Comments (4)
Shiv's repo does this where it makes a set of 50 images of the classification prompt if the class folder is empty. Right now in the webui I have to generate the prompt manually in txt2img and then move them to a new folder that I feed into the extension. Would be good if that process can be automated.
So lets say i want to train an anime girl, do i just search up anime girls and download them and set those as my class images? or do i have to do something else? i think thats why im getting bad results, because i don't have class images.
from sd_dreambooth_extension.
Shiv's repo does this where it makes a set of 50 images of the classification prompt if the class folder is empty. Right now in the webui I have to generate the prompt manually in txt2img and then move them to a new folder that I feed into the extension. Would be good if that process can be automated.
This does this as well, you just specify the number of class images and prompt, and it will generate the images.
from sd_dreambooth_extension.
Shiv's repo does this where it makes a set of 50 images of the classification prompt if the class folder is empty. Right now in the webui I have to generate the prompt manually in txt2img and then move them to a new folder that I feed into the extension. Would be good if that process can be automated.
This does this as well, you just specify the number of class images and prompt, and it will generate the images.
"Total number of classification images to use. Set to 0 to disable." right?
Oops. You can close this then. Thanks for the info
from sd_dreambooth_extension.
when training with classification images, i get CUDA out of memory.
CUDA SETUP: Loading binary C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll...
The config attributes {'set_alpha_to_one': False, 'skip_prk_steps': True, 'steps_offset': 1} were passed to DDPMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.
Scheduler Loaded
Allocated: 0.2GB
Reserved: 0.2GB
Total target lifetime optimization steps = 1000
CPU: False Adam: True, Prec: fp16, Prior: True, Grad: True, TextTr: True
Allocated: 3.8GB
Reserved: 3.9GB
Steps: 0%| | 0/1000 [00:00<?, ?it/s]Error completing request
Arguments: ('Sakie', 'C:\Users\Hector\Pictures\New folder\Sakie\New folder (2)', 'C:\SUPER_SD_2.0\stable-diffusion-webui-master\models\dreambooth\Sakie\New folder', 'Sakie', 'person', '', '', 1.0, 7.5, 40.0, 57, 512, False, True, 1, 1, 1, 1000, 1, True, 5e-06, False, 'constant', 0, True, 0.9, 0.999, 0.01, 1e-08, 1, 100, 500, 'fp16', True, '', False) {}
Traceback (most recent call last):
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\modules\ui.py", line 185, in f
res = list(func(*args, **kwargs))
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\webui.py", line 54, in f
res = func(*args, **kwargs)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\extensions\sd_dreambooth_extension\dreambooth\dreambooth.py", line 256, in start_training
trained_steps = main(config)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\extensions\sd_dreambooth_extension\dreambooth\train_dreambooth.py", line 766, in main
accelerator.backward(loss)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\accelerate\accelerator.py", line 882, in backward
self.scaler.scale(loss).backward(**kwargs)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\torch_tensor.py", line 396, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\torch\autograd_init_.py", line 173, in backward
Variable.execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\torch\autograd\function.py", line 253, in apply
return user_fn(self, *args)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\torch\utils\checkpoint.py", line 146, in backward
torch.autograd.backward(outputs_with_grad, args_with_grad)
File "C:\SUPER_SD_2.0\stable-diffusion-webui-master\venv\lib\site-packages\torch\autograd_init.py", line 173, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 12.00 GiB total capacity; 9.67 GiB already allocated; 0 bytes free; 10.37 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
Steps: 0%| | 0/1000 [00:09<?, ?it/s]
from sd_dreambooth_extension.
Related Issues (20)
- [Bug]: Can't create model using trained & generated model using dreambooth. HOT 3
- [Bug]: Exception training model: 'Cannot copy out of meta tensor; no data!'. HOT 2
- [Bug]: HOT 1
- [Bug]: The deprecation tuple ('LoRAAttnProcessor2_0', '0.26.0', 'Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`') should be removed since diffusers' version 0.26.1 is >= 0.26.0 HOT 4
- [Bug]: Completely unable to train any LORA with CUDA out of memory error HOT 2
- RuntimeError: Expected query, key, and value to have the same dtype, but got query.dtype: c10::Half key.dtype: float and value.dtype: float instead. HOT 9
- [Bug]: HOT 1
- [Bug]: OSError: Can't load tokenizer for 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k'. HOT 2
- [Bug]: TypeError: intercept_args() got an unexpected keyword argument 'multiprocessing_context' HOT 1
- Error al cargar sd_dreambooth_extension en Windows 10: 'LoRAAttnProcessor2_0' no definido HOT 1
- [Bug]: Dreambooth (input tab) not showing correctly HOT 10
- [Bug]: Exception training model: 'type object 'LoraLoaderMixin' has no attribute '_modify_text_encoder''. HOT 4
- [Bug]: AttributeError: 'NoneType' object has no attribute 'unscale_grads' HOT 1
- [Bug]: Unable to further train using previously trained ckpt in dreambooth. HOT 1
- [Bug]: Dreambooth can not start training HOT 3
- AttributeError: module 'jax.random' has no attribute 'KeyArray'[Bug]: HOT 1
- [Bug]: Fast api of concepts don't work HOT 2
- [Bug]: AttributeError: 'NoneType' object has no attribute 'keys' HOT 1
- [Bug]: Unable to do training on sdxl model HOT 4
- [Bug]: Memory Attention default try to use xformers if Class Images Per Instance Image is greather that zero and need to generate images HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from sd_dreambooth_extension.