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# SPDX-License-Identifier: Apache-2.0
import csv
import json
# must be the very first lines!
import os
import statistics
import time
import urllib
import pytest
import torch
from diffusers import StableDiffusion3Pipeline
from loguru import logger
from ..clip_encoder import CLIPEncoder
from ..fid_score import calculate_fid_score
class SimpleProfiler:
def __init__(self):
self.times = {}
self.start_times = {}
def start(self, name):
self.start_times[name] = time.time()
if name not in self.times:
self.times[name] = []
def end(self, name):
if name in self.start_times:
elapsed = time.time() - self.start_times[name]
self.times[name].append(elapsed)
del self.start_times[name]
def get(self, name):
if name in self.times and self.times[name]:
return sum(self.times[name]) / len(self.times[name])
return 0.0
profiler = SimpleProfiler()
COCO_CAPTIONS_DOWNLOAD_PATH = "https://github.com/mlcommons/inference/raw/4b1d1156c23965172ae56eacdd8372f8897eb771/text_to_image/coco2014/captions/captions_source.tsv"
OUT_ROOT, RESULTS_FILE_NAME = "test_reports", "sd35_test_results.json"
@pytest.mark.parametrize(
"device_params",
[{"device": "cuda" if torch.cuda.is_available() else "cpu"}],
indirect=True,
)
@pytest.mark.parametrize(
"model_name, image_w, image_h, guidance_scale, num_inference_steps, max_sequence_length",
[
("stable-diffusion-3.5-large", 1024, 1024, 3.5, 40, 256),
],
)
@pytest.mark.parametrize("captions_path", ["captions.tsv"])
@pytest.mark.parametrize("coco_statistics_path", ["val2014.npz"])
def test_accuracy_sd35(
device_params,
model_name,
image_w,
image_h,
guidance_scale,
num_inference_steps,
max_sequence_length,
captions_path,
coco_statistics_path,
evaluation_range,
):
start_from, num_prompts = evaluation_range
prompts = sd35_get_prompts(captions_path, start_from, num_prompts)
logger.info(f"start inference from prompt index: {start_from} to {start_from + num_prompts}")
device = device_params.get("device", "cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")
if device == "cuda":
logger.info(f"GPU: {torch.cuda.get_device_name(0)}")
logger.info(f"GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")
model_id = "./sd35_large"
logger.info(f"Loading model: {model_id}")
logger.info(f"T5 text encoder enabled with max_sequence_length={max_sequence_length}")
pipeline = StableDiffusion3Pipeline.from_pretrained(
model_id,
torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32, # Use bfloat16 for H100
use_safetensors=True,
)
pipeline = pipeline.to(device)
logger.info(f"Pipeline loaded on {device}")
if device == "cuda":
# enable optimizations for H100
try:
pipeline.enable_attention_slicing()
logger.info("✓ Attention slicing enabled")
except Exception as e:
logger.warning(f"Could not enable attention slicing: {e}")
# enable VAE slicing for memory efficiency
try:
if hasattr(pipeline, "enable_vae_slicing"):
pipeline.enable_vae_slicing()
logger.info("✓ VAE slicing enabled")
except Exception as e:
logger.warning(f"Could not enable VAE slicing: {e}")
# set generator for reproducibility
generator = torch.Generator(device=device).manual_seed(0)
images = []
total_times = []
logger.info(f"Starting generation of {len(prompts)} images at {image_w}x{image_h}...")
for i, prompt in enumerate(prompts):
logger.info(f"Generating image {i+1}/{len(prompts)}: {prompt[:50]}...")
start_total = time.time()
profiler.start("denoising_loop")
# generate image with triple prompt (SD3.5 uses 3 text encoders)
with torch.no_grad():
generated_images = pipeline(
prompt=prompt,
prompt_2=prompt,
prompt_3=prompt,
negative_prompt="",
negative_prompt_2="",
negative_prompt_3="",
height=image_h,
width=image_w,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
max_sequence_length=max_sequence_length,
generator=generator,
output_type="pil",
).images
profiler.end("denoising_loop")
end_total = time.time()
total_time = end_total - start_total
total_times.append(total_time)
images.append(generated_images[0])
logger.info(f"Image {i+1} completed in {total_time:.2f}s")
# optional: save image
os.makedirs("generated_images", exist_ok=True)
generated_images[0].save(f"generated_images/image_{i+1}.png")
# calculate metrics
logger.info("Calculating CLIP scores...")
clip = CLIPEncoder()
clip_scores = [100 * clip.get_clip_score(prompts[i], img).item() for i, img in enumerate(images)]
average_clip_score = sum(clip_scores) / len(clip_scores)
deviation_clip_score = "N/A"
fid_score = "N/A"
# Calculate CLIP standard deviation if we have multiple prompts
if num_prompts >= 2:
deviation_clip_score = statistics.stdev(clip_scores)
logger.info(f"CLIP scores individual: {[f'{score:.2f}' for score in clip_scores]}")
# Calculate FID score if we have the COCO statistics file
if num_prompts >= 2 and os.path.isfile(coco_statistics_path):
logger.info("Calculating FID score...")
