-
Notifications
You must be signed in to change notification settings - Fork 10
/
nodes.py
385 lines (302 loc) · 14.9 KB
/
nodes.py
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
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
import os
import torch
import comfy
import yaml
import folder_paths
from einops import rearrange
from comfy import model_base, model_management, model_detection, latent_formats, model_sampling
from .lvdm.modules.networks.openaimodel3d import UNetModel as DynamiCrafterUNetModel
from .utils.model_utils import DynamiCrafterBase, DYNAMICRAFTER_CONFIG, \
load_image_proj_dict, load_dynamicrafter_dict, get_image_proj_model, load_vae_dict
from .utils.utils import get_models_directory
MODEL_DIR= "dynamicrafter_models"
MODEL_DIR_PATH = os.path.join(folder_paths.models_dir, MODEL_DIR)
class DynamiCrafterProcessor:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL", ),
"clip_vision": ("CLIP_VISION", ),
"vae": ("VAE", ),
"image_proj_model": ("IMAGE_PROJ_MODEL", ),
"images": ("IMAGE", ),
"use_interpolate": ("BOOLEAN", {"default": False}),
"fps": ("INT", {"default": 15, "min": 1, "max": 30, "step": 1}, ),
"frames": ("INT", {"default": 16}),
"scale_latents": ("BOOLEAN", {"default": False})
},
}
CATEGORY = "Native_DynamiCrafter/Processing"
RETURN_TYPES = ("MODEL", "LATENT", "LATENT", )
RETURN_NAMES = ("model", "empty_latent", "latent_img", )
FUNCTION = "process_image_conditioning"
def __init__(self):
self.model_patcher = None
# There is probably a better way to do this, but with the apply_model callback, this seems necessary.
# The model gets wrapped around a CFG Denoiser class, and handles the conditioning parts there.
# We cannot access it, so we must find the conditioning according to how ComfyUI handles it.
def get_conditioning_pair(self, c_crossattn, use_cfg: bool):
if not use_cfg:
return c_crossattn
conditioning_group = []
for i in range(c_crossattn.shape[0]):
# Get the positive and negative conditioning.
positive_idx = i + 1
negative_idx = i
if positive_idx >= c_crossattn.shape[0]:
break
if not torch.equal(c_crossattn[[positive_idx]], c_crossattn[[negative_idx]]):
conditioning_group = [
c_crossattn[[positive_idx]],
c_crossattn[[negative_idx]]
]
break
if len(conditioning_group) == 0:
raise ValueError("Could not get the appropriate conditioning group.")
return torch.cat(conditioning_group)
# apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}
def _forward(self, *args):
transformer_options = self.model_patcher.model_options['transformer_options']
conditioning = transformer_options['conditioning']
apply_model = args[0]
# forward_dict
fd = args[1]
x, t, model_in_kwargs, _ = fd['input'], fd['timestep'], fd['c'], fd['cond_or_uncond']
c_crossattn = model_in_kwargs.pop("c_crossattn")
c_concat = conditioning['c_concat']
num_video_frames = conditioning['num_video_frames']
fs = conditioning['fs']
original_num_frames = num_video_frames
# Better way to determine if we're using CFG
# The cond batch will always be num_frames >= 2 since we're doing video,
# so we need get this condition differently here.
if x.shape[0] > num_video_frames:
num_video_frames *= 2
batch_size = 2
use_cfg = True
else:
use_cfg = False
batch_size = 1
if use_cfg:
c_concat = torch.cat([c_concat] * 2)
self.validate_forwardable_latent(x, c_concat, num_video_frames, use_cfg)
x_in, c_concat = map(lambda xc: rearrange(xc, '(b t) c h w -> b c t h w', b=batch_size), (x, c_concat))
# We always assume video, so there will always be batched conditionings.
c_crossattn = self.get_conditioning_pair(c_crossattn, use_cfg)
c_crossattn = c_crossattn[:2] if use_cfg else c_crossattn[:1]
context_in = c_crossattn
img_embs = conditioning['image_emb']
if use_cfg:
img_emb_uncond = conditioning['image_emb_uncond']
img_embs = torch.cat([img_embs, img_emb_uncond])
fs = torch.cat([fs] * x_in.shape[0])
outs = []
for i in range(batch_size):
model_in_kwargs['transformer_options']['cond_idx'] = i
x_out = apply_model(
x_in[[i]],
t=torch.cat([t[:1]]),
context_in=context_in[[i]],
c_crossattn=c_crossattn,
cc_concat=c_concat[[i]], # "cc" is to handle naming conflict with apply_model wrapper.
