import gradio as gr
from PIL import Image
import torch
import numpy as np
from os.path import exists as path_exists
from git.repo.base import Repo
from einops import rearrange
import torchvision.transforms as transforms
from torchvision.utils import make_grid
if not (path_exists(f"rudalle-aspect-ratio")):
Repo.clone_from("https://github.com/shonenkov-AI/rudalle-aspect-ratio", "rudalle-aspect-ratio")
import sys
sys.path.append('./rudalle-aspect-ratio')
from rudalle_aspect_ratio import RuDalleAspectRatio, get_rudalle_model
from rudalle import get_vae, get_tokenizer
from rudalle.pipelines import show
#model_path_e = hf_hub_download(repo_id="multimodalart/compvis-latent-diffusion-text2img-large", filename="txt2img-f8-large.ckpt")
device = 'cuda'
dalle_surreal = get_rudalle_model('Surrealist_XL', fp16=True, device=device)
dalle_real = get_rudalle_model('Malevich',fp16=True,device=device)
dalle_emoji = get_rudalle_model('Emojich',fp16=True,device=device)
vae, tokenizer = get_vae().to(device), get_tokenizer()
def np_gallery(array, ncols=3):
nindex, height, width, intensity = array.shape
nrows = nindex//ncols
assert nindex == nrows*ncols
# want result.shape = (height*nrows, width*ncols, intensity)
result = (array.reshape(nrows, ncols, height, width, intensity)
.swapaxes(1,2)
.reshape(height*nrows, width*ncols, intensity))
return result
def image_to_np(image):
return np.asarray(image)
def run(prompt, aspect_ratio, model):
if(model=='Surrealism'):
dalle = dalle_surreal
elif(model=='Realism'):
dalle = dalle_real
elif(model=='Emoji'):
dalle = dalle_emoji
if(aspect_ratio == 'Square'):
aspect_ratio_value = 1
top_k = 512
elif(aspect_ratio == 'Horizontal'):
aspect_ratio_value = 32/9
top_k = 1024
elif(aspect_ratio == 'Vertical'):
aspect_ratio_value = 9/32
top_k = 512
rudalle_ar = RuDalleAspectRatio(
dalle=dalle, vae=vae, tokenizer=tokenizer,
aspect_ratio=aspect_ratio_value, bs=1, device=device
)
_, result_pil_images = rudalle_ar.generate_images(prompt, top_k, 0.975, 1)
#np_images = map(image_to_np,result_pil_images)
#np_grid = np_gallery(np.array(list(np_images)),2)
#result_grid = Image.fromarray(np_grid)
return(result_pil_images[0])
image = gr.outputs.Image(type="pil", label="Your result")
iface = gr.Interface(fn=run, inputs=[
gr.inputs.Textbox(label="Prompt (if not in Russian, it will be automatically translated to Russian)",default="chalk pastel drawing of a dog wearing a funny hat"),
#gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=45,maximum=50,minimum=1,step=1),
gr.inputs.Radio(label="Aspect Ratio", choices=["Square", "Horizontal", "Vertical"],default="Horizontal"),
gr.inputs.Dropdown(label="Model", choices=["Surrealism","Realism", "Emoji"], default="Surrealism")
#gr.inputs.Radio(label="Height", choices=[32,64,128,256,512],default=256),
#gr.inputs.Slider(label="Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4),
#gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=5.0, minimum=1.0, maximum=15.0),
#gr.inputs.Slider(label="ETA - between 0 and 1. Lower values can provide better quality, higher values can be more diverse",default=0.0,minimum=0.0, maximum=1.0,step=0.1),
],
outputs=image,
#css=css,
title="Generate images from text with ruDALLE",
description="By typing a prompt and pressing submit you can generate images based on this prompt.
ruDALLE is an open source text-to-image model, this Arbitrary Aspect ration implementation was created by
Alex ShonenkovThis UI to the model was assembled by
@multimodalartBiases acknowledgment
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exarcbates societal biases. According to the
Latent Diffusion paper:
\"Deep learning modules tend to reproduce or exacerbate biases that are already present in the data\". The models are meant to be used for research purposes, such as this one.
Who owns the images produced by this demo?
")
iface.launch(enable_queue=True)