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178 lines
8.4 KiB
YAML
178 lines
8.4 KiB
YAML
---
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# This is in yaml format. You can use json if you prefer
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# I like both but yaml is easier to write
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# Plus it has comments which is nice for documentation
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# This is the config I use on my sliders, It is solid and tested
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job: train
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config:
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# the name will be used to create a folder in the output folder
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# it will also replace any [name] token in the rest of this config
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name: pet_slider_v1
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# folder will be created with name above in folder below
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# it can be relative to the project root or absolute
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training_folder: "output/LoRA"
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device: cuda:0 # cpu, cuda:0, etc
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# for tensorboard logging, we will make a subfolder for this job
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log_dir: "output/.tensorboard"
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# you can stack processes for other jobs, It is not tested with sliders though
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# just use one for now
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process:
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- type: slider # tells runner to run the slider process
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# network is the LoRA network for a slider, I recommend to leave this be
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network:
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# network type lierla is traditional LoRA that works everywhere, only linear layers
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type: "lierla"
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# rank / dim of the network. Bigger is not always better. Especially for sliders. 8 is good
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rank: 8
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alpha: 1.0 # just leave it
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# training config
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train:
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# this is also used in sampling. Stick with ddpm unless you know what you are doing
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noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
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# how many steps to train. More is not always better. I rarely go over 1000
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steps: 500
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# I have had good results with 4e-4 to 1e-4 at 500 steps
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lr: 1e-4
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# train the unet. I recommend leaving this true
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train_unet: true
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# train the text encoder. I don't recommend this unless you have a special use case
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# for sliders we are adjusting representation of the concept (unet),
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# not the description of it (text encoder)
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train_text_encoder: false
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# just leave unless you know what you are doing
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# also supports "dadaptation" but set lr to 1 if you use that,
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# but it learns too fast and I don't recommend it
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optimizer: "adamw"
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# only constant for now
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lr_scheduler: "constant"
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# we randomly denoise random num of steps form 1 to this number
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# while training. Just leave it
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max_denoising_steps: 40
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# works great at 1. I do 1 even with my 4090.
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batch_size: 1
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# bf16 works best if your GPU supports it (modern)
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dtype: bf16 # fp32, bf16, fp16
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# if you have it, use it. It is faster and better
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xformers: true
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# I don't recommend using unless you are trying to make a darker lora. Then do 0.1 MAX
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# although, the way we train sliders is comparative, so it probably won't work anyway
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noise_offset: 0.0
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# the model to train the LoRA network on
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model:
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# huggingface name, relative prom project path, or absolute path to .safetensors or .ckpt
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name_or_path: "runwayml/stable-diffusion-v1-5"
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is_v2: false # for v2 models
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is_v_pred: false # for v-prediction models (most v2 models)
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# saving config
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save:
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dtype: float16 # precision to save. I recommend float16
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save_every: 50 # save every this many steps
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# sampling config
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sample:
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# must match train.noise_scheduler, this is not used here
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# but may be in future and in other processes
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sampler: "ddpm"
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# sample every this many steps
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sample_every: 20
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# image size
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width: 512
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height: 512
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# prompts to use for sampling. Do as many as you want, but it slows down training
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# pick ones that will best represent the concept you are trying to adjust
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# allows some flags after the prompt
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# --m [number] # network multiplier. LoRA weight. -3 for the negative slide, 3 for the positive
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# slide are good tests. will inherit sample.network_multiplier if not set
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# --n [string] # negative prompt, will inherit sample.neg if not set
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# Only 75 tokens allowed currently
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prompts: # our example is an animal slider, neg: dog, pos: cat
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- "a golden retriever --m -5"
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- "a golden retriever --m -3"
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- "a golden retriever --m 3"
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- "a golden retriever --m 5"
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- "calico cat --m -5"
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- "calico cat --m -3"
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- "calico cat --m 3"
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- "calico cat --m 5"
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- "an elephant --m -5"
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- "an elephant --m -3"
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- "an elephant --m 3"
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- "an elephant --m 5"
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# negative prompt used on all prompts above as default if they don't have one
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neg: "cartoon, fake, drawing, illustration, cgi, animated, anime, monochrome"
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# seed for sampling. 42 is the answer for everything
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seed: 42
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# walks the seed so s1 is 42, s2 is 43, s3 is 44, etc
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# will start over on next sample_every so s1 is always seed
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# works well if you use same prompt but want different results
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walk_seed: false
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# cfg scale (4 to 10 is good)
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guidance_scale: 7
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# sampler steps (20 to 30 is good)
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sample_steps: 20
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# default network multiplier for all prompts
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# since we are training a slider, I recommend overriding this with --m [number]
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# in the prompts above to get both sides of the slider
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network_multiplier: 1.0
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# logging information
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logging:
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log_every: 10 # log every this many steps
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use_wandb: false # not supported yet
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verbose: false # probably done need unless you are debugging
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# slider training config, best for last
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slider:
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# resolutions to train on. [ width, height ]. This is less important for sliders
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# as we are not teaching the model anything it doesn't already know
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# but must be a size it understands [ 512, 512 ] for sd_v1.5 and [ 768, 768 ] for sd_v2.1
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# you can do as many as you want here
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resolutions:
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- [ 512, 512 ]
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# - [ 512, 768 ]
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# - [ 768, 768 ]
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# These are the concepts to train on. You can do as many as you want here,
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# but they can conflict outweigh each other. Other than experimenting, I recommend
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# just doing one for good results
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targets:
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# target_class is the base concept we are adjusting the representation of
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# for example, if we are adjusting the representation of a person, we would use "person"
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# if we are adjusting the representation of a cat, we would use "cat" It is not
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# a keyword necessarily but what the model understands the concept to represent.
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# "person" will affect men, women, children, etc but will not affect cats, dogs, etc
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# it is the models base general understanding of the concept and everything it represents
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- target_class: "animal"
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# positive is the prompt for the positive side of the slider.
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# It is the concept that will be excited and amplified in the model when we slide the slider
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# to the positive side and forgotten / inverted when we slide
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# the slider to the negative side. It is generally best to include the target_class in
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# the prompt. You want it to be the extreme of what you want to train on. For example,
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# if you want to train on fat people, you would use "an extremely fat, morbidly obese person"
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# as the prompt. Not just "fat person"
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positive: "cat"
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# negative is the prompt for the negative side of the slider and works the same as positive
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# it does not necessarily work the same as a negative prompt when generating images
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negative: "dog"
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# the loss for this target is multiplied by this number.
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# if you are doing more than one target it may be good to set less important ones
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# to a lower number like 0.1 so they dont outweigh the primary target
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weight: 1.0
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# You can put any information you want here, and it will be saved in the model.
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# The below is an example, but you can put your grocery list in it if you want.
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# It is saved in the model so be aware of that. The software will include this
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# plus some other information for you automatically
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meta:
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# [name] gets replaced with the name above
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name: "[name]"
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# version: '1.0'
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# creator:
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# name: Your Name
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# email: your@gmail.com
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# website: https://your.website
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