diff --git a/CLAUDE.md b/CLAUDE.md
index 1796bc16..c3286c2d 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -62,10 +62,11 @@ JSON workflow → schema validation → variable substitution → sequential ste
 | `dw/step.py` | Step executor: generates iterations, dispatches to pipeline/task/workflow |
 | `dw/pipeline_processors/pipeline.py` | Pipeline loading, components, quantization, LoRA, schedulers, offloading |
 | `dw/pipeline_processors/config_objects.py` | Quantization and group offload config creation |
-| `dw/tasks/task.py` | Task dispatcher (image processing, QR codes, gathering, video, segmentation, captioning, frame interpolation) |
+| `dw/tasks/task.py` | Task dispatcher (image processing, QR codes, gathering, video, segmentation, captioning, text generation, diffusion upscaling, frame interpolation) |
 | `dw/tasks/segment.py` | GroundingDINO + SAM2 text-prompted object segmentation |
 | `dw/tasks/image_to_text.py` | Image captioning via transformers image-to-text pipeline (BLIP, BLIP-2, etc.) |
 | `dw/tasks/text_generation.py` | Text generation / prompt expansion via transformers text-generation pipeline |
+| `dw/tasks/diffusion_upscale.py` | Diffusion-based image upscaling via SD upscale pipelines (x2/x4) |
 | `dw/tasks/interpolate_frames.py` | RIFE frame interpolation (2x/4x/8x) with vendored IFNet v4.6 |
 | `dw/tasks/rife_model.py` | Vendored RIFE IFNet v4.6 architecture (MIT License, Megvii Inc.) |
 | `dw/previous_results.py` | Cross-step data flow via cartesian products |
diff --git a/dw/pipeline_processors/config_objects.py b/dw/pipeline_processors/config_objects.py
index fe9c39c3..eaa5a118 100644
--- a/dw/pipeline_processors/config_objects.py
+++ b/dw/pipeline_processors/config_objects.py
@@ -24,7 +24,15 @@ def get_quantization_configuration(configuration):
             quantization_config_type = quantization_config["configuration"][
                 "config_type"
             ]
-            return quantization_config_type(**quantization_config["arguments"])
+            # Some quantization configs (e.g. TorchAoConfig) require argument values
+            # to be instances rather than classes. realize_args converts *_type keys to
+            # classes; instantiate them here with no args so callers can write e.g.
+            # "quant_type": "torchao.quantization.Int8WeightOnlyConfig" in JSON.
+            args = {
+                k: v() if isinstance(v, type) else v
+                for k, v in quantization_config["arguments"].items()
+            }
+            return quantization_config_type(**args)
         except Exception as e:
             logger.error(
                 f"Failed to create quantization_config: {str(e)}", exc_info=True