fid_score = calculate_fid_score(images, coco_statistics_path)
elif num_prompts >= 2 and not os.path.isfile(coco_statistics_path):
logger.warning(f"FID calculation skipped: COCO stats file not found at {coco_statistics_path}")
logger.info("To enable FID calculation, download COCO validation statistics:")
logger.info(
"wget https://github.com/mlcommons/inference/raw/4b1d1156c23965172ae56eacdd8372f8897eb771/text_to_image/tools/val2014.npz"
)
elif num_prompts < 2:
logger.info("FID calculation requires at least 2 images")
average_inference_time = sum(total_times) / len(total_times) if total_times else 0
min_inference_time = min(total_times) if total_times else 0
max_inference_time = max(total_times) if total_times else 0
# results
logger.info("=== RESULTS ===")
logger.info(f"Average CLIP Score: {average_clip_score:.2f}")
logger.info(f"CLIP Score Std Dev: {deviation_clip_score}")
logger.info(f"Average Inference Time: {average_inference_time:.2f}s")
logger.info(f"Min/Max Inference Time: {min_inference_time:.2f}s / {max_inference_time:.2f}s")
logger.info(f"FID Score: {fid_score}")
print(f"FID score: {fid_score}")
print(f"Average CLIP Score: {average_clip_score}")
print(f"Standard Deviation of CLIP Scores: {deviation_clip_score}")
# check if T5 encoder is being used
t5_enabled = hasattr(pipeline, "text_encoder_3") and pipeline.text_encoder_3 is not None
logger.info(f"T5 text encoder enabled: {t5_enabled}")
# ensure values are JSON serializable
clip_std_serializable = float(deviation_clip_score) if deviation_clip_score != "N/A" else "N/A"
fid_serializable = float(fid_score) if fid_score != "N/A" else "N/A"
data = {
"model": model_name,
"metadata": {
"device": device.upper(),
"model_name": model_name,
"actual_model": model_id,
"start_from": start_from,
"num_prompts": num_prompts,
"image_width": image_w,
"image_height": image_h,
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
"max_sequence_length": max_sequence_length,
"t5_enabled": t5_enabled,
"backend": "diffusers",
"dtype": str(torch.bfloat16 if device == "cuda" else torch.float32),
"optimizations": ["attention_slicing", "vae_slicing"],
},
"benchmarks_summary": [
{
"device": device.upper(),
"model": model_name,
"average_denoising_time": profiler.get("denoising_loop"),
"average_vae_time": 0.0, # VAE time is included in denoising for diffusers
"average_inference_time": average_inference_time,
"min_inference_time": min_inference_time,
"max_inference_time": max_inference_time,
"average_clip": average_clip_score,
"deviation_clip": clip_std_serializable,
"fid_score": fid_serializable,
"individual_clip_scores": [float(score) for score in clip_scores],
}
],
}
os.makedirs(OUT_ROOT, exist_ok=True)
with open(f"{OUT_ROOT}/{RESULTS_FILE_NAME}", "w") as f:
json.dump(data, f, indent=4)
logger.info(f"test results saved to {OUT_ROOT}/{RESULTS_FILE_NAME}")
# cleanup
if device == "cuda":
torch.cuda.empty_cache()
print(f"\n🎉 Test completed successfully!")
print(f"📊 Average CLIP Score: {average_clip_score:.2f}")
print(f"⚡ Average Inference Time: {average_inference_time:.2f}s")
print(f"🖼️ Images saved to: generated_images/")
def sd35_get_prompts(captions_path, start_from, num_prompts):
assert (
0 <= start_from < 5000 and start_from + num_prompts <= 5000
), "start_from must be between 0 and 4999, and start_from + num_prompts must not exceed 5000."
prompts = []
if not os.path.isfile(captions_path):
logger.info(f"file {captions_path} not found. downloading...")
os.makedirs(os.path.dirname(captions_path), exist_ok=True)
urllib.request.urlretrieve(COCO_CAPTIONS_DOWNLOAD_PATH, captions_path)
logger.info("download complete.")
with open(captions_path, "r") as tsv_file:
reader = csv.reader(tsv_file, delimiter="\t")
next(reader) # skip header
for index, row in enumerate(reader):
if index < start_from:
continue
if index >= start_from + num_prompts:
break
prompts.append(row[2])
return prompts
# fixture for device parameters
@pytest.fixture
def device_params(request):
return request.param
# python -m pytest model.py::test_accuracy_sd35 -v --start-from=0 --num-prompts=5 -s
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