Merge branch 'master' into cursor/oss-spec-cloud-fields-1c03

comfy/cli_args.py

@@ -90,7 +90,6 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 
 class LatentPreviewMethod(enum.Enum):

comfy/model_management.py

@@ -112,10 +112,6 @@ if args.directml is not None:
     # torch_directml.disable_tiled_resources(True)
     lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
 
-try:
-    import intel_extension_for_pytorch as ipex  # noqa: F401
-except:
-    pass
 
 try:
     _ = torch.xpu.device_count()
@@ -583,9 +579,6 @@ class LoadedModel:
 
         real_model = self.model.model
 
-        if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None:
-            with torch.no_grad():
-                real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
 
         self.real_model = weakref.ref(real_model)
         self.model_finalizer = weakref.finalize(real_model, cleanup_models)
@@ -1581,10 +1574,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
         return False
 
     if is_intel_xpu():
-        if torch_version_numeric < (2, 3):
-            return True
-        else:
-            return torch.xpu.get_device_properties(device).has_fp16
+        return torch.xpu.get_device_properties(device).has_fp16
 
     if is_ascend_npu():
         return True
@@ -1650,10 +1640,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         return False
 
     if is_intel_xpu():
-        if torch_version_numeric < (2, 3):
-            return True
-        else:
-            return torch.xpu.is_bf16_supported()
+        return torch.xpu.is_bf16_supported()
 
     if is_ascend_npu():
         return True
@@ -1784,6 +1771,7 @@ def soft_empty_cache(force=False):
     if cpu_state == CPUState.MPS:
         torch.mps.empty_cache()
     elif is_intel_xpu():
+        torch.xpu.synchronize()
         torch.xpu.empty_cache()
     elif is_ascend_npu():
         torch.npu.empty_cache()

comfy_extras/nodes_sdpose.py

@@ -459,27 +459,23 @@ class SDPoseKeypointExtractor(io.ComfyNode):
         total_images = image.shape[0]
         captured_feat = None
 
-        model_h = int(head.heatmap_size[0]) * 4   # e.g. 192 * 4 = 768
-        model_w = int(head.heatmap_size[1]) * 4   # e.g. 256 * 4 = 1024
+        model_w = int(head.heatmap_size[0]) * 4   # 192 * 4 = 768
+        model_h = int(head.heatmap_size[1]) * 4   # 256 * 4 = 1024
 
         def _resize_to_model(imgs):
-            """Aspect-preserving resize + zero-pad BHWC images to (model_h, model_w). Returns (resized_bhwc, scale, pad_top, pad_left)."""
+            """Stretch BHWC images to (model_h, model_w), model expects no aspect preservation."""
             h, w = imgs.shape[-3], imgs.shape[-2]
-            scale = min(model_h / h, model_w / w)
-            sh, sw = int(round(h * scale)), int(round(w * scale))
-            pt, pl = (model_h - sh) // 2, (model_w - sw) // 2
+            method = "area" if (model_h <= h and model_w <= w) else "bilinear"
             chw = imgs.permute(0, 3, 1, 2).float()
-            scaled = comfy.utils.common_upscale(chw, sw, sh, upscale_method="bilinear", crop="disabled")
-            padded = torch.zeros(scaled.shape[0], scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device)
-            padded[:, :, pt:pt + sh, pl:pl + sw] = scaled
-            return padded.permute(0, 2, 3, 1), scale, pt, pl
+            scaled = comfy.utils.common_upscale(chw, model_w, model_h, upscale_method=method, crop="disabled")
+            return scaled.permute(0, 2, 3, 1), model_w / w, model_h / h
 
-        def _remap_keypoints(kp, scale, pad_top, pad_left, offset_x=0, offset_y=0):
+        def _remap_keypoints(kp, scale_x, scale_y, offset_x=0, offset_y=0):
             """Remap keypoints from model space back to original image space."""
             kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32)
             invalid = kp[..., 0] < 0
-            kp[..., 0] = (kp[..., 0] - pad_left) / scale + offset_x
-            kp[..., 1] = (kp[..., 1] - pad_top)  / scale + offset_y
+            kp[..., 0] = kp[..., 0] / scale_x + offset_x
+            kp[..., 1] = kp[..., 1] / scale_y + offset_y
             kp[invalid] = -1
             return kp
 
@@ -529,18 +525,18 @@ class SDPoseKeypointExtractor(io.ComfyNode):
                             continue
 
                         crop = img[:, y1:y2, x1:x2, :]  # (1, crop_h, crop_w, C)
-                        crop_resized, scale, pad_top, pad_left = _resize_to_model(crop)
+                        crop_resized, sx, sy = _resize_to_model(crop)
 
                         latent_crop = vae.encode(crop_resized)
                         kp_batch, sc_batch = _run_on_latent(latent_crop)
-                        kp = _remap_keypoints(kp_batch[0], scale, pad_top, pad_left, x1, y1)
+                        kp = _remap_keypoints(kp_batch[0], sx, sy, x1, y1)
                         img_keypoints.append(kp)
                         img_scores.append(sc_batch[0])
                 else:
-                    img_resized, scale, pad_top, pad_left = _resize_to_model(img)
+                    img_resized, sx, sy = _resize_to_model(img)
                     latent_img = vae.encode(img_resized)
                     kp_batch, sc_batch = _run_on_latent(latent_img)
-                    img_keypoints.append(_remap_keypoints(kp_batch[0], scale, pad_top, pad_left))
+                    img_keypoints.append(_remap_keypoints(kp_batch[0], sx, sy))
                     img_scores.append(sc_batch[0])
 
                 all_keypoints.append(img_keypoints)
@@ -549,12 +545,12 @@ class SDPoseKeypointExtractor(io.ComfyNode):
 
         else: # full-image mode, batched
             for batch_start in tqdm(range(0, total_images, batch_size), desc="Extracting keypoints"):
-                batch_resized, scale, pad_top, pad_left = _resize_to_model(image[batch_start:batch_start + batch_size])
+                batch_resized, sx, sy = _resize_to_model(image[batch_start:batch_start + batch_size])
                 latent_batch = vae.encode(batch_resized)
                 kp_batch, sc_batch = _run_on_latent(latent_batch)
 
                 for kp, sc in zip(kp_batch, sc_batch):
-                    all_keypoints.append([_remap_keypoints(kp, scale, pad_top, pad_left)])
+                    all_keypoints.append([_remap_keypoints(kp, sx, sy)])
                     all_scores.append([sc])
 
                 pbar.update(len(kp_batch))
@@ -727,13 +723,13 @@ class CropByBBoxes(io.ComfyNode):
                 scale = min(output_width / crop_w, output_height / crop_h)
                 scaled_w = int(round(crop_w * scale))
                 scaled_h = int(round(crop_h * scale))
-                scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
+                scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="area", crop="disabled")
                 pad_left = (output_width  - scaled_w) // 2
                 pad_top  = (output_height - scaled_h) // 2
                 resized = torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device)
                 resized[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
             else:  # "stretch"
-                resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
+                resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="area", crop="disabled")
             crops.append(resized)
 
         if not crops: