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diffusers-backend (#6)

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- Delete pytest.ini (f3f18e3224b316f8894e505b90f05f05aeaad782)
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  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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- *.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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- *.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

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  • Size of remote file: 4.19 MB
assets/test_image.png DELETED
Binary file (494 kB)
 
assets/tile.gif DELETED

Git LFS Details

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  • 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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
data/DejaVuSans.ttf DELETED
Binary file (757 kB)
 
images/blink_meme.png ADDED
images/confused2_meme.png ADDED
images/confused_meme.png ADDED
images/disaster_meme.png ADDED
images/distracted_meme.png ADDED
images/hide_meme.png ADDED
images/nazare_meme.png ADDED
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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- STABLE VIDEO DIFFUSION NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT
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- Dated: November 21, 2023
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- β€œAUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
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- "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Software Products set forth herein.
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- "Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model.
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- β€œDocumentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software.
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- "Stability AI" or "we" means Stability AI Ltd.
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- "Software" means, collectively, Stability AI’s proprietary StableCode made available under this Agreement.
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- β€œSoftware Products” means Software and Documentation.
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- License Rights and Redistribution.
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model_licenses/LICENSE-SDXL0.9 DELETED
@@ -1,75 +0,0 @@
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- SDXL 0.9 RESEARCH LICENSE AGREEMENT
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- Copyright (c) Stability AI Ltd.
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- This License Agreement (as may be amended in accordance with this License Agreement, β€œLicense”), between you, or your employer or other entity (if you are entering into this agreement on behalf of your employer or other entity) (β€œLicensee” or β€œyou”) and Stability AI Ltd. (β€œStability AI” or β€œwe”) applies to your use of any computer program, algorithm, source code, object code, or software that is made available by Stability AI under this License (β€œSoftware”) and any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software (β€œDocumentation”).
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- 1. LICENSE GRANT
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- 3. ATTRIBUTION
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- Together with any copies of the Software Products (as well as derivative works thereof or works incorporating the Software Products) that you distribute, you must provide (i) a copy of this License, and (ii) the following attribution notice: β€œSDXL 0.9 is licensed under the SDXL Research License, Copyright (c) Stability AI Ltd. All Rights Reserved.”
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- THE SOFTWARE PRODUCTS ARE PROVIDED β€œAS IS” AND β€œWITH ALL FAULTS” WITH NO WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. STABILITY AIEXPRESSLY DISCLAIMS ALL REPRESENTATIONS AND WARRANTIES, EXPRESS OR IMPLIED, WHETHER BY STATUTE, CUSTOM, USAGE OR OTHERWISE AS TO ANY MATTERS RELATED TO THE SOFTWARE PRODUCTS, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, SATISFACTORY QUALITY, OR NON-INFRINGEMENT. STABILITY AI MAKES NO WARRANTIES OR REPRESENTATIONS THAT THE SOFTWARE PRODUCTS WILL BE ERROR FREE OR FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS, OR PRODUCE ANY PARTICULAR RESULTS.
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- TO THE FULLEST EXTENT PERMITTED BY LAW, IN NO EVENT WILL STABILITY AI BE LIABLE TO YOU (A) UNDER ANY THEORY OF LIABILITY, WHETHER BASED IN CONTRACT, TORT, NEGLIGENCE, STRICT LIABILITY, WARRANTY, OR OTHERWISE UNDER THIS LICENSE, OR (B) FOR ANY INDIRECT, CONSEQUENTIAL, EXEMPLARY, INCIDENTAL, PUNITIVE OR SPECIAL DAMAGES OR LOST PROFITS, EVEN IF STABILITY AI HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THE SOFTWARE PRODUCTS, THEIR CONSTITUENT COMPONENTS, AND ANY OUTPUT (COLLECTIVELY, β€œSOFTWARE MATERIALS”) ARE NOT DESIGNED OR INTENDED FOR USE IN ANY APPLICATION OR SITUATION WHERE FAILURE OR FAULT OF THE SOFTWARE MATERIALS COULD REASONABLY BE ANTICIPATED TO LEAD TO SERIOUS INJURY OF ANY PERSON, INCLUDING POTENTIAL DISCRIMINATION OR VIOLATION OF AN INDIVIDUAL’S PRIVACY RIGHTS, OR TO SEVERE PHYSICAL, PROPERTY, OR ENVIRONMENTAL DAMAGE (EACH, A β€œHIGH-RISK USE”). IF YOU ELECT TO USE ANY OF THE SOFTWARE MATERIALS FOR A HIGH-RISK USE, YOU DO SO AT YOUR OWN RISK. YOU AGREE TO DESIGN AND IMPLEMENT APPROPRIATE DECISION-MAKING AND RISK-MITIGATION PROCEDURES AND POLICIES IN CONNECTION WITH A HIGH-RISK USE SUCH THAT EVEN IF THERE IS A FAILURE OR FAULT IN ANY OF THE SOFTWARE MATERIALS, THE SAFETY OF PERSONS OR PROPERTY AFFECTED BY THE ACTIVITY STAYS AT A LEVEL THAT IS REASONABLE, APPROPRIATE, AND LAWFUL FOR THE FIELD OF THE HIGH-RISK USE.
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- 6. INDEMNIFICATION
