File size: 5,741 Bytes
3c3014b
 
ada0ab1
3c3014b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ada0ab1
 
 
 
 
 
3c3014b
 
bff87b0
3c3014b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ada0ab1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c3014b
ada0ab1
e1136c8
3c3014b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ada0ab1
2387a4c
ada0ab1
 
 
 
 
3c3014b
 
 
 
 
d4011b4
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
import gradio as gr
import numpy as np
from infer import infer, CONTROLNET_MODE

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

examples = [
    "The image of a cartoonish mouse eating from a red bowl of yellow triangle chips, her cheeks are full. The mouse is gray with big pink ears, small white eyes and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
    "The image of a cartoonish mouse with red hearts instead of eyes meaning that the mouse is in love with something. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
    "The image of a cartoonish mouse with sunglasses and smiling. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

def on_checkbox_change(use_advanced):
    visible = use_advanced
    return (gr.update(visible=visible, interactive=visible),
            gr.update(visible=visible, interactive=visible),
            gr.update(visible=visible, interactive=visible))

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Maria Lashina T2I Rat Stickers Generation App")

        MODEL_LIST = [
            "CompVis/stable-diffusion-v1-4",
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            "Maria_Lashina_LoRA"
        ]
        with gr.Row():
            model_id = gr.Dropdown(
                label="Model",
                choices=MODEL_LIST
            )

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            use_controlnet = gr.Checkbox(label="Use ControlNet")
            control_strength = gr.Slider(
                label="ControlNet strength",
                minimum=0,
                maximum=1,
                step=0.01,
                value=0.8,
                visible=False
            )
            controlnet_mode = gr.Dropdown(CONTROLNET_MODE.keys(), label="ControlNet mode", visible=False)
            controlnet_image = gr.Image(label="ControlNet image", visible=False)
            use_controlnet.change(on_checkbox_change, use_controlnet, [control_strength, controlnet_mode, controlnet_image])

            use_ip_adapter = gr.Checkbox(label="Use IPAdapter")
            ip_adapter_scale = gr.Slider(
                label="IPAdapter scale",
                minimum=0,
                maximum=1,
                step=0.01,
                value=0.8,
                visible=False
            )
            ip_adapter_image = gr.Image(label="IPAdapter image", visible=False)
            use_ip_adapter.change(on_checkbox_change, use_ip_adapter, [ip_adapter_scale, ip_adapter_image])

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=20,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            use_controlnet,
            control_strength,
            controlnet_mode,
            controlnet_image,
            use_ip_adapter,
            ip_adapter_scale,
            ip_adapter_image
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch(share=True, debug=True)