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Co-authored-by: Timothy Boie <[email protected]>

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  1. .gitattributes +33 -0
  2. Dockerfile +19 -0
  3. LICENSE +201 -0
  4. LICENSE_MODEL +82 -0
  5. README.md +3 -0
  6. demo.py +74 -0
  7. demo_web.py +124 -0
  8. docker-compose.yaml +10 -0
  9. requirements.txt +12 -0
  10. stable_diffusion_engine.py +212 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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Dockerfile ADDED
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+ FROM python:3.9.9-bullseye
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+
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+ WORKDIR /src
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+
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+ RUN apt-get update && \
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+ apt-get install -y \
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+ libgl1 libglib2.0-0
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+
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+ COPY requirements.txt /src/
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+
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+ RUN pip3 install -r requirements.txt
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+
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+ COPY stable_diffusion_engine.py demo.py demo_web.py /src/
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+ COPY data/ /src/data/
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+
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+ # download models
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+ RUN python3 demo.py --num-inference-steps 1 --prompt "test" --output /tmp/test.jpg
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+
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+ ENTRYPOINT ["python3", "demo.py"]
LICENSE ADDED
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LICENSE_MODEL ADDED
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+ Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
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+ Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
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+ Use Restrictions
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+ - To provide medical advice and medical results interpretation;
82
+ - To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
README.md ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ ---
2
+ duplicated_from: timboie/test
3
+ ---
demo.py ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -- coding: utf-8 --`
2
+ import argparse
3
+ import os
4
+ # engine
5
+ from stable_diffusion_engine import StableDiffusionEngine
6
+ # scheduler
7
+ from diffusers import LMSDiscreteScheduler, PNDMScheduler
8
+ # utils
9
+ import cv2
10
+ import numpy as np
11
+
12
+
13
+ def main(args):
14
+ if args.seed is not None:
15
+ np.random.seed(args.seed)
16
+ if args.init_image is None:
17
+ scheduler = LMSDiscreteScheduler(
18
+ beta_start=args.beta_start,
19
+ beta_end=args.beta_end,
20
+ beta_schedule=args.beta_schedule,
21
+ tensor_format="np"
22
+ )
23
+ else:
24
+ scheduler = PNDMScheduler(
25
+ beta_start=args.beta_start,
26
+ beta_end=args.beta_end,
27
+ beta_schedule=args.beta_schedule,
28
+ skip_prk_steps = True,
29
+ tensor_format="np"
30
+ )
31
+ engine = StableDiffusionEngine(
32
+ model = args.model,
33
+ scheduler = scheduler,
34
+ tokenizer = args.tokenizer
35
+ )
36
