├── DeepCache_Fix.py
├── README.md
├── __init__.py
└── doc
├── DeepCache-Fix.json
├── icon.png
└── img.png
/DeepCache_Fix.py:
--------------------------------------------------------------------------------
1 | import torch
2 | from comfy.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, timestep_embedding, th, apply_control
3 |
4 | class DeepCache_Fix:
5 |
6 | @classmethod
7 | def INPUT_TYPES(s):
8 |
9 | """
10 | 静态方法，用于定义输入参数的类型和默认值。
11 |
12 | 该方法返回一个字典，其中包含了不同输入参数的配置信息。每个输入参数都是一个键值对，
13 | 键表示参数名，值是一个元组，包含参数的类型和一个字典，该字典描述了参数的更多细节，
14 | 如默认值、最小值、最大值等。
15 |
16 | 返回:
17 | dict: 包含所有输入参数配置的字典。
18 | """
19 | return {
20 | "required": {
21 | "model": ("MODEL",),
22 | "cache_interval": ("INT", {
23 | "default": 3,
24 | "min": 1,
25 | "max": 1000,
26 | "step": 1,
27 | "display": "number"
28 | }),
29 | "cache_depth": ("INT", {
30 | "default": 3,
31 | "min": 0,
32 | "max": 12,
33 | "step": 1,
34 | "display": "number"
35 | }),
36 | "start_steps": ("INT", {
37 | "default": 0,
38 | "min": 0,
39 | "max": 100,
40 | "step": 1,
41 | "display": "number"
42 | }),
43 | "end_steps": ("INT", {
44 | "default": 12,
45 | "min": 0,
46 | "max": 100,
47 | "step": 1,
48 | "display": "number"
49 | }),
50 | "input_cache": (["No", "Yes"], {"default":"Yes"}),
51 | "middle_cahce": (["No", "Yes"], {"default":"Yes"}),
52 | "output_cache": (["No", "Yes"], {"default":"Yes"}),
53 | },
54 | }
55 |
56 | RETURN_TYPES = ("MODEL",)
57 | FUNCTION = "apply"
58 | CATEGORY = "loaders"
59 |
60 | def apply(self, model, cache_interval, cache_depth, start_steps, end_steps, input_cache, middle_cahce, output_cache):
61 | # 初始化一些变量
62 | current_time = -1
63 | current_step = -1
64 | model_step = 0
65 | cache_h = None
66 |
67 | # 创建一个新的模型副本，用于存储修改后的模型。
68 | new_model = model.clone()
69 | # 获取并初始化模型的扩散部分。
70 | unet = new_model.model.diffusion_model
71 | # 获取并初始化模型的扩散部分。
72 | dtype = new_model.model.get_dtype()
73 |
74 | def cache_apply_methon(current, start, end):
75 | """
76 | 判断当前步骤是否在指定的开始步骤和结束步骤范围内。
77 |
78 | 参数:
79 | current -- 当前的步骤数
80 | start -- 范围的起始步骤数
81 | end -- 范围的结束步骤数
82 |
83 | 返回:
84 | 如果当前步骤在指定范围内，则返回True；否则返回False。
85 | """
86 | return start <= current <= end
87 |
88 | def apply_model(model_function, kwargs):
89 | """
90 | 应用模型函数到给定的输入上。
91 |
92 | 这个函数处理模型的输入和输出，包括数据类型转换、条件的添加和模型的分块计算。
93 | 它还处理缓存机制，以在多次调用之间复用计算结果，提高效率。
94 |
95 | :param model_function: 模型函数，一个接受kwargs参数的函数。
96 | :param kwargs: 包含模型输入和配置的字典。包括输入数据、时间步、条件等。
97 | :return: 模型处理后的输出。
98 | """
99 |
100 | # 声明一些非局部变量，用于处理缓存和当前时间步等状态。
101 | nonlocal model_step, cache_h, current_time, current_step
102 |
103 | # 从kwargs中提取必要的输入和配置。
104 | xa = kwargs["input"]
105 | t = kwargs["timestep"]
106 | c_concat = kwargs["c"].get("c_concat", None)
107 | c_crossattn = kwargs["c"].get("c_crossattn", None)
108 | y = kwargs["c"].get("y", None)
109 | control = kwargs["c"].get("control", None)
110 | transformer_options = kwargs["c"].get("transformer_options", None)
111 |
112 | # 根据当前时间步计算输入xc。
113 | sigma = t
114 | xc = new_model.model.model_sampling.calculate_input(sigma, xa)
115 | if c_concat is not None:
116 | # 将输入xc与跨注意力的上下文c_concat进行拼接。
117 | xc = torch.cat([xc] + [c_concat], dim=1)
118 |
119 | # 处理跨注意力的上下文和数据类型的转换。
120 | context = c_crossattn
