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  1. .gitattributes +1001 -0
  2. added_tokens.json +27 -0
  3. config.json +84 -0
  4. diffusion-decoder/.gitattributes +35 -0
  5. diffusion-decoder/README.md +137 -0
  6. diffusion-decoder/feature_extractor/preprocessor_config.json +28 -0
  7. diffusion-decoder/model_index.json +32 -0
  8. diffusion-decoder/pipeline_ar_gen.py +292 -0
  9. diffusion-decoder/pipeline_emu2_gen.py +250 -0
  10. diffusion-decoder/pipeline_llava_gen.py +287 -0
  11. diffusion-decoder/safety_checker_none/config.json +168 -0
  12. diffusion-decoder/safety_checker_none/model.bf16.safetensors +3 -0
  13. diffusion-decoder/scheduler/scheduler_config.json +18 -0
  14. diffusion-decoder/tokenizer/added_tokens.json +274 -0
  15. diffusion-decoder/tokenizer/special_tokens_map.json +285 -0
  16. diffusion-decoder/tokenizer/tokenizer.json +0 -0
  17. diffusion-decoder/tokenizer/tokenizer.model +3 -0
  18. diffusion-decoder/tokenizer/tokenizer_config.json +34 -0
  19. diffusion-decoder/unet/config.json +72 -0
  20. diffusion-decoder/unet/diffusion_pytorch_model.bf16.safetensors +3 -0
  21. diffusion-decoder/vae/config.json +32 -0
  22. diffusion-decoder/vae/diffusion_pytorch_model.bf16.safetensors +3 -0
  23. gen_projector.bin +3 -0
  24. generation_config.json +15 -0
  25. merges.txt +0 -0
  26. mm_projector.bin +3 -0
  27. model-00001-of-00006.safetensors +3 -0
  28. model-00002-of-00006.safetensors +3 -0
  29. model-00003-of-00006.safetensors +3 -0
  30. model-00004-of-00006.safetensors +3 -0
  31. model-00005-of-00006.safetensors +3 -0
  32. model-00006-of-00006.safetensors +3 -0
  33. model.safetensors.index.json +0 -0
  34. results.jsonl +0 -0
  35. special_tokens_map.json +21 -0
  36. tokenizer.json +3 -0
  37. tokenizer_config.json +222 -0
  38. trainer_state.json +0 -0
  39. training_args.bin +3 -0
  40. vocab.json +0 -0
  41. wise/1.png +3 -0
  42. wise/10.png +3 -0
  43. wise/100.png +3 -0
  44. wise/1000.png +3 -0
  45. wise/101.png +3 -0
  46. wise/102.png +3 -0
  47. wise/103.png +3 -0
  48. wise/104.png +3 -0
  49. wise/105.png +3 -0
  50. wise/106.png +3 -0
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diffusion-decoder/README.md ADDED
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1
+ ---
2
+ language:
3
+ - en
4
+ ---
5
+
6
+
7
+ # Emu2-Gen
8
+
9
+ [Paper](https://arxiv.org/abs/2312.13286) | [🤗HF Demo](https://huggingface.co/spaces/BAAI/Emu2) | [Demo](https://emu.ssi.plus) | [Project Page](https://baaivision.github.io/emu2/) | [Github](https://github.com/baaivision/Emu)
10
+
11
+
12
+ ## Model Weights
13
+
14
+ | Model name | Weight |
15
+ | ------------------ | ------------------------------------------------------- |
16
+ | **Emu2** | [🤗 HF link](https://huggingface.co/BAAI/Emu2) |
17
+ | **Emu2-Chat** | [🤗 HF link](https://huggingface.co/BAAI/Emu2-Chat) |
18
+ | **Emu2-Gen** | [🤗 HF link](https://huggingface.co/BAAI/Emu2-Gen) |
19
+
20
+
21
+ ## Inference (Huggingface Version)
22
+
23
+ ### Emu2-Gen
24
+
25
+ ```python
26
+ import cv2
27
+ from diffusers import DiffusionPipeline
28
+ import numpy as np
29
+ from PIL import Image
30
+ import requests
31
+ from transformers import AutoModelForCausalLM, AutoTokenizer
32
+ import torch
33
+
34
+ # For the first time of using,
35
+ # you need to download the huggingface repo "BAAI/Emu2-GEN" to local first
36
+ path = "path to local BAAI/Emu2-GEN"
37
+
38
+ multimodal_encoder = AutoModelForCausalLM.from_pretrained(
39
+ f"{path}/multimodal_encoder",
40
+ trust_remote_code=True,
41
+ torch_dtype=torch.bfloat16,
42
+ use_safetensors=True,
43
+ variant="bf16"
44
+ )
45
+ tokenizer = AutoTokenizer.from_pretrained(f"{path}/tokenizer")
46
+
47
+ pipe = DiffusionPipeline.from_pretrained(
48
+ path,
49
+ custom_pipeline="pipeline_emu2_gen",
50
+ torch_dtype=torch.bfloat16,
51
+ use_safetensors=True,
52
+ variant="bf16",
53
+ multimodal_encoder=multimodal_encoder,
54
+ tokenizer=tokenizer,
55
+ )
56
+
57
+ # For the non-first time of using, you can init the pipeline directly
58
+ pipe = DiffusionPipeline.from_pretrained(
59
+ path,
60
+ custom_pipeline="pipeline_emu2_gen",
61
+ torch_dtype=torch.bfloat16,
62
+ use_safetensors=True,
63
+ variant="bf16",
64
+ )
65
+
66
+ pipe.to("cuda")
67
+
68
+ # text-to-image
69
+ prompt = "impressionist painting of an astronaut in a jungle"
70
+ ret = pipe(prompt)
71
+ ret.image.save("astronaut.png")
72
+
73
+ # image editing
74
+ image = Image.open(requests.get('https://github.com/baaivision/Emu/Emu2/examples/dog.jpg?raw=true',stream=True).raw).convert('RGB')
75
+ prompt = [image, "wearing a red hat on the beach."]
