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| import gradio as gr | |
| import requests | |
| import io | |
| import random | |
| import os | |
| from PIL import Image | |
| # List of available models | |
| list_models = [ | |
| "SDXL 1.0", "SD 1.5", "OpenJourney", "Anything V4.0", | |
| "Disney Pixar Cartoon", "Pixel Art XL", "Dalle 3 XL", | |
| "Midjourney V4 XL", "Open Diffusion V1", "SSD 1B", | |
| "Segmind Vega", "Animagine XL-2.0", "Animagine XL-3.0", | |
| "OpenDalle", "OpenDalle V1.1", "PlaygroundV2 1024px aesthetic", | |
| ] | |
| # Function to generate images from text | |
| def generate_txt2img(current_model, prompt, is_negative=False, image_style="None style", steps=50, cfg_scale=7, seed=None): | |
| 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": | |
| API_URL = "https://api-inference.huggingface.co/models/prompthero/openjourney" | |
| elif current_model == "Anything V4.0": | |
| 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" | |
| elif current_model == "Open Diffusion V1": | |
| API_URL = "https://api-inference.huggingface.co/models/openskyml/open-diffusion-v1" | |
| elif current_model == "SSD 1B": | |
| API_URL = "https://api-inference.huggingface.co/models/segmind/SSD-1B" | |
| elif current_model == "Segmind Vega": | |
| API_URL = "https://api-inference.huggingface.co/models/segmind/Segmind-Vega" | |
| elif current_model == "Animagine XL-2.0": | |
| API_URL = "https://api-inference.huggingface.co/models/Linaqruf/animagine-xl-2.0" | |
| elif current_model == "Animagine XL-3.0": | |
| API_URL = "https://api-inference.huggingface.co/models/cagliostrolab/animagine-xl-3.0" | |
| elif current_model == "OpenDalle": | |
| API_URL = "https://api-inference.huggingface.co/models/dataautogpt3/OpenDalle" | |
| elif current_model == "OpenDalle V1.1": | |
| API_URL = "https://api-inference.huggingface.co/models/dataautogpt3/OpenDalleV1.1" | |
| elif current_model == "PlaygroundV2 1024px aesthetic": | |
| API_URL = "https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic" | |
| API_TOKEN = os.environ.get("HF_READ_TOKEN") | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| 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)) | |
| return image | |
| # Function to read CSS from file | |
| def read_css_from_file(filename): | |
| with open(filename, "r") as file: | |
| return file.read() | |
| # Read CSS from file | |
| css = read_css_from_file("style.css") | |
| PTI_SD_DESCRIPTION = ''' | |
| <div id="content_align"> | |
| <span style="color:darkred;font-size:32px;font-weight:bold"> | |
| Stable Diffusion Models Image Generation | |
| </span> | |
| </div> | |
| <div id="content_align"> | |
| <span style="color:blue;font-size:16px;font-weight:bold"> | |
| Generate images directly from text prompts (no parameter tuning required) | |
| </span> | |
| </div> | |
| <div id="content_align" style="margin-top: 10px;"> | |
| </div> | |
| ''' | |
| # Prompt examples | |
| prompt_examples = [ | |
| "A blue jay standing on a large basket of rainbow macarons.", | |
| "A dog looking curiously in the mirror, seeing a cat.", | |
| # "A robot couple fine dining with Eiffel Tower in the background.", | |
| # "A chrome-plated duck with a golden beak arguing with an angry turtle in a forest.", | |
| # "A transparent sculpture of a duck made out of glass. The sculpture is in front of a painting of a landscape.", | |
| # "A cute corgi lives in a house made out of sushi.", | |
| # "A single beam of light enter the room from the ceiling. The beam of light is illuminating an easel. On the easel there is a Rembrandt painting of a raccoon.", | |
| # "A photo of a Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat." | |
| ] | |
| # Creating Gradio interface | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown(PTI_SD_DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| current_model = gr.Dropdown(label="Select Model", choices=list_models, value=list_models[1]) | |
| text_prompt = gr.Textbox(label="Input Prompt", placeholder="Example: A blue jay ", lines=2) | |
| text_prompt.examples = prompt_examples | |
| with gr.Column(): | |
| negative_prompt = gr.Textbox(label="Negative Prompt (optional)", placeholder="Example: blurry, unfocused", lines=2) | |
| image_style = gr.Dropdown(label="Select Style", choices=["None style", "Cinematic", "Digital Art", "Portrait"], value="None style") | |
| generate_button = gr.Button("Generate Image", variant='primary') | |
| with gr.Row(): | |
| image_output = gr.Image(type="pil", label="Image Output") | |
| generate_button.click(generate_txt2img, inputs=[current_model, text_prompt, negative_prompt, image_style], outputs=image_output) | |
| # Launch the app | |
| demo.launch() | |