File size: 2,494 Bytes
d91410a 71e298d d91410a 71e298d d91410a 71e298d |
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 |
---
license: apache-2.0
---
# Qwen-Image Image Structure Control Model - Depth ControlNet
## Model Introduction
This model is an image structure control model based on [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image), with a ControlNet architecture that enables structural control of generated images using depth maps. The training framework is built upon [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio), and the dataset used for training is [BLIP3o](https://modelscope.cn/datasets/BLIP3o/BLIP3o-60k).
## Result Demonstration
|Depth Map|Generated Image 1|Generated Image 2|
|-|-|-|
||||
||||
||||
## Inference Code
```
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
```
```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
from PIL import Image
import torch
from modelscope import dataset_snapshot_download
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/example_image_dataset",
local_dir="./data/example_image_dataset",
allow_file_pattern="depth/image_1.jpg"
)
controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
```
prompt = "Exquisite portrait, underwater girl, flowing blue dress, gently floating hair, translucent lighting, surrounded by bubbles, serene expression, intricate details, dreamy and ethereal."
image = pipe(
prompt, seed=0,
blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
)
image.save("image.jpg") |