File size: 9,527 Bytes
4f4509d
363ce98
4f4509d
 
 
 
 
 
 
 
d32adcb
 
8b300d9
 
6abad74
9850537
363ce98
 
 
 
bf9abdd
363ce98
1841b37
363ce98
 
1841b37
bf9abdd
363ce98
2afea1f
6abad74
 
ba76dfd
dc8c3f2
 
 
5103f57
 
ba76dfd
 
f3e34b0
4f4509d
 
 
 
f3e34b0
 
 
 
a03fe94
 
4f4509d
 
d32e597
bd7215a
 
f3e34b0
 
cce1831
bd7215a
cce1831
 
55e476e
 
 
bd7215a
55e476e
 
f3e34b0
 
 
 
 
 
 
d32e597
 
 
 
 
f3e34b0
a03fe94
 
f3e34b0
 
 
bd7215a
a03fe94
f3e34b0
 
 
 
 
4f4509d
 
 
 
d32e597
f3e34b0
 
 
 
 
c4a54a2
f3e34b0
c4a54a2
 
f3e34b0
819cc0a
c4a54a2
819cc0a
f3e34b0
 
 
d32e597
f3e34b0
12a8812
d32e597
f3e34b0
 
4f4509d
 
 
 
f3e34b0
 
 
bd7215a
 
f3e34b0
 
6abad74
 
dc8c3f2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6abad74
 
bd7215a
ba76dfd
f3e34b0
ba76dfd
 
bf9abdd
ba76dfd
 
 
9850537
ba76dfd
 
 
 
 
 
363ce98
 
 
777823b
d2b2961
ba76dfd
2b66265
4f4509d
ba76dfd
4f4509d
c4a54a2
4f4509d
bf9abdd
4f4509d
ba76dfd
5b4ede2
d82f553
d2b2961
 
d82f553
f3e34b0
4f4509d
363ce98
a03fe94
4f4509d
363ce98
5b4ede2
 
f3e34b0
55e476e
f3e34b0
6abad74
363ce98
6abad74
f3e34b0
2feda0d
 
 
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
import tempfile
from share_btn import community_icon_html, loading_icon_html, share_js, save_js
import huggingface_hub
import gradio as gr
from fromage import utils
from fromage import models
import matplotlib.pyplot as plt
from PIL import Image
import torch
import numpy as np
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False"


css = """
    #chatbot { min-height: 300px; }
    #save-btn {
        background-image: linear-gradient(to right bottom, rgba(130,217,244, 0.9), rgba(158,231,214, 1.0));
    }
    #save-btn:hover {
        background-image: linear-gradient(to right bottom, rgba(110,197,224, 0.9), rgba(138,211,194, 1.0));
    }
    #share-btn {
        background-image: linear-gradient(to right bottom, rgba(130,217,244, 0.9), rgba(158,231,214, 1.0));
    }
    #share-btn:hover {
        background-image: linear-gradient(to right bottom, rgba(110,197,224, 0.9), rgba(138,211,194, 1.0));
    }
    #gallery img:hover {transform: scale(4.0);}
"""

examples = [
    'examples/sparrow.png',
    'examples/beaver.png',
    'examples/couch.png',
    'examples/guac.png',
    'examples/scraped_knee.png'
]

# Download model from HF Hub.
ckpt_path = huggingface_hub.hf_hub_download(
    repo_id='jykoh/fromage', filename='pretrained_ckpt.pth.tar')
args_path = huggingface_hub.hf_hub_download(
    repo_id='jykoh/fromage', filename='model_args.json')
model = models.load_fromage('./', args_path, ckpt_path)


def upload_image(state, image_input):
    conversation = state[0]
    chat_history = state[1]
    input_image = Image.open(image_input.name).resize(
        (224, 224)).convert('RGB')
    input_image.save(image_input.name)  # Overwrite with smaller image.
    conversation += [(f'<img src="/file={image_input.name}" style="display: inline-block;">', "")]
    return [conversation, chat_history + [input_image, ""]], conversation


def reset():
    return [[], []], []


def reset_last(state):
    conversation = state[0][:-1]
    chat_history = state[1][:-2]
    return [conversation, chat_history], conversation


def save_image_to_local(image: Image.Image):
    # TODO(jykoh): Update so the url path is used, to prevent repeat saving.
    filename = next(tempfile._get_candidate_names()) + '.png'
    image.save(filename)
    return filename


def generate_for_prompt(input_text, state, ret_scale_factor, max_num_rets, num_words, temperature):
    # Ignore empty inputs.
    if len(input_text) == 0:
        return state, state[0], gr.update(visible=True)

    input_prompt = 'Q: ' + input_text + '\nA:'
    conversation = state[0]
    chat_history = state[1]
    print('Generating for', chat_history, flush=True)

