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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 = "stable-diffusion-v1-5/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) |