local-image-gen

Local Image Generator

Generate images locally using Stable Diffusion. Auto-detects your hardware and picks the optimal model, device, and resolution.

Phase 0: Detect Compute Environment

Run this at the start of every invocation. It determines everything downstream.

python3 -c "
import platform, shutil, subprocess, json

info = {'os': platform.system(), 'arch': platform.machine(), 'ram_gb': 0, 'gpu': 'none', 'vram_gb': 0, 'device': 'cpu', 'dtype': 'float32'}

# RAM
try:
    if platform.system() == 'Darwin':
        import os; info['ram_gb'] = round(os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES') / (1024**3))
    elif platform.system() == 'Linux':
        with open('/proc/meminfo') as f:
            for line in f:
                if line.startswith('MemTotal'):
                    info['ram_gb'] = round(int(line.split()[1]) / (1024**2))
                    break
    else:
        import ctypes
        mem = ctypes.c_ulonglong(0)
        ctypes.windll.kernel32.GetPhysicallyInstalledMemory(ctypes.byref(mem))
        info['ram_gb'] = round(mem.value / (1024**2))
except: pass

# GPU detection
try:
    import torch
    if torch.cuda.is_available():
        info['gpu'] = torch.cuda.get_device_name(0)
        info['vram_gb'] = round(torch.cuda.get_device_properties(0).total_mem / (1024**3))
        info['device'] = 'cuda'
        info['dtype'] = 'float16'
    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
        info['gpu'] = 'Apple Silicon (MPS)'
        info['vram_gb'] = info['ram_gb']  # unified memory
        info['device'] = 'mps'
        info['dtype'] = 'float16'
    elif hasattr(torch, 'hip') or 'AMD' in str(getattr(torch, '_C', '')):
        info['gpu'] = 'AMD (ROCm)'
        info['device'] = 'cuda'  # ROCm uses cuda API
        info['dtype'] = 'float16'
except ImportError:
    pass

print(json.dumps(info))
"

Parse the JSON output and store it internally as COMPUTE. Present the results to the user:

Detected hardware:

• OS: {os} ({arch})
• RAM: {ram_gb} GB
• GPU: {gpu} ({vram_gb} GB VRAM)
• Compute device: {device}

Model Selection Matrix

Based on the detected hardware, recommend a model from this table:

Condition	Recommended Model	Reason
VRAM >= 8 GB (CUDA or MPS)	stabilityai/sdxl-turbo	Best quality, fast with GPU
VRAM 4-7 GB (CUDA)	stabilityai/sd-turbo	Lighter model, fits in low VRAM
VRAM < 4 GB or CPU + RAM >= 16 GB	stabilityai/sd-turbo + CPU offload	Slow but works
CPU + RAM < 16 GB	segmind/tiny-sd	Smallest model, runs on anything

Present the recommendation and let the user choose:

AskUserQuestion: "Which image generation model should I use?"
Options:
- [Recommended model] (Recommended) — [reason based on their hardware]
- SDXL-Turbo — Best quality, needs 8+ GB VRAM, ~6 GB download
- SD-Turbo — Good quality, needs 4+ GB VRAM, ~3 GB download
- Tiny-SD — Lower quality, runs on any hardware, ~1 GB download

Store the chosen model as MODEL.

Resolution Selection

Based on model and VRAM:

VRAM	SDXL-Turbo	SD-Turbo	Tiny-SD
>= 16 GB	1200x640	1200x640	768x408
8-15 GB	1024x576	1200x640	768x408
4-7 GB	N/A	768x408	512x272
CPU	N/A	512x272	512x272

Steps Selection

Device	SDXL-Turbo	SD-Turbo	Tiny-SD
CUDA	4-6	4-6	20-30
MPS	6	6	25
CPU	6-8	6-8	30-40

Phase 1: Determine What to Generate

If the user provided a slug and prompt (argument after the skill name), parse them and skip to Phase 3.

Expected argument format: {slug} {prompt} (e.g., beginners-guide-to-rag abstract knowledge retrieval system with floating documents)

If only a slug was provided, read the blog post to generate an appropriate prompt:

# Find the blog post
cat content/blog/{SLUG}.mdx 2>/dev/null | head -50

Extract the title, description, and key themes. Generate a prompt using one of the SAI style templates below that best fits the post's topic. Each image MUST use a different style to avoid visual repetition across blog posts.

SAI Style Templates (pick ONE per image)

Each template wraps your subject description in a distinct visual style. Replace {subject} with a short, vivid description of the post's core concept as a visual metaphor.

