Skip to content

inference.md

Latest commit

 

History

History
135 lines (105 loc) · 3.96 KB

inference.md

File metadata and controls

135 lines (105 loc) · 3.96 KB

Inference Guide

Quick Start

# Download checkpoint first
python scripts/download_checkpoint.py

# Basic inference
python src/inference.py \
  --prompt "a beautiful sunset over mountain peaks" \
  --checkpoint checkpoints/sd_epoch_042.pt

# Apple Silicon (MPS) — auto-detected
python src/inference.py \
  --prompt "a cat wearing a spacesuit" \
  --checkpoint checkpoints/sd_epoch_042.pt

Usage Options

Basic Parameters

Flag Default Description
--prompt (required) Text prompt
--batch — .txt file with one prompt per line (overrides --prompt)
--negative "" Negative prompt for CFG
--checkpoint sd_epoch_042.pt Path to checkpoint
--steps 50 DDIM steps: 25 (fast), 50 (good), 100 (best)
--guidance 7.5 CFG scale: 1.0 (no guidance), 7.5 (balanced), 15+ (high)
--seed 42 Random seed for reproducibility
--width / --height 512 Output dimensions (must be multiples of 8)
--batch_size 1 Images per generation call (for batch prompts)
--output output.png Output filename (single prompt)
--output_dir ./outputs Output directory (batch mode)

Examples

# High quality, deterministic
python src/inference.py \
  --prompt "a cinematic shot of a mountain lake at sunrise, professional photography" \
  --steps 100 --guidance 7.5 --seed 42

# With negative prompt
python src/inference.py \
  --prompt "a portrait of a woman" \
  --negative "blurry, low quality, deformed hands, extra fingers" \
  --steps 50 --guidance 9.0

# Batch mode — generate from 100 prompts
python src/inference.py \
  --batch prompts.txt \
  --output_dir ./generated \
  --steps 50 --guidance 7.5

# Stochastic sampling (DDPM-like, more variety)
python src/inference.py \
  --prompt "fantasy landscape" \
  --eta 1.0 --seed 999

Scripts

src/inference.py — Apple Silicon + CUDA

Designed for MacBook (MPS) and single-GPU inference. Automatically detects MPS, CUDA, or CPU.

Features:

  • Automatic device selection
  • Negative prompt support
  • Batch processing from text file
  • DDIM with optional stochastic (eta)
  • EMA weight loading from checkpoint

src/SD_ImageGen.py — Alternative CLI

Full-featured CLI with additional options:

  • Supports both raw UNet weights and EMA shadow weights
  • Negative prompt per-sample broadcasting
  • autocast BF16 on CUDA
  • Image grid generation for multiple outputs

src/generate.py — Programmatic API

from generate import generate_images

images = generate_images(
    prompts=["a cosmic nebula with vibrant colors"],
    checkpoint_path="checkpoints/sd_epoch_042.pt",
    num_steps=50,
    guidance_scale=7.5,
    seed=42,
    device="cuda",           # or "mps", "cpu"
)
images[0].save("nebula.png")

DDIM Parameters

Steps

Steps Quality Speed
25 Good 2× faster
50 Recommended Baseline
100 Excellent 2× slower
200+ Diminishing Not worth it

Eta (Stochasticity)

Eta Behavior
0.0 Deterministic — same seed always produces the same image
0.5 Moderate stochasticity — small variations
1.0 DDPM-like — maximum variety, but may lose fidelity

CFG Scale

Scale Effect
1.0 No guidance — pure model prior, often blurry/unrelated
5.0–7.5 Balanced — recommended range
9.0–12.0 Strong guidance — more prompt alignment, may oversaturate
15.0+ Excessive — often produces artifacts, burned-in look

Output Quality Tips

  1. Use descriptive prompts: "a cinematic shot of..." works better than "a photo of..."
  2. Negative prompts help: Common negatives: "blurry, low quality, deformed, extra limbs, bad anatomy, ugly, text, watermark"
  3. Seed selection: For a given prompt, try seeds 0–20 and pick the best
  4. Steps vs. quality: 50 steps is usually sufficient; 100+ gives marginal gains
  5. CFG tuning: Start at 7.5, adjust ±2 based on output character