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| import json | |
| import random | |
| import gradio as gr | |
| from pathlib import Path | |
| import os | |
| import requests | |
| from PIL import Image | |
| import io | |
| import pathlib | |
| API_TOKEN = os.environ.get("HF_READ_TOKEN") | |
| base_dir = "." | |
| dropdown_options_file = Path(base_dir, "json/dropdown_options.json") | |
| category_data_file = Path(base_dir, "json/category_data.json") | |
| style_data_file = Path(base_dir, "json/style_data.json") | |
| prefix_data_file = Path(base_dir, "json/prefix_data.json") | |
| lightning_data_file = Path(base_dir, "json/lightning_data.json") | |
| lens_data_file = Path(base_dir, "json/lens_data.json") | |
| class Model: | |
| ''' | |
| Small strut to hold data for the text generator | |
| ''' | |
| def __init__(self, name) -> None: | |
| self.name = name | |
| pass | |
| def populate_dropdown_options(): | |
| path = dropdown_options_file | |
| with open(path, 'r') as f: | |
| data = json.load(f) | |
| category_choices = data["category"] | |
| style_choices = data["style"] | |
| lightning_choices = data["lightning"] | |
| lens_choices = data["lens"] | |
| return tuple(category_choices), tuple(style_choices), tuple(lightning_choices), tuple(lens_choices), | |
| def add_to_prompt(*args): | |
| prompt, use_default_negative_prompt, base_prompt, negative_base_prompt = args | |
| default_negative_prompt = "(worst quality:1.2), (low quality:1.2), (lowres:1.1), (monochrome:1.1), (greyscale), multiple views, comic, sketch, (((bad anatomy))), (((deformed))), (((disfigured))), watermark, multiple_views, mutation hands, mutation fingers, extra fingers, missing fingers, watermark" | |
| if(use_default_negative_prompt): | |
| return "{} {}".format(base_prompt ,prompt), default_negative_prompt | |
| else: | |
| return "{} {}".format(base_prompt ,prompt), "" | |
| def get_random_prompt(data): | |
| random_key = random.choice(list(data.keys())) | |
| random_array = random.choice(data[random_key]) | |
| random_strings = random.sample(random_array, 3) | |
| return random_strings | |
| def get_correct_prompt(data, selected_dropdown): | |
| correct_array = data[selected_dropdown] | |
| random_array = random.choice(correct_array) | |
| random_strings = random.sample(random_array, 3) | |
| random_strings.insert(0, selected_dropdown) | |
| return random_strings | |
| def generate_prompt_output(*args): | |
| #all imported files | |
| prefix_path = prefix_data_file | |
| category_path = category_data_file | |
| style_path = style_data_file | |
| lightning_path = lightning_data_file | |
| lens_path = lens_data_file | |
| #destructure args | |
| category, style, lightning, lens, negative_prompt = args | |
| # Convert variables to lowercase | |
| category = category.lower() | |
| style = style.lower() | |
| lightning = lightning.lower() | |
| lens = lens.lower() | |
| # Open category_data.json and grab correct text | |
| with open(prefix_path, 'r') as f: | |
| prefix_data = json.load(f) | |
| prefix_prompt = random.sample(prefix_data, 6) | |
| modified_prefix_prompt = [f"(({item}))" for item in prefix_prompt] | |
| # Open category_data.json and grab correct text | |
| with open(category_path, 'r') as f2: | |
| category_data = json.load(f2) | |
| if category == "none": | |
| category_prompt = "" | |
| elif category == "random": | |
| category_prompt = get_random_prompt(category_data) | |
| else: | |
| category_prompt = get_correct_prompt(category_data, category) | |
| # Open style_data.json and grab correct text | |
| with open(style_path, 'r') as f3: | |
| style_data = json.load(f3) | |
| if style == "none": | |
| style_prompt = "" | |
| elif style == "random": | |
| style_prompt = get_random_prompt(style_data) | |
