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update README (#1)

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- update README (ac8f9e356081e6d322fadc4eb23908edba9a1e5b)


Co-authored-by: Will Berman <[email protected]>

README.md CHANGED
@@ -18,136 +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
28
- $ pip install opencv-contrib-python
29
- ```
30
-
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
-
38
- image = Image.open('images/bird.png')
39
- image = np.array(image)
40
-
41
- low_threshold = 100
42
- high_threshold = 200
43
-
44
- image = cv2.Canny(image, low_threshold, high_threshold)
45
- image = image[:, :, None]
46
- 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
- ```
62
-
63
- ![bird](./images/bird.png)
64
-
65
- ![bird_canny](./images/bird_canny.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.
@@ -158,7 +34,7 @@ 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")
@@ -184,15 +60,23 @@ color_seg = color_seg.astype(np.uint8)
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
  ```
@@ -203,179 +87,6 @@ image.save('./images/house_seg_out.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
  ## Semantic Segmentation
24
 
25
+ ### Diffusers
26
+
27
  Semantic segmentation relies on transformers. Transformers is a
28
  dependency of diffusers for running controlnet, so you should
29
  have it installed already.
 
34
  import numpy as np
35
  from controlnet_utils import ade_palette
36
  import torch
37
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
38
 
39
  image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
40
  image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
 
60
  image = Image.fromarray(color_seg)
61
 
62
  controlnet = ControlNetModel.from_pretrained(
63
+ "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=torch.float16
64
  )
65
 
66
  pipe = StableDiffusionControlNetPipeline.from_pretrained(
67
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
68
  )
 
69
 
70
+ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
71
+
72
+ # Remove if you do not have xformers installed
73
+ # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
74
+ # for installation instructions
75
+ pipe.enable_xformers_memory_efficient_attention()
76
+
77
+ pipe.enable_model_cpu_offload()
78
+
79
+ image = pipe("house", image, num_inference_steps=20).images[0]
80
 
81
  image.save('./images/house_seg_out.png')
82
  ```
 
87
 
88
  ![house_seg_out](images/house_seg_out.png)
89
 
90
+ ### Training
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91
 
92
+ The semantic segmentation model was trained on 164K segmentation-image, caption pairs from ADE20K. The model was trained for 200 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model.
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