+ image = engine(
37
+ prompt = args.prompt,
38
+ init_image = None if args.init_image is None else cv2.imread(args.init_image),
39
+ mask = None if args.mask is None else cv2.imread(args.mask, 0),
40
+ strength = args.strength,
41
+ num_inference_steps = args.num_inference_steps,
42
+ guidance_scale = args.guidance_scale,
43
+ eta = args.eta
44
+ )
45
+ cv2.imwrite(args.output, image)
46
+
47
+
48
+ if __name__ == "__main__":
49
+ parser = argparse.ArgumentParser()
50
+ # pipeline configure
51
+ parser.add_argument("--model", type=str, default="bes-dev/stable-diffusion-v1-4-openvino", help="model name")
52
+ # randomizer params
53
+ parser.add_argument("--seed", type=int, default=None, help="random seed for generating consistent images per prompt")
54
+ # scheduler params
55
+ parser.add_argument("--beta-start", type=float, default=0.00085, help="LMSDiscreteScheduler::beta_start")
56
+ parser.add_argument("--beta-end", type=float, default=0.012, help="LMSDiscreteScheduler::beta_end")
57
+ parser.add_argument("--beta-schedule", type=str, default="scaled_linear", help="LMSDiscreteScheduler::beta_schedule")
58
+ # diffusion params
59
+ parser.add_argument("--num-inference-steps", type=int, default=32, help="num inference steps")
60
+ parser.add_argument("--guidance-scale", type=float, default=7.5, help="guidance scale")
61
+ parser.add_argument("--eta", type=float, default=0.0, help="eta")
62
+ # tokenizer
63
+ parser.add_argument("--tokenizer", type=str, default="openai/clip-vit-large-patch14", help="tokenizer")
64
+ # prompt
65
+ parser.add_argument("--prompt", type=str, default="Street-art painting of Emilia Clarke in style of Banksy, photorealism", help="prompt")
66
+ # img2img params
67
+ parser.add_argument("--init-image", type=str, default=None, help="path to initial image")
68
+ parser.add_argument("--strength", type=float, default=0.5, help="how strong the initial image should be noised [0.0, 1.0]")
69
+ # inpainting
70
+ parser.add_argument("--mask", type=str, default=None, help="mask of the region to inpaint on the initial image")
71
+ # output name
72
+ parser.add_argument("--output", type=str, default="output.png", help="output image name")
73
+ args = parser.parse_args()
74
+ main(args)
demo_web.py ADDED
@@ -0,0 +1,124 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -- coding: utf-8 --`
2
+ import argparse
3
+ import os
4
+ import random
5
+ import streamlit as st
6
+ from streamlit_drawable_canvas import st_canvas
7
+ import numpy as np
8
+ import cv2
9
+ from PIL import Image, ImageEnhance
10
+ import numpy as np
11
+ # engine
12
+ from stable_diffusion_engine import StableDiffusionEngine
13
+ # scheduler
14
+ from diffusers import PNDMScheduler
15
+
16
+
17
+ def run(engine):
18
+ with st.form(key="request"):
19
+ with st.sidebar:
20
+ prompt = st.text_area(label='Enter prompt')
21
+
22
+ with st.expander("Initial image"):
23
+ init_image = st.file_uploader("init_image", type=['jpg','png','jpeg'])
24
+ stroke_width = st.slider("stroke_width", 1, 100, 50)
25
+ stroke_color = st.color_picker("stroke_color", "#00FF00")
26
+ canvas_result = st_canvas(
27
+ fill_color="rgb(0, 0, 0)",
28
+ stroke_width = stroke_width,
29
+ stroke_color = stroke_color,
30
+ background_color = "#000000",
31
+ background_image = Image.open(init_image) if init_image else None,
32
+ height = 512,
33
+ width = 512,
34
+ drawing_mode = "freedraw",
35
+ key = "canvas"
36
+ )
37
+
38
+ if init_image is not None:
39
+ init_image = cv2.cvtColor(np.array(Image.open(init_image)), cv2.COLOR_RGB2BGR)
40
+
41
+ if canvas_result.image_data is not None:
42