76
+ ret = pipe(prompt)
77
+ ret.image.save("dog_hat_beach.png")
78
+
79
+ # grounding generation
80
+ def draw_box(left, top, right, bottom):
81
+ mask = np.zeros((448, 448, 3), dtype=np.uint8)
82
+ mask = cv2.rectangle(mask, (left, top), (right, bottom), (255, 255, 255), 3)
83
+ mask = Image.fromarray(mask)
84
+ return mask
85
+
86
+ dog1 = Image.open(requests.get('https://github.com/baaivision/Emu/Emu2/examples/dog1.jpg?raw=true',stream=True).raw).convert('RGB')
87
+ dog2 = Image.open(requests.get('https://github.com/baaivision/Emu/Emu2/examples/dog2.jpg?raw=true',stream=True).raw).convert('RGB')
88
+ dog3 = Image.open(requests.get('https://github.com/baaivision/Emu/Emu2/examples/dog3.jpg?raw=true',stream=True).raw).convert('RGB')
89
+ dog1_mask = draw_box( 22, 14, 224, 224)
90
+ dog2_mask = draw_box(224, 10, 448, 224)
91
+ dog3_mask = draw_box(120, 264, 320, 438)
92
+
93
+ prompt = [
94
+ "<grounding>",
95
+ "An oil painting of three dogs,",
96
+ "<phrase>the first dog</phrase>"
97
+ "<object>",
98
+ dog1_mask,
99
+ "</object>",
100
+ dog1,
101
+ "<phrase>the second dog</phrase>"
102
+ "<object>",
103
+ dog2_mask,
104
+ "</object>",
105
+ dog2,
106
+ "<phrase>the third dog</phrase>"
107
+ "<object>",
108
+ dog3_mask,
109
+ "</object>",
110
+ dog3,
111
+ ]
112
+ ret = pipe(prompt)
113
+ ret.image.save("three_dogs.png")
114
+
115
+ # Autoencoding
116
+ # to enable the autoencoding mode, you can only input exactly one image as prompt
117
+ # if you want the model to generate an image,
118
+ # please input extra empty text "" besides the image, e.g.
119
+ # autoencoding mode: prompt = image or [image]
120
+ # generation mode: prompt = ["", image] or [image, ""]
121
+ prompt = Image.open("./examples/doodle.jpg").convert("RGB")
122
+ ret = pipe(prompt)
123
+ ret.image.save("doodle_ae.png")
124
+ ```
125
+
126
+ ## Citation
127
+
128
+ If you find Emu2 useful for your research and applications, please consider starring this repository and citing:
129
+
130
+ ```
131
+ @article{Emu2,
132
+ title={Generative Multimodal Models are In-Context Learners},
133
+ author={Quan Sun and Yufeng Cui and Xiaosong Zhang and Fan Zhang and Qiying Yu and Zhengxiong Luo and Yueze Wang and Yongming Rao and Jingjing Liu and Tiejun Huang and Xinlong Wang},
134
+ publisher={arXiv preprint arXiv:2312.13286},
135
+ year={2023},
136
+ }
137
+ ```
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+ "width": 224
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+ "do_normalize": true,
9
+ "do_rescale": true,
10
+ "do_resize": true,
11
+ "feature_extractor_type": "CLIPFeatureExtractor",
12
+ "image_mean": [
13
+ 0.48145466,
14
+ 0.4578275,
15
+ 0.40821073
16
+ ],
17
+ "image_processor_type": "CLIPImageProcessor",
18
+ "image_std": [
19
+ 0.26862954,
20
+ 0.26130258,
21
+ 0.27577711
22
+ ],
23
+ "resample": 3,
24
+ "rescale_factor": 0.00392156862745098,
25
+ "size": {
26
+ "shortest_edge": 224
27
+ }
28
+ }
diffusion-decoder/model_index.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "EmuVisualGenerationPipeline",
3
+ "_diffusers_version": "0.21.2",
4
+ "feature_extractor": [
5
+ "transformers",
6
+ "CLIPImageProcessor"
7
+ ],
8
+ "multimodal_encoder": [
9
+ "transformers_modules.multimodal_encoder.modeling_emu",
10
+ "EmuForCausalLM"
11
+ ],
12
+ "safety_checker": [
13
+ "stable_diffusion",
14
+ "StableDiffusionSafetyChecker"
15
+ ],
16
+ "scheduler": [
17
+ "diffusers",
18
+ "EulerDiscreteScheduler"
19
+ ],
20
+ "tokenizer": [
21
+ "transformers",
22
+ "LlamaTokenizerFast"
23
+ ],
24
+ "unet": [
25
+ "diffusers",
26
+ "UNet2DConditionModel"
27
+ ],
28
+ "vae": [
29
+ "diffusers",
30
+ "AutoencoderKL"
31
+ ]
32
+ }
diffusion-decoder/pipeline_ar_gen.py ADDED
@@ -0,0 +1,292 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+
3
+
4
+ # ===========================================================================================
5
+ #
6
+ # Copyright (c) Beijing Academy of Artificial Intelligence (BAAI). All rights reserved.