121 | xc = xc.to(dtype)
122 | # 将时间步转换为指定的数据类型。
123 | t = new_model.model.model_sampling.timestep(t).float()
124 | context = context.to(dtype)
125 |
126 | # 将所有额外的条件转换为指定的数据类型。
127 | extra_conds = {}
128 | for o in kwargs:
129 | extra = kwargs[o]
130 | if hasattr(extra, "to"):
131 | extra = extra.to(dtype)
132 | extra_conds[o] = extra
133 |
134 | # 初始化模型的输入和配置。
135 | x = xc
136 | timesteps = t
137 | y = None if y is None else y.to(dtype)
138 | transformer_options["original_shape"] = list(x.shape)
139 | transformer_options["current_index"] = 0
140 | transformer_patches = transformer_options.get("patches", {})
141 |
142 | model_step += 1
143 | # 更新当前时间步和缓存状态，根据当前时间步决定是否应用模型。
144 | if t[0].item() > current_time:
145 | model_step = 0
146 | current_step = -1
147 | # 判断是否需要应用模型，根据当前时间步和指定的时间范围。
148 | cache_apply = cache_apply_methon(model_step, start_steps, end_steps)
149 | if cache_apply:
150 | current_step += 1
151 | else:
152 | current_step = -1
153 | current_time = t[0].item()
154 | # print(f"model_step: {model_step}, {cache_apply}")
155 |
156 | # 确保如果模型是分类的，那么必须提供标签y。
157 | assert (y is not None) == (
158 | unet.num_classes is not None
159 | ), "must specify y if and only if the model is class-conditional"
160 |
161 | # 处理时间嵌入和模型的输入、中间和输出块。
162 | hs = []
163 | t_emb = timestep_embedding(timesteps, unet.model_channels, repeat_only=False).to(unet.dtype)
164 | emb = unet.time_embed(t_emb)
165 | if unet.num_classes is not None:
166 | assert y.shape[0] == x.shape[0]
167 | emb = emb + unet.label_emb(y)
168 | xuh = x.type(unet.dtype)
169 | # current_step 是 cache_interval 的整数倍?
170 | step_cache_interval = current_step % cache_interval
171 | # 循环处理输入块。
172 | for id, module in enumerate(unet.input_blocks):
173 | transformer_options["block"] = ("input", id)
174 | xuh = forward_timestep_embed(module, xuh, emb, context, transformer_options)
175 | xuh = apply_control(xuh, control, 'input')
176 | if "input_block_patch" in transformer_patches:
177 | patch = transformer_patches["input_block_patch"]
178 | for p in patch:
179 | xuh = p(xuh, transformer_options)
180 | hs.append(xuh)
181 | if "input_block_patch_after_skip" in transformer_patches:
182 | patch = transformer_patches["input_block_patch_after_skip"]
183 | for p in patch:
184 | xuh = p(xuh, transformer_options)
185 |
186 | # 根据缓存策略决定是否继续处理或使用缓存。
187 | if id == cache_depth and cache_apply and input_cache:
188 | if not step_cache_interval == 0:
189 | break
190 |
191 | # 处理中间块，同样考虑缓存策略。
192 | # 如果 current_step 是 cache_interval 的整数倍
193 | # 或者 cache_apply 为 False
194 | # 或者 middle_cahce 为 False (开关关闭)
195 | # 则执行中间块的处理。
196 | if step_cache_interval == 0 or not cache_apply or not middle_cahce:
197 | transformer_options["block"] = ("middle", 0)
198 | xuh = forward_timestep_embed(unet.middle_block, xuh, emb, context, transformer_options)
199 | xuh = apply_control(xuh, control, 'middle')
200 |
201 | # 处理输出块，包括缓存的加载和使用。
202 | for id, module in enumerate(unet.output_blocks):
203 | if id < len(unet.output_blocks) - cache_depth - 1 and cache_apply and output_cache:
204 | if not step_cache_interval == 0:
205 | continue
206 | if id == len(unet.output_blocks) - cache_depth - 1 and cache_apply and output_cache:
207 | if step_cache_interval == 0:
