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update README (#1)

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- update README (2579df16b27d26f15371480035b35eca8a04ba81)


Co-authored-by: Will Berman <williamberman@users.noreply.huggingface.co>

README.md CHANGED
@@ -18,10 +18,12 @@ Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimenta
18
  The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required.
19
 
20
  Some of the additional conditionings can be extracted from images via additional models. We extracted these
21
- additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/human_pose.git).
22
 
23
  ## Canny edge detection
24
 
 
 
25
  Install opencv
26
 
27
  ```sh
@@ -31,7 +33,7 @@ $ pip install opencv-contrib-python
31
  ```python
32
  import cv2
33
  from PIL import Image
34
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
35
  import torch
36
  import numpy as np
37
 
@@ -47,15 +49,23 @@ image = np.concatenate([image, image, image], axis=2)
47
  image = Image.fromarray(image)
48
 
49
  controlnet = ControlNetModel.from_pretrained(
50
- "fusing/stable-diffusion-v1-5-controlnet-canny",
51
  )
52
 
53
  pipe = StableDiffusionControlNetPipeline.from_pretrained(
54
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
55
  )
56
- pipe.to('cuda')
57
 
58
- image = pipe("bird", image).images[0]
 
 
 
 
 
 
 
 
 
59
 
60
  image.save('images/bird_canny_out.png')
61
  ```
@@ -66,316 +76,6 @@ image.save('images/bird_canny_out.png')
66
 
67
  ![bird_canny_out](./images/bird_canny_out.png)
68
 
69
- ## M-LSD Straight line detection
70
-
71
- Install the additional controlnet models package.
72
-
73
- ```sh
74
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
75
- ```
76
-
77
- ```py
78
- from PIL import Image
79
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
80
- import torch
81
- from human_pose import MLSDdetector
82
-
83
- mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
84
-
85
- image = Image.open('images/room.png')
86
-
87
- image = mlsd(image)
88
-
89
- controlnet = ControlNetModel.from_pretrained(
90
- "fusing/stable-diffusion-v1-5-controlnet-mlsd",
91
- )
92
-
93
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
94
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
95
- )
96
- pipe.to('cuda')
97
-
98
- image = pipe("room", image).images[0]
99
-
100
- image.save('images/room_mlsd_out.png')
101
- ```
102
-
103
- ![room](./images/room.png)
104
-
105
- ![room_mlsd](./images/room_mlsd.png)
106
-
107
- ![room_mlsd_out](./images/room_mlsd_out.png)
108
-
109
- ## Pose estimation
110
-
111
- Install the additional controlnet models package.
112
-
113
- ```sh
114
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
115
- ```
116
-
117
- ```py
118
- from PIL import Image
119
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
120
- import torch
121
- from human_pose import OpenposeDetector
122
-
123
- openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
124
-
125
- image = Image.open('images/pose.png')
126
-
127
- image = openpose(image)
128
-
129
- controlnet = ControlNetModel.from_pretrained(
130
- "fusing/stable-diffusion-v1-5-controlnet-openpose",
131
- )
132
-
133
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
134
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
135
- )
136
- pipe.to('cuda')
137
-
138
- image = pipe("chef in the kitchen", image).images[0]
139
-
140
- image.save('images/chef_pose_out.png')
141
- ```
142
-
143
- ![pose](./images/pose.png)
144
-
145
- ![openpose](./images/openpose.png)
146
-
147
- ![chef_pose_out](./images/chef_pose_out.png)
148
-
149
- ## Semantic Segmentation
150
-
151
- Semantic segmentation relies on transformers. Transformers is a
152
- dependency of diffusers for running controlnet, so you should
153
- have it installed already.
154
-
155
- ```py
156
- from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
157
- from PIL import Image
158
- import numpy as np
159
- from controlnet_utils import ade_palette
160
- import torch
161
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
162
-
163
- image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
164
- image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
165
-
166
- image = Image.open("./images/house.png").convert('RGB')
167
-
168
- pixel_values = image_processor(image, return_tensors="pt").pixel_values
169
-
170
- with torch.no_grad():
171
- outputs = image_segmentor(pixel_values)
172
-
173
- seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
174
-
175
- color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
176
-
177
- palette = np.array(ade_palette())
178
-
179
- for label, color in enumerate(palette):
180
- color_seg[seg == label, :] = color
181
-
182
- color_seg = color_seg.astype(np.uint8)
183
-
184
- image = Image.fromarray(color_seg)
185
-
186
- controlnet = ControlNetModel.from_pretrained(
187
- "fusing/stable-diffusion-v1-5-controlnet-seg",
188
- )
189
-
190
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
191
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
192
- )
193
- pipe.to('cuda')
194
-
195
- image = pipe("house", image).images[0]
196
-
197
- image.save('./images/house_seg_out.png')
198
- ```
199
-
200
- ![house](images/house.png)
201
-
202
- ![house_seg](images/house_seg.png)
203
-
204
- ![house_seg_out](images/house_seg_out.png)