7
+ #
8
+ # Author : Fan Zhang
9
+ # Email : [email protected]
10
+ # Institute : Beijing Academy of Artificial Intelligence (BAAI)
11
+ # Create On : 2023-12-19 10:45
12
+ # Last Modified : 2023-12-25 07:59
13
+ # File Name : pipeline_emu2_gen.py
14
+ # Description :
15
+ #
16
+ # ===========================================================================================
17
+
18
+
19
+ from dataclasses import dataclass
20
+ from typing import List, Optional
21
+
22
+
23
+ from PIL import Image
24
+ import numpy as np
25
+ import torch
26
+ from torchvision import transforms as TF
27
+ from tqdm import tqdm
28
+ import pdb
29
+
30
+
31
+ from diffusers import DiffusionPipeline
32
+ from diffusers.utils import BaseOutput
33
+
34
+
35
+ from diffusers import UNet2DConditionModel, EulerDiscreteScheduler, AutoencoderKL
36
+ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
37
+ from transformers import CLIPImageProcessor
38
+ from transformers import AutoModelForCausalLM, AutoTokenizer
39
+
40
+
41
+ EVA_IMAGE_SIZE = 448
42
+ OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
43
+ OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
44
+ DEFAULT_IMG_PLACEHOLDER = "<image>"
45
+
46
+
47
+ @dataclass
48
+ class EmuVisualGenerationPipelineOutput(BaseOutput):
49
+ image: Image.Image
50
+ nsfw_content_detected: Optional[bool]
51
+
52
+
53
+
54
+
55
+ class EmuVisualGenerationPipeline(DiffusionPipeline):
56
+
57
+
58
+ def __init__(
59
+ self,
60
+ tokenizer: AutoTokenizer,
61
+ multimodal_encoder: AutoModelForCausalLM,
62
+ scheduler: EulerDiscreteScheduler,
63
+ unet: UNet2DConditionModel,
64
+ vae: AutoencoderKL,
65
+ feature_extractor: CLIPImageProcessor,
66
+ safety_checker: StableDiffusionSafetyChecker,
67
+ eva_size=EVA_IMAGE_SIZE,
68
+ eva_mean=OPENAI_DATASET_MEAN,
69
+ eva_std=OPENAI_DATASET_STD,
70
+ ):
71
+ super().__init__()
72
+ self.register_modules(
73
+ tokenizer=tokenizer,
74
+ multimodal_encoder=multimodal_encoder,
75
+ scheduler=scheduler,
76
+ unet=unet,
77
+ vae=vae,
78
+ feature_extractor=feature_extractor,
79
+ safety_checker=safety_checker,
80
+ )
81
+
82
+
83
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
84
+
85
+
86
+ self.transform = TF.Compose([
87
+ TF.Resize((eva_size, eva_size), interpolation=TF.InterpolationMode.BICUBIC),
88
+ TF.ToTensor(),
89
+ TF.Normalize(mean=eva_mean, std=eva_std),
90
+ ])
91
+
92
+
93
+ self.negative_prompt = {}
94
+
95
+
96
+ def device(self, module):
97
+ return next(module.parameters()).device
98
+
99
+
100
+ def dtype(self, module):
101
+ return next(module.parameters()).dtype
102
+
103
+
104
+ @torch.no_grad()
105
+ def __call__(
106
+ self,
107
+ inputs: List[Image.Image | str] | str | Image.Image,
108
+ height: int = 1024,
109
+ width: int = 1024,
110
+ num_inference_steps: int = 50,
111
+ guidance_scale: float = 3.,
112
+ crop_info: List[int] = [0, 0],
113
+ original_size: List[int] = [1024, 1024],
114
+ ):
115
+ if not isinstance(inputs, list):
116
+ inputs = [inputs]
117
+
118
+
119
+ # 0. Default height and width to unet
120
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
121
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
122
+
123
+
124
+ device = self.device(self.unet)
125
+ dtype = self.dtype(self.unet)
126
+
127
+
128
+ do_classifier_free_guidance = guidance_scale > 1.0
129
+
130
+
131
+ # 1. Encode input prompt
132
+ prompt_embeds = self._prepare_and_encode_inputs(
133
+ inputs,
134
+ do_classifier_free_guidance,
135
+ ).to(dtype).to(device)
136
+ batch_size = prompt_embeds.shape[0] // 2 if do_classifier_free_guidance else prompt_embeds.shape[0]
137
+
138
+
139
+ unet_added_conditions = {}
140
+ time_ids = torch.LongTensor(original_size + crop_info + [height, width]).to(device)
141
+ if do_classifier_free_guidance:
142
+ unet_added_conditions["time_ids"] = torch.cat([time_ids, time_ids], dim=0)
143
+ else:
144
+ unet_added_conditions["time_ids"] = time_ids
145
+ unet_added_conditions["text_embeds"] = torch.mean(prompt_embeds, dim=1)
146
+
147
+
148
+ # 2. Prepare timesteps
149
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
150
+ timesteps = self.scheduler.timesteps
151
+
152
+
153
+ # 3. Prepare latent variables
154
+ shape = (
155
+ batch_size,
156
+ self.unet.config.in_channels,
157
+ height // self.vae_scale_factor,
158
+ width // self.vae_scale_factor,
159
+ )
160
+ latents = torch.randn(shape, device=device, dtype=dtype)
161
+ latents = latents * self.scheduler.init_noise_sigma
162
+
163
+
164
+ # 4. Denoising loop
165
+ for t in tqdm(timesteps):
166
+ # expand the latents if we are doing classifier free guidance
167
+ # 2B x 4 x H x W
168
+ latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
169
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
170
+
171
+
172
+ noise_pred = self.unet(
173
+ latent_model_input,
174
+ t,
175
+ encoder_hidden_states=prompt_embeds,
176
+ added_cond_kwargs=unet_added_conditions,
177
+ ).sample
178
+
179
+
180
+ # perform guidance
181
+ if do_classifier_free_guidance:
182
+ noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
183
+ noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
184
+
185
+
186
+ # compute the previous noisy sample x_t -> x_t-1
187
+ latents = self.scheduler.step(noise_pred, t, latents).prev_sample
188
+
189
+
190
+ # 5. Post-processing
191
+ images = self.decode_latents(latents)
192
+
193
+
194
+ # 6. Run safety checker
195
+ images, has_nsfw_concept = self.run_safety_checker(images)
196
+
197
+
198
+ # 7. Convert to PIL
199
+ images = self.numpy_to_pil(images)
200
+ return EmuVisualGenerationPipelineOutput(
201
