app.py

from PIL import Image
import gradio as gr
import torch, os, random
from accelerate import Accelerator
from transformers import pipeline
from diffusers.utils import load_image
from diffusers import DiffusionPipeline, DDPMScheduler
from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS

accelerator = Accelerator(cpu=True)
warp_prior = accelerator.prepare(WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=torch.bfloat16, use_safetensors=True, safety_cheker=None))
###warp_prior.scheduler = DDPMWuerstchenScheduler.from_config(warp_prior.scheduler.config)
warp_prior = warp_prior.to("cpu")
warp = accelerator.prepare(WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.bfloat16, use_safetensors=True, safety_checker=None))
###warp.scheduler = DDPMWuerstchenScheduler.from_config(warp.scheduler.config)
warp = warp.to("cpu")
generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4876364))

def plex(cook, one, two):
    ###goof = load_image(img).resize((512, 512))
    negative_prompt = "lowres,text,bad quality,low quality,jpeg artifacts,ugly,bad hands,bad face,blurry,bad eyes,watermark,signature"
    warp_out = warp_prior(prompt=cook, height=512,width=512,negative_prompt=negative_prompt,guidance_scale=two, num_inference_steps=one,generator=generator,output_type="np")
    warp_out = warp_out.cpu().numpy()
    primpt = ""
    imas = warp(warp_out, height=512, width=512, num_inference_steps=one, prompt=cook,negative_prompt=primpt,guidance_scale=0.0,output_type="pil",generator=generator).images[0]
    return imas

iface = gr.Interface(fn=plex,outputs=gr.Image(label="Generated Output Image"),inputs=[gr.Textbox(label="prompt"), gr.Slider(label="Inference steps",minimum=1,step=1,maximum=10,value=5), gr.Slider(label="Prior guidance scale",minimum=4.1,step=0.1,maximum=19.9,value=4.1)], title="Txt2Img Wrstchn SD", description="Txt2Img Wrstchn SD")
iface.queue(max_size=1)
iface.launch(max_threads=1)