ComfyUI-DareMerge

Merge two checkpoint models by dare ties (https://github.com/yule-BUAA/MergeLM). Now with CLIP support.

Node List

U-Net

category	node name	input type	output type	desc.
unet	Model Merger (Masked)	MODEL, MODEL, MODEL_MASK	MODEL	Performs a masked block merge
unet	Model Merger (DARE)	MODEL, MODEL, MODEL_MASK (optional)	MODEL	Performs a DARE block merge
unet	MBW Merger (DARE)	MODEL, MODEL, MODEL_MASK (optional)	MODEL	Performs a DARE block merge, with full layer control (like MBW)

CLIP

category	node name	input type	output type	desc.
clip	CLIP Merger (DARE)	CLIP, CLIP	CLIP	Performs a DARE merge on two CLIP

Masking

category	node name	input type	output type	desc.
mask	Simple Masker	MODEL	MODEL_MASK	Creates a new mask for a model
mask	Magnitude Masker	MODEL, MODEL	MODEL_MASK	Creates a mask based on the deltas of the parameters
mask	Quad Masker	MODEL	MODEL_MASK, MODEL_MASK, MODEL_MASK, MODEL_MASK	Creates four random non-overlapping masks
mask	Mask Operations	MODEL_MASK, MODEL_MASK	MODEL_MASK	Allows set operations to be performed on masks
mask	Mask Edit	MODEL_MASK	MODEL_MASK	Allows the direct editing of mask layers

LoRA

category	node name	input type	output type	desc.
lora	LoRA Loader (Tags)	MODEL, CLIP	MODEL, CLIP, STRING	Loads a LoRA model, returning the tags from the metadata

Utilities

category	node name	input type	output type	desc.
util	Normalize Model	MODEL, MODEL	MODEL	Normalizes one models parameter norm to another model

Reporting

category	node name	input type	output type	desc.
report	Mask Reporting	MODEL_MASK	STRING	Returns basic layer statistics for the mask
report	Model Reporting	MODEL	STRING, IMAGE	Returns a plot of a model layer
report	LoRA Reporting		STRING, IMAGE	Returns stats and information about a LoRA

Merging

• In general, one means keep first model, zero means keep second model
• For DARE, we use the base model to determine which values to protect or include.
• For TIES, we can use the sum of the delta ties (as in the paper), or the count, or off to disable.
• Can accept a model mask, which will restrict changes to only modify the masked areas.

How to use

DARE-TIES does a stochastic selection of the parameters to keep, and then only performs updates in either the 'up' or 'down' direction. According to the paper, the workflow should be as such:

• Take our model A, and build a magnitude mask based on a base model.
• Take model B, and merge it in to model A using the mask to protect model A's largest parameters.

Of note, this merge method does use random sampling, so you should not just assume that your first random seed is the best one for your merge, and if it is not set to fixed that the merge will change every run.

Masks

• A mask creates a signature of a model to filter parts of a model merge. Areas that are selected in the mask will be included in the merge, while areas that are not selected will be excluded.
• Using the mask you can select the parameters to target either stronger, by using the 'above' or weaker, by using the 'below' option. The threshold will determine quantile of the parameters to target.
• Since these tensors are large they have to be chunked, which is where the thredhold type comes in as it is used to determine the set level of the threshold. The options 'median' and 'quantile' refer to how to handle the chunks. 'median' will err more towards the middle, while 'quantile' will err more towards the edges.
• Training (and overtraining) will generate high magnitide parameters. Different models have high strength parameters in different parts of their model, so filtering out high magnitude parameters (by selecting 'below', with model A as your filter target and SD1.5 as the base) will allow a merge to not disturb the high strength parameters of model A. There is an example of this in the examples folder.

Set Operations

• The set operations are performed on the masks, and are used to combine masks together. The operations are:
  • Union: Selects the union of the two masks
  • Intersection: Selects the intersection of the two masks
  • Difference: Subtracts the second mask from the first
  • Xor: Selects the symmetric difference of the two masks - the union minus the intersection

What does this mean?

I don't know if it means you could potentially bin the parameters of a model up with some fancy set thresholding, and then merge a different model in to each slice... It might mean that though; and probably does.

Mask Editing

Selecting which parameters by magnitude masking may be one approach, but a potentially more powerful approach would be pure random selection; until we can find a pattern in our latent space and account for it, we can assume that the distribution of the parameters for a given state is random. The mask editor can target individual layers of the mask, and generate a boolean or random (bernoulli or gaussian) mask for that layer. * is a wildcard and will match all layers. If you need to find the layer names, you can see them in the mask reporting node 'details' report.

Normalization

I am testing out a new normalization method, which is to normalize the norm of the parameters of one model to another. This is done by taking the ratio of the norms, and then scaling the parameters of the first model by that ratio. This is done in the Normalize Model node. There are a few options, of most interest is the 'attn_only' option, which only scales Q and K relative to each other, and just that. You should see no difference in the model's performance, but it might make the merge more stable.