+ image=images[0],
202
+ nsfw_content_detected=None if has_nsfw_concept is None else has_nsfw_concept[0],
203
+ )
204
+
205
+
206
+ def _prepare_and_encode_inputs(
207
+ self,
208
+ inputs: List[str | Image.Image],
209
+ do_classifier_free_guidance: bool = False,
210
+ placeholder: str = DEFAULT_IMG_PLACEHOLDER,
211
+ ):
212
+ # pdb.set_trace()
213
+ device = self.device(self.multimodal_encoder.model)
214
+ dtype = self.dtype(self.multimodal_encoder.model)
215
+
216
+
217
+ has_image, has_text = False, False
218
+ text_prompt, image_prompt = "", []
219
+ for x in inputs:
220
+ if isinstance(x, str):
221
+ has_text = True
222
+ text_prompt += x
223
+ else:
224
+ has_image = True
225
+ text_prompt += placeholder
226
+ image_prompt.append(self.transform(x))
227
+
228
+
229
+ if len(image_prompt) == 0:
230
+ image_prompt = None
231
+ else:
232
+ image_prompt = torch.stack(image_prompt)
233
+ image_prompt = image_prompt.type(dtype).to(device)
234
+
235
+
236
+ if has_image and not has_text:
237
+ prompt = self.multimodal_encoder.model.encode_image(image=image_prompt)
238
+ if do_classifier_free_guidance:
239
+ key = "[NULL_IMAGE]"
240
+ if key not in self.negative_prompt:
241
+ negative_image = torch.zeros_like(image_prompt)
242
+ self.negative_prompt[key] = self.multimodal_encoder.model.encode_image(image=negative_image)
243
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
244
+ else:
245
+ prompt = self.multimodal_encoder.generate_image(text=[text_prompt], image=image_prompt, tokenizer=self.tokenizer)
246
+ if do_classifier_free_guidance:
247
+ key = ""
248
+ if key not in self.negative_prompt:
249
+ self.negative_prompt[key] = self.multimodal_encoder.generate_image(text=[""], tokenizer=self.tokenizer)
250
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
251
+
252
+
253
+ return prompt
254
+
255
+
256
+ def decode_latents(self, latents: torch.Tensor) -> np.ndarray:
257
+ latents = 1 / self.vae.config.scaling_factor * latents
258
+ image = self.vae.decode(latents).sample
259
+ image = (image / 2 + 0.5).clamp(0, 1)
260
+ image = image.cpu().permute(0, 2, 3, 1).float().numpy()
261
+ return image
262
+
263
+
264
+ def numpy_to_pil(self, images: np.ndarray) -> List[Image.Image]:
265
+ """
266
+ Convert a numpy image or a batch of images to a PIL image.
267
+ """
268
+ if images.ndim == 3:
269
+ images = images[None, ...]
270
+ images = (images * 255).round().astype("uint8")
271
+ if images.shape[-1] == 1:
272
+ # special case for grayscale (single channel) images
273
+ pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
274
+ else:
275
+ pil_images = [Image.fromarray(image) for image in images]
276
+
277
+
278
+ return pil_images
279
+
280
+
281
+ def run_safety_checker(self, images: np.ndarray):
282
+ if self.safety_checker is not None:
283
+ device = self.device(self.safety_checker)
284
+ dtype = self.dtype(self.safety_checker)
285
+ safety_checker_input = self.feature_extractor(self.numpy_to_pil(images), return_tensors="pt").to(device)
286
+ images, has_nsfw_concept = self.safety_checker(
287
+ images=images, clip_input=safety_checker_input.pixel_values.to(dtype)
288
+ )
289
+ else:
290
+ has_nsfw_concept = None
291
+ return images, has_nsfw_concept
292
+
diffusion-decoder/pipeline_emu2_gen.py ADDED
@@ -0,0 +1,250 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+
3
+ # ===========================================================================================
4
+ #
5
+ # Copyright (c) Beijing Academy of Artificial Intelligence (BAAI). All rights reserved.
6
+ #
7
+ # Author : Fan Zhang
8
+ # Email : [email protected]
9
+ # Institute : Beijing Academy of Artificial Intelligence (BAAI)
10
+ # Create On : 2023-12-19 10:45
11
+ # Last Modified : 2023-12-25 07:59
12
+ # File Name : pipeline_emu2_gen.py
13
+ # Description :
14
+ #
15
+ # ===========================================================================================
16
+
17
+ from dataclasses import dataclass
18
+ from typing import List, Optional
19
+
20
+ from PIL import Image
21
+ import numpy as np
22
+ import torch
23
+ from torchvision import transforms as TF
24
+ from tqdm import tqdm
25
+
26
+ from diffusers import DiffusionPipeline
27
+ from diffusers.utils import BaseOutput
28
+
29
+ from diffusers import UNet2DConditionModel, EulerDiscreteScheduler, AutoencoderKL
30
+ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
31
+ from transformers import CLIPImageProcessor
32
+ from transformers import AutoModelForCausalLM, AutoTokenizer
33
+
34
+ EVA_IMAGE_SIZE = 448
35
+ OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
36
+ OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
37
+ DEFAULT_IMG_PLACEHOLDER = "[<IMG_PLH>]"
38
+
39
+ @dataclass
40
+ class EmuVisualGenerationPipelineOutput(BaseOutput):
41
+ image: Image.Image
42
+ nsfw_content_detected: Optional[bool]
43
+
44
+
45
+ class EmuVisualGenerationPipeline(DiffusionPipeline):
46
+
47
+ def __init__(
48
+ self,
49
+ tokenizer: AutoTokenizer,
50
+ multimodal_encoder: AutoModelForCausalLM,
51
+ scheduler: EulerDiscreteScheduler,
52
+ unet: UNet2DConditionModel,
53
+ vae: AutoencoderKL,
54
+ feature_extractor: CLIPImageProcessor,
55
+ safety_checker: StableDiffusionSafetyChecker,
56
+ eva_size=EVA_IMAGE_SIZE,
57
+ eva_mean=OPENAI_DATASET_MEAN,
58
+ eva_std=OPENAI_DATASET_STD,
59
+ ):
60
+ super().__init__()
61
+ self.register_modules(
62
+ tokenizer=tokenizer,
63
+ multimodal_encoder=multimodal_encoder,
64
+ scheduler=scheduler,
65
+ unet=unet,
66
+ vae=vae,
67
+ feature_extractor=feature_extractor,
68
+ safety_checker=safety_checker,
69
+ )
70
+
