diffusers-backend

#6
by multimodalart HF staff - opened
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Files changed (50) hide show
  1. .gitattributes +0 -38
  2. CODEOWNERS +0 -1
  3. LICENSE-CODE +0 -21
  4. app.py +45 -211
  5. assets/000.jpg +0 -0
  6. assets/001_with_eval.png +0 -3
  7. assets/test_image.png +0 -0
  8. assets/tile.gif +0 -3
  9. configs/.DS_Store +0 -0
  10. configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml +0 -104
  11. configs/example_training/autoencoder/kl-f4/imagenet-kl_f8_8chn.yaml +0 -105
  12. configs/example_training/imagenet-f8_cond.yaml +0 -185
  13. configs/example_training/toy/cifar10_cond.yaml +0 -98
  14. configs/example_training/toy/mnist.yaml +0 -79
  15. configs/example_training/toy/mnist_cond.yaml +0 -98
  16. configs/example_training/toy/mnist_cond_discrete_eps.yaml +0 -103
  17. configs/example_training/toy/mnist_cond_l1_loss.yaml +0 -99
  18. configs/example_training/toy/mnist_cond_with_ema.yaml +0 -100
  19. configs/example_training/txt2img-clipl-legacy-ucg-training.yaml +0 -182
  20. configs/example_training/txt2img-clipl.yaml +0 -184
  21. configs/inference/sd_2_1.yaml +0 -60
  22. configs/inference/sd_2_1_768.yaml +0 -60
  23. configs/inference/sd_xl_base.yaml +0 -93
  24. configs/inference/sd_xl_refiner.yaml +0 -86
  25. configs/inference/svd.yaml +0 -131
  26. configs/inference/svd_image_decoder.yaml +0 -114
  27. data/DejaVuSans.ttf +0 -0
  28. images/blink_meme.png +0 -0
  29. images/confused2_meme.png +0 -0
  30. images/confused_meme.png +0 -0
  31. images/disaster_meme.png +0 -0
  32. images/distracted_meme.png +0 -0
  33. images/hide_meme.png +0 -0
  34. images/nazare_meme.png +0 -0
  35. images/success_meme.png +0 -0
  36. images/willy_meme.png +0 -0
  37. images/wink_meme.png +0 -0
  38. main.py +0 -943
  39. model_licenses/LICENSE-SDV +0 -31
  40. model_licenses/LICENSE-SDXL0.9 +0 -75
  41. model_licenses/LICENSE-SDXL1.0 +0 -175
  42. pyproject.toml +0 -48
  43. pytest.ini +0 -3
  44. requirements.txt +5 -40
  45. requirements/pt13.txt +0 -40
  46. requirements/pt2.txt +0 -39
  47. scripts/.DS_Store +0 -0
  48. scripts/__init__.py +0 -0
  49. scripts/demo/__init__.py +0 -0
  50. scripts/demo/detect.py +0 -156
.gitattributes DELETED
@@ -1,38 +0,0 @@
1
- *.7z filter=lfs diff=lfs merge=lfs -text
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- *.arrow filter=lfs diff=lfs merge=lfs -text
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- *.bin filter=lfs diff=lfs merge=lfs -text
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- *.bz2 filter=lfs diff=lfs merge=lfs -text
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- *.ckpt filter=lfs diff=lfs merge=lfs -text
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- *.ftz filter=lfs diff=lfs merge=lfs -text
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- *.gz filter=lfs diff=lfs merge=lfs -text
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- *.h5 filter=lfs diff=lfs merge=lfs -text
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- *.joblib filter=lfs diff=lfs merge=lfs -text
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- *.lfs.* filter=lfs diff=lfs merge=lfs -text
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- *.mlmodel filter=lfs diff=lfs merge=lfs -text
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- *.model filter=lfs diff=lfs merge=lfs -text
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- *.msgpack filter=lfs diff=lfs merge=lfs -text
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- *.npy filter=lfs diff=lfs merge=lfs -text
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- *.npz filter=lfs diff=lfs merge=lfs -text
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- *.onnx filter=lfs diff=lfs merge=lfs -text
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- *.ot filter=lfs diff=lfs merge=lfs -text
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- *.parquet filter=lfs diff=lfs merge=lfs -text
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- *.pb filter=lfs diff=lfs merge=lfs -text
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- *.pickle filter=lfs diff=lfs merge=lfs -text
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- *.pkl filter=lfs diff=lfs merge=lfs -text
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- *.pt filter=lfs diff=lfs merge=lfs -text
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- *.pth filter=lfs diff=lfs merge=lfs -text
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- *.rar filter=lfs diff=lfs merge=lfs -text
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- *.safetensors filter=lfs diff=lfs merge=lfs -text
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- saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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- *.tar.* filter=lfs diff=lfs merge=lfs -text
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- *.tar filter=lfs diff=lfs merge=lfs -text
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- *.tflite filter=lfs diff=lfs merge=lfs -text
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- *.tgz filter=lfs diff=lfs merge=lfs -text
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- *.wasm filter=lfs diff=lfs merge=lfs -text
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- *.xz filter=lfs diff=lfs merge=lfs -text
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- *.zip filter=lfs diff=lfs merge=lfs -text
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- *.zst filter=lfs diff=lfs merge=lfs -text
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- *tfevents* filter=lfs diff=lfs merge=lfs -text
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- assets/001_with_eval.png filter=lfs diff=lfs merge=lfs -text
37
- assets/tile.gif filter=lfs diff=lfs merge=lfs -text
38
- outputs/000004.mp4 filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CODEOWNERS DELETED
@@ -1 +0,0 @@
1
- .github @Stability-AI/infrastructure
 
 
LICENSE-CODE DELETED
@@ -1,21 +0,0 @@
1
- MIT License
2
-
3
- Copyright (c) 2023 Stability AI
4
-
5
- Permission is hereby granted, free of charge, to any person obtaining a copy
6
- of this software and associated documentation files (the "Software"), to deal
7
- in the Software without restriction, including without limitation the rights
8
- to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
- copies of the Software, and to permit persons to whom the Software is
10
- furnished to do so, subject to the following conditions:
11
-
12
- The above copyright notice and this permission notice shall be included in all
13
- copies or substantial portions of the Software.
14
-
15
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
- OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
- SOFTWARE.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app.py CHANGED
@@ -1,243 +1,59 @@
1
- import math
 
 
2
  import os
3
  from glob import glob
4
  from pathlib import Path
5
  from typing import Optional
6
 
7
- import cv2
8
- import numpy as np
9
- import torch
10
- from einops import rearrange, repeat
11
- from fire import Fire
12
- from omegaconf import OmegaConf
13
  from PIL import Image
14
- from torchvision.transforms import ToTensor
15
 
16
- from scripts.util.detection.nsfw_and_watermark_dectection import \
17
- DeepFloydDataFiltering
18
- from sgm.inference.helpers import embed_watermark
19
- from sgm.util import default, instantiate_from_config
20
-
21
- import gradio as gr
22
  import uuid
23
  import random
24
  from huggingface_hub import hf_hub_download
25
 
26
- hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints")
27
-
28
- version = "svd_xt"
29
- device = "cuda"
30
- max_64_bit_int = 2**63 - 1
31
-
32
- def load_model(
33
- config: str,
34
- device: str,
35
- num_frames: int,
36
- num_steps: int,
37
- ):
38
- config = OmegaConf.load(config)
39
- if device == "cuda":
40
- config.model.params.conditioner_config.params.emb_models[
41
- 0
42
- ].params.open_clip_embedding_config.params.init_device = device
43
-
44
- config.model.params.sampler_config.params.num_steps = num_steps
45
- config.model.params.sampler_config.params.guider_config.params.num_frames = (
46
- num_frames
47
- )
48
- if device == "cuda":
49
- with torch.device(device):
50
- model = instantiate_from_config(config.model).to(device).eval()
51
- else:
52
- model = instantiate_from_config(config.model).to(device).eval()
53
-
54
- filter = DeepFloydDataFiltering(verbose=False, device=device)
55
- return model, filter
56
-
57
- if version == "svd_xt":
58
- num_frames = 25
59
- num_steps = 30
60
- model_config = "scripts/sampling/configs/svd_xt.yaml"
61
- else:
62
- raise ValueError(f"Version {version} does not exist.")
63
 
64
- model, filter = load_model(
65
- model_config,
66
- device,
67
- num_frames,
68
- num_steps,
69
  )
 
 
 
 
 
70
 
71
  def sample(
72
  image: Image,
73
- seed: Optional[int] = None,
74
  randomize_seed: bool = True,
75
  motion_bucket_id: int = 127,
76
  fps_id: int = 6,
77
  version: str = "svd_xt",
78
  cond_aug: float = 0.02,
79
- decoding_t: int = 5, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
80
  device: str = "cuda",
81
  output_folder: str = "outputs",
82
- progress=gr.Progress(track_tqdm=True)
83
  ):
 
 
 
84
  if(randomize_seed):
85
  seed = random.randint(0, max_64_bit_int)
86
-
87
- torch.manual_seed(seed)
88
 
89
- if image.mode == "RGBA":
90
- image = image.convert("RGB")
91
- w, h = image.size
92
 
93
- if h % 64 != 0 or w % 64 != 0:
94
- width, height = map(lambda x: x - x % 64, (w, h))
95
- image = image.resize((width, height))
96
- print(
97
- f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
98
- )
99
-
100
- image = ToTensor()(image)
101
- image = image * 2.0 - 1.0
102
- image = image.unsqueeze(0).to(device)
103
- H, W = image.shape[2:]
104
- assert image.shape[1] == 3
105
- F = 8
106
- C = 4
107
- shape = (num_frames, C, H // F, W // F)
108
- if (H, W) != (576, 1024):
109
- print(
110
- "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
111
- )
112
- if motion_bucket_id > 255:
113
- print(
114
- "WARNING: High motion bucket! This may lead to suboptimal performance."
115
- )
116
-
117
- if fps_id < 5:
118
- print("WARNING: Small fps value! This may lead to suboptimal performance.")
119
-
120
- if fps_id > 30:
121
- print("WARNING: Large fps value! This may lead to suboptimal performance.")
122
-
123
- value_dict = {}
124
- value_dict["motion_bucket_id"] = motion_bucket_id
125
- value_dict["fps_id"] = fps_id
126
- value_dict["cond_aug"] = cond_aug
127
- value_dict["cond_frames_without_noise"] = image
128
- value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
129
- value_dict["cond_aug"] = cond_aug
130
-
131
- with torch.no_grad():
132
- with torch.autocast(device):
133
- batch, batch_uc = get_batch(
134
- get_unique_embedder_keys_from_conditioner(model.conditioner),
135
- value_dict,
136
- [1, num_frames],
137
- T=num_frames,
138
- device=device,
139
- )
140
- c, uc = model.conditioner.get_unconditional_conditioning(
141
- batch,
142
- batch_uc=batch_uc,
143
- force_uc_zero_embeddings=[
144
- "cond_frames",
145
- "cond_frames_without_noise",
146
- ],
147
- )
148
-
149
- for k in ["crossattn", "concat"]:
150
- uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
151
- uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
152
- c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
153
- c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
154
-
155
- randn = torch.randn(shape, device=device)
156
-
157
- additional_model_inputs = {}
158
- additional_model_inputs["image_only_indicator"] = torch.zeros(
159
- 2, num_frames
160
- ).to(device)
161
- additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
162
-
163
- def denoiser(input, sigma, c):
164
- return model.denoiser(
165
- model.model, input, sigma, c, **additional_model_inputs
166
- )
167
-
168
- samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
169
- model.en_and_decode_n_samples_a_time = decoding_t
170
- samples_x = model.decode_first_stage(samples_z)
171
- samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
172
-
173
- os.makedirs(output_folder, exist_ok=True)
174
- base_count = len(glob(os.path.join(output_folder, "*.mp4")))
175
- video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
176
- writer = cv2.VideoWriter(
177
- video_path,
178
- cv2.VideoWriter_fourcc(*"mp4v"),
179
- fps_id + 1,
180
- (samples.shape[-1], samples.shape[-2]),
181
- )
182
-
183
- samples = embed_watermark(samples)
184
- samples = filter(samples)
185
- vid = (
186
- (rearrange(samples, "t c h w -> t h w c") * 255)
187
- .cpu()
188
- .numpy()
189
- .astype(np.uint8)
190
- )
191
- for frame in vid:
192
- frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
193
- writer.write(frame)
194
- writer.release()
195
  return video_path, seed
196
 
197
- def get_unique_embedder_keys_from_conditioner(conditioner):
198
- return list(set([x.input_key for x in conditioner.embedders]))
199
-
200
-
201
- def get_batch(keys, value_dict, N, T, device):
202
- batch = {}
203
- batch_uc = {}
204
-
205
- for key in keys:
206
- if key == "fps_id":
207
- batch[key] = (
208
- torch.tensor([value_dict["fps_id"]])
209
- .to(device)
210
- .repeat(int(math.prod(N)))
211
- )
212
- elif key == "motion_bucket_id":
213
- batch[key] = (
214
- torch.tensor([value_dict["motion_bucket_id"]])
215
- .to(device)
216
- .repeat(int(math.prod(N)))
217
- )
218
- elif key == "cond_aug":
219
- batch[key] = repeat(
220
- torch.tensor([value_dict["cond_aug"]]).to(device),
221
- "1 -> b",
222
- b=math.prod(N),
223
- )
224
- elif key == "cond_frames":
225
- batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
226
- elif key == "cond_frames_without_noise":
227
- batch[key] = repeat(
228
- value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
229
- )
230
- else:
231
- batch[key] = value_dict[key]
232
-
233
- if T is not None:
234
- batch["num_video_frames"] = T
235
-
236
- for key in batch.keys():
237
- if key not in batch_uc and isinstance(batch[key], torch.Tensor):
238
- batch_uc[key] = torch.clone(batch[key])
239
- return batch, batch_uc
240
-
241
  def resize_image(image, output_size=(1024, 576)):
242
  # Calculate aspect ratios
243
  target_aspect = output_size[0] / output_size[1] # Aspect ratio of the desired size
@@ -286,7 +102,25 @@ with gr.Blocks() as demo:
286
 
