Mann-E is a text to image model which has been developed by Muhammadreza Haghiri in order to be part of the Cognitive Web movement and projects. This is revision 3 of the model and it's the first one to have a code name. The code name is Prompt Muse.
It's a play with the name Mani, 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.
You easily can click on this link and use model's inference notebook.
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:
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler, DiffusionPipeline, DPMSolverMultistepScheduler import torch
and then, create a pipeline (this pipeline is made with Euler Scheduler):
model_id = "mann-e/mann-e_rev-3" 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:
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:
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
image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=100, width=width, height=height, guidance_scale=10).images image.save("My_image.png")
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