license: apache-2.0
Templates-Super-Resolution (FLUX.2-klein-base-4B)
This model is part of the open-source Diffusion Templates series by DiffSynth-Studio. Specifically designed for image super-resolution, it takes low-resolution input images and redraws them with rich high-definition details while preserving the original composition and semantics.
Results
Prompt: A cat is sitting on a stone.
| Input (100px) | Output | Input (512px) | Output |
|---|---|---|---|
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Prompt: An anime girl under a cherry blossom tree, looking at the sky.
| Input (100px) | Output | Input (512px) | Output |
|---|---|---|---|
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Prompt: A hamburger with fries on a plate.
| Input (100px) | Output | Input (512px) | Output |
|---|---|---|---|
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Inference Code
- Install DiffSynth-Studio
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
- Direct inference (requires 40G GPU memory)
from diffsynth.diffusion.template import TemplatePipeline
from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch
from modelscope import dataset_snapshot_download
from PIL import Image
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/"),
)
template = TemplatePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[ModelConfig(model_id="DiffSynth-Studio/Template-KleinBase4B-Upscaler")],
)
dataset_snapshot_download(
"DiffSynth-Studio/examples_in_diffsynth",
allow_file_pattern=["templates/*"],
local_dir="data/examples",
)
image = template(
pipe,
prompt="A cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs=[{
"image": Image.open("data/examples/templates/image_lowres_512.jpg"),
"prompt": "A cat is sitting on a stone.",
}],
negative_template_inputs=[{
"image": Image.open("data/examples/templates/image_lowres_512.jpg"),
"prompt": "",
}],
)
image.save("image_Upscaler_1.png")
image = template(
pipe,
prompt="A cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs=[{
"image": Image.open("data/examples/templates/image_lowres_100.jpg"),
"prompt": "A cat is sitting on a stone.",
}],
negative_template_inputs=[{
"image": Image.open("data/examples/templates/image_lowres_100.jpg"),
"prompt": "",
}],
)
image.save("image_Upscaler_2.png")
- Enable lazy loading and memory management, requires 24G GPU memory
from diffsynth.diffusion.template import TemplatePipeline
from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
import torch
from modelscope import dataset_snapshot_download
from PIL import Image
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-Upscaler")], lazy_loading=True, ) dataset_snapshot_download( "DiffSynth-Studio/examples_in_diffsynth", allow_file_pattern=["templates/"], local_dir="data/examples", ) image = template( pipe, prompt="A cat is sitting on a stone.", seed=0, cfg_scale=4, num_inference_steps=50, template_inputs = [{ "image": Image.open("data/examples/templates/image_lowres_512.jpg"), "prompt": "A cat is sitting on a stone.", }], negative_template_inputs = [{ "image": Image.open("data/examples/templates/image_lowres_512.jpg"), "prompt": "", }], ) image.save("image_Upscaler_1.png") image = template( pipe, prompt="A cat is sitting on a stone.", seed=0, cfg_scale=4, num_inference_steps=50, template_inputs = [{ "image": Image.open("data/examples/templates/image_lowres_100.jpg"), "prompt": "A cat is sitting on a stone.", }], negative_template_inputs = [{ "image": Image.open("data/examples/templates/image_lowres_100.jpg"), "prompt": "", }], ) image.save("image_Upscaler_2.png")
## Training Script
After installing DiffSynth-Studio, use the following script to start training. For more information, please refer to the [DiffSynth-Studio Documentation](https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/).
```shell
modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "flux2/Template-KleinBase4B-Upscaler/*" --local_dir ./data/diffsynth_example_dataset
accelerate launch examples/flux2/model_training/train.py \
--dataset_base_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-Upscaler \
--dataset_metadata_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-Upscaler/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-Upscaler:" \
--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-Upscaler_full" \
--trainable_models "template_model" \
--use_gradient_checkpointing \
--find_unused_parameters