71
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
72
+
73
+ self.transform = TF.Compose([
74
+ TF.Resize((eva_size, eva_size), interpolation=TF.InterpolationMode.BICUBIC),
75
+ TF.ToTensor(),
76
+ TF.Normalize(mean=eva_mean, std=eva_std),
77
+ ])
78
+
79
+ self.negative_prompt = {}
80
+
81
+ def device(self, module):
82
+ return next(module.parameters()).device
83
+
84
+ def dtype(self, module):
85
+ return next(module.parameters()).dtype
86
+
87
+ @torch.no_grad()
88
+ def __call__(
89
+ self,
90
+ inputs: List[Image.Image | str] | str | Image.Image,
91
+ height: int = 1024,
92
+ width: int = 1024,
93
+ num_inference_steps: int = 50,
94
+ guidance_scale: float = 3.,
95
+ crop_info: List[int] = [0, 0],
96
+ original_size: List[int] = [1024, 1024],
97
+ ):
98
+ if not isinstance(inputs, list):
99
+ inputs = [inputs]
100
+
101
+ # 0. Default height and width to unet
102
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
103
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
104
+
105
+ device = self.device(self.unet)
106
+ dtype = self.dtype(self.unet)
107
+
108
+ do_classifier_free_guidance = guidance_scale > 1.0
109
+
110
+ # 1. Encode input prompt
111
+ prompt_embeds = self._prepare_and_encode_inputs(
112
+ inputs,
113
+ do_classifier_free_guidance,
114
+ ).to(dtype).to(device)
115
+ batch_size = prompt_embeds.shape[0] // 2 if do_classifier_free_guidance else prompt_embeds.shape[0]
116
+
117
+ unet_added_conditions = {}
118
+ time_ids = torch.LongTensor(original_size + crop_info + [height, width]).to(device)
119
+ if do_classifier_free_guidance:
120
+ unet_added_conditions["time_ids"] = torch.cat([time_ids, time_ids], dim=0)
121
+ else:
122
+ unet_added_conditions["time_ids"] = time_ids
123
+ unet_added_conditions["text_embeds"] = torch.mean(prompt_embeds, dim=1)
124
+
125
+ # 2. Prepare timesteps
126
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
127
+ timesteps = self.scheduler.timesteps
128
+
129
+ # 3. Prepare latent variables
130
+ shape = (
131
+ batch_size,
132
+ self.unet.config.in_channels,
133
+ height // self.vae_scale_factor,
134
+ width // self.vae_scale_factor,
135
+ )
136
+ latents = torch.randn(shape, device=device, dtype=dtype)
137
+ latents = latents * self.scheduler.init_noise_sigma
138
+
139
+ # 4. Denoising loop
140
+ for t in tqdm(timesteps):
141
+ # expand the latents if we are doing classifier free guidance
142
+ # 2B x 4 x H x W
143
+ latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
144
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
145
+
146
+ noise_pred = self.unet(
147
+ latent_model_input,
148
+ t,
149
+ encoder_hidden_states=prompt_embeds,
150
+ added_cond_kwargs=unet_added_conditions,
151
+ ).sample
152
+
153
+ # perform guidance
154
+ if do_classifier_free_guidance:
155
+ noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
156
+ noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
157
+
158
+ # compute the previous noisy sample x_t -> x_t-1
159
+ latents = self.scheduler.step(noise_pred, t, latents).prev_sample
160
+
161
+ # 5. Post-processing
162
+ images = self.decode_latents(latents)
163
+
164
+ # 6. Run safety checker
165
+ images, has_nsfw_concept = self.run_safety_checker(images)
166
+
167
+ # 7. Convert to PIL
168
+ images = self.numpy_to_pil(images)
169
+ return EmuVisualGenerationPipelineOutput(
170
+ image=images[0],
171
+ nsfw_content_detected=None if has_nsfw_concept is None else has_nsfw_concept[0],
172
+ )
173
+
174
+ def _prepare_and_encode_inputs(
175
+ self,
176
+ inputs: List[str | Image.Image],
177
+ do_classifier_free_guidance: bool = False,
178
+ placeholder: str = DEFAULT_IMG_PLACEHOLDER,
179
+ ):
180
+ device = self.device(self.multimodal_encoder.model.visual)
181
+ dtype = self.dtype(self.multimodal_encoder.model.visual)
182
+
183
+ has_image, has_text = False, False
184
+ text_prompt, image_prompt = "", []
185
+ for x in inputs:
186
+ if isinstance(x, str):
187
+ has_text = True
188
+ text_prompt += x
189
+ else:
190
+ has_image = True
191
+ text_prompt += placeholder
192
+ image_prompt.append(self.transform(x))
193
+
194
+ if len(image_prompt) == 0:
195
+ image_prompt = None
196
+ else:
197
+ image_prompt = torch.stack(image_prompt)
198
+ image_prompt = image_prompt.type(dtype).to(device)
199
+
200
+ if has_image and not has_text:
201
+ prompt = self.multimodal_encoder.model.encode_image(image=image_prompt)
202
+ if do_classifier_free_guidance:
203
+ key = "[NULL_IMAGE]"
204
+ if key not in self.negative_prompt:
205
+ negative_image = torch.zeros_like(image_prompt)
206
+ self.negative_prompt[key] = self.multimodal_encoder.model.encode_image(image=negative_image)
207
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
208
+ else:
209
+ prompt = self.multimodal_encoder.generate_image(text=[text_prompt], image=image_prompt, tokenizer=self.tokenizer)
210
+ if do_classifier_free_guidance:
211
+ key = ""
212
+ if key not in self.negative_prompt:
213
+ self.negative_prompt[key] = self.multimodal_encoder.generate_image(text=[""], tokenizer=self.tokenizer)
214
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
215
+
216
+ return prompt
217
+
218
+ def decode_latents(self, latents: torch.Tensor) -> np.ndarray:
219
+ latents = 1 / self.vae.config.scaling_factor * latents
220
+ image = self.vae.decode(latents).sample
221
+ image = (image / 2 + 0.5).clamp(0, 1)
222
+ image = image.cpu().permute(0, 2, 3, 1).float().numpy()
223
+ return image
224
+
225
+ def numpy_to_pil(self, images: np.ndarray) -> List[Image.Image]:
226
+ """
227
+ Convert a numpy image or a batch of images to a PIL image.