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- You will indemnify, defend and hold harmless Stability AI and our subsidiaries and affiliates, and each of our respective shareholders, directors, officers, employees, agents, successors, and assigns (collectively, the β€œStability AI Parties”) from and against any losses, liabilities, damages, fines, penalties, and expenses (including reasonable attorneys’ fees) incurred by any Stability AI Party in connection with any claim, demand, allegation, lawsuit, proceeding, or investigation (collectively, β€œClaims”) arising out of or related to: (a) your access to or use of the Software Products (as well as any results or data generated from such access or use), including any High-Risk Use (defined below); (b) your violation of this License; or (c) your violation, misappropriation or infringement of any rights of another (including intellectual property or other proprietary rights and privacy rights). You will promptly notify the Stability AI Parties of any such Claims, and cooperate with Stability AI Parties in defending such Claims. You will also grant the Stability AI Parties sole control of the defense or settlement, at Stability AI’s sole option, of any Claims. This indemnity is in addition to, and not in lieu of, any other indemnities or remedies set forth in a written agreement between you and Stability AI or the other Stability AI Parties.
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- 7. TERMINATION; SURVIVAL
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- a. This License will automatically terminate upon any breach by you of the terms of this License.
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- c. The following sections survive termination of this License: 2 (Restrictions), 3 (Attribution), 4 (Disclaimers), 5 (Limitation on Liability), 6 (Indemnification) 7 (Termination; Survival), 8 (Third Party Materials), 9 (Trademarks), 10 (Applicable Law; Dispute Resolution), and 11 (Miscellaneous).
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- 8. THIRD PARTY MATERIALS
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- The Software Products may contain third-party software or other components (including free and open source software) (all of the foregoing, β€œThird Party Materials”), which are subject to the license terms of the respective third-party licensors. Your dealings or correspondence with third parties and your use of or interaction with any Third Party Materials are solely between you and the third party. Stability AI does not control or endorse, and makes no representations or warranties regarding, any Third Party Materials, and your access to and use of such Third Party Materials are at your own risk.
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- 9. TRADEMARKS
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- Licensee has not been granted any trademark license as part of this License and may not use any name or mark associated with Stability AI without the prior written permission of Stability AI, except to the extent necessary to make the reference required by the β€œATTRIBUTION” section of this Agreement.
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- 10. APPLICABLE LAW; DISPUTE RESOLUTION
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- This License will be governed and construed under the laws of the State of California without regard to conflicts of law provisions. Any suit or proceeding arising out of or relating to this License will be brought in the federal or state courts, as applicable, in San Mateo County, California, and each party irrevocably submits to the jurisdiction and venue of such courts.
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- 11. MISCELLANEOUS
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- If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Stability AI to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Stability AI regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Stability AI regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Stability AI.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- Copyright (c) 2023 Stability AI CreativeML Open RAIL++-M License dated July 26, 2023
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- Section I: PREAMBLE Multimodal generative models are being widely adopted and used, and
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- use of the output can contravene any provision as stated in the License.
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- Section IV: OTHER PROVISIONS Updates and Runtime Restrictions. To the maximum extent
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- permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage
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- the Model and the Complementary Material (and each Contributor provides its
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-
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- END OF TERMS AND CONDITIONS
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-
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- Attachment A Use Restrictions
154
- You agree not to use the Model or Derivatives of the Model:
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- In any way that violates any applicable national, federal, state, local or
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- international law or regulation; For the purpose of exploiting, harming or attempting to
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- exploit or harm minors in any way; To generate or disseminate verifiably false
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- information and/or content with the purpose of harming others; To generate or
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- defame, disparage or otherwise harass others; For fully automated decision making that
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- adversely impacts an individual’s legal rights or otherwise creates or modifies a
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- social behavior or known or predicted personal or personality characteristics; To
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- exploit any of the vulnerabilities of a specific group of persons based on their age,
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- social, physical or mental characteristics, in order to materially distort the behavior
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- of a person pertaining to that group in a manner that causes or is likely to cause that
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- person or another person physical or psychological harm; For any use intended to or
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- predicting an individual will commit fraud/crime commitment (e.g. by text profiling,
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- drawing causal relationships between assertions made in documents, indiscriminate and
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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
- )