nodes/ssitu-ComfyUI_UltimateSDUpscale
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refactor: reuse functions in patch for batched tiles

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ssitu
2026-02-11 19:02:29 -05:00
parent 5d3887fad7
commit 6a4594da4c
3 changed files with 19 additions and 94 deletions
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test/test_main_workflow.py
+4 -4
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@@ -49,7 +49,7 @@ class TestMainWorkflow:
cfg=8,
sampler_name="euler",
scheduler="normal",
denoise=0.6,
denoise=0.7,
upscale_model=upscale_model,
mode_type="Chess",
tile_width=512,
@@ -125,7 +125,7 @@ class TestMainWorkflow:
cfg=8,
sampler_name="euler",
scheduler="normal",
denoise=0.6,
denoise=0.7,
mode_type="Chess",
tile_width=512,
tile_height=512,
@@ -185,8 +185,8 @@ class TestMainWorkflow:
with torch.inference_mode():
# Setup custom scheduler and sampler
custom_scheduler = node_classes["KarrasScheduler"]
(sigmas,) = execute(custom_scheduler, 9, 14.614642, 0.0291675, 7.0)
(_, sigmas) = execute(node_classes["SplitSigmasDenoise"], sigmas, 0.6)
(sigmas,) = execute(custom_scheduler, 10, 14.614642, 0.0291675, 7.0)
(_, sigmas) = execute(node_classes["SplitSigmasDenoise"], sigmas, 0.7)
custom_sampler = node_classes["KSamplerSelect"]
(sampler,) = execute(custom_sampler, "dpmpp_2m")
test/test_settings.py
+7 -8
View File
@@ -68,15 +68,14 @@ def test_minimal_tile_sizes(
# Save and reload sample image
sample_dir = test_dirs.sample_images
filename = image_name_format("non_uniform_tiles", EXT, batch_size)
filename_path = CATEGORY / filename
save_image(upscaled[0], sample_dir / filename_path)
upscaled = load_image(sample_dir / filename_path)
filename = CATEGORY / image_name_format("non_uniform_tiles", EXT, batch_size)
save_image(upscaled[0], sample_dir / filename)
upscaled = load_image(sample_dir / filename)
# Compare with reference
test_image_dir = test_dirs.test_images
test_image = load_image(test_image_dir / filename_path)
diff = img_tensor_mae(blur(upscaled), blur(test_image))
test_image = load_image(test_image_dir / filename)
logger = logging.getLogger(__name__)
logger.info(f"{filename_path} MAE: {diff}")
assert diff < 0.01, f"{filename_path} output doesn't match reference"
diff = img_tensor_mae(blur(upscaled), blur(test_image))
logger.info(f"{filename} MAE: {diff}")
assert diff < 0.02, f"{filename} does not match reference (MAE {diff})"
usdu_patch.py
+8 -82
View File
@@ -24,6 +24,7 @@ from comfy_extras.nodes_custom_sampler import SamplerCustom
import modules.shared as shared
from nodes import common_ksampler, VAEEncode, VAEDecode, VAEDecodeTiled
from repositories import ultimate_upscale as usdu
import usdu_utils
logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler())
@@ -45,81 +46,6 @@ def round_length(length: int, multiple: int = 8) -> int:
return round(length / multiple) * multiple
def _pil_to_tensor(image: Image.Image) -> torch.Tensor:
"""Convert PIL image to CHW-like float tensor in [0,1], with batch dim omitted."""
arr = np.array(image).astype(np.float32) / 255.0
t = torch.from_numpy(arr)
# Ensure a channel dimension: HxW -> HxWx1, or HxWxC
if t.ndim == 2:
t = t.unsqueeze(-1)
# Move channel last to channel-first if needed by your VAE? You used unsqueeze(0) previously,
# so preserve the previous behavior: add batch dim at dim=0 but do not permute channels.
t = t.unsqueeze(0)
return t
def _tensor_to_pil(img_tensor: torch.Tensor, batch_index: int = 0) -> Image.Image:
"""Convert tensor (with batch) to PIL image for a specific batch index."""
safe = torch.nan_to_num(img_tensor[batch_index])
arr = (255 * safe.cpu().numpy()).astype(np.uint8)
return Image.fromarray(arr)
def _fix_crop_region(region: Tuple[int, int, int, int], image_size: Tuple[int, int]) -> Tuple[int, int, int, int]:
"""Adjust crop region to remove trailing pixel if not touching border."""
image_width, image_height = image_size
x1, y1, x2, y2 = region
if x2 < image_width:
x2 -= 1
if y2 < image_height:
y2 -= 1
return x1, y1, x2, y2
def _get_crop_region(mask: Image.Image, pad: int = 0) -> Tuple[int, int, int, int]:
"""Get the bounding box of the white region in a mask and pad it."""
