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"""
Train a diffusion model on images.
"""
import gradio as gr
import argparse
from einops import rearrange
from glide_text2im import dist_util, logger
from torchvision.utils import make_grid
from glide_text2im.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
args_to_dict,
add_dict_to_argparser,
)
from glide_text2im.image_datasets_sketch import get_tensor
from glide_text2im.train_util import TrainLoop
from glide_text2im.glide_util import sample
import torch
import os
import torch as th
import torchvision.utils as tvu
import torch.distributed as dist
from PIL import Image
import cv2
import numpy as np
def run(image, mode, sample_c=1.3, num_samples=3, sample_step=100):
parser, parser_up = create_argparser()
args = parser.parse_args()
args_up = parser_up.parse_args()
dist_util.setup_dist()
if mode == 'sketch':
args.mode = 'coco-edge'
args_up.mode = 'coco-edge'
args.model_path = './ckpt/base_edge.pt'
args.sr_model_path = './ckpt/upsample_edge.pt'
elif mode == 'mask':
args.mode = 'coco'
args_up.mode = 'coco'
args.model_path = './ckpt/base_mask.pt'
args.sr_model_path = './ckpt/upsample_mask.pt'
args.val_data_dir = image
args.sample_c = sample_c
args.num_samples = num_samples
options=args_to_dict(args, model_and_diffusion_defaults(0.).keys())
model, diffusion = create_model_and_diffusion(**options)
options_up=args_to_dict(args_up, model_and_diffusion_defaults(True).keys())
model_up, diffusion_up = create_model_and_diffusion(**options_up)
if args.model_path:
print('loading model')
model_ckpt = dist_util.load_state_dict(args.model_path, map_location="cpu")
model.load_state_dict(
model_ckpt , strict=True )
if args.sr_model_path:
print('loading sr model')
model_ckpt2 = dist_util.load_state_dict(args.sr_model_path, map_location="cpu")
model_up.load_state_dict(
model_ckpt2 , strict=True )
model.to(dist_util.dev())
model_up.to(dist_util.dev())
model.eval()
model_up.eval()
########### dataset
# logger.log("creating data loader...")
if args.mode == 'coco':
pil_image = image
label_pil = pil_image.convert("RGB").resize((256, 256), Image.NEAREST)
label_tensor = get_tensor()(label_pil)
data_dict = {"ref":label_tensor.unsqueeze(0).repeat(args.num_samples, 1, 1, 1)}
elif args.mode == 'coco-edge':
# pil_image = Image.open(image)
pil_image = image
label_pil = pil_image.convert("L").resize((256, 256), Image.NEAREST)
im_dist = cv2.distanceTransform(255-np.array(label_pil), cv2.DIST_L1, 3)
im_dist = np.clip((im_dist) , 0, 255).astype(np.uint8)
im_dist = Image.fromarray(im_dist).convert("RGB")
label_tensor = get_tensor()(im_dist)[:1]
data_dict = {"ref":label_tensor.unsqueeze(0).repeat(args.num_samples, 1, 1, 1)}
print("sampling...")
sampled_imgs = []
grid_imgs = []
img_id = 0
while (True):
if img_id >= args.num_samples:
break
model_kwargs = data_dict
with th.no_grad():
samples_lr =sample(
glide_model= model,
glide_options= options,
side_x= 64,
side_y= 64,
prompt=model_kwargs,
batch_size= args.num_samples,
guidance_scale=args.sample_c,
device=dist_util.dev(),
prediction_respacing= str(sample_step),
upsample_enabled= False,
upsample_temp=0.997,
mode = args.mode,
)
samples_lr = samples_lr.clamp(-1, 1)
tmp = (127.5*(samples_lr + 1.0)).int()
model_kwargs['low_res'] = tmp/127.5 - 1.
samples_hr =sample(
glide_model= model_up,
glide_options= options_up,
side_x=256,
side_y=256,
prompt=model_kwargs,
batch_size=args.num_samples,
guidance_scale=1,
device=dist_util.dev(),
prediction_respacing= "fast27",
upsample_enabled=True,
upsample_temp=0.997,
mode = args.mode,
)
samples_hr = samples_hr
for hr in samples_hr:
hr = 255. * rearrange((hr.cpu().numpy()+1.0)*0.5, 'c h w -> h w c')
sample_img = Image.fromarray(hr.astype(np.uint8))
sampled_imgs.append(sample_img)
img_id += 1
grid_imgs.append(samples_hr)
grid = torch.stack(grid_imgs, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=2)
# to image
grid = 255. * rearrange((grid+1.0)*0.5, 'c h w -> h w c').cpu().numpy()
return Image.fromarray(grid.astype(np.uint8))
def create_argparser():
defaults = dict(
data_dir="",
val_data_dir="",
model_path="./base_edge.pt",
sr_model_path="./upsample_edge.pt",
encoder_path="",
schedule_sampler="uniform",
lr=1e-4,
weight_decay=0.0,
lr_anneal_steps=0,
batch_size=2,
microbatch=-1, # -1 disables microbatches
ema_rate="0.9999", # comma-separated list of EMA values
log_interval=100,
save_interval=20000,
resume_checkpoint="",
use_fp16=False,
fp16_scale_growth=1e-3,
sample_c=1.,
sample_respacing="100",
uncond_p=0.2,
num_samples=3,
finetune_decoder = False,
mode = '',
)
defaults_up = defaults
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
defaults_up.update(model_and_diffusion_defaults(True))
parser_up = argparse.ArgumentParser()
add_dict_to_argparser(parser_up, defaults_up)
return parser, parser_up
image = gr.outputs.Image(type="pil", label="Sampled results")
css = ".output-image{height: 528px !important} .output-carousel .output-image{height:272px !important} a{text-decoration: underline}"
demo = gr.Interface(fn=run, inputs=[
gr.inputs.Image(type="pil", label="Input Sketch" ) ,
# gr.Image(image_mode="L", source="canvas", type="pil", shape=(256,256), invert_colors=False, tool="editor"),
gr.inputs.Radio(label="Input Mode - The type of your input", choices=["mask", "sketch"],default="sketch"),
gr.inputs.Slider(label="sample_c - The strength of classifier-free guidance",default=1.4, minimum=1.0, maximum=2.0),
gr.inputs.Slider(label="Number of samples - How many samples you wish to generate", default=4, step=1, minimum=1, maximum=16),
gr.inputs.Slider(label="Number of Steps - How many steps you want to use", default=100, step=10, minimum=50, maximum=1000),
],
outputs=[image],
css=css,
title="Generate images from sketches with PITI",
description="<div>By uploading a sketch map or a semantic map and pressing submit, you can generate images based on your input.</div>")
demo.launch(enable_queue=True)
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