---
license: apache-2.0
datasets:
- TopAI-1/Image-Dataset
language:
- en
pipeline_tag: text-to-image
library_name: transformers
tags:
- art
---

![image](https://cdn-uploads.huggingface.co/production/uploads/6883b03536f0c50bc30bda75/M3WTacD5y08KCgTHaCD0e.png)

# Pixel-1: From-Scratch Text-to-Image Generator 🎨

Pixel-1 is a lightweight, experimental text-to-image model built and trained entirely from scratch. Unlike many modern generators that rely on massive pre-trained diffusion backbones, Pixel-1 explores the potential of a compact architecture to understand and render complex semantic prompts.

## 🚀 The Achievement
Pixel-1 was designed to prove that even a small model can achieve high logical alignment with user prompts. It successfully renders complex concepts like **window bars**, **fence shadows**, and **specific color contrasts**—features usually reserved for much larger models.

### Key Features:
* **Built from Scratch:** The Generator architecture (Upsampling, Residual Blocks, and Projections) was designed and trained without pre-trained image weights.
* **High Prompt Adherence:** Exceptional ability to "listen" to complex instructions (e.g., "Window with metal bars and fence shadow").
* **Efficient Architecture:** Optimized for fast inference and training on consumer-grade GPUs (like Kaggle's T4).
* **Latent Understanding:** Uses a CLIP-based text encoder to bridge the gap between human language and pixel space.

---

## 🏗️ Architecture
The model uses a series of Transposed Convolutional layers combined with Residual Blocks to upsample a latent text vector into a 128x128 image.

* **Encoder:** CLIP (OpenAI/clip-vit-large-patch14)
* **Decoder:** Custom CNN-based Generator with Skip Connections
* **Loss Function:** L1/MSE transition
* **Resolution:** 128x128 (v1)



---

## 🖼️ Samples & Prompting
Pixel-1 shines when given high-contrast, descriptive prompts. 

**Recommended Prompting Style:**
> *"Window with metal bars and fence shadow, high contrast, vivid colors, detailed structure"*

**Observations:**
While the current version (v1) produces stylistic, slightly "painterly" or "pixelated" results, its spatial reasoning is remarkably accurate, correctly placing shadows and structural elements according to the text.

---

## 🛠️ How to use
```python
import torch
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
from transformers import AutoTokenizer, CLIPTextModel, AutoModel, AutoConfig

def generate_fixed_from_hub(prompt, model_id="TopAI-1/Pixel-1"):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"🚀 Working on {device}...")

    # 1. ניקוי Cache כדי לוודא שאתה מושך את התיקונים החדשים מה-Hub
    cache_path = os.path.expanduser(f"~/.cache/huggingface/hub/models--{model_id.replace('/', '--')}")
    if os.path.exists(cache_path):
        print("🧹 Clearing old cache to fetch your latest fixes...")
        shutil.rmtree(cache_path)

    # 2. טעינת CLIP
    tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-large-patch14")
    text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(device)

    # 3. טעינת המודל והקונפיג אוטומטית מה-Hub
    # בזכות ה-auto_map ב-config.json, transformers ימצא לבד את המחלקות
    print("📥 Downloading architecture and weights directly from Hub...")
    model = AutoModel.from_pretrained(
        model_id, 
        trust_remote_code=True, 
        force_download=True
    ).to(device)
    
    model.eval()
    print("✅ Model loaded successfully!")

    # 4. יצירה
    print(f"🎨 Generating: {prompt}")
    inputs = tokenizer(prompt, padding="max_length", max_length=77, truncation=True, return_tensors="pt").to(device)
    
    with torch.no_grad():
        emb = text_encoder(inputs.input_ids).pooler_output
        out = model(emb)

    # 5. תצוגה
    img = (out.squeeze(0).cpu().permute(1, 2, 0).numpy() + 1.0) / 2.0
    plt.figure(figsize=(8, 8))
    plt.imshow(np.clip(img, 0, 1))
    plt.axis('off')
    plt.show()

# הרצה
generate_fixed_from_hub("Window with metal bars and fence shadow")