205
-
206
- ## Depth control
207
-
208
- Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so
209
- you should have it installed already.
210
-
211
- ```py
212
- from transformers import pipeline
213
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
214
- from PIL import Image
215
- import numpy as np
216
-
217
- depth_estimator = pipeline('depth-estimation')
218
-
219
- image = Image.open('./images/stormtrooper.png')
220
- image = depth_estimator(image)['depth']
221
- image = np.array(image)
222
- image = image[:, :, None]
223
- image = np.concatenate([image, image, image], axis=2)
224
- image = Image.fromarray(image)
225
-
226
- controlnet = ControlNetModel.from_pretrained(
227
- "fusing/stable-diffusion-v1-5-controlnet-depth",
228
- )
229
-
230
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
231
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
232
- )
233
- pipe.to('cuda')
234
-
235
- image = pipe("Stormtrooper's lecture", image).images[0]
236
-
237
- image.save('./images/stormtrooper_depth_out.png')
238
- ```
239
-
240
- ![stormtrooper](./images/stormtrooper.png)
241
-
242
- ![stormtrooler_depth](./images/stormtrooper_depth.png)
243
-
244
- ![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
245
-
246
-
247
- ## Normal map
248
-
249
- ```py
250
- from PIL import Image
251
- from transformers import pipeline
252
- import numpy as np
253
- import cv2
254
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
255
-
256
- image = Image.open("images/toy.png").convert("RGB")
257
-
258
- depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas" )
259
-
260
- image = depth_estimator(image)['predicted_depth'][0]
261
-
262
- image = image.numpy()
263
-
264
- image_depth = image.copy()
265
- image_depth -= np.min(image_depth)
266
- image_depth /= np.max(image_depth)
267
-
268
- bg_threhold = 0.4
269
-
270
- x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
271
- x[image_depth < bg_threhold] = 0
272
-
273
- y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
274
- y[image_depth < bg_threhold] = 0
275
-
276
- z = np.ones_like(x) * np.pi * 2.0
277
-
278
- image = np.stack([x, y, z], axis=2)
279
- image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5
280
- image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
281
- image = Image.fromarray(image)
282
-
283
- controlnet = ControlNetModel.from_pretrained(
284
- "fusing/stable-diffusion-v1-5-controlnet-normal",
285
- )
286
-
287
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
288
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
289
- )
290
- pipe.to('cuda')
291
-
292
- image = pipe("cute toy", image).images[0]
293
-
294
- image.save('images/toy_normal_out.png')
295
- ```
296
-
297
- ![toy](./images/toy.png)
298
-
299
- ![toy_normal](./images/toy_normal.png)
300
-
301
- ![toy_normal_out](./images/toy_normal_out.png)
302
-
303
- ## Scribble
304
-
305
- Install the additional controlnet models package.
306
-
307
- ```sh
308
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
309
- ```
310
-
311
- ```py
312
- from PIL import Image
313
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
314
- import torch
315
- from human_pose import HEDdetector
316
-
317
- hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
318
-
319
- image = Image.open('images/bag.png')
320
-
321
- image = hed(image, scribble=True)
322
-
323
- controlnet = ControlNetModel.from_pretrained(
324
- "fusing/stable-diffusion-v1-5-controlnet-scribble",
325
- )
326
-
327
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
328
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
329
- )
330
- pipe.to('cuda')
331
-
332
- image = pipe("bag", image).images[0]
333
-
334
- image.save('images/bag_scribble_out.png')
335
- ```
336
-
337
- ![bag](./images/bag.png)
338
-
339
- ![bag_scribble](./images/bag_scribble.png)
340
-
341
- ![bag_scribble_out](./images/bag_scribble_out.png)
342
-
343
- ## HED Boundary
344
-
345
- Install the additional controlnet models package.
346
-
347
- ```sh
348
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
349
- ```
350
-
351
- ```py
352
- from PIL import Image
353
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
354
- import torch
355
- from human_pose import HEDdetector
356
-
357
- hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
358
-
359
- image = Image.open('images/man.png')
360
-
361
- image = hed(image)
362
-
363
- controlnet = ControlNetModel.from_pretrained(
364
- "fusing/stable-diffusion-v1-5-controlnet-hed",
365
- )
366
-
367
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
368
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
369
- )
370
- pipe.to('cuda')
371
-
372
- image = pipe("oil painting of handsome old man, masterpiece", image).images[0]
373
-
374
- image.save('images/man_hed_out.png')
375
- ```
376
-
377
- ![man](./images/man.png)
378
-
379
- ![man_hed](./images/man_hed.png)
380
 
381
- ![man_hed_out](./images/man_hed_out.png)
 
18
  The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required.
19
 
20
  Some of the additional conditionings can be extracted from images via additional models. We extracted these
21
+ additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/controlnet_aux.git).
22
 
23
  ## Canny edge detection
24
 
25
+ ### Diffusers
26
+
27
  Install opencv
28
 
29
  ```sh
 
