app.py

import gradio as gr
import numpy as np
import torch
from PIL import Image
from diffusers import StableDiffusionPipeline
from transformers import pipeline, set_seed
import random
import re

model_id = "runwayml/stable-diffusion-v1-5"

pipe = StableDiffusionPipeline.from_pretrained(model_id).to('cpu')

gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
gpt2_pipe2 = pipeline('text-generation', model='succinctly/text2image-prompt-generator')

def infer1(starting_text):
    seed = random.randint(100, 1000000)
    set_seed(seed)

    if starting_text == "":
        starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)

    response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
    response_list = []
    for x in response:
        resp = x['generated_text'].strip()
        if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
            response_list.append(resp+'\n')

    response_end = "\n".join(response_list)
    response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
    response_end = response_end.replace("<", "").replace(">", "")

    if response_end != "":
        return response_end

def infer2(starting_text):
    for count in range(6):
        seed = random.randint(100, 1000000)
        set_seed(seed)
    
        # If the text field is empty
        if starting_text == "":
            starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
            starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
            print(starting_text)
    
        response = gpt2_pipe2(starting_text, max_length=random.randint(60, 90), num_return_sequences=8)
        response_list = []
        for x in response:
            resp = x['generated_text'].strip()
            if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
                response_list.append(resp)
    
        response_end = "\n".join(response_list)
        response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
        response_end = response_end.replace("<", "").replace(">", "")
        if response_end != "":
            return response_end
        if count == 5:
            return response_end

def infer3(prompt, negative, steps, scale, seed):
    generator = torch.Generator(device='cpu').manual_seed(seed)
    img = pipe(
            prompt,
            height=512, 
            width=512,
            num_inference_steps=steps,
            guidance_scale=scale,
            negative_prompt = negative,
            generator=generator,
        ).images
    return img

block = gr.Blocks()

with block:
  with gr.Group():
    with gr.Box():
      gr.Markdown(
          """
          Model: Gustavosta/MagicPrompt-Stable-Diffusion
          """
      )
      with gr.Row() as row:
        with gr.Column():
          txt = gr.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
          gpt_btn = gr.Button("Generate prompt").style(
                            margin=False,
                            rounded=(False, True, True, False),
                        ) 
        with gr.Column():
          out = gr.Textbox(lines=4, label="Generated Prompts")
    
    with gr.Box():
      gr.Markdown(
          """
          Model: succinctly/text2image-prompt-generator
          """
      )
      with gr.Row() as row:
        with gr.Column():
          txt2 = gr.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
          gpt_btn2 = gr.Button("Generate prompt").style(
                            margin=False,
                            rounded=(False, True, True, False),
                        ) 
        with gr.Column():
          out2 = gr.Textbox(lines=4, label="Generated Prompts")
        
    with gr.Box():
      gr.Markdown(
          """
          Model: stable diffusion v1.5
          """
      )
      with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
            with gr.Column():
                text = gr.Textbox(
                    label="Enter your prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    ).style(
                        border=(True, False, True, True),
                        rounded=(True, False, False, True),
                        container=False,
                    )

                negative = gr.Textbox(
                    label="Enter your negative prompt",
                    show_label=False,
                    placeholder="Enter a negative prompt",
                    elem_id="negative-prompt-text-input",
                    ).style(
                        border=(True, False, True, True),
                        rounded=(True, False, False, True),container=False,
                    )
                
            btn = gr.Button("Generate image").style(
                        margin=False,
                        rounded=(False, True, True, False),
                    )  
    gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(columns=(1, 2), height="auto")
        
    with gr.Row(elem_id="advanced-options"):
          samples = gr.Slider(label="Images", minimum=1, maximum=1, value=1, step=1, interactive=False)
          steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=12, step=1, interactive=True)
          scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1, interactive=True)
          seed = gr.Slider(label="Random seed",minimum=0,maximum=2147483647,step=1,randomize=True,interactive=True)

    gpt_btn.click(infer1,inputs=txt,outputs=out)
    gpt_btn2.click(infer2,inputs=txt2,outputs=out2)
    btn.click(infer3, inputs=[text, negative, steps, scale, seed], outputs=[gallery])

block.launch(show_api=False,enable_queue=True, debug=True)