208 | cache_h = xuh # cache
209 | else:
210 | xuh = cache_h # load cache
211 | transformer_options["block"] = ("output", id)
212 | hsp = hs.pop()
213 | hsp = apply_control(hsp, control, 'output')
214 | if "output_block_patch" in transformer_patches:
215 | patch = transformer_patches["output_block_patch"]
216 | for p in patch:
217 | xuh, hsp = p(xuh, hsp, transformer_options)
218 | xuh = th.cat([xuh, hsp], dim=1)
219 | del hsp
220 | if len(hs) > 0:
221 | output_shape = hs[-1].shape
222 | else:
223 | output_shape = None
224 | xuh = forward_timestep_embed(module, xuh, emb, context, transformer_options, output_shape)
225 |
226 | # 将输出转换回原始数据类型，并根据模型配置进行噪声消除计算。
227 | xuh = xuh.type(x.dtype)
228 | if unet.predict_codebook_ids:
229 | model_output = unet.id_predictor(xuh)
230 | else:
231 | model_output = unet.out(xuh)
232 |
233 | # 返回计算得到的最终输出。
234 | return new_model.model.model_sampling.calculate_denoised(sigma, model_output, xa)
235 |
236 | new_model.set_model_unet_function_wrapper(apply_model)
237 |
238 | return (new_model,)
239 |
240 |
241 | NODE_CLASS_MAPPINGS = {
242 | "DeepCache_Fix": DeepCache_Fix,
243 | }
244 |
245 | NODE_DISPLAY_NAME_MAPPINGS = {
246 | "DeepCache_Fix": "DeepCache_Fix",
247 | }
248 |
249 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
250 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
2 |
3 |
5 | 富强、民主、文明、和谐、合规
6 |
7 |
8 | 自由、平等、公正、法治、合法
9 |
10 |
11 | 爱国、敬业、诚信、友善、合理
12 |
13 |
14 | # ComfyUI-DeepCache-Fix
15 |
16 | ## 介绍
17 |
18 | For SDXL LCM,ComfyUI加速节点，生成图片时更快，同时保证了加速前后的一致性，适合大批量生图。[Accelerate ComfyUI Nodes for Faster Image Generation, Ensuring Consistency Pre and Post-Acceleration, Ideal for Bulk Image Production.]
19 |
20 | ## 用法
21 |
22 | 将该库放在 ComfyUI/custom_nodes/ 下即可。[Simply place the library in the 'ComfyUI/custom_nodes/' directory to get started.]
23 |
24 | ## 工作流例子
25 |
26 | [DeepCache-Fix.json](doc%2FDeepCache-Fix.json)
27 |
28 | ## 使用说明
29 |
30 | ### 插件参数
31 |
32 | - cache_interval: 缓存间隔, 单位: 步, 默认:3
33 | - cache_depth: 缓存深度, 默认:3
34 | - start_steps: 使用缓存的开始步数, 默认:0
35 | - end_steps: 使用缓存的结束步数, 默认:12
36 | - input_cache: 使用输入层缓存，默认:True 开启
37 | - middle_cahce: 使用中间层缓存，默认:True 开启
38 | - output_cache: 使用输出层缓存，默认:True 开启
39 |
40 | ### 举例(实践)
41 |
42 | #### 模型(蒸馏)
43 |
44 | https://www.liblib.art/modelinfo/386109978c19484298d810d6f2830780
45 |
46 | #### 生成
47 |
48 | 在总共16步的执行过程中，我们计划采取分阶段的策略。具体来说，如下
49 |
50 | 1. 0-12步(start_steps=0, end_steps=12)将利用特定的插件来执行，以提高效率和效果。
51 | 2. 从第13步开始，我们将切换回原始模型，完成剩下的4步。 (一般预留3步以上，不使用加速)
52 |
53 | 这样的安排旨在结合两者的优势，确保整个流程的顺利进行。
54 |
55 | 
56 |
57 | ## 参考
58 |
59 | 原始代码参考: https://gist.github.com/laksjdjf/435c512bc19636e9c9af4ee7bea9eb86
60 |
61 | 感谢 laksjdjf 分享的代码。
62 |
--------------------------------------------------------------------------------
/__init__.py:
--------------------------------------------------------------------------------
1 | try:
2 | import comfy.utils
3 | except ImportError:
4 | pass
5 | else:
6 | from .DeepCache_Fix import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
7 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
8 |
--------------------------------------------------------------------------------
/doc/DeepCache-Fix.json:
--------------------------------------------------------------------------------