33
  ```python
34
  import cv2
35
  from PIL import Image
36
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
37
  import torch
38
  import numpy as np
39
 
 
49
  image = Image.fromarray(image)
50
 
51
  controlnet = ControlNetModel.from_pretrained(
52
+ "fusing/stable-diffusion-v1-5-controlnet-canny", torch_dtype=torch.float16
53
  )
54
 
55
  pipe = StableDiffusionControlNetPipeline.from_pretrained(
56
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
57
  )
 
58
 
59
+ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
60
+
61
+ # Remove if you do not have xformers installed
62
+ # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
63
+ # for installation instructions
64
+ pipe.enable_xformers_memory_efficient_attention()
65
+
66
+ pipe.enable_model_cpu_offload()
67
+
68
+ image = pipe("bird", image, num_inference_steps=20).images[0]
69
 
70
  image.save('images/bird_canny_out.png')
71
  ```
 
76
 
77
  ![bird_canny_out](./images/bird_canny_out.png)
78
 
79
+ ### Training
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
80
 
81
+ The canny edge model was trained on 3M edge-image, caption pairs. The model was trained for 600 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model.
controlnet_utils.py DELETED
@@ -1,40 +0,0 @@
1
- def ade_palette():
2
- """ADE20K palette that maps each class to RGB values."""
3
- return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
4
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
5
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
6
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
7
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
8
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
9
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
10
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
11
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
12
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
13
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
14
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
15
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
16
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
17
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
18
- [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
19
- [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
20
- [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
21
- [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
22
- [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
23
- [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
24
- [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
25
- [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
26
- [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
27
- [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
28
- [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
29
- [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
30
- [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
31
- [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
32
- [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
33
- [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
34
- [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
35
- [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
36
- [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
37
- [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
38
- [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
39
- [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
40
- [102, 255, 0], [92, 0, 255]]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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