nodes.py 11.8 KB
Newer Older
comfyanonymous's avatar
comfyanonymous committed
1
2
3
4
5
import torch

import os
import sys
import json
6
import hashlib
comfyanonymous's avatar
comfyanonymous committed
7
import copy
comfyanonymous's avatar
comfyanonymous committed
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

from PIL import Image
from PIL.PngImagePlugin import PngInfo
import numpy as np

sys.path.append(os.path.join(sys.path[0], "comfy"))


import comfy.samplers
import comfy.sd

supported_ckpt_extensions = ['.ckpt']
try:
    import safetensors.torch
    supported_ckpt_extensions += ['.safetensors']
except:
    print("Could not import safetensors, safetensors support disabled.")

def filter_files_extensions(files, extensions):
    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))

class CLIPTextEncode:
    @classmethod
    def INPUT_TYPES(s):
32
        return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
comfyanonymous's avatar
comfyanonymous committed
33
34
35
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "encode"

36
37
    CATEGORY = "conditioning"

comfyanonymous's avatar
comfyanonymous committed
38
    def encode(self, clip, text):
comfyanonymous's avatar
comfyanonymous committed
39
40
41
42
43
44
45
46
47
        return ([[clip.encode(text), {}]], )

class ConditioningCombine:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "combine"

48
49
    CATEGORY = "conditioning"

comfyanonymous's avatar
comfyanonymous committed
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
    def combine(self, conditioning_1, conditioning_2):
        return (conditioning_1 + conditioning_2, )

class ConditioningSetArea:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                              "width": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
                              "height": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
                              "x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
                              "y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "append"

66
67
    CATEGORY = "conditioning"

comfyanonymous's avatar
comfyanonymous committed
68
69
70
71
72
73
74
75
    def append(self, conditioning, width, height, x, y, strength, min_sigma=0.0, max_sigma=99.0):
        c = copy.deepcopy(conditioning)
        for t in c:
            t[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
            t[1]['strength'] = strength
            t[1]['min_sigma'] = min_sigma
            t[1]['max_sigma'] = max_sigma
        return (c, )
comfyanonymous's avatar
comfyanonymous committed
76
77
78
79
80
81
82
83
84
85
86

class VAEDecode:
    def __init__(self, device="cpu"):
        self.device = device

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "decode"

87
88
    CATEGORY = "latent"

comfyanonymous's avatar
comfyanonymous committed
89
90
91
92
93
94
95
96
97
98
99
100
101
    def decode(self, vae, samples):
        return (vae.decode(samples), )

class VAEEncode:
    def __init__(self, device="cpu"):
        self.device = device

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "encode"

102
103
    CATEGORY = "latent"

comfyanonymous's avatar
comfyanonymous committed
104
    def encode(self, vae, pixels):
105
106
107
108
        x = (pixels.shape[1] // 64) * 64
        y = (pixels.shape[2] // 64) * 64
        if pixels.shape[1] != x or pixels.shape[2] != y:
            pixels = pixels[:,:x,:y,:]
comfyanonymous's avatar
comfyanonymous committed
109
110
111
112
113
114
115
116
117
118
119
120
121
122
        return (vae.encode(pixels), )

class CheckpointLoader:
    models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
    config_dir = os.path.join(models_dir, "configs")
    ckpt_dir = os.path.join(models_dir, "checkpoints")

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "config_name": (filter_files_extensions(os.listdir(s.config_dir), '.yaml'), ),
                              "ckpt_name": (filter_files_extensions(os.listdir(s.ckpt_dir), supported_ckpt_extensions), )}}
    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
    FUNCTION = "load_checkpoint"

123
124
    CATEGORY = "loaders"

comfyanonymous's avatar
comfyanonymous committed
125
126
127
128
129
130
131
132
133
134
135
136
137
138
    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
        config_path = os.path.join(self.config_dir, config_name)
        ckpt_path = os.path.join(self.ckpt_dir, ckpt_name)
        return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True)

class VAELoader:
    models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
    vae_dir = os.path.join(models_dir, "vae")
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "vae_name": (filter_files_extensions(os.listdir(s.vae_dir), supported_ckpt_extensions), )}}
    RETURN_TYPES = ("VAE",)
    FUNCTION = "load_vae"

139
140
    CATEGORY = "loaders"

comfyanonymous's avatar
comfyanonymous committed
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
    #TODO: scale factor?
    def load_vae(self, vae_name):
        vae_path = os.path.join(self.vae_dir, vae_name)
        vae = comfy.sd.VAE(ckpt_path=vae_path)
        return (vae,)

class EmptyLatentImage:
    def __init__(self, device="cpu"):
        self.device = device

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
                              "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "generate"

159
160
    CATEGORY = "latent"

comfyanonymous's avatar
comfyanonymous committed
161
162
163
164
165
166
    def generate(self, width, height, batch_size=1):
        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
        return (latent, )

class LatentUpscale:
    upscale_methods = ["nearest-exact", "bilinear", "area"]
167
    crop_methods = ["disabled", "center"]
comfyanonymous's avatar
comfyanonymous committed
168
169
170
171
172

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
                              "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
173
174
                              "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
                              "crop": (s.crop_methods,)}}
comfyanonymous's avatar
comfyanonymous committed
175
176
177
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "upscale"

