Comments (52)
https://github.com/ShivamShrirao/diffusers/tree/main/examples/dreambooth
New version now trains in 10 GB.
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@Jarfeh This repo seems abandoned. Use ShivamShrirao's diffusers fork instead. It includes all the optimizations discussed here and some new ones
from dreambooth-stable-diffusion.
@Blucknote hopefully pretty soon. I have gotten the GPU usage to 11.187 GB, but there are a few bugs due to which the model output quality isn't good right now even for higher precision. Will update once quality gets better.
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Wow, Using the 8bit adam optimizer from bitsandbytes along with xformers reduces the memory usage to 12.5 GB.
Colab: https://colab.research.google.com/github/ShivamShrirao/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb
Code: https://github.com/ShivamShrirao/diffusers/blob/main/examples/dreambooth/
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@roar-emaus These are the diffuser version of weights. I have added an inference example in colab on how to use them in diffusers. For others you will need to convert them.
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@pdjohntony try to update transformers library pip install -U transformers
from dreambooth-stable-diffusion.
@ShivamShrirao I'm assuming you mean only the items in the imv folder make up the ckpt file, I deleted my colab and only saved those items to the google drive
from dreambooth-stable-diffusion.
@JoeMcGuire you will need to compile the xformers, current wheels only support T4 GPU.
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Very cool. Doing what I can for 16gb too.
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I'm running into issues with it finding the gpus I think. 4xA10G. I'll post code tomorrow.
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There is no such file.
404 Client Error: Entry Not Found for url: https://huggingface.co/CompVis/stable-diffusion-v1-4/resolve/main/config.json
Edit: Issue resolved.
from dreambooth-stable-diffusion.
Do you have a donation link? I don't have much, but you are doing great work.
from dreambooth-stable-diffusion.
Do you have a donation link? I don't have much, but you are doing great work.
Hey, Thanks. No donation link haha. Good to hear you liked it. It has been quite fun to do for me.
from dreambooth-stable-diffusion.
@ShivamShrirao I've been trying to run your notebook on Runpod with Pytorch and an A5000 but I'm getting an error during pip install "Building wheel for xformers (setup.py) ... error".
Training starts with a bitsandbytes bug report but runs and eventually after 20 min of training it crashes.
I'd also love to donate if I can get this working.
from dreambooth-stable-diffusion.
There is no such file. 404 Client Error: Entry Not Found for url: https://huggingface.co/CompVis/stable-diffusion-v1-4/resolve/main/config.json
Edit: Issue resolved.
@Daniel-Kelvich How did you fix this?
from dreambooth-stable-diffusion.
@pdjohntony What error are you facing ? If 404, it may be due to not being authenticated with huggingface cli.
from dreambooth-stable-diffusion.
@ShivamShrirao I managed to get your dreambooth example working but its been running for 2 hours now on an A5000.
Since thats taking so long, I spun up another instance on vast with 2 A5000's but now I'm getting the 404. It shouldn't be an auth issue with huggingface as a logged in on the CLI and it appeared to download the model for a while before getting this 404 error.
The following values were not passed to `accelerate launch` and had defaults used instead:
`--num_cpu_threads_per_process` was set to `24` to improve out-of-box performance
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
Traceback (most recent call last):
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/transformers/configuration_utils.py", line 596, in _get_config_dict
resolved_config_file = cached_path(
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/transformers/utils/hub.py", line 282, in cached_path
output_path = get_from_cache(
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/transformers/utils/hub.py", line 486, in get_from_cache
_raise_for_status(r)
File "/opt/conda/envs/ldm/lib/python3.8/site-packages/transformers/utils/hub.py", line 409, in _raise_for_status
raise EntryNotFoundError(f"404 Client Error: Entry Not Found for url: {request.url}")
transformers.utils.hub.EntryNotFoundError: 404 Client Error: Entry Not Found for url: https://huggingface.co/CompVis/stable-diffusion-v1-4/resolve/main/config.json
from dreambooth-stable-diffusion.
