added seamless tiling mode and commands

ldm/simplet2i.py

@@ -14,6 +14,7 @@ from PIL import Image
 from tqdm import tqdm, trange
 from itertools import islice
 from einops import rearrange, repeat
+from torch import nn
 from torchvision.utils import make_grid
 from pytorch_lightning import seed_everything
 from torch import autocast
@@ -109,6 +110,7 @@ class T2I:
         downsampling_factor
         precision
         strength
+        seamless
         embedding_path
 
     The vast majority of these arguments default to reasonable values.
@@ -132,6 +134,7 @@ class T2I:
             precision='autocast',
             full_precision=False,
             strength=0.75,  # default in scripts/img2img.py
+            seamless=False,
             embedding_path=None,
             device_type = 'cuda',
             # just to keep track of this parameter when regenerating prompt
@@ -153,6 +156,7 @@ class T2I:
         self.precision                = precision
         self.full_precision           = full_precision
         self.strength                 = strength
+        self.seamless                 = seamless
         self.embedding_path           = embedding_path
         self.device_type              = device_type
         self.model                    = None     # empty for now
@@ -217,6 +221,7 @@ class T2I:
             step_callback  =    None,
             width          =    None,
             height         =    None,
+            seamless       =    False,
             # these are specific to img2img
             init_img       =    None,
             fit            =    False,
@@ -238,6 +243,7 @@ class T2I:
            width                           // width of image, in multiples of 64 (512)
            height                          // height of image, in multiples of 64 (512)
            cfg_scale                       // how strongly the prompt influences the image (7.5) (must be >1)
+           seamless                        // whether the generated image should tile
            init_img                        // path to an initial image - its dimensions override width and height
            strength                        // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
            gfpgan_strength                 // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
@@ -265,6 +271,7 @@ class T2I:
         seed                  = seed       or self.seed
         width                 = width      or self.width
         height                = height     or self.height
+        seamless              = seamless   or self.seamless
         cfg_scale             = cfg_scale  or self.cfg_scale
         ddim_eta              = ddim_eta   or self.ddim_eta
         iterations            = iterations or self.iterations
@@ -274,6 +281,10 @@ class T2I:
         model = (
             self.load_model()
         )  # will instantiate the model or return it from cache
+        for m in model.modules():
+            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                m.padding_mode = 'circular' if seamless else m._orig_padding_mode
+        
         assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0'
         assert (
             0.0 <= strength <= 1.0
@@ -562,6 +573,10 @@ class T2I:
 
             self._set_sampler()
 
+            for m in self.model.modules():
+                if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                    m._orig_padding_mode = m.padding_mode
+
         return self.model
 
     def _set_sampler(self):

scripts/dream.py

@@ -9,6 +9,7 @@ import sys
 import copy
 import warnings
 import time
+import torch.nn as nn
 from ldm.dream.devices import choose_torch_device
 import ldm.dream.readline
 from ldm.dream.pngwriter import PngWriter, PromptFormatter
@@ -60,6 +61,7 @@ def main():
         grid  = opt.grid,
         # this is solely for recreating the prompt
         latent_diffusion_weights=opt.laion400m,
+        seamless=opt.seamless,
         embedding_path=opt.embedding_path,
         device_type=opt.device
     )
@@ -92,6 +94,14 @@ def main():
         f'>> model loaded in', '%4.2fs' % (time.time() - tic)
     )
 
+    for m in t2i.model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+            m._orig_padding_mode = m.padding_mode
+            if opt.seamless:
+                m.padding_mode = 'circular'
+    if opt.seamless:
+        print(">> changed to seamless tiling mode")
+
     if not infile:
         print(
             "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
@@ -374,6 +384,11 @@ def create_argv_parser():
         default='outputs/img-samples',
         help='Directory to save generated images and a log of prompts and seeds. Default: outputs/img-samples',
     )
+    parser.add_argument(
+        '--seamless',
+        action='store_true',
+        help='Change the model to seamless tiling (circular) mode',
+    )
     parser.add_argument(
         '--embedding_path',
         type=str,
@@ -474,6 +489,11 @@ def create_cmd_parser():
         default=None,
         help='Directory to save generated images and a log of prompts and seeds',
     )
+    parser.add_argument(
+        '--seamless',
+        action='store_true',
+        help='Change the model to seamless tiling (circular) mode',
+    )
     parser.add_argument(
         '-i',
         '--individual',