stablepy

Description:

The goal of this project is to make Stable Diffusion more accessible, simple and easy-to-use with python. Stablepy is constructed on top of the Diffusers library

Installation:

pip install stablepy==0.4.1

Usage:

To use the project, simply create a new instance of the Model_Diffusers class. This class takes several arguments, including the path to the Stable Diffusion model file and the task name.

Once you have created a new instance of the Model_Diffusers class, you can call the model() method to generate an image. The model() method takes several arguments, including the prompt, the number of steps, the guidance scale, the sampler, the image width, the image height, the path to the upscaler model (if using), etc.

Interactive tutorial:

See stablepy_demo.ipynb

Examples:

The following code examples show how to use the project to generate a text-to-image and a ControlNet diffusion:

from stablepy import Model_Diffusers

# Generate a text-to-image diffusion
model = Model_Diffusers(
    base_model_id='./models/toonyou_beta6.safetensors',
    task_name= 'txt2img',
)

image, path_image = model(
    prompt='highly detailed portrait of an underwater city, with towering spires and domes rising up from the ocean floor',
    num_steps = 30,
    guidance_scale = 7.5,
    sampler = "DPM++ 2M",
    img_width = 512,
    img_height = 1024,
    upscaler_model_path = "./upscaler/RealESRGAN_x4plus_anime_6B.pth",
    upscaler_increases_size = 1.5,
    hires_steps = 25,
)

image[0]

Multiple LoRAs can also be used, as well as optimizations to the generation such as FreeU.

from stablepy import Model_Diffusers

# Generate an ControlNet diffusion
model = Model_Diffusers(
    base_model_id='./models/toonyou_beta6.safetensors',
    task_name= 'canny',
)

images, path_images = model(
    prompt='highly detailed portrait of an underwater city, with towering spires and domes rising up from the ocean floor',
    num_steps = 30,
    image_resolution = 768,
    preprocessor_name = "Canny",
    guidance_scale = 7.5,
    seed = 567,
    FreeU = True,
    lora_A = "./loras/lora14552.safetensors",
    lora_scale_A = 0.8,
    lora_B = "./loras/example_lora3.safetensors",
    lora_scale_B = 0.5,
    image = "./examples/image001.png",
)

images[1]

📖 News:

🔥 Version 0.4.0: New Update Details

IP Adapter with the variants FaceID and Instant-Style
New samplers
Appropriate support for SDXL safetensors models
ControlNet for SDXL: OpenPose, Canny, Scribble, SoftEdge, Depth, LineArt, and SDXL_Tile_Realistic
New variant prompt weight with emphasis
ControlNet pattern for SD1.5 and SDXL
ControlNet Canny now needs the preprocessor_name="Canny"
Similarly, ControlNet MLSD requires the preprocessor_name="MLSD"
Task names like "sdxl_canny" have been changed to "sdxl_canny_t2i" to refer to the T2I adapter that uses them.

Contributing:

We welcome contributions to the project. If you have any suggestions or bug fixes, please feel free to open an issue or submit a pull request.

R3gm/stablepy

stablepy