# We want to handle this in the UNet forward.
num_video_frames=num_video_frames // 2 if batch_size > 1 else num_video_frames,
img_emb=img_embs[[i]],
fs=fs[[i]],
**model_in_kwargs
)
outs.append(x_out)
x_out = torch.cat(list(reversed(outs)))
x_out = rearrange(x_out, 'b c t h w -> (b t) c h w')
return x_out
def assign_forward_args(
self,
model,
c_concat,
image_emb,
image_emb_uncond,
fs,
frames,
):
model.model_options['transformer_options']['conditioning'] = {
"c_concat": c_concat,
"image_emb": image_emb,
'image_emb_uncond': image_emb_uncond,
"fs": fs,
"num_video_frames": frames,
}
def validate_forwardable_latent(self, latent, c_concat, num_video_frames, use_cfg):
check_no_cfg = latent.shape[0] != num_video_frames
check_with_cfg = latent.shape[0] != (num_video_frames * 2)
latent_batch_size = latent.shape[0] if not use_cfg else latent.shape[0] // 2
num_frames = num_video_frames if not use_cfg else num_video_frames // 2
if all([check_no_cfg, check_with_cfg]):
raise ValueError(
"Please make sure your latent inputs match the number of frames in the DynamiCrafter Processor."
f"Got a latent batch size of ({latent_batch_size}) with number of frames being ({num_frames})."
)
latent_h, latent_w = latent.shape[-2:]
c_concat_h, c_concat_w = c_concat.shape[-2:]
if not all([latent_h == c_concat_h, latent_w == c_concat_w]):
raise ValueError(
"Please make sure that your input latent and image frames are the same height and width.",
f"Image Size: {c_concat_w * 8}, {c_concat_h * 8}, Latent Size: {latent_h * 8}, {latent_w * 8}"
)
def process_image_conditioning(
self,
model,
clip_vision,
vae,
image_proj_model,
images,
use_interpolate,
fps: int,
frames: int,
scale_latents: bool
):
self.model_patcher = model
encoded_latent = vae.encode(images[:, :, :, :3])
encoded_image = clip_vision.encode_image(images[:1])['last_hidden_state']
image_emb = image_proj_model(encoded_image)
encoded_image_uncond = clip_vision.encode_image(torch.zeros_like(images)[:1])['last_hidden_state']
image_emb_uncond = image_proj_model(encoded_image_uncond)
c_concat = encoded_latent
if scale_latents:
vae_process_input = vae.process_input
vae.process_input = lambda image: (image - .5) * 2
c_concat = vae.encode(images[:, :, :, :3])
vae.process_input = vae_process_input
c_concat = model.model.process_latent_in(c_concat) * 1.3
else:
c_concat = model.model.process_latent_in(c_concat)
fs = torch.tensor([fps], dtype=torch.long, device=model_management.intermediate_device())
model.set_model_unet_function_wrapper(self._forward)
used_interpolate_processing = False
if use_interpolate and frames > 16:
raise ValueError(
"When using interpolation mode, the maximum amount of frames are 16."
"If you're doing long video generation, consider using the last frame\
from the first generation for the next one (autoregressive)."
)
if encoded_latent.shape[0] == 1:
c_concat = torch.cat([c_concat] * frames, dim=0)[:frames]
if use_interpolate:
mask = torch.zeros_like(c_concat)
mask[:1] = c_concat[:1]
c_concat = mask
used_interpolate_processing = True
else:
if use_interpolate and c_concat.shape[0] in [2, 3]:
input_frame_count = c_concat.shape[0]
# We're just padding to the same type an size of the concat
masked_frames = torch.zeros_like(torch.cat([c_concat[:1]] * frames))[:frames]
# Start frame
masked_frames[:1] = c_concat[:1]
end_frame_idx = -1
# TODO
speed = 1.0
if speed < 1.0:
possible_speeds = list(torch.linspace(0, 1.0, c_concat.shape[0]))
speed_from_frames = enumerate(possible_speeds)
speed_idx = min(speed_from_frames, key=lambda n: n[1] - speed)[0]
end_frame_idx = speed_idx
# End frame
masked_frames[-1:] = c_concat[[end_frame_idx]]
# Possible middle frame, but not working at the moment.
if input_frame_count == 3:
middle_idx = masked_frames.shape[0] // 2
middle_idx_frame = c_concat.shape[0] // 2
masked_frames[[middle_idx]] = c_concat[[middle_idx_frame]]
c_concat = masked_frames
used_interpolate_processing = True
print(f"Using interpolation mode with {input_frame_count} frames.")
