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# AnimatedDiff ControlNet SDXL Example
This document provides a step-by-step guide to setting up and running the `animatediff_controlnet_sdxl.py` script from the Hugging Face repository. The script leverages the `diffusers-sdxl-controlnet` library to generate animated images using ControlNet and SDXL models.
## Prerequisites
Before running the script, ensure you have the necessary dependencies installed. You can install them using the following commands:
### System Dependencies
```bash
sudo apt-get update && sudo apt-get install git-lfs cbm ffmpeg
```
### Python Dependencies
```bash
pip install git+https://huggingface.co/svjack/diffusers-sdxl-controlnet
pip install transformers peft sentencepiece moviepy controlnet_aux
```
### Clone the Repository
```bash
git clone https://huggingface.co/svjack/diffusers-sdxl-controlnet
cp diffusers-sdxl-controlnet/girl-pose.gif .
```
## Script Modifications
The script requires some modifications to work correctly. Specifically, you need to comment out certain lines related to LoRA processors:
```python
'''
drop #LoRAAttnProcessor2_0,
#LoRAXFormersAttnProcessor,
'''
```
## GIF to Frames Conversion
The script includes a function to convert a GIF into individual frames. This is useful for preparing input data for the animation pipeline.
```python
from PIL import Image, ImageSequence
import os
def gif_to_frames(gif_path, output_folder):
# Open the GIF file
gif = Image.open(gif_path)
# Ensure the output folder exists
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Iterate through each frame of the GIF
for i, frame in enumerate(ImageSequence.Iterator(gif)):
# Copy the frame
frame_copy = frame.copy()
# Save the frame to the specified folder
frame_path = os.path.join(output_folder, f"frame_{i:04d}.png")
frame_copy.save(frame_path)
print(f"Successfully extracted {i + 1} frames to {output_folder}")
# Example call
gif_to_frames("girl-pose.gif", "girl_pose_frames")
```
### Use this girl pose as pose source video (gif)
## Running the Script
To run the script, follow these steps:
1. **Add the Script Path to System Path**:
```python
import sys
sys.path.insert(0, "diffusers-sdxl-controlnet/examples/community/")
from animatediff_controlnet_sdxl import *
from controlnet_aux.processor import Processor
```
2. **Load Necessary Libraries and Models**:
```python
import torch
from diffusers.models import MotionAdapter
from diffusers import DDIMScheduler
from diffusers.utils import export_to_gif
from diffusers import AutoPipelineForText2Image, ControlNetModel
from diffusers.utils import load_image
from PIL import Image
```
3. **Load the MotionAdapter Model**:
```python
adapter = MotionAdapter.from_pretrained(
"a-r-r-o-w/animatediff-motion-adapter-sdxl-beta",
torch_dtype=torch.float16
)
```
4. **Configure the Scheduler and ControlNet**:
```python
model_id = "svjack/GenshinImpact_XL_Base"
scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
controlnet = ControlNetModel.from_pretrained(
"thibaud/controlnet-openpose-sdxl-1.0",
torch_dtype=torch.float16,
).to("cuda")
```
5. **Load the AnimateDiffSDXLControlnetPipeline**:
```python
pipe = AnimateDiffSDXLControlnetPipeline.from_pretrained(
model_id,
controlnet=controlnet,
motion_adapter=adapter,
scheduler=scheduler,
torch_dtype=torch.float16,
).to("cuda")
```
6. **Enable Memory Saving Features**:
```python
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()
```
7. **Load Conditioning Frames**:
```python
import os
folder_path = "girl_pose_frames/"
frames = os.listdir(folder_path)
frames = list(filter(lambda x: x.endswith(".png"), frames))
frames.sort()
conditioning_frames = list(map(lambda x: Image.open(os.path.join(folder_path ,x)).resize((1024, 1024)), frames))[:16]
```
8. **Process Conditioning Frames**:
```python
p2 = Processor("openpose")
cn2 = [p2(frame) for frame in conditioning_frames]
```
9. **Define Prompts**:
```python
prompt = '''
solo,Xiangling\(genshin impact\),1girl,
full body professional photograph of a stunning detailed, sharp focus, dramatic
cinematic lighting, octane render unreal engine (film grain, blurry background
'''
prompt = "solo,Xiangling\(genshin impact\),1girl,full body professional photograph of a stunning detailed"
negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly"
```
10. **Generate Output**: (Use Genshin Impact character Xiangling)
```python
prompt = '''
solo,Xiangling\(genshin impact\),1girl,
full body professional photograph of a stunning detailed, sharp focus, dramatic
cinematic lighting, octane render unreal engine (film grain, blurry background
'''
prompt = "solo,Xiangling\(genshin impact\),1girl,full body professional photograph of a stunning detailed"
#prompt = "solo,Xiangling\(genshin impact\),1girl"
negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly"
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=50,
guidance_scale=20,
controlnet_conditioning_scale = 1.0,
width=512,
height=768,
num_frames=16,
conditioning_frames=cn2,
generator = generator
)
```
11. **Export Frames to GIF**:
```python
frames = output.frames[0]
export_to_gif(frames, "xiangling_animation.gif")
```
12. **Display the Result**:
```python
from IPython import display
display.Image("xiangling_animation.gif")
```
### Target gif
## Conclusion
This script demonstrates how to use the `diffusers-sdxl-controlnet` library to generate animated images with ControlNet and SDXL models. By following the steps outlined above, you can create and visualize your own animated sequences.
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