File size: 10,264 Bytes
bdc1819
1264271
bdc1819
 
 
 
 
 
 
 
 
 
 
9e720d9
bdc1819
 
 
 
 
 
 
 
 
 
9e720d9
de7bb6a
bdc1819
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4e30dae
933cc37
4e30dae
bdc1819
 
 
8d13dd9
bdc1819
 
 
 
 
483d89f
 
 
 
 
bdc1819
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8d13dd9
bdc1819
 
 
 
 
 
 
 
 
 
80d3ffa
bdc1819
 
 
 
5c442f8
bdc1819
 
442e0e4
bdc1819
 
 
 
 
80d3ffa
bdc1819
 
 
 
442e0e4
bdc1819
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import gradio as gr
#test
from io import BytesIO
import requests
import PIL
from PIL import Image
import numpy as np
import os
import uuid
import torch
from torch import autocast
import cv2
from matplotlib import pyplot as plt
from diffusers import DiffusionPipeline
from torchvision import transforms
from clipseg.models.clipseg import CLIPDensePredT

auth_token = os.environ.get("API_TOKEN") or True

def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    revision="fp16", 
    torch_dtype=torch.float16,
    use_auth_token=auth_token,
).to(device)

model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
model.eval()
model.load_state_dict(torch.load('./clipseg/weights/rd64-uni.pth', map_location=torch.device('cuda')), strict=False)

transform = transforms.Compose([
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
      transforms.Resize((512, 512)),
])

def predict(radio, dict, word_mask, prompt=""):
    if(radio == "draw a mask above"):
        with autocast("cuda"):
            init_image = dict["image"].convert("RGB").resize((512, 512))
            mask = dict["mask"].convert("RGB").resize((512, 512))
    else:
        img = transform(dict["image"]).unsqueeze(0)
        word_masks = [word_mask]
        with torch.no_grad():
            preds = model(img.repeat(len(word_masks),1,1,1), word_masks)[0]
        init_image = dict['image'].convert('RGB').resize((512, 512))
        filename = f"{uuid.uuid4()}.png"
        plt.imsave(filename,torch.sigmoid(preds[0][0]))
        img2 = cv2.imread(filename)
        gray_image = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
        (thresh, bw_image) = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY)
        cv2.cvtColor(bw_image, cv2.COLOR_BGR2RGB)
        mask = Image.fromarray(np.uint8(bw_image)).convert('RGB')
        os.remove(filename)
    #with autocast("cuda"):
    output = pipe(prompt = prompt, image=init_image, mask_image=mask, strength=0.8)
    return output.images[0]

# examples = [[dict(image="init_image.png", mask="mask_image.png"), "A panda sitting on a bench"]]
css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
'''
def swap_word_mask(radio_option):
    if(radio_option == "type what to mask below"):
        return gr.update(interactive=True, placeholder="A cat")
    else:
        return gr.update(interactive=False, placeholder="Disabled")

image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <svg
                  width="0.65em"
                  height="0.65em"
                  viewBox="0 0 115 115"
                  fill="none"
                  xmlns="http://www.w3.org/2000/svg"
                >
                  <rect width="23" height="23" fill="white"></rect>
                  <rect y="69" width="23" height="23" fill="white"></rect>
                  <rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="46" width="23" height="23" fill="white"></rect>
                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
                  <rect x="69" width="23" height="23" fill="black"></rect>
                  <rect x="69" y="69" width="23" height="23" fill="black"></rect>
                  <rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="115" y="46" width="23" height="23" fill="white"></rect>
                  <rect x="115" y="115" width="23" height="23" fill="white"></rect>
                  <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="92" y="69" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="46" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="115" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="46" y="46" width="23" height="23" fill="black"></rect>
                  <rect x="46" y="115" width="23" height="23" fill="black"></rect>
                  <rect x="46" y="69" width="23" height="23" fill="black"></rect>
                  <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="23" y="69" width="23" height="23" fill="black"></rect>
                </svg>
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Stable Diffusion Multi Inpainting
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Inpaint Stable Diffusion by either drawing a mask or typing what to replace
              </p>
            </div>
        """
    )
    with gr.Row():
        with gr.Column():
            image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
            with gr.Box(elem_id="mask_radio").style(border=False):
                radio = gr.Radio(["draw a mask above", "type what to mask below"], value="draw a mask above", show_label=False, interactive=True).style(container=False)
                word_mask = gr.Textbox(label = "What to find in your image", interactive=False, elem_id="word_mask", placeholder="Disabled").style(container=False)
            prompt = gr.Textbox(label = 'Your prompt (what you want to add in place of what you are removing)')
            radio.change(fn=swap_word_mask, inputs=radio, outputs=word_mask,show_progress=False)
            radio.change(None, inputs=[], outputs=image_blocks, _js = """
            () => {
                css_style = document.styleSheets[document.styleSheets.length - 1]
                last_item = css_style.cssRules[css_style.cssRules.length - 1]
                last_item.style.display = ["flex", ""].includes(last_item.style.display) ? "none" : "flex";
            }""")
            btn = gr.Button("Run")
        with gr.Column():
            result = gr.Image(label="Result")
        btn.click(fn=predict, inputs=[radio, image, word_mask, prompt], outputs=result)
    gr.HTML(
            """
                <div class="footer">
                    <p>Model by <a href="https://huggingface.co/CompVis" style="text-decoration: underline;" target="_blank">CompVis</a> and <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">Stability AI</a> - Inpainting by <a href="https://github.com/nagolinc" style="text-decoration: underline;" target="_blank">nagolinc</a> and <a href="https://github.com/patil-suraj" style="text-decoration: underline;">patil-suraj</a>, inpainting with words by <a href="https://twitter.com/yvrjsharma/" style="text-decoration: underline;" target="_blank">@yvrjsharma</a> and <a href="https://twitter.com/1littlecoder" style="text-decoration: underline;">@1littlecoder</a> - Gradio Demo by 🤗 Hugging Face
                    </p>
                </div>
                <div class="acknowledgments">
                    <p><h4>LICENSE</h4>
The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
                    <p><h4>Biases and content acknowledgment</h4>
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
               </div>
           """
        )
demo.launch()