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add controlnet

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  1. README.md +1 -0
  2. app.py +91 -57
  3. gradio_cached_examples/19/component 0/e33a451dfb129bc30b9f/image.png +0 -3
  4. gradio_cached_examples/19/component 0/f04e50c024c6a9afe961/image.png +0 -3
  5. gradio_cached_examples/19/component 1/10e16f2dc59d089b20b5/img_8fd6f06e-6eb6-48a3-8bd4-9298b01a2285_2048.jpg +0 -0
  6. gradio_cached_examples/19/component 1/1bf228b9acd9fb445d09/img_0e41dc12-93c8-4f9d-aa2f-0a00f7081e66_2048.jpg +0 -0
  7. gradio_cached_examples/19/component 1/2059998b38ef7fa9ea52/img_83478ba8-56ad-4ad9-ba9d-49b5f236b065_1024.jpg +0 -0
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  9. gradio_cached_examples/19/component 1/2cfcce2908a6182e88b3/img_8fd6f06e-6eb6-48a3-8bd4-9298b01a2285_1024.jpg +0 -0
  10. gradio_cached_examples/19/component 1/32488afdf4bea3199b37/img_fe14ac56-9968-4a67-8af8-e7be456eaa6b_1024.jpg +0 -0
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  14. gradio_cached_examples/19/component 1/7fbf1b7dc2b095710833/img_fe14ac56-9968-4a67-8af8-e7be456eaa6b_2048.jpg +0 -0
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  16. gradio_cached_examples/19/component 1/a8236eaf57eb4c00a675/img_aa7a6207-935a-4d96-95c8-83a84bef7e2d_1024.jpg +0 -0
  17. gradio_cached_examples/19/component 1/a8af99b541e23f5a226d/img_aa7a6207-935a-4d96-95c8-83a84bef7e2d_2048.jpg +0 -0
  18. gradio_cached_examples/19/component 1/ba24df0d074b7630e53f/img_fe14ac56-9968-4a67-8af8-e7be456eaa6b_1024.jpg +0 -0
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  21. gradio_cached_examples/19/component 1/d8301fa9c8d8e203bab3/img_83478ba8-56ad-4ad9-ba9d-49b5f236b065_1024.jpg +0 -0
  22. gradio_cached_examples/19/component 1/e9c81c6c4654c62b7b6b/img_0e41dc12-93c8-4f9d-aa2f-0a00f7081e66_1024.jpg +0 -0
  23. gradio_cached_examples/19/component 1/fe9c1f1d45c63a37a11a/img_a2bfff99-69e6-4ff9-b9bc-9dd629a9c66a_1024.jpg +0 -0
  24. gradio_cached_examples/19/log.csv +0 -7
  25. gradio_cached_examples/{19/component 0/f6f69d54afbbc4bd71cb → 26/component 0/05278c335b8cbc37e6e9}/image.png +0 -0
  26. gradio_cached_examples/{19/component 0/c563eafa73fbc0612108 → 26/component 0/0e1b694b9f853ef25b2d}/image.png +0 -0
  27. gradio_cached_examples/{19/component 0/21abf21d2e8b22047b17 → 26/component 0/2172f5bce50a165095d7}/image.png +0 -0
  28. gradio_cached_examples/{19/component 0/03129640ecbf969b455b → 26/component 0/43c83f140c0e7e40df3f}/image.png +2 -2
  29. gradio_cached_examples/{19/component 0/223924b0c75c36d80549 → 26/component 0/4f78b7a98d242044e045}/image.png +2 -2
  30. gradio_cached_examples/{19/component 0/176506a57aab17e55184 → 26/component 0/852cc94666acec8c678b}/image.png +0 -0
  31. gradio_cached_examples/{19/component 0/3f5d4b211c66ddd81c42 → 26/component 0/87fb854bfedb1c1dbba7}/image.png +2 -2
  32. gradio_cached_examples/{19/component 0/807af9bebd567b4d0200 → 26/component 0/a809ca545a9024bf41fd}/image.png +2 -2
  33. gradio_cached_examples/{19/component 0/c1b325bec4f04dd7a097 → 26/component 0/a968a5b504e7afa21616}/image.png +0 -0
  34. gradio_cached_examples/{19/component 0/c99293e18af806dbedbe → 26/component 0/cae38a1e5d767031ea11}/image.png +0 -0
  35. gradio_cached_examples/26/component 0/cc001089d949637bfacb/image.png +3 -0
  36. gradio_cached_examples/26/component 0/df134be50ac8e17a9ddb/image.png +3 -0
  37. gradio_cached_examples/26/component 1/04ef3fbe84cc54034e2c/img_b8f5aba6-750d-452a-8e2f-78ff4d323cd5_1024.jpg +0 -0
  38. gradio_cached_examples/26/component 1/15a4af899bf1ba05d19d/img_b8f5aba6-750d-452a-8e2f-78ff4d323cd5_2048.jpg +0 -0
  39. gradio_cached_examples/26/component 1/2e18d7f82880a84a79c3/img_d00f5d85-29ea-4527-95a9-9e881d992154_1024.jpg +0 -0
  40. gradio_cached_examples/26/component 1/3153fc03211f8b7474f1/img_30bb400b-cf42-41d7-bbff-d7068b421b15_2048.jpg +0 -0
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  42. gradio_cached_examples/26/component 1/3cc28e7b24c1884348e1/img_30bb400b-cf42-41d7-bbff-d7068b421b15_1024.jpg +0 -0
  43. gradio_cached_examples/26/component 1/3d6157f24622d58483a2/img_65e0d120-53f7-4be8-8e00-7e1b53f86a45_1024.jpg +0 -0
  44. gradio_cached_examples/26/component 1/3e6b594512eedb39a1df/img_65e0d120-53f7-4be8-8e00-7e1b53f86a45_3072.jpg +0 -0
  45. gradio_cached_examples/26/component 1/5331f153d00c300e9a71/img_65e0d120-53f7-4be8-8e00-7e1b53f86a45_1024.jpg +0 -0
  46. gradio_cached_examples/26/component 1/ac3c51cc52d84efc87f1/img_30bb400b-cf42-41d7-bbff-d7068b421b15_1024.jpg +0 -0
  47. gradio_cached_examples/26/component 1/ad302a505dedcdcce6fd/img_d00f5d85-29ea-4527-95a9-9e881d992154_2048.jpg +0 -0
  48. gradio_cached_examples/26/component 1/adf183dd596531f6b693/img_c6e188f5-b005-4850-8d0d-f78de1c14a5a_1024.jpg +0 -0
  49. gradio_cached_examples/26/component 1/becd945790d06d3d36a8/img_65e0d120-53f7-4be8-8e00-7e1b53f86a45_2048.jpg +0 -0
  50. gradio_cached_examples/26/component 1/c624b0e2fb73f4c9771a/img_c6e188f5-b005-4850-8d0d-f78de1c14a5a_1024.jpg +0 -0
README.md CHANGED
@@ -8,6 +8,7 @@ sdk_version: 4.8.0
8
  app_file: app.py
9
  pinned: false
10
  suggested_hardware: t4-medium
 