    # If an image was uploaded, prepend it to the model.
    model_inputs = chat_history
    model_inputs.append(input_prompt)

    top_p = 1.0
    if temperature != 0.0:
        top_p = 0.95

    print('Running model.generate_for_images_and_texts with',
          model_inputs, flush=True)
    model_outputs = model.generate_for_images_and_texts(model_inputs,
                                                        num_words=max(num_words, 1), ret_scale_factor=ret_scale_factor, top_p=top_p,
                                                        temperature=temperature, max_num_rets=max_num_rets)
    print('model_outputs', model_outputs, flush=True)

    im_names = []
    response = ''
    text_outputs = []
    for output_i, output in enumerate(model_outputs):
        if type(output) == str:
            if output_i > 0:
                response += '<br/>'
            text_outputs.append(output)
            response += output
            if len(model_outputs) > 1:
                response += '<br/>'
        elif type(output) == list:
            for image in output:
                filename = save_image_to_local(image)
                response += f'<img src="/file={filename}" style="display: inline-block;">'
        elif type(output) == Image.Image:
            filename = save_image_to_local(output)
            response += f'<img src="/file={filename}" style="display: inline-block;">'

    # TODO(jykoh): Persist image inputs.
    chat_history = model_inputs + \
        [' '.join([s for s in model_outputs if type(s) == str]) + '\n']
    # Remove [RET] from outputs.
    conversation.append((input_text, response.replace('[RET]', '')))

    # Set input image to None.
    print('state', state, flush=True)
    print('updated state', [conversation, chat_history], flush=True)
    return [conversation, chat_history], conversation, gr.update(visible=True), gr.update(visible=True)


with gr.Blocks(css=css) as demo:
    gr.HTML("""
        <h1>🧀 FROMAGe</h1>
        <p>This is the official Gradio demo for the FROMAGe model, a model that can process arbitrarily interleaved image and text inputs, and produce image and text outputs.</p>

        <strong>Paper:</strong> <a href="https://arxiv.org/abs/2301.13823" target="_blank">Grounding Language Models to Images for Multimodal Generation</a>
        <br/>
        <strong>Project Website:</strong> <a href="https://jykoh.com/fromage" target="_blank">FROMAGe Website</a>
        <br/>
        <strong>Code and Models:</strong> <a href="https://github.com/kohjingyu/fromage" target="_blank">GitHub</a>
        <br/>
        <br/>

        <strong>Tips:</strong>
        <ul>
        <li>Start by inputting either image or text prompts (or both) and chat with FROMAGe to get image-and-text replies.</li>
        <li>Tweak the level of sensitivity to images and text using the parameters on the right.</li>
        <li>Check out cool conversations in the examples or community tab for inspiration and share your own!</li>
        <li>For faster inference without waiting in queue, you may duplicate the space and use your own GPU: <a href="https://huggingface.co/spaces/jykoh/fromage?duplicate=true"><img style="display: inline-block; margin-top: 0em; margin-bottom: 0em" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></li>
        </ul>
    """)

    gr_state = gr.State([[], []])  # conversation, chat_history

    with gr.Row():
        with gr.Column(scale=0.7, min_width=500):
            with gr.Row():
                chatbot = gr.Chatbot(elem_id="chatbot", label="🧀 FROMAGe Chatbot")
            with gr.Row():
                image_btn = gr.UploadButton("🖼️ Upload Image", file_types=["image"])

                text_input = gr.Textbox(label="Message", placeholder="Type a message")

                with gr.Column():
                    submit_btn = gr.Button(
                        "Submit", interactive=True, variant="primary")
                    clear_last_btn = gr.Button("Undo")
                    clear_btn = gr.Button("Reset All")
                    with gr.Row(visible=False) as save_group:
                        save_button = gr.Button("💾 Save Conversation as .png", elem_id="save-btn")

                    with gr.Row(visible=False) as share_group:
                        share_button = gr.Button("🤗 Share to Community (opens new window)", elem_id="share-btn")

        with gr.Column(scale=0.3, min_width=400):
            ret_scale_factor = gr.Slider(minimum=0.0, maximum=3.0, value=1.0, step=0.1, interactive=True,
                                         label="Frequency multiplier for returning images (higher means more frequent)")
            max_ret_images = gr.Number(
                minimum=0, maximum=3, value=2, precision=1, interactive=True, label="Max images to return")
            gr_max_len = gr.Slider(minimum=1, maximum=64, value=32,
                                   step=1, interactive=True, label="Max # of words")
            gr_temperature = gr.Slider(
                minimum=0.0, maximum=1.0, value=0.0, interactive=True, label="Temperature (0 for deterministic, higher for more randomness)")

    with gr.Row():
        gallery = gr.Gallery(
            value=[Image.open(e) for e in examples], label="Example Conversations", show_label=True, elem_id="gallery",
        ).style(grid=[5], height="auto")

    text_input.submit(generate_for_prompt, [text_input, gr_state, ret_scale_factor,
                      max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot, share_group, save_group])
    text_input.submit(lambda: "", None, text_input)  # Reset chatbox.
    submit_btn.click(generate_for_prompt, [text_input, gr_state, ret_scale_factor,
                     max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot, share_group, save_group])
    submit_btn.click(lambda: "", None, text_input)  # Reset chatbox.

    image_btn.upload(upload_image, [gr_state, image_btn], [gr_state, chatbot])
    clear_last_btn.click(reset_last, [gr_state], [gr_state, chatbot])
    clear_btn.click(reset, [], [gr_state, chatbot])
    share_button.click(None, [], [], _js=share_js)
    save_button.click(None, [], [], _js=save_js)


demo.queue(concurrency_count=1, api_open=False, max_size=16)
demo.launch(debug=True, server_name="0.0.0.0")
# demo.launch(debug=True, server_name="127.0.0.1")