import torch, os, gc, random
import gradio as gr
from PIL import Image
from diffusers.utils import load_image
from accelerate import Accelerator
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
accelerator = Accelerator(cpu=True)

pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/lllyasviel/fav_models/fav/realisticStockPhoto_v10.safetensors", torch_dtype=torch.bfloat16, use_safetensors=True, variant=None, safety_checker=None)
pipe.load_lora_weights("./", weight_name="SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors", local_files_only="True")
pipe.fuse_lora(fuse_unet=True,fuse_text_encoder=False)
pipe.unet.to(memory_format=torch.channels_last)
accelerator.prepare(pipe.to("cpu"))
apol=[]
def plex(prompt,neg_prompt,stips,nut):
    apol=[]
    if nut == 0:
        nm = random.randint(1, 2147483616)
        while nm % 32 != 0:
            nm = random.randint(1, 2147483616)
    else:
        nm=nut
    lora_scale=0.8
    generator = torch.Generator(device="cpu").manual_seed(nm)
    image = pipe(prompt=prompt, negative_prompt=neg_prompt, denoising_end=1.0,num_inference_steps=stips, output_type="pil",cross_attention_kwargs={"scale": lora_scale},generator=generator)
    for i, imge in enumerate(image["images"]):
        apol.append(imge)
    return apol

iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=10, value=8),gr.Slider(label="manual seed (leave 0 for random)", minimum=0,step=32,maximum=2147483616,value=0)], outputs=gr.Gallery(label="out", columns=1),description="Running on cpu, very slow! by JoPmt.")
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1)