228
+ """
229
+ if images.ndim == 3:
230
+ images = images[None, ...]
231
+ images = (images * 255).round().astype("uint8")
232
+ if images.shape[-1] == 1:
233
+ # special case for grayscale (single channel) images
234
+ pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
235
+ else:
236
+ pil_images = [Image.fromarray(image) for image in images]
237
+
238
+ return pil_images
239
+
240
+ def run_safety_checker(self, images: np.ndarray):
241
+ if self.safety_checker is not None:
242
+ device = self.device(self.safety_checker)
243
+ dtype = self.dtype(self.safety_checker)
244
+ safety_checker_input = self.feature_extractor(self.numpy_to_pil(images), return_tensors="pt").to(device)
245
+ images, has_nsfw_concept = self.safety_checker(
246
+ images=images, clip_input=safety_checker_input.pixel_values.to(dtype)
247
+ )
248
+ else:
249
+ has_nsfw_concept = None
250
+ return images, has_nsfw_concept
diffusion-decoder/pipeline_llava_gen.py ADDED
@@ -0,0 +1,287 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ # ===========================================================================================
3
+ #
4
+ # Copyright (c) Beijing Academy of Artificial Intelligence (BAAI). All rights reserved.
5
+ #
6
+ # Author : Fan Zhang
7
+ # Email : [email protected]
8
+ # Institute : Beijing Academy of Artificial Intelligence (BAAI)
9
+ # Create On : 2023-12-19 10:45
10
+ # Last Modified : 2023-12-25 07:59
11
+ # File Name : pipeline_emu2_gen.py
12
+ # Description :
13
+ #
14
+ # ===========================================================================================
15
+
16
+ from dataclasses import dataclass
17
+ from typing import List, Optional
18
+
19
+ from PIL import Image
20
+ import numpy as np
21
+ import torch
22
+ from torchvision import transforms as TF
23
+ from tqdm import tqdm
24
+ import pdb
25
+
26
+ from diffusers import DiffusionPipeline
27
+ from diffusers.utils import BaseOutput
28
+
29
+ from diffusers import UNet2DConditionModel, EulerDiscreteScheduler, AutoencoderKL
30
+ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
31
+ from transformers import CLIPImageProcessor
32
+ from transformers import AutoModelForCausalLM, AutoTokenizer
33
+
34
+ EVA_IMAGE_SIZE = 448
35
+ OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
36
+ OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
37
+ DEFAULT_IMG_PLACEHOLDER = "<image>"
38
+
39
+ from transformers import AutoProcessor
40
+ image_processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct").image_processor
41
+
42
+
43
+ @dataclass
44
+ class EmuVisualGenerationPipelineOutput(BaseOutput):
45
+ image: Image.Image
46
+ nsfw_content_detected: Optional[bool]
47
+
48
+
49
+ class EmuVisualGenerationPipeline(DiffusionPipeline):
50
+
51
+ def __init__(
52
+ self,
53
+ tokenizer: AutoTokenizer,
54
+ multimodal_encoder: AutoModelForCausalLM,
55
+ scheduler: EulerDiscreteScheduler,
56
+ unet: UNet2DConditionModel,
57
+ vae: AutoencoderKL,
58
+ feature_extractor: CLIPImageProcessor,
59
+ safety_checker: StableDiffusionSafetyChecker,
60
+ eva_size=EVA_IMAGE_SIZE,
61
+ eva_mean=OPENAI_DATASET_MEAN,
62
+ eva_std=OPENAI_DATASET_STD,
63
+ ):
64
+ super().__init__()
65
+ self.register_modules(
66
+ tokenizer=tokenizer,
67
+ multimodal_encoder=multimodal_encoder,
68
+ scheduler=scheduler,
69
+ unet=unet,
70
+ vae=vae,
71
+ feature_extractor=feature_extractor,
72
+ safety_checker=None,
73
+ )
74
+
75
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
76
+
77
+ self.transform = TF.Compose([
78
+ TF.Resize((eva_size, eva_size), interpolation=TF.InterpolationMode.BICUBIC),
79
+ TF.ToTensor(),
80
+ TF.Normalize(mean=eva_mean, std=eva_std),
81
+ ])
82
+
83
+ self.negative_prompt = {}
84
+
85
+ def device(self, module):
86
+ return next(module.parameters()).device
87
+
88
+ def dtype(self, module):
89
+ return next(module.parameters()).dtype
90
+
91
+ @torch.no_grad()
92
+ def __call__(
93
+ self,
94
+ inputs: List[Image.Image | str] | str | Image.Image,
95
+ height: int = 1024,
96
+ width: int = 1024,
97
+ num_inference_steps: int = 50,
98
+ guidance_scale: float = 3.0,
99
+ crop_info: List[int] = [0, 0],
100
+ original_size: List[int] = [1024, 1024],
101
+ ):
102
+ if not isinstance(inputs, list):
103
+ inputs = [inputs]
104
+
105
+ # 0. Default height and width to unet
106
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
107
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
108
+
109
+ device = self.device(self.unet)
110
+ dtype = self.dtype(self.unet)
111
+
112
+ do_classifier_free_guidance = guidance_scale > 1.0
113
+
114
+ # 1. Encode input prompt
115
+ prompt_embeds = self._prepare_and_encode_inputs(
116
+ inputs,
117
+ do_classifier_free_guidance,
118
+ ).to(dtype).to(device)
119
+ batch_size = prompt_embeds.shape[0] // 2 if do_classifier_free_guidance else prompt_embeds.shape[0]
120
+
121
+ unet_added_conditions = {}
122
+ time_ids = torch.LongTensor(original_size + crop_info + [height, width]).to(device)
123
+ if do_classifier_free_guidance:
124
+ unet_added_conditions["time_ids"] = torch.cat([time_ids, time_ids], dim=0)
125
+ else:
126
+ unet_added_conditions["time_ids"] = time_ids
