Qwen-Image-Layered-MRP-MLX/tests/image_generation/helpers/image_generation_test_helper.py
Filip Strand f9768b2e1c
Qwen image edit (#274)
Co-authored-by: Filip Strand <filip@Host-022.local>
2025-11-11 16:25:58 +01:00

97 lines
3.4 KiB
Python

import os
from pathlib import Path
from typing import Type, Union
from mflux.config.config import Config
from mflux.config.model_config import ModelConfig
from mflux.models.flux.variants.txt2img.flux import Flux1
from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage
from mflux.utils.image_compare import ImageCompare
class ImageGeneratorTestHelper:
@staticmethod
def assert_matches_reference_image(
reference_image_path: str,
output_image_path: str,
model_class: Type[Union[Flux1, QwenImage]],
model_config: ModelConfig,
prompt: str,
steps: int,
seed: int,
quantize=8,
height: int | None = None,
width: int | None = None,
image_path: str | None = None,
image_strength: float | None = None,
lora_paths: list[str] | None = None,
lora_scales: list[float] | None = None,
lora_names: list[str] | None = None,
lora_repo_id: str | None = None,
negative_prompt: str | None = None,
guidance: float | None = None,
):
# resolve paths
reference_image_path = ImageGeneratorTestHelper.resolve_path(reference_image_path)
output_image_path = ImageGeneratorTestHelper.resolve_path(output_image_path)
lora_paths = [str(ImageGeneratorTestHelper.resolve_path(p)) for p in lora_paths] if lora_paths else None
try:
# given
model_kwargs = {
"model_config": model_config,
"quantize": quantize,
"lora_paths": lora_paths,
"lora_scales": lora_scales,
}
# Add HuggingFace LoRA parameters if provided
if lora_names is not None:
model_kwargs["lora_names"] = lora_names
if lora_repo_id is not None:
model_kwargs["lora_repo_id"] = lora_repo_id
model = model_class(**model_kwargs)
config_kwargs = {
"num_inference_steps": steps,
"image_path": ImageGeneratorTestHelper.resolve_path(image_path),
"image_strength": image_strength,
"height": height,
"width": width,
}
# Add guidance if provided
if guidance is not None:
config_kwargs["guidance"] = guidance
generate_kwargs = {
"seed": seed,
"prompt": prompt,
"config": Config(**config_kwargs),
}
# Add negative_prompt for Qwen models
if model_class == QwenImage and negative_prompt is not None:
generate_kwargs["negative_prompt"] = negative_prompt
# when
image = model.generate_image(**generate_kwargs)
image.save(path=output_image_path, overwrite=True)
# then
ImageCompare.check_images_close_enough(
output_image_path,
reference_image_path,
"Generated image doesn't match reference image.",
)
finally:
# cleanup
if os.path.exists(output_image_path) and "MFLUX_PRESERVE_TEST_OUTPUT" not in os.environ:
os.remove(output_image_path)
@staticmethod
def resolve_path(path) -> Path | None:
if path is None:
return None
return Path(__file__).parent.parent.parent / "resources" / path