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Madras1 commited on 13 days ago

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Dockerfile +21 -0
README.md +48 -10
app.py +255 -0
requirements.txt +28 -0

Dockerfile ADDED Viewed

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+FROM python:3.10-slim
+WORKDIR /app
+# Install system deps
+RUN apt-get update && apt-get install -y \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+# Copy requirements first for caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+# Copy app
+COPY app.py .
+# Expose port
+EXPOSE 7860
+# Run
+CMD ["python", "app.py"]

README.md CHANGED Viewed

@@ -1,10 +1,48 @@
----
-title: Imagineai
-emoji: 📉
-colorFrom: purple
-colorTo: green
-sdk: docker
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

+---
+title: Z-Image-Turbo API
+emoji: 🎨
+colorFrom: purple
+colorTo: blue
+sdk: docker
+pinned: false
+license: apache-2.0
+---
+# 🎨 Z-Image-Turbo API
+Generate high-quality images from text using Alibaba's **Z-Image-Turbo** model.
+## ⚠️ Performance Note
+This Space runs on **CPU only**. Expect generation times of **2-5 minutes per image**.
+## 📡 API Usage
+### POST /generate
+```python
+import requests
+response = requests.post(
+    "https://YOUR_SPACE.hf.space/generate",
+    json={
+        "prompt": "a beautiful sunset over mountains",
+        "width": 512,
+        "height": 512,
+        "seed": -1,
+        "num_steps": 8
+    }
+)
+data = response.json()
+# data["image_base64"] contains the PNG image as base64
+# data["seed"] contains the seed used
+```
+### cURL
+```bash
+curl -X POST "https://YOUR_SPACE.hf.space/generate" \
+  -H "Content-Type: application/json" \
+  -d '{"prompt": "a cat in space", "width": 512, "height": 512, "seed": -1, "num_steps": 8}'
+```

