File size: 1,837 Bytes
05d7e61 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import gradio as gr
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline
from diffusers import StableDiffusionPipeline
summarizer = pipeline("summarization")
model_id = "runwayml/stable-diffusion-v1-5"
SAVED_CHECKPOINT = 'mikegarts/distilgpt2-lotr'
MIN_WORDS = 120
def get_image_pipe():
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
pipe.to(pipe.device)
return pipe
def get_model():
model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT)
tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT)
return model, tokenizer
def generate(prompt):
model, tokenizer = get_model()
input_context = prompt
input_ids = tokenizer.encode(input_context, return_tensors="pt").to(model.device)
outputs = model.generate(
input_ids=input_ids,
max_length=100,
temperature=0.7,
num_return_sequences=3,
do_sample=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True).rsplit('.', 1)[0] + '.'
def make_image(prompt):
pipe = get_image_pipe()
image = pipe(prompt).images[0]
def predict(prompt):
story = generate(prompt=prompt)
summary = summarizer(story, min_length=5, max_length=20)[0]['summary_text']
image = make_image(summary)
return story, summarizer(story, min_length=5, max_length=20), image
title = "Lord of the rings app"
description = """A Lord of the rings insired app that combines text and image generation"""
gr.Interface(
fn=predict,
inputs="textbox",
outputs=["text", "text", "image"],
title=title,
description=description,
examples=[["My new adventure would be"], ["Then I a hobbit appeared"], ["Frodo told me"]]
).launch(share=True) |