coords = mask.getbbox()
if coords is not None:
x1, y1, x2, y2 = coords
else:
# empty bbox => use inverted (no area)
x1, y1, x2, y2 = mask.width, mask.height, 0, 0
x1 = max(x1 - pad, 0)
y1 = max(y1 - pad, 0)
x2 = min(x2 + pad, mask.width)
y2 = min(y2 + pad, mask.height)
return _fix_crop_region((x1, y1, x2, y2), (mask.width, mask.height))
def _expand_crop(region: Tuple[int, int, int, int], width: int, height: int, target_width: int, target_height: int) -> Tuple[Tuple[int, int, int, int], Tuple[int, int]]:
"""Expand a crop region to target size while keeping it inside image."""
x1, y1, x2, y2 = region
actual_w = x2 - x1
actual_h = y2 - y1
# Expand horizontally
w_diff = target_width - actual_w
x2 = min(x2 + w_diff // 2, width)
w_diff = target_width - (x2 - x1)
x1 = max(x1 - w_diff, 0)
w_diff = target_width - (x2 - x1)
x2 = min(x2 + w_diff, width)
# Expand vertically
h_diff = target_height - actual_h
y2 = min(y2 + h_diff // 2, height)
h_diff = target_height - (y2 - y1)
y1 = max(y1 - h_diff, 0)
h_diff = target_height - (y2 - y1)
y2 = min(y2 + h_diff, height)
return (x1, y1, x2, y2), (target_width, target_height)
def _crop_cond(cond, region, init_size, canvas_size, tile_size, w_pad: int = 0, h_pad: int = 0):
"""Placeholder simplified crop conditioning for batch processing (keeps original behavior)."""
# This intentionally mirrors your simplified version: returns same conditioning.
return cond
def _sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise, custom_sampler, custom_sigmas):
@@ -287,7 +213,7 @@ def _prepare_tile_for_batch(calc_rectangle_fn, current_image: Image.Image, tx: i
tile_draw = ImageDraw.Draw(tile_mask)
tile_draw.rectangle(calc_rectangle_fn(tx, ty), fill="white")
crop_region = _get_crop_region(tile_mask, p.inpaint_full_res_padding)
crop_region = usdu_utils.get_crop_region(tile_mask, p.inpaint_full_res_padding)
if p.uniform_tile_mode:
x1, y1, x2, y2 = crop_region
@@ -301,7 +227,7 @@ def _prepare_tile_for_batch(calc_rectangle_fn, current_image: Image.Image, tx: i
else:
target_w = round(crop_h * p_ratio)
target_h = crop_h
crop_region, _ = _expand_crop(crop_region, tile_mask.width, tile_mask.height, target_w, target_h)
crop_region, _ = usdu_utils.expand_crop(crop_region, tile_mask.width, tile_mask.height, target_w, target_h)
tile_size = (p.width, p.height)
else:
x1, y1, x2, y2 = crop_region
@@ -309,7 +235,7 @@ def _prepare_tile_for_batch(calc_rectangle_fn, current_image: Image.Image, tx: i
crop_h = y2 - y1
target_w = math.ceil(crop_w / 8) * 8
target_h = math.ceil(crop_h / 8) * 8
crop_region, tile_size = _expand_crop(crop_region, tile_mask.width, tile_mask.height, target_w, target_h)
crop_region, tile_size = usdu_utils.expand_crop(crop_region, tile_mask.width, tile_mask.height, target_w, target_h)
# Optional blur
if getattr(p, "mask_blur", 0) > 0:
@@ -347,14 +273,14 @@ def _process_batch_tiles(p,
batch_tile_sizes.append(tile_size)
# Encode tiles -> latent
batched_tensors = torch.cat([_pil_to_tensor(tile) for tile, _ in batch_tiles], dim=0)
batched_tensors = torch.cat([usdu_utils.pil_to_tensor(tile) for tile, _ in batch_tiles], dim=0)
(latent,) = vae_encoder.encode(p.vae, batched_tensors)
# Condition from first tile (assume same)
first_crop_region = batch_crop_regions[0]
first_tile_size = batch_tile_sizes[0]
positive_cropped = _crop_cond(p.positive, first_crop_region, p.init_size, current_image.size, first_tile_size)
negative_cropped = _crop_cond(p.negative, first_crop_region, p.init_size, current_image.size, first_tile_size)
positive_cropped = usdu_utils.crop_cond(p.positive, first_crop_region, p.init_size, current_image.size, first_tile_size)
negative_cropped = usdu_utils.crop_cond(p.negative, first_crop_region, p.init_size, current_image.size, first_tile_size)
# Sampling
samples = _sample(p.model, p.seed, p.steps, p.cfg, p.sampler_name, p.scheduler,
@@ -374,7 +300,7 @@ def _process_batch_tiles(p,
# Composite tiles back
result_img = current_image
for idx, (tx, ty) in enumerate(tiles_coords):
tile_sampled = _tensor_to_pil(decoded, idx)
tile_sampled = usdu_utils.tensor_to_pil(decoded, idx)
initial_tile_size = batch_tiles[idx][1]
crop_region = batch_crop_regions[idx]
tile_mask = batch_masks[idx]