| else: | |
| style_prompt = get_correct_prompt(style_data, style) | |
| # Open lightning_data.json and grab correct text | |
| with open(lightning_path, 'r') as f4: | |
| lightning_data = json.load(f4) | |
| if lightning == "none": | |
| lightning_prompt = "" | |
| elif lightning == "random": | |
| lightning_prompt = get_random_prompt(lightning_data) | |
| else: | |
| lightning_prompt = get_correct_prompt(lightning_data, lightning) | |
| # Open lens_data.json and grab correct text | |
| with open(lens_path, 'r') as f5: | |
| lens_data = json.load(f5) | |
| if lens == "none": | |
| lens_prompt = "" | |
| elif lens == "random": | |
| lens_prompt = get_random_prompt(lens_data) | |
| else: | |
| lens_prompt = get_correct_prompt(lens_data, lens) | |
| prompt_output = modified_prefix_prompt, category_prompt, style_prompt, lightning_prompt, lens_prompt | |
| prompt_strings = [] | |
| for sublist in prompt_output: | |
| # Join the sublist elements into a single string | |
| prompt_string = ", ".join(str(item) for item in sublist) | |
| if prompt_string: # Check if the prompt_string is not empty | |
| prompt_strings.append(prompt_string) | |
| # Join the non-empty prompt_strings | |
| final_output = ", ".join(prompt_strings) | |
| return final_output | |
| list_models = [ | |
| "SDXL-1.0", | |
| "SD-1.5", | |
| "OpenJourney-V4", | |
| "Anything-V4", | |
| "Disney-Pixar-Cartoon", | |
| "Pixel-Art-XL", | |
| "Dalle-3-XL", | |
| "Midjourney-V4-XL", | |
| ] | |
| def generate_txt2img(current_model, prompt, is_negative=False, image_style="None style", steps=50, cfg_scale=7, | |
| seed=None, API_TOKEN = API_TOKEN): | |
| print("call {} {} one time".format(current_model, prompt)) | |
| ''' | |
| import shutil | |
| im_save_dir = "local_img_dir" | |
| if not os.path.exists(im_save_dir): | |
| #shutil.rmtree(im_save_dir) | |
| os.mkdir(im_save_dir) | |
| ''' | |
| if current_model == "SD-1.5": | |
| API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5" | |
| elif current_model == "SDXL-1.0": | |
| API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0" | |
| elif current_model == "OpenJourney-V4": | |
| API_URL = "https://api-inference.huggingface.co/models/prompthero/openjourney" | |
| elif current_model == "Anything-V4": | |
| API_URL = "https://api-inference.huggingface.co/models/xyn-ai/anything-v4.0" | |
| elif current_model == "Disney-Pixar-Cartoon": | |
| API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/disney-pixar-cartoon" | |
| elif current_model == "Pixel-Art-XL": | |
| API_URL = "https://api-inference.huggingface.co/models/nerijs/pixel-art-xl" | |
| elif current_model == "Dalle-3-XL": | |
| API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl" | |
| elif current_model == "Midjourney-V4-XL": | |
| API_URL = "https://api-inference.huggingface.co/models/openskyml/midjourney-v4-xl" | |
| #API_TOKEN = os.environ.get("HF_READ_TOKEN") | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| if type(prompt) != type(""): | |
| prompt = DEFAULT_PROMPT | |
| if image_style == "None style": | |
| payload = { | |
| "inputs": prompt + ", 8k", | |
| "is_negative": is_negative, | |
| "steps": steps, | |
| "cfg_scale": cfg_scale, | |
| "seed": seed if seed is not None else random.randint(-1, 2147483647) | |
| } | |
| elif image_style == "Cinematic": | |
| payload = { | |
| "inputs": prompt + ", realistic, detailed, textured, skin, hair, eyes, by Alex Huguet, Mike Hill, Ian Spriggs, JaeCheol Park, Marek Denko", | |
| "is_negative": is_negative + ", abstract, cartoon, stylized", | |
| "steps": steps, | |
| "cfg_scale": cfg_scale, | |
| "seed": seed if seed is not None else random.randint(-1, 2147483647) | |
| } | |
| elif image_style == "Digital Art": | |
