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 Shonenkov
This UI to the model was assembled by @multimodalart
", article="

Biases 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?

Definetly not me! Probably you do. I say probably because the Copyright discussion about AI generated art is ongoing. So it may be the case that everything produced here falls automatically into the public domain. But in any case it is either yours or is in the public domain.
") iface.launch(enable_queue=True)