287
  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
288
  generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
289
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
290
  if __name__ == "__main__":
291
  demo.queue(max_size=20)
292
  demo.launch(share=True)
 
1
+ import gradio as gr
2
+ import gradio.helpers
3
+ import torch
4
  import os
5
  from glob import glob
6
  from pathlib import Path
7
  from typing import Optional
8
 
9
+ from diffusers import StableVideoDiffusionPipeline
10
+ from diffusers.utils import load_image, export_to_video
 
 
 
 
11
  from PIL import Image
 
12
 
 
 
 
 
 
 
13
  import uuid
14
  import random
15
  from huggingface_hub import hf_hub_download
16
 
17
+ gradio.helpers.CACHED_FOLDER = '/data/cache'
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18
 
19
+ pipe = StableVideoDiffusionPipeline.from_pretrained(
20
+ "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
 
 
 
21
  )
22
+ pipe.to("cuda")
23
+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
24
+ pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
25
+
26
+ max_64_bit_int = 2**63 - 1
27
 
28
  def sample(
29
  image: Image,
30
+ seed: Optional[int] = 42,
31
  randomize_seed: bool = True,
32
  motion_bucket_id: int = 127,
33
  fps_id: int = 6,
34
  version: str = "svd_xt",
35
  cond_aug: float = 0.02,
36
+ decoding_t: int = 3, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
37
  device: str = "cuda",
38
  output_folder: str = "outputs",
 
39
  ):
40
+ if image.mode == "RGBA":
41
+ image = image.convert("RGB")
42
+
43
  if(randomize_seed):
44
  seed = random.randint(0, max_64_bit_int)
45
+ generator = torch.manual_seed(seed)
 
46
 
47
+ os.makedirs(output_folder, exist_ok=True)
48
+ base_count = len(glob(os.path.join(output_folder, "*.mp4")))
49
+ video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
50
 
51
+ frames = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1).frames[0]
52
+ export_to_video(frames, video_path, fps=fps_id)
53
+ torch.manual_seed(seed)
54
+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55
  return video_path, seed
56
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
57
  def resize_image(image, output_size=(1024, 576)):
58
  # Calculate aspect ratios
59
  target_aspect = output_size[0] / output_size[1] # Aspect ratio of the desired size
 
102
 
103
  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
104
  generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
105
+ gr.Examples(
106
+ examples=[
107
+ "images/blink_meme.png",
108
+ "images/confused2_meme.png",
109
+ "images/confused_meme.png",
110
+ "images/disaster_meme.png",
111
+ "images/distracted_meme.png",
112
+ "images/hide_meme.png",
113
+ "images/nazare_meme.png",
114
+ "images/success_meme.png",
115
+ "images/willy_meme.png",
116
+ "images/wink_meme.png"
117
+ ],
118
+ inputs=image,
119
+ outputs=[video, seed],
120
+ fn=sample,
121
+ cache_examples=True,
122
+ )
123
+
124
  if __name__ == "__main__":
125
  demo.queue(max_size=20)
126
  demo.launch(share=True)
assets/000.jpg DELETED
Binary file (728 kB)
 
assets/001_with_eval.png DELETED

Git LFS Details

  • SHA256: 026fa14e30098729064a00fb7fcec41bb57dcddb33b36b548d553f601bc53634
  • Pointer size: 132 Bytes
  • Size of remote file: 4.19 MB
assets/test_image.png DELETED
Binary file (494 kB)
 
assets/tile.gif DELETED

Git LFS Details

  • SHA256: 2340a9809e36fa9634633c7cc5fd256737c620ba47151726c85173512dc5c8ff
  • Pointer size: 133 Bytes
  • Size of remote file: 18.6 MB
configs/.DS_Store DELETED
Binary file (6.15 kB)
 
configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml DELETED
@@ -1,104 +0,0 @@
1
- model:
2
- base_learning_rate: 4.5e-6
3
- target: sgm.models.autoencoder.AutoencodingEngine
4
- params:
5
- input_key: jpg
6
- monitor: val/rec_loss
7
-
8
- loss_config:
9
- target: sgm.modules.autoencoding.losses.GeneralLPIPSWithDiscriminator
10
- params:
11
- perceptual_weight: 0.25
12
- disc_start: 20001
13
- disc_weight: 0.5
14
- learn_logvar: True
15
-
16
- regularization_weights:
17
- kl_loss: 1.0
18
-
19
- regularizer_config:
20
- target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
21
-
22
- encoder_config:
23
- target: sgm.modules.diffusionmodules.model.Encoder
24
- params:
25
- attn_type: none
26
- double_z: True
27
- z_channels: 4
28
- resolution: 256
29
- in_channels: 3
30
- out_ch: 3
31
- ch: 128
32
- ch_mult: [1, 2, 4]
33
- num_res_blocks: 4
34
- attn_resolutions: []
35
- dropout: 0.0
36
-
37
- decoder_config:
38
- target: sgm.modules.diffusionmodules.model.Decoder
39
- params: ${model.params.encoder_config.params}
40
-
41
- data:
42
- target: sgm.data.dataset.StableDataModuleFromConfig
43
- params:
44
- train:
45
- datapipeline:
46
- urls:
47
- - DATA-PATH
48
- pipeline_config:
49
- shardshuffle: 10000
50
- sample_shuffle: 10000
51
-
52
- decoders:
53
- - pil
54
-
55
- postprocessors:
56
- - target: sdata.mappers.TorchVisionImageTransforms
57
- params:
58
- key: jpg
59
- transforms:
60
- - target: torchvision.transforms.Resize
61
- params:
62
- size: 256
63
- interpolation: 3
64
- - target: torchvision.transforms.ToTensor
65
- - target: sdata.mappers.Rescaler
66
- - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
67
- params:
68
- h_key: height
69
- w_key: width
70
-
71
- loader:
72
- batch_size: 8
73
- num_workers: 4
74
-
75
-
76
- lightning:
77
- strategy:
78
- target: pytorch_lightning.strategies.DDPStrategy
79
- params:
80
- find_unused_parameters: True
81
-
82
- modelcheckpoint:
83
- params:
84
- every_n_train_steps: 5000
85
-
86
- callbacks:
87
- metrics_over_trainsteps_checkpoint:
88
- params:
89
- every_n_train_steps: 50000
90
-
91
- image_logger:
92
- target: main.ImageLogger
93
- params:
94
- enable_autocast: False
95
- batch_frequency: 1000
96
- max_images: 8
97
- increase_log_steps: True
98
-
99
- trainer:
100
- devices: 0,
101
- limit_val_batches: 50
102
- benchmark: True
103
- accumulate_grad_batches: 1
104
- val_check_interval: 10000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/autoencoder/kl-f4/imagenet-kl_f8_8chn.yaml DELETED
@@ -1,105 +0,0 @@
1
- model:
2
- base_learning_rate: 4.5e-6
3
- target: sgm.models.autoencoder.AutoencodingEngine
4
- params:
5
- input_key: jpg
6
- monitor: val/loss/rec
7
- disc_start_iter: 0
8
-
9
- encoder_config:
10
- target: sgm.modules.diffusionmodules.model.Encoder
11
- params:
12
- attn_type: vanilla-xformers
13
- double_z: true
14
- z_channels: 8
15
- resolution: 256
16
- in_channels: 3
17
- out_ch: 3
18
- ch: 128
19
- ch_mult: [1, 2, 4, 4]
20
- num_res_blocks: 2
21
- attn_resolutions: []
22
- dropout: 0.0
23
-
24
- decoder_config:
25
- target: sgm.modules.diffusionmodules.model.Decoder
26
- params: ${model.params.encoder_config.params}
27
-
28
- regularizer_config:
29
- target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
30
-
31
- loss_config:
32
- target: sgm.modules.autoencoding.losses.GeneralLPIPSWithDiscriminator
33
- params:
34
- perceptual_weight: 0.25
35
- disc_start: 20001
36
- disc_weight: 0.5
37
- learn_logvar: True
38
-
39
- regularization_weights:
40
- kl_loss: 1.0
41
-
42
- data:
43
- target: sgm.data.dataset.StableDataModuleFromConfig
44
- params:
45
- train:
46
- datapipeline:
47
- urls:
48
- - DATA-PATH
49
- pipeline_config:
50
- shardshuffle: 10000
51
- sample_shuffle: 10000
52
-
53
- decoders:
54
- - pil
55
-
56
- postprocessors:
57
- - target: sdata.mappers.TorchVisionImageTransforms
58
- params:
59
- key: jpg
60
- transforms:
61
- - target: torchvision.transforms.Resize
62
- params:
63
- size: 256
64
- interpolation: 3
65
- - target: torchvision.transforms.ToTensor
66
- - target: sdata.mappers.Rescaler
67
- - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
68
- params:
69
- h_key: height
70
- w_key: width
71
-
72
- loader:
73
- batch_size: 8
74
- num_workers: 4
75
-
76
-
77
- lightning:
78
- strategy:
79
- target: pytorch_lightning.strategies.DDPStrategy
80
- params:
81
- find_unused_parameters: True
82
-
83
- modelcheckpoint:
84
- params:
85
- every_n_train_steps: 5000
86
-
87
- callbacks:
88
- metrics_over_trainsteps_checkpoint:
89
- params:
90
- every_n_train_steps: 50000
91
-
92
- image_logger:
93
- target: main.ImageLogger
94
- params:
95
- enable_autocast: False
96
- batch_frequency: 1000
97
- max_images: 8
98
- increase_log_steps: True
99
-
100
- trainer:
101
- devices: 0,
102
- limit_val_batches: 50
103
- benchmark: True
104
- accumulate_grad_batches: 1
105
- val_check_interval: 10000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/imagenet-f8_cond.yaml DELETED
@@ -1,185 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- scale_factor: 0.13025
6
- disable_first_stage_autocast: True
7
- log_keys:
8
- - cls
9
-
10
- scheduler_config:
11
- target: sgm.lr_scheduler.LambdaLinearScheduler
12
- params:
13
- warm_up_steps: [10000]
14
- cycle_lengths: [10000000000000]
15
- f_start: [1.e-6]
16
- f_max: [1.]
17
- f_min: [1.]
18
-
19
- denoiser_config:
20
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
21
- params:
22
- num_idx: 1000
23
-
24
- scaling_config:
25
- target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
26
- discretization_config:
27
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
28
-
29
- network_config:
30
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
31
- params:
32
- use_checkpoint: True
33
- in_channels: 4
34
- out_channels: 4
35
- model_channels: 256
36
- attention_resolutions: [1, 2, 4]
37
- num_res_blocks: 2
38
- channel_mult: [1, 2, 4]
39
- num_head_channels: 64
40
- num_classes: sequential
41
- adm_in_channels: 1024
42
- transformer_depth: 1
43
- context_dim: 1024
44
- spatial_transformer_attn_type: softmax-xformers
45
-
46
- conditioner_config:
47
- target: sgm.modules.GeneralConditioner
48
- params:
49
- emb_models:
50
- - is_trainable: True
51
- input_key: cls
52
- ucg_rate: 0.2
53
- target: sgm.modules.encoders.modules.ClassEmbedder
54
- params:
55
- add_sequence_dim: True
56
- embed_dim: 1024
57
- n_classes: 1000
58
-
59
- - is_trainable: False
60
- ucg_rate: 0.2
61
- input_key: original_size_as_tuple
62
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
63
- params:
64
- outdim: 256
65
-
66
- - is_trainable: False
67
- input_key: crop_coords_top_left
68
- ucg_rate: 0.2
69
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
70
- params:
71
- outdim: 256
72
-
73
- first_stage_config:
74
- target: sgm.models.autoencoder.AutoencoderKL
75
- params:
76
- ckpt_path: CKPT_PATH
77
- embed_dim: 4
78
- monitor: val/rec_loss
79
- ddconfig:
80
- attn_type: vanilla-xformers
81
- double_z: true
82
- z_channels: 4
83
- resolution: 256
84
- in_channels: 3
85
- out_ch: 3
86
- ch: 128
87
- ch_mult: [1, 2, 4, 4]
88
- num_res_blocks: 2
89
- attn_resolutions: []
90
- dropout: 0.0
91
- lossconfig:
92
- target: torch.nn.Identity
93
-
94
- loss_fn_config:
95
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
96
- params:
97
- loss_weighting_config:
98
- target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
99
- sigma_sampler_config:
100
- target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
101
- params:
102
- num_idx: 1000
103
-
104
- discretization_config:
105
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
106
-
107
- sampler_config:
108
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
109
- params:
110
- num_steps: 50
111
-
112
- discretization_config:
113
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
114
-
115
- guider_config:
116
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
117
- params:
118
- scale: 5.0
119
-
120
- data:
121
- target: sgm.data.dataset.StableDataModuleFromConfig
122
- params:
123
- train:
124
- datapipeline:
125
- urls:
126
- # USER: adapt this path the root of your custom dataset
127
- - DATA_PATH
128
- pipeline_config:
129
- shardshuffle: 10000
130
- sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
131
-
132
- decoders:
133
- - pil
134
-
135
- postprocessors:
136
- - target: sdata.mappers.TorchVisionImageTransforms
137
- params:
138
- key: jpg # USER: you might wanna adapt this for your custom dataset
139
- transforms:
140
- - target: torchvision.transforms.Resize
141
- params:
142
- size: 256
143
- interpolation: 3
144
- - target: torchvision.transforms.ToTensor
145
- - target: sdata.mappers.Rescaler
146
-
147
- - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
148
- params:
149
- h_key: height # USER: you might wanna adapt this for your custom dataset
150
- w_key: width # USER: you might wanna adapt this for your custom dataset
151
-
152
- loader:
153
- batch_size: 64
154
- num_workers: 6
155
-
156
- lightning:
157
- modelcheckpoint:
158
- params:
159
- every_n_train_steps: 5000
160
-
161
- callbacks:
162
- metrics_over_trainsteps_checkpoint:
163
- params:
164
- every_n_train_steps: 25000
165
-
166
- image_logger:
167
- target: main.ImageLogger
168
- params:
169
- disabled: False
170
- enable_autocast: False
171
- batch_frequency: 1000
172
- max_images: 8
173
- increase_log_steps: True
174
- log_first_step: False
175
- log_images_kwargs:
176
- use_ema_scope: False
177
- N: 8
178
- n_rows: 2
179
-
180
- trainer:
181
- devices: 0,
182
- benchmark: True
183
- num_sanity_val_steps: 0
184
- accumulate_grad_batches: 1
185
- max_epochs: 1000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/toy/cifar10_cond.yaml DELETED
@@ -1,98 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- denoiser_config:
6
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
7
- params:
8
- scaling_config:
9
- target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
10
- params:
11
- sigma_data: 1.0
12
-
13
- network_config:
14
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
15
- params:
16
- in_channels: 3
17
- out_channels: 3
18
- model_channels: 32
19
- attention_resolutions: []
20
- num_res_blocks: 4
21
- channel_mult: [1, 2, 2]
22
- num_head_channels: 32
23
- num_classes: sequential
24
- adm_in_channels: 128
25
-
26
- conditioner_config:
27
- target: sgm.modules.GeneralConditioner
28
- params:
29
- emb_models:
30
- - is_trainable: True
31
- input_key: cls
32
- ucg_rate: 0.2
33
- target: sgm.modules.encoders.modules.ClassEmbedder
34
- params:
35
- embed_dim: 128
36
- n_classes: 10
37
-
38
- first_stage_config:
39
- target: sgm.models.autoencoder.IdentityFirstStage
40
-
41
- loss_fn_config:
42
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
43
- params:
44
- loss_weighting_config:
45
- target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
46
- params:
47
- sigma_data: 1.0
48
- sigma_sampler_config:
49
- target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
50
-
51
- sampler_config:
52
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
53
- params:
54
- num_steps: 50
55
-
56
- discretization_config:
57
- target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
58
-
59
- guider_config:
60
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
61
- params:
62
- scale: 3.0
63
-
64
- data:
65
- target: sgm.data.cifar10.CIFAR10Loader
66
- params:
67
- batch_size: 512
68
- num_workers: 1
69
-
70
- lightning:
71
- modelcheckpoint:
72
- params:
73
- every_n_train_steps: 5000
74
-
75
- callbacks:
76
- metrics_over_trainsteps_checkpoint:
77
- params:
78
- every_n_train_steps: 25000
79
-
80
- image_logger:
81
- target: main.ImageLogger
82
- params:
83
- disabled: False
84
- batch_frequency: 1000
85
- max_images: 64
86
- increase_log_steps: True
87
- log_first_step: False
88
- log_images_kwargs:
89
- use_ema_scope: False
90
- N: 64
91
- n_rows: 8
92
-
93
- trainer:
94
- devices: 0,
95
- benchmark: True
96
- num_sanity_val_steps: 0
97
- accumulate_grad_batches: 1
98
- max_epochs: 20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/toy/mnist.yaml DELETED
@@ -1,79 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- denoiser_config:
6
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
7
- params:
8
- scaling_config:
9
- target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
10
- params:
11
- sigma_data: 1.0
12
-
13
- network_config:
14
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
15
- params:
16
- in_channels: 1
17
- out_channels: 1
18
- model_channels: 32
19
- attention_resolutions: []
20
- num_res_blocks: 4
21
- channel_mult: [1, 2, 2]
22
- num_head_channels: 32
23
-
24
- first_stage_config:
25
- target: sgm.models.autoencoder.IdentityFirstStage
26
-
27
- loss_fn_config:
28
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
29
- params:
30
- loss_weighting_config:
31
- target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
32
- params:
33
- sigma_data: 1.0
34
- sigma_sampler_config:
35
- target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
36
-
37
- sampler_config:
38
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
39
- params:
40
- num_steps: 50
41
-
42
- discretization_config:
43
- target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
44
-
45
- data:
46
- target: sgm.data.mnist.MNISTLoader
47
- params:
48
- batch_size: 512
49
- num_workers: 1
50
-
51
- lightning:
52
- modelcheckpoint:
53
- params:
54
- every_n_train_steps: 5000
55
-
56
- callbacks:
57
- metrics_over_trainsteps_checkpoint:
58
- params:
59
- every_n_train_steps: 25000
60
-
61
- image_logger:
62
- target: main.ImageLogger
63
- params:
64
- disabled: False
65
- batch_frequency: 1000
66
- max_images: 64
67
- increase_log_steps: False
68
- log_first_step: False
69
- log_images_kwargs:
70
- use_ema_scope: False
71
- N: 64
72
- n_rows: 8
73
-
74
- trainer:
75
- devices: 0,
76
- benchmark: True
77
- num_sanity_val_steps: 0
78
- accumulate_grad_batches: 1
79
- max_epochs: 10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/toy/mnist_cond.yaml DELETED
@@ -1,98 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- denoiser_config:
6
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
7
- params:
8
- scaling_config:
9
- target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
10
- params:
11
- sigma_data: 1.0
12
-
13
- network_config:
14
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
15
- params:
16
- in_channels: 1
17
- out_channels: 1
18
- model_channels: 32
19
- attention_resolutions: []
20
- num_res_blocks: 4
21
- channel_mult: [1, 2, 2]
22
- num_head_channels: 32
23
- num_classes: sequential
24
- adm_in_channels: 128
25
-
26
- conditioner_config:
27
- target: sgm.modules.GeneralConditioner
28
- params:
29
- emb_models:
30
- - is_trainable: True
31
- input_key: cls
32
- ucg_rate: 0.2
33
- target: sgm.modules.encoders.modules.ClassEmbedder
34
- params:
35
- embed_dim: 128
36
- n_classes: 10
37
-
38
- first_stage_config:
39
- target: sgm.models.autoencoder.IdentityFirstStage
40
-
41
- loss_fn_config:
42
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
43
- params:
44
- loss_weighting_config:
45
- target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
46
- params:
47
- sigma_data: 1.0
48
- sigma_sampler_config:
49
- target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
50
-
51
- sampler_config:
52
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
53
- params:
54
- num_steps: 50
55
-
56
- discretization_config:
57
- target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
58
-
59
- guider_config:
60
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
61
- params:
62
- scale: 3.0
63
-
64
- data:
65
- target: sgm.data.mnist.MNISTLoader
66
- params:
67
- batch_size: 512
68
- num_workers: 1
69
-
70
- lightning:
71
- modelcheckpoint:
72
- params:
73
- every_n_train_steps: 5000
74
-
75
- callbacks:
76
- metrics_over_trainsteps_checkpoint:
77
- params:
78
- every_n_train_steps: 25000
79
-
80
- image_logger:
81
- target: main.ImageLogger
82
- params:
83
- disabled: False
84
- batch_frequency: 1000
85
- max_images: 16
86
- increase_log_steps: True
87
- log_first_step: False
88
- log_images_kwargs:
89
- use_ema_scope: False
90
- N: 16
91
- n_rows: 4
92
-
93
- trainer:
94
- devices: 0,
95
- benchmark: True
96
- num_sanity_val_steps: 0
97
- accumulate_grad_batches: 1
98
- max_epochs: 20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/toy/mnist_cond_discrete_eps.yaml DELETED
@@ -1,103 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- denoiser_config:
6
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
7
- params:
8
- num_idx: 1000
9
-
10
- scaling_config:
11
- target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
12
- discretization_config:
13
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
14
-
15
- network_config:
16
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
17
- params:
18
- in_channels: 1
19
- out_channels: 1
20
- model_channels: 32
21
- attention_resolutions: []
22
- num_res_blocks: 4
23
- channel_mult: [1, 2, 2]
24
- num_head_channels: 32
25
- num_classes: sequential
26
- adm_in_channels: 128
27
-
28
- conditioner_config:
29
- target: sgm.modules.GeneralConditioner
30
- params:
31
- emb_models:
32
- - is_trainable: True
33
- input_key: cls
34
- ucg_rate: 0.2
35
- target: sgm.modules.encoders.modules.ClassEmbedder
36
- params:
37
- embed_dim: 128
38
- n_classes: 10
39
-
40
- first_stage_config:
41
- target: sgm.models.autoencoder.IdentityFirstStage
42
-
43
- loss_fn_config:
44
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
45
- params:
46
- loss_weighting_config:
47
- target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
48
- sigma_sampler_config:
49
- target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
50
- params:
51
- num_idx: 1000
52
-
53
- discretization_config:
54
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
55
-
56
- sampler_config:
57
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
58
- params:
59
- num_steps: 50
60
-
61
- discretization_config:
62
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
63
-
64
- guider_config:
65
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
66
- params:
67
- scale: 5.0
68
-
69
- data:
70
- target: sgm.data.mnist.MNISTLoader
71
- params:
72
- batch_size: 512
73
- num_workers: 1
74
-
75
- lightning:
76
- modelcheckpoint:
77
- params:
78
- every_n_train_steps: 5000
79
-
80
- callbacks:
81
- metrics_over_trainsteps_checkpoint:
82
- params:
83
- every_n_train_steps: 25000
84
-
85
- image_logger:
86
- target: main.ImageLogger
87
- params:
88
- disabled: False
89
- batch_frequency: 1000
90
- max_images: 16
91
- increase_log_steps: True
92
- log_first_step: False
93
- log_images_kwargs:
94
- use_ema_scope: False
95
- N: 16
96
- n_rows: 4
97
-
98
- trainer:
99
- devices: 0,
100
- benchmark: True
101
- num_sanity_val_steps: 0
102
- accumulate_grad_batches: 1
103
- max_epochs: 20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/toy/mnist_cond_l1_loss.yaml DELETED
@@ -1,99 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- denoiser_config:
6
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
7
- params:
8
- scaling_config:
9
- target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
10
- params:
11
- sigma_data: 1.0
12
-
13
- network_config:
14
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
15
- params:
16
- in_channels: 1
17
- out_channels: 1
18
- model_channels: 32
19
- attention_resolutions: []
20
- num_res_blocks: 4
21
- channel_mult: [1, 2, 2]
22
- num_head_channels: 32
23
- num_classes: sequential
24
- adm_in_channels: 128
25
-
26
- conditioner_config:
27
- target: sgm.modules.GeneralConditioner
28
- params:
29
- emb_models:
30
- - is_trainable: True
31
- input_key: cls
32
- ucg_rate: 0.2
33
- target: sgm.modules.encoders.modules.ClassEmbedder
34
- params:
35
- embed_dim: 128
36
- n_classes: 10
37
-
38
- first_stage_config:
39
- target: sgm.models.autoencoder.IdentityFirstStage
40
-
41
- loss_fn_config:
42
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
43
- params:
44
- loss_type: l1
45
- loss_weighting_config:
46
- target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
47
- params:
48
- sigma_data: 1.0
49
- sigma_sampler_config:
50
- target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
51
-
52
- sampler_config:
53
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
54
- params:
55
- num_steps: 50
56
-
57
- discretization_config:
58
- target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
59
-
60
- guider_config:
61
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
62
- params:
63
- scale: 3.0
64
-
65
- data:
66
- target: sgm.data.mnist.MNISTLoader
67
- params:
68
- batch_size: 512
69
- num_workers: 1
70
-
71
- lightning:
72
- modelcheckpoint:
73
- params:
74
- every_n_train_steps: 5000
75
-
76
- callbacks:
77
- metrics_over_trainsteps_checkpoint:
78
- params:
79
- every_n_train_steps: 25000
80
-
81
- image_logger:
82
- target: main.ImageLogger
83
- params:
84
- disabled: False
85
- batch_frequency: 1000
86
- max_images: 64
87
- increase_log_steps: True
88
- log_first_step: False
89
- log_images_kwargs:
90
- use_ema_scope: False
91
- N: 64
92
- n_rows: 8
93
-
94
- trainer:
95
- devices: 0,
96
- benchmark: True
97
- num_sanity_val_steps: 0
98
- accumulate_grad_batches: 1
99
- max_epochs: 20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/toy/mnist_cond_with_ema.yaml DELETED
@@ -1,100 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- use_ema: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
9
- params:
10
- scaling_config:
11
- target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
12
- params:
13
- sigma_data: 1.0
14
-
15
- network_config:
16
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
17
- params:
18
- in_channels: 1
19
- out_channels: 1
20
- model_channels: 32
21
- attention_resolutions: []
22
- num_res_blocks: 4
23
- channel_mult: [1, 2, 2]
24
- num_head_channels: 32
25
- num_classes: sequential
26
- adm_in_channels: 128
27
-
28
- conditioner_config:
29
- target: sgm.modules.GeneralConditioner
30
- params:
31
- emb_models:
32
- - is_trainable: True
33
- input_key: cls
34
- ucg_rate: 0.2
35
- target: sgm.modules.encoders.modules.ClassEmbedder
36
- params:
37
- embed_dim: 128
38
- n_classes: 10
39
-
40
- first_stage_config:
41
- target: sgm.models.autoencoder.IdentityFirstStage
42
-
43
- loss_fn_config:
44
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
45
- params:
46
- loss_weighting_config:
47
- target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
48
- params:
49
- sigma_data: 1.0
50
- sigma_sampler_config:
51
- target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
52
-
53
- sampler_config:
54
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
55
- params:
56
- num_steps: 50
57
-
58
- discretization_config:
59
- target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
60
-
61
- guider_config:
62
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
63
- params:
64
- scale: 3.0
65
-
66
- data:
67
- target: sgm.data.mnist.MNISTLoader
68
- params:
69
- batch_size: 512
70
- num_workers: 1
71
-
72
- lightning:
73
- modelcheckpoint:
74
- params:
75
- every_n_train_steps: 5000
76
-
77
- callbacks:
78
- metrics_over_trainsteps_checkpoint:
79
- params:
80
- every_n_train_steps: 25000
81
-
82
- image_logger:
83
- target: main.ImageLogger
84
- params:
85
- disabled: False
86
- batch_frequency: 1000
87
- max_images: 64
88
- increase_log_steps: True
89
- log_first_step: False
90
- log_images_kwargs:
91
- use_ema_scope: False
92
- N: 64
93
- n_rows: 8
94
-
95
- trainer:
96
- devices: 0,
97
- benchmark: True
98
- num_sanity_val_steps: 0
99
- accumulate_grad_batches: 1
100
- max_epochs: 20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/txt2img-clipl-legacy-ucg-training.yaml DELETED
@@ -1,182 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- scale_factor: 0.13025
6
- disable_first_stage_autocast: True
7
- log_keys:
8
- - txt
9
-
10
- scheduler_config:
11
- target: sgm.lr_scheduler.LambdaLinearScheduler
12
- params:
13
- warm_up_steps: [10000]
14
- cycle_lengths: [10000000000000]
15
- f_start: [1.e-6]
16
- f_max: [1.]
17
- f_min: [1.]
18
-
19
- denoiser_config:
20
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
21
- params:
22
- num_idx: 1000
23
-
24
- scaling_config:
25
- target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
26
- discretization_config:
27
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
28
-
29
- network_config:
30
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
31
- params:
32
- use_checkpoint: True
33
- in_channels: 4
34
- out_channels: 4
35
- model_channels: 320
36
- attention_resolutions: [1, 2, 4]
37
- num_res_blocks: 2
38
- channel_mult: [1, 2, 4, 4]
39
- num_head_channels: 64
40
- num_classes: sequential
41
- adm_in_channels: 1792
42
- num_heads: 1
43
- transformer_depth: 1
44
- context_dim: 768
45
- spatial_transformer_attn_type: softmax-xformers
46
-
47
- conditioner_config:
48
- target: sgm.modules.GeneralConditioner
49
- params:
50
- emb_models:
51
- - is_trainable: True
52
- input_key: txt
53
- ucg_rate: 0.1
54
- legacy_ucg_value: ""
55
- target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
56
- params:
57
- always_return_pooled: True
58
-
59
- - is_trainable: False
60
- ucg_rate: 0.1
61
- input_key: original_size_as_tuple
62
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
63
- params:
64
- outdim: 256
65
-
66
- - is_trainable: False
67
- input_key: crop_coords_top_left
68
- ucg_rate: 0.1
69
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
70
- params:
71
- outdim: 256
72
-
73
- first_stage_config:
74
- target: sgm.models.autoencoder.AutoencoderKL
75
- params:
76
- ckpt_path: CKPT_PATH
77
- embed_dim: 4
78
- monitor: val/rec_loss
79
- ddconfig:
80
- attn_type: vanilla-xformers
81
- double_z: true
82
- z_channels: 4
83
- resolution: 256
84
- in_channels: 3
85
- out_ch: 3
86
- ch: 128
87
- ch_mult: [ 1, 2, 4, 4 ]
88
- num_res_blocks: 2
89
- attn_resolutions: [ ]
90
- dropout: 0.0
91
- lossconfig:
92
- target: torch.nn.Identity
93
-
94
- loss_fn_config:
95
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
96
- params:
97
- loss_weighting_config:
98
- target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
99
- sigma_sampler_config:
100
- target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
101
- params:
102
- num_idx: 1000
103
-
104
- discretization_config:
105
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
