stablepy 0.5.1

A tool for easy use of stable diffusion

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==0.5.1

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, info_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, info_image = 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]

📖 New Update Details:

🔥 Version 0.5.1:

• After generation, PIL images are now returned along with sublists containing the seeds, image paths, and metadata with the parameters used in generation. [pil_image], [seed, image_path, metadata]
• The use of image_previews=True has been improved, and now preview images can be obtained during the generation steps using a generator. For more details, refer to the Colab notebook.

🔥 Version 0.5.0:

• Fix LoRA SDXL compatibility.
• Latent upscaler and variants.
• Perturbed Attention Guidance (PAG) enhances image generation quality without the need for training.
• Multiple images for one FaceID adapter.
• ControlNet for SDXL: MLSD, Segmentation, Normalbae.
• ControlNet "lineart_anime" task accessible and able to load a model different from the "lineart" task.
• ControlNet Tile and Recolor for SD1.5 and SDXL ("tile" replaces the previous task called "sdxl_tile_realistic").

🔥 Version 0.4.0:

• 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.