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| import os | |
| import random | |
| import spaces | |
| import torch | |
| import gradio as gr | |
| from huggingface_hub import login | |
| if os.environ.get("HF_TOKEN"): | |
| login(token=os.environ["HF_TOKEN"]) | |
| from diffusers import Krea2Pipeline | |
| DTYPE = torch.bfloat16 | |
| TURBO_REPO = "krea/Krea-2-Turbo" | |
| MAX_SEED = 2**31 - 1 | |
| pipe = Krea2Pipeline.from_pretrained(TURBO_REPO, torch_dtype=DTYPE) | |
| pipe.to("cuda") | |
| RESOLUTIONS = { | |
| "Square 1024": (1024, 1024), | |
| "Portrait 1024": (832, 1216), | |
| "Landscape 1024": (1216, 832), | |
| } | |
| EXAMPLE_PROMPTS = [ | |
| ["immense rocket launch exhaust as seen from extremely close up"], | |
| [ | |
| "A tiny, russet-brown harvest mouse clings to a slender diagonal branch amid vibrant green " | |
| "lobed leaves and small round buds. The mouse has soft textured fur, glossy black eyes, a pink " | |
| "nose, fine whiskers, and delicate pink paws firmly gripping the wood. In this macro photograph, " | |
| "an extremely shallow depth of field sharply focuses on the animal's face. The deep green " | |
| "background dissolves into a smooth, creamy bokeh, illuminated by soft, diffused natural lighting " | |
| "that highlights the intricate details of the fur and foliage." | |
| ], | |
| [ | |
| "high-fashion editorial portrait of a young East Asian woman, short choppy platinum blonde bob " | |
| "with heavy bangs, looking over her bare shoulder to the right, lips playfully pursed, wearing a " | |
| "structured black top with an architectural protruding bust detail and thin straps, delicate gold " | |
| "hoop earrings, arm bent with hand resting on hip, warm skin tones, solid striking crimson red " | |
| "background, soft directional studio lighting, cinematic color palette, medium close-up shot" | |
| ], | |
| [ | |
| "A surreal retro-futuristic space scene features liquid chrome forming an abstract face merging " | |
| "with a glowing planetary horizon. The foreground is dominated by swirling, highly reflective " | |
| "metallic fluid that distorts into a stylized, melting facial profile with deep shadows and bright " | |
| "silver highlights. This undulating chrome form rests against the curved, atmospheric edge of a " | |
| "massive planet bathed in a soft electric blue and purple glow. Set against a deep black starfield, " | |
| "the artwork employs a vintage 1980s airbrush aesthetic with smooth gradients, ethereal lighting, " | |
| "and high-contrast metallic rendering." | |
| ], | |
| [ | |
| "Stylized digital painting of a menacing jester figure rendered with bold, expressive brushstrokes " | |
| "and a vibrant, almost psychedelic color palette against a pitch-black background. Dynamic low-angle " | |
| "perspective forces a dramatic, imposing composition as the character leans forward, one leg raised " | |
| "high. The jester wears a classic multi-pointed hat with bells, a ruffled collar, and striped tights " | |
| "in alternating shades of purple, blue, and chartreuse. The figure's face is a smooth, faceless, pale " | |
| "mauve mask with a single glowing white point of light at the center, and it grips a massive ornate " | |
| "sword with a glowing ethereal white blade. Theatrical lighting, dark fantasy concept-art aesthetic." | |
| ], | |
| [ | |
| "A minimalist flat-color illustration of a person wading through expansive shallow ocean waves " | |
| "beneath a pale peach sky. The dark-skinned figure, wearing an orange swim cap, light blue top, and " | |