106
-
107
- sampler_config:
108
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
109
- params:
110
- num_steps: 50
111
-
112
- discretization_config:
113
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
114
-
115
- guider_config:
116
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
117
- params:
118
- scale: 7.5
119
-
120
- data:
121
- target: sgm.data.dataset.StableDataModuleFromConfig
122
- params:
123
- train:
124
- datapipeline:
125
- urls:
126
- # USER: adapt this path the root of your custom dataset
127
- - DATA_PATH
128
- pipeline_config:
129
- shardshuffle: 10000
130
- sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
131
-
132
- decoders:
133
- - pil
134
-
135
- postprocessors:
136
- - target: sdata.mappers.TorchVisionImageTransforms
137
- params:
138
- key: jpg # USER: you might wanna adapt this for your custom dataset
139
- transforms:
140
- - target: torchvision.transforms.Resize
141
- params:
142
- size: 256
143
- interpolation: 3
144
- - target: torchvision.transforms.ToTensor
145
- - target: sdata.mappers.Rescaler
146
- - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
147
- # USER: you might wanna use non-default parameters due to your custom dataset
148
-
149
- loader:
150
- batch_size: 64
151
- num_workers: 6
152
-
153
- lightning:
154
- modelcheckpoint:
155
- params:
156
- every_n_train_steps: 5000
157
-
158
- callbacks:
159
- metrics_over_trainsteps_checkpoint:
160
- params:
161
- every_n_train_steps: 25000
162
-
163
- image_logger:
164
- target: main.ImageLogger
165
- params:
166
- disabled: False
167
- enable_autocast: False
168
- batch_frequency: 1000
169
- max_images: 8
170
- increase_log_steps: True
171
- log_first_step: False
172
- log_images_kwargs:
173
- use_ema_scope: False
174
- N: 8
175
- n_rows: 2
176
-
177
- trainer:
178
- devices: 0,
179
- benchmark: True
180
- num_sanity_val_steps: 0
181
- accumulate_grad_batches: 1
182
- max_epochs: 1000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/example_training/txt2img-clipl.yaml DELETED
@@ -1,184 +0,0 @@
1
- model:
2
- base_learning_rate: 1.0e-4
3
- target: sgm.models.diffusion.DiffusionEngine
4
- params:
5
- scale_factor: 0.13025
6
- disable_first_stage_autocast: True
7
- log_keys:
8
- - txt
9
-
10
- scheduler_config:
11
- target: sgm.lr_scheduler.LambdaLinearScheduler
12
- params:
13
- warm_up_steps: [10000]
14
- cycle_lengths: [10000000000000]
15
- f_start: [1.e-6]
16
- f_max: [1.]
17
- f_min: [1.]
18
-
19
- denoiser_config:
20
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
21
- params:
22
- num_idx: 1000
23
-
24
- scaling_config:
25
- target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
26
- discretization_config:
27
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
28
-
29
- network_config:
30
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
31
- params:
32
- use_checkpoint: True
33
- in_channels: 4
34
- out_channels: 4
35
- model_channels: 320
36
- attention_resolutions: [1, 2, 4]
37
- num_res_blocks: 2
38
- channel_mult: [1, 2, 4, 4]
39
- num_head_channels: 64
40
- num_classes: sequential
41
- adm_in_channels: 1792
42
- num_heads: 1
43
- transformer_depth: 1
44
- context_dim: 768
45
- spatial_transformer_attn_type: softmax-xformers
46
-
47
- conditioner_config:
48
- target: sgm.modules.GeneralConditioner
49
- params:
50
- emb_models:
51
- - is_trainable: True
52
- input_key: txt
53
- ucg_rate: 0.1
54
- legacy_ucg_value: ""
55
- target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
56
- params:
57
- always_return_pooled: True
58
-
59
- - is_trainable: False
60
- ucg_rate: 0.1
61
- input_key: original_size_as_tuple
62
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
63
- params:
64
- outdim: 256
65
-
66
- - is_trainable: False
67
- input_key: crop_coords_top_left
68
- ucg_rate: 0.1
69
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
70
- params:
71
- outdim: 256
72
-
73
- first_stage_config:
74
- target: sgm.models.autoencoder.AutoencoderKL
75
- params:
76
- ckpt_path: CKPT_PATH
77
- embed_dim: 4
78
- monitor: val/rec_loss
79
- ddconfig:
80
- attn_type: vanilla-xformers
81
- double_z: true
82
- z_channels: 4
83
- resolution: 256
84
- in_channels: 3
85
- out_ch: 3
86
- ch: 128
87
- ch_mult: [1, 2, 4, 4]
88
- num_res_blocks: 2
89
- attn_resolutions: []
90
- dropout: 0.0
91
- lossconfig:
92
- target: torch.nn.Identity
93
-
94
- loss_fn_config:
95
- target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
96
- params:
97
- loss_weighting_config:
98
- target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
99
- sigma_sampler_config:
100
- target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
101
- params:
102
- num_idx: 1000
103
-
104
- discretization_config:
105
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
106
-
107
- sampler_config:
108
- target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
109
- params:
110
- num_steps: 50
111
-
112
- discretization_config:
113
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
114
-
115
- guider_config:
116
- target: sgm.modules.diffusionmodules.guiders.VanillaCFG
117
- params:
118
- scale: 7.5
119
-
120
- data:
121
- target: sgm.data.dataset.StableDataModuleFromConfig
122
- params:
123
- train:
124
- datapipeline:
125
- urls:
126
- # USER: adapt this path the root of your custom dataset
127
- - DATA_PATH
128
- pipeline_config:
129
- shardshuffle: 10000
130
- sample_shuffle: 10000
131
-
132
-
133
- decoders:
134
- - pil
135
-
136
- postprocessors:
137
- - target: sdata.mappers.TorchVisionImageTransforms
138
- params:
139
- key: jpg # USER: you might wanna adapt this for your custom dataset
140
- transforms:
141
- - target: torchvision.transforms.Resize
142
- params:
143
- size: 256
144
- interpolation: 3
145
- - target: torchvision.transforms.ToTensor
146
- - target: sdata.mappers.Rescaler
147
- # USER: you might wanna use non-default parameters due to your custom dataset
148
- - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
149
- # USER: you might wanna use non-default parameters due to your custom dataset
150
-
151
- loader:
152
- batch_size: 64
153
- num_workers: 6
154
-
155
- lightning:
156
- modelcheckpoint:
157
- params:
158
- every_n_train_steps: 5000
159
-
160
- callbacks:
161
- metrics_over_trainsteps_checkpoint:
162
- params:
163
- every_n_train_steps: 25000
164
-
165
- image_logger:
166
- target: main.ImageLogger
167
- params:
168
- disabled: False
169
- enable_autocast: False
170
- batch_frequency: 1000
171
- max_images: 8
172
- increase_log_steps: True
173
- log_first_step: False
174
- log_images_kwargs:
175
- use_ema_scope: False
176
- N: 8
177
- n_rows: 2
178
-
179
- trainer:
180
- devices: 0,
181
- benchmark: True
182
- num_sanity_val_steps: 0
183
- accumulate_grad_batches: 1
184
- max_epochs: 1000
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/inference/sd_2_1.yaml DELETED
@@ -1,60 +0,0 @@
1
- model:
2
- target: sgm.models.diffusion.DiffusionEngine
3
- params:
4
- scale_factor: 0.18215
5
- disable_first_stage_autocast: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
9
- params:
10
- num_idx: 1000
11
-
12
- scaling_config:
13
- target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
14
- discretization_config:
15
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
16
-
17
- network_config:
18
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
19
- params:
20
- use_checkpoint: True
21
- in_channels: 4
22
- out_channels: 4
23
- model_channels: 320
24
- attention_resolutions: [4, 2, 1]
25
- num_res_blocks: 2
26
- channel_mult: [1, 2, 4, 4]
27
- num_head_channels: 64
28
- use_linear_in_transformer: True
29
- transformer_depth: 1
30
- context_dim: 1024
31
-
32
- conditioner_config:
33
- target: sgm.modules.GeneralConditioner
34
- params:
35
- emb_models:
36
- - is_trainable: False
37
- input_key: txt
38
- target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder
39
- params:
40
- freeze: true
41
- layer: penultimate
42
-
43
- first_stage_config:
44
- target: sgm.models.autoencoder.AutoencoderKL
45
- params:
46
- embed_dim: 4
47
- monitor: val/rec_loss
48
- ddconfig:
49
- double_z: true
50
- z_channels: 4
51
- resolution: 256
52
- in_channels: 3
53
- out_ch: 3
54
- ch: 128
55
- ch_mult: [1, 2, 4, 4]
56
- num_res_blocks: 2
57
- attn_resolutions: []
58
- dropout: 0.0
59
- lossconfig:
60
- target: torch.nn.Identity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/inference/sd_2_1_768.yaml DELETED
@@ -1,60 +0,0 @@
1
- model:
2
- target: sgm.models.diffusion.DiffusionEngine
3
- params:
4
- scale_factor: 0.18215
5
- disable_first_stage_autocast: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
9
- params:
10
- num_idx: 1000
11
-
12
- scaling_config:
13
- target: sgm.modules.diffusionmodules.denoiser_scaling.VScaling
14
- discretization_config:
15
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
16
-
17
- network_config:
18
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
19
- params:
20
- use_checkpoint: True
21
- in_channels: 4
22
- out_channels: 4
23
- model_channels: 320
24
- attention_resolutions: [4, 2, 1]
25
- num_res_blocks: 2
26
- channel_mult: [1, 2, 4, 4]
27
- num_head_channels: 64
28
- use_linear_in_transformer: True
29
- transformer_depth: 1
30
- context_dim: 1024
31
-
32
- conditioner_config:
33
- target: sgm.modules.GeneralConditioner
34
- params:
35
- emb_models:
36
- - is_trainable: False
37
- input_key: txt
38
- target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder
39
- params:
40
- freeze: true
41
- layer: penultimate
42
-
43
- first_stage_config:
44
- target: sgm.models.autoencoder.AutoencoderKL
45
- params:
46
- embed_dim: 4
47
- monitor: val/rec_loss
48
- ddconfig:
49
- double_z: true
50
- z_channels: 4
51
- resolution: 256
52
- in_channels: 3
53
- out_ch: 3
54
- ch: 128
55
- ch_mult: [1, 2, 4, 4]
56
- num_res_blocks: 2
57
- attn_resolutions: []
58
- dropout: 0.0
59
- lossconfig:
60
- target: torch.nn.Identity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/inference/sd_xl_base.yaml DELETED
@@ -1,93 +0,0 @@
1
- model:
2
- target: sgm.models.diffusion.DiffusionEngine
3
- params:
4
- scale_factor: 0.13025
5
- disable_first_stage_autocast: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
9
- params:
10
- num_idx: 1000
11
-
12
- scaling_config:
13
- target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
14
- discretization_config:
15
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
16
-
17
- network_config:
18
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
19
- params:
20
- adm_in_channels: 2816
21
- num_classes: sequential
22
- use_checkpoint: True
23
- in_channels: 4
24
- out_channels: 4
25
- model_channels: 320
26
- attention_resolutions: [4, 2]
27
- num_res_blocks: 2
28
- channel_mult: [1, 2, 4]
29
- num_head_channels: 64
30
- use_linear_in_transformer: True
31
- transformer_depth: [1, 2, 10]
32
- context_dim: 2048
33
- spatial_transformer_attn_type: softmax-xformers
34
-
35
- conditioner_config:
36
- target: sgm.modules.GeneralConditioner
37
- params:
38
- emb_models:
39
- - is_trainable: False
40
- input_key: txt
41
- target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
42
- params:
43
- layer: hidden
44
- layer_idx: 11
45
-
46
- - is_trainable: False
47
- input_key: txt
48
- target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
49
- params:
50
- arch: ViT-bigG-14
51
- version: laion2b_s39b_b160k
52
- freeze: True
53
- layer: penultimate
54
- always_return_pooled: True
55
- legacy: False
56
-
57
- - is_trainable: False
58
- input_key: original_size_as_tuple
59
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
60
- params:
61
- outdim: 256
62
-
63
- - is_trainable: False
64
- input_key: crop_coords_top_left
65
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
66
- params:
67
- outdim: 256
68
-
69
- - is_trainable: False
70
- input_key: target_size_as_tuple
71
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
72
- params:
73
- outdim: 256
74
-
75
- first_stage_config:
76
- target: sgm.models.autoencoder.AutoencoderKL
77
- params:
78
- embed_dim: 4
79
- monitor: val/rec_loss
80
- ddconfig:
81
- attn_type: vanilla-xformers
82
- double_z: true
83
- z_channels: 4
84
- resolution: 256
85
- in_channels: 3
86
- out_ch: 3
87
- ch: 128
88
- ch_mult: [1, 2, 4, 4]
89
- num_res_blocks: 2
90
- attn_resolutions: []
91
- dropout: 0.0
92
- lossconfig:
93
- target: torch.nn.Identity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/inference/sd_xl_refiner.yaml DELETED
@@ -1,86 +0,0 @@
1
- model:
2
- target: sgm.models.diffusion.DiffusionEngine
3
- params:
4
- scale_factor: 0.13025
5
- disable_first_stage_autocast: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
9
- params:
10
- num_idx: 1000
11
-
12
- scaling_config:
13
- target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
14
- discretization_config:
15
- target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
16
-
17
- network_config:
18
- target: sgm.modules.diffusionmodules.openaimodel.UNetModel
19
- params:
20
- adm_in_channels: 2560
21
- num_classes: sequential
22
- use_checkpoint: True
23
- in_channels: 4
24
- out_channels: 4
25
- model_channels: 384
26
- attention_resolutions: [4, 2]
27
- num_res_blocks: 2
28
- channel_mult: [1, 2, 4, 4]
29
- num_head_channels: 64
30
- use_linear_in_transformer: True
31
- transformer_depth: 4
32
- context_dim: [1280, 1280, 1280, 1280]
33
- spatial_transformer_attn_type: softmax-xformers
34
-
35
- conditioner_config:
36
- target: sgm.modules.GeneralConditioner
37
- params:
38
- emb_models:
39
- - is_trainable: False
40
- input_key: txt
41
- target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
42
- params:
43
- arch: ViT-bigG-14
44
- version: laion2b_s39b_b160k
45
- legacy: False
46
- freeze: True
47
- layer: penultimate
48
- always_return_pooled: True
49
-
50
- - is_trainable: False
51
- input_key: original_size_as_tuple
52
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
53
- params:
54
- outdim: 256
55
-
56
- - is_trainable: False
57
- input_key: crop_coords_top_left
58
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
59
- params:
60
- outdim: 256
61
-
62
- - is_trainable: False
63
- input_key: aesthetic_score
64
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
65
- params:
66
- outdim: 256
67
-
68
- first_stage_config:
69
- target: sgm.models.autoencoder.AutoencoderKL
70
- params:
71
- embed_dim: 4
72
- monitor: val/rec_loss
73
- ddconfig:
74
- attn_type: vanilla-xformers
75
- double_z: true
76
- z_channels: 4
77
- resolution: 256
78
- in_channels: 3
79
- out_ch: 3
80
- ch: 128
81
- ch_mult: [1, 2, 4, 4]
82
- num_res_blocks: 2
83
- attn_resolutions: []
84
- dropout: 0.0
85
- lossconfig:
86
- target: torch.nn.Identity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/inference/svd.yaml DELETED
@@ -1,131 +0,0 @@
1
- model:
2
- target: sgm.models.diffusion.DiffusionEngine
3
- params:
4
- scale_factor: 0.18215
5
- disable_first_stage_autocast: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
9
- params:
10
- scaling_config:
11
- target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
12
-
13
- network_config:
14
- target: sgm.modules.diffusionmodules.video_model.VideoUNet
15
- params:
16
- adm_in_channels: 768
17
- num_classes: sequential
18
- use_checkpoint: True
19
- in_channels: 8
20
- out_channels: 4
21
- model_channels: 320
22
- attention_resolutions: [4, 2, 1]
23
- num_res_blocks: 2
24
- channel_mult: [1, 2, 4, 4]
25
- num_head_channels: 64
26
- use_linear_in_transformer: True
27
- transformer_depth: 1
28
- context_dim: 1024
29
- spatial_transformer_attn_type: softmax-xformers
30
- extra_ff_mix_layer: True
31
- use_spatial_context: True
32
- merge_strategy: learned_with_images
33
- video_kernel_size: [3, 1, 1]
34
-
35
- conditioner_config:
36
- target: sgm.modules.GeneralConditioner
37
- params:
38
- emb_models:
39
- - is_trainable: False
40
- input_key: cond_frames_without_noise
41
- target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
42
- params:
43
- n_cond_frames: 1
44
- n_copies: 1
45
- open_clip_embedding_config:
46
- target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
47
- params:
48
- freeze: True
49
-
50
- - input_key: fps_id
51
- is_trainable: False
52
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
53
- params:
54
- outdim: 256
55
-
56
- - input_key: motion_bucket_id
57
- is_trainable: False
58
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
59
- params:
60
- outdim: 256
61
-
62
- - input_key: cond_frames
63
- is_trainable: False
64
- target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
65
- params:
66
- disable_encoder_autocast: True
67
- n_cond_frames: 1
68
- n_copies: 1
69
- is_ae: True
70
- encoder_config:
71
- target: sgm.models.autoencoder.AutoencoderKLModeOnly
72
- params:
73
- embed_dim: 4
74
- monitor: val/rec_loss
75
- ddconfig:
76
- attn_type: vanilla-xformers
77
- double_z: True
78
- z_channels: 4
79
- resolution: 256
80
- in_channels: 3
81
- out_ch: 3
82
- ch: 128
83
- ch_mult: [1, 2, 4, 4]
84
- num_res_blocks: 2
85
- attn_resolutions: []
86
- dropout: 0.0
87
- lossconfig:
88
- target: torch.nn.Identity
89
-
90
- - input_key: cond_aug
91
- is_trainable: False
92
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
93
- params:
94
- outdim: 256
95
-
96
- first_stage_config:
97
- target: sgm.models.autoencoder.AutoencodingEngine
98
- params:
99
- loss_config:
100
- target: torch.nn.Identity
101
- regularizer_config:
102
- target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
103
- encoder_config:
104
- target: sgm.modules.diffusionmodules.model.Encoder
105
- params:
106
- attn_type: vanilla
107
- double_z: True
108
- z_channels: 4
109
- resolution: 256
110
- in_channels: 3
111
- out_ch: 3
112
- ch: 128
113
- ch_mult: [1, 2, 4, 4]
114
- num_res_blocks: 2
115
- attn_resolutions: []
116
- dropout: 0.0
117
- decoder_config:
118
- target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
119
- params:
120
- attn_type: vanilla
121
- double_z: True
122
- z_channels: 4
123
- resolution: 256
124
- in_channels: 3
125
- out_ch: 3
126
- ch: 128
127
- ch_mult: [1, 2, 4, 4]
128
- num_res_blocks: 2
129
- attn_resolutions: []
130
- dropout: 0.0
131
- video_kernel_size: [3, 1, 1]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
configs/inference/svd_image_decoder.yaml DELETED
@@ -1,114 +0,0 @@
1
- model:
2
- target: sgm.models.diffusion.DiffusionEngine
3
- params:
4
- scale_factor: 0.18215
5
- disable_first_stage_autocast: True
6
-
7
- denoiser_config:
8
- target: sgm.modules.diffusionmodules.denoiser.Denoiser
9
- params:
10
- scaling_config:
11
- target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
12
-
13
- network_config:
14
- target: sgm.modules.diffusionmodules.video_model.VideoUNet
15
- params:
16
- adm_in_channels: 768
17
- num_classes: sequential
18
- use_checkpoint: True
19
- in_channels: 8
20
- out_channels: 4
21
- model_channels: 320
22
- attention_resolutions: [4, 2, 1]
23
- num_res_blocks: 2
24
- channel_mult: [1, 2, 4, 4]
25
- num_head_channels: 64
26
- use_linear_in_transformer: True
27
- transformer_depth: 1
28
- context_dim: 1024
29
- spatial_transformer_attn_type: softmax-xformers
30
- extra_ff_mix_layer: True
31
- use_spatial_context: True
32
- merge_strategy: learned_with_images
33
- video_kernel_size: [3, 1, 1]
34
-
35
- conditioner_config:
36
- target: sgm.modules.GeneralConditioner
37
- params:
38
- emb_models:
39
- - is_trainable: False
40
- input_key: cond_frames_without_noise
41
- target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
42
- params:
43
- n_cond_frames: 1
44
- n_copies: 1
45
- open_clip_embedding_config:
46
- target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
47
- params:
48
- freeze: True
49
-
50
- - input_key: fps_id
51
- is_trainable: False
52
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
53
- params:
54
- outdim: 256
55
-
56
- - input_key: motion_bucket_id
57
- is_trainable: False
58
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
59
- params:
60
- outdim: 256
61
-
62
- - input_key: cond_frames
63
- is_trainable: False
64
- target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
65
- params:
66
- disable_encoder_autocast: True
67
- n_cond_frames: 1
68
- n_copies: 1
69
- is_ae: True
70
- encoder_config:
71
- target: sgm.models.autoencoder.AutoencoderKLModeOnly
72
- params:
73
- embed_dim: 4
74
- monitor: val/rec_loss
75
- ddconfig:
76
- attn_type: vanilla-xformers
77
- double_z: True
78
- z_channels: 4
79
- resolution: 256
80
- in_channels: 3
81
- out_ch: 3
82
- ch: 128
83
- ch_mult: [1, 2, 4, 4]
84
- num_res_blocks: 2
85
- attn_resolutions: []
86
- dropout: 0.0
87
- lossconfig:
88
- target: torch.nn.Identity
89
-
90
- - input_key: cond_aug
91
- is_trainable: False
92
- target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
93
- params:
94
- outdim: 256
95
-
96
- first_stage_config:
97
- target: sgm.models.autoencoder.AutoencoderKL
98
- params:
99
- embed_dim: 4
100
- monitor: val/rec_loss
101
- ddconfig:
102
- attn_type: vanilla-xformers
103
- double_z: True
104
- z_channels: 4
105
- resolution: 256
106
- in_channels: 3
107
- out_ch: 3
108
- ch: 128
109
- ch_mult: [1, 2, 4, 4]
110
- num_res_blocks: 2
111
- attn_resolutions: []
112
- dropout: 0.0
113
- lossconfig:
114
- target: torch.nn.Identity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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images/confused_meme.png ADDED
images/disaster_meme.png ADDED
images/distracted_meme.png ADDED
images/hide_meme.png ADDED
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images/success_meme.png ADDED
images/willy_meme.png ADDED
images/wink_meme.png ADDED
main.py DELETED
@@ -1,943 +0,0 @@
1
- import argparse
2
- import datetime
3
- import glob
4
- import inspect
5
- import os
6
- import sys
7
- from inspect import Parameter
8
- from typing import Union
9
-
10
- import numpy as np
11
- import pytorch_lightning as pl
12
- import torch
13
- import torchvision
14
- import wandb
15
- from matplotlib import pyplot as plt
16
- from natsort import natsorted
17
- from omegaconf import OmegaConf
18
- from packaging import version
19
- from PIL import Image
20
- from pytorch_lightning import seed_everything
21
- from pytorch_lightning.callbacks import Callback
22
- from pytorch_lightning.loggers import WandbLogger
23
- from pytorch_lightning.trainer import Trainer
24
- from pytorch_lightning.utilities import rank_zero_only
25
-
26
- from sgm.util import exists, instantiate_from_config, isheatmap
27
-
28
- MULTINODE_HACKS = True
29
-
30
-
31
- def default_trainer_args():
32
- argspec = dict(inspect.signature(Trainer.__init__).parameters)
33
- argspec.pop("self")
34
- default_args = {
35
- param: argspec[param].default
36
- for param in argspec
37
- if argspec[param] != Parameter.empty
38
- }
39
- return default_args
40
-
41
-
42
- def get_parser(**parser_kwargs):
43
- def str2bool(v):
44
- if isinstance(v, bool):
45
- return v
46
- if v.lower() in ("yes", "true", "t", "y", "1"):
47
- return True
48
- elif v.lower() in ("no", "false", "f", "n", "0"):
49
- return False
50
- else:
51
- raise argparse.ArgumentTypeError("Boolean value expected.")
52
-
53
- parser = argparse.ArgumentParser(**parser_kwargs)
54
- parser.add_argument(
55
- "-n",
56
- "--name",
57
- type=str,
58
- const=True,
59
- default="",
60
- nargs="?",
61
- help="postfix for logdir",
62
- )
63
- parser.add_argument(
64
- "--no_date",
65
- type=str2bool,
66
- nargs="?",
67
- const=True,
68
- default=False,
69
- help="if True, skip date generation for logdir and only use naming via opt.base or opt.name (+ opt.postfix, optionally)",
70
- )
71
- parser.add_argument(
72
- "-r",
73
- "--resume",
74
- type=str,
75
- const=True,
76
- default="",
77
- nargs="?",
78
- help="resume from logdir or checkpoint in logdir",
79
- )
80
- parser.add_argument(
81
- "-b",
82
- "--base",
83
- nargs="*",
84
- metavar="base_config.yaml",
85
- help="paths to base configs. Loaded from left-to-right. "
86
- "Parameters can be overwritten or added with command-line options of the form `--key value`.",
87
- default=list(),
88
- )
89
- parser.add_argument(
90
- "-t",
91
- "--train",
92
- type=str2bool,
93
- const=True,
94
- default=True,
95
- nargs="?",
96
- help="train",
97
- )
98
- parser.add_argument(
99
- "--no-test",
100
- type=str2bool,
101
- const=True,
102
- default=False,
103
- nargs="?",
104
- help="disable test",
105
- )
106
- parser.add_argument(
107
- "-p", "--project", help="name of new or path to existing project"
108
- )
109
- parser.add_argument(
110
- "-d",
111
- "--debug",
112
- type=str2bool,
113
- nargs="?",
114
- const=True,
115
- default=False,
116
- help="enable post-mortem debugging",
117
- )
118
- parser.add_argument(
119
- "-s",
120
- "--seed",
121
- type=int,
122
- default=23,
123
- help="seed for seed_everything",
124
- )
125
- parser.add_argument(
126
- "-f",
127
- "--postfix",
128
- type=str,
129
- default="",
130
- help="post-postfix for default name",
131
- )
132
- parser.add_argument(
133
- "--projectname",
134
- type=str,
135
- default="stablediffusion",
136
- )
137
- parser.add_argument(
138
- "-l",
139
- "--logdir",
140
- type=str,
141
- default="logs",
142
- help="directory for logging dat shit",
143
- )
144
- parser.add_argument(
145
- "--scale_lr",
146
- type=str2bool,
147
- nargs="?",
148
- const=True,
149
- default=False,
150
- help="scale base-lr by ngpu * batch_size * n_accumulate",
151
- )
152
- parser.add_argument(
153
- "--legacy_naming",
154
- type=str2bool,
155
- nargs="?",
156
- const=True,
157
- default=False,
158
- help="name run based on config file name if true, else by whole path",
159
- )
160
- parser.add_argument(
161
- "--enable_tf32",
162
- type=str2bool,
163
- nargs="?",
164
- const=True,
165
- default=False,
166
- help="enables the TensorFloat32 format both for matmuls and cuDNN for pytorch 1.12",
167
- )
168
- parser.add_argument(
169
- "--startup",
170
- type=str,
171
- default=None,
172
- help="Startuptime from distributed script",
173
- )
174
- parser.add_argument(
175
- "--wandb",
176
- type=str2bool,
177
- nargs="?",
178
- const=True,
179
- default=False, # TODO: later default to True
180
- help="log to wandb",
181
- )
182
- parser.add_argument(
183
- "--no_base_name",
184
- type=str2bool,
185
- nargs="?",
186
- const=True,
187
- default=False, # TODO: later default to True
188
- help="log to wandb",
189
- )
190
- if version.parse(torch.__version__) >= version.parse("2.0.0"):
191
- parser.add_argument(
192
- "--resume_from_checkpoint",
