UDiffText / app.py
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import cv2
import torch
import os, glob
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from contextlib import nullcontext
from pytorch_lightning import seed_everything
from os.path import join as ospj
from util import *
def predict(cfgs, model, sampler, batch):
context = nullcontext if cfgs.aae_enabled else torch.no_grad
with context():
batch, batch_uc_1, batch_uc_2 = prepare_batch(cfgs, batch)
if cfgs.dual_conditioner:
c, uc_1, uc_2 = model.conditioner.get_unconditional_conditioning(
batch,
batch_uc_1=batch_uc_1,
batch_uc_2=batch_uc_2,
force_uc_zero_embeddings=cfgs.force_uc_zero_embeddings,
)
else:
c, uc_1 = model.conditioner.get_unconditional_conditioning(
batch,
batch_uc=batch_uc_1,
force_uc_zero_embeddings=cfgs.force_uc_zero_embeddings,
)
if cfgs.dual_conditioner:
x = sampler.get_init_noise(cfgs, model, cond=c, batch=batch, uc_1=uc_1, uc_2=uc_2)
samples_z = sampler(model, x, cond=c, batch=batch, uc_1=uc_1, uc_2=uc_2, init_step=0,
aae_enabled = cfgs.aae_enabled, detailed = cfgs.detailed)
else:
x = sampler.get_init_noise(cfgs, model, cond=c, batch=batch, uc=uc_1)
samples_z = sampler(model, x, cond=c, batch=batch, uc=uc_1, init_step=0,
aae_enabled = cfgs.aae_enabled, detailed = cfgs.detailed)
samples_x = model.decode_first_stage(samples_z)
samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
return samples, samples_z
def demo_predict(input_blk, text, num_samples, steps, scale, seed, show_detail):
global cfgs, global_index
global_index += 1
if num_samples > 1: cfgs.noise_iters = 0
cfgs.batch_size = num_samples
cfgs.steps = steps
cfgs.scale[0] = scale
cfgs.detailed = show_detail
seed_everything(seed)
sampler = init_sampling(cfgs)
image = input_blk["image"]
mask = input_blk["mask"]
image = cv2.resize(image, (cfgs.W, cfgs.H))
mask = cv2.resize(mask, (cfgs.W, cfgs.H))
mask = (mask == 0).astype(np.int32)
image = torch.from_numpy(image.transpose(2,0,1)).to(dtype=torch.float32) / 127.5 - 1.0
mask = torch.from_numpy(mask.transpose(2,0,1)).to(dtype=torch.float32).mean(dim=0, keepdim=True)
masked = image * mask
mask = 1 - mask
seg_mask = torch.cat((torch.ones(len(text)), torch.zeros(cfgs.seq_len-len(text))))
# additional cond
txt = f"\"{text}\""
original_size_as_tuple = torch.tensor((cfgs.H, cfgs.W))
crop_coords_top_left = torch.tensor((0, 0))
target_size_as_tuple = torch.tensor((cfgs.H, cfgs.W))
image = torch.tile(image[None], (num_samples, 1, 1, 1))
mask = torch.tile(mask[None], (num_samples, 1, 1, 1))
masked = torch.tile(masked[None], (num_samples, 1, 1, 1))
seg_mask = torch.tile(seg_mask[None], (num_samples, 1))
original_size_as_tuple = torch.tile(original_size_as_tuple[None], (num_samples, 1))
crop_coords_top_left = torch.tile(crop_coords_top_left[None], (num_samples, 1))
target_size_as_tuple = torch.tile(target_size_as_tuple[None], (num_samples, 1))
text = [text for i in range(num_samples)]
txt = [txt for i in range(num_samples)]
name = [str(global_index) for i in range(num_samples)]
batch = {
"image": image,
"mask": mask,
"masked": masked,
"seg_mask": seg_mask,
"label": text,
"txt": txt,
"original_size_as_tuple": original_size_as_tuple,
"crop_coords_top_left": crop_coords_top_left,
"target_size_as_tuple": target_size_as_tuple,
"name": name
}
samples, samples_z = predict(cfgs, model, sampler, batch)
samples = samples.cpu().numpy().transpose(0, 2, 3, 1) * 255