| payload = { | |
| "inputs": prompt + ", faded , vintage , nostalgic , by Jose Villa , Elizabeth Messina , Ryan Brenizer , Jonas Peterson , Jasmine Star", | |
| "is_negative": is_negative + ", sharp , modern , bright", | |
| "steps": steps, | |
| "cfg_scale": cfg_scale, | |
| "seed": seed if seed is not None else random.randint(-1, 2147483647) | |
| } | |
| elif image_style == "Portrait": | |
| payload = { | |
| "inputs": prompt + ", soft light, sharp, exposure blend, medium shot, bokeh, (hdr:1.4), high contrast, (cinematic, teal and orange:0.85), (muted colors, dim colors, soothing tones:1.3), low saturation, (hyperdetailed:1.2), (noir:0.4), (natural skin texture, hyperrealism, soft light, sharp:1.2)", | |
| "is_negative": is_negative, | |
| "steps": steps, | |
| "cfg_scale": cfg_scale, | |
| "seed": seed if seed is not None else random.randint(-1, 2147483647) | |
| } | |
| image_bytes = requests.post(API_URL, headers=headers, json=payload).content | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| ''' | |
| from uuid import uuid1 | |
| path = os.path.join(im_save_dir ,"{}.png".format(uuid1())) | |
| image.save(path) | |
| return path | |
| ''' | |
| return image | |
| #yield image | |
| #return [image] | |
| def on_ui_tabs(): | |
| ''' | |
| # UI structure | |
| txt2img_prompt = modules.ui.txt2img_paste_fields[0][0] | |
| img2img_prompt = modules.ui.img2img_paste_fields[0][0] | |
| txt2img_negative_prompt = modules.ui.txt2img_paste_fields[1][0] | |
| img2img_negative_prompt = modules.ui.img2img_paste_fields[1][0] | |
| ''' | |
| with gr.Blocks(css = ''' | |
| .header img { | |
| float: middle; | |
| width: 33px; | |
| height: 33px; | |
| } | |
| .header h1 { | |
| top: 18px; | |
| left: 10px; | |
| } | |
| ''' | |
| ) as prompt_generator: | |
| gr.HTML( | |
| ''' | |
| <center> | |
| <div class="header"> | |
| <h1 class = "logo"> <img src="https://huggingface.co/spaces/svjack/Next-Diffusion-Prompt-Generator/resolve/main/images/nextdiffusion_logo.png" alt="logo" /> 🧑🎨 Next Diffusion Prompt On Stable Diffuison </h1> | |
| </center> | |
| ''') | |
| with gr.Tab("Prompt Generator"): | |
| with gr.Row(): # Use Row to arrange two columns side by side | |
| with gr.Column(): # Left column for dropdowns | |
| category_choices, style_choices, lightning_choices, lens_choices = populate_dropdown_options() | |
| with gr.Row(): | |
| gr.HTML('''<h2 id="input_header">Input 👇</h2>''') | |
| with gr.Row(): | |
| # Create a dropdown to select | |
| with gr.Row(): | |
| txt2img_prompt = gr.Textbox(label = "txt2img_prompt", interactive = True) | |
| txt2img_negative_prompt = gr.Textbox(label = "txt2img_negative_prompt", interactive = True) | |
| ''' | |
| with gr.Row(): | |
| img2img_prompt = gr.Textbox(label = "img2img_prompt", interactive = True) | |
| img2img_negative_prompt = gr.Textbox(label = "img2img_negative_prompt", interactive = True) | |
| ''' | |
| with gr.Row(): | |
| current_model = gr.Dropdown(label="Current Model", choices=list_models, value=list_models[1]) | |
| text_button = gr.Button("Generate image by Stable Diffusion") | |
| with gr.Row(): | |
| image_output = gr.Image(label="Output Image", type = "filepath", elem_id="gallery", height = 512, | |
| show_share_button = True | |
| ) | |
| #image_gallery = gr.Gallery(height = 512, label = "Output Gallery") | |
| #image_file = gr.File(label="Output Image File") | |
| with gr.Column(): # Right column for result_textbox and generate_button | |
| # Add a Textbox to display the generated text | |
| with gr.Row(): | |
| gr.HTML('''<h2 id="output_header">Prompt Extender by Rule 👋 (aid Input 👈)</h2>''') | |
| with gr.Row().style(equal_height=True): # Place dropdowns side by side | |
| category_dropdown = gr.Dropdown( | |