193
- type=str,
194
- default=None,
195
- help="single checkpoint file to resume from",
196
- )
197
- default_args = default_trainer_args()
198
- for key in default_args:
199
- parser.add_argument("--" + key, default=default_args[key])
200
- return parser
201
-
202
-
203
- def get_checkpoint_name(logdir):
204
- ckpt = os.path.join(logdir, "checkpoints", "last**.ckpt")
205
- ckpt = natsorted(glob.glob(ckpt))
206
- print('available "last" checkpoints:')
207
- print(ckpt)
208
- if len(ckpt) > 1:
209
- print("got most recent checkpoint")
210
- ckpt = sorted(ckpt, key=lambda x: os.path.getmtime(x))[-1]
211
- print(f"Most recent ckpt is {ckpt}")
212
- with open(os.path.join(logdir, "most_recent_ckpt.txt"), "w") as f:
213
- f.write(ckpt + "\n")
214
- try:
215
- version = int(ckpt.split("/")[-1].split("-v")[-1].split(".")[0])
216
- except Exception as e:
217
- print("version confusion but not bad")
218
- print(e)
219
- version = 1
220
- # version = last_version + 1
221
- else:
222
- # in this case, we only have one "last.ckpt"
223
- ckpt = ckpt[0]
224
- version = 1
225
- melk_ckpt_name = f"last-v{version}.ckpt"
226
- print(f"Current melk ckpt name: {melk_ckpt_name}")
227
- return ckpt, melk_ckpt_name
228
-
229
-
230
- class SetupCallback(Callback):
231
- def __init__(
232
- self,
233
- resume,
234
- now,
235
- logdir,
236
- ckptdir,
237
- cfgdir,
238
- config,
239
- lightning_config,
240
- debug,
241
- ckpt_name=None,
242
- ):
243
- super().__init__()
244
- self.resume = resume
245
- self.now = now
246
- self.logdir = logdir
247
- self.ckptdir = ckptdir
248
- self.cfgdir = cfgdir
249
- self.config = config
250
- self.lightning_config = lightning_config
251
- self.debug = debug
252
- self.ckpt_name = ckpt_name
253
-
254
- def on_exception(self, trainer: pl.Trainer, pl_module, exception):
255
- if not self.debug and trainer.global_rank == 0:
256
- print("Summoning checkpoint.")
257
- if self.ckpt_name is None:
258
- ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
259
- else:
260
- ckpt_path = os.path.join(self.ckptdir, self.ckpt_name)
261
- trainer.save_checkpoint(ckpt_path)
262
-
263
- def on_fit_start(self, trainer, pl_module):
264
- if trainer.global_rank == 0:
265
- # Create logdirs and save configs
266
- os.makedirs(self.logdir, exist_ok=True)
267
- os.makedirs(self.ckptdir, exist_ok=True)
268
- os.makedirs(self.cfgdir, exist_ok=True)
269
-
270
- if "callbacks" in self.lightning_config:
271
- if (
272
- "metrics_over_trainsteps_checkpoint"
273
- in self.lightning_config["callbacks"]
274
- ):
275
- os.makedirs(
276
- os.path.join(self.ckptdir, "trainstep_checkpoints"),
277
- exist_ok=True,
278
- )
279
- print("Project config")
280
- print(OmegaConf.to_yaml(self.config))
281
- if MULTINODE_HACKS:
282
- import time
283
-
284
- time.sleep(5)
285
- OmegaConf.save(
286
- self.config,
287
- os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)),
288
- )
289
-
290
- print("Lightning config")
291
- print(OmegaConf.to_yaml(self.lightning_config))
292
- OmegaConf.save(
293
- OmegaConf.create({"lightning": self.lightning_config}),
294
- os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)),
295
- )
296
-
297
- else:
298
- # ModelCheckpoint callback created log directory --- remove it
299
- if not MULTINODE_HACKS and not self.resume and os.path.exists(self.logdir):
300
- dst, name = os.path.split(self.logdir)
301
- dst = os.path.join(dst, "child_runs", name)
302
- os.makedirs(os.path.split(dst)[0], exist_ok=True)
303
- try:
304
- os.rename(self.logdir, dst)
305
- except FileNotFoundError:
306
- pass
307
-
308
-
309
- class ImageLogger(Callback):
310
- def __init__(
311
- self,
312
- batch_frequency,
313
- max_images,
314
- clamp=True,
315
- increase_log_steps=True,
316
- rescale=True,
317
- disabled=False,
318
- log_on_batch_idx=False,
319
- log_first_step=False,
320
- log_images_kwargs=None,
321
- log_before_first_step=False,
322
- enable_autocast=True,
323
- ):
324
- super().__init__()
325
- self.enable_autocast = enable_autocast
326
- self.rescale = rescale
327
- self.batch_freq = batch_frequency
328
- self.max_images = max_images
329
- self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
330
- if not increase_log_steps:
331
- self.log_steps = [self.batch_freq]
332
- self.clamp = clamp
333
- self.disabled = disabled
334
- self.log_on_batch_idx = log_on_batch_idx
335
- self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
336
- self.log_first_step = log_first_step
337
- self.log_before_first_step = log_before_first_step
338
-
339
- @rank_zero_only
340
- def log_local(
341
- self,
342
- save_dir,
343
- split,
344
- images,
345
- global_step,
346
- current_epoch,
347
- batch_idx,
348
- pl_module: Union[None, pl.LightningModule] = None,
349
- ):
350
- root = os.path.join(save_dir, "images", split)
351
- for k in images:
352
- if isheatmap(images[k]):
353
- fig, ax = plt.subplots()
354
- ax = ax.matshow(
355
- images[k].cpu().numpy(), cmap="hot", interpolation="lanczos"
356
- )
357
- plt.colorbar(ax)
358
- plt.axis("off")
359
-
360
- filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
361
- k, global_step, current_epoch, batch_idx
362
- )
363
- os.makedirs(root, exist_ok=True)
364
- path = os.path.join(root, filename)
365
- plt.savefig(path)
366
- plt.close()
367
- # TODO: support wandb
368
- else:
369
- grid = torchvision.utils.make_grid(images[k], nrow=4)
370
- if self.rescale:
371
- grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
372
- grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
373
- grid = grid.numpy()
374
- grid = (grid * 255).astype(np.uint8)
375
- filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
376
- k, global_step, current_epoch, batch_idx
377
- )
378
- path = os.path.join(root, filename)
379
- os.makedirs(os.path.split(path)[0], exist_ok=True)
380
- img = Image.fromarray(grid)
381
- img.save(path)
382
- if exists(pl_module):
383
- assert isinstance(
384
- pl_module.logger, WandbLogger
385
- ), "logger_log_image only supports WandbLogger currently"
386
- pl_module.logger.log_image(
387
- key=f"{split}/{k}",
388
- images=[
389
- img,
390
- ],
391
- step=pl_module.global_step,
392
- )
393
-
394
- @rank_zero_only
395
- def log_img(self, pl_module, batch, batch_idx, split="train"):
396
- check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
397
- if (
398
- self.check_frequency(check_idx)
399
- and hasattr(pl_module, "log_images") # batch_idx % self.batch_freq == 0
400
- and callable(pl_module.log_images)
401
- and
402
- # batch_idx > 5 and
403
- self.max_images > 0
404
- ):
405
- logger = type(pl_module.logger)
406
- is_train = pl_module.training
407
- if is_train:
408
- pl_module.eval()
409
-
410
- gpu_autocast_kwargs = {
411
- "enabled": self.enable_autocast, # torch.is_autocast_enabled(),
412
- "dtype": torch.get_autocast_gpu_dtype(),
413
- "cache_enabled": torch.is_autocast_cache_enabled(),
414
- }
415
- with torch.no_grad(), torch.cuda.amp.autocast(**gpu_autocast_kwargs):
416
- images = pl_module.log_images(
417
- batch, split=split, **self.log_images_kwargs
418
- )
419
-
420
- for k in images:
421
- N = min(images[k].shape[0], self.max_images)
422
- if not isheatmap(images[k]):
423
- images[k] = images[k][:N]
424
- if isinstance(images[k], torch.Tensor):
425
- images[k] = images[k].detach().float().cpu()
426
- if self.clamp and not isheatmap(images[k]):
427
- images[k] = torch.clamp(images[k], -1.0, 1.0)
428
-
429
- self.log_local(
430
- pl_module.logger.save_dir,
431
- split,
432
- images,
433
- pl_module.global_step,
434
- pl_module.current_epoch,
435
- batch_idx,
436
- pl_module=pl_module
437
- if isinstance(pl_module.logger, WandbLogger)
438
- else None,
439
- )
440
-
441
- if is_train:
442
- pl_module.train()
443
-
444
- def check_frequency(self, check_idx):
445
- if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
446
- check_idx > 0 or self.log_first_step
447
- ):
448
- try:
449
- self.log_steps.pop(0)
450
- except IndexError as e:
451
- print(e)
452
- pass
453
- return True
454
- return False
455
-
456
- @rank_zero_only
457
- def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
458
- if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
459
- self.log_img(pl_module, batch, batch_idx, split="train")
460
-
461
- @rank_zero_only
462
- def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
463
- if self.log_before_first_step and pl_module.global_step == 0:
464
- print(f"{self.__class__.__name__}: logging before training")
465
- self.log_img(pl_module, batch, batch_idx, split="train")
466
-
467
- @rank_zero_only
468
- def on_validation_batch_end(
469
- self, trainer, pl_module, outputs, batch, batch_idx, *args, **kwargs
470
- ):
471
- if not self.disabled and pl_module.global_step > 0:
472
- self.log_img(pl_module, batch, batch_idx, split="val")
473
- if hasattr(pl_module, "calibrate_grad_norm"):
474
- if (
475
- pl_module.calibrate_grad_norm and batch_idx % 25 == 0
476
- ) and batch_idx > 0:
477
- self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
478
-
479
-
480
- @rank_zero_only
481
- def init_wandb(save_dir, opt, config, group_name, name_str):
482
- print(f"setting WANDB_DIR to {save_dir}")
483
- os.makedirs(save_dir, exist_ok=True)
484
-
485
- os.environ["WANDB_DIR"] = save_dir
486
- if opt.debug:
487
- wandb.init(project=opt.projectname, mode="offline", group=group_name)
488
- else:
489
- wandb.init(
490
- project=opt.projectname,
491
- config=config,
492
- settings=wandb.Settings(code_dir="./sgm"),
493
- group=group_name,
494
- name=name_str,
495
- )
496
-
497
-
498
- if __name__ == "__main__":
499
- # custom parser to specify config files, train, test and debug mode,
500
- # postfix, resume.
501
- # `--key value` arguments are interpreted as arguments to the trainer.
502
- # `nested.key=value` arguments are interpreted as config parameters.
503
- # configs are merged from left-to-right followed by command line parameters.
504
-
505
- # model:
506
- # base_learning_rate: float
507
- # target: path to lightning module
508
- # params:
509
- # key: value
510
- # data:
511
- # target: main.DataModuleFromConfig
512
- # params:
513
- # batch_size: int
514
- # wrap: bool
515
- # train:
516
- # target: path to train dataset
517
- # params:
518
- # key: value
519
- # validation:
520
- # target: path to validation dataset
521
- # params:
522
- # key: value
523
- # test:
524
- # target: path to test dataset
525
- # params:
526
- # key: value
527
- # lightning: (optional, has sane defaults and can be specified on cmdline)
528
- # trainer:
529
- # additional arguments to trainer
530
- # logger:
531
- # logger to instantiate
532
- # modelcheckpoint:
533
- # modelcheckpoint to instantiate
534
- # callbacks:
535
- # callback1:
536
- # target: importpath
537
- # params:
538
- # key: value
539
-
540
- now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
541
-
542
- # add cwd for convenience and to make classes in this file available when
543
- # running as `python main.py`
544
- # (in particular `main.DataModuleFromConfig`)
545
- sys.path.append(os.getcwd())
546
-
547
- parser = get_parser()
548
-
549
- opt, unknown = parser.parse_known_args()
550
-
551
- if opt.name and opt.resume:
552
- raise ValueError(
553
- "-n/--name and -r/--resume cannot be specified both."
554
- "If you want to resume training in a new log folder, "
555
- "use -n/--name in combination with --resume_from_checkpoint"
556
- )
557
- melk_ckpt_name = None
558
- name = None
559
- if opt.resume:
560
- if not os.path.exists(opt.resume):
561
- raise ValueError("Cannot find {}".format(opt.resume))
562
- if os.path.isfile(opt.resume):
563
- paths = opt.resume.split("/")
564
- # idx = len(paths)-paths[::-1].index("logs")+1
565
- # logdir = "/".join(paths[:idx])
566
- logdir = "/".join(paths[:-2])
567
- ckpt = opt.resume
568
- _, melk_ckpt_name = get_checkpoint_name(logdir)
569
- else:
570
- assert os.path.isdir(opt.resume), opt.resume
571
- logdir = opt.resume.rstrip("/")
572
- ckpt, melk_ckpt_name = get_checkpoint_name(logdir)
573
-
574
- print("#" * 100)
575
- print(f'Resuming from checkpoint "{ckpt}"')
576
- print("#" * 100)
577
-
578
- opt.resume_from_checkpoint = ckpt
579
- base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
580
- opt.base = base_configs + opt.base
581
- _tmp = logdir.split("/")
582
- nowname = _tmp[-1]
583
- else:
584
- if opt.name:
585
- name = "_" + opt.name
586
- elif opt.base:
587
- if opt.no_base_name:
588
- name = ""
589
- else:
590
- if opt.legacy_naming:
591
- cfg_fname = os.path.split(opt.base[0])[-1]
592
- cfg_name = os.path.splitext(cfg_fname)[0]
593
- else:
594
- assert "configs" in os.path.split(opt.base[0])[0], os.path.split(
595
- opt.base[0]
596
- )[0]
597
- cfg_path = os.path.split(opt.base[0])[0].split(os.sep)[
598
- os.path.split(opt.base[0])[0].split(os.sep).index("configs")
599
- + 1 :
600
- ] # cut away the first one (we assert all configs are in "configs")
601
- cfg_name = os.path.splitext(os.path.split(opt.base[0])[-1])[0]
602
- cfg_name = "-".join(cfg_path) + f"-{cfg_name}"
603
- name = "_" + cfg_name
604
- else:
605
- name = ""
606
- if not opt.no_date:
607
- nowname = now + name + opt.postfix
608
- else:
609
- nowname = name + opt.postfix
610
- if nowname.startswith("_"):
611
- nowname = nowname[1:]
612
- logdir = os.path.join(opt.logdir, nowname)
613
- print(f"LOGDIR: {logdir}")
614
-
615
- ckptdir = os.path.join(logdir, "checkpoints")
616
- cfgdir = os.path.join(logdir, "configs")
617
- seed_everything(opt.seed, workers=True)
618
-
619
- # move before model init, in case a torch.compile(...) is called somewhere
620
- if opt.enable_tf32:
621
- # pt_version = version.parse(torch.__version__)
622
- torch.backends.cuda.matmul.allow_tf32 = True
623
- torch.backends.cudnn.allow_tf32 = True
624
- print(f"Enabling TF32 for PyTorch {torch.__version__}")
625
- else:
626
- print(f"Using default TF32 settings for PyTorch {torch.__version__}:")
627
- print(
628
- f"torch.backends.cuda.matmul.allow_tf32={torch.backends.cuda.matmul.allow_tf32}"
629
- )
630
- print(f"torch.backends.cudnn.allow_tf32={torch.backends.cudnn.allow_tf32}")
631
-
632
- try:
633
- # init and save configs
634
- configs = [OmegaConf.load(cfg) for cfg in opt.base]
635
- cli = OmegaConf.from_dotlist(unknown)
636
- config = OmegaConf.merge(*configs, cli)
637
- lightning_config = config.pop("lightning", OmegaConf.create())
638
- # merge trainer cli with config
639
- trainer_config = lightning_config.get("trainer", OmegaConf.create())
640
-
641
- # default to gpu
642
- trainer_config["accelerator"] = "gpu"
643
- #
644
- standard_args = default_trainer_args()
645
- for k in standard_args:
646
- if getattr(opt, k) != standard_args[k]:
647
- trainer_config[k] = getattr(opt, k)
648
-
649
- ckpt_resume_path = opt.resume_from_checkpoint
650
-
651
- if not "devices" in trainer_config and trainer_config["accelerator"] != "gpu":
652
- del trainer_config["accelerator"]
653
- cpu = True
654
- else:
655
- gpuinfo = trainer_config["devices"]
656
- print(f"Running on GPUs {gpuinfo}")
657
- cpu = False
658
- trainer_opt = argparse.Namespace(**trainer_config)
659
- lightning_config.trainer = trainer_config
660
-
661
- # model
662
- model = instantiate_from_config(config.model)
663
-
664
- # trainer and callbacks
665
- trainer_kwargs = dict()
666
-
667
- # default logger configs
668
- default_logger_cfgs = {
669
- "wandb": {
670
- "target": "pytorch_lightning.loggers.WandbLogger",
671
- "params": {
672
- "name": nowname,
673
- # "save_dir": logdir,
674
- "offline": opt.debug,
675
- "id": nowname,
676
- "project": opt.projectname,
677
- "log_model": False,
678
- # "dir": logdir,
679
- },
680
- },
681
- "csv": {
682
- "target": "pytorch_lightning.loggers.CSVLogger",
683
- "params": {
684
- "name": "testtube", # hack for sbord fanatics
685
- "save_dir": logdir,
686
- },
687
- },
688
- }
689
- default_logger_cfg = default_logger_cfgs["wandb" if opt.wandb else "csv"]
690
- if opt.wandb:
691
- # TODO change once leaving "swiffer" config directory
692
- try:
693
- group_name = nowname.split(now)[-1].split("-")[1]
694
- except:
695
- group_name = nowname
696
- default_logger_cfg["params"]["group"] = group_name
697
- init_wandb(
698
- os.path.join(os.getcwd(), logdir),
699
- opt=opt,
700
- group_name=group_name,
701
- config=config,
702
- name_str=nowname,
703
- )
704
- if "logger" in lightning_config:
705
- logger_cfg = lightning_config.logger
706
- else:
707
- logger_cfg = OmegaConf.create()
708
- logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
709
- trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
710
-
711
- # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
712
- # specify which metric is used to determine best models
713
- default_modelckpt_cfg = {
714
- "target": "pytorch_lightning.callbacks.ModelCheckpoint",
715
- "params": {
716
- "dirpath": ckptdir,
717
- "filename": "{epoch:06}",
718
- "verbose": True,
719
- "save_last": True,
720
- },
721
- }
722
- if hasattr(model, "monitor"):
723
- print(f"Monitoring {model.monitor} as checkpoint metric.")
724
- default_modelckpt_cfg["params"]["monitor"] = model.monitor
725
- default_modelckpt_cfg["params"]["save_top_k"] = 3
726
-
727
- if "modelcheckpoint" in lightning_config:
728
- modelckpt_cfg = lightning_config.modelcheckpoint
729
- else:
730
- modelckpt_cfg = OmegaConf.create()
731
- modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
732
- print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
733
-
734
- # https://pytorch-lightning.readthedocs.io/en/stable/extensions/strategy.html
735
- # default to ddp if not further specified
736
- default_strategy_config = {"target": "pytorch_lightning.strategies.DDPStrategy"}
737
-
738
- if "strategy" in lightning_config:
739
- strategy_cfg = lightning_config.strategy
740
- else:
741
- strategy_cfg = OmegaConf.create()
742
- default_strategy_config["params"] = {
743
- "find_unused_parameters": False,
744
- # "static_graph": True,
745
- # "ddp_comm_hook": default.fp16_compress_hook # TODO: experiment with this, also for DDPSharded
746
- }
747
- strategy_cfg = OmegaConf.merge(default_strategy_config, strategy_cfg)
748
- print(
749
- f"strategy config: \n ++++++++++++++ \n {strategy_cfg} \n ++++++++++++++ "
750
- )
751
- trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
752
-
753
- # add callback which sets up log directory
754
- default_callbacks_cfg = {
755
- "setup_callback": {
756
- "target": "main.SetupCallback",
757
- "params": {
758
- "resume": opt.resume,
759
- "now": now,
760
- "logdir": logdir,
761
- "ckptdir": ckptdir,
762
- "cfgdir": cfgdir,
763
- "config": config,
764
- "lightning_config": lightning_config,
765
- "debug": opt.debug,
766
- "ckpt_name": melk_ckpt_name,
767
- },
768
- },
769
- "image_logger": {
770
- "target": "main.ImageLogger",
771
- "params": {"batch_frequency": 1000, "max_images": 4, "clamp": True},
772
- },
773
- "learning_rate_logger": {
774
- "target": "pytorch_lightning.callbacks.LearningRateMonitor",
775
- "params": {
776
- "logging_interval": "step",
777
- # "log_momentum": True
778
- },
779
- },
780
- }
781
- if version.parse(pl.__version__) >= version.parse("1.4.0"):
782
- default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
783
-
784
- if "callbacks" in lightning_config:
785
- callbacks_cfg = lightning_config.callbacks
786
- else:
787
- callbacks_cfg = OmegaConf.create()
788
-
789
- if "metrics_over_trainsteps_checkpoint" in callbacks_cfg:
790
- print(
791
- "Caution: Saving checkpoints every n train steps without deleting. This might require some free space."
792
- )
793
- default_metrics_over_trainsteps_ckpt_dict = {
794
- "metrics_over_trainsteps_checkpoint": {
795
- "target": "pytorch_lightning.callbacks.ModelCheckpoint",
796
- "params": {
797
- "dirpath": os.path.join(ckptdir, "trainstep_checkpoints"),
798
- "filename": "{epoch:06}-{step:09}",
799
- "verbose": True,
800
- "save_top_k": -1,
801
- "every_n_train_steps": 10000,
802
- "save_weights_only": True,
803
- },
804
- }
805
- }
806
- default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
807
-
808
- callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
809
- if "ignore_keys_callback" in callbacks_cfg and ckpt_resume_path is not None:
810
- callbacks_cfg.ignore_keys_callback.params["ckpt_path"] = ckpt_resume_path
811
- elif "ignore_keys_callback" in callbacks_cfg:
812
- del callbacks_cfg["ignore_keys_callback"]
813
-
814
- trainer_kwargs["callbacks"] = [
815
- instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg
816
- ]
817
- if not "plugins" in trainer_kwargs:
818
- trainer_kwargs["plugins"] = list()
819
-
820
- # cmd line trainer args (which are in trainer_opt) have always priority over config-trainer-args (which are in trainer_kwargs)
821
- trainer_opt = vars(trainer_opt)
822
- trainer_kwargs = {
823
- key: val for key, val in trainer_kwargs.items() if key not in trainer_opt
824
- }
825
- trainer = Trainer(**trainer_opt, **trainer_kwargs)
826
-
827
- trainer.logdir = logdir ###
828
-
829
- # data
830
- data = instantiate_from_config(config.data)
831
- # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
832
- # calling these ourselves should not be necessary but it is.
833
- # lightning still takes care of proper multiprocessing though
834
- data.prepare_data()
835
- # data.setup()
836
- print("#### Data #####")
837
- try:
838
- for k in data.datasets:
839
- print(
840
- f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}"
841
- )
842
- except:
843
- print("datasets not yet initialized.")
844
-
845
- # configure learning rate
846
- if "batch_size" in config.data.params:
847
- bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
848
- else:
849
- bs, base_lr = (
850
- config.data.params.train.loader.batch_size,
851
- config.model.base_learning_rate,
852
- )
853
- if not cpu:
854
- ngpu = len(lightning_config.trainer.devices.strip(",").split(","))
855
- else:
856
- ngpu = 1
857
- if "accumulate_grad_batches" in lightning_config.trainer:
858
- accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
859
- else:
860
- accumulate_grad_batches = 1
861
- print(f"accumulate_grad_batches = {accumulate_grad_batches}")
862
- lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
863
- if opt.scale_lr:
864
- model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
865
- print(
866
- "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
867
- model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr
868
- )
869
- )
870
- else:
871
- model.learning_rate = base_lr
872
- print("++++ NOT USING LR SCALING ++++")
873
- print(f"Setting learning rate to {model.learning_rate:.2e}")
874
-
875
- # allow checkpointing via USR1
876
- def melk(*args, **kwargs):
877
- # run all checkpoint hooks
878
- if trainer.global_rank == 0:
879
- print("Summoning checkpoint.")
880
- if melk_ckpt_name is None:
881
- ckpt_path = os.path.join(ckptdir, "last.ckpt")
882
- else:
883
- ckpt_path = os.path.join(ckptdir, melk_ckpt_name)
884
- trainer.save_checkpoint(ckpt_path)
885
-
886
- def divein(*args, **kwargs):
887
- if trainer.global_rank == 0:
888
- import pudb
889
-
890
- pudb.set_trace()
891
-
892
- import signal
893
-
894
- signal.signal(signal.SIGUSR1, melk)
895
- signal.signal(signal.SIGUSR2, divein)
896
-
897
- # run
898
- if opt.train:
899
- try:
900
- trainer.fit(model, data, ckpt_path=ckpt_resume_path)
901
- except Exception:
902
- if not opt.debug:
903
- melk()
904
- raise
905
- if not opt.no_test and not trainer.interrupted:
906
- trainer.test(model, data)
907
- except RuntimeError as err:
908
- if MULTINODE_HACKS:
909
- import datetime
910
- import os
911
- import socket
912
-
913
- import requests
914
-
915
- device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
916
- hostname = socket.gethostname()
917
- ts = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")
918
- resp = requests.get("http://169.254.169.254/latest/meta-data/instance-id")
919
- print(
920
- f"ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}",
921
- flush=True,
922
- )
923
- raise err
924
- except Exception:
925
- if opt.debug and trainer.global_rank == 0:
926
- try:
927
- import pudb as debugger
928
- except ImportError:
929
- import pdb as debugger
930
- debugger.post_mortem()
931
- raise
932
- finally:
933
- # move newly created debug project to debug_runs
934
- if opt.debug and not opt.resume and trainer.global_rank == 0:
935
- dst, name = os.path.split(logdir)
936
- dst = os.path.join(dst, "debug_runs", name)
937
- os.makedirs(os.path.split(dst)[0], exist_ok=True)
938
- os.rename(logdir, dst)
939
-
940
- if opt.wandb:
941
- wandb.finish()
942
- # if trainer.global_rank == 0:
943
- # print(trainer.profiler.summary())
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- arbitrarily-targeted use).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
pyproject.toml DELETED
@@ -1,48 +0,0 @@
1
- [build-system]
2
- requires = ["hatchling"]
3
- build-backend = "hatchling.build"
4
-
5
- [project]
6
- name = "sgm"
7
- dynamic = ["version"]
8
- description = "Stability Generative Models"
9
- readme = "README.md"
10
- license-files = { paths = ["LICENSE-CODE"] }
11
- requires-python = ">=3.8"
12
-
13
- [project.urls]
14
- Homepage = "https://github.com/Stability-AI/generative-models"
15
-
16
- [tool.hatch.version]
17
- path = "sgm/__init__.py"
18
-
19
- [tool.hatch.build]
20
- # This needs to be explicitly set so the configuration files
21
- # grafted into the `sgm` directory get included in the wheel's
22
- # RECORD file.
23
- include = [
24
- "sgm",
25
- ]
26
- # The force-include configurations below make Hatch copy
27
- # the configs/ directory (containing the various YAML files required
28
- # to generatively model) into the source distribution and the wheel.
29
-
30
- [tool.hatch.build.targets.sdist.force-include]
31
- "./configs" = "sgm/configs"
32
-
33
- [tool.hatch.build.targets.wheel.force-include]
34
- "./configs" = "sgm/configs"
35
-
36
- [tool.hatch.envs.ci]
37
- skip-install = false
38
-
39
- dependencies = [
40
- "pytest"
41
- ]
42
-
43
- [tool.hatch.envs.ci.scripts]
44
- test-inference = [
45
- "pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --index-url https://download.pytorch.org/whl/cu118",
46
- "pip install -r requirements/pt2.txt",
47
- "pytest -v tests/inference/test_inference.py {args}",
48
- ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
pytest.ini DELETED
@@ -1,3 +0,0 @@
1
- [pytest]
2
- markers =
3
- inference: mark as inference test (deselect with '-m "not inference"')
 
