| from diffusers import AutoPipelineForText2Image | |
| import torch | |
| import gradio as gr | |
| from PIL import Image | |
| import os, random | |
| import PIL.Image | |
| from transformers import pipeline | |
| from diffusers.utils import load_image | |
| from accelerate import Accelerator | |
| accelerator = Accelerator() | |
| def plex(prompt,neg_prompt): | |
| apol=[] | |
| pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained("openskyml/overall-v1", torch_dtype=torch.float32, variant=None, use_safetensors=False, safety_checker=None)) | |
| pipe = accelerator.prepare(pipe.to("cpu")) | |
| image = pipe(prompt=prompt, negative_prompt=neg_prompt,num_inference_steps=10) | |
| for a, imze in enumerate(image["images"]): | |
| apol.append(imze) | |
| return apol | |
| iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality")],outputs=gr.Gallery(label="Generated Output Image", columns=1), title="Txt2Img_Overall_v1_SD",description="Running on cpu, very slow!") | |
| iface.queue(max_size=1,api_open=False) | |
| iface.launch(max_threads=1) |