when comparing checkpoints (steps, methods, etc), what parameters do you guys use on XYZ? only seed
when comparing checkpoints (steps, methods, etc), what parameters do you guys use on XYZ? only seed and checkpoint?
Fi do batches of 4 so each cell gets 4 seeds anyways. checkpoint and cfg are ones i'll try. sometimes prompt s/r. you can try different prompts with it or different lora weights. the :1> can s/r to :0.5>, :1.5>, etc. more a lora thing
Fwhat cfgs you try? and why?
Fyou can try cfg
Fand perhaps pre selected seeds
Fadded some more prompts : https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Midjourney-Level-Realism.md
GitHub
Best Stable Diffusion and AI Tutorials, Guides, News, Tips and Tricks - Stable-Diffusion/Midjourney-Level-Realism.md at main · FurkanGozukara/Stable-Diffusion
F5-9[5] is my usual setting. when i'm generating its around 7 usually but if im testing a model i want to see what a range looks like
Fpre selected seeds = try seeds until you have some that look great, so you can compare on best possible seeds?
Fif you are our patreon supporter i can connect your computer and install
Fmaybe sometime thanks
Fwhat cfgs do you try?
F4,7,11 is fine?
Fi try usually 4,5,6,7,8,9,10,11,12
Fif i do
Fbut at the same time? X=seed, Y=checkpoint, Z=cfg?
Fyes you can use
Fbut which order looks best
Fyou need to test
Fi'm gonna use seed as Z i think, as it's separated in "another dimension"
Flet's see if the freaking plot script is not still broken... Xd 8640 steps
Pit should start without a problem if you already installed the dependencies (by launching with your internet on at least once). what exactly are you doing to disable your wifi
F@Furkan Gözükara SECourses those were great pointers, but i'll come back to this problem another time. thanks for the help!
Fyou are welcome
R@Furkan Gözükara SECourses I am attempting to train a Lora following your video, however I am getting this error when clicking (Train).
Traceback (most recent call last):
File "/workspace/kohya_ss/train_network.py", line 783, in <module>
train(args)
File "/workspace/kohya_ss/train_network.py", line 146, in train
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
File "/workspace/kohya_ss/library/train_util.py", line 3023, in load_target_model
text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model(
File "/workspace/kohya_ss/library/train_util.py", line 2989, in _load_target_modelRHow could I solve this?
RI looked in my files and I am missing the train_network.py, file
RI read all of these solutions and tried, them but none worked, https://www.reddit.com/r/StableDiffusion/comments/114e0nj/kohya_ss_error_how_do_i_solve_this/
reddit
3 votes and 33 comments so far on Reddit
Fi dont see error messsage here. but try adamw instead of adam8bit
RI did, same result
Rsubprocess.CalledProcessError: Command '['C:\\Stable-Diffusion\\kohya_ss\\venv\\Scripts\\python.exe', 'train_network.py', '--enable_bucket', '--pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5', '--train_data_dir=C:/Stable-Diffusion/Emma/Process lora/Emma lora/image/', '--resolution=512,512', '--output_dir=C:/Stable-Diffusion/Emma/Process lora/Emma lora/model', '--logging_dir=C:/Stable-Diffusion/Emma/Process lora/Emma lora/log', '--network_alpha=1', '--save_model_as=safetensors', '--network_module=networks.lora', '--text_encoder_lr=5e-5', '--unet_lr=0.0001', '--network_dim=8', '--network_weights=C:/Users/archi/Downloads/LoraLowVRAMSettings.json', '--output_name=last', '--lr_scheduler_num_cycles=1', '--learning_rate=0.0001', '--lr_scheduler=cosine', '--lr_warmup_steps=150', '--train_batch_size=1', '--max_train_steps=1496', '--save_every_n_epochs=1', '--mixed_precision=fp16', '--save_precision=fp16', '--seed=1234', '--cache_latents', '--bucket_reso_steps=64', '--mem_eff_attn', '--gradient_checkpointing', '--xformers', '--use_8bit_adam', '--bucket_no_upscale']' returned non-zero exit status 1.
Khow would i use the dev version on the colab? I'm using TheLastBen's colab here: https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb
Fwell this may not work as good as on pc
Fthat is why
Fyou can use runpod alternatively
ROk, thank you
Kmy pc doesn't have enough vram, so i paid for google colab compute GPUs. i guess if i pay for runpod then it'll work?
Fyes runpod works
Fi have necessary tutorials
FGitHub
Best Stable Diffusion and AI Tutorials, Guides, News, Tips and Tricks - GitHub - FurkanGozukara/Stable-Diffusion: Best Stable Diffusion and AI Tutorials, Guides, News, Tips and Tricks
Fsearch runpod word
Fi am going to sleep hopefully see you tomorrow
Kthank you! is there a way to use google colab to do it? just because i already have some compute on google colab and don't want to waste them
Tme: i don't really understand truncation error with regards to image diffusion samplers. can you lay it out in eli5 terms for me?
ChatGPT said:
Sure! Let's say you're baking cookies and the recipe calls for 1/3 cup of sugar. But your measuring cups only have 1/4 cup and 1/2 cup.
If you use the 1/4 cup, you won't be adding enough sugar. If you use the 1/2 cup, it will be too much. So, you decide to fill the 1/4 cup and add a little bit more, hoping it's about 1/3 cup.
The difference between the amount of sugar you actually added and the exact 1/3 cup the recipe called for is like the truncation error. It's a small error that comes from having to stop (or "truncate") your measuring because you don't have the exact tool (in this case, the right measuring cup) to get the precise amount.
In the case of the image diffusion, the "sampling steps" are like the measuring cups. If you could do an infinite number of steps, you could get the exact, perfect result. But in reality, you have to stop at some point (just like you can't keep adding sugar forever), and this results in a small error. Increasing the number of steps is like adding a bit more sugar to the 1/4 cup to try and get closer to the exact 1/3 cup you need.
ChatGPT said:
Sure! Let's say you're baking cookies and the recipe calls for 1/3 cup of sugar. But your measuring cups only have 1/4 cup and 1/2 cup.
If you use the 1/4 cup, you won't be adding enough sugar. If you use the 1/2 cup, it will be too much. So, you decide to fill the 1/4 cup and add a little bit more, hoping it's about 1/3 cup.
The difference between the amount of sugar you actually added and the exact 1/3 cup the recipe called for is like the truncation error. It's a small error that comes from having to stop (or "truncate") your measuring because you don't have the exact tool (in this case, the right measuring cup) to get the precise amount.
In the case of the image diffusion, the "sampling steps" are like the measuring cups. If you could do an infinite number of steps, you could get the exact, perfect result. But in reality, you have to stop at some point (just like you can't keep adding sugar forever), and this results in a small error. Increasing the number of steps is like adding a bit more sugar to the 1/4 cup to try and get closer to the exact 1/3 cup you need.
Thow correct did chatGPT get this i wonder?
生hey guys i try this vedio but the cmd show this is means i dont process in GPU? my gpu is RX6600
生YouTubeSECourses
Databricks’ #Dolly v2 is a free, open source, commercially useable ChatGPT-style #AI model. Dolly 2.0 could spark a new wave of fully open source LLMs similar to #ChatGPT . Open source community working hardest to bring up a model that can compete with GPT4. Our discord: https://bit.ly/SECoursesDiscord
If I have been of assistance to you and yo...
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Ti'm a nooby, but from what others have told me, AMD doesn't work well with machine learning stuff like stable diffusion (yet)
生oh that too bad QQ