| "bright green shorts, steps carefully through knee-deep water. The ocean is rendered in muted mint " | |
| "green with delicate, thin black linework detailing the continuous ripples and gentle whitecaps. " | |
| "Soft pinkish-peach reflections echo the sky on the water's surface. The high-angle wide perspective " | |
| "emphasizes the vast negative space of the water, utilizing a clean ligne claire drawing aesthetic " | |
| "with a subtle paper texture." | |
| ], | |
| ] | |
| PLACEHOLDER = ( | |
| "Describe your image in natural language. e.g. a russet harvest mouse clinging to a " | |
| 'branch, macro photograph, shallow depth of field, creamy green bokeh, soft natural light. ' | |
| 'Wrap words in "quotes" to render them as text.' | |
| ) | |
| def _duration(prompt, steps, guidance, width, height, seed, randomize, *args, **kwargs): | |
| megapixels = max(1.0, (int(width) * int(height)) / (1024 * 1024)) | |
| return int(int(steps) * 2 * megapixels + 25) | |
| def generate( | |
| prompt, | |
| steps=8, | |
| guidance=0.0, | |
| width=1024, | |
| height=1024, | |
| seed=42, | |
| randomize=False, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| """Generate an image from a text prompt using Krea 2 Turbo.""" | |
| if not prompt or not prompt.strip(): | |
| raise gr.Error("Enter a prompt to generate an image.") | |
| if randomize: | |
| seed = random.randint(0, MAX_SEED) | |
| seed = int(seed) | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| try: | |
| image = pipe( | |
| prompt=prompt, | |
| height=int(height), | |
| width=int(width), | |
| num_inference_steps=int(steps), | |
| guidance_scale=float(guidance), | |
| generator=generator, | |
| ).images[0] | |
| except RuntimeError as exc: | |
| torch.cuda.empty_cache() | |
| raise gr.Error( | |
| f"Generation failed at {int(width)}x{int(height)}. Try 1024x1024 or a smaller size." | |
| ) from exc | |
| return image, seed | |
| def on_resolution_change(label): | |
| w, h = RESOLUTIONS[label] | |
| return gr.update(value=w), gr.update(value=h) | |
| KREA_ACCENT = "#2b5cff" | |
| theme = gr.themes.Base( | |
| primary_hue=gr.themes.colors.blue, | |
| neutral_hue=gr.themes.colors.neutral, | |
| font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"], | |
| font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui-monospace", "monospace"], | |
| ).set( | |
| body_background_fill="#000000", | |
| body_background_fill_dark="#000000", | |
| body_text_color="#f5f5f5", | |
| background_fill_primary="#0d0d0d", | |
| background_fill_secondary="#0d0d0d", | |
| block_background_fill="#0d0d0d", | |
| block_border_color="#262626", | |
| block_border_width="1px", | |
| block_label_background_fill="#0d0d0d", | |
| block_label_text_color="#737373", | |
| block_title_text_color="#d4d4d5", | |
| border_color_primary="#262626", | |
| input_background_fill="#000000", | |
| input_border_color="#262626", | |
| input_border_color_focus=KREA_ACCENT, | |
| button_primary_background_fill=KREA_ACCENT, | |
| button_primary_background_fill_hover="#1f4fff", | |
| button_primary_text_color="#ffffff", | |
| button_primary_border_color=KREA_ACCENT, | |
| button_secondary_background_fill="#171717", | |
| button_secondary_background_fill_hover="#262626", | |
| button_secondary_text_color="#f5f5f5", | |
| button_secondary_border_color="#262626", | |
| slider_color=KREA_ACCENT, | |
| ) | |
| CSS = """ | |
| .gradio-container { background: #000 !important; } | |
