app.py

import torch
import os
import gradio as gr
from torch import autocast
from diffusers import StableDiffusionPipeline, DDIMScheduler
from IPython.display import display
from text_generation import Client, InferenceAPIClient

model_path = WEIGHTS_DIR             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive

pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
g_cuda = None

#@markdown Can set random seed here for reproducibility.
g_cuda = torch.Generator(device='cuda')
seed = 52362 #@param {type:"number"}
g_cuda.manual_seed(seed)

#@title Run for generating images.

prompt = "photo of zwx dog in a bucket" #@param {type:"string"}
negative_prompt = "" #@param {type:"string"}
num_samples = 4 #@param {type:"number"}
guidance_scale = 7.5 #@param {type:"number"}
num_inference_steps = 24 #@param {type:"number"}
height = 512 #@param {type:"number"}
width = 512 #@param {type:"number"}

with autocast("cuda"), torch.inference_mode():
    images = pipe(
        prompt,
        height=height,
        width=width,
        negative_prompt=negative_prompt,
        num_images_per_prompt=num_samples,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=g_cuda
    ).images

for img in images:
    display(img)