Great work! I managed to run it in a google colab. I was just wondering, how do I get checkpoint files that I can use later on from the model files that are stored?
I could only find the
feature_extractor logs model_index.json safety_checker scheduler text_encoder tokenizer unet vae
folders/files that were stored in the --output_dir=$OUTPUT_DIR
after it was done training.
from dreambooth-stable-diffusion.
@roar-emaus These are the diffuser version of weights. I have added an inference example in colab on how to use them in diffusers. For others you will need to convert them.
Thank you! will test it tomorrow :)
from dreambooth-stable-diffusion.
finally got it to work, how can we use the model to reuse in a stable colab @ShivamShrirao ? I have used the inference but how do i save my model, i havent even been able to find what folder its in lol, any info on how to convert it into a ckpt?? great work !!
from dreambooth-stable-diffusion.
finally got it to work, how can we use the model to reuse in a stable colab @ShivamShrirao ? I have used the inference but how do i save my model, i havent even been able to find what folder its in lol, any info on how to convert it into a ckpt?? great work !!
I haven't figured out yet how to convert to single ckpt to use in other repos. Currently the whole folder is your model, you can save the whole folder until someone figures it out. This needs to be reversed https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
from dreambooth-stable-diffusion.
@ShivamShrirao If I'm reading things right, 8bit AdamW should be a drop in replacement and the modified CrossAttention class seems like it should just be able to replace the one in ldm/modules/attention.py in this repository. Sadly can't test it myself because bitsandbytes has a C extension that uses CUDA and I'm on AMD
from dreambooth-stable-diffusion.
successfully trained one model, but my second time training im getting an error @ShivamShrirao
Steps: 2% 18/1000 [00:56<45:45, 2.80s/it, loss=0.536, lr=5e-6]Traceback (most recent call last):
File "train_dreambooth.py", line 606, in
main()
File "train_dreambooth.py", line 527, in main
for step, batch in enumerate(train_dataloader):
File "/usr/local/lib/python3.7/dist-packages/accelerate/data_loader.py", line 357, in iter
next_batch = next(dataloader_iter)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 681, in next
data = self._next_data()
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 721, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "train_dreambooth.py", line 268, in getitem
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
File "/usr/local/lib/python3.7/dist-packages/PIL/Image.py", line 2843, in open
fp = builtins.open(filename, "rb")
IsADirectoryError: [Errno 21] Is a directory: '/content/data/sks/.ipynb_checkpoints'
Steps: 2% 18/1000 [00:56<51:30, 3.15s/it, loss=0.536, lr=5e-6]
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in
sys.exit(main())
File "/usr/local/lib/python3.7/dist-packages/accelerate/commands/accelerate_cli.py", line 43, in main
args.func(args)
File "/usr/local/lib/python3.7/dist-packages/accelerate/commands/launch.py", line 837, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.7/dist-packages/accelerate/commands/launch.py", line 354, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3', 'train_dreambooth.py', '--pretrained_model_name_or_path=CompVis/stable-diffusion-v1-4', '--use_auth_token', '--instance_data_dir=/content/data/sks', '--class_data_dir=/content/data/gfx', '--output_dir=/content/models/sks', '--with_prior_preservation', '--instance_prompt=photo of sks gfx', '--class_prompt=photo of a gfx', '--resolution=512', '--use_8bit_adam', '--train_batch_size=1', '--gradient_accumulation_steps=1', '--learning_rate=5e-6', '--lr_scheduler=constant', '--lr_warmup_steps=0', '--num_class_images=200', '--max_train_steps=1000']' returned non-zero exit status 1.
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Very nice progress! Digging in more now
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in the collab
--instance_prompt="photo of imv{CLASS_NAME}" \
--class_prompt="photo of a {CLASS_NAME}" \
are no f strings, they should be right ?
cheers
from dreambooth-stable-diffusion.
@binarymind Not required here cause it executes as a shell command.