11
  ---
12
 
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
8
  app_file: app.py
9
  pinned: false
10
  suggested_hardware: t4-medium
11
+ disable_embedding: true
12
  ---
13
 
14
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py CHANGED
@@ -1,7 +1,7 @@
1
  import gradio as gr
2
  from gradio_imageslider import ImageSlider
3
  import torch
4
- from diffusers import DiffusionPipeline, AutoencoderKL
5
  from compel import Compel, ReturnedEmbeddingsType
6
  from PIL import Image
7
  from torchvision import transforms
@@ -9,6 +9,8 @@ import tempfile
9
  import os
10
  import time
11
  import uuid
 
 
12
 
13
 
14
  device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -22,9 +24,13 @@ print(f"low memory: {LOW_MEMORY}")
22
 
23
 
24
  vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
 
 
 
25
  pipe = DiffusionPipeline.from_pretrained(
26
  "stabilityai/stable-diffusion-xl-base-1.0",
27
- custom_pipeline="pipeline_demofusion_sdxl.py",
 
28
  custom_revision="main",
29
  torch_dtype=dtype,
30
  variant="fp16",
@@ -76,6 +82,7 @@ def predict(
76
  prompt,
77
  negative_prompt,
78
  seed,
 
79
  guidance_scale=8.5,
80
  cosine_scale_1=3,
81
  cosine_scale_2=1,
@@ -91,6 +98,11 @@ def predict(
91
  conditioning, pooled = compel([prompt, negative_prompt])
92
  generator = torch.manual_seed(seed)
93
  last_time = time.time()
 
 
 
 
 
94
  images = pipe(
95
  prompt_embeds=conditioning[0:1],
96
  pooled_prompt_embeds=pooled[0:1],
@@ -100,6 +112,8 @@ def predict(
100
  width=1024 * scale,
101
  height=1024 * scale,
102
  view_batch_size=16,
 
