File size: 14,126 Bytes
b5b4980
 
025a281
b5b4980
 
 
 
 
 
 
 
 
 
 
 
 
 
5b04683
 
 
b5b4980
 
025a281
b5b4980
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e814de0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b5b4980
2dbd667
b5b4980
 
2dbd667
 
b5b4980
 
 
 
 
 
 
 
 
 
 
 
 
 
0f1d017
868c4e3
b5b4980
 
eef1455
 
 
 
 
 
 
 
63296c8
 
eef1455
b5b4980
025a281
 
 
 
 
 
 
 
 
 
 
b5b4980
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e814de0
edadc03
 
b5b4980
8245707
 
 
 
 
 
e814de0
 
8245707
 
 
e814de0
 
 
 
8245707
 
 
64a5a18
e5117b9
5bb1103
b5b4980
 
 
 
 
edadc03
 
b9954e9
b5b4980
 
 
 
 
 
 
 
025a281
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5b04683
 
025a281
8433342
b5b4980
8433342
025a281
ac150a8
025a281
b5b4980
 
ac150a8
 
 
b5b4980
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import gradio as gr
from prompt_generator import PromptGenerator
from huggingface_inference_node import LLMInferenceNode
from caption_models import florence_caption, qwen_caption
import random
from prompt_generator import ARTFORM, PHOTO_TYPE, FEMALE_BODY_TYPES, MALE_BODY_TYPES, FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, ROLES, HAIRSTYLES, FEMALE_CLOTHING, MALE_CLOTHING, PLACE, LIGHTING, COMPOSITION, POSE, BACKGROUND, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM


title = """<h1 align="center">FLUX Prompt Generator</h1>
<p><center>
<a href="https://x.com/gokayfem" target="_blank">[X gokaygokay]</a>
<a href="https://github.com/gokayfem" target="_blank">[Github gokayfem]</a>
<a href="https://github.com/dagthomas/comfyui_dagthomas" target="_blank">[comfyui_dagthomas]</a>
<p align="center">Create long prompts from images or simple words. Enhance your short prompts with prompt enhancer.</p>
</center></p>
"""

# Add this global variable
selected_prompt_type = "happy"  # Default value

def create_interface():
    prompt_generator = PromptGenerator()
    llm_node = LLMInferenceNode()

    with gr.Blocks(theme='bethecloud/storj_theme') as demo:
        
        gr.HTML(title)

        with gr.Row():
            with gr.Column(scale=2):
                with gr.Accordion("Basic Settings"):
                    custom = gr.Textbox(label="Custom Input Prompt (optional)")
                    subject = gr.Textbox(label="Subject (optional)")
                    gender = gr.Radio(["female", "male"], label="Gender", value="female")
                    
                    global_option = gr.Radio(
                        ["Disabled", "Random", "No Figure Rand"],
                        label="Set all options to:",
                        value="Disabled"
                    )
                
                with gr.Accordion("Artform and Photo Type", open=False):
                    artform = gr.Dropdown(["disabled", "random"] + ARTFORM, label="Artform", value="disabled")
                    photo_type = gr.Dropdown(["disabled", "random"] + PHOTO_TYPE, label="Photo Type", value="disabled")
            
                with gr.Accordion("Character Details", open=False):
                    body_types = gr.Dropdown(["disabled", "random"] + FEMALE_BODY_TYPES + MALE_BODY_TYPES, label="Body Types", value="disabled")
                    default_tags = gr.Dropdown(["disabled", "random"] + FEMALE_DEFAULT_TAGS + MALE_DEFAULT_TAGS, label="Default Tags", value="disabled")
                    roles = gr.Dropdown(["disabled", "random"] + ROLES, label="Roles", value="disabled")
                    hairstyles = gr.Dropdown(["disabled", "random"] + HAIRSTYLES, label="Hairstyles", value="disabled")
                    clothing = gr.Dropdown(["disabled", "random"] + FEMALE_CLOTHING + MALE_CLOTHING, label="Clothing", value="disabled")
            