| choices=category_choices, | |
| value=category_choices[1], | |
| label="Category", show_label=True | |
| ) | |
| style_dropdown = gr.Dropdown( | |
| choices=style_choices, | |
| value=style_choices[1], | |
| label="Style", show_label=True | |
| ) | |
| with gr.Row(): | |
| lightning_dropdown = gr.Dropdown( | |
| choices=lightning_choices, | |
| value=lightning_choices[1], | |
| label="Lightning", show_label=True | |
| ) | |
| lens_dropdown = gr.Dropdown( | |
| choices=lens_choices, | |
| value=lens_choices[1], | |
| label="Lens", show_label=True | |
| ) | |
| result_textbox = gr.Textbox(label="Generated Prompt", lines=3) | |
| use_default_negative_prompt = gr.Checkbox(label="Include Negative Prompt", value=True, interactive=True, elem_id="negative_prompt_checkbox") | |
| setattr(use_default_negative_prompt,"do_not_save_to_config",True) | |
| with gr.Row(): | |
| generate_button = gr.Button(value="Generate", elem_id="generate_button") | |
| clear_button = gr.Button(value="Clear") | |
| with gr.Row(): | |
| txt2img = gr.Button("Send to txt2img") | |
| #img2img = gr.Button("Send to img2img") | |
| with gr.Row(): | |
| gr.HTML(''' | |
| <hr class="rounded" id="divider"> | |
| ''') | |
| with gr.Row(): | |
| gr.HTML('''<h2 id="input_header">Links</h2>''') | |
| with gr.Row(): | |
| gr.HTML(''' | |
| <h3>Stable Diffusion Tutorials⚡</h3> | |
| <container> | |
| <a href="https://nextdiffusion.ai" target="_blank"> | |
| <button id="website_button" class="external-link">Website</button> | |
| </a> | |
| <a href="https://www.youtube.com/channel/UCd9UIUkLnjE-Fj-CGFdU74Q?sub_confirmation=1" target="_blank"> | |
| <button id="youtube_button" class="external-link">YouTube</button> | |
| </a> | |
| </container> | |
| ''') | |
| ''' | |
| with gr.Accordion("Advanced settings", open=True): | |
| negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry, fuzziness", lines=1, elem_id="negative-prompt-text-input") | |
| image_style = gr.Dropdown(label="Style", choices=["None style", "Cinematic", "Digital Art", "Portrait"], value="None style", allow_custom_value=False) with gr.Row(): | |
| ''' | |
| # Create a button to trigger text generation | |
| txt2img.click(add_to_prompt, inputs=[result_textbox, use_default_negative_prompt, txt2img_prompt, txt2img_negative_prompt], outputs=[txt2img_prompt, txt2img_negative_prompt ]) | |
| #img2img.click(add_to_prompt, inputs=[result_textbox, use_default_negative_prompt, img2img_prompt, img2img_negative_prompt], outputs=[img2img_prompt, img2img_negative_prompt]) | |
| clear_button.click(lambda x: [""] * 3 + ["Random", "Random", "Random", "Random"], None, | |
| [result_textbox, txt2img_prompt, txt2img_negative_prompt, | |
| category_dropdown, style_dropdown, lightning_dropdown, lens_dropdown | |
| ]) | |
| text_button.click(generate_txt2img, inputs=[current_model, txt2img_prompt, txt2img_negative_prompt], outputs=image_output, | |
| ) | |
| # Register the callback for the Generate button | |
| generate_button.click(fn=generate_prompt_output, inputs=[category_dropdown, style_dropdown, lightning_dropdown, lens_dropdown, use_default_negative_prompt], outputs=[result_textbox]) | |
| gr.Examples( | |
| [ | |
| #["A lovely cat", "low quality, blur", "OpenJourney-V4", "Anime", "Drawing", "Bloom light", "F/14"], | |
| ["Forest house", "low quality, blur", "SD-1.5", "None", "Photograph", "Beautifully lit", "800mm lens"], | |
| ["A girl in pink", "low quality, blur", "SD-1.5", "Anime", "3D style", "None", "Random"], | |
| ], | |
| inputs = [txt2img_prompt, txt2img_negative_prompt, current_model, category_dropdown, style_dropdown, lightning_dropdown, lens_dropdown] | |
| ) | |
| return prompt_generator | |
| with on_ui_tabs() as demo: | |
| pass | |
| demo.launch(show_api = False) | |