File size: 12,374 Bytes
61bbe53
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7479d
61bbe53
 
 
 
 
 
 
 
 
 
 
0c7479d
 
 
61bbe53
 
 
 
 
 
 
 
 
 
 
0c7479d
 
61bbe53
 
 
0c7479d
61bbe53
 
 
 
 
 
 
 
0c7479d
 
94fe49e
61bbe53
0c7479d
 
 
 
61bbe53
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7479d
61bbe53
 
 
 
 
 
 
 
 
 
 
 
 
0c7479d
 
61bbe53
 
 
 
0c7479d
61bbe53
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7479d
 
 
61bbe53
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7479d
61bbe53
 
 
 
 
 
 
 
 
 
 
 
0c7479d
 
 
61bbe53
0c7479d
 
61bbe53
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7479d
61bbe53
 
 
 
0c7479d
61bbe53
 
 
 
 
0c7479d
 
 
61bbe53
 
 
0c7479d
61bbe53
 
 
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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
# Edit Anything trained with Stable Diffusion + ControlNet + SAM  + BLIP2
from diffusers.utils import load_image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from torchvision.utils import save_image
from PIL import Image
from pytorch_lightning import seed_everything
import subprocess
from collections import OrderedDict

import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
import os
from annotator.util import resize_image, HWC3


def create_demo():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    use_blip = True
    use_gradio = True

    # Diffusion init using diffusers.
    # diffusers==0.14.0 required.

    base_model_path = "stabilityai/stable-diffusion-2-1"

    config_dict = OrderedDict([('SAM Pretrained(v0-1)', 'shgao/edit-anything-v0-1-1'),
                               ('LAION Pretrained(v0-3)', 'shgao/edit-anything-v0-3'),
                               ('LAION Pretrained(v0-4)', 'shgao/edit-anything-v0-4-sd21'),
                               ])

    def obtain_generation_model(controlnet_path):
        controlnet = ControlNetModel.from_pretrained(
            controlnet_path, torch_dtype=torch.float16)
        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            base_model_path, controlnet=controlnet, torch_dtype=torch.float16
        )
        # speed up diffusion process with faster scheduler and memory optimization
        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
        # remove following line if xformers is not installed
        pipe.enable_xformers_memory_efficient_attention()
        # pipe.enable_model_cpu_offload() # disable for now because of unknow bug in accelerate
        pipe.to(device)
        return pipe

    global default_controlnet_path
    default_controlnet_path = config_dict['LAION Pretrained(v0-4)']
    pipe = obtain_generation_model(default_controlnet_path)

    # Segment-Anything init.
    # pip install git+https://github.com/facebookresearch/segment-anything.git
    try:
        from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
    except ImportError:
        print('segment_anything not installed')
        result = subprocess.run(['pip', 'install', 'git+https://github.com/facebookresearch/segment-anything.git'],
                                check=True)
        print(f'Install segment_anything {result}')
    if not os.path.exists('./models/sam_vit_h_4b8939.pth'):
        result = subprocess.run(
            ['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth', '-P', 'models'],
            check=True)
        print(f'Download sam_vit_h_4b8939.pth {result}')
    sam_checkpoint = "models/sam_vit_h_4b8939.pth"
    model_type = "default"
    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
    sam.to(device=device)
    mask_generator = SamAutomaticMaskGenerator(sam)

    # BLIP2 init.
    if use_blip:
        # need the latest transformers
        # pip install git+https://github.com/huggingface/transformers.git
        from transformers import AutoProcessor, Blip2ForConditionalGeneration

        processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
        blip_model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
        blip_model.to(device)
        blip_model.to(device)

    def get_blip2_text(image):
        inputs = processor(image, return_tensors="pt").to(device, torch.float16)
        generated_ids = blip_model.generate(**inputs, max_new_tokens=50)
        generated_text = processor.batch_decode(
            generated_ids, skip_special_tokens=True)[0].strip()
        return generated_text

    def show_anns(anns):
        if len(anns) == 0:
            return
        sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
        full_img = None

        # for ann in sorted_anns:
        for i in range(len(sorted_anns)):
            ann = anns[i]
            m = ann['segmentation']
            if full_img is None:
                full_img = np.zeros((m.shape[0], m.shape[1], 3))
                map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16)
            map[m != 0] = i + 1
            color_mask = np.random.random((1, 3)).tolist()[0]
            full_img[m != 0] = color_mask
        full_img = full_img * 255
        # anno encoding from https://github.com/LUSSeg/ImageNet-S
        res = np.zeros((map.shape[0], map.shape[1], 3))
        res[:, :, 0] = map % 256
        res[:, :, 1] = map // 256
        res.astype(np.float32)
        full_img = Image.fromarray(np.uint8(full_img))
        return full_img, res

    def get_sam_control(image):
        masks = mask_generator.generate(image)
        full_img, res = show_anns(masks)
        return full_img, res

    def process(condition_model, input_image, enable_auto_prompt, prompt, a_prompt, n_prompt, num_samples,
                image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):

        global default_controlnet_path
        global pipe
        print("To Use:", config_dict[condition_model], "Current:", default_controlnet_path)
        if default_controlnet_path != config_dict[condition_model]:
            print("Change condition model to:", config_dict[condition_model])
            pipe = obtain_generation_model(config_dict[condition_model])
            default_controlnet_path = config_dict[condition_model]

