import gradio as gr import requests import os import gradio as gr from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cpu") name = "andite/anything-v4.0" model = gr.Interface.load(f"models/{name}") o = os.getenv("P") h = "Q" def ac(): def im_fn(put): if h == o: return model(put,negative_prompt = "blury") elif h != o: return(None) def im_pipe(put): return pipe(put, negative_prompt="blury").images[0] ''' num_images_per_prompt=n_images, num_inference_steps = int(steps), guidance_scale = guidance, width = width, height = height, generator = generator, callback=pipe_callback) ''' with gr.Blocks() as b: put = gr.Textbox() with gr.Row(): out1 = gr.Image() out2 = gr.Image() with gr.Row(): btn1 = gr.Button() btn2 = gr.Button() btn1.click(im_fn,put,out1) btn2.click(im_pipe,put,out2) b.queue(concurrency_count=100).launch() ac()