 
103
  stride=64,
104
  generator=generator,
105
  num_inference_steps=40,
@@ -159,49 +173,6 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
159
  label="Negative Prompt",
160
  value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
161
  )
162
- guidance_scale = gr.Slider(
163
- minimum=0,
164
- maximum=50,
165
- value=8.5,
166
- step=0.001,
167
- label="Guidance Scale",
168
- )
169
- scale = gr.Slider(
170
- minimum=1,
171
- maximum=5,
172
- value=2,
173
- step=1,
174
- label="x Scale",
175
- interactive=False,
176
- )
177
- cosine_scale_1 = gr.Slider(
178
- minimum=0,
179
- maximum=5,
180
- value=3,
181
- step=0.01,
182
- label="Cosine Scale 1",
183
- )
184
- cosine_scale_2 = gr.Slider(
185
- minimum=0,
186
- maximum=5,
187
- value=1,
188
- step=0.01,
189
- label="Cosine Scale 2",
190
- )
191
- cosine_scale_3 = gr.Slider(
192
- minimum=0,
193
- maximum=5,
194
- value=1,
195
- step=0.01,
196
- label="Cosine Scale 3",
197
- )
198
- sigma = gr.Slider(
199
- minimum=0,
200
- maximum=1,
201
- value=0.8,
202
- step=0.01,
203
- label="Sigma",
204
- )
205
  seed = gr.Slider(
206
  minimum=0,
207
  maximum=2**64 - 1,
@@ -210,32 +181,89 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
210
  label="Seed",
211
  randomize=True,
212
  )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
213
  btn = gr.Button()
214
  with gr.Column(scale=2):
215
  image_slider = ImageSlider(position=0.5)
216
  files = gr.Files()
217
- # inputs = [
218
- # image_input,
219
- # prompt,
220
- # negative_prompt,
221
- # seed,
222
- # guidance_scale,
223
- # cosine_scale_1,
224
- # cosine_scale_2,
225
- # cosine_scale_3,
226
- # sigma,
227
- # scale,
228
- # ]
229
  inputs = [
230
  image_input,
231
  prompt,
232
  negative_prompt,
233
  seed,
 
234
  guidance_scale,
235
  cosine_scale_1,
236
  cosine_scale_2,
237
  cosine_scale_3,
238
  sigma,
 
239
  ]
240
  outputs = [image_slider, files]
241
  btn.click(predict, inputs=inputs, outputs=outputs, concurrency_limit=1)
@@ -247,6 +275,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
247
  "photography of lara croft 8k high definition award winning",
248
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
249
  5436236241,
 
250
  8.5,
251
  3,
252
  1,
@@ -259,6 +288,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
259
  "photo of tesla cybertruck futuristic car 8k high definition on a sand dune in mars, future",
260
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
261
  383472451451,
 
262
  8.5,
263
  3,
264
  1,
@@ -271,6 +301,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
271
  "a photorealistic painting of Jesus Christ, 4k high definition",
272
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
273
  13317204146129588000,
 
274
  8.5,
275
  3,
276
  1,
@@ -283,6 +314,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
283
  "A crowded stadium with enthusiastic fans watching a daytime sporting event, the stands filled with colorful attire and the sun casting a warm glow",
284
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
285
  5623124123512,
 
286
  8.5,
287
  3,
288
  1,
@@ -295,6 +327,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
295
  "a large red flower on a black background 4k high definition",
296
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
297
  23123412341234,
 
298
  8.5,
299
  3,
300
  1,
@@ -307,6 +340,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
307
  "photo realistic huggingface human+++ emoji costume, round, yellow, skin+++ texture+++",
308
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic, emoji cartoon, drawing, pixelated",
309
  5532144938416372000,
 
310
  25.206,
311
  4.64,
312
  1,
 
1
  import gradio as gr
2
  from gradio_imageslider import ImageSlider
3
  import torch
4
+ from diffusers import DiffusionPipeline, AutoencoderKL, ControlNetModel
5
  from compel import Compel, ReturnedEmbeddingsType
6
  from PIL import Image
7
  from torchvision import transforms
 
9
  import os
10
  import time
11
  import uuid
12
+ import cv2
13
+ import numpy as np
14
 
15
 
16
  device = "cuda" if torch.cuda.is_available() else "cpu"
 
24
 
25
 
26
  vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
27
+ controlnet = ControlNetModel.from_pretrained(
28
+ "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
29
+ )
30
  pipe = DiffusionPipeline.from_pretrained(
31
  "stabilityai/stable-diffusion-xl-base-1.0",
32
+ custom_pipeline="pipeline_demofusion_sdxl_controlnet.py",
33
+ controlnet=controlnet,
34
  custom_revision="main",
35
  torch_dtype=dtype,
36
  variant="fp16",
 
82
  prompt,
83
  negative_prompt,
84
  seed,
85
+ controlnet_conditioning_scale,
86
  guidance_scale=8.5,
87
  cosine_scale_1=3,
88
  cosine_scale_2=1,
 