                with gr.Accordion("Scene Details", open=False):
                    place = gr.Dropdown(["disabled", "random"] + PLACE, label="Place", value="disabled")
                    lighting = gr.Dropdown(["disabled", "random"] + LIGHTING, label="Lighting", value="disabled")
                    composition = gr.Dropdown(["disabled", "random"] + COMPOSITION, label="Composition", value="disabled")
                    pose = gr.Dropdown(["disabled", "random"] + POSE, label="Pose", value="disabled")
                    background = gr.Dropdown(["disabled", "random"] + BACKGROUND, label="Background", value="disabled")
            
                with gr.Accordion("Style and Artist", open=False):
                    additional_details = gr.Dropdown(["disabled", "random"] + FEMALE_ADDITIONAL_DETAILS + MALE_ADDITIONAL_DETAILS, label="Additional Details", value="disabled")
                    photography_styles = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled")
                    device = gr.Dropdown(["disabled", "random"] + DEVICE, label="Device", value="disabled")
                    photographer = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHER, label="Photographer", value="disabled")
                    artist = gr.Dropdown(["disabled", "random"] + ARTIST, label="Artist", value="disabled")
                    digital_artform = gr.Dropdown(["disabled", "random"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled")

                # Add Next components
                with gr.Accordion("More Detailed Prompt Options", open=False):
                    next_components = {}
                    for category, fields in prompt_generator.next_data.items():
                        with gr.Accordion(f"{category.capitalize()} Options", open=False):
                            category_components = {}
                            for field, data in fields.items():
                                if isinstance(data, list):
                                    options = ["None", "Random", "Multiple Random"] + data
                                elif isinstance(data, dict):
                                    options = ["None", "Random", "Multiple Random"] + data.get("items", [])
                                else:
                                    options = ["None", "Random", "Multiple Random"]
                                category_components[field] = gr.Dropdown(options, label=field.capitalize(), value="None")
                            next_components[category] = category_components
                
                

            with gr.Column(scale=2):
                generate_button = gr.Button("Generate Prompt")

                with gr.Accordion("Image and Caption", open=False):
                    input_image = gr.Image(label="Input Image (optional)")
                    caption_output = gr.Textbox(label="Generated Caption", lines=3)
                    caption_model = gr.Radio(["Florence-2", "Qwen2-VL"], label="Caption Model", value="Florence-2")
                    create_caption_button = gr.Button("Create Caption")
                    add_caption_button = gr.Button("Add Caption to Prompt")

                with gr.Accordion("Prompt Generation", open=True):
                    output = gr.Textbox(label="Generated Prompt / Input Text", lines=4)
                    t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True)
                    clip_l_output = gr.Textbox(label="CLIP L Output", visible=True)
                    clip_g_output = gr.Textbox(label="CLIP G Output", visible=True)
            
            with gr.Column(scale=2):
                with gr.Accordion("""Prompt Generation with LLM 
                                (You need to use Generate Prompt first)""", open=False):
                    happy_talk = gr.Checkbox(label="Happy Talk", value=True)
                    compress = gr.Checkbox(label="Compress", value=True)
                    compression_level = gr.Dropdown(
                        choices=["soft", "medium", "hard"],
                        label="Compression Level",
                        value="hard"
                    )
                    prompt_type = gr.Dropdown(
                        choices=["happy", "simple", "poster", "only_objects", "no_figure", "landscape", "fantasy"],
                        label="Prompt Type",
                        value="happy",
                        interactive=True
                    )
                    custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5)
                    
                    # Add new components for LLM provider selection
                    llm_provider = gr.Dropdown(
                        choices=["Hugging Face", "OpenAI", "Anthropic", "Groq"],
                        label="LLM Provider",
                        value="Hugging Face"
                    )
                    api_key = gr.Textbox(label="API Key", type="password", visible=False)
                    model = gr.Dropdown(label="Model", choices=["meta-llama/Meta-Llama-3.1-70B-Instruct"], value="meta-llama/Meta-Llama-3.1-70B-Instruct")

                generate_text_button = gr.Button("Generate Prompt with LLM")
                text_output = gr.Textbox(label="Generated Text", lines=10)

        def create_caption(image, model):
            if image is not None:
                if model == "Florence-2":
                    return florence_caption(image)
                elif model == "Qwen2-VL":
                    return qwen_caption(image)
            return ""

        create_caption_button.click(
            create_caption,
            inputs=[input_image, caption_model],
            outputs=[caption_output]
        )

        

        def generate_prompt_with_dynamic_seed(*args, **kwargs):
            dynamic_seed = random.randint(0, 1000000)
            