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ok thanks !
during this cell I got the following result
The following values were not passed to `accelerate launch` and had defaults used instead:
`--num_processes` was set to a value of `1`
`--num_machines` was set to a value of `1`
`--mixed_precision` was set to a value of `'no'`
`--num_cpu_threads_per_process` was set to `32` to improve out-of-box performance
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
/opt/conda/lib/python3.7/site-packages/accelerate/accelerator.py:179: UserWarning: `log_with=tensorboard` was passed but no supported trackers are currently installed.
warnings.warn(f"`log_with={log_with}` was passed but no supported trackers are currently installed.")
Fetching 16 files: 100%|█████████████████████| 16/16 [00:00<00:00, 13678.94it/s]
Generating class images: 0%| | 0/25 [00:00<?, ?it/s]FATAL: this function is for sm80, but was built for sm750
FATAL: this function is for sm80, but was built for sm750
my nvidia-smi is the following
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.94 Driver Version: 470.94 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| 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 RTX A6000 On | 00000000:0F:00.0 Off | Off |
| 30% 27C P8 26W / 300W | 1MiB / 48685MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
I tried also to do the
%pip install git+https://github.com/facebookresearch/xformers@1d31a3a#egg=xformers
some cells above as it was not working.
currently stucked there
from dreambooth-stable-diffusion.
Lol I fixed my problem by removing the f strings I added.... sorry
edit: ah nope was not that, launched again the notebook on a new repo and the problem appear again, looking at it
from dreambooth-stable-diffusion.
I'm hoping for a (fingers crossed not too distant) future version of this that can run on requirements of a 3080. Will put it into reach of many more people including myself. Keep up the great work!!
from dreambooth-stable-diffusion.
I'm not having any success. Trying to use V100 on colab.
Generating class images: 0% 0/50 [00:06<?, ?it/s]
Traceback (most recent call last):
File "train_dreambooth.py", line 606, in <module>
main()
File "train_dreambooth.py", line 362, in main
images = pipeline(example["prompt"]).images
File "/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py", line 259, in __call__
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/diffusers/models/unet_2d_condition.py", line 254, in forward
encoder_hidden_states=encoder_hidden_states,
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/diffusers/models/unet_blocks.py", line 565, in forward
hidden_states = attn(hidden_states, context=encoder_hidden_states)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py", line 155, in forward
hidden_states = block(hidden_states, context=context)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py", line 204, in forward
hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/diffusers/models/attention.py", line 288, in forward
hidden_states = xformers.ops.memory_efficient_attention(query, key, value)
File "/usr/local/lib/python3.7/dist-packages/xformers/ops.py", line 575, in memory_efficient_attention
query=query, key=key, value=value, attn_bias=attn_bias, p=p
File "/usr/local/lib/python3.7/dist-packages/xformers/ops.py", line 196, in forward_no_grad
causal=isinstance(attn_bias, LowerTriangularMask),
File "/usr/local/lib/python3.7/dist-packages/torch/_ops.py", line 143, in __call__
return self._op(*args, **kwargs or {})
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.7/dist-packages/accelerate/commands/accelerate_cli.py", line 43, in main
args.func(args)
File "/usr/local/lib/python3.7/dist-packages/accelerate/commands/launch.py", line 837, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.7/dist-packages/accelerate/commands/launch.py", line 354, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3', 'train_dreambooth.py', '--pretrained_model_name_or_path=CompVis/stable-diffusion-v1-4', '--use_auth_token', '--instance_data_dir=/content/data/sks', '--class_data_dir=/content/data/dog', '--output_dir=/content/models/sks', '--with_prior_preservation', '--instance_prompt=photo of sks dog', '--class_prompt=photo of a dog', '--resolution=512', '--use_8bit_adam', '--train_batch_size=1', '--gradient_accumulation_steps=1', '--learning_rate=5e-6', '--lr_scheduler=constant', '--lr_warmup_steps=0', '--num_class_images=200', '--max_train_steps=600']' returned non-zero exit status 1
from dreambooth-stable-diffusion.
there are xformers for p100 on this colab precompiled, how to incorporate those into dreambooth ? It will cover colab pro
https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb#scrollTo=a---cT2rwUQj
under installing xformers
Also how about optional googledrive cell to upload trained model + prune cell to get it to 2gb?
