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# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import ldm_patched.modules.sd
import ldm_patched.modules.utils
import ldm_patched.modules.model_base
import ldm_patched.modules.model_management
import ldm_patched.utils.path_utils
import json
import os
from ldm_patched.modules.args_parser import args
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()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
return (m, )
class ModelSubtract:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
"multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, model1, model2, multiplier):
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, - multiplier, multiplier)
return (m, )
class ModelAdd:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, model1, model2):
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, 1.0, 1.0)
return (m, )
class CLIPMergeSimple:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip1": ("CLIP",),
"clip2": ("CLIP",),
"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, clip1, clip2, ratio):
m = clip1.clone()
kp = clip2.get_key_patches()
for k in kp:
if k.endswith(".position_ids") or k.endswith(".logit_scale"):
continue
m.add_patches({k: kp[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()
kp = model2.get_key_patches("diffusion_model.")
default_ratio = next(iter(kwargs.values()))
for k in kp:
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: kp[k]}, 1.0 - ratio, ratio)
return (m, )
def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, output_dir)
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {}
enable_modelspec = True
if isinstance(model.model, ldm_patched.modules.model_base.SDXL):
metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner):
metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
else:
enable_modelspec = False
if enable_modelspec:
metadata["modelspec.sai_model_spec"] = "1.0.0"
metadata["modelspec.implementation"] = "sgm"
metadata["modelspec.title"] = "{} {}".format(filename, counter)
#TODO:
# "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
# "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
# "v2-inpainting"
if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS:
metadata["modelspec.predict_key"] = "epsilon"
elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION:
metadata["modelspec.predict_key"] = "v"
if not args.disable_server_info:
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)
ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata)
class CheckpointSave:
def __init__(self):
self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),},
"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):
save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
return {}
class CLIPSave:
def __init__(self):
self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip": ("CLIP",),
"filename_prefix": ("STRING", {"default": "clip/ldm_patched"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "advanced/model_merging"
def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {}
if not args.disable_server_info:
metadata["prompt"] = prompt_info
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata[x] = json.dumps(extra_pnginfo[x])
ldm_patched.modules.model_management.load_models_gpu([clip.load_model()])
clip_sd = clip.get_sd()
for prefix in ["clip_l.", "clip_g.", ""]:
k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys()))
current_clip_sd = {}
for x in k:
current_clip_sd[x] = clip_sd.pop(x)
if len(current_clip_sd) == 0:
continue
p = prefix[:-1]
replace_prefix = {}
filename_prefix_ = filename_prefix
if len(p) > 0:
filename_prefix_ = "{}_{}".format(filename_prefix_, p)
replace_prefix[prefix] = ""
replace_prefix["transformer."] = ""
full_output_folder, filename, counter, subfolder, filename_prefix_ = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix_, self.output_dir)
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
current_clip_sd = ldm_patched.modules.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
ldm_patched.modules.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
return {}
class VAESave:
def __init__(self):
self.output_dir = ldm_patched.utils.path_utils.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "vae/ldm_patched_vae"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "advanced/model_merging"
def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {}
if not args.disable_server_info:
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)
ldm_patched.modules.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)
return {}
NODE_CLASS_MAPPINGS = {
"ModelMergeSimple": ModelMergeSimple,
"ModelMergeBlocks": ModelMergeBlocks,
"ModelMergeSubtract": ModelSubtract,
"ModelMergeAdd": ModelAdd,
"CheckpointSave": CheckpointSave,
"CLIPMergeSimple": CLIPMergeSimple,
"CLIPSave": CLIPSave,
"VAESave": VAESave,
}
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