98
  conditioning, pooled = compel([prompt, negative_prompt])
99
  generator = torch.manual_seed(seed)
100
  last_time = time.time()
101
+ canny_image = np.array(padded_image)
102
+ canny_image = cv2.Canny(canny_image, 100, 200)
103
+ canny_image = canny_image[:, :, None]
104
+ canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
105
+ canny_image = Image.fromarray(canny_image)
106
  images = pipe(
107
  prompt_embeds=conditioning[0:1],
108
  pooled_prompt_embeds=pooled[0:1],
 
112
  width=1024 * scale,
113
  height=1024 * scale,
114
  view_batch_size=16,
115
+ controlnet_conditioning_scale=controlnet_conditioning_scale,
116
+ condition_image=canny_image,
117
  stride=64,
118
  generator=generator,
119
  num_inference_steps=40,
 
173
  label="Negative Prompt",
174
  value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
175
  )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
176
  seed = gr.Slider(
177
  minimum=0,
178
  maximum=2**64 - 1,
 
181
  label="Seed",
182
  randomize=True,
183
  )
184
+ with gr.Accordion(label="DemoFusion Params", open=False):
185
+ guidance_scale = gr.Slider(
186
+ minimum=0,
187
+ maximum=50,
188
+ value=8.5,
189
+ step=0.001,
190
+ label="Guidance Scale",
191
+ )
192
+ scale = gr.Slider(
193
+ minimum=1,
194
+ maximum=5,
195
+ value=2,
196
+ step=1,
197
+ label="Magnification Scale",
198
+ interactive=False,
199
+ )
200
+ cosine_scale_1 = gr.Slider(
201
+ minimum=0,
202
+ maximum=5,
203
+ value=3,
204
+ step=0.01,
205
+ label="Cosine Scale 1",
206
+ )
207
+ cosine_scale_2 = gr.Slider(
208
+ minimum=0,
209
+ maximum=5,
210
+ value=1,
211
+ step=0.01,
212
+ label="Cosine Scale 2",
213
+ )
214
+ cosine_scale_3 = gr.Slider(
215
+ minimum=0,
216
+ maximum=5,
217
+ value=1,
218
+ step=0.01,
219
+ label="Cosine Scale 3",
220
+ )
221
+ sigma = gr.Slider(
222
+ minimum=0,
223
+ maximum=1,
224
+ value=0.8,
225
+ step=0.01,
226
+ label="Sigma",
227
+ )
228
+ with gr.Accordion(label="ControlNet Params", open=False):
229
+ controlnet_conditioning_scale = gr.Slider(
230
+ minimum=0,
231
+ maximum=1,
232
+ step=0.001,
233
+ value=0.5,
234
+ label="ControlNet Conditioning Scale",
235
+ )
236
+ controlnet_start = gr.Slider(
237
+ minimum=0,
238
+ maximum=1,
239
+ step=0.001,
240
+ value=0.0,
241
+ label="ControlNet Start",
242
+ )
243
+ controlnet_end = gr.Slider(
244
+ minimum=0.0,
245
+ maximum=1.0,
246
+ step=0.001,
247
+ value=1.0,
248
+ label="ControlNet End",
249
+ )
250
+
251
  btn = gr.Button()
252
  with gr.Column(scale=2):
253
  image_slider = ImageSlider(position=0.5)
254
  files = gr.Files()
 
 
 
 
 
 
 
 
 
 
 
 
255
  inputs = [
256
  image_input,
257
  prompt,
258
  negative_prompt,
259
  seed,
260
+ controlnet_conditioning_scale,
261
  guidance_scale,
262
  cosine_scale_1,
263
  cosine_scale_2,
264
  cosine_scale_3,
265
  sigma,
266
+ # scale,
267
  ]
268
  outputs = [image_slider, files]
269
  btn.click(predict, inputs=inputs, outputs=outputs, concurrency_limit=1)
 
275
  "photography of lara croft 8k high definition award winning",
276
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
277
  5436236241,
278
+ 0.5,
279
  8.5,
280
  3,
281
  1,
 
288
  "photo of tesla cybertruck futuristic car 8k high definition on a sand dune in mars, future",
289
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
290
  383472451451,
291
+ 0.5,
292
  8.5,
293
  3,
294
  1,
 
301
  "a photorealistic painting of Jesus Christ, 4k high definition",
302
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
303
  13317204146129588000,
304
+ 0.5,
305
  8.5,
306
  3,
307
  1,
 
314
  "A crowded stadium with enthusiastic fans watching a daytime sporting event, the stands filled with colorful attire and the sun casting a warm glow",
315
  "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
316
  5623124123512,
317
+ 0.5,
318
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