127
+ unet_added_conditions["text_embeds"] = torch.mean(prompt_embeds, dim=1)
128
+
129
+ # 2. Prepare timesteps
130
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
131
+ timesteps = self.scheduler.timesteps
132
+
133
+ # 3. Prepare latent variables
134
+ shape = (
135
+ batch_size,
136
+ self.unet.config.in_channels,
137
+ height // self.vae_scale_factor,
138
+ width // self.vae_scale_factor,
139
+ )
140
+ latents = torch.randn(shape, device=device, dtype=dtype)
141
+ latents = latents * self.scheduler.init_noise_sigma
142
+
143
+ # 4. Denoising loop
144
+ for t in tqdm(timesteps):
145
+ # Expand the latents if doing classifier free guidance: 2B x 4 x H x W
146
+ latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
147
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
148
+
149
+ noise_pred = self.unet(
150
+ latent_model_input,
151
+ t,
152
+ encoder_hidden_states=prompt_embeds,
153
+ added_cond_kwargs=unet_added_conditions,
154
+ ).sample
155
+
156
+ # Perform guidance
157
+ if do_classifier_free_guidance:
158
+ noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
159
+ noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
160
+
161
+ # Compute the previous noisy sample x_t -> x_t-1
162
+ latents = self.scheduler.step(noise_pred, t, latents).prev_sample
163
+
164
+ # 5. Post-processing
165
+ images = self.decode_latents(latents)
166
+ # 6. Run safety checker
167
+ # images, has_nsfw_concept = self.run_safety_checker(images)
168
+
169
+ # 7. Convert to PIL
170
+ images = self.numpy_to_pil(images)
171
+
172
+ # return EmuVisualGenerationPipelineOutput(
173
+ # image=images[0],
174
+ # nsfw_content_detected=None if has_nsfw_concept is None else has_nsfw_concept[0],
175
+ # )
176
+
177
+ return EmuVisualGenerationPipelineOutput(
178
+ image=images[0],
179
+ nsfw_content_detected=None
180
+ )
181
+
182
+ def _prepare_and_encode_inputs(
183
+ self,
184
+ inputs: List[str | Image.Image],
185
+ do_classifier_free_guidance: bool = False,
186
+ placeholder: str = DEFAULT_IMG_PLACEHOLDER,
187
+ ):
188
+ # pdb.set_trace()
189
+ device = self.device(self.multimodal_encoder.model)
190
+ dtype = self.dtype(self.multimodal_encoder.model)
191
+
192
+ has_image, has_text = False, False
193
+ text_prompt, image_prompt, image_grid_thw = "", [], []
194
+ for x in inputs:
195
+ if isinstance(x, str):
196
+ has_text = True
197
+ text_prompt += x
198
+ else:
199
+ has_image = True
200
+ text_prompt = text_prompt.replace(
201
+ "<image>",
202
+ "<|vision_start|>" + "<|image_pad|>" * 256 + "<|vision_end|>"
203
+ )
204
+ resized_images = x.resize((448, 448))
205
+ image_inputs = image_processor(resized_images, return_tensors="pt")
206
+ image_prompt.append(image_inputs.pixel_values)
207
+ image_grid_thw.append(image_inputs.image_grid_thw)
208
+
209
+ if len(image_prompt) == 0:
210
+ image_prompt = None
211
+ image_grid_thw = None
212
+ else:
213
+ image_prompt = torch.cat(image_prompt, dim=0)
214
+ image_grid_thw = torch.cat(image_grid_thw, dim=0)
215
+ # breakpoint()
216
+ if has_image and not has_text:
217
+ prompt = self.multimodal_encoder.model.encode_image(image=image_prompt)
218
+ if do_classifier_free_guidance:
219
+ key = "[NULL_IMAGE]"
220
+ if key not in self.negative_prompt:
221
+ negative_image = torch.zeros_like(image_prompt)
222
+ self.negative_prompt[key] = self.multimodal_encoder.model.encode_image(image=negative_image)
223
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
224
+ elif has_text and not has_image:
225
+
226
+ prompt = self.multimodal_encoder.generate_image(
227
+ text=[text_prompt], tokenizer=self.tokenizer
228
+ )
229
+ if do_classifier_free_guidance:
230
+ key = ""
231
+ if key not in self.negative_prompt:
232
+ self.negative_prompt[key] = self.multimodal_encoder.generate_image(
233
+ text=[" "],
234
+ tokenizer=self.tokenizer
235
+ )
236
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
237
+ elif has_text and has_image:
238
+ prompt = self.multimodal_encoder.generate_image(
239
+ text=[text_prompt],
240
+ pixel_values=image_prompt.cuda(),
241
+ image_grid_thw=image_grid_thw.cuda(),
242
+ tokenizer=self.tokenizer
243
+ )
244
+ if do_classifier_free_guidance:
245
+ key = ""
246
+ if key not in self.negative_prompt:
247
+ self.negative_prompt[key] = self.multimodal_encoder.generate_image(
248
+ text=[" "],
249
+ tokenizer=self.tokenizer
250
+ )
251
+ prompt = torch.cat([prompt, self.negative_prompt[key]], dim=0)
252
+ return prompt
253
+
254
+ def decode_latents(self, latents: torch.Tensor) -> np.ndarray:
255
+ latents = 1 / self.vae.config.scaling_factor * latents
256
+ image = self.vae.decode(latents).sample
257
+ image = (image / 2 + 0.5).clamp(0, 1)
258
+ image = image.cpu().permute(0, 2, 3, 1).float().numpy()
259
+ return image
260
+
261
+ def numpy_to_pil(self, images: np.ndarray) -> List[Image.Image]:
262
+ """
263
+ Convert a numpy image or a batch of images to a PIL image.
264
+ """
265
+ if images.ndim == 3:
266
+ images = images[None, ...]
267
+ images = (images * 255).round().astype("uint8")
268
+ if images.shape[-1] == 1:
269
+ # Special case for grayscale (single channel) images.