1 | {
2 | "last_node_id": 112,
3 | "last_link_id": 306,
4 | "nodes": [
5 | {
6 | "id": 8,
7 | "type": "VAEDecode",
8 | "pos": [
9 | 1979.6826171875,
10 | 130
11 | ],
12 | "size": {
13 | "0": 210,
14 | "1": 46
15 | },
16 | "flags": {},
17 | "order": 8,
18 | "mode": 0,
19 | "inputs": [
20 | {
21 | "name": "samples",
22 | "type": "LATENT",
23 | "link": 175,
24 | "label": "Latent"
25 | },
26 | {
27 | "name": "vae",
28 | "type": "VAE",
29 | "link": 230,
30 | "label": "VAE"
31 | }
32 | ],
33 | "outputs": [
34 | {
35 | "name": "IMAGE",
36 | "type": "IMAGE",
37 | "links": [
38 | 279
39 | ],
40 | "slot_index": 0,
41 | "label": "图像"
42 | }
43 | ],
44 | "properties": {
45 | "Node name for S&R": "VAEDecode"
46 | }
47 | },
48 | {
49 | "id": 44,
50 | "type": "EmptyLatentImage",
51 | "pos": [
52 | 649.6826171875,
53 | 130
54 | ],
55 | "size": {
56 | "0": 315,
57 | "1": 106
58 | },
59 | "flags": {},
60 | "order": 2,
61 | "mode": 0,
62 | "inputs": [
63 | {
64 | "name": "width",
65 | "type": "INT",
66 | "link": 293,
67 | "widget": {
68 | "name": "width"
69 | },
70 | "label": "宽度"
71 | },
72 | {
73 | "name": "height",
74 | "type": "INT",
75 | "link": 294,
76 | "widget": {
77 | "name": "height"
78 | },
79 | "label": "高度"
80 | }
81 | ],
82 | "outputs": [
83 | {
84 | "name": "LATENT",
85 | "type": "LATENT",
86 | "links": [
87 | 174
88 | ],
89 | "shape": 3,
90 | "label": "Latent"
91 | }
92 | ],
93 | "properties": {
94 | "Node name for S&R": "EmptyLatentImage"
95 | },
96 | "widgets_values": [
97 | 512,
98 | 1024,
99 | 1
100 | ]
101 | },
102 | {
103 | "id": 92,
104 | "type": "Image Save",
105 | "pos": [
106 | 2289.6826171875,
107 | 130
108 | ],
109 | "size": {
110 | "0": 403.4533996582031,
111 | "1": 1203.6630859375
112 | },
113 | "flags": {},
114 | "order": 9,
115 | "mode": 0,
116 | "inputs": [
117 | {
118 | "name": "images",
119 | "type": "IMAGE",
120 | "link": 279
121 | }
122 | ],
123 | "outputs": [
124 | {
125 | "name": "images",
126 | "type": "IMAGE",
127 | "links": null,
128 | "shape": 3
129 | }
130 | ],
131 | "properties": {
132 | "Node name for S&R": "Image Save"
133 | },
134 | "widgets_values": [
135 | "[time(%Y-%m-%d)]",
136 | "ComfyUI",
137 | "_",
138 | 4,
139 | "false",
140 | "jpg",
141 | 72,
142 | 95,
143 | "true",
144 | "false",
145 | "false",
146 | "false",
147 | "true",
148 | "true",
149 | "true"
150 | ]
151 | },
152 | {
153 | "id": 106,
154 | "type": "CLIPTextEncodeSDXL",
155 | "pos": [
156 | 649.6826171875,
157 | 578
158 | ],
159 | "size": {
160 | "0": 400,
161 | "1": 269.9999694824219
162 | },
163 | "flags": {
164 | "pinned": false
165 | },
166 | "order": 4,
167 | "mode": 0,
168 | "inputs": [
169 | {
170 | "name": "clip",
171 | "type": "CLIP",
172 | "link": 250,
173 | "label": "CLIP"
174 | },
175 | {
176 | "name": "target_width",
177 | "type": "INT",
178 | "link": 280,
179 | "widget": {
180 | "name": "target_width"
181 | },
182 | "label": "目标宽度"
183 | },
184 | {
185 | "name": "target_height",
186 | "type": "INT",
187 | "link": 281,
188 | "widget": {
189 | "name": "target_height"
190 | },
191 | "label": "目标高度"
192 | },
193 | {
194 | "name": "width",
195 | "type": "INT",
196 | "link": 286,
197 | "widget": {
198 | "name": "width"
199 | },
