metadata

language: en
license: other
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - en
  - english
inference: false
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English Stable Diffusion Pokemon Model Card

Stable-Diffusion-Pokemon-en is a English-specific latent text-to-image diffusion model capable of generating Pokemon images given any text input.

This model was trained by using a powerful text-to-image model, diffusers For more information about our training method, see train_text_to_image.py.

Model Details

Developed by: Zhipeng Yang
Model type: Diffusion-based text-to-image generation model
Language(s): English
License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based.
Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model (LDM) that used Stable Diffusion as a pre-trained model.
Resources for more information: https://github.com/svjack/Stable-Diffusion-Pokemon

Examples

Firstly, install our package as follows. This package is modified 🤗's Diffusers library to run English Stable Diffusion.

pip install diffusers==0.4.1

Run this command to log in with your HF Hub token if you haven't before:

huggingface-cli login

Running the pipeline with the LMSDiscreteScheduler scheduler:

import torch
import pandas as pd

from torch import autocast
from diffusers import LMSDiscreteScheduler, StableDiffusionPipeline

scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012,
     beta_schedule="scaled_linear", num_train_timesteps=1000)

#pretrained_model_name_or_path = "en_model_26000"
pretrained_model_name_or_path = "svjack/Stable-Diffusion-Pokemon-en"
pipe = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path,
                                                           scheduler=scheduler, use_auth_token=True)

pipe = pipe.to("cuda")

disable safety_checker
pipe.safety_checker = lambda images, clip_input: (images, False)

imgs = pipe("A cartoon character with a potted plant on his head",
                    num_inference_steps = 100
)
image = imgs.images[0]
    
image.save("output.png")

Generator Results comparison

https://github.com/svjack/Stable-Diffusion-Pokemon