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import gradio as gr
import torch
import random
import transformers
from transformers import T5Tokenizer, T5ForConditionalGeneration
if torch.cuda.is_available():
device = "cuda"
print("Using GPU")
else:
device = "cpu"
print("Using CPU")
tokenizer = T5Tokenizer.from_pretrained("imranali291/flux-prompt-enhancer")
model = T5ForConditionalGeneration.from_pretrained("imranali291/flux-prompt-enhancer", device_map="auto", torch_dtype="auto")
model.to(device)
def generate(your_prompt, max_new_tokens, repetition_penalty, temperature, model_precision_type, top_p, top_k, seed):
if seed == 0:
seed = random.randint(1, 2**32-1)
transformers.set_seed(seed)
if model_precision_type == "fp16":
dtype = torch.float16
elif model_precision_type == "fp32":
dtype = torch.float32
model.to(dtype)
repetition_penalty = float(repetition_penalty)
input_text = f"{your_prompt}"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
outputs = model.generate(
input_ids,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
do_sample=True,
temperature=temperature,
top_p=top_p,
top_k=top_k,
)
better_prompt = tokenizer.decode(outputs[0], skip_special_tokens=True)
return better_prompt
your_prompt = gr.Textbox(label="Your Prompt", info="Your Prompt that you want to enhanced")
max_new_tokens = gr.Slider(value=128, minimum=25, maximum=512, step=1, label="Max New Tokens", info="The maximum numbers of new tokens, controls how long is the output")
repetition_penalty = gr.Slider(value=2.5, minimum=0, maximum=3.0, step=0.05, label="Repetition Penalty", info="Penalize repeated tokens, making the AI repeat less itself")
temperature = gr.Slider(value=0.7, minimum=0, maximum=1, step=0.05, label="Temperature", info="Higher values produce more diverse outputs")
model_precision_type = gr.Dropdown(["fp16", "fp32"], value="fp16", label="Model Precision Type", info="The precision type to load the model, like fp16 which is faster, or fp32 which is more precise but more resource consuming")
top_p = gr.Slider(value=0.9, minimum=0, maximum=2, step=0.05, label="Top P", info="Higher values sample more low-probability tokens")
top_k = gr.Slider(value=50, minimum=1, maximum=100, step=1, label="Top K", info="Higher k means more diverse outputs by considering a range of tokens")
seed = gr.Slider(value=42, minimum=0, maximum=2**32-1, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
examples = [
["Futuristic cityscape at twilight descent.", 128, 2.5, 0.5, "fp16", 0.9, 50, 42]
]
gr.Interface(
fn=generate,
inputs=[your_prompt, max_new_tokens, repetition_penalty, temperature, model_precision_type, top_p, top_k, seed],
outputs=gr.Textbox(label="Prompt Enhancer"),
title="Prompt Enhancer",
description='Make your prompts more detailed!',
examples=examples,
).launch(share=True) |