---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
inference: true
license: mit
library_name: diffusers
instance_prompt: a professional studio photograph of an attractive model wearing a
  teal top with lace detail
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
---

 
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# ControlNet for cloth- Docty/cloth_controlnet

These are ControlNet for stable-diffusion-v1-5/stable-diffusion-v1-5.  You can find some example images in the following. 

![img_0](./image_control.png)
![img_1](./images_0.png)
![img_2](./images_1.png)

```python
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch

base_model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
controlnet_path = "Docty/cloth_controlnet"

controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    base_model_path, controlnet=controlnet, torch_dtype=torch.float16
)

# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed or when using Torch 2.0.
#pipe.enable_xformers_memory_efficient_attention()
# memory optimization.
pipe.enable_model_cpu_offload()

control_image = load_image("./cond1.jpg")
prompt = "a professional studio photograph of an attractive model wearing a teal top with lace detail"

# generate image
#generator = torch.manual_seed(0)
image = pipe(
    prompt, num_inference_steps=20, image=control_image
).images[0]
image

```