Hugging Face's logo

Spaces:
flatcherlee
/
ioatol2
Runtime error

App Files Community
ioatol2
File size: 12,992 Bytes 
ac6acf2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
 
import comfy.sd
import comfy.utils
import comfy.model_base
import comfy.model_management
import comfy.model_sampling

import torch
import folder_paths
import json
import os

from comfy.cli_args 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 CLIPSubtract:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip1": ("CLIP",),
                              "clip2": ("CLIP",),
                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("CLIP",)
    FUNCTION = "merge"

    CATEGORY = "advanced/model_merging"

    def merge(self, clip1, clip2, multiplier):
        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]}, - multiplier, multiplier)
        return (m, )


class CLIPAdd:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip1": ("CLIP",),
                              "clip2": ("CLIP",),
                              }}
    RETURN_TYPES = ("CLIP",)
    FUNCTION = "merge"

    CATEGORY = "advanced/model_merging"

    def merge(self, clip1, clip2):
        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, 1.0)
        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 = folder_paths.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, comfy.model_base.SDXL):
        if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix):
            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit"
        else:
            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
    elif isinstance(model.model, comfy.model_base.SDXLRefiner):
        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
    elif isinstance(model.model, comfy.model_base.SVD_img2vid):
        metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
    elif isinstance(model.model, comfy.model_base.SD3):
        metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants
    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"

    extra_keys = {}
    model_sampling = model.get_model_object("model_sampling")
    if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM):
        if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION):
            extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float()
            extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float()

    if model.model.model_type == comfy.model_base.ModelType.EPS:
        metadata["modelspec.predict_key"] = "epsilon"
    elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
        metadata["modelspec.predict_key"] = "v"

    if not args.disable_metadata:
        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, clip_vision, metadata=metadata, extra_keys=extra_keys)

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):
        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 = folder_paths.get_output_directory()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip": ("CLIP",),
                              "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),},
                "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_metadata:
            metadata["prompt"] = prompt_info
            if extra_pnginfo is not None:
                for x in extra_pnginfo:
                    metadata[x] = json.dumps(extra_pnginfo[x])

        comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True)
        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_ = folder_paths.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 = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)

            comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
        return {}

class VAESave:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "vae": ("VAE",),
                              "filename_prefix": ("STRING", {"default": "vae/ComfyUI_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 = folder_paths.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_metadata:
            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.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,
    "CLIPMergeSubtract": CLIPSubtract,
    "CLIPMergeAdd": CLIPAdd,
    "CLIPSave": CLIPSave,
    "VAESave": VAESave,
}