270
+ pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
271
+ else:
272
+ pil_images = [Image.fromarray(image) for image in images]
273
+ return pil_images
274
+
275
+ def run_safety_checker(self, images: np.ndarray):
276
+ if self.safety_checker is not None:
277
+ device = self.device(self.safety_checker)
278
+ dtype = self.dtype(self.safety_checker)
279
+ safety_checker_input = self.feature_extractor(
280
+ self.numpy_to_pil(images), return_tensors="pt"
281
+ ).to(device)
282
+ images, has_nsfw_concept = self.safety_checker(
283
+ images=images, clip_input=safety_checker_input.pixel_values.to(dtype)
284
+ )
285
+ else:
286
+ has_nsfw_concept = None
287
+ return images, has_nsfw_concept
diffusion-decoder/safety_checker_none/config.json ADDED
@@ -0,0 +1,168 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_commit_hash": null,
3
+ "_name_or_path": "/share/project/quansun/release_hf/Emu2-VisualGeneration/safety_checker",
4
+ "architectures": [
5
+ "StableDiffusionSafetyChecker"
6
+ ],
7
+ "initializer_factor": 1.0,
8
+ "logit_scale_init_value": 2.6592,
9
+ "model_type": "clip",
10
+ "projection_dim": 768,
11
+ "text_config": {
12
+ "_name_or_path": "",
13
+ "add_cross_attention": false,
14
+ "architectures": null,
15
+ "attention_dropout": 0.0,
16
+ "bad_words_ids": null,
17
+ "begin_suppress_tokens": null,
18
+ "bos_token_id": 49406,
19
+ "chunk_size_feed_forward": 0,
20
+ "cross_attention_hidden_size": null,
21
+ "decoder_start_token_id": null,
22
+ "diversity_penalty": 0.0,
23
+ "do_sample": false,
24
+ "dropout": 0.0,
25
+ "early_stopping": false,
26
+ "encoder_no_repeat_ngram_size": 0,
27
+ "eos_token_id": 49407,
28
+ "exponential_decay_length_penalty": null,
29
+ "finetuning_task": null,
30
+ "forced_bos_token_id": null,
31
+ "forced_eos_token_id": null,
32
+ "hidden_act": "quick_gelu",
33
+ "hidden_size": 768,
34
+ "id2label": {
35
+ "0": "LABEL_0",
36
+ "1": "LABEL_1"
37
+ },
38
+ "initializer_factor": 1.0,
39
+ "initializer_range": 0.02,
40
+ "intermediate_size": 3072,
41
+ "is_decoder": false,
42
+ "is_encoder_decoder": false,
43
+ "label2id": {
44
+ "LABEL_0": 0,
45
+ "LABEL_1": 1
46
+ },
47
+ "layer_norm_eps": 1e-05,
48
+ "length_penalty": 1.0,
49
+ "max_length": 20,
50
+ "max_position_embeddings": 77,
51
+ "min_length": 0,
52
+ "model_type": "clip_text_model",
53
+ "no_repeat_ngram_size": 0,
54
+ "num_attention_heads": 12,
55
+ "num_beam_groups": 1,
56
+ "num_beams": 1,
57
+ "num_hidden_layers": 12,
58
+ "num_return_sequences": 1,
59
+ "output_attentions": false,
60
+ "output_hidden_states": false,
61
+ "output_scores": false,
62
+ "pad_token_id": 1,
63
+ "prefix": null,
64
+ "problem_type": null,
65
+ "projection_dim": 512,
66
+ "pruned_heads": {},
67
+ "remove_invalid_values": false,
68
+ "repetition_penalty": 1.0,
69
+ "return_dict": true,
70
+ "return_dict_in_generate": false,
71
+ "sep_token_id": null,
72
+ "suppress_tokens": null,
73
+ "task_specific_params": null,
74
+ "temperature": 1.0,
75
+ "tf_legacy_loss": false,
76
+ "tie_encoder_decoder": false,
77
+ "tie_word_embeddings": true,
78
+ "tokenizer_class": null,
79
+ "top_k": 50,
80
+ "top_p": 1.0,
81
+ "torch_dtype": null,
82
+ "torchscript": false,
83
+ "transformers_version": "4.31.0",
84
+ "typical_p": 1.0,
85
+ "use_bfloat16": false,
86
+ "vocab_size": 49408
87
+ },
88
+ "torch_dtype": "bfloat16",
89
+ "transformers_version": null,
90
+ "vision_config": {
91
+ "_name_or_path": "",
92
+ "add_cross_attention": false,
93
+ "architectures": null,
94
+ "attention_dropout": 0.0,
95
+ "bad_words_ids": null,
96
+ "begin_suppress_tokens": null,
97
+ "bos_token_id": null,
98
+ "chunk_size_feed_forward": 0,
99
+ "cross_attention_hidden_size": null,
100
+ "decoder_start_token_id": null,
101
+ "diversity_penalty": 0.0,
102
+ "do_sample": false,
103
+ "dropout": 0.0,
104
+ "early_stopping": false,
105
+ "encoder_no_repeat_ngram_size": 0,
106
+ "eos_token_id": null,
107
+ "exponential_decay_length_penalty": null,
108
+ "finetuning_task": null,
109
+ "forced_bos_token_id": null,
110
+ "forced_eos_token_id": null,
111
+ "hidden_act": "quick_gelu",
112
+ "hidden_size": 1024,
113
+ "id2label": {
114
+ "0": "LABEL_0",
115
+ "1": "LABEL_1"
116
+ },
117
+ "image_size": 224,
118
+ "initializer_factor": 1.0,
119
+ "initializer_range": 0.02,
120
+ "intermediate_size": 4096,
121
+ "is_decoder": false,
122
+ "is_encoder_decoder": false,
123
+ "label2id": {
124
+ "LABEL_0": 0,
125
+ "LABEL_1": 1
126
+ },
127
+ "layer_norm_eps": 1e-05,
128
+ "length_penalty": 1.0,
129
+ "max_length": 20,
130
+ "min_length": 0,
131
+ "model_type": "clip_vision_model",
132
+ "no_repeat_ngram_size": 0,
133
+ "num_attention_heads": 16,
134
+ "num_beam_groups": 1,
135
+ "num_beams": 1,
136
+ "num_channels": 3,
137
+ "num_hidden_layers": 24,
138
+ "num_return_sequences": 1,
139
+ "output_attentions": false,
140
+ "output_hidden_states": false,
141
+ "output_scores": false,
142
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