dreamgen 1.8.0

Generate unlimited AI images locally with no subscriptions - your machine dreams with you

✨ DreamGen

Generate unlimited AI images locally with no subscriptions, no cloud APIs, and complete privacy. Your machine dreams with you! ✨

✨ Modern Web Interface

Beautiful, VS Code-inspired dark theme with real-time generation and organized galleries. The web interface features:

• 🎨 Smart Generation Dashboard - AI-enhanced prompts with contextual plugins
• 🖼️ Weekly Gallery Organization - Browse your creations by week with thumbnail previews
• ⚙️ Plugin Management - Configure time-aware and artistic enhancement plugins
• 📊 Real-time Status - Monitor API, GPU, and generation progress

🚀 Install

Option 1: Install the CLI from PyPI

Use this path if you want to run DreamGen as an installed command and do not need to edit the source code.

uv venv --python 3.11
source .venv/bin/activate  # Windows PowerShell: .venv\Scripts\Activate.ps1
uv pip install dreamgen

Verify the install without downloading image models or requiring a GPU:

dreamgen generate --mock

The plain uv pip install dreamgen command resolves from PyPI. For NVIDIA systems where you specifically want CUDA 12.4 PyTorch wheels from the PyTorch index, include the extra index during install:

uv pip install dreamgen --extra-index-url https://download.pytorch.org/whl/cu124

Option 2: Run from source

Use this path for development, local web UI work, or Docker review.

# Clone the repository
git clone https://github.com/Agentic-Insights/dreamgen
cd dreamgen

# Install Python dependencies
uv sync

# Configure the app
cp .env.example .env
# Edit .env for your machine:
# - OLLAMA_MODEL must point at a local Ollama model
# - OLLAMA_IMAGE_MODEL is optional and only used for IMAGE_BACKEND=ollama
# - HF_TOKEN is optional for small/turbo/smoke public models
# - IMAGE_BACKEND=qwen enables the NF4 Qwen-Image backend for text-heavy posters and signage
# - IMAGE_BACKEND=ernie enables ERNIE-Image-Turbo for prompt-enhanced multilingual text rendering
# - IMAGE_BACKEND=auto uses FLUX if cached, otherwise the small public fallback

# Generate from the CLI
uv run dreamgen generate

# Start the API (terminal 1)
uv run uvicorn src.api.server:app --host 127.0.0.1 --port 25800

# Start the web UI (terminal 2)
cd web-ui
npm install
npm run dev

The local dev UI runs at http://localhost:3000 and talks to the API at http://localhost:25800.

Source checkouts use uv run dreamgen .... PyPI installs use dreamgen ....

Option 3: Run with Docker Compose

For a production-style local run with the shipped ports and wiring:

cp .env.docker.example .env.docker
docker compose --env-file .env.docker up --build

That exposes:

• UI: http://localhost:7860
• API: http://localhost:25800
• API docs: http://localhost:25800/api/docs

For Z-Image review in Docker:

• put LoRAs under ./loras/<name>/*.safetensors
• use Settings → Models to download Z-Image-Turbo
• use Settings → Models to switch the active backend to Z-Image
• use Settings → Plugins to enable lora and then select active LoRAs in the Models panel

For Ollama-backed image generation:

• install at least one Ollama image model such as x/z-image-turbo or x/flux2-klein
• use Settings → Ollama to pick the Ollama prompt model and Ollama image model separately
• use Settings → Models to switch the active backend to Ollama Image

For Qwen-Image typography generation:

• use Settings → Models to download diffusers/qwen-image-nf4
• switch the active backend to Qwen-Image
• use prompts with explicit text such as signs, menus, posters, labels, or bilingual English/Chinese layouts
• set QWEN_IMAGE_MODEL=Qwen/Qwen-Image only on machines with enough memory for the full model
• enable QWEN_IMAGE_LIGHTNING=true only if you also want the optional Lightning LoRA few-step path

For ERNIE-Image-Turbo generation:

• use Settings → Models to download baidu/ERNIE-Image-Turbo
• switch the active backend to ERNIE-Image
• keep the prompt enhancer on for richer image prompts, or turn it off for stricter prompt following
• use prompts with visible English, Chinese, or Japanese text when you want to stress its text-in-image behavior

🔑 Why Choose This?