if c_concat.shape[0] < frames and not used_interpolate_processing:
print(
"Multiple images found, but interpolation mode is unset. Using the first frame as condition.",
)
c_concat = torch.cat([c_concat[:1]] * frames)
c_concat = c_concat[:frames]
if encoded_latent.shape[0] == 1:
encoded_latent = torch.cat([encoded_latent] * frames)[:frames]
if encoded_latent.shape[0] < frames and encoded_latent.shape[0] != 1:
encoded_latent = torch.cat(
[encoded_latent] + [encoded_latent[-1:]] * abs(encoded_latent.shape[0] - frames)
)[:frames]
# We could store this as a state in this Node Class Instance, but to prevent any weird edge cases,
# this should always be passed through the 'stateless' way, and let ComfyUI handle the transformer_options state.
self.assign_forward_args(model, c_concat, image_emb, image_emb_uncond, fs, frames)
return (model, {"samples": torch.zeros_like(c_concat)}, {"samples": encoded_latent},)
class DynamiCrafterLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model_path": (get_models_directory(os.listdir(MODEL_DIR_PATH)), ),
},
}
CATEGORY = "Native_DynamiCrafter/Loaders"
RETURN_TYPES = ("MODEL", "IMAGE_PROJ_MODEL", )
RETURN_NAMES = ("model", "image_proj_model", )
FUNCTION = "load_dynamicrafter"
def load_model_sicts(self, model_path: str):
model_state_dict = comfy.utils.load_torch_file(model_path)
dynamicrafter_dict = load_dynamicrafter_dict(model_state_dict)
image_proj_dict = load_image_proj_dict(model_state_dict)
return dynamicrafter_dict, image_proj_dict
def get_prediction_type(self, is_eps: bool, model_config):
if not is_eps and "image_cross_attention_scale_learnable" in model_config.unet_config.keys():
model_config.unet_config["image_cross_attention_scale_learnable"] = False
return model_base.ModelType.EPS if is_eps else model_base.ModelType.V_PREDICTION
def handle_model_management(self, dynamicrafter_dict: dict, model_config):
parameters = comfy.utils.calculate_parameters(dynamicrafter_dict, "model.diffusion_model.")
load_device = model_management.get_torch_device()
unet_dtype = model_management.unet_dtype(
model_params=parameters,
supported_dtypes=model_config.supported_inference_dtypes
)
manual_cast_dtype = model_management.unet_manual_cast(
unet_dtype,
load_device,
model_config.supported_inference_dtypes
)
model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
offload_device = model_management.unet_offload_device()
return load_device, inital_load_device
def check_leftover_keys(self, state_dict: dict):
left_over = state_dict.keys()
if len(left_over) > 0:
print("left over keys:", left_over)
def load_dynamicrafter(self, model_path):
model_path = os.path.join(MODEL_DIR_PATH, model_path)
if os.path.exists(model_path):
dynamicrafter_dict, image_proj_dict = self.load_model_sicts(model_path)
model_config = DynamiCrafterBase(DYNAMICRAFTER_CONFIG)
dynamicrafter_dict, is_eps = model_config.process_dict_version(state_dict=dynamicrafter_dict)
MODEL_TYPE = self.get_prediction_type(is_eps, model_config)
load_device, inital_load_device = self.handle_model_management(dynamicrafter_dict, model_config)
model = model_base.BaseModel(
model_config,
model_type=MODEL_TYPE,
device=inital_load_device,
unet_model=DynamiCrafterUNetModel
)
image_proj_model = get_image_proj_model(image_proj_dict)
model.load_model_weights(dynamicrafter_dict, "model.diffusion_model.")
self.check_leftover_keys(dynamicrafter_dict)
model_patcher = comfy.model_patcher.ModelPatcher(
model,
load_device=load_device,
offload_device=model_management.unet_offload_device(),
current_device=inital_load_device
)
return (model_patcher, image_proj_model, )
NODE_CLASS_MAPPINGS = {
"DynamiCrafterLoader": DynamiCrafterLoader,
"DynamiCrafterProcessor": DynamiCrafterProcessor,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DynamiCrafterLoader": "Load a DynamiCrafter Checkpoint",
"DynamiCrafterProcessor": "Apply DynamiCrafter",
}