File size: 2,908 Bytes
05d7e61 79c9a48 3e4033b 79c9a48 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b 05d7e61 3e4033b |
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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import gradio as gr
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline
from diffusers import StableDiffusionPipeline
READ_TOKEN = os.environ.get('HF_ACCESS_TOKEN', None)
model_id = "runwayml/stable-diffusion-v1-5"
# model_id = "CompVis/stable-diffusion-v1-4"
has_cuda = torch.cuda.is_available()
device = "cpu"
if has_cuda:
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=READ_TOKEN)
device = "cuda"
else:
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", use_auth_token=READ_TOKEN)
device = "cpu"
pipe.to(device)
def safety_checker(images, clip_input):
return images, False
pipe.safety_checker = safety_checker
SAVED_CHECKPOINT = 'mikegarts/distilgpt2-lotr'
model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT)
tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT)
summarizer = pipeline("summarization")
def break_until_dot(txt):
return txt.rsplit('.', 1)[0] + '.'
def generate(prompt):
input_context = prompt
input_ids = tokenizer.encode(input_context, return_tensors="pt").to(model.device)
outputs = model.generate(
input_ids=input_ids,
max_length=180,
temperature=0.7,
num_return_sequences=3,
do_sample=True
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
return break_until_dot(decoded)
def generate_image(prompt, inference_steps):
prompt = prompt + ', masterpiece charcoal pencil art lord of the rings illustration'
img = pipe(prompt, height=512, width=512, num_inference_steps=inference_steps)
return img.images[0]
def generate_story(prompt):
story = generate(prompt=prompt)
summary = summarizer(story, min_length=5, max_length=15)[0]['summary_text']
summary = break_until_dot(summary)
return story, summary, gr.update(visible=True)
with gr.Blocks() as demo:
title = gr.Markdown('## Lord of the rings app')
description = gr.Markdown('### A Lord of the rings insired app that combines text and image generation')
prompt = gr.Textbox(label="Your prompt", value="And then the hobbit said")
story = gr.Textbox(label="Your story")
summary = gr.Textbox(label="Summary")
bt_make_text = gr.Button("Generate text")
bt_make_image = gr.Button("Generate and image (takes about 10-15 minutes on CPU)", visible=False)
image = gr.Image(label='Illustration for your story')
inference_steps = gr.Slider(5, 35, value=15, step=1, label="Num inference steps (more steps makes a better image but takes more time)")
bt_make_text.click(fn=generate_story, inputs=prompt, outputs=[story, summary, bt_make_image])
bt_make_image.click(fn=generate_image, inputs=[summary, inference_steps], outputs=image)
demo.launch(share=True) |