Initial commit: Juggernaut Z Image ZeroGPU Space

README.md

@@ -1,13 +1,59 @@
 ---
-title: JUGGERNAUT Z IMAGE
-emoji: 🐢
-colorFrom: red
-colorTo: gray
+title: JUGGERNAUT-Z-IMAGE
+emoji: 🎨
+colorFrom: yellow
+colorTo: red
 sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
+sdk_version: "6.9.0"
+python_version: "3.10"
 app_file: app.py
 pinned: false
+tags:
+- text-to-image
+- image-generation
+- juggernaut
+- z-image
+- lumina2
+- rundiffusion
+- zero-gpu
+short_description: Juggernaut Z - Cinematic fine-tune of Z-Image Base model
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Juggernaut Z Image
+
+A ZeroGPU Space for generating images with **Juggernaut Z** by RunDiffusion — a cinematic fine-tune of the Z-Image Base model.
+
+## Features
+
+- 🎨 **Cinematic Quality**: Tuned for dramatic lighting, sharper focus, and refined skin texture
+- ⚡ **ZeroGPU**: Runs on Hugging Face's free GPU infrastructure
+- 🖼️ **Multiple Resolutions**: From 720x720 up to 1680x720 (and custom sizes)
+- 🎛️ **Full CFG Control**: Supports Classifier-Free Guidance for precise prompt adherence
+- 🌱 **Seed Control**: Reproducible generation with seed randomization option
+
+## Model Details
+
+- **Base Model**: [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)
+- **Fine-tune**: [RunDiffusion/Juggernaut-Z-Image](https://huggingface.co/RunDiffusion/Juggernaut-Z-Image)
+- **Architecture**: Lumina2 (Single-Stream Diffusion Transformer)
+- **License**: Apache 2.0
+
+## Recommended Settings
+
+| Parameter | Default | Range |
+|-----------|---------|-------|
+| CFG Scale | 6.0 | 3.0 – 9.0 |
+| Steps | 35 | 25 – 50 |
+| Resolution | 1024×1024 | 512×512 – 2048×2048 |
+
+## Links
+
+- [RunDiffusion](https://www.rundiffusion.com/)
+- [Prompt Guide](https://www.rundiffusion.com/juggernaut-z-prompt-guide)
+- [Base Model](https://huggingface.co/Tongyi-MAI/Z-Image)
+
+## Credits
+
+- Fine-tuned by **Team Juggernaut**
+- Training by **KandooAI**
+- Published by **RunDiffusion**

app.py

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+"""
+Juggernaut Z Image Generation Demo
+ZeroGPU Space for RunDiffusion/Juggernaut-Z-Image
+"""
+
+import spaces
+import random
+import re
+import torch
+import gradio as gr
+from diffusers import ZImagePipeline
+
+# ==================== Configuration ====================
+MODEL_PATH = "RunDiffusion/Juggernaut-Z-Image"
+
+# ==================== Resolution Choices ====================
+RES_CHOICES = {
+    "720": [
+        "720x720 ( 1:1 )",
+        "896x512 ( 16:9 )",
+        "512x896 ( 9:16 )",
+        "832x544 ( 3:2 )",
+        "544x832 ( 2:3 )",
+        "800x576 ( 4:3 )",
+        "576x800 ( 3:4 )",
+    ],
+    "1024": [
+        "1024x1024 ( 1:1 )",
+        "1152x896 ( 9:7 )",
+        "896x1152 ( 7:9 )",
+        "1152x864 ( 4:3 )",
+        "864x1152 ( 3:4 )",
+        "1248x832 ( 3:2 )",
+        "832x1248 ( 2:3 )",
+        "1280x720 ( 16:9 )",
+        "720x1280 ( 9:16 )",
+        "1344x576 ( 21:9 )",
+        "576x1344 ( 9:21 )",
+    ],
+    "1280": [
+        "1280x1280 ( 1:1 )",
+        "1440x1120 ( 9:7 )",
+        "1120x1440 ( 7:9 )",
+        "1472x1104 ( 4:3 )",
+        "1104x1472 ( 3:4 )",
+        "1536x1024 ( 3:2 )",
+        "1024x1536 ( 2:3 )",
+        "1536x864 ( 16:9 )",
+        "864x1536 ( 9:16 )",
+        "1680x720 ( 21:9 )",
+        "720x1680 ( 9:21 )",
+    ],
+}
+
+RESOLUTION_SET = []
+for resolutions in RES_CHOICES.values():
+    RESOLUTION_SET.extend(resolutions)
+
+EXAMPLE_PROMPTS = [
+    ["Cinematic portrait of a cyberpunk warrior, neon lights reflecting off chrome armor, rain-soaked streets, dramatic lighting, 8k, photorealistic"],
+    ["Ethereal forest scene with bioluminescent mushrooms, misty atmosphere, magical lighting, fantasy art style"],
+    ["Majestic mountain landscape at golden hour, snow-capped peaks, alpine lake reflection, cinematic photography"],
+    ["Futuristic cityscape at night, flying cars, holographic billboards, cyberpunk aesthetic, highly detailed"],
+    ["Portrait of an elegant woman in Victorian dress, ornate jewelry, soft natural lighting, studio portrait"],
+]
+
+# ==================== Helper Functions ====================
+def get_resolution(resolution: str) -> tuple[int, int]:
+    """Parse resolution string to width and height."""
+    match = re.search(r"(\d+)\s*[×x]\s*(\d+)", resolution)
+    if match:
+        return int(match.group(1)), int(match.group(2))
+    return 1024, 1024
+
+
+# ==================== Model Loading (Global Context) ====================
+print(f"Loading Juggernaut Z pipeline from {MODEL_PATH}...")
+pipe = ZImagePipeline.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=False,
+)
+pipe.to("cuda")
+print("Pipeline loaded successfully!")
+
+
+# ==================== Generation Function ====================
+@spaces.GPU
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    resolution: str = "1024x1024 ( 1:1 )",
+    seed: int = 42,
+    num_inference_steps: int = 35,
+    guidance_scale: float = 6.0,
+    cfg_normalization: bool = False,
+):
+    if not prompt or not prompt.strip():
+        raise gr.Error("Prompt is required.")
+    
+    width, height = get_resolution(resolution)
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+    
+    image = pipe(
+        prompt=prompt.strip(),
+        negative_prompt=negative_prompt.strip() if negative_prompt else None,
+        height=height,
+        width=width,
+        num_inference_steps=int(num_inference_steps),
+        guidance_scale=float(guidance_scale),
+        cfg_normalization=bool(cfg_normalization),
+        generator=generator,
+    ).images[0]
+    
+    meta = {
+        "model": MODEL_PATH,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "resolution": f"{width} x {height}",
+        "guidance_scale": guidance_scale,
+        "steps": num_inference_steps,
+        "seed": seed,
+        "cfg_normalization": cfg_normalization,
+    }
+    
+    return image, meta
+
+
+# ==================== Custom Theme ====================
+CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;500;600;700;800&family=Fira+Code:wght@400;500&display=swap');
+
+:root {
+    --bg:      #080a0e;
+    --surf:    #0d1017;
+    --card:    #111520;
+    --border:  #1c2133;
+    --border2: #252d45;
+    --amber:   #f59e0b;
+    --gold:    #fbbf24;
+    --cream:   #fef3c7;
+    --text:    #e2e8f8;
+    --muted:   #4a5578;
+    --r:       14px;
+    --r-sm:    8px;
+}
+
+*, *::before, *::after { box-sizing: border-box; }
+
+body, .gradio-container {
+    background: var(--bg) !important;
+    font-family: 'Outfit', sans-serif !important;
+    color: var(--text) !important;
+}
+
+.gradio-container::before {
+    content: '';
+    position: fixed; inset: 0; pointer-events: none; z-index: 0;
+    background:
+        radial-gradient(ellipse 70% 50% at 50% -10%, rgba(245,158,11,0.07) 0%, transparent 65%),
+        radial-gradient(ellipse 40% 30% at 90% 90%, rgba(251,191,36,0.04) 0%, transparent 60%);
+}
+
+.app-hero { padding: 52px 0 28px; text-align: center; }
+
+.app-hero h1 {
+    font-size: 3rem; font-weight: 800; letter-spacing: -0.05em;
+    line-height: 1; margin: 0 0 12px;
+    background: linear-gradient(135deg, var(--cream) 0%, var(--gold) 40%, var(--amber) 100%);
+    -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;
