{{ message }}
Repository navigation
Expand file tree
/
Copy pathcallbacks.py
More file actions
209 lines (151 loc) · 8.54 KB
/
Copy pathcallbacks.py
File metadata and controls
209 lines (151 loc) · 8.54 KB
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
from typing import Any, Dict, List
from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME
class PipelineCallback(ConfigMixin):
"""
Base class for all the official callbacks used in a pipeline. This class provides a structure for implementing
custom callbacks and ensures that all callbacks have a consistent interface.
Please implement the following:
`tensor_inputs`: This should return a list of tensor inputs specific to your callback. You will only be able to
include
variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.
`callback_fn`: This method defines the core functionality of your callback.
"""
config_name = CONFIG_NAME
@register_to_config
def __init__(self, cutoff_step_ratio=1.0, cutoff_step_index=None):
super().__init__()
if (cutoff_step_ratio is None and cutoff_step_index is None) or (
cutoff_step_ratio is not None and cutoff_step_index is not None
):
raise ValueError("Either cutoff_step_ratio or cutoff_step_index should be provided, not both or none.")
if cutoff_step_ratio is not None and (
not isinstance(cutoff_step_ratio, float) or not (0.0 <= cutoff_step_ratio <= 1.0)
):
raise ValueError("cutoff_step_ratio must be a float between 0.0 and 1.0.")
@property
def tensor_inputs(self) -> List[str]:
raise NotImplementedError(f"You need to set the attribute `tensor_inputs` for {self.__class__}")
def callback_fn(self, pipeline, step_index, timesteps, callback_kwargs) -> Dict[str, Any]:
raise NotImplementedError(f"You need to implement the method `callback_fn` for {self.__class__}")
def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
return self.callback_fn(pipeline, step_index, timestep, callback_kwargs)
class MultiPipelineCallbacks:
"""
This class is designed to handle multiple pipeline callbacks. It accepts a list of PipelineCallback objects and
provides a unified interface for calling all of them.
"""
def __init__(self, callbacks: List[PipelineCallback]):
self.callbacks = callbacks
@property
def tensor_inputs(self) -> List[str]:
return [input for callback in self.callbacks for input in callback.tensor_inputs]
def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
"""
Calls all the callbacks in order with the given arguments and returns the final callback_kwargs.
"""
for callback in self.callbacks:
callback_kwargs = callback(pipeline, step_index, timestep, callback_kwargs)
return callback_kwargs
class SDCFGCutoffCallback(PipelineCallback):
"""
Callback function for Stable Diffusion Pipelines. After certain number of steps (set by `cutoff_step_ratio` or
`cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
"""
tensor_inputs = ["prompt_embeds"]
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
prompt_embeds = prompt_embeds[-1:] # "-1" denotes the embeddings for conditional text tokens.
pipeline._guidance_scale = 0.0
callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
return callback_kwargs
class SDXLCFGCutoffCallback(PipelineCallback):
"""
Callback function for the base Stable Diffusion XL Pipelines. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
"""
tensor_inputs = [
"prompt_embeds",
"add_text_embeds",
"add_time_ids",
]
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
prompt_embeds = prompt_embeds[-1:] # "-1" denotes the embeddings for conditional text tokens.
add_text_embeds = callback_kwargs[self.tensor_inputs[1]]
add_text_embeds = add_text_embeds[-1:] # "-1" denotes the embeddings for conditional pooled text tokens
add_time_ids = callback_kwargs[self.tensor_inputs[2]]
add_time_ids = add_time_ids[-1:] # "-1" denotes the embeddings for conditional added time vector
pipeline._guidance_scale = 0.0
callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
callback_kwargs[self.tensor_inputs[1]] = add_text_embeds
callback_kwargs[self.tensor_inputs[2]] = add_time_ids
return callback_kwargs
class SDXLControlnetCFGCutoffCallback(PipelineCallback):
"""
Callback function for the Controlnet Stable Diffusion XL Pipelines. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
"""
tensor_inputs = [
"prompt_embeds",
"add_text_embeds",
"add_time_ids",
"image",
]
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
prompt_embeds = prompt_embeds[-1:] # "-1" denotes the embeddings for conditional text tokens.
add_text_embeds = callback_kwargs[self.tensor_inputs[1]]
add_text_embeds = add_text_embeds[-1:] # "-1" denotes the embeddings for conditional pooled text tokens
add_time_ids = callback_kwargs[self.tensor_inputs[2]]
add_time_ids = add_time_ids[-1:] # "-1" denotes the embeddings for conditional added time vector
# For Controlnet
image = callback_kwargs[self.tensor_inputs[3]]
image = image[-1:]
pipeline._guidance_scale = 0.0
callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
callback_kwargs[self.tensor_inputs[1]] = add_text_embeds
callback_kwargs[self.tensor_inputs[2]] = add_time_ids
callback_kwargs[self.tensor_inputs[3]] = image
return callback_kwargs
class IPAdapterScaleCutoffCallback(PipelineCallback):
"""
Callback function for any pipeline that inherits `IPAdapterMixin`. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will set the IP Adapter scale to `0.0`.
Note: This callback mutates the IP Adapter attention processors by setting the scale to 0.0 after the cutoff step.
"""
tensor_inputs = []
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
pipeline.set_ip_adapter_scale(0.0)
return callback_kwargs
You can’t perform that action at this time.