If some of You will compile whl for p100 please download and store it in gdrive to share
from dreambooth-stable-diffusion.
yeah , now its kinda not useable on webuis and most people are on webuis, huggingface love their bins also default 600 steps are pretty bad, not sure why its default ? should be more like at least 2000
from dreambooth-stable-diffusion.
Any chances to run on 12GB rtx 3060?
I'm getting Tried to allocate 4.00 GiB (GPU 0; 12.00 GiB total capacity; 4.81 GiB already allocated; 890.00 MiB free; 8.81 GiB reserved in total by PyTorch)
error even with --use_8bit_adam
flag
from dreambooth-stable-diffusion.
Can we get a link to the json or description on that?
from dreambooth-stable-diffusion.
The following values were not passed to accelerate launch
and had defaults used instead:
--num_cpu_threads_per_process
was set to 4
to improve out-of-box performance
To avoid this warning pass in values for each of the problematic parameters or run accelerate config
.
Traceback (most recent call last):
File "train_dreambooth.py", line 608, in
main()
File "train_dreambooth.py", line 394, in main
tokenizer = CLIPTokenizer.from_pretrained(
File "c:\users\urban\anaconda3\envs\ldm\lib\site-packages\transformers\tokenization_utils_base.py", line 1764, in from_pretrained
raise EnvironmentError(
OSError: Can't load tokenizer for '/CompVis/stable-diffusion-v1-4'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/CompVis/stable-diffusion-v1-4' is the correct path to a directory containing all relevant files for a CLIPTokenizer tokenizer.
Traceback (most recent call last):
File "c:\users\urban\anaconda3\envs\ldm\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\urban\anaconda3\envs\ldm\lib\runpy.py", line 87, in run_code
exec(code, run_globals)
File "C:\Users\Urban\anaconda3\envs\ldm\Scripts\accelerate.exe_main.py", line 7, in
File "c:\users\urban\anaconda3\envs\ldm\lib\site-packages\accelerate\commands\accelerate_cli.py", line 43, in main
args.func(args)
File "c:\users\urban\anaconda3\envs\ldm\lib\site-packages\accelerate\commands\launch.py", line 837, in launch_command
simple_launcher(args)
File "c:\users\urban\anaconda3\envs\ldm\lib\site-packages\accelerate\commands\launch.py", line 354, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
SOLVE = pip install --upgrade transformers
from dreambooth-stable-diffusion.
I've tried both local directories matching and making sure there are zero that match. So close. Would appreciate any help anybody has to offer.
from dreambooth-stable-diffusion.
Hello, i have trained on an RTX 2060 with a stable consumption of 10.8GB of VRAM and at an amazing speed, between 5 and 10 minutes!
These are the details of my configuration:
- torch and torchvision compiled with support for cuda 11.6
- accelerate configured to use --mixed_precision with bf16
- reduced size of training images with --resolution=256
- with 3-5 images for instance, and 12-20 images for class, 1000 training steps.
I obtain very good results.
from dreambooth-stable-diffusion.
@konimaki2022 can you share your notebook?
from dreambooth-stable-diffusion.
@guumaster sorry I haven't created a notebook in Google Colab yet, I run it on my local computer with Ubuntu 20.04, no cloud.
from dreambooth-stable-diffusion.
@guumaster sorry I haven't created a notebook in Google Colab yet, I run it on my local computer with Ubuntu 20.04, no cloud.
I think Ubuntu is the key. Because we have to redirect Cuda drivers to invoke adam right in windows it's cause two straight days of work. Close hopefully
from dreambooth-stable-diffusion.
I've learned a lot and I think a more stable and universal windows local solution is close.
from dreambooth-stable-diffusion.
https://github.com/ShivamShrirao/diffusers/tree/main/examples/dreambooth
New version now trains in 10 GB.
