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 = accelerator.prepare(StableDiffusionXLPipeline.from_single_file("https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors", torch_dtype=torch.bfloat16, use_safetensors=True, variant=None, safety_checker=False)) ##pipe.scheduler = accelerator.prepare(EulerDiscreteScheduler.from_config(pipe.scheduler.config)) ##pipe.unet.to(memory_format=torch.channels_last) 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 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",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=6),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)