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1951446 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | #!/usr/bin/env python3
"""
Auto quality check: generate full-quality 1024px samples, compare with previous step, upload to HF.
Run after each 2000-step checkpoint.
"""
import sys
import time
import json
import shutil
from pathlib import Path
import torch
import numpy as np
from PIL import Image
PROMPTS = [
"a beautiful mountain landscape at sunset, 4k, highly detailed, cinematic lighting",
"a cute cat sitting on a windowsill, natural lighting, sharp focus",
"a futuristic city skyline at night with neon lights, cyberpunk, ultra detailed",
"portrait of a woman with flowers in her hair, oil painting style, masterpiece",
]
CKPT_DIR = Path("/data0/checkpoints/flux_lora_train")
OUTPUT_DIR = Path("/data1/outputs/quality_check")
HISTORY_FILE = OUTPUT_DIR / "history.json"
def load_history():
if HISTORY_FILE.exists():
return json.loads(HISTORY_FILE.read_text())
return {}
def save_history(history):
HISTORY_FILE.write_text(json.dumps(history, indent=2))
def analyze_image(img):
arr = np.array(img).astype(float)
r, g, b = arr[:,:,0].mean(), arr[:,:,1].mean(), arr[:,:,2].mean()
std = arr.std()
gray = arr.mean(axis=2)
dx = np.diff(gray, axis=1)
dy = np.diff(gray, axis=0)
edge = (np.abs(dx).mean() + np.abs(dy).mean()) / 2
sat = (arr.max(axis=2) - arr.min(axis=2)).mean()
return {"std": round(std, 1), "edge": round(edge, 1), "sat": round(sat, 1), "rgb": f"({r:.0f},{g:.0f},{b:.0f})"}
def generate_full_quality(step):
from diffusers import FluxTransformer2DModel, FluxPipeline
from peft import LoraConfig, get_peft_model, set_peft_model_state_dict
import safetensors.torch
ckpt_path = CKPT_DIR / f"checkpoint-{step}" / "adapter_model.safetensors"
if not ckpt_path.exists():
print(f" Checkpoint {step} not found")
return None
print(f" Loading Flux Dev + LoRA (step {step})...")
transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev", subfolder="transformer",
torch_dtype=torch.bfloat16, cache_dir="/data0/models",
)
lora_target_modules = [
"attn.to_q", "attn.to_k", "attn.to_v", "attn.to_out.0",
"attn.add_k_proj", "attn.add_q_proj", "attn.add_v_proj", "attn.to_add_out",
"ff.net.0.proj", "ff.net.2", "ff_context.net.0.proj", "ff_context.net.2",
]
lora_config = LoraConfig(r=128, lora_alpha=64, target_modules=lora_target_modules, lora_dropout=0.0)
transformer = get_peft_model(transformer, lora_config)
state_dict = safetensors.torch.load_file(str(ckpt_path))
set_peft_model_state_dict(transformer, state_dict)
transformer.eval()
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer,
torch_dtype=torch.bfloat16, cache_dir="/data0/models",
)
pipe = pipe.to("cuda:0")
step_dir = OUTPUT_DIR / f"step_{step}"
step_dir.mkdir(parents=True, exist_ok=True)
results = []
print(f" Generating 4 images (1024px, 28 steps, guidance 3.5)...")
for i, prompt in enumerate(PROMPTS):
image = pipe(prompt=prompt, num_inference_steps=28, guidance_scale=3.5, height=1024, width=1024).images[0]
image.save(step_dir / f"sample_{i}.png")
stats = analyze_image(image)
stats["prompt"] = prompt[:50]
results.append(stats)
print(f" Sample {i}: std={stats['std']}, sat={stats['sat']}, edge={stats['edge']}")
del pipe, transformer
torch.cuda.empty_cache()
return results
def compare_and_report(step, current_stats, history):
print(f"\n{'='*60}")
print(f" REPORT: Step {step}")
print(f"{'='*60}")
avg_std = np.mean([s["std"] for s in current_stats])
avg_edge = np.mean([s["edge"] for s in current_stats])
avg_sat = np.mean([s["sat"] for s in current_stats])
print(f" Avg std={avg_std:.1f}, edge={avg_edge:.1f}, sat={avg_sat:.1f}")
# Compare with previous
steps = sorted([int(k) for k in history.keys()])
if steps:
prev_step = steps[-1]
prev = history[str(prev_step)]
prev_avg_std = np.mean([s["std"] for s in prev["stats"]])
prev_avg_edge = np.mean([s["edge"] for s in prev["stats"]])
prev_avg_sat = np.mean([s["sat"] for s in prev["stats"]])
print(f"\n vs Step {prev_step}:")
print(f" Std: {prev_avg_std:.1f} → {avg_std:.1f} {'↑' if avg_std > prev_avg_std else '↓'} ({avg_std-prev_avg_std:+.1f})")
print(f" Edge: {prev_avg_edge:.1f} → {avg_edge:.1f} {'↑' if avg_edge > prev_avg_edge else '↓'} ({avg_edge-prev_avg_edge:+.1f})")
print(f" Sat: {prev_avg_sat:.1f} → {avg_sat:.1f} {'↑' if avg_sat > prev_avg_sat else '↓'} ({avg_sat-prev_avg_sat:+.1f})")
# Verdict
if avg_sat > 40 and avg_std > 50 and avg_edge > 5:
verdict = "GOOD"
elif avg_sat > 30 and avg_std > 40:
verdict = "OK"
else:
verdict = "NEEDS MORE TRAINING"
print(f"\n Verdict: {verdict}")
print(f"{'='*60}\n")
return {"avg_std": avg_std, "avg_edge": avg_edge, "avg_sat": avg_sat, "verdict": verdict}
def upload_to_hf(step):
from huggingface_hub import upload_folder
token = open('/home/adminuser/.cache/huggingface/token').read().strip()
repo_id = "memoryai/4k-training-samples"
step_dir = OUTPUT_DIR / f"step_{step}"
print(f" Uploading step_{step} to HF...")
upload_folder(
folder_path=str(step_dir),
repo_id=repo_id,
path_in_repo=f"step_{step}_fullquality",
repo_type="dataset",
token=token,
)
print(f" Uploaded!")
def main():
step = int(sys.argv[1]) if len(sys.argv) > 1 else None
if step is None:
# Auto-detect latest checkpoint
checkpoints = sorted(
[d for d in CKPT_DIR.iterdir() if d.is_dir() and d.name.startswith("checkpoint-")],
key=lambda p: int(p.name.split("-")[1]),
)
if not checkpoints:
print("No checkpoints found")
return
step = int(checkpoints[-1].name.split("-")[1])
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
history = load_history()
# Skip if already done
if str(step) in history:
print(f" Step {step} already evaluated")
return
print(f"\n=== Quality Check: Step {step} ===")
t0 = time.time()
stats = generate_full_quality(step)
if stats is None:
return
summary = compare_and_report(step, stats, history)
# Save to history
history[str(step)] = {"stats": stats, "summary": summary, "time": time.strftime("%Y-%m-%d %H:%M")}
save_history(history)
# Upload
upload_to_hf(step)
print(f" Total time: {time.time()-t0:.0f}s")
if __name__ == "__main__":
main()
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