200 | "label": "宽度"
201 | },
202 | {
203 | "name": "height",
204 | "type": "INT",
205 | "link": 287,
206 | "widget": {
207 | "name": "height"
208 | },
209 | "label": "高度"
210 | },
211 | {
212 | "name": "text_g",
213 | "type": "STRING",
214 | "link": 288,
215 | "widget": {
216 | "name": "text_g"
217 | },
218 | "label": "G文本"
219 | },
220 | {
221 | "name": "text_l",
222 | "type": "STRING",
223 | "link": 289,
224 | "widget": {
225 | "name": "text_l"
226 | },
227 | "label": "L文本"
228 | }
229 | ],
230 | "outputs": [
231 | {
232 | "name": "CONDITIONING",
233 | "type": "CONDITIONING",
234 | "links": [
235 | 296
236 | ],
237 | "shape": 3,
238 | "label": "条件",
239 | "slot_index": 0
240 | }
241 | ],
242 | "properties": {
243 | "Node name for S&R": "CLIPTextEncodeSDXL"
244 | },
245 | "widgets_values": [
246 | 1024,
247 | 1024,
248 | 0,
249 | 0,
250 | 1024,
251 | 1024,
252 | "",
253 | ""
254 | ]
255 | },
256 | {
257 | "id": 104,
258 | "type": "CLIPTextEncodeSDXL",
259 | "pos": [
260 | 649.6826171875,
261 | 977.9999694824219
262 | ],
263 | "size": {
264 | "0": 400,
265 | "1": 269.9999694824219
266 | },
267 | "flags": {
268 | "pinned": false
269 | },
270 | "order": 5,
271 | "mode": 0,
272 | "inputs": [
273 | {
274 | "name": "clip",
275 | "type": "CLIP",
276 | "link": 251,
277 | "label": "CLIP"
278 | },
279 | {
280 | "name": "target_width",
281 | "type": "INT",
282 | "link": 282,
283 | "widget": {
284 | "name": "target_width"
285 | },
286 | "label": "目标宽度"
287 | },
288 | {
289 | "name": "target_height",
290 | "type": "INT",
291 | "link": 283,
292 | "widget": {
293 | "name": "target_height"
294 | },
295 | "label": "目标高度"
296 | },
297 | {
298 | "name": "width",
299 | "type": "INT",
300 | "link": 284,
301 | "widget": {
302 | "name": "width"
303 | },
304 | "label": "宽度"
305 | },
306 | {
307 | "name": "height",
308 | "type": "INT",
309 | "link": 285,
310 | "widget": {
311 | "name": "height"
312 | },
313 | "label": "高度"
314 | },
315 | {
316 | "name": "text_g",
317 | "type": "STRING",
318 | "link": 297,
319 | "widget": {
320 | "name": "text_g"
321 | },
322 | "label": "G文本"
323 | },
324 | {
325 | "name": "text_l",
326 | "type": "STRING",
327 | "link": 298,
328 | "widget": {
329 | "name": "text_l"
330 | },
331 | "label": "L文本"
332 | }
333 | ],
334 | "outputs": [
335 | {
336 | "name": "CONDITIONING",
337 | "type": "CONDITIONING",
338 | "links": [
339 | 295
340 | ],
341 | "shape": 3,
342 | "label": "条件",
343 | "slot_index": 0
344 | }
345 | ],
346 | "properties": {
347 | "Node name for S&R": "CLIPTextEncodeSDXL"
348 | },
349 | "widgets_values": [
350 | 1024,
351 | 1024,
352 | 0,
353 | 0,
354 | 1024,
355 | 1024,
356 | "",
357 | ""
358 | ]
359 | },
360 | {
361 | "id": 110,
362 | "type": "MexxSDXLPromptStyler",
363 | "pos": [
364 | 100,
365 | 130
366 | ],
367 | "size": {
368 | "0": 449.6826171875,
369 | "1": 793.7354125976562
370 | },
371 | "flags": {},
372 | "order": 0,
373 | "mode": 0,
374 | "outputs": [
375 | {
376 | "name": "text_positive",
377 | "type": "STRING",
378 | "links": [
379 | 288,
380 | 289
381 | ],
382 | "shape": 3,
383 | "label": "text_positive",
384 | "slot_index": 0
385 | },
386 | {
387 | "name": "text_negative",
388 | "type": "STRING",
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