metadata
frameworks:
- Pytorch
license: Apache License 2.0
tags: []
tasks:
- text-to-image-synthesis
Templates-美学对齐（FLUX.2-klein-base-4B）
本模型是 DiffSynth-Studio 开源的 Diffusion Templates 系列模型之一。该模型为 Aesthetic（美学）对齐模型，能够通过修改 scale 参数来调整图像的美学对齐程度。
效果展示
Prompt: A cat is sitting on a stone.
| base model | scale=1.0 | scale=2.5 |
|---|---|---|
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Prompt: A cute anime girl with pink hair and cat ears, pastel colors.
| base model | scale=1.0 | scale=2.5 |
|---|---|---|
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Prompt: A cyberpunk apartment with a view of neon lights.
| base model | scale=1.0 | scale=2.5 |
|---|---|---|
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推理代码
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
- 直接推理，需 40G 显存
from diffsynth.diffusion.template import TemplatePipeline
from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch
pipe = Flux2ImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
)
pipe.dit = pipe.enable_lora_hot_loading(pipe.dit) # Important!
template = TemplatePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[ModelConfig(model_id="DiffSynth-Studio/Template-KleinBase4B-Aesthetic")],
)
image = template(
pipe,
prompt="A cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 1.0,
"merge_type": "mean",
}],
negative_template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 1.0,
"merge_type": "mean",
}],
)
image.save("image_Aesthetic_1.0.jpg")
image = template(
pipe,
prompt="A cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 2.5,
"merge_type": "mean",
}],
negative_template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 2.5,
"merge_type": "mean",
}],
)
image.save("image_Aesthetic_2.5.jpg")
- 开启惰性加载和显存管理，需 24G 显存
from diffsynth.diffusion.template import TemplatePipeline
from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch
vram_config = {
"offload_dtype": "disk",
"offload_device": "disk",
"onload_dtype": torch.float8_e4m3fn,
"onload_device": "cpu",
"preparing_dtype": torch.float8_e4m3fn,
"preparing_device": "cuda",
"computation_dtype": torch.bfloat16,
"computation_device": "cuda",
}
pipe = Flux2ImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors", **vram_config),
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)
template = TemplatePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[ModelConfig(model_id="DiffSynth-Studio/Template-KleinBase4B-Aesthetic")],
lazy_loading=True,
)
image = template(
pipe,
prompt="A cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 1.0,
"merge_type": "mean",
}],
negative_template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 1.0,
"merge_type": "mean",
}],
)
image.save("image_Aesthetic_1.0.jpg")
image = template(
pipe,
prompt="A cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 2.5,
"merge_type": "mean",
}],
negative_template_inputs = [{
"lora_ids": list(range(1, 180, 2)),
"lora_scales": 2.5,
"merge_type": "mean",
}],
)
image.save("image_Aesthetic_2.5.jpg")
训练代码
安装 DiffSynth-Studio 后，使用以下脚本可开启训练，更多信息请参考 DiffSynth-Studio 文档。
modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "flux2/Template-KleinBase4B-Aesthetic/*" --local_dir ./data/diffsynth_example_dataset
accelerate launch examples/flux2/model_training/train.py \
--dataset_base_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-Aesthetic \
--dataset_metadata_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-Aesthetic/metadata.jsonl \
--extra_inputs "template_inputs" \
--max_pixels 1048576 \
--dataset_repeat 50 \
--model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
--template_model_id_or_path "DiffSynth-Studio/Template-KleinBase4B-Aesthetic:" \
--tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
--learning_rate 1e-4 \
--num_epochs 2 \
--remove_prefix_in_ckpt "pipe.template_model." \
--output_path "./models/train/Template-KleinBase4B-Aesthetic_full" \
--trainable_models "template_model" \
--use_gradient_checkpointing \
--find_unused_parameters \
--enable_lora_hot_loading