 
 
 
requirements.txt CHANGED
@@ -1,42 +1,7 @@
1
  https://gradio-builds.s3.amazonaws.com/756e3431d65172df986a7e335dce8136206a293a/gradio-4.7.1-py3-none-any.whl
2
- black==23.7.0
3
- chardet==5.1.0
4
- clip @ git+https://github.com/openai/CLIP.git
5
- einops>=0.6.1
6
- fairscale>=0.4.13
7
- fire>=0.5.0
8
- fsspec>=2023.6.0
9
- invisible-watermark>=0.2.0
10
- kornia==0.6.9
11
- matplotlib>=3.7.2
12
- natsort>=8.4.0
13
- ninja>=1.11.1
14
- numpy>=1.24.4
15
- omegaconf>=2.3.0
16
- open-clip-torch>=2.20.0
17
- opencv-python==4.6.0.66
18
- pandas>=2.0.3
19
- pillow>=9.5.0
20
- pudb>=2022.1.3
21
- pytorch-lightning==2.0.1
22
- pyyaml>=6.0.1
23
- scipy>=1.10.1
24
- streamlit>=0.73.1
25
- tensorboardx==2.6
26
- timm>=0.9.2
27
- tokenizers==0.12.1
28
- torch>=2.0.1
29
- torchaudio>=2.0.2
30
- torchdata==0.6.1
31
- torchmetrics>=1.0.1
32
- torchvision>=0.15.2
33
- tqdm>=4.65.0
34
- transformers==4.19.1
35
- triton==2.0.0
36
- urllib3<1.27,>=1.25.4
37
- wandb>=0.15.6
38
- webdataset>=0.2.33
39
- wheel>=0.41.0
40
- xformers>=0.0.20
41
- fire
42
  uuid
 
1
  https://gradio-builds.s3.amazonaws.com/756e3431d65172df986a7e335dce8136206a293a/gradio-4.7.1-py3-none-any.whl
2
+ git+https://github.com/huggingface/diffusers.git@refs/pull/5895/head
3
+ transformers
4
+ accelerate
5
+ safetensors
6
+ opencv-python
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
  uuid
requirements/pt13.txt DELETED
@@ -1,40 +0,0 @@
1
- black==23.7.0
2
- chardet>=5.1.0
3
- clip @ git+https://github.com/openai/CLIP.git
4
- einops>=0.6.1
5
- fairscale>=0.4.13
6
- fire>=0.5.0
7
- fsspec>=2023.6.0
8
- invisible-watermark>=0.2.0
9
- kornia==0.6.9
10
- matplotlib>=3.7.2
11
- natsort>=8.4.0
12
- numpy>=1.24.4
13
- omegaconf>=2.3.0
14
- onnx<=1.12.0
15
- open-clip-torch>=2.20.0
16
- opencv-python==4.6.0.66
17
- pandas>=2.0.3
18
- pillow>=9.5.0
19
- pudb>=2022.1.3
20
- pytorch-lightning==1.8.5
21
- pyyaml>=6.0.1
22
- scipy>=1.10.1
23
- streamlit>=1.25.0
24
- tensorboardx==2.5.1
25
- timm>=0.9.2
26
- tokenizers==0.12.1
27
- --extra-index-url https://download.pytorch.org/whl/cu117
28
- torch==1.13.1+cu117
29
- torchaudio==0.13.1
30
- torchdata==0.5.1
31
- torchmetrics>=1.0.1
32
- torchvision==0.14.1+cu117
33
- tqdm>=4.65.0
34
- transformers==4.19.1
35
- triton==2.0.0.post1
36
- urllib3<1.27,>=1.25.4
37
- wandb>=0.15.6
38
- webdataset>=0.2.33
39
- wheel>=0.41.0
40
- xformers==0.0.16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
requirements/pt2.txt DELETED
@@ -1,39 +0,0 @@
1
- black==23.7.0
2
- chardet==5.1.0
3
- clip @ git+https://github.com/openai/CLIP.git
4
- einops>=0.6.1
5
- fairscale>=0.4.13
6
- fire>=0.5.0
7
- fsspec>=2023.6.0
8
- invisible-watermark>=0.2.0
9
- kornia==0.6.9
10
- matplotlib>=3.7.2
11
- natsort>=8.4.0
12
- ninja>=1.11.1
13
- numpy>=1.24.4
14
- omegaconf>=2.3.0
15
- open-clip-torch>=2.20.0
16
- opencv-python==4.6.0.66
17
- pandas>=2.0.3
18
- pillow>=9.5.0
19
- pudb>=2022.1.3
20
- pytorch-lightning==2.0.1
21
- pyyaml>=6.0.1
22
- scipy>=1.10.1
23
- streamlit>=0.73.1
24
- tensorboardx==2.6
25
- timm>=0.9.2
26
- tokenizers==0.12.1
27
- torch>=2.0.1
28
- torchaudio>=2.0.2
29
- torchdata==0.6.1
30
- torchmetrics>=1.0.1
31
- torchvision>=0.15.2
32
- tqdm>=4.65.0
33
- transformers==4.19.1
34
- triton==2.0.0
35
- urllib3<1.27,>=1.25.4
36
- wandb>=0.15.6
37
- webdataset>=0.2.33
38
- wheel>=0.41.0
39
- xformers>=0.0.20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
scripts/.DS_Store DELETED
Binary file (6.15 kB)
 
