointing.
You can trade-off memory for compute by using checkpoint.
My GPU memory isn’t freed properly¶
PyTorch uses a caching memory allocator to speed up memory allocations. As a
result, the values shown in nvidia-smi usually don’t reflect the true
memory usage. See Memory management for more details about GPU
memory management.
If your GPU memory isn’t freed even after Python quits, it is very likely that
some Python subprocesses are still alive. You may find them via
ps -elf | grep python and manually kill them with kill -9 [pid].
My out of memory exception handler can’t allocate memory¶
You may have some code that tries to recover from out of memory errors.
try: run_model(batch_size) except RuntimeError: # Out of memory for _ in range(batch_size): run_model(1)
But find that when you do run out of memory, your recovery code can’t allocate
either. That’s because the python exception object holds a reference to the
stack frame where the error was raised. Which prevents the original tensor
objects from being freed. The solution is to move you OOM recovery code outside
of the except clause.
oom = False try: run_model(batch_size) except RuntimeError: # Out of memory oom = True if oom: for _ in range(batch_size): run_model(1)
My data loader workers return identical random numbers¶
You are likely using other libraries to generate random numbers in the dataset
and worker subprocesses are started via fork. See
torch.utils.data.DataLoader’s documentation for how to
properly set up random seeds in workers with its worker_init_fn option.
My recurrent network doesn’t work with data parallelism¶
There is a subtlety in using the
pack sequence -> recurrent network -> unpack sequence pattern in a
Module with DataParallel or
data_parallel(). Input to each the forward() on
each device will only be part of the entire input. Because the unpack operation
torch.nn.utils.rnn.pad_packed_sequence() by default only pads up to the
longest input it sees, i.e., the longest on that particular device, size
mismatches will happen when results are gathered together. Therefore, you can
instead take advantage of the total_length argument of
pad_packed_sequence() to make sure that the
forward() calls return sequences of same length. For example, you can
write:
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence class MyModule(nn.Module): # ... __init__, other methods, etc. # padded_input is of shape [B x T x *] (batch_first mode) and contains # the sequences sorted by lengths # B is the batch size # T is max sequence length def forward(self, padded_input, input_lengths): total_length = padded_input.size(1) # get the max sequence length packed_input = pack_padded_sequence(padded_input, input_lengths, batch_first=True) packed_output, _ = self.my_lstm(packed_input) output, _ = pad_packed_sequence(packed_output, batch_first=True, total_length=total_length) return output m = MyModule().cuda() dp_m = nn.DataParallel(m)
Additionally, extra care needs to be taken when batch dimension is dim 1
(i.e., batch_first=False) with data parallelism. In this case, the first
argument of pack_padded_sequence padding_input will be of shape
[T x B x *] and should be scattered along dim 1, but the second argument
input_lengths will be of shape [B] and should be scattered along dim
0. Extra code to manipulate the tensor shapes will be needed.
11/05/2023 9:11 am
Topic starter
The Runtimeerror cuda out of memory stable diffusion error can be frustrating and confusing.
This error message shows that the program has exhausted the available GPU memory, preventing the execution of the task at hand.
In this article, we will explain the causes of this error and discuss the solutions to resolve it completely.
Why this Error Occur?
The RuntimeError: CUDA out of memory error usually occurs when the GPU fails to allocate sufficient memory for the current operation.
GPUs have a limited amount of memory, and if the required memory exceeds the available capacity, then an error will occur.
Causes of CUDA out of memory error
The following are the common causes of the CUDA out of memory error:
- Insufficient GPU memory
- Large batch sizes or model sizes
- Memory leaks
- Concurrent GPU tasks
- High GPU Memory Utilization
- Memory Fragmentation
- Inefficient Memory Usage
How to Fix the Error?
Now that we have identified the common causes of the Runtimeerror: cuda out of memory stable diffusion, let’s discuss into the solutions to resolve the issue.
Solution 1: Check GPU Memory Availability
Before running your CUDA program, it is essential to check the GPU memory availability.
This can help you to identify whether your GPU has sufficient memory to accommodate the computations and data needed by your application.
You can use different tools and libraries to monitor GPU memory usages, like the NVIDIA System Management Interface (nvidia-smi) or CUDA Toolkit utilities.
Solution 2: Reduce Memory Consumption
The other way to solve this error is to reduce memory consumption.
If you find out that your GPU memory is insufficient, consider reducing the memory consumption within your CUDA program.
Here are some strategies to achieve this:
- Use Smaller Batch Sizes:
- Optimize Data Structures
- Remove Redundant Data
- Use Data Streaming
Solution 3: Optimize Memory Usage
The solution to solve this error is to optimize memory usage.
By implementing efficient memory management system, you can maximize the available GPU memory and prevent memory deficiency issues.
