The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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import comfy.sd
import comfy.utils
import folder_paths
import json
import os
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 = "advanced/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 = "advanced/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."):]
last_arg_size = 0
for arg in kwargs:
if k_unet.startswith(arg) and last_arg_size < len(arg):
ratio = kwargs[arg]
last_arg_size = len(arg)
m.add_patches({k: (sd[k], )}, 1.0 - ratio, ratio)
return (m, )
class CheckpointSave:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "advanced/model_merging"
def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {"prompt": prompt_info}
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata[x] = json.dumps(extra_pnginfo[x])
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
return {}
NODE_CLASS_MAPPINGS = {
"ModelMergeSimple": ModelMergeSimple,
"ModelMergeBlocks": ModelMergeBlocks,
"CheckpointSave": CheckpointSave,
}