milyiyo/testing-diffusers

1from diffusers import LDMTextToImagePipeline2import gradio as gr3import PIL.Image4import numpy as np5import random6import torch7 8ldm_pipeline = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")9 10def predict(prompt, steps=100, seed=42, guidance_scale=6.0):11    torch.cuda.empty_cache()12    generator = torch.manual_seed(seed)13    images = ldm_pipeline([prompt], generator=generator, num_inference_steps=steps, eta=0.3, guidance_scale=guidance_scale)["sample"]14    return images[0]15 16random_seed = random.randint(0, 2147483647)17gr.Interface(18    predict,19    inputs=[20        gr.inputs.Textbox(label='Prompt', default='a chalk pastel drawing of a llama wearing a wizard hat'),21        gr.inputs.Slider(1, 100, label='Inference Steps', default=50, step=1),22        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),23        gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1),24    ],25    outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"),26    css="#output_image{width: 256px}",27).launch()