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import gradio as gr |
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import numpy as np |
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import random |
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from diffusers import DiffusionPipeline |
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from safetensors.torch import load_file |
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from huggingface_hub import hf_hub_download |
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import torch |
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from typing import Tuple |
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style_list = [ |
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{ |
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"name": "(No style)", |
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"prompt": "{prompt}", |
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"negative_prompt": "", |
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}, |
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{ |
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"name": "Cinematic", |
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"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", |
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"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", |
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}, |
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{ |
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"name": "Photographic", |
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"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", |
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"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", |
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}, |
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{ |
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"name": "Anime", |
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"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", |
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"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", |
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}, |
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{ |
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"name": "Digital Art", |
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"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", |
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"negative_prompt": "photo, photorealistic, realism, ugly", |
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}, |
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{ |
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"name": "Fantasy art", |
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"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", |
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"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", |
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}, |
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{ |
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"name": "3D Model", |
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"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", |
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"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", |
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}, |
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] |
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} |
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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "(No style)" |
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def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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return p.replace("{prompt}", positive), n + negative |
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") |
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pipe.to("cuda") |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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def infer(prompt, negative_prompt, width, height, guidance_scale, style_name=None): |
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seed = random.randint(0,4294967295) |
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generator = torch.Generator().manual_seed(seed) |
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prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) |
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image = [pipe( |
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prompt = prompt, |
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negative_prompt = negative_prompt, |
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guidance_scale = guidance_scale, |
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width = width, |
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height = height, |
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generator = generator |
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).images[0] for _ in range(4)] |
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return image |
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examples = [ |
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", |
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"An astronaut riding a green horse", |
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"A delicious ceviche cheesecake slice", |
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] |
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css=""" |
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#col-container { |
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margin: 0 auto; |
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max-width: 600px; |
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padding-top: 20px; |
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text-align: center; |
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} |
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.footer { |
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margin-bottom: 45px; |
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margin-top: 25px; |
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text-align: center; |
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} |
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.footer>p { |
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font-size: .8rem; |
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display: inline-block; |
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padding: 0 10px; |
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transform: translateY(10px); |
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} |
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""" |
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if torch.cuda.is_available(): |
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power_device = "GPU" |
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else: |
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power_device = "CPU" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f""" |
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# Welcome to Metamorph: Your Creative Gateway |
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#### Transform your words into stunning visuals with our advanced AI-powered Text-to-Image generator |
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Currently running on {power_device} |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Generate", scale=0) |
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result = gr.Gallery(label="Results", show_label=False, format="png", show_share_button=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Textbox( |
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label="Negative prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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elem_id="negative-prompt-text-input" |
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) |
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style_selection = gr.Radio( |
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show_label=True, container=True, interactive=True, |
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choices=STYLE_NAMES, |
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value=DEFAULT_STYLE_NAME, |
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label='Image Style' |
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) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.0, |
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maximum=50.0, |
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step=0.1, |
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value=10, |
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) |
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gr.Examples( |
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examples = examples, |
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inputs = [prompt] |
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) |
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gr.HTML( |
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""" |
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<div class="footer"> |
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<p>This application harnesses the cutting-edge Stable Diffusion XL (SDXL) model by <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">StabilityAI</a>, offering unparalleled text-to-image generation, while acknowledging potential biases and content considerations outlined in the model card.</p> |
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</p> |
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</div> |
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""" |
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) |
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run_button.click( |
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fn = infer, |
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inputs = [prompt, negative_prompt, width, height, guidance_scale, style_selection], |
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outputs = [result] |
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) |
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demo.queue().launch() |
