Merge branch 'prixt-seamless' into development

README.md

@@ -138,6 +138,13 @@ You may also pass a -v<count> option to generate count variants on the original
 passing the first generated image back into img2img the requested number of times. It generates interesting
 variants.
 
+## Seamless Tiling
+
+The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the --seamless option when starting the script which will result in all generated images to tile, or for each dream> prompt as shown here:
+```
+dream> "pond garden with lotus by claude monet" --seamless -s100 -n4
+```
+
 ## GFPGAN and Real-ESRGAN Support
 
 The script also provides the ability to do face restoration and
@@ -400,7 +407,11 @@ repository and associated paper for details and limitations.
 
 # Latest Changes
 
-- v1.13 (3 September 2022)
+- v1.14 (In progress)
+
+ - Add "seamless mode" for circular tiling of image. Generates beautiful effects. ([prixt](https://github.com/prixt))
+
+- v1.13 (3 September 2022
 
  - Support image variations (see [VARIATIONS](VARIATIONS.md) ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
  - Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)

ldm/dream/pngwriter.py

@@ -59,6 +59,8 @@ def normalize_prompt(self):
  switches.append(f'-H{opt.height or t2i.height}')
  switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
  switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
+ if opt.seamless or t2i.seamless:
+ switches.append(f'--seamless')
  if opt.init_img:
  switches.append(f'-I{opt.init_img}')
  if opt.fit:

ldm/dream/server.py

@@ -70,7 +70,8 @@ def do_POST(self):
  steps = int(post_data['steps'])
  width = int(post_data['width'])
  height = int(post_data['height'])
- fit = 'fit' in post_data
+ fit = 'fit' in post_data
+ seamless = 'seamless' in post_data
  cfgscale = float(post_data['cfgscale'])
  sampler_name = post_data['sampler']
  gfpgan_strength = float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0
@@ -164,6 +165,7 @@ def image_progress(sample, step):
  gfpgan_strength = gfpgan_strength,
  upscale = upscale,
  sampler_name = sampler_name,
+ seamless = seamless,
  step_callback=image_progress,
  image_callback=image_done)
  else:
@@ -185,6 +187,7 @@ def image_progress(sample, step):
  width = width,
  height = height,
  fit = fit,
+ seamless = seamless,
  gfpgan_strength=gfpgan_strength,
  upscale = upscale,
  step_callback=image_progress,

ldm/simplet2i.py

@@ -14,6 +14,7 @@
 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 @@ def __init__(
  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 @@ def __init__(
  self.precision = precision
  self.full_precision = True if choose_torch_device() == 'mps' else 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 @@ def prompt2image(
  step_callback = None,
  width = None,
  height = None,
+ seamless = False,
  # these are specific to img2img
  init_img = None,
  fit = False,
@@ -240,6 +245,7 @@ def prompt2image(
  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
@@ -268,6 +274,7 @@ def process_image(image,seed):
  steps = steps or self.steps
  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
@@ -278,6 +285,10 @@ def process_image(image,seed):
  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
@@ -603,6 +614,10 @@ def load_model(self):
 
  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
 
  # returns a tensor filled with random numbers from a normal distribution

scripts/dream.py

@@ -62,6 +62,7 @@ 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
  )
@@ -87,6 +88,9 @@ def main():
  print(f'{e}. Aborting.')
  sys.exit(-1)
 
+ if opt.seamless:
+ print(">> changed to seamless tiling mode")
+
  # preload the model
  tic = time.time()
  t2i.load_model()
@@ -418,6 +422,11 @@ 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,
@@ -540,6 +549,11 @@ 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',

static/dream_web/index.html

@@ -37,6 +37,8 @@ <h2 id="header">Stable Diffusion Dream Server</h2>
  <option value="k_euler_a">KEULER_A</option>
  <option value="k_heun">KHEUN</option>
  </select>
+ <input type="checkbox" name="seamless" id="seamless">
+ <label for="seamless">Seamless circular tiling</label>
  <br>
  <label title="Set to multiple of 64" for="width">Width:</label>
  <select id="width" name="width" value="512">
@@ -64,7 +66,7 @@ <h2 id="header">Stable Diffusion Dream Server</h2>
  <input value="-1" type="number" id="seed" name="seed">
  <button type="button" id="reset-seed">&olarr;</button>
  <input type="checkbox" name="progress_images" id="progress_images">
- <label for="progress_images">Display in-progress images (slows down generation):</label>
+ <label for="progress_images">Display in-progress images (slower)</label>
  <button type="button" id="reset-all">Reset to Defaults</button>
  </div>
  <div id="img2img">
@@ -74,7 +76,7 @@ <h2 id="header">Stable Diffusion Dream Server</h2>
  <label for="strength">Img2Img Strength:</label>
  <input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
  <input type="checkbox" id="fit" name="fit" checked>
- <label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height:</label>
+ <label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height</label>
  </div>
  <div id="gfpgan">
  <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>