simplify/fix saving
This commit is contained in:
Mackerel
@@ -31,7 +31,6 @@ The classifier models have been taken from the sdweb-auto-MBW repo.
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- the resulting model will contain the text encoder and VAE sent to the node
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### Bugs
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- filename box doesn't use the standard comfy "prefix" method
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- merging process doesn't use the comfy ModelPatcher method and takes hundreds of milliseconds
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- - as a result, --highvram flag recommended. both models will be kept in VRAM and the process is much faster
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- the unet will (probably) be fp16 and the rest fp32. that's how they're sent to the node
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+6
-31
@@ -15,11 +15,12 @@ import nodes
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sys.path.append(str(pathlib.Path(__file__).parent))
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import classifiers
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from nodes_model_merging import CheckpointSave
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BLOCK_ORDER = [12, 11, 13, 10, 14, 9, 15, 8, 16, 7, 17, 6, 18,
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5, 19, 4, 20, 3, 21, 2, 22, 1, 23, 0, 24]
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class AutoMBW:
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class AutoMBW(CheckpointSave):
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def __init__(self):
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self.type = "output"
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@@ -42,7 +43,7 @@ class AutoMBW:
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"search_depth": ("INT", {"default": 4, "min": 2}),
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"sample_count": ("INT", {"default": 1, "min": 1}),
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"classifier": (classifiers.__all__,),
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"filename": ("STRING", { "multiline": False, "default": "ambw" }),
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"filename_prefix": ("STRING", { "multiline": False, "default": "ambw" }),
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}}
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RETURN_TYPES = ()
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@@ -103,8 +104,9 @@ class AutoMBW:
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return maximum
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def ambw(self, model1, model2, clip, vae, prompt, negative, search_depth,
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sample_count, classifier, filename):
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sample_count, classifier, filename_prefix):
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# python setup
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self.output_dir = folder_paths.get_output_directory()
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self.model1 = model1
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self.model2 = model2
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self.vae = vae
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@@ -142,34 +144,7 @@ class AutoMBW:
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self.merge(block, self.ratios[block])
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print(self.ratios)
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sd1 = self.model1.model.state_dict()
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vae = vae.first_stage_model.state_dict()
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for key in vae:
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sd1[f"first_stage_model.{key}"] = vae[key]
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clip = clip.cond_stage_model.state_dict()
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for key in clip:
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sd1[f"cond_stage_model.{key}"] = clip[key]
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create_ckpt = False
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if filename.endswith(".safetensors"):
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filename = filename[0:-12]
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elif filename.endswith(".ckpt"):
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filename = filename[0:-5]
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create_ckpt = True
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filename = pathlib.Path(folder_paths.folder_names_and_paths[
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"checkpoints"][0][0]).joinpath(f"{filename}")
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print(f"saving as {filename}", end="")
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if not create_ckpt:
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try:
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import safetensors.torch
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print(".safetensors")
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safetensors.torch.save_file(sd1, f"{filename}.safetensors")
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except ModuleNotFoundError:
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create_ckpt = True
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if create_ckpt:
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print(".ckpt")
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torch.save(sd1, f"{filename}.ckpt")
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self.save(self.model1, clip, vae, filename_prefix)
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return ()
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@@ -0,0 +1,149 @@
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import comfy.sd
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import comfy.utils
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import comfy.model_base
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import folder_paths
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import json
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import os
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from comfy.cli_args import args
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class ModelMergeSimple:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model1": ("MODEL",),
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"model2": ("MODEL",),
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"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "merge"
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CATEGORY = "advanced/model_merging"
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def merge(self, model1, model2, ratio):
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m = model1.clone()
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kp = model2.get_key_patches("diffusion_model.")
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for k in kp:
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m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
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return (m, )
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class CLIPMergeSimple:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip1": ("CLIP",),
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"clip2": ("CLIP",),
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"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "merge"
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CATEGORY = "advanced/model_merging"
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def merge(self, clip1, clip2, ratio):
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m = clip1.clone()
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kp = clip2.get_key_patches()
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for k in kp:
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if k.endswith(".position_ids") or k.endswith(".logit_scale"):
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continue
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m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
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return (m, )
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class ModelMergeBlocks:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model1": ("MODEL",),
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"model2": ("MODEL",),
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"input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "merge"
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CATEGORY = "advanced/model_merging"
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def merge(self, model1, model2, **kwargs):
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m = model1.clone()
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kp = model2.get_key_patches("diffusion_model.")
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default_ratio = next(iter(kwargs.values()))
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for k in kp:
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ratio = default_ratio
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k_unet = k[len("diffusion_model."):]
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last_arg_size = 0
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for arg in kwargs:
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if k_unet.startswith(arg) and last_arg_size < len(arg):
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ratio = kwargs[arg]
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last_arg_size = len(arg)
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m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
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return (m, )
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class CheckpointSave:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"clip": ("CLIP",),
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"vae": ("VAE",),
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"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
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RETURN_TYPES = ()
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FUNCTION = "save"
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OUTPUT_NODE = True
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CATEGORY = "advanced/model_merging"
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def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
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prompt_info = ""
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if prompt is not None:
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prompt_info = json.dumps(prompt)
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metadata = {}
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enable_modelspec = True
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if isinstance(model.model, comfy.model_base.SDXL):
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metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
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elif isinstance(model.model, comfy.model_base.SDXLRefiner):
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metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
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else:
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enable_modelspec = False
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if enable_modelspec:
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metadata["modelspec.sai_model_spec"] = "1.0.0"
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metadata["modelspec.implementation"] = "sgm"
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metadata["modelspec.title"] = "{} {}".format(filename, counter)
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#TODO:
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# "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
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# "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
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# "v2-inpainting"
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if model.model.model_type == comfy.model_base.ModelType.EPS:
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metadata["modelspec.predict_key"] = "epsilon"
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elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
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metadata["modelspec.predict_key"] = "v"
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if not args.disable_metadata:
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metadata["prompt"] = prompt_info
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata[x] = json.dumps(extra_pnginfo[x])
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output_checkpoint = f"{filename}_{counter:05}_.safetensors"
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output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
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comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
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return {}
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NODE_CLASS_MAPPINGS = {
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"ModelMergeSimple": ModelMergeSimple,
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"ModelMergeBlocks": ModelMergeBlocks,
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"CheckpointSave": CheckpointSave,
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"CLIPMergeSimple": CLIPMergeSimple,
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}
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