Add CfgShardMetadata

resources/image_model_cards/exolabs--Qwen-Image-4bit.toml

@@ -3,6 +3,7 @@ n_layers = 60
 hidden_size = 1
 supports_tensor = false
 tasks = ["TextToImage"]
+uses_cfg = true
 
 [storage_size]
 in_bytes = 26799533856

resources/image_model_cards/exolabs--Qwen-Image-8bit.toml

@@ -3,6 +3,7 @@ n_layers = 60
 hidden_size = 1
 supports_tensor = false
 tasks = ["TextToImage"]
+uses_cfg = true
 
 [storage_size]
 in_bytes = 37014734400

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-4bit.toml

@@ -3,6 +3,7 @@ n_layers = 60
 hidden_size = 1
 supports_tensor = false
 tasks = ["ImageToImage"]
+uses_cfg = true
 
 [storage_size]
 in_bytes = 26799533856

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-8bit.toml

@@ -3,6 +3,7 @@ n_layers = 60
 hidden_size = 1
 supports_tensor = false
 tasks = ["ImageToImage"]
+uses_cfg = true
 
 [storage_size]
 in_bytes = 37014734400

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509.toml

@@ -3,6 +3,7 @@ n_layers = 60
 hidden_size = 1
 supports_tensor = false
 tasks = ["ImageToImage"]
+uses_cfg = true
 
 [storage_size]
 in_bytes = 57445135488

resources/image_model_cards/exolabs--Qwen-Image.toml

@@ -3,6 +3,7 @@ n_layers = 60
 hidden_size = 1
 supports_tensor = false
 tasks = ["TextToImage"]
+uses_cfg = true
 
 [storage_size]
 in_bytes = 57445135488

src/exo/master/api.py

@@ -1,6 +1,7 @@
 import base64
 import contextlib
 import json
+import random
 import time
 from collections.abc import AsyncGenerator, Awaitable, Callable
 from datetime import datetime, timezone
@@ -150,6 +151,15 @@ def _format_to_content_type(image_format: Literal["png", "jpeg", "webp"] | None)
     return f"image/{image_format or 'png'}"
 
 
+def _ensure_seed(params: AdvancedImageParams | None) -> AdvancedImageParams:
+    """Ensure advanced params has a seed set for distributed consistency."""
+    if params is None:
+        return AdvancedImageParams(seed=random.randint(0, 2**32 - 1))
+    if params.seed is None:
+        return params.model_copy(update={"seed": random.randint(0, 2**32 - 1)})
+    return params
+
+
 class API:
     def __init__(
         self,
@@ -709,6 +719,9 @@ class API:
         with SSE-formatted events for partial and final images.
         """
         payload.model = await self._validate_image_model(ModelId(payload.model))
+        payload = payload.model_copy(
+            update={"advanced_params": _ensure_seed(payload.advanced_params)}
+        )
 
         command = ImageGeneration(
             task_params=payload,
@@ -957,6 +970,9 @@ class API:
 
         payload.stream = False
         payload.partial_images = 0
+        payload = payload.model_copy(
+            update={"advanced_params": _ensure_seed(payload.advanced_params)}
+        )
 
         command = ImageGeneration(
             task_params=payload,
@@ -988,6 +1004,7 @@ class API:
     ) -> ImageEdits:
         """Prepare and send an image edits command with chunked image upload."""
         resolved_model = await self._validate_image_model(model)
+        advanced_params = _ensure_seed(advanced_params)
 
         image_content = await image.read()
         image_data = base64.b64encode(image_content).decode("utf-8")