• 🏠 100% Local: No cloud APIs, no usage limits, complete privacy
• 🧠 Smart Prompts: AI-enhanced prompts with time, holidays, and art styles
• 🌐 Modern UI: Professional web interface with galleries and real-time updates
• 💰 Zero Cost: Generate unlimited images after initial setup
• 🔌 Extensible: Plugin system for custom prompt enhancements

🎮 Quick Commands

These examples assume a source checkout. If you installed from PyPI, drop uv run and run dreamgen ... directly.

# Generate a single image
uv run dreamgen generate

# Generate with interactive prompt refinement
uv run dreamgen generate --interactive

# Generate multiple images in a batch
uv run dreamgen loop --batch-size 10 --interval 300

# Use mock mode (no GPU required)
uv run dreamgen generate --mock

# Force the local Z-Image backend
uv run dreamgen generate --backend zimage

# Force Qwen-Image for text-rich posters and signs
uv run dreamgen generate --backend qwen

# Force ERNIE-Image-Turbo for prompt-enhanced multilingual text rendering
uv run dreamgen generate --backend ernie

# List prompt plugins
uv run dreamgen plugins list

# Get help
uv run dreamgen --help

🔧 Requirements

• Python 3.11+ with uv package manager
• Ollama for prompt generation (ollama.ai)
• Hugging Face Token for gated/private model downloads only
• GPU recommended: NVIDIA (8GB+ VRAM) or Apple Silicon

📖 Full Documentation

For detailed setup, Docker usage, and development workflow:

• docs/DOCKER.md
• docs/CONTRIBUTING.md

☁️ Optional: Cloudflare Hosting

DreamGen includes two Cloudflare Workers for free, global image hosting:

1. Single Image Worker (host-image/)

Purpose: Host a single showcase image from R2 storage Use Case: README badges, social media previews, landing pages

Features:

• Serves the most recent PNG from R2
• R2 bucket binding: DREAM_BUCKET → continuous-image-gen
• CORS enabled, 1-day cache
• Simple TypeScript worker

Setup:

cd host-image
npx wrangler deploy

Configuration (wrangler.jsonc):

{
  "name": "host-image",
  "main": "src/index.ts",
  "r2_buckets": [
    { "binding": "DREAM_BUCKET", "bucket_name": "continuous-image-gen" }
  ]
}

2. Gallery API Worker (cloudflare-gallery/)

Purpose: Full gallery API with listing and image retrieval Use Case: Web UI backend, public gallery, API integrations

Features:

• Dynamic routing via Cloudflare Pages Functions ([[path]].js)
• List endpoint: GET /api/images → returns sorted image keys
• Serve endpoint: GET /api/images/{path} → streams image files
• R2 bucket binding: GALLERY → dreamgen-gallery
• Auto-detects content types (png/jpg/webp/gif)
• CORS enabled, 1-year cache for images

Setup:

cd cloudflare-gallery
npx wrangler pages deploy public

Configuration (wrangler.toml):

name = "dreamgen-gallery"
pages_build_output_dir = "public"
[[r2_buckets]]
binding = "GALLERY"
bucket_name = "dreamgen-gallery"

API Endpoints:

# List all images (sorted by upload date, newest first)
curl https://your-worker.pages.dev/api/images

# Get specific image
curl https://your-worker.pages.dev/api/images/2024/week_52/image.png

Key Differences:

Feature	host-image	cloudflare-gallery
Type	Cloudflare Worker	Pages Function
Routing	Single endpoint	Dynamic catch-all
Images	1 hardcoded	Full R2 listing
Cache	1 day	1 year
Use Case	Static showcase	Dynamic gallery API

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