+ mask = cv2.cvtColor(canvas_result.image_data, cv2.COLOR_BGRA2GRAY)
43
+ mask[mask > 0] = 255
44
+ else:
45
+ mask = None
46
+
47
+ num_inference_steps = st.select_slider(
48
+ label='num_inference_steps',
49
+ options=range(1, 150),
50
+ value=32
51
+ )
52
+
53
+ guidance_scale = st.select_slider(
54
+ label='guidance_scale',
55
+ options=range(1, 21),
56
+ value=7
57
+ )
58
+
59
+ strength = st.slider(
60
+ label='strength',
61
+ min_value = 0.0,
62
+ max_value = 1.0,
63
+ value = 0.5
64
+ )
65
+
66
+ seed = st.number_input(
67
+ label='seed',
68
+ min_value = 0,
69
+ max_value = 2 ** 31,
70
+ value = random.randint(0, 2 ** 31)
71
+ )
72
+
73
+ generate = st.form_submit_button(label = 'Generate')
74
+
75
+ if prompt:
76
+ np.random.seed(seed)
77
+ image = engine(
78
+ prompt = prompt,
79
+ init_image = init_image,
80
+ mask = mask,
81
+ strength = strength,
82
+ num_inference_steps = num_inference_steps,
83
+ guidance_scale = guidance_scale
84
+ )
85
+ st.image(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), width=512)
86
+
87
+ @st.cache(allow_output_mutation=True)
88
+ def load_engine(args):
89
+ scheduler = PNDMScheduler(
90
+ beta_start=args.beta_start,
91
+ beta_end=args.beta_end,
92
+ beta_schedule=args.beta_schedule,
93
+ skip_prk_steps = True,
94
+ tensor_format="np"
95
+ )
96
+ engine = StableDiffusionEngine(
97
+ model = args.model,
98
+ scheduler = scheduler,
99
+ tokenizer = args.tokenizer
100
+ )
101
+ return engine
102
+
103
+
104
+ if __name__ == "__main__":
105
+ parser = argparse.ArgumentParser()
106
+ # pipeline configure
107
+ parser.add_argument("--model", type=str, default="bes-dev/stable-diffusion-v1-4-openvino", help="model name")
108
+ # scheduler params
109
+ parser.add_argument("--beta-start", type=float, default=0.00085, help="LMSDiscreteScheduler::beta_start")
110
+ parser.add_argument("--beta-end", type=float, default=0.012, help="LMSDiscreteScheduler::beta_end")
111
+ parser.add_argument("--beta-schedule", type=str, default="scaled_linear", help="LMSDiscreteScheduler::beta_schedule")
112
+ # tokenizer
113
+ parser.add_argument("--tokenizer", type=str, default="openai/clip-vit-large-patch14", help="tokenizer")
114
+
115
+ try:
116
+ args = parser.parse_args()
117
+ except SystemExit as e:
118
+ # This exception will be raised if --help or invalid command line arguments
119
+ # are used. Currently streamlit prevents the program from exiting normally
120
+ # so we have to do a hard exit.
121
+ os._exit(e.code)
122
+
123
+ engine = load_engine(args)
124
+ run(engine)
docker-compose.yaml ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ version: '3.9'
2
+ services:
3
+ stable-diffusion:
4
+ build:
5
+ context: .
6
+ dockerfile: Dockerfile
7
+ volumes:
8
+ # - /tmp/cache:/root/.cache
9
+ - /tmp/output:/tmp/output
10
+ # - /tmp/models:/root/models
requirements.txt ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ numpy==1.19.5
2
+ opencv-python==4.5.5.64
3
+ transformers==4.16.2
4
+ diffusers==0.2.4
5
+ tqdm==4.64.0
6
+ openvino==2022.1.0
7
+ huggingface_hub==0.9.0
8
+ scipy==1.9.0
9
+ streamlit==1.12.0
10
+ streamlit-drawable-canvas==0.8.0
11
+ watchdog==2.1.9
12
+ ftfy==6.1.1
stable_diffusion_engine.py ADDED
@@ -0,0 +1,212 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import inspect
2
+ import numpy as np
3
+ # openvino
4
+ from openvino.runtime import Core
5
+ # tokenizer
6
+ from transformers import CLIPTokenizer
7
+ # utils
8
+ from tqdm import tqdm
9
+ from huggingface_hub import hf_hub_download
10
+ from diffusers import LMSDiscreteScheduler, PNDMScheduler
11
+ import cv2
12
+
13
+