+}
+
+.app-hero .tagline {
+    color: var(--muted); font-size: 0.88rem; font-weight: 300;
+    letter-spacing: 0.06em; text-transform: uppercase; margin: 0 0 20px;
+}
+
+.app-hero .pills { display: flex; justify-content: center; gap: 8px; flex-wrap: wrap; }
+
+.app-hero .pill {
+    background: var(--card); border: 1px solid var(--border2); border-radius: 100px;
+    padding: 4px 14px; font-size: 0.74rem; font-weight: 500; color: var(--muted);
+    font-family: 'Fira Code', monospace;
+}
+
+.app-hero .pill.gold { color: var(--amber); border-color: rgba(245,158,11,0.3); }
+
+.sec-label {
+    font-size: 0.62rem !important; font-weight: 700 !important;
+    letter-spacing: 0.15em !important; text-transform: uppercase !important;
+    color: var(--amber) !important; margin: 0 0 8px !important; display: block;
+}
+
+label > span {
+    font-family: 'Outfit', sans-serif !important; font-size: 0.72rem !important;
+    font-weight: 500 !important; color: var(--muted) !important;
+    text-transform: uppercase; letter-spacing: 0.08em;
+}
+
+textarea, input[type="text"] {
+    background: var(--surf) !important; border: 1px solid var(--border) !important;
+    border-radius: var(--r-sm) !important; color: var(--text) !important;
+    font-family: 'Outfit', sans-serif !important; font-size: 0.95rem !important;
+    transition: border-color 0.2s, box-shadow 0.2s;
+}
+
+textarea:focus, input[type="text"]:focus {
+    border-color: var(--amber) !important;
+    box-shadow: 0 0 0 3px rgba(245,158,11,0.12) !important;
+    outline: none !important;
+}
+
+.gen-btn {
+    background: linear-gradient(135deg, var(--amber), #d97706) !important;
+    border: none !important; border-radius: var(--r) !important;
+    color: #000 !important; font-family: 'Outfit', sans-serif !important;
+    font-weight: 700 !important; font-size: 1rem !important;
+    height: 54px !important; width: 100% !important;
+    letter-spacing: 0.02em !important; cursor: pointer !important;
+    transition: opacity 0.18s, transform 0.15s, box-shadow 0.2s !important;
+    box-shadow: 0 4px 20px rgba(245,158,11,0.28) !important;
+}
+
+.gen-btn:hover {
+    opacity: 0.88 !important; transform: translateY(-1px) !important;
+    box-shadow: 0 8px 30px rgba(245,158,11,0.48) !important;
+}
+
+.gen-btn:active { transform: translateY(0) !important; }
+
+.result-gallery .grid-wrap {
+    background: var(--surf) !important;
+    border: 1px solid var(--border) !important;
+    border-radius: var(--r) !important;
+}
+
+.result-gallery img { border-radius: 10px !important; }
+
+.gr-accordion {
+    background: var(--card) !important; border: 1px solid var(--border) !important;
+    border-radius: var(--r) !important; margin-top: 10px !important;
+}
+
+::-webkit-scrollbar { width: 5px; }
+::-webkit-scrollbar-track { background: var(--surf); }
+::-webkit-scrollbar-thumb { background: var(--border2); border-radius: 3px; }
+::-webkit-scrollbar-thumb:hover { background: var(--amber); }
+"""
+
+
+# ==================== Gradio Interface ====================
+with gr.Blocks(css=CSS) as demo:
+    gr.HTML("""
+    <div class="app-hero">
+        <h1>Juggernaut Z</h1>
+        <p class="tagline">Cinematic Fine-tune of Z-Image Base</p>
+        <div class="pills">
+            <span class="pill gold">ZeroGPU ⚡</span>
+            <span class="pill">RunDiffusion</span>
+            <span class="pill">Lumina2</span>
+            <span class="pill">bfloat16</span>
+        </div>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1, min_width=320):
+            gr.HTML('<span class="sec-label">① Prompt</span>')
+            prompt = gr.Textbox(
