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fix skip processing

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-07-19 10:52:55 +02:00
parent 5d989a6cc6
commit bed5a51f8b
3 changed files with 23 additions and 15 deletions
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extensions-builtin/sdnext-modernui
+1 -1
Submodule extensions-builtin/sdnext-modernui updated: b987359783...235e3f71ab
modules/processing_prompt.py
+1 -1
View File
@@ -23,7 +23,7 @@ def fix_prompt_batch(p, prompts, negative_prompts, prompts_2, negative_prompts_2
if type(negative_prompts) is str:
negative_prompts = [negative_prompts]
if hasattr(p, 'init_images') and p.init_images is not None and len(p.init_images) > 1:
if hasattr(p, 'init_images') and (p.init_images is not None) and (len(p.init_images) > 1) and not getattr(p, 'skip_processing', False):
while len(prompts) < len(p.init_images):
prompts.append(prompts[-1] if prompts else '')
while len(negative_prompts) < len(p.init_images):
modules/seedvr/src/models/dit/patch.py
+21 -13
View File
@@ -77,19 +77,23 @@ class NaPatchIn(PatchIn):
self,
vid: torch.Tensor, # l c
vid_shape: torch.LongTensor,
cache: Cache = Cache(disable=True),
) -> torch.Tensor:
cache = cache.namespace("patch")
vid_shape_before_patchify = cache("vid_shape_before_patchify", lambda: vid_shape)
t, h, w = self.patch_size
if not t == h == w == 1:
vid, vid_shape = na.rearrange(
vid, vid_shape, "(T t) (H h) (W w) c -> T H W (t h w c)", t=t, h=h, w=w
)
vid = na.unflatten(vid, vid_shape)
for i in range(len(vid)):
if h > 1 and vid_shape[i, 1] % h != 0:
if t > 1 and vid_shape_before_patchify[i, 0] % t != 0:
vid[i] = torch.cat([vid[i][:1]] * (t - vid[i].size(0) % t) + [vid[i]], dim=0)
if h > 1 and vid_shape_before_patchify[i, 1] % h != 0:
vid[i] = torch.cat([vid[i][:, :1]] * (h - vid[i].size(1) % h) + [vid[i]], dim=1)
if w > 1 and vid_shape[i, 2] % w != 0:
if w > 1 and vid_shape_before_patchify[i, 2] % w != 0:
vid[i] = torch.cat([vid[i][:, :, :1]] * (w - vid[i].size(2) % w) + [vid[i]], dim=2)
vid[i] = rearrange(vid[i], "(T t) (H h) (W w) c -> T H W (t h w c)", t=t, h=h, w=w)
vid, vid_shape = na.flatten(vid)
# slice vid after patching in when using sequence parallelism
# slice vid after patchting in when using sequence parallelism
vid = slice_inputs(vid, dim=0)
if vid.dtype != self.proj.weight.dtype:
vid = vid.to(self.proj.weight.dtype)
@@ -107,6 +111,8 @@ class NaPatchOut(PatchOut):
torch.FloatTensor,
torch.LongTensor,
]:
cache = cache.namespace("patch")
vid_shape_before_patchify = cache.get("vid_shape_before_patchify")
t, h, w = self.patch_size
if vid.dtype != self.proj.weight.dtype:
vid = vid.to(self.proj.weight.dtype)
@@ -120,12 +126,14 @@ class NaPatchOut(PatchOut):
cache=cache.namespace("vid"),
)
if not t == h == w == 1:
vid, vid_shape = na.rearrange(
vid, vid_shape, "T H W (t h w c) -> (T t) (H h) (W w) c", t=t, h=h, w=w
)
vid = na.unflatten(vid, vid_shape)
for i in range(len(vid)):
if h > 1 and vid_shape[i, 1] % h != 0:
vid[i] = vid[i][:, (h - vid_shape[i, 1] % h) :]
if w > 1 and vid_shape[i, 2] % w != 0:
vid[i] = vid[i][:, :, (w - vid_shape[i, 2] % w) :]
vid[i] = rearrange(vid[i], "T H W (t h w c) -> (T t) (H h) (W w) c", t=t, h=h, w=w)
if t > 1 and vid_shape_before_patchify is not None and vid_shape_before_patchify[i, 0] % t != 0:
vid[i] = vid[i][(t - vid_shape_before_patchify[i, 0] % t) :]
if h > 1 and vid_shape_before_patchify is not None and vid_shape_before_patchify[i, 1] % h != 0:
vid[i] = vid[i][:, (h - vid_shape_before_patchify[i, 1] % h) :]
if w > 1 and vid_shape_before_patchify is not None and vid_shape_before_patchify[i, 2] % w != 0:
vid[i] = vid[i][:, :, (w - vid_shape_before_patchify[i, 2] % w) :]
vid, vid_shape = na.flatten(vid)
return vid, vid_shape