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: {ramgb} GB > - GPU: {gpu} ({vramgb} 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 = 16 GB | stabilityai/sd-turbo + CPU offload | Slow but works | | CPU + RAM = 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

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

1. 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 |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sacredvoid
• Source: sacredvoid/skillkit
• License: MIT

Install and usage instructions live in the source repository linked above.