Archer Image Generator

Summary

• Introduction
• Parameters
• Examples
• Usage for developers
• Reference
• License

Introduction

Archer Image Generator user Archer Diffusion, is a highly specialized Image generation AI Model of type Safetensors / Checkpoint AI Model created by AI community user civitai. Derived from the powerful Stable Diffusion (SD 1.5) model, Archer Diffusion has undergone an extensive fine-tuning process, leveraging the power of a dataset consisting of images generated by other AI models or user-contributed data. This fine-tuning process ensures that Archer Diffusion is capable of generating images that are highly relevant to the specific use-cases it was designed for, such as landscapes, nitrosocke, archer.

This is the fine-tuned Stable Diffusion model trained on screenshots from the TV-show Archer. Use the archer style tokens in your prompts for the effect.

Parameters

Inputs

• prompt - (text): This is the input text or question that you want to use to create the image. It can be a detailed description of the image you want to create or a short sentence that describes the general idea.

Output

• output - (image -.png): The generated images are created based on the descriptions and parameters provided by the user. The default size is 512x512.

Examples

prompt	output
a magical princess with golden hair, archer style

Usage for developers

Please find below the details to track the information and access the code for processing the model on our platform.

Requirements

python~=3.10
torch~=2.0.0
diffusers

Code based on AIOZ structure

from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch, os

...
def do_ai_task(
        prompt: Union[str, Path],
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda", "gpu"] = "cpu",
        *args, **kwargs) -> Any:
        
        model_id = os.path.abspath(model_storage_directory + "...")
        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
        pipe = pipe.to(device)

        image = pipe(prompt).images[0]
        image.save("output.png")
        output = open("output.png", "rb")  # io.BufferedReader
    return output

Reference

This repository is based on and inspired by Nitrosocke's work. We sincerely appreciate their generosity in sharing the code.

License

We respect and comply with the terms of the author's license cited in the Reference section, including the main contents below:

This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:

1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here