Consider the following procedure:
- Memory Pools
- Implement memory pools or caches to manage memory allocation and deallocation efficiently.
- Asynchronous Memory Transfers
- Utilize asynchronous memory transfers to overlap data transfers between the host and the GPU with kernel execution.
- Memory Alignment
- Make sure that memory allocations are aligned correctly to minimize padding and reduce memory wastage.
Solution 4: Close Unnecessary Applications and Processes
If you are still encountering the runtimeerror: CUDA out of memory, it is important to check for any unnecessary applications or processes running in the background that might be consuming GPU memory.
You can close any non-necessary programs and make sure that only the needed applications are utilizing the GPU resources.
Solution 5: Upgrade GPU or Add Additional Memory
If the previous solutions cannot resolve the issue, it may need to upgrade your GPU or add additional memory to your existing GPU.
Upgrading to a GPU with a higher memory capacity or adding more memory modules can provide the necessary resources to overcome the out of memory error.
Additional Resources
Here are the following articles that can help you to know more about CUDA errors:
- Runtimeerror: cuda out of memory.
- runtimeerror: cudnn error: cudnn_status_execution_failed
- Runtimeerror: no cuda gpus are available
- Runtimeerror: cuda out of memory. tried to allocate
Conclusion
In conclusion, getting the Runtimeerror cuda out of memory stable diffusion can be frustrating, but with the proper knowledge and provided solutions, you can avoid this error.
By knowing the causes of the error and implementing memory optimization solutions, you can make sure that run smoothly and efficiently your CUDA programs.
FAQs
How do I check GPU memory usage?
You can use tools such as NVIDIA System Management Interface (nvidia-smi), CUDA Toolkit utilities, or GPU monitoring libraries to check GPU memory usage.
Is the runtime error CUDA out of memory specific to stable diffusion or can it occur in other CUDA applications?
The runtimeerror: CUDA out of memory can stable diffusion occur in any CUDA application that exceeds the available GPU memory.
Are there any tools or libraries available to help optimize memory usage in CUDA programs?
Yes, there are different tools and libraries available to assist in optimizing memory usage in CUDA programs.
Just want the answer? In most cases, you can fix this error by setting a lower image resolution or fewer images per generation. Or, use an app like NightCafe that runs Stable Diffusion online in the cloud so you don’t need to deal with CUDA errors at all.
One of the best AI image generators currently available is Stable Diffusion online. It’s a text-to-image technology that enables individuals to produce beautiful works of art in a matter of seconds. If you take the time to study a Stable Diffusion prompt guide, you can quickly make quality images with your computer or on the cloud, and learn what to do if you get a CUDA out-of-memory error message.
If Stable Diffusion is used locally on a computer rather than via a website or application programming interface, the machine will need to have certain capabilities to handle the program. Your graphics card is the most critical component when using Stable Diffusion because it operates almost entirely on a graphics processing unit (GPU)—and usually on a CUDA-based Nvidia GPU.
The Nvidia CUDA parallel computing platform is the foundation for thousands of GPU-accelerated applications. It is the platform of choice for developing and implementing novel deep learning and parallel computing algorithms due to CUDA’s flexibility and programmability.
What Is CUDA?
NVIDIA developed the parallel computing platform and programming language called Compute Unified Device Architecture, or CUDA. Through GPU accelerators, CUDA has assisted developers in speeding up their apps with more than twenty million downloads.
In addition to speeding up applications for high-performance computing and research, CUDA has gained widespread use in consumer and commercial ecosystems, as well as open-source AI generators such as Stable Diffusion.
What Happens With a Memory Error in Stable Diffusion?
Running Stable Diffusion on your computer may occasionally cause memory problems and prevent the model from functioning correctly. This occurs when your GPU memory allocation is exhausted. It is important to note that running Stable Diffusion requires at least four gigabytes (GB) of video random access memory (VRAM). One recommendation is a 3xxx series NVIDIA GPU that starts with six GB of VRAM. Other components of your computer, such as your central processing unit (CPU), RAM, and storage devices, are less important.
To train an AI model on a GPU, you need to differentiate labels and predictions to be accurate. To produce reliable predictions, you need both the model and the input data to be allocated in CUDA memory. A memory error occurs when the project becomes too complex to be cached in the GPU’s memory.
Each project has a specific quantity of data that needs to be uploaded, either to the VRAM (the GPU’s memory when the CUDA or RTX GPU engine resides) or the RAM (when the CPU engine operates).
GPUs typically contain a significantly smaller amount of memory than a computer’s RAM. A project may occasionally be too big and fail because it is fully uploaded to the VRAM. The geometry’s intricacy, extent to which high-resolution textures are used, render settings, and other elements can all play a part.
One of the easiest ways to fix a memory error issue is by simply restarting the computer. If this doesn’t work, another potential remedy is to reduce the resolution. Reduce your image to 256 x 256 resolution by making an input of -W 256 -H 256 in the command line.