14
+ def result(var):
15
+ return next(iter(var.values()))
16
+
17
+
18
+ class StableDiffusionEngine:
19
+ def __init__(
20
+ self,
21
+ scheduler,
22
+ model="bes-dev/stable-diffusion-v1-4-openvino",
23
+ tokenizer="openai/clip-vit-large-patch14",
24
+ device="CPU"
25
+ ):
26
+ self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer)
27
+ self.scheduler = scheduler
28
+ # models
29
+ self.core = Core()
30
+ # text features
31
+ self._text_encoder = self.core.read_model(
32
+ hf_hub_download(repo_id=model, filename="text_encoder.xml"),
33
+ hf_hub_download(repo_id=model, filename="text_encoder.bin")
34
+ )
35
+ self.text_encoder = self.core.compile_model(self._text_encoder, device)
36
+ # diffusion
37
+ self._unet = self.core.read_model(
38
+ hf_hub_download(repo_id=model, filename="unet.xml"),
39
+ hf_hub_download(repo_id=model, filename="unet.bin")
40
+ )
41
+ self.unet = self.core.compile_model(self._unet, device)
42
+ self.latent_shape = tuple(self._unet.inputs[0].shape)[1:]
43
+ # decoder
44
+ self._vae_decoder = self.core.read_model(
45
+ hf_hub_download(repo_id=model, filename="vae_decoder.xml"),
46
+ hf_hub_download(repo_id=model, filename="vae_decoder.bin")
47
+ )
48
+ self.vae_decoder = self.core.compile_model(self._vae_decoder, device)
49
+ # encoder
50
+ self._vae_encoder = self.core.read_model(
51
+ hf_hub_download(repo_id=model, filename="vae_encoder.xml"),
52
+ hf_hub_download(repo_id=model, filename="vae_encoder.bin")
53
+ )
54
+ self.vae_encoder = self.core.compile_model(self._vae_encoder, device)
55
+ self.init_image_shape = tuple(self._vae_encoder.inputs[0].shape)[2:]
56
+
57
+ def _preprocess_mask(self, mask):
58
+ h, w = mask.shape
59
+ if h != self.init_image_shape[0] and w != self.init_image_shape[1]:
60
+ mask = cv2.resize(
61
+ mask,
62
+ (self.init_image_shape[1], self.init_image_shape[0]),
63
+ interpolation = cv2.INTER_NEAREST
64
+ )
65
+ mask = cv2.resize(
66
+ mask,
67
+ (self.init_image_shape[1] // 8, self.init_image_shape[0] // 8),
68
+ interpolation = cv2.INTER_NEAREST
69
+ )
70
+ mask = mask.astype(np.float32) / 255.0
71
+ mask = np.tile(mask, (4, 1, 1))
72
+ mask = mask[None].transpose(0, 1, 2, 3)
73
+ mask = 1 - mask
74
+ return mask
75
+
76
+ def _preprocess_image(self, image):
77
+ image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
78
+ h, w = image.shape[1:]
79
+ if h != self.init_image_shape[0] and w != self.init_image_shape[1]:
80
+ image = cv2.resize(
81
+ image,
82
+ (self.init_image_shape[1], self.init_image_shape[0]),
83
+ interpolation=cv2.INTER_LANCZOS4
84
+ )
85
+ # normalize
86
+ image = image.astype(np.float32) / 255.0
87
+ image = 2.0 * image - 1.0
88
+ # to batch
89
+ image = image[None].transpose(0, 3, 1, 2)
90
+ return image
91
+
92
+ def _encode_image(self, init_image):
93
+ moments = result(self.vae_encoder.infer_new_request({
94
+ "init_image": self._preprocess_image(init_image)
95
+ }))
96
+ mean, logvar = np.split(moments, 2, axis=1)
97
+ std = np.exp(logvar * 0.5)
98
+ latent = (mean + std * np.random.randn(*mean.shape)) * 0.18215
99
+ return latent
100
+
101
+ def __call__(
102
+ self,
103
+ prompt,
104
+ init_image = None,
105
+ mask = None,
106
+ strength = 0.5,
107
+ num_inference_steps = 32,
108
+ guidance_scale = 7.5,
109
+ eta = 0.0
110
+ ):
111
+ # extract condition
112
+ tokens = self.tokenizer(
113
+ prompt,
114
+ padding="max_length",
115
+ max_length=self.tokenizer.model_max_length,
116
+ truncation=True
117
+ ).input_ids
118
+ text_embeddings = result(
119
+ self.text_encoder.infer_new_request({"tokens": np.array([tokens])})
120
+ )
121
+
122