            # Extract the main arguments
            main_args = args[:22]  # Assuming there are 22 main arguments before the next_params
            
            # Extract next_params
            next_params = {}
            next_args = args[22:]  # All arguments after the main ones are for next_params
            next_arg_index = 0
            for category, fields in prompt_generator.next_data.items():
                category_params = {}
                for field in fields:
                    value = next_args[next_arg_index]
                    # Include all values, even "None", "Random", and "Multiple Random"
                    category_params[field] = value
                    next_arg_index += 1
                if category_params:
                    next_params[category] = category_params
            # Call generate_prompt with the correct arguments
            result = prompt_generator.generate_prompt(dynamic_seed, *main_args, next_params=next_params)
            
            return [dynamic_seed] + list(result)

        generate_button.click(
            generate_prompt_with_dynamic_seed,
            inputs=[custom, subject, gender, artform, photo_type, body_types, default_tags, roles, hairstyles,
                    additional_details, photography_styles, device, photographer, artist, digital_artform,
                    place, lighting, clothing, composition, pose, background, input_image] + 
                    [component for category in next_components.values() for component in category.values()],
            outputs=[gr.Number(label="Used Seed", visible=False), output, gr.Number(visible=False), t5xxl_output, clip_l_output, clip_g_output]
        )

        add_caption_button.click(
            prompt_generator.add_caption_to_prompt,
            inputs=[output, caption_output],
            outputs=[output]
        )

        def update_model_choices(provider):
            provider_models = {
                "Hugging Face": ["meta-llama/Meta-Llama-3.1-70B-Instruct"],
                "Groq": ["llama-3.1-70b-versatile"],
                "OpenAI": ["gpt-4o", "gpt-4o-mini"],
                "Anthropic": ["claude-3-5-sonnet-20240620"],
            }
            models = provider_models[provider]
            return gr.Dropdown(choices=models, value=models[0])

        def update_api_key_visibility(provider):
            return gr.update(visible=(provider in ["OpenAI", "Anthropic"]))

        llm_provider.change(update_model_choices, inputs=[llm_provider], outputs=[model])
        llm_provider.change(update_api_key_visibility, inputs=[llm_provider], outputs=[api_key])

        def generate_text_with_llm(output, happy_talk, compress, compression_level, custom_base_prompt, provider, api_key, model):
            global selected_prompt_type
            print(f"Prompt type selected in UI: {selected_prompt_type}")  # Debug print
            return llm_node.generate(output, happy_talk, compress, compression_level, False, selected_prompt_type, custom_base_prompt, provider, api_key, model)

        generate_text_button.click(
            generate_text_with_llm,
            inputs=[output, happy_talk, compress, compression_level, custom_base_prompt, llm_provider, api_key, model],
            outputs=text_output,
            api_name="generate_text"
        )

        # Add this line to disable caching for the generate_text_with_llm function
        generate_text_with_llm.cache_examples = False

        def update_all_options(choice):
            updates = {}
            if choice == "Disabled":
                for dropdown in [
                    artform, photo_type, body_types, default_tags, roles, hairstyles, clothing,
                    place, lighting, composition, pose, background, additional_details,
                    photography_styles, device, photographer, artist, digital_artform
                ]:
                    updates[dropdown] = gr.update(value="disabled")
            elif choice == "Random":
                for dropdown in [
                    artform, photo_type, body_types, default_tags, roles, hairstyles, clothing,
                    place, lighting, composition, pose, background, additional_details,
                    photography_styles, device, photographer, artist, digital_artform
                ]:
                    updates[dropdown] = gr.update(value="random")
            else:  # No Figure Random
                for dropdown in [photo_type, body_types, default_tags, roles, hairstyles, clothing, pose, additional_details]:
                    updates[dropdown] = gr.update(value="disabled")
                for dropdown in [artform, place, lighting, composition, background, photography_styles, device, photographer, artist, digital_artform]:
                    updates[dropdown] = gr.update(value="random")
            return updates
        
        global_option.change(
            update_all_options,
            inputs=[global_option],
            outputs=[
                artform, photo_type, body_types, default_tags, roles, hairstyles, clothing,
                place, lighting, composition, pose, background, additional_details,
                photography_styles, device, photographer, artist, digital_artform
            ]
        )

    return demo