You can also try increasing the memory that the CUDA device has access to. You do this by modifying your system’s GPU settings. Changing the configuration file or using command-line options frequently resolves the issue.
Another option is to buy a new GPU. If you go this route, get a GPU with more memory to replace the existing GPU if VRAM is consistently causing runtime problems that other methods can’t solve.
Divide the data into smaller batches. Processing smaller sets of data may be needed to avoid memory overload. This tactic reduces overall memory utilisation and the task can be completed without running out of memory.
You can also use a new framework. If you are using TensorFlow or PyTorch, you can switch to a more memory-efficient framework.
Finally, make your code more efficient to avoid memory issues. You can decrease the data size, use more effective methods, or try other speed enhancements.
In Conclusion
The best way to solve a memory problem in Stable Diffusion will depend on the specifics of your situation, including the volume of data being processed and the hardware and software employed.
You can further enhance your creations with Stable Diffusion samplers such as k_LMS, DDIM and k_euler_a. The incredible results happen without any pre- or post-processing.
Ready to take a deep dive into the Stable Diffusion universe? Sign up for a free account on NightCafe and let your creative ideas flow.
Stable Diffusion — это один из инструментов искусственного интеллекта, который люди используют для создания искусства искусственного интеллекта, потому что он находится в открытом доступе и бесплатен для использования. Программу можно использовать локально на компьютере с выделенным графическим процессором или удаленно через Демо HuggingFace. Следующий пост должен помочь вам устранить ошибку «Cuda Out of Memory» и включить Stable Diffusion, если вы пытались использовать его на своем компьютере, но столкнулись с проблемами.
Исправьте «Cuda Out of Memory» в Stable Diffusion, используя эти 7 методов.
Вы должны быть в состоянии исправить ошибку «Cuda Out of Memory» в Stable Diffusion с помощью следующего списка исправлений.
1. Перезагрузите систему
Если ранее Stable Diffusion работал без каких-либо проблем, возможно, простой перезапуск системы решит проблему, поскольку программное обеспечение Stable Diffusion могло потерять доступ к некоторым компонентам графического процессора. После перезагрузки системы некоторые пользователи (1,2) смогли быстро устранить ошибку «Cuda Out of Memory» на своем ПК.
2. Установите Anaconda вместе с Nvidia CUDA Toolkit.
Установка и использование приглашения Anaconda — еще один обходной путь, предлагаемый пользователями (1,2), чтобы без проблем запускать Stable Diffusion. Для тех из вас, кто не знает, Анаконда — это бесплатный инструмент управления средой, который может устанавливать и запускать пакеты приложений Python. Чтобы без проблем использовать Stable Diffusion, установите Anaconda (видео-инструкции), получите NVIDIA CUDA Toolkit, а затем следуйте указаниям из репозитория Python GitHub по вашему выбору.
3. Используйте оптимизированную версию Stable Diffusion.
Если проблема «Cuda Out of Memory» не устранена, вы можете попробовать использовать оптимизированную версию Stable Diffusion, которая доступна здесь. Чтобы решить эту проблему, загрузите оптимизированную версию Stable Diffusion и вставьте ее содержимое в папку stable-diffusion-main, если на вашем компьютере уже установлена исходная версия Stable Diffusion.
Подробные инструкции о том, как это сделать, см. Reddit пост.
4. Попробуйте создать изображения с более низким разрешением
Вы можете столкнуться с проблемой «Cuda Out of Memory», если попытаетесь создать фотографии с более высоким разрешением. Это связано с тем, что фотографии более высокого качества требуют гораздо большей памяти графического процессора. Понижение разрешения изображения, которое можно сделать, изменив значения высоты и ширины внутри Stable Diffusion, позволило пользователям (1,2), чтобы решить проблему. Если объем оперативной памяти вашего графического процессора меньше 4 ГБ, вы можете попробовать выбрать 512 x 512 или 256 x 256 в качестве требуемых разрешений или выбрать что-то меньшее.
5. Уменьшите размер выборки до одного
Stable Diffusion по умолчанию создает множество изображений одновременно, как и любой другой генератор изображений. Но если вы используете много фотографий, вашему графическому процессору может не хватить памяти, и вы получите ошибку «Cuda Out of Memory». Используйте «-n образцы 1» в приглашении ввода, чтобы исправить это, уменьшив размер выборки до 1. Этот пост Reddit показывает, что многие пользователи, похоже, добились успеха с это решение.
6. Проверьте память графического процессора
Рекомендуется использовать графический процессор с объемом памяти не менее 6 ГБ для запуска Stable Diffusion без каких-либо проблем, хотя вы можете обойтись и графическим процессором с 4 ГБ ОЗУ (см. 1,2,3). Все, что меньше, не позволит программному обеспечению Stable Diffusion использовать память вашего графического процессора, заставляя вас запускать его непосредственно на вашем процессоре, что может увеличить время создания каждого изображения как минимум до двух минут.