+ # do classifier free guidance
123
+ if guidance_scale > 1.0:
124
+ tokens_uncond = self.tokenizer(
125
+ "",
126
+ padding="max_length",
127
+ max_length=self.tokenizer.model_max_length,
128
+ truncation=True
129
+ ).input_ids
130
+ uncond_embeddings = result(
131
+ self.text_encoder.infer_new_request({"tokens": np.array([tokens_uncond])})
132
+ )
133
+ text_embeddings = np.concatenate((uncond_embeddings, text_embeddings), axis=0)
134
+
135
+ # set timesteps
136
+ accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
137
+ extra_set_kwargs = {}
138
+ offset = 0
139
+ if accepts_offset:
140
+ offset = 1
141
+ extra_set_kwargs["offset"] = 1
142
+
143
+ self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
144
+
145
+ # initialize latent latent
146
+ if init_image is None:
147
+ latents = np.random.randn(*self.latent_shape)
148
+ init_timestep = num_inference_steps
149
+ else:
150
+ init_latents = self._encode_image(init_image)
151
+ init_timestep = int(num_inference_steps * strength) + offset
152
+ init_timestep = min(init_timestep, num_inference_steps)
153
+ timesteps = np.array([[self.scheduler.timesteps[-init_timestep]]]).astype(np.long)
154
+ noise = np.random.randn(*self.latent_shape)
155
+ latents = self.scheduler.add_noise(init_latents, noise, timesteps)[0]
156
+
157
+ if init_image is not None and mask is not None:
158
+ mask = self._preprocess_mask(mask)
159
+ else:
160
+ mask = None
161
+
162
+ # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
163
+ if isinstance(self.scheduler, LMSDiscreteScheduler):
164
+ latents = latents * self.scheduler.sigmas[0]
165
+
166
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
167
+ # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
168
+ # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
169
+ # and should be between [0, 1]
170
+ accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
171
+ extra_step_kwargs = {}
172
+ if accepts_eta:
173
+ extra_step_kwargs["eta"] = eta
174
+
175
+ t_start = max(num_inference_steps - init_timestep + offset, 0)
176
+ for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
177
+ # expand the latents if we are doing classifier free guidance
178
+ latent_model_input = np.stack([latents, latents], 0) if guidance_scale > 1.0 else latents[None]
179
+ if isinstance(self.scheduler, LMSDiscreteScheduler):
180
+ sigma = self.scheduler.sigmas[i]
181
+ latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
182
+
183
+ # predict the noise residual
184
+ noise_pred = result(self.unet.infer_new_request({
185
+ "latent_model_input": latent_model_input,
186
+ "t": t,
187
+ "encoder_hidden_states": text_embeddings
188
+ }))
189
+
190
+ # perform guidance
191
+ if guidance_scale > 1.0:
192
+ noise_pred = noise_pred[0] + guidance_scale * (noise_pred[1] - noise_pred[0])
193
+
194
+ # compute the previous noisy sample x_t -> x_t-1
195
+ if isinstance(self.scheduler, LMSDiscreteScheduler):
196
+ latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs)["prev_sample"]
197
+ else:
198
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
199
+
200
+ # masking for inapinting
201
+ if mask is not None:
202
+ init_latents_proper = self.scheduler.add_noise(init_latents, noise, t)
203
+ latents = ((init_latents_proper * mask) + (latents * (1 - mask)))[0]
204
+
205
+ image = result(self.vae_decoder.infer_new_request({
206
+ "latents": np.expand_dims(latents, 0)
207
+ }))
208
+
209
+ # convert tensor to opencv's image format
210
+ image = (image / 2 + 0.5).clip(0, 1)
211
+ image = (image[0].transpose(1, 2, 0)[:, :, ::-1] * 255).astype(np.uint8)
212
+ return image