Style	Template	Best for
Isometric	isometric style {subject}. vibrant, beautiful, crisp, detailed, ultra detailed, intricate	Architecture, systems, infrastructure
Low-poly	low-poly style {subject}. low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition	Tutorials, beginner guides, fundamentals
Neonpunk	neonpunk style {subject}. cyberpunk, vaporwave, neon, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic	AI/ML, cutting-edge tech, future-facing
Concept art	concept art {subject}. digital artwork, illustrative, painterly, matte painting, highly detailed	Opinion pieces, deep dives, strategy
Line art	line art drawing {subject}. professional, sleek, modern, minimalist, graphic, line art, vector graphics	Comparisons, frameworks, decision guides
3D model	professional 3d model {subject}. octane render, highly detailed, volumetric, dramatic lighting	Product/tool reviews, practical guides
Fantasy	ethereal fantasy concept art of {subject}. magnificent, celestial, ethereal, painterly, epic, majestic, magical	Vision pieces, thought leadership
Cinematic	cinematic film still {subject}. shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous	Case studies, real-world stories

Subject Description Guidelines

Write the {subject} as a vivid visual metaphor, not a literal description. Never include hands, fingers, faces, or human figures.

• Good: "a crystalline data pipeline splitting light into rainbow streams"
• Bad: "data pipeline architecture diagram"
• Good: "mechanical clockwork gears meshing with glowing circuit traces"
• Bad: "AI system with nodes and connections"

Vary across posts: color palette, physical metaphor (clockwork, rivers, crystals, bridges, constellations), and composition.

If no input was provided, use AskUserQuestion to ask for a slug and description.

Phase 2: Confirm with User

Present the generation plan:

I'll generate a hero image for {slug}: Model: {MODEL} on {device} Resolution: {width}x{height} ({steps} steps) Prompt: "{prompt}" Seed: {seed or "random"} Estimated time: {estimate based on device and model}

Want me to adjust anything before generating?

Time estimates:

Device	SDXL-Turbo	SD-Turbo	Tiny-SD
CUDA (RTX 3060+)	5-10s	3-8s	15-25s
MPS (M1/M2/M3/M4)	25-35s	15-25s	30-45s
CPU (16GB+ RAM)	3-8 min	2-5 min	5-10 min

Phase 3: Install Dependencies

Check and install what's needed based on platform:

# Check Python + torch
python3 -c "import torch; print(torch.__version__)" 2>&1

If torch is missing, install based on platform:

Platform	Install command
macOS (MPS)	pip3 install torch torchvision
Linux (CUDA)	pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121
Linux (ROCm)	pip3 install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.0
Linux/Windows (CPU)	pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
Windows (CUDA)	pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121

Then install diffusers:

pip3 install diffusers accelerate Pillow

Phase 4: Generate Image

Run inline Python generation (no external script needed):

import torch
from diffusers import AutoPipelineForText2Image
from PIL import Image
import os

MODEL = "{MODEL}"
DEVICE = "{device}"
DTYPE = torch.float16 if "{dtype}" == "float16" else torch.float32
WIDTH = {width}
HEIGHT = {height}
STEPS = {steps}
PROMPT = "{PROMPT}"
SEED = {SEED}
SLUG = "{SLUG}"

pipe = AutoPipelineForText2Image.from_pretrained(MODEL, torch_dtype=DTYPE, variant="fp16" if DTYPE == torch.float16 else None)
pipe = pipe.to(DEVICE)

if DEVICE == "cuda":
    pipe.enable_attention_slicing()  # Reduce VRAM usage

generator = torch.Generator(device="cpu").manual_seed(SEED)

image = pipe(prompt=PROMPT, num_inference_steps=STEPS, guidance_scale=0.0, width=WIDTH, height=HEIGHT, generator=generator).images[0]

out_dir = f"public/blog/{SLUG}"
os.makedirs(out_dir, exist_ok=True)
image.save(f"{out_dir}/hero.jpg", "JPEG", quality=90)
print(f"Saved to {out_dir}/hero.jpg ({WIDTH}x{HEIGHT})")

Run in the background for GPU, or warn the user about wait time for CPU.

For batch generation, loop over entries with the model loaded once.

Phase 5: Verify and Present

After generation:

1. Check the output file exists:

file public/blog/{SLUG}/hero.jpg

2. Show the image to the user using the Read tool.

3. Ask:

AskUserQuestion: "How does this look?"
Options:
- Looks good, use it
- Regenerate with a different seed
- Adjust the prompt and try again
- Try a different model
- Discard

If "try a different model": go back to Phase 0's model selection and re-run.

SDXL-Turbo / SD-Turbo Prompting Rules

Critical: These turbo models use guidance_scale=0.0, which means negative prompts are IGNORED. All steering must come from the positive prompt alone.

DO:

• Always lead with a SAI style prefix (see table above)
• Use vivid, concrete visual metaphors as the subject
• Specify a dominant color palette in the subject

DON'T:

• Don't include hands, fingers, or human body parts (renders badly)
• Don't include faces or people as focal subjects
• Don't request text or labels (diffusion models can't render text reliably)
• Don't rely on negative prompts (ignored at guidance_scale=0.0)

Error Handling

Error	Cause	Fix
torch not found	Python deps missing	Install per platform table above
CUDA out of memory	Model too large for VRAM	Switch to smaller model or reduce resolution
MPS out of memory	Not enough unified memory	Close other apps, reduce resolution
RuntimeError: slow_conv2d_cpu	Running on CPU without float32	Set DTYPE = torch.float32 for CPU
Black/noisy image	Bad seed or too few steps	Try a different seed or increase steps
Model download fails	Network issue	Check connection, model is cached at ~/.cache/huggingface/ after first download