lvjiameng/GalMeasDiff

GalaxyDiff Release

Self-contained Measurement Alignment (MA) inference package for conditional galaxy image generation.

Given 8 physical parameters, the package:

1. Samples a 64×64 image with a conditional diffusion model
2. Upscales to 224×224 with SwinIR
3. Measures the image with a frozen regression proxy
4. Reports consistency metrics (MAE / RMSE / MAE(_z))

All paths resolve under this folder (Paper_code/GalaxyDiff_release/). No external data download is required once weights/ is present (~2.5 GB).

---

Pipeline

8-D conditions c
      │
      ▼
 CondNorm (z-score)
      │
      ▼
 MA diffusion  ──►  x₆₄  ──►  SwinIR SR  ──►  x₂₂₄
                                              │
                                              ▼
                                    frozen Reg proxy R(·)
                                              │
                                              ▼
                                         ĉ  vs  c
                                   MAE / RMSE / MAE_z

---

Layout

GalaxyDiff_release/
├── generate_and_eval.py      # CLI entry
├── run.sh                    # one-command demo
├── demo_generate_and_eval.ipynb
├── README.md
├── ma/                       # release helpers
│   ├── pipeline.py           # end-to-end generate → SR → measure
│   ├── cond.py               # 8-D parse / CondNorm
│   ├── joint_ckpt.py         # load joint diffusion+SwinIR ckpt
│   ├── ddim.py               # fast sampling
│   ├── sr.py                 # SwinIR wrap
│   └── metrics.py            # MAE / RMSE / MAE_z
├── vendor/                   # model code only (no training)
│   ├── Daldiff_cond_norm/
│   ├── SwinIR-main/
│   └── Regression/
├── weights/                  # checkpoints (see MANIFEST.json)
│   ├── 00003000-joint.pt
│   ├── best_regression_head.pt
│   ├── cond_norm_stats.json
│   └── backbone/             # DINOv3 + LoRA / LN finetune
├── scripts/
│   └── assemble_bundle.sh    # pull weights/vendor from this repo (once)
└── runs/                     # example outputs

---

Quick start

cd /home/zhangbi/LJM/diffusion/Paper_code/GalaxyDiff_release

# If weights/ is empty, assemble once from the parent repo:
bash scripts/assemble_bundle.sh

# Demo (GPU 0)
bash run.sh

Or call the CLI directly:

CUDA_VISIBLE_DEVICES=0 python generate_and_eval.py \
  --cond-values "0.0784,144.40,310.56,551.57,-0.3088,0.2504,5.1633,2.2935" \
  --n-samples 4 \
  --outdir runs/demo \
  --sampling-timesteps 50

Interactive walkthrough: open demo_generate_and_eval.ipynb from this directory.

---

CLI options

Flag	Default	Meaning
--cond-values	(required)	8 comma-separated floats (order below)
--n-samples	4	number of stochastic samples
--outdir	runs/demo	output directory
--device	cuda	cuda or cpu
--rank	0	GPU index for DataParallel
--base-seed	42	RNG seed for sampling
--batch-size	4	generation batch size
--sampling-timesteps	50	DDIM steps; 0 or 1000 → full 1000-step DDPM
--mc-samples	10	MC Dropout passes in the proxy
--skip-generate	off	reuse existing LR images in outdir
--skip-sr	off	skip SwinIR (LR-only path)

run.sh env overrides: COND_VALUES, N_SAMPLES, OUTDIR, SAMPLING_TIMESTEPS, MC_SAMPLES, CUDA_VISIBLE_DEVICES.

---

Input conditions (fixed order)

#	Name	Meaning
1	redshift	spectroscopic / photometric redshift
2	flux_g	g-band flux
3	flux_r	r-band flux
4	flux_z	z-band flux
5	shape_e1	ellipticity (e_1)
6	shape_e2	ellipticity (e_2)
7	shape_r	effective radius (r_e)
8	sersic	Sérsic index

Values are physical units (not z-scored). CondNorm stats live in weights/cond_norm_stats.json.

---

Outputs (--outdir)

Path	Content
lr/output/*.png	generated 64×64 images
sr/sr/*.png	224×224 super-resolved images
param_comparison.csv	input (c) vs measured (\hat{c})
summary.json / metrics.json	MAE, RMSE, MAE(_z) (when (N\ge 2))
input_cond.json	echo of the requested conditions

---

Weights

See weights/MANIFEST.json:

File	Role
00003000-joint.pt	joint MA checkpoint (diffusion + SwinIR, step 3000)
best_regression_head.pt	frozen measurement proxy
cond_norm_stats.json	condition z-score mean / std
backbone/dinov3-vitl16-…	DINOv3 ViT-L/16
backbone/finetune_2addLN/	LoRA + LN adapters for the proxy

Approximate disk: ~2.5 GB under weights/.

---

Assemble from this repository

Only needed if weights/ or vendor/ are missing (e.g. after a fresh clone without the bundle):

cd /home/zhangbi/LJM/diffusion/Paper_code/GalaxyDiff_release
bash scripts/assemble_bundle.sh

The script copies / hardlinks diffusion, SwinIR, and regression code from diffusion_model/, and checkpoints from related Paper_code/experiment* paths.

---

Dependencies

torch
torchvision
transformers
timm
Pillow
numpy
pandas
matplotlib
tqdm

CUDA GPU recommended. Full DDPM (--sampling-timesteps 0) is much slower than DDIM 50.

---

Notes

• This package is inference-only; training lives in diffusin_model_fits/ and diffusion_model/.
• Folder was formerly named model_release; paths in older docs may still say that — use GalaxyDiff_release instead.
• Prefer --sampling-timesteps 50 for demos; use 1000 / 0 only when comparing to paper-quality DDPM sampling.