Лучший вариант — обновить видеокарту до версии с не менее 6 ГБ ОЗУ, если вы хотите предотвратить появление сообщения «Cuda Out of Memory».
7. Отредактируйте файл webui-user.bat с оптимизированными командами.
Stable Diffusion выполняет команды для создания образов на вашем компьютере через файл webui-user.bat. Чтобы проверить, устраняет ли обновление этого файла оптимизированными командами системную ошибку «Cuda Out of Memory», попробуйте сделать это. Найдите файл webui-user.bat в папке Stable Diffusion, щелкните его правой кнопкой мыши и выберите «Правка» > «Блокнот», чтобы начать. Затем вы можете протестировать каждую оптимизацию командной строки на этом Страница GitHub чтобы увидеть, какой из них лучше всего подходит для вас. Подробные инструкции см. в этих сообщениях Reddit (1,2,3).
Вам не нужно ничего делать, чтобы решить проблему «Cuda Out of Memory» в Stable Diffusion.
В этом руководстве мы покажем вам, как исправить ошибку Stable Diffusion Cuda Out of Memory. Стабильная диффузия, одна из самых популярных моделей глубокого обучения и преобразования текста в изображение, способна генерировать впечатляющие детализированные изображения на основе текстовых описаний. Однако, несмотря на все преимущества, которые он может предложить, он также не свободен от своей доли проблем.
В связи с этим недавно мы рассмотрели его сбой с помощью Automatic1111, и теперь в этот список ошибок добавлена еще одна нежелательная запись. Многие пользователи выразили обеспокоенность по поводу того, что в Stable Diffusion Cuda возникает ошибка «Недостаточно памяти». В результате они не могут в полной мере использовать этот инструмент. Если вы также в настоящее время сталкиваетесь с этой проблемой, то это руководство познакомит вас с многочисленными обходными путями для устранения этой проблемы. Следуйте вместе.
Рекомендуется попробовать каждый из нижеперечисленных обходных путей, а затем посмотреть, какой из них приводит к успеху. Итак, имея это в виду, давайте начнем.
ИСПРАВЛЕНИЕ 1. Перезагрузите компьютер.
Как бы очевидно это ни звучало, но простой перезапуск OPC помог решить эту проблему для нескольких пользователей. Поэтому, прежде чем переходить к расширенным исправлениям, попробуйте это базовое и проверьте результат.
ИСПРАВЛЕНИЕ 2. Установите Anaconda с помощью Nvidia CUDA Toolkit
Для некоторых пользователей установка Анаконда также известная как система управления средой с открытым исходным кодом, которая позволяет вам устанавливать и запускать пакеты для Python вместе с Инструментарий Nvidia CUDA сделал работу. Поэтому установите оба этих программного обеспечения на свой компьютер, а затем проверьте, исправляет ли оно ошибку Stable Diffusion Cuda Out of Memory.
ИСПРАВЛЕНИЕ 3. Попробуйте оптимизированный вариант стабильной диффузии.
Существует также гораздо более усовершенствованная, эффективная и оптимизированная версия Stable Diffusion с открытым исходным кодом, которая в настоящее время свободна от этой ошибки Cuda Out of Memory. Таким образом, вы можете попробовать его Страница GitHub и проверьте, работает ли это для вас.
ИСПРАВЛЕНИЕ 4. Создание изображений с более низким разрешением
Вы также можете рассмотреть возможность создания изображений с более низким разрешением, поскольку они менее требовательны к ресурсам вашего графического процессора. Для этого просто измените значения высоты и ширины выходных данных, которые будут сгенерированы внутри Stable Diffusion.
ИСПРАВЛЕНИЕ 5. Уменьшите размер выборки до одного
Чем больше, тем лучше может быть неверно для таких ресурсоемких задач, поэтому вам следует подумать уменьшение размера выходной выборки до 1 чтобы снизить общую нагрузку на GPU. Для этого вам нужно будет ввести «–n_samples 1».
ИСПРАВЛЕНИЕ 6: отредактируйте webui-user.bat
Для тех, кто не знает, Stable Diffusion использует пакетный файл пользователя WebUI для выполнения необходимых команд для создания изображений на вашем ПК. Таким образом, вы можете отредактировать этот файл с помощью блокнота и добавить несколько настроек оптимизации, как указано в эта страница GitHub или проверьте эти сообщения Reddit (1,2,3).
Это были различные методы исправления ошибки Stable Diffusion Cuda Out of Memory. Если у вас есть какие-либо вопросы относительно вышеупомянутых шагов, сообщите нам об этом в комментариях. Мы вернемся к вам с решением в ближайшее время.