File size: 3,547 Bytes
95bf78b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | #!/usr/bin/env python3
"""Run controlled semantic inpainting evaluation from a Modal Volume candidate."""
from __future__ import annotations
import os
import subprocess
import sys
from pathlib import Path
import modal
APP_NAME = "clover-image-tiny-inpaint-semantic-eval"
OUTPUT_VOLUME_NAME = "clover-image-tiny-inpaint-output"
CACHE_VOLUME_NAME = "clover-image-tiny-inpaint-cache"
OUTPUT_ROOT = Path("/outputs")
CACHE_ROOT = Path("/cache")
image = (
modal.Image.debian_slim(python_version="3.11")
.pip_install(
"accelerate==1.14.0",
"diffusers==0.39.0",
"numpy==2.2.6",
"pillow==12.3.0",
"safetensors==0.8.0",
"torch==2.7.0",
"torchvision==0.22.0",
"transformers==4.57.6",
)
.add_local_dir("inpainting", remote_path="/root/inpainting")
)
output_volume = modal.Volume.from_name(OUTPUT_VOLUME_NAME, create_if_missing=True)
cache_volume = modal.Volume.from_name(CACHE_VOLUME_NAME, create_if_missing=True)
app = modal.App(
APP_NAME,
image=image,
volumes={
str(OUTPUT_ROOT): output_volume,
str(CACHE_ROOT): cache_volume,
},
)
@app.function(gpu="A10", timeout=3 * 60 * 60, cpu=4, memory=24576)
def evaluate(
candidate_name: str,
evaluation_name: str,
guidance_scale: float = 7.5,
steps: int = 30,
mask_crop_padding: int = 0,
) -> str:
candidate = OUTPUT_ROOT / candidate_name
if not (candidate / "training-complete.json").exists():
raise RuntimeError(f"Candidate is not complete: {candidate}")
destination = OUTPUT_ROOT / "evaluations" / evaluation_name
if destination.exists():
raise RuntimeError(f"Evaluation output already exists: {destination}")
env = os.environ.copy()
env.update(
{
"HF_HOME": str(CACHE_ROOT / "huggingface"),
"HF_HUB_CACHE": str(CACHE_ROOT / "huggingface" / "hub"),
"TOKENIZERS_PARALLELISM": "false",
}
)
command = [
sys.executable,
"-u",
"/root/inpainting/evaluate_semantic.py",
"--base_model",
"neonforestmist/Clover-Image-Tiny",
"--base_revision",
"63b0e9f6be9c00888ff464f342a9ef052bf76681",
"--baseline_model",
"neonforestmist/Clover-Image-Tiny-Inpaint",
"--baseline_revision",
"1b6f8ae3db51900520369d5522c7dc7c2a97e21e",
"--teacher_model",
"stable-diffusion-v1-5/stable-diffusion-inpainting",
"--teacher_revision",
"8a4288a76071f7280aedbdb3253bdb9e9d5d84bb",
"--teacher_variant",
"fp16",
"--candidate_model",
str(candidate),
"--clip_model",
"openai/clip-vit-base-patch32",
"--clip_revision",
"3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268",
"--output_dir",
str(destination),
"--guidance_scale",
str(guidance_scale),
"--steps",
str(steps),
"--mask_crop_padding",
str(mask_crop_padding),
]
subprocess.run(command, check=True, env=env)
output_volume.commit()
cache_volume.commit()
return str(destination)
@app.local_entrypoint()
def main(
candidate_name: str,
evaluation_name: str,
guidance_scale: float = 7.5,
steps: int = 30,
mask_crop_padding: int = 0,
) -> None:
result = evaluate.remote(
candidate_name,
evaluation_name,
guidance_scale,
steps,
mask_crop_padding,
)
print(f"Evaluation is available in Modal Volume {OUTPUT_VOLUME_NAME}: {result}")
|