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support qwen-image-layered

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Artiprocher
2025-12-19 19:06:37 +08:00
parent 11315d7a40
commit c6722b3f56
18 changed files with 417 additions and 27 deletions
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30
examples/qwen_image/model_training/train.py
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@@ -2,6 +2,7 @@ import torch, os, argparse, accelerate
from diffsynth.core import UnifiedDataset
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
from diffsynth.diffusion import *
from diffsynth.core.data.operators import *
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -58,11 +59,6 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
inputs_posi = {"prompt": data["prompt"]}
inputs_nega = {"negative_prompt": ""}
inputs_shared = {
# Assume you are using this pipeline for inference,
# please fill in the input parameters.
"input_image": data["image"],
"height": data["image"].size[1],
"width": data["image"].size[0],
# Please do not modify the following parameters
# unless you clearly know what this will cause.
"cfg_scale": 1,
@@ -72,6 +68,20 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
"edit_image_auto_resize": True,
"zero_cond_t": self.zero_cond_t,
}
# Assume you are using this pipeline for inference,
# please fill in the input parameters.
if isinstance(data["image"], list):
inputs_shared.update({
"input_image": data["image"],
"height": data["image"][0].size[1],
"width": data["image"][0].size[0],
})
else:
inputs_shared.update({
"input_image": data["image"],
"height": data["image"].size[1],
"width": data["image"].size[0],
})
inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
return inputs_shared, inputs_posi, inputs_nega
@@ -113,7 +123,15 @@ if __name__ == "__main__":
width=args.width,
height_division_factor=16,
width_division_factor=16,
)
),
special_operator_map={
# Qwen-Image-Layered
"layer_input_image": ToAbsolutePath(args.dataset_base_path) >> LoadImage(convert_RGB=False, convert_RGBA=True) >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16),
"image": RouteByType(operator_map=[
(str, ToAbsolutePath(args.dataset_base_path) >> LoadImage() >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16)),
(list, SequencialProcess(ToAbsolutePath(args.dataset_base_path) >> LoadImage(convert_RGB=False, convert_RGBA=True) >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16))),
])
}
)
model = QwenImageTrainingModule(
model_paths=args.model_paths,