SD Training Methods

stable diffusion training methods

fine-tuning

• retrains parts of the hypernetwork with new data thus modifying original weights
  requires large and precisely labelled dataset
• size is same as original model size, ~2-7gb
• verdict: prohibitive due to large dataset and effort required

model merge

• combines weights from multiple models according to specified rules
• verdict: highly desired to create pre-set models for specific use-case

textual inversion

• assign vector to a new concept with originally one vector per embedding, hacks to enable multi-vector embeddings
  works by expanding vocabulary of a model, but majority of learned content is actually assembled from existing concepts
  can be considered as a formula on which already learned weights should be combined to achieve learned concept
• size 768/1024b per vector
• verdict: best currently viable short-term training solution

aesthetic gradient

• uses low-precision trained embeddings to steer clip using classifier guidance
  training is very cheap, but classifier guidance sloes down image generation
  result is basic transfer of style from learned image to generated image
• size is same as embedding
• origin: independent work
• verdict: inconsistent results with minimal value

custom diffusion

• fine-tuning specific model matrices with textual inversion
  similar speed and memory requirements to embedding training and supposedly gives better results in less steps
• size ~50mb
• origin: cmu
• verdict: possibly promising, requires further investigation, surprisingly low chatter on this topic

hypernetwork

• similar to model fine-tuning, but adds small a small neural network that on-the-fly modifies weights of the last two layers of the main model
  works like adaptive head that steers model in a learned direction so primary use-case is style transfer, not concept transfer
• size is limited to learned layers, ~100-200mb
• origin: leaked from novel.ai
• verdict: lower priority as concept transfer is more important than style transfer

null-text inversion

• similar concept to textual inversion, but trains unconditional embedding that is used for classifier free guidance instead of text embedding
  resulting embedding is apparently more detailed than standard textual embedding
• size is larger but comparable to textual inversion
• origin: google
• verdict: possibly promising, requires further investigation, but no working prototype as of yet

clip inversion

• similar concept to textual inversion, but uses clip embedding instead of text embedding
• size is same as textual inversion
• origin: google
• verdict: prohibitive due to requirement of specially fine-tuned model as a starting point

dream artist

• variation on ti training where both positive and negative embeddings are created
• size is same as textual inversion
• origin: independent work
• verdict: skip for now as solution does not appear to be sufficiently maintained

dreambooth

• similar to model fine-tuning except it adds information on top of model instead of forgetting/overwriting existing concepts
• size is equal to original model size, ~2-7gb
• origin: google, but heavily modified by independent work
• verdict: prohibitive due to resulting size and requirement to load full model on-demand

lora

• "low-rank adaptation of large language models"
  injects trainable layers to steer cross attention layers
  very flexible, but memory intensive so limited training opportunities on normal gpu
  multiple incompatible implementations: should choose which implementation to use
• size varies from ~5mb to full-model size, average ~150-300mb
• origin: microsoft
• verdict: very promising, but memory prohibitive until further optimizations