        with torch.no_grad():
            if use_blip and (enable_auto_prompt or len(prompt) == 0):
                print("Generating text:")
                blip2_prompt = get_blip2_text(input_image)
                print("Generated text:", blip2_prompt)
                if len(prompt) > 0:
                    prompt = blip2_prompt + ',' + prompt
                else:
                    prompt = blip2_prompt
                print("All text:", prompt)

            input_image = HWC3(input_image)

            img = resize_image(input_image, image_resolution)
            H, W, C = img.shape

            print("Generating SAM seg:")
            # the default SAM model is trained with 1024 size.
            full_segmask, detected_map = get_sam_control(
                resize_image(input_image, detect_resolution))

            detected_map = HWC3(detected_map.astype(np.uint8))
            detected_map = cv2.resize(
                detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

            control = torch.from_numpy(
                detected_map.copy()).float().cuda()
            control = torch.stack([control for _ in range(num_samples)], dim=0)
            control = einops.rearrange(control, 'b h w c -> b c h w').clone()

            if seed == -1:
                seed = random.randint(0, 65535)
            seed_everything(seed)
            print("control.shape", control.shape)
            generator = torch.manual_seed(seed)
            x_samples = pipe(
                prompt=[prompt + ', ' + a_prompt] * num_samples,
                negative_prompt=[n_prompt] * num_samples,
                num_images_per_prompt=num_samples,
                num_inference_steps=ddim_steps,
                generator=generator,
                height=H,
                width=W,
                image=control.type(torch.float16),
            ).images

            results = [x_samples[i] for i in range(num_samples)]
        return [full_segmask] + results, prompt

    # disable gradio when not using GUI.
    if not use_gradio:
        # This part is not updated, it's just a example to use it without GUI.
        condition_model = 'shgao/edit-anything-v0-1-1'
        image_path = "images/sa_309398.jpg"
        input_image = Image.open(image_path)
        input_image = np.array(input_image, dtype=np.uint8)
        prompt = ""
        a_prompt = 'best quality, extremely detailed'
        n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
        num_samples = 4
        image_resolution = 512
        detect_resolution = 512
        ddim_steps = 100
        guess_mode = False
        strength = 1.0
        scale = 9.0
        seed = 10086
        eta = 0.0

        outputs, full_text = process(condition_model, input_image, prompt, a_prompt, n_prompt, num_samples,
                                     image_resolution,
                                     detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta)

        image_list = []
        input_image = resize_image(input_image, 512)
        image_list.append(torch.tensor(input_image))
        for i in range(len(outputs)):
            each = outputs[i]
            if type(each) is not np.ndarray:
                each = np.array(each, dtype=np.uint8)
            each = resize_image(each, 512)
            print(i, each.shape)
            image_list.append(torch.tensor(each))

        image_list = torch.stack(image_list).permute(0, 3, 1, 2)

        save_image(image_list, "sample.jpg", nrow=3,
                   normalize=True, value_range=(0, 255))
    else:
        block = gr.Blocks()
        with block as demo:
            with gr.Row():
                gr.Markdown(
                    "## Generate Anything")
            with gr.Row():
                with gr.Column():
                    input_image = gr.Image(source='upload', type="numpy")
                    prompt = gr.Textbox(label="Prompt (Optional)")
                    run_button = gr.Button(label="Run")
                    condition_model = gr.Dropdown(choices=list(config_dict.keys()),
                                                  value=list(config_dict.keys())[0],
                                                  label='Model',
                                                  multiselect=False)
                    num_samples = gr.Slider(
                        label="Images", minimum=1, maximum=12, value=1, step=1)

                    enable_auto_prompt = gr.Checkbox(label='Auto generated BLIP2 prompt', value=True)
                    with gr.Accordion("Advanced options", open=False):
                        image_resolution = gr.Slider(
                            label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
                        strength = gr.Slider(
                            label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                        guess_mode = gr.Checkbox(label='Guess Mode', value=False)
                        detect_resolution = gr.Slider(
                            label="SAM Resolution", minimum=128, maximum=2048, value=1024, step=1)
                        ddim_steps = gr.Slider(
                            label="Steps", minimum=1, maximum=100, value=20, step=1)
                        scale = gr.Slider(
                            label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                        seed = gr.Slider(label="Seed", minimum=-1,
                                         maximum=2147483647, step=1, randomize=True)
                        eta = gr.Number(label="eta (DDIM)", value=0.0)
                        a_prompt = gr.Textbox(
                            label="Added Prompt", value='best quality, extremely detailed')
                        n_prompt = gr.Textbox(label="Negative Prompt",
                                              value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')

                with gr.Column():
                    result_gallery = gr.Gallery(
                        label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
                    result_text = gr.Text(label='BLIP2+Human Prompt Text')
            ips = [condition_model, input_image, enable_auto_prompt, prompt, a_prompt, n_prompt, num_samples,
                   image_resolution,
                   detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
            run_button.click(fn=process, inputs=ips, outputs=[result_gallery, result_text])
            return demo


if __name__ == '__main__':
    demo = create_demo()
    demo.queue().launch(server_name='0.0.0.0')