clean and propagate tracebacks to ui

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -12,6 +12,7 @@
   - sdnq separate dit/te settings
 - **Features**
   - SeedVR enhanced support
+  - Propagate server tracebacks to client
 - **Fixes**
   - upscaler auto-refresh to catch chainner upscalers that are not loaded on first attempt  
   - lora support diffusers trainer

modules/logger.py

@@ -108,15 +108,29 @@ def setup_logging(debug=None, trace=None, filename=None):
             return ansi_escape.sub('', str(line))
 
         def emit(self, record):
-            if record.msg is not None and not isinstance(record.msg, str):
-                record.msg = str(record.msg)
+            if record.msg is None:
+                record.msg = ""
+            msg = record.getMessage()
+            msg = msg.replace('"', "'")
+            msg = self.strip(msg)
             try:
-                record.msg = record.msg.replace('"', "'")
+                if '❱ ' in msg: # only last 3 lines of traceback
+                    lines = [l.strip() for l in msg.splitlines() if l.strip() and not l.startswith(' ')]
+                    if len(lines) > 3:
+                        lines = lines[-3:]
+                    lines = [l.replace('│   ', '').strip() for l in lines]
+                    lines.insert(0, 'Exception traceback:')
+                    msg = '\n'.join(lines)
             except Exception:
                 pass
-            line = self.format(record)
-            line = self.strip(line)
-            self.buffer.append(line[:1024])
+            try:
+                if len(msg) > 1024:
+                    msg = msg[:1024] + '...'
+            except Exception:
+                pass
+            record.msg = msg
+            formatted = self.format(record)
+            self.buffer.append(formatted)
             if len(self.buffer) > self.capacity:
                 self.buffer.pop(0)
 

modules/seedvr/src/models/dit/patch.py

@@ -77,23 +77,19 @@ 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 = na.unflatten(vid, vid_shape)
+            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
+            )
             for i in range(len(vid)):
-                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:
+                if h > 1 and vid_shape[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_before_patchify[i, 2] % w != 0:
+                if w > 1 and vid_shape[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 patchting in when using sequence parallelism
+        # slice vid after patching 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)
@@ -111,8 +107,6 @@ 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)
@@ -126,14 +120,12 @@ class NaPatchOut(PatchOut):
             cache=cache.namespace("vid"),
         )
         if not t == h == w == 1:
-            vid = na.unflatten(vid, vid_shape)
+            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
+            )
             for i in range(len(vid)):
-                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)
+                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) :]
         return vid, vid_shape

modules/seedvr/src/models/dit_v2/nablocks/attention/mmattn.py

@@ -13,12 +13,11 @@
 # // limitations under the License.
 
 from typing import Optional, Tuple, Union
+from itertools import chain
 import torch
 from einops import rearrange
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.modules.utils import _triple
-
 from .....common.cache import Cache
 from .....common.distributed.ops import gather_heads_scatter_seq, gather_seq_scatter_heads_qkv
 from .....common.half_precision_fixes import safe_pad_operation
@@ -29,7 +28,6 @@ from ...mm import MMArg, MMModule
 from ...normalization import norm_layer_type
 from ...rope import get_na_rope
 from ...window import get_window_op
-from itertools import chain
 
 
 class NaMMAttention(nn.Module):

scripts/postprocessing_seedvr.py

@@ -1,5 +1,8 @@
+from typing import TYPE_CHECKING
 import gradio as gr
 from modules import scripts_postprocessing
+if TYPE_CHECKING:
+    from modules.postprocess.seedvr_model import UpscalerSeedVR
 
 
 class ScriptSeedVR(scripts_postprocessing.ScriptPostprocessing):
@@ -17,7 +20,7 @@ class ScriptSeedVR(scripts_postprocessing.ScriptPostprocessing):
                 seedvr_seed = gr.Number(step=1, value=-1, label="SeedVR seed", elem_id="extras_seedvr_seed")
                 seedvr_steps = gr.Number(step=1, value=1, minimum=1, maximum=99, label="SeedVR steps", elem_id="extras_seedvr_steps", visible=False)
             with gr.Row():
-                seedvr_cfg_scale = gr.Slider(minimum=0.0, maximum=15.0, step=0.01, value=3.5, label="SeedVR guidance scale", elem_id="extras_seedvr_cfg_scale")
+                seedvr_cfg_scale = gr.Slider(minimum=0.0, maximum=15.0, step=0.01, value=1.5, label="SeedVR guidance scale", elem_id="extras_seedvr_cfg_scale")
                 seedvr_cfg_rescale = gr.Slider(minimum=0.0, maximum=15.0, step=0.01, value=0.0, label="SeedVR guidance rescale", elem_id="extras_seedvr_cfg_rescale")
             with gr.Row():
                 seedvr_tile_size = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="SeedVR tile size", elem_id="extras_seedvr_tile_size")
@@ -52,7 +55,7 @@ class ScriptSeedVR(scripts_postprocessing.ScriptPostprocessing):
         from modules.logger import log
         image = pp.image
         instance: upscaler.UpscalerData = next(iter([x for x in shared.sd_upscalers if x.name == seedvr_selected]), None)
-        scaler: upscaler.Upscaler = instance.scaler
+        scaler: UpscalerSeedVR = instance.scaler
 
         log.info(f'Upscaler: type="SeedVR" model="{seedvr_selected}" scale={seedvr_scale} seed={seedvr_seed} steps={seedvr_steps} cfg_scale={seedvr_cfg_scale} cfg_rescale={seedvr_cfg_rescale} tile_size={seedvr_tile_size} tile_overlap={seedvr_tile_overlap}')
 

ui/dist/sdnext.mjs

@@ -13774,7 +13774,6 @@ async function gallerySort(key) {
   rootFiles.forEach((node) => fragment.appendChild(node));
   const folderNames = Array.from(folderGroups.keys());
   const sortedFolderNames = currentSort.endsWith("A") ? folderNames.sort((a, b) => a.localeCompare(b)) : folderNames.sort((a, b) => b.localeCompare(a));
-  console.log("HERE", sortedFolderNames);
   for (const folderName of sortedFolderNames) {
     const files = folderGroups.get(folderName);
     files.sort(sortMode.func);