+                label="",
+                lines=5,
+                placeholder="Cinematic portrait of a warrior queen, golden armor, dramatic lighting, 8k, photorealistic...",
+                container=False,
+            )
+            
+            gr.HTML('<div style="height:8px"></div>')
+            negative_prompt = gr.Textbox(
+                label="Negative prompt",
+                lines=2,
+                placeholder="Optional: describe what to avoid...",
+                value="",
+            )
+            
+            gr.HTML('<div style="height:10px"></div>')
+            run_btn = gr.Button("▶  Generate", variant="primary", elem_classes=["gen-btn"])
+            
+            gr.Examples(
+                examples=EXAMPLE_PROMPTS,
+                inputs=[prompt],
+                label="Example prompts",
+            )
+        
+        with gr.Column(scale=1, min_width=320):
+            gr.HTML('<span class="sec-label">② Result</span>')
+            result = gr.Image(
+                label="",
+                type="pil",
+                height=512,
+                container=False,
+                elem_classes=["result-gallery"],
+            )
+            
+            gr.HTML('<div style="height:8px"></div>')
+            gr.HTML('<span class="sec-label">Generation Metadata</span>')
+            metadata = gr.JSON(label="", show_label=False)
+    
+    with gr.Accordion("⚙  Generation Settings", open=False):
+        gr.HTML('<span class="sec-label" style="margin-top:4px">Resolution</span>')
+        resolution = gr.Dropdown(
+            label="",
+            choices=RESOLUTION_SET,
+            value="1024x1024 ( 1:1 )",
+            container=False,
+        )
+        
+        gr.HTML('<div style="height:10px"></div>')
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=3.0,
+                maximum=9.0,
+                step=0.5,
+                value=6.0,
+            )
+            num_inference_steps = gr.Slider(
+                label="Steps",
+                minimum=25,
+                maximum=50,
+                step=1,
+                value=35,
+            )
+        
+        with gr.Row():
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=2_147_483_647,
+                step=1,
+                value=42,
+            )
+            randomize_seed = gr.Checkbox(
+                label="Randomize seed",
+                value=False,
+            )
+        
+        cfg_normalization = gr.Checkbox(
+            label="CFG Normalization",
+            value=False,
+            info="Enable for more stable CFG behavior at high values",
+        )
+    
+    def generate_wrapper(prompt, negative_prompt, resolution, seed, num_inference_steps, guidance_scale, cfg_normalization, randomize_seed):
+        if randomize_seed:
+            seed = random.randint(0, 2_147_483_647)
+        return generate(prompt, negative_prompt, resolution, seed, num_inference_steps, guidance_scale, cfg_normalization)
+    
+    inputs = [
+        prompt, negative_prompt, resolution, seed,
+        num_inference_steps, guidance_scale, cfg_normalization, randomize_seed,
+    ]
+    
+    run_btn.click(
+        fn=generate_wrapper,
+        inputs=inputs,
+        outputs=[result, metadata],
+        api_name="generate",
+    )
+    prompt.submit(
+        fn=generate_wrapper,
+        inputs=inputs,
+        outputs=[result, metadata],
+        api_name=False,
+    )
+
+
+demo.queue(max_size=20)
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+diffusers>=0.33.0
+torch>=2.0.0
+gradio>=6.9.0
+spaces>=0.30.0
+accelerate>=0.25.0
+safetensors>=0.4.0
+huggingface-hub>=0.25.0