import gc
import math
import sys
from IPython import display
import torch
from torchvision import utils as tv_utils
from torchvision.transforms import functional as TF
import gradio as gr
from git.repo.base import Repo
from os.path import exists as path_exists
if not (path_exists(f"v-diffusion-pytorch")):
Repo.clone_from("https://github.com/crowsonkb/v-diffusion-pytorch", "v-diffusion-pytorch")
if not (path_exists(f"CLIP")):
Repo.clone_from("https://github.com/openai/CLIP", "CLIP")
sys.path.append('v-diffusion-pytorch')
from huggingface_hub import hf_hub_download
from CLIP import clip
from diffusion import get_model, sampling, utils
cc12m_model = hf_hub_download(repo_id="multimodalart/crowsonkb-v-diffusion-cc12m-1-cfg", filename="cc12m_1_cfg.pth")
model = get_model('cc12m_1_cfg')()
_, side_y, side_x = model.shape
model.load_state_dict(torch.load(cc12m_model, map_location='cpu'))
model = model.half().cuda().eval().requires_grad_(False)
clip_model = clip.load(model.clip_model, jit=False, device='cpu')[0]
def run_all(prompt, steps, n_images, weight):
import random
seed = int(random.randint(0, 2147483647))
target_embed = clip_model.encode_text(clip.tokenize(prompt)).float().cuda()
def cfg_model_fn(x, t):
"""The CFG wrapper function."""
n = x.shape[0]
x_in = x.repeat([2, 1, 1, 1])
t_in = t.repeat([2])
clip_embed_repeat = target_embed.repeat([n, 1])
clip_embed_in = torch.cat([torch.zeros_like(clip_embed_repeat), clip_embed_repeat])
v_uncond, v_cond = model(x_in, t_in, clip_embed_in).chunk(2, dim=0)
v = v_uncond + (v_cond - v_uncond) * weight
return v
gc.collect()
torch.cuda.empty_cache()
torch.manual_seed(seed)
x = torch.randn([n_images, 3, side_y, side_x], device='cuda')
t = torch.linspace(1, 0, steps + 1, device='cuda')[:-1]
step_list = utils.get_spliced_ddpm_cosine_schedule(t)
outs = sampling.plms_sample(cfg_model_fn, x, step_list, {})#, callback=display_callback)
images_out = []
for i, out in enumerate(outs):
images_out.append(utils.to_pil_image(out))
return(images_out)
##################### START GRADIO HERE ############################
#image = gr.outputs.Image(type="pil", label="Your result")
gallery = gr.Gallery(css={"height": "256px","width":"256px"})
iface = gr.Interface(
fn=run_all,
inputs=[
gr.inputs.Textbox(label="Prompt - try adding increments to your prompt such as 'oil on canvas', 'a painting', 'a book cover'",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=50,maximum=250,minimum=1,step=1),
gr.inputs.Slider(label="Number of images in parallel", default=2, maximum=4, minimum=1,step=1),
gr.inputs.Slider(label="Weight", default=5, maximum=15, minimum=0, step=1),
#gr.inputs.Checkbox(label="CLIP Guided"),
#gr.inputs.Dropdown(label="Flavor",choices=["ginger", "cumin", "holywater", "zynth", "wyvern", "aaron", "moth", "juu", "custom"]),
#markdown,
#gr.inputs.Dropdown(label="Style",choices=["Default","Balanced","Detailed","Consistent Creativity","Realistic","Smooth","Subtle MSE","Hyper Fast Results"],default="Hyper Fast Results"),
#gr.inputs.Radio(label="Width", choices=[32,64,128,256,512],default=512),
#gr.inputs.Radio(label="Height", choices=[32,64,128,256,512],default=512),
],
outputs=gallery,
title="Generate images from text with V-Diffusion CC12M",
#description="",
#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 model was trained on an unfiltered version the LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the the removal of illegal content) and is meant to be used for research purposes, such as this one.
You can read more on LAION's websiteWho owns the images produced by this demo?
"
)
iface.launch()