scripts/__init__.py DELETED
File without changes
scripts/demo/__init__.py DELETED
File without changes
scripts/demo/detect.py DELETED
@@ -1,156 +0,0 @@
1
- import argparse
2
-
3
- import cv2
4
- import numpy as np
5
-
6
- try:
7
- from imwatermark import WatermarkDecoder
8
- except ImportError as e:
9
- try:
10
- # Assume some of the other dependencies such as torch are not fulfilled
11
- # import file without loading unnecessary libraries.
12
- import importlib.util
13
- import sys
14
-
15
- spec = importlib.util.find_spec("imwatermark.maxDct")
16
- assert spec is not None
17
- maxDct = importlib.util.module_from_spec(spec)
18
- sys.modules["maxDct"] = maxDct
19
- spec.loader.exec_module(maxDct)
20
-
21
- class WatermarkDecoder(object):
22
- """A minimal version of
23
- https://github.com/ShieldMnt/invisible-watermark/blob/main/imwatermark/watermark.py
24
- to only reconstruct bits using dwtDct"""
25
-
26
- def __init__(self, wm_type="bytes", length=0):
27
- assert wm_type == "bits", "Only bits defined in minimal import"
28
- self._wmType = wm_type
29
- self._wmLen = length
30
-
31
- def reconstruct(self, bits):
32
- if len(bits) != self._wmLen:
33
- raise RuntimeError("bits are not matched with watermark length")
34
-
35
- return bits
36
-
37
- def decode(self, cv2Image, method="dwtDct", **configs):
38
- (r, c, channels) = cv2Image.shape
39
- if r * c < 256 * 256:
40
- raise RuntimeError("image too small, should be larger than 256x256")
41
-
42
- bits = []
43
- assert method == "dwtDct"
44
- embed = maxDct.EmbedMaxDct(watermarks=[], wmLen=self._wmLen, **configs)
45
- bits = embed.decode(cv2Image)
46
- return self.reconstruct(bits)
47
-
48
- except:
49
- raise e
50
-
51
-
52
- # A fixed 48-bit message that was choosen at random
53
- # WATERMARK_MESSAGE = 0xB3EC907BB19E
54
- WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
55
- # bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
56
- WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
57
- MATCH_VALUES = [
58
- [27, "No watermark detected"],
59
- [33, "Partial watermark match. Cannot determine with certainty."],
60
- [
61
- 35,
62
- (
63
- "Likely watermarked. In our test 0.02% of real images were "
64
- 'falsely detected as "Likely watermarked"'
65
- ),
66
- ],
67
- [
68
- 49,
69
- (
70
- "Very likely watermarked. In our test no real images were "
71
- 'falsely detected as "Very likely watermarked"'
72
- ),
73
- ],
74
- ]
75
-
76
-
77
- class GetWatermarkMatch:
78
- def __init__(self, watermark):
79
- self.watermark = watermark
80
- self.num_bits = len(self.watermark)
81
- self.decoder = WatermarkDecoder("bits", self.num_bits)
82
-
83
- def __call__(self, x: np.ndarray) -> np.ndarray:
84
- """
85
- Detects the number of matching bits the predefined watermark with one
86
- or multiple images. Images should be in cv2 format, e.g. h x w x c BGR.
87
-
88
- Args:
89
- x: ([B], h w, c) in range [0, 255]
90
-
91
- Returns:
92
- number of matched bits ([B],)
93
- """
94
- squeeze = len(x.shape) == 3
95
- if squeeze:
96
- x = x[None, ...]
97
-
98
- bs = x.shape[0]
99
- detected = np.empty((bs, self.num_bits), dtype=bool)
100
- for k in range(bs):
101
- detected[k] = self.decoder.decode(x[k], "dwtDct")
102
- result = np.sum(detected == self.watermark, axis=-1)
103
- if squeeze:
104
- return result[0]
105
- else:
106
- return result
107
-
108
-
109
- get_watermark_match = GetWatermarkMatch(WATERMARK_BITS)
110
-
111
-
112
- if __name__ == "__main__":
113
- parser = argparse.ArgumentParser()
114
- parser.add_argument(
115
- "filename",
116
- nargs="+",
117
- type=str,
118
- help="Image files to check for watermarks",
119
- )
120
- opts = parser.parse_args()
121
-
122
- print(
123
- """
124
- This script tries to detect watermarked images. Please be aware of
125
- the following:
126
- - As the watermark is supposed to be invisible, there is the risk that
127
- watermarked images may not be detected.
128
- - To maximize the chance of detection make sure that the image has the same
129
- dimensions as when the watermark was applied (most likely 1024x1024
130
- or 512x512).
131
- - Specific image manipulation may drastically decrease the chance that
132
- watermarks can be detected.
133
- - There is also the chance that an image has the characteristics of the
134
- watermark by chance.
135
- - The watermark script is public, anybody may watermark any images, and
136
- could therefore claim it to be generated.
137
- - All numbers below are based on a test using 10,000 images without any
138
- modifications after applying the watermark.
139
- """
140
- )
141
-
142
- for fn in opts.filename:
143
- image = cv2.imread(fn)
144
- if image is None:
145
- print(f"Couldn't read {fn}. Skipping")
146
- continue
147
-
148
- num_bits = get_watermark_match(image)
149
- k = 0
150
- while num_bits > MATCH_VALUES[k][0]:
151
- k += 1
152
- print(
153
- f"{fn}: {MATCH_VALUES[k][1]}",
154
- f"Bits that matched the watermark {num_bits} from {len(WATERMARK_BITS)}\n",
155
- sep="\n\t",
156
- )