File size: 4,341 Bytes
f8eb38f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5da9c03
f8eb38f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from threading import Thread

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

import gradio as gr

MODEL_NAME = "isek-ai/SDPrompt-RetNet-300M"

DEFAULT_INPUT_TEXT = "1girl,"

EXAMPLE_INPUTS = [DEFAULT_INPUT_TEXT, "oil painting of", "high quality photo of"]

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)
model.eval()

# streamer = TextStreamer(
#     tokenizer,
#     skip_prompt=False,
#     skip_special_tokens=True,
# )


@torch.no_grad()
def generate(
    input_text,
    max_new_tokens=128,
    do_sample=True,
    temperature=1.0,
    top_p=0.95,
    top_k=20,
    # no_repeat_ngram_size=3,
    repetition_penalty=1.2,
    num_beams=1,
):
    if input_text.strip() == "":
        return ""

    inputs = tokenizer(
        f"<s>{input_text}", return_tensors="pt", add_special_tokens=False
    )["input_ids"]

    generated = model.generate(
        inputs,
        max_new_tokens=max_new_tokens,
        do_sample=do_sample,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        # no_repeat_ngram_size=no_repeat_ngram_size,
        repetition_penalty=repetition_penalty,
        num_beams=num_beams,
        # streamer=streamer,
    )

    return tokenizer.batch_decode(generated, skip_special_tokens=True)[0]


def continue_generate(
    input_text,
    *args,
):
    return input_text, generate(input_text, *args)


with gr.Blocks() as demo:
    gr.Markdown(
        """\
# SDPrompt-RetNet-300M-Demo
A RetNet model trained with Stable Diffusion prompts and Danbooru tags.

Model: https://huggingface.co/isek-ai/SDPrompt-RetNet-300M

### Reference:
- https://github.com/syncdoth/RetNet
"""
    )

    input_text = gr.Textbox(
        label="Input text",
        value=DEFAULT_INPUT_TEXT,
        placeholder="beautiful photo of ...",
        lines=2,
    )
    output_text = gr.Textbox(
        label="Output text",
        value="",
        placeholder="Output will appear here...",
        lines=8,
        interactive=False,
    )

    with gr.Row():
        generate_btn = gr.Button("Generate ✒️", variant="primary")
        continue_btn = gr.Button("Continue ➡️", variant="secondary")
        clear_btn = gr.ClearButton(
            value="Clear 🧹",
            components=[input_text, output_text],
        )

    with gr.Accordion("Advanced settings", open=False):
        max_tokens = gr.Slider(
            label="Max tokens",
            minimum=8,
            maximum=512,
            value=75,
            step=4,
        )
        do_sample = gr.Checkbox(
            label="Do sample",
            value=True,
        )
        temperature = gr.Slider(
            label="Temperature",
            minimum=0,
            maximum=1,
            value=0.9,
            step=0.05,
        )
        top_p = gr.Slider(
            label="Top p",
            minimum=0,
            maximum=1,
            value=0.95,
            step=0.05,
        )
        top_k = gr.Slider(
            label="Top k",
            minimum=0,
            maximum=100,
            value=50,
            step=1,
        )
        repetition_penalty = gr.Slider(
            label="Repetition penalty",
            minimum=0,
            maximum=2,
            value=1,
            step=0.1,
        )
        num_beams = gr.Slider(
            label="Num beams",
            minimum=1,
            maximum=10,
            value=1,
            step=1,
        )

    gr.Examples(
        examples=EXAMPLE_INPUTS,
        inputs=input_text,
    )

    generate_btn.click(
        fn=generate,
        inputs=[
            input_text,
            max_tokens,
            do_sample,
            temperature,
            top_p,
            top_k,
            repetition_penalty,
            num_beams,
        ],
        outputs=output_text,
        queue=False,
    )
    continue_btn.click(
        fn=continue_generate,
        inputs=[
            output_text,
            max_tokens,
            do_sample,
            temperature,
            top_p,
            top_k,
            repetition_penalty,
            num_beams,
        ],
        outputs=[input_text, output_text],
        queue=False,
    )

demo.queue()
demo.launch(
    debug=True,
    show_error=True,
)