results = [Image.fromarray(sample.astype(np.uint8)) for sample in samples]
if cfgs.detailed:
sections = []
attn_map = Image.open(f"./temp/attn_map/attn_map_{global_index}.png")
seg_maps = np.load(f"./temp/seg_map/seg_{global_index}.npy")
for i, seg_map in enumerate(seg_maps):
seg_map = cv2.resize(seg_map, (cfgs.W, cfgs.H))
sections.append((seg_map, text[0][i]))
seg = (results[0], sections)
else:
attn_map = None
seg = None
return results, attn_map, seg
if __name__ == "__main__":
cfgs = OmegaConf.load("./configs/demo.yaml")
model = init_model(cfgs)
global_index = 0
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.HTML(
"""
<div style="text-align: center; max-width: 1200px; margin: 20px auto;">
<h1 style="font-weight: 600; font-size: 2rem; margin: 0rem">
UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models
</h1>
<h3 style="font-weight: 450; font-size: 1rem; margin: 0rem">
[<a href="" style="color:blue;">arXiv</a>]
[<a href="" style="color:blue;">Code</a>]
[<a href="" style="color:blue;">ProjectPage</a>]
</h3>
<h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem">
Our proposed UDiffText is capable of synthesizing accurate and harmonious text in either synthetic or real-word images, thus can be applied to tasks like scene text editing (a), arbitrary text generation (b) and accurate T2I generation (c)
</h2>
<div align=center><img src="file/demo/teaser.png" alt="UDiffText" width="80%"></div>
</div>
"""
)
with gr.Row():
with gr.Column():
input_blk = gr.Image(source='upload', tool='sketch', type="numpy", label="Input", height=512)
text = gr.Textbox(label="Text to render:", info="the text you want to render at the masked region")
run_button = gr.Button(variant="primary")
with gr.Accordion("Advanced options", open=False):
num_samples = gr.Slider(label="Images", info="number of generated images, locked as 1", minimum=1, maximum=1, value=1, step=1)
steps = gr.Slider(label="Steps", info ="denoising sampling steps", minimum=1, maximum=200, value=50, step=1)
scale = gr.Slider(label="Guidance Scale", info="the scale of classifier-free guidance (CFG)", minimum=0.0, maximum=10.0, value=4.0, step=0.1)
seed = gr.Slider(label="Seed", info="random seed for noise initialization", minimum=0, maximum=2147483647, step=1, randomize=True)
show_detail = gr.Checkbox(label="Show Detail", info="show the additional visualization results", value=True)
with gr.Column():
gallery = gr.Gallery(label="Output", height=512, preview=True)
with gr.Accordion("Visualization results", open=True):
with gr.Tab(label="Attention Maps"):
gr.Markdown("### Attention maps for each character (extracted from middle blocks at intermediate sampling step):")
attn_map = gr.Image(show_label=False, show_download_button=False)
with gr.Tab(label="Segmentation Maps"):
gr.Markdown("### Character-level segmentation maps (using upscaled attention maps):")
seg_map = gr.AnnotatedImage(height=384, show_label=False, show_download_button=False)
# examples
examples = []
example_paths = sorted(glob.glob(ospj("./demo/examples", "*")))
for example_path in example_paths:
label = example_path.split(os.sep)[-1].split(".")[0].split("_")[0]
examples.append([example_path, label])
gr.Markdown("## Examples:")
gr.Examples(
examples=examples,
inputs=[input_blk, text]
)
run_button.click(fn=demo_predict, inputs=[input_blk, text, num_samples, steps, scale, seed, show_detail], outputs=[gallery, attn_map, seg_map])
block.launch()