README.md

---
library_name: diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- text-to-image
license: openrail++
inference: false
---

# What is different about this fork from the original (h1t/oms_b_openclip_xl)?

The code has been modified to work with the current final version (0.27.2) of diffusers.  
The behavior remains the same. Enjoy.

<img src="kaeru-dev.png" width="600"/>

```diff
- OMSPipeline.from_pretrained('h1t/oms_b_openclip_xl', ...)
+ OMSPipeline.from_pretrained('kaeru-shigure/oms_b_openclip_xl', ...)
```

```diff
--- a/diffusers_patch/models/unet_2d_condition_woct.py
+++ b/diffusers_patch/models/unet_2d_condition_woct.py
@@ -35,7 +35,7 @@ from diffusers.models.embeddings import (
     Timesteps,
 )
 from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.unet_2d_blocks import (
+from diffusers.models.unets.unet_2d_blocks import (
     CrossAttnDownBlock2D,
     CrossAttnUpBlock2D,
     DownBlock2D,
@@ -159,6 +159,7 @@ class UNet2DConditionWoCTModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMi
         conv_out_kernel: int = 3,
         mid_block_only_cross_attention: Optional[bool] = None,
         cross_attention_norm: Optional[str] = None,
+        subfolder: Optional[str] = None,
     ):
         super().__init__()
```

```diff
--- a/diffusers_patch/pipelines/oms/pipeline_oms.py
+++ b/diffusers_patch/pipelines/oms/pipeline_oms.py
@@ -8,6 +8,7 @@ from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokeniz

 from diffusers.loaders import FromSingleFileMixin

+from huggingface_hub.constants import HF_HUB_CACHE, HF_HUB_OFFLINE
 from diffusers.utils import (
     USE_PEFT_BACKEND,
     deprecate,
@@ -17,6 +18,7 @@ from diffusers.utils.torch_utils import randn_tensor
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.pipeline_utils import *
 from diffusers.pipelines.pipeline_utils import _get_pipeline_class
+from diffusers.pipelines.pipeline_loading_utils import *
 from diffusers.models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT

 from diffusers_patch.models.unet_2d_condition_woct import UNet2DConditionWoCTModel
@@ -164,7 +166,8 @@ class OMSPipeline(DiffusionPipeline, FromSingleFileMixin):
         sd_pipeline: DiffusionPipeline,
         oms_text_encoder:Optional[Union[CLIPTextModel, SDXLTextEncoder]],
         oms_tokenizer:Optional[Union[CLIPTokenizer, SDXLTokenizer]],
-        sd_scheduler = None
+        sd_scheduler = None,
+        trust_remote_code: bool = False,
     ):
         # assert sd_pipeline is not None

@@ -279,7 +282,7 @@ class OMSPipeline(DiffusionPipeline, FromSingleFileMixin):

     @classmethod
 os.PathLike]], **kwargs):
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        cache_dir = kwargs.pop("cache_dir", HF_HUB_CACHE)
         resume_download = kwargs.pop("resume_download", False)
         force_download = kwargs.pop("force_download", False)
         proxies = kwargs.pop("proxies", None)
```

-----

# One More Step

One More Step (OMS) module was proposed in [One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls](https://github.com/mhh0318/OneMoreStep)
by *Minghui Hu, Jianbin Zheng, Chuanxia Zheng, Tat-Jen Cham et al.*


By **adding one small step** on the top the sampling process, we can address the issues caused by the current schedule flaws of diffusion models **without changing the original model parameters**. This also allows for some control over low-frequency information, such as color. 

Our model is **versatile** and can be integrated into almost all widely-used Stable Diffusion frameworks. It's compatible with community favorites such as **LoRA, ControlNet, Adapter, and foundational models**.


## Usage

OMS now is supported 🤗 `diffusers` with a customized pipeline [github](https://github.com/mhh0318/OneMoreStep).  To run the model (especially with `LCM` variant), first install the latest version of `diffusers` library as well as `accelerate` and `transformers`.

```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate
```

And then we clone the repo
```bash
git clone https://github.com/mhh0318/OneMoreStep.git
cd OneMoreStep
```


### SDXL

The OMS module can be loaded with SDXL base model `stabilityai/stable-diffusion-xl-base-1.0`. 
And all the SDXL based model and its LoRA can **share the same OMS** `h1t/oms_b_openclip_xl`.

Here is an example for SDXL with LCM-LoRA.
Firstly import the related packages and choose SDXL based backbone and LoRA:

```python
import torch
from diffusers import StableDiffusionXLPipeline, LCMScheduler

sd_pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", add_watermarker=False).to('cuda')

sd_scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.load_lora_weights('latent-consistency/lcm-lora-sdxl', variant="fp16")
```

Following import the customized OMS pipeline to wrap the backbone and add OMS for sampling. We have uploaded the `.safetensors` to [HuggingFace Hub](https://huggingface.co/h1t/). There are 2 choices for SDXL backbone currently, one is base OMS module with OpenCLIP text encoder [h1t/oms_b_openclip_xl)](https://huggingface.co/h1t/oms_b_openclip_xl) and the other is large OMS module with two text encoder followed by SDXL architecture [h1t/oms_l_mixclip_xl)](https://huggingface.co/h1t/oms_b_mixclip_xl).
```python
from diffusers_patch import OMSPipeline

pipe = OMSPipeline.from_pretrained('h1t/oms_b_openclip_xl', sd_pipeline = sd_pipe, torch_dtype=torch.float16, variant="fp16", trust_remote_code=True, sd_scheduler=sd_scheduler)
pipe.to('cuda')
```

After setting a random seed, we can easily generate images with the OMS module.
```python
prompt = 'close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux'
generator = torch.Generator(device=pipe.device).manual_seed(1024)

image = pipe(prompt, guidance_scale=1, num_inference_steps=4, generator=generator)
image['images'][0]
```
![oms_xl](sdxl_oms.png)

Or we can offload the OMS module and generate a image only using backbone
```python
image = pipe(prompt, guidance_scale=1, num_inference_steps=4, generator=generator, oms_flag=False)
image['images'][0]
```
![oms_xl](sdxl_wo_oms.png)

For more models and more functions like diverse prompt, please refer to [OMS Repo](https://github.com/mhh0318/OneMoreStep).