app.py ADDED Viewed

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+"""
+Z-Image-Turbo API - FastAPI Implementation
+Avoids Gradio/diffusers schema serialization bug
+"""
+import os
+import io
+import base64
+import random
+import gc
+from PIL import Image
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import HTMLResponse, Response
+from pydantic import BaseModel
+import uvicorn
+app = FastAPI(title="Z-Image-Turbo API")
+# Global pipeline
+pipe = None
+class GenerateRequest(BaseModel):
+    prompt: str
+    width: int = 512
+    height: int = 512
+    seed: int = -1
+    num_steps: int = 8
+class GenerateResponse(BaseModel):
+    image_base64: str
+    seed: int
+    status: str
+def load_model():
+    """Lazy load the model"""
+    global pipe
+    if pipe is None:
+        print("Loading Z-Image-Turbo model...")
+        import torch
+        from diffusers import ZImagePipeline
+        pipe = ZImagePipeline.from_pretrained(
+            "Tongyi-MAI/Z-Image-Turbo",
+            torch_dtype=torch.float32,
+            low_cpu_mem_usage=True,
+        )
+        pipe.to("cpu")
+        print("Model loaded!")
+    return pipe
+@app.get("/", response_class=HTMLResponse)
+async def root():
+    """Simple HTML interface"""
+    return """
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>Z-Image-Turbo API</title>
+        <style>
+            * { box-sizing: border-box; }
+            body {
+                font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+                background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
+                color: white;
+                min-height: 100vh;
+                margin: 0;
+                padding: 20px;
+            }
+            .container { max-width: 800px; margin: 0 auto; }
+            h1 { text-align: center; font-size: 2.5em; margin-bottom: 10px; }
+            .subtitle { text-align: center; opacity: 0.7; margin-bottom: 30px; }
+            .form-group { margin-bottom: 20px; }
+            label { display: block; margin-bottom: 8px; font-weight: 500; }
+            input, textarea {
+                width: 100%;
+                padding: 12px;
+                border: none;
+                border-radius: 8px;
+                background: rgba(255,255,255,0.1);
+                color: white;
+                font-size: 16px;
+            }
+            textarea { min-height: 100px; resize: vertical; }
+            input:focus, textarea:focus { outline: 2px solid #667eea; }
+            button {
+                width: 100%;
+                padding: 15px;
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                border: none;
+                border-radius: 8px;
+                color: white;
+                font-size: 18px;
+                font-weight: 600;
+                cursor: pointer;
+                transition: transform 0.2s;
+            }
+            button:hover { transform: scale(1.02); }
+            button:disabled { opacity: 0.5; cursor: not-allowed; }
+            .result {
+                margin-top: 30px;
+                text-align: center;
+                padding: 20px;
+                background: rgba(255,255,255,0.05);
+                border-radius: 12px;
+            }
+            .result img { max-width: 100%; border-radius: 8px; }
+            .warning {
+                background: rgba(255,193,7,0.2);
+                padding: 15px;
+                border-radius: 8px;
+                margin-bottom: 20px;
+                border-left: 4px solid #ffc107;
+            }
+            .row { display: flex; gap: 15px; }
+            .row .form-group { flex: 1; }
+            #status { margin-top: 15px; font-style: italic; opacity: 0.8; }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🎨 Z-Image-Turbo API</h1>
+            <p class="subtitle">Generate images from text using AI</p>
+            <div class="warning">
+                ⚠️ <strong>Running on CPU</strong> - Generation takes 2-5 minutes per image
+            </div>
+            <div class="form-group">
+                <label>Prompt</label>
+                <textarea id="prompt" placeholder="A majestic dragon flying over a crystal castle at sunset..."></textarea>
+            </div>
+            <div class="row">
+                <div class="form-group">
+                    <label>Width</label>
+                    <input type="number" id="width" value="512" min="256" max="768" step="64">
+                </div>
+                <div class="form-group">
+                    <label>Height</label>
+                    <input type="number" id="height" value="512" min="256" max="768" step="64">
+                </div>
+                <div class="form-group">
+                    <label>Seed (-1 = random)</label>
+                    <input type="number" id="seed" value="-1">
+                </div>
+            </div>
+            <button id="generateBtn" onclick="generate()">🚀 Generate Image</button>
+            <p id="status"></p>
+            <div class="result" id="result" style="display:none;">
+                <img id="resultImg" src="" alt="Generated image">
+                <p id="resultInfo"></p>
+            </div>
+        </div>
+        <script>
+            async function generate() {
+                const btn = document.getElementById('generateBtn');
+                const status = document.getElementById('status');
+                const result = document.getElementById('result');
+                btn.disabled = true;
+                status.textContent = 'Generating... This will take 2-5 minutes...';
+                result.style.display = 'none';
+                try {
+                    const response = await fetch('/generate', {
+                        method: 'POST',
+                        headers: {'Content-Type': 'application/json'},
+                        body: JSON.stringify({
+                            prompt: document.getElementById('prompt').value,
+                            width: parseInt(document.getElementById('width').value),
+                            height: parseInt(document.getElementById('height').value),
+                            seed: parseInt(document.getElementById('seed').value),
+                            num_steps: 8
+                        })
+                    });
+                    const data = await response.json();
+                    if (response.ok) {
+                        document.getElementById('resultImg').src = 'data:image/png;base64,' + data.image_base64;
+                        document.getElementById('resultInfo').textContent = 'Seed: ' + data.seed;
+                        result.style.display = 'block';
+                        status.textContent = '✅ Done!';
+                    } else {
+                        status.textContent = '❌ Error: ' + (data.detail || 'Unknown error');
+                    }
+                } catch (e) {
+                    status.textContent = '❌ Error: ' + e.message;
+                }
+                btn.disabled = false;
+            }
+        </script>
+    </body>
+    </html>
+    """
+@app.post("/generate", response_model=GenerateResponse)
+async def generate(request: GenerateRequest):
+    """Generate an image from text prompt"""
+    import torch
+    try:
+        pipeline = load_model()
+        seed = request.seed
+        if seed == -1:
+            seed = random.randint(0, 2147483647)
+        generator = torch.Generator("cpu").manual_seed(seed)
+        width = min(max(request.width, 256), 768)
+        height = min(max(request.height, 256), 768)
+        print(f"Generating: '{request.prompt[:50]}...' at {width}x{height}, seed={seed}")
+        result = pipeline(
+            prompt=request.prompt,
+            width=width,
+            height=height,
+            num_inference_steps=request.num_steps,
+            guidance_scale=0.0,
+            generator=generator,
+        )
+        image = result.images[0]
+        # Convert to base64
+        buffer = io.BytesIO()
+        image.save(buffer, format="PNG")
+        image_base64 = base64.b64encode(buffer.getvalue()).decode()
+        gc.collect()
+        return GenerateResponse(
+            image_base64=image_base64,
+            seed=seed,
+            status="success"
+        )
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=str(e))
+@app.get("/health")
+async def health():
+    return {"status": "ok"}
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)

requirements.txt ADDED Viewed

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+# Z-Image-Turbo API Requirements
+# Fix numpy first (torch needs numpy<2)
+numpy<2
+# PyTorch
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch>=2.5.0
+# Diffusers from source (has Z-Image support)
+git+https://github.com/huggingface/diffusers
+# Other deps
+transformers>=4.40.0
+accelerate>=0.30.0
+safetensors
+sentencepiece
+# FastAPI (no Gradio!)
+fastapi
+uvicorn[standard]
+python-multipart
+# Image processing
+Pillow
+# Utils
+huggingface_hub