| #page { max-width: 1120px; margin: 0 auto; padding: 4px 8px 32px; } | |
| #krea-header { | |
| padding: 32px 6px 22px; | |
| border-bottom: 1px solid #1a1a1a; | |
| margin-bottom: 22px; | |
| } | |
| #krea-header .eyebrow { | |
| font-family: 'JetBrains Mono', ui-monospace, monospace; | |
| font-size: 11px; | |
| letter-spacing: 0.24em; | |
| text-transform: uppercase; | |
| color: #737373; | |
| } | |
| #krea-header h1 { | |
| font-size: 42px; | |
| font-weight: 600; | |
| letter-spacing: -0.025em; | |
| line-height: 1.05; | |
| margin: 10px 0 6px; | |
| color: #fff; | |
| } | |
| #krea-header .subtitle { | |
| font-size: 15px; | |
| line-height: 1.5; | |
| color: #a3a3a3; | |
| margin: 0; | |
| max-width: 60ch; | |
| } | |
| #krea-header .badges { | |
| margin-top: 18px; | |
| display: flex; | |
| gap: 8px; | |
| } | |
| #krea-header .badge { | |
| font-family: 'JetBrains Mono', ui-monospace, monospace; | |
| font-size: 10px; | |
| letter-spacing: 0.12em; | |
| text-transform: uppercase; | |
| color: #d4d4d5; | |
| border: 1px solid #262626; | |
| border-radius: 999px; | |
| padding: 4px 10px; | |
| } | |
| #generate-btn { font-weight: 600; letter-spacing: 0.01em; } | |
| #result-image { min-height: 420px; border-radius: 10px; overflow: hidden; } | |
| footer { display: none !important; } | |
| """ | |
| with gr.Blocks(title="Krea 2 Turbo Demo", theme=theme, css=CSS) as demo: | |
| with gr.Column(elem_id="page"): | |
| gr.HTML( | |
| """ | |
| <header id="krea-header"> | |
| <div class="eyebrow">KREA · TEXT-TO-IMAGE</div> | |
| <h1>Krea 2 Turbo</h1> | |
| <p class="subtitle">Fast, few-step image generation from natural language. 8 steps, zero guidance.</p> | |
| <div class="badges"> | |
| <span class="badge">13B params</span> | |
| <span class="badge">8 steps</span> | |
| <span class="badge">Turbo distilled</span> | |
| </div> | |
| </header> | |
| """ | |
| ) | |
| with gr.Row(equal_height=False): | |
| with gr.Column(scale=5, elem_classes="panel"): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| lines=4, | |
| placeholder=PLACEHOLDER, | |
| show_label=True, | |
| autofocus=True, | |
| ) | |
| run = gr.Button("Generate", variant="primary", elem_id="generate-btn") | |
| resolution = gr.Radio( | |
| list(RESOLUTIONS.keys()), | |
| value="Square 1024", | |
| label="Resolution", | |
| ) | |
| with gr.Accordion("Advanced", open=False): | |
| steps = gr.Slider(1, 50, value=8, step=1, label="Steps") | |
| guidance = gr.Slider(0.0, 10.0, value=0.0, step=0.1, label="Guidance scale") | |
| with gr.Row(): | |
| width = gr.Number(value=1024, label="Width", precision=0, interactive=False) | |
| height = gr.Number(value=1024, label="Height", precision=0, interactive=False) | |
| with gr.Row(): | |
| seed = gr.Slider(0, MAX_SEED, value=0, step=1, label="Seed") | |
| randomize = gr.Checkbox(value=True, label="Randomize seed") | |
| with gr.Column(scale=6, elem_classes="panel"): | |
| output = gr.Image(label="Result", format="png", elem_id="result-image") | |
| gr.Examples( | |
| fn=generate, | |
| examples=EXAMPLE_PROMPTS, | |
| inputs=[prompt], | |
| outputs=[output, seed], | |
| cache_examples=True, | |
| cache_mode="lazy", | |
| label="Example prompts", | |
| examples_per_page=4, | |
| ) | |
| resolution.change(on_resolution_change, resolution, [width, height]) | |
| inputs = [prompt, steps, guidance, width, height, seed, randomize] | |
| run.click(generate, inputs, [output, seed]) | |
| prompt.submit(generate, inputs, [output, seed]) | |
| demo.launch(mcp_server=True) | |