178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
    def upscale(self, samples, upscale_method, width, height, crop):
        if crop == "center":
            old_width = samples.shape[3]
            old_height = samples.shape[2]
            old_aspect = old_width / old_height
            new_aspect = width / height
            x = 0
            y = 0
            if old_aspect > new_aspect:
                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
            elif old_aspect < new_aspect:
                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
            s = samples[:,:,y:old_height-y,x:old_width-x]
        else:
            s = samples
        s = torch.nn.functional.interpolate(s, size=(height // 8, width // 8), mode=upscale_method)
comfyanonymous's avatar
comfyanonymous committed
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
        return (s,)

class KSampler:
    def __init__(self, device="cuda"):
        self.device = device

    @classmethod
    def INPUT_TYPES(s):
        return {"required": 
                    {"model": ("MODEL",),
                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
                    "positive": ("CONDITIONING", ),
                    "negative": ("CONDITIONING", ),
                    "latent_image": ("LATENT", ),
                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                    }}

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "sample"

218
219
    CATEGORY = "sampling"

comfyanonymous's avatar
comfyanonymous committed
220
221
222
223
224
225
    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
        noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
        model = model.to(self.device)
        noise = noise.to(self.device)
        latent_image = latent_image.to(self.device)

comfyanonymous's avatar
comfyanonymous committed
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
        positive_copy = []
        negative_copy = []

        for p in positive:
            t = p[0]
            if t.shape[0] < noise.shape[0]:
                t = torch.cat([t] * noise.shape[0])
            t = t.to(self.device)
            positive_copy += [[t] + p[1:]]
        for n in negative:
            t = n[0]
            if t.shape[0] < noise.shape[0]:
                t = torch.cat([t] * noise.shape[0])
            t = t.to(self.device)
            negative_copy += [[t] + n[1:]]
comfyanonymous's avatar
comfyanonymous committed
241
242
243
244
245
246
247

        if sampler_name in comfy.samplers.KSampler.SAMPLERS:
            sampler = comfy.samplers.KSampler(model, steps=steps, device=self.device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
        else:
            #other samplers
            pass

comfyanonymous's avatar
comfyanonymous committed
248
        samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image)
comfyanonymous's avatar
comfyanonymous committed
249
250
251
252
253
254
255
256
257
258
259
260
        samples = samples.cpu()
        model = model.cpu()
        return (samples, )


class SaveImage:
    def __init__(self):
        self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")

    @classmethod
    def INPUT_TYPES(s):
        return {"required": 
261
262
                    {"images": ("IMAGE", ),
                     "filename_prefix": ("STRING", {"default": "ComfyUI"})},
comfyanonymous's avatar
comfyanonymous committed
263
264
265
266
267
268
269
270
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }

    RETURN_TYPES = ()
    FUNCTION = "save_images"

    OUTPUT_NODE = True

271
272
    CATEGORY = "image"

273
274
275
276
277
278
279
280
281
282
283
284
285
    def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
        def map_filename(filename):
            prefix_len = len(filename_prefix)
            prefix = filename[:prefix_len + 1]
            try:
                digits = int(filename[prefix_len + 1:].split('_')[0])
            except:
                digits = 0
            return (digits, prefix)
        try:
            counter = max(filter(lambda a: a[1][:-1] == filename_prefix and a[1][-1] == "_", map(map_filename, os.listdir(self.output_dir))))[0] + 1
        except ValueError:
            counter = 1
comfyanonymous's avatar
comfyanonymous committed
286
287
288
289
290
291
292
293
294
        for image in images:
            i = 255. * image.cpu().numpy()
            img = Image.fromarray(i.astype(np.uint8))
            metadata = PngInfo()
            if prompt is not None:
                metadata.add_text("prompt", json.dumps(prompt))
            if extra_pnginfo is not None:
                for x in extra_pnginfo:
                    metadata.add_text(x, json.dumps(extra_pnginfo[x]))
295
296
            img.save(f"output/{filename_prefix}_{counter:05}_.png", pnginfo=metadata, optimize=True)
            counter += 1
comfyanonymous's avatar
comfyanonymous committed
297

298
299
300
301
302
303
304
class LoadImage:
    input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"image": (os.listdir(s.input_dir), )},
                }
305
306

    CATEGORY = "image"
307
308
309
310
311
312
313
314
315
316

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "load_image"
    def load_image(self, image):
        image_path = os.path.join(self.input_dir, image)
        image = Image.open(image_path).convert("RGB")
        image = np.array(image).astype(np.float32) / 255.0
        image = torch.from_numpy(image[None])[None,]
        return image

317
318
319
320
321
322
323
324
    @classmethod
    def IS_CHANGED(s, image):
        image_path = os.path.join(s.input_dir, image)
        m = hashlib.sha256()
        with open(image_path, 'rb') as f:
            m.update(f.read())
        return m.digest().hex()

325

comfyanonymous's avatar
comfyanonymous committed
326
327
328
329
330
331
332
333
334
335
336

NODE_CLASS_MAPPINGS = {
    "KSampler": KSampler,
    "CheckpointLoader": CheckpointLoader,
    "CLIPTextEncode": CLIPTextEncode,
    "VAEDecode": VAEDecode,
    "VAEEncode": VAEEncode,
    "VAELoader": VAELoader,
    "EmptyLatentImage": EmptyLatentImage,
    "LatentUpscale": LatentUpscale,
    "SaveImage": SaveImage,
comfyanonymous's avatar
comfyanonymous committed
337
338
339
    "LoadImage": LoadImage,
    "ConditioningCombine": ConditioningCombine,
    "ConditioningSetArea": ConditioningSetArea,
comfyanonymous's avatar
comfyanonymous committed
340
341
342
}