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from __future__ import annotations
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
from diffusers import DPMSolverMultistepScheduler
import torch
import PIL.Image
import numpy as np
device = "cpu"
class Model:
def __init__(self):
modelID = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(modelID, torch_dtype=torch.float16)
pipe = pipe.to(device)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
#self.pipe = StableDiffusionPipeline.from_pretrained(modelID)
#prompt = "a photo of an astronaut riding a horse on mars"
#n_prompt = "deformed, disfigured"
def process(self,
prompt: str,
negative_prompt: str,
guidance_scale:int = 7,
num_images:int = 1,
num_steps:int = 20,
):
seed = np.random.randint(0, np.iinfo(np.int64).max)
generator = torch.Generator(device).manual_seed(seed)
return self.pipe(prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images,
num_inference_steps=num_steps,
generator=generator).images
# image = pipeline(prompt=prompt,
# negative_prompt = n_prompt,
# num_inference_steps = 2,
# guidance_scale = 7).images
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