Awesome!! I assume this wont work with a 10GB GPU still, due to other apps using it. If anyone knows of a way to get it working with that, such as utilising shared memory (not worrying about a decrease in performance), that would be fantastic!! If not, I look forward to future progressions!
from dreambooth-stable-diffusion.
@TheChapster It might work on linux where you can have no other application running on the GPU, or might need just a few modifications. I don't have a 10GB GPU to test it so can't confirm.
from dreambooth-stable-diffusion.
https://github.com/ShivamShrirao/diffusers/tree/main/examples/dreambooth
New version now trains in 10 GB.
Can we get a row or two in the table with all optimizations on except for use_8bit_adam
? The bitsandbytes library relies on a C extension to wrap some CUDA functions, so it can't be used on AMD
from dreambooth-stable-diffusion.
@hopibel Check the last row.
from dreambooth-stable-diffusion.
Ah, missed it somehow. Dang, looks too close to 16GB to fit
from dreambooth-stable-diffusion.
With xformers
and triton
in this my fork at FP16 it trains with slightly less than 14 GB... I haven't pushed the branch but it seems fine.
This is using stable-diffusion
and EMA weights, not diffusers
at all.
from dreambooth-stable-diffusion.
Now you can convert diffusers weights to ckpt, thanks to https://gist.github.com/jachiam/8a5c0b607e38fcc585168b90c686eb05
I have updated it in my colab.
from dreambooth-stable-diffusion.
With
xformers
andtriton
in this my fork at FP16 it trains with slightly less than 14 GB... I haven't pushed the branch but it seems fine.This is using
stable-diffusion
and EMA weights, notdiffusers
at all.
can you push it? thanks
from dreambooth-stable-diffusion.
With
xformers
andtriton
in this my fork at FP16 it trains with slightly less than 14 GB... I haven't pushed the branch but it seems fine.This is using
stable-diffusion
and EMA weights, notdiffusers
at all.
Like andrae293, I too would like to see you push this to be available :)
from dreambooth-stable-diffusion.
@Jarfeh I agree with @feffy380
https://github.com/ShivamShrirao/diffusers/tree/main/examples/dreambooth
from dreambooth-stable-diffusion.
I tried to run the Google Colab, I have RTX 3060 12Gb but doesnt work
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.25 GiB (GPU 0; 11.75 GiB total capacity; 8.06 GiB already allocated; 1.95 GiB free; 8.12 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
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 7772) of binary: /home/merexai-dev/miniconda3/envs/tf/bin/python
Traceback (most recent call last):
from dreambooth-stable-diffusion.
Related Issues (20)
- Interface changed for add_argparse_args() of lightning.Trainer HOT 1
- RuntimeError HOT 4
- AttributeError: module 'torch.linalg' has no attribute 'solve'
- Is there any method for loop t-step denoising to restore images and parallel speed up in stable diffusion?
- .
- This repo has many problem on windows
- cuda out of memory on RTX 24gb 3090 HOT 4
- ERROR: Failed building wheel for dlib
- Nothing Habben when Traning
- How to use DreamBooth for unconditional image synthesis.
- Questions about parameters
- ERROR: huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':
- Implementation of metrics in the Dreambooth paper HOT 1
- RuntimeError: Error(s) in loading state_dict for LatentDiffusion: size mismatch
- Unable to train Dreambooth on Mac M1
- Dreambooth training with image captions HOT 1
- Size of the trained checkpoint (ckpt) file HOT 1
- Support for inpainting training for dreambooth?
- TypeError: __init__() missing 1 required positional argument: 'personalization_config' HOT 2
- 支持多GPU训练吗 HOT 3
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Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
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Microsoft
Open source projects and samples from Microsoft.
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Google
Google ❤️ Open Source for everyone.
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Alibaba
Alibaba Open Source for everyone
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D3
Data-Driven Documents codes.
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Tencent
China tencent open source team.
from dreambooth-stable-diffusion.