File size: 9,674 Bytes
9355758 76acc61 9355758 4d5e683 9355758 76acc61 9355758 76acc61 9355758 76acc61 9355758 76acc61 9355758 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | ---
license: apache-2.0
---
# Templates-Inpainting (FLUX.2-klein-base-4B)
This model is one of the open-source Diffusion Templates series models from [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio). Specifically designed for inpainting tasks, it accepts an original image and a mask image, then generates new content within the masked region based on natural language prompts, seamlessly blending with the surrounding unmasked background.
* Open-source code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Technical report: [arXiv](https://arxiv.org/abs/2604.24351)
* Project page: [GitHub](https://modelscope.github.io/diffusion-templates-web/)
* Documentation: [English Version](https://diffsynth-studio-doc.readthedocs.io/en/latest/Diffusion_Templates/Introducing_Diffusion_Templates.html)、[中文版](https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/Diffusion_Templates/Introducing_Diffusion_Templates.html)
* Online demo: [ModelScope](https://modelscope.cn/studios/DiffSynth-Studio/Diffusion-Templates)
* Models: [ModelScope](https://modelscope.cn/collections/DiffSynth-Studio/KleinBase4B-Templates)、[ModelScope International](https://modelscope.ai/collections/DiffSynth-Studio/KleinBase4B-Templates)、[HuggingFace](https://huggingface.co/collections/DiffSynth-Studio/kleinbase4b-templates)
* Datasets: [ModelScope](https://modelscope.cn/collections/DiffSynth-Studio/ImagePulseV2)、[ModelScope International](https://modelscope.cn/collections/DiffSynth-Studio/ImagePulseV2)、[HuggingFace](https://huggingface.co/collections/DiffSynth-Studio/imagepulsev2)
## Results
| Reference | Prompt | Mask | Generated |
|:---:|:---|:---:|:---:|
|  | An orange cat is sitting on a stone. |  |  |
|  | A cat wearing sunglasses is sitting on a stone. |  |  |
| Reference | Prompt | Mask | Generated |
|:---:|:---|:---:|:---:|
|  | A beautiful young woman wearing a woven straw hat with a ribbon standing in a sunflower field. |  |  |
|  | A beautiful young woman wearing an elegant white dress standing in a glowing sunflower field. |  |  |
| Reference | Prompt | Mask | Generated |
|:---:|:---|:---:|:---:|
|  | A sleek glass vase with a single blooming white lily and an open minimalist art book resting on the circular white marble coffee table. |  |  |
|  | A large, minimalist flower painting hanging on the clean off-white wall above the sofa, soft shadows. |  |  |
## Inference Code
* Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
```
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
```
* Direct inference (requires 40GB GPU memory)
```python
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
```
```python
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-Inpaint")],
)
dataset_snapshot_download(
"DiffSynth-Studio/examples_in_diffsynth",
allow_file_pattern=["templates/*"],
local_dir="data/examples",
)
image = template(
pipe,
prompt="An orange cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs = [{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_1.jpg"),
"force_inpaint": True,
}],
negative_template_inputs = [{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_1.jpg"),
}],
)
image.save("image_Inpaint_1.jpg")
image = template(
pipe,
prompt="A cat wearing sunglasses is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs = [{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_2.jpg"),
}],
negative_template_inputs = [{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_2.jpg"),
}],
)
image.save("image_Inpaint_2.jpg")
```
* Enable lazy loading and memory management, requires 24G GPU memory
```python
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
```
```python
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-Inpaint")],
lazy_loading=True,
)
dataset_snapshot_download(
"DiffSynth-Studio/examples_in_diffsynth",
allow_file_pattern=["templates/*"],
local_dir="data/examples",
)
image = template(
pipe,
prompt="An orange cat is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs=[{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_1.jpg"),
"force_inpaint": True,
}],
negative_template_inputs=[{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_1.jpg"),
}],
)
image.save("image_Inpaint_1.jpg")
image = template(
pipe,
prompt="A cat wearing sunglasses is sitting on a stone.",
seed=0, cfg_scale=4, num_inference_steps=50,
template_inputs=[{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_2.jpg"),
}],
negative_template_inputs=[{
"image": Image.open("data/examples/templates/image_reference.jpg"),
"mask": Image.open("data/examples/templates/image_mask_2.jpg"),
}],
)
image.save("image_Inpaint_2.jpg")
```
## Training Code
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-Inpaint/*" --local_dir ./data/diffsynth_example_dataset
accelerate launch examples/flux2/model_training/train.py \
--dataset_base_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-Inpaint \
--dataset_metadata_path data/diffsynth_example_dataset/flux2/Template-KleinBase4B-Inpaint/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-Inpaint:" \
--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-Inpaint_full" \
--trainable_models "template_model" \
--use_gradient_checkpointing \
--find_unused_parameters
``` |