readme: ---
  license: mit
  tags:
  - text-to-image
  ---
  
  ![Sample](https://huggingface.co/mann-e/mann-e_4_rev-0-1/resolve/main/futuristic-city.jpg)
  
  # Mann-E 4 Revision 0.1
  
  __Mann-E__ is a _text to image_ model which has been developed by [Muhammadreza Haghiri](https://haghiri75.com/en) in order to be part of the [Cognitive Web](https://opencognitives.com) movement and projects.
  This is revision 0.1 of the 4th version of the model.
  
  ### What does _Mann-E_ mean?
  
  It's a play with the name [Mani](https://en.wikipedia.org/wiki/Mani_(prophet)), who was a Persian religious leader at the early Sassanian era and also a painter and he's famous for both his religious and artistic works. His artistic side was more considered for naming the model of course.
  
  ## How to use the model
  
  ### Colab
  
  Colab hasn't been updated with this version yet. 
  
  ### Code
  
  The following code is written for _CUDA_ supported devices. If you use UI's or inference tools on other devices, you may need to tweak them in order to get them to the work. Otherwise, it will be fine.
  
  First, you need to install required libraries:
  
  ```
  pip3 install diffusers transformers scipy ftfy accelerate
  ```
  
  _NOTE: installation of `accelerate` library makes the inference process amazingly faster. but it's totally optional_. 
  
  Then, you need to import required libraries:
  
  ```python
  from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler, DiffusionPipeline, DPMSolverMultistepScheduler
  import torch
  ```
  
  and then, create a pipeline (this pipeline is made with Euler Scheduler):
  
  ```python
  model_id = "mann-e/mann-e_4_rev-0-1"
  
  scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
  
  pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
  pipe = pipe.to("cuda")
  ```
  
  and of course, since you may get NSFW filteration warnings even on simplest prompts, you may consider disabling it:
  
  ```python
  def dummy(images, **kwargs): 
      return images, False 
  
  pipe.safety_checker = dummy
  ```
  
  _NOTE: Please consider consequences of disabling this filter as well. we do not want people to get any sort of damage or injury from the image generation results_. 
  
  And after that, you easily can start inference:
  
  ```python
  prompt = "Concept art of a hostile alien planet with unbreathable purple air and toxic clouds, sinister atmosphere, deep shadows, sharp details" 
  negative_prompt = "low quality, blurry" 
  width = 768 
  height = 512 
  ```
  
  then: 
  
  ```python
  image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=100, width=width, height=height, guidance_scale=10).images[0]
  image.save("My_image.png")
  ```
  