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| 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", "Emojis"], 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="<div>By typing a prompt and pressing submit you can generate images based on this prompt. <a href='https://github.com/CompVis/latent-diffusion' target='_blank'>ruDALLE</a> is an open source text-to-image model, this Arbitrary Aspect ration implementation was created by <a href='https://github.com/shonenkov-AI' target='_blank'>Alex Shonenkov</a><br>This UI to the model was assembled by <a style='color: rgb(245, 158, 11);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a></div>", | |
| article="<h4 style='font-size: 110%;margin-top:.5em'>Biases acknowledgment</h4><div>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 <a href='https://arxiv.org/abs/2112.10752' target='_blank'>Latent Diffusion paper</a>:<i> \"Deep learning modules tend to reproduce or exacerbate biases that are already present in the data\"</i>. The models are meant to be used for research purposes, such as this one.</div><h4 style='font-size: 110%;margin-top:1em'>Who owns the images produced by this demo?</h4><div>Definetly not me! Probably you do. I say probably because the Copyright discussion about AI generated art is ongoing. So <a href='https://www.theverge.com/2022/2/21/22944335/us-copyright-office-reject-ai-generated-art-recent-entrance-to-paradise' target='_blank'>it may be the case that everything produced here falls automatically into the public domain</a>. But in any case it is either yours or is in the public domain.</div>") | |
| iface.launch(enable_queue=True) |