The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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class ModelMergeSimple:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "_for_testing/model_merging"
def merge(self, model1, model2, ratio):
m = model1.clone()
sd = model2.model_state_dict("diffusion_model.")
for k in sd:
m.add_patches({k: (sd[k], )}, 1.0 - ratio, ratio)
return (m, )
class ModelMergeBlocks:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
"input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "_for_testing/model_merging"
def merge(self, model1, model2, **kwargs):
m = model1.clone()
sd = model2.model_state_dict("diffusion_model.")
default_ratio = next(iter(kwargs.values()))
for k in sd:
ratio = default_ratio
k_unet = k[len("diffusion_model."):]
for arg in kwargs:
if k_unet.startswith(arg):
ratio = kwargs[arg]
m.add_patches({k: (sd[k], )}, 1.0 - ratio, ratio)
return (m, )
NODE_CLASS_MAPPINGS = {
"ModelMergeSimple": ModelMergeSimple,
"ModelMergeBlocks": ModelMergeBlocks
}