src/exo/master/placement_utils.py

@@ -10,6 +10,7 @@ from exo.shared.types.profiling import MemoryUsage, NodeNetworkInfo
 from exo.shared.types.topology import Cycle, RDMAConnection, SocketConnection
 from exo.shared.types.worker.runners import RunnerId, ShardAssignments
 from exo.shared.types.worker.shards import (
+    CfgShardMetadata,
     PipelineShardMetadata,
     Sharding,
     ShardMetadata,
@@ -79,6 +80,118 @@ def get_shard_assignments_for_pipeline_parallel(
     cycle: Cycle,
     node_memory: Mapping[NodeId, MemoryUsage],
 ):
+    """Create shard assignments for pipeline parallel execution.
+
+    Routes to CFG-aware or standard pipeline placement based on model_card.uses_cfg.
+    """
+    # Determine CFG parallelism topology
+    # CFG parallel only for even node counts with CFG models (2+ nodes)
+    world_size = len(cycle)
+    use_cfg_parallel = model_card.uses_cfg and world_size >= 2 and world_size % 2 == 0
+
+    if use_cfg_parallel:
+        return _get_shard_assignments_for_cfg_parallel(model_card, cycle, node_memory)
+    else:
+        return _get_shard_assignments_for_pure_pipeline(model_card, cycle, node_memory)
+
+
+def _get_shard_assignments_for_cfg_parallel(
+    model_card: ModelCard,
+    cycle: Cycle,
+    node_memory: Mapping[NodeId, MemoryUsage],
+) -> ShardAssignments:
+    """Create shard assignments for CFG parallel execution (returns CfgShardMetadata)."""
+    if not cycle.node_ids:
+        raise ValueError("Cannot create shard assignments for empty node cycle")
+
+    cycle_memory = sum(
+        (node_memory[node_id].ram_available for node_id in cycle.node_ids),
+        start=Memory(),
+    )
+    if cycle_memory.in_bytes == 0:
+        raise ValueError("Cannot create shard assignments: total available memory is 0")
+
+    total_layers = model_card.n_layers
+    world_size = len(cycle)
+    runner_to_shard: dict[RunnerId, ShardMetadata] = {}
+    node_to_runner: dict[NodeId, RunnerId] = {}
+
+    cfg_world_size = 2
+    pipeline_world_size = world_size // cfg_world_size
+
+    # For CFG parallel, we only need to allocate layers for one pipeline group
+    # (both CFG groups run the same layers). Use the first pipeline group's nodes.
+    pipeline_node_ids = cycle.node_ids[:pipeline_world_size]
+    pipeline_memory = sum(
+        (node_memory[node_id].ram_available for node_id in pipeline_node_ids),
+        start=Memory(),
+    )
+
+    layer_allocations = allocate_layers_proportionally(
+        total_layers=total_layers,
+        memory_fractions=[
+            node_memory[node_id].ram_available.in_bytes / pipeline_memory.in_bytes
+            for node_id in pipeline_node_ids
+        ],
+    )
+
+    # Validate each pipeline node has sufficient memory for its assigned layers
+    # Use integer arithmetic to avoid floating point precision issues
+    total_storage_bytes = model_card.storage_size.in_bytes
+    for i, node_id in enumerate(pipeline_node_ids):
+        node_layers = layer_allocations[i]
+        # Integer division then multiply to get conservative estimate
+        required_memory = (total_storage_bytes * node_layers) // total_layers
+        available_memory = node_memory[node_id].ram_available.in_bytes
+        if required_memory > available_memory:
+            raise ValueError(
+                f"Node {i} ({node_id}) has insufficient memory: "
+                f"requires {required_memory / (1024**3):.2f} GB for {node_layers} layers, "
+                f"but only has {available_memory / (1024**3):.2f} GB available"
+            )
+
+    # CFG group 0: pipeline ranks in ascending order (0, 1, 2, ...)
+    # CFG group 1: pipeline ranks in descending order (reversed)
+    # This places both "last stages" as ring neighbors for CFG exchange.
+    position_to_cfg_pipeline = [(0, r) for r in range(pipeline_world_size)] + [
+        (1, r) for r in reversed(range(pipeline_world_size))
+    ]
+
+    for i, node_id in enumerate(cycle.node_ids):
+        cfg_rank, pipeline_rank = position_to_cfg_pipeline[i]
+
+        layers_before = sum(layer_allocations[:pipeline_rank])
+        node_layers = layer_allocations[pipeline_rank]
+
+        runner_id = RunnerId()
+
+        shard = CfgShardMetadata(
+            model_card=model_card,
+            device_rank=i,
+            world_size=world_size,
+            start_layer=layers_before,
+            end_layer=layers_before + node_layers,
+            n_layers=total_layers,
+            cfg_rank=cfg_rank,
+            cfg_world_size=cfg_world_size,
+        )
+
+        runner_to_shard[runner_id] = shard
+        node_to_runner[node_id] = runner_id
+
+    return ShardAssignments(
+        model_id=model_card.model_id,
+        runner_to_shard=runner_to_shard,
+        node_to_runner=node_to_runner,
+    )
+
+
+def _get_shard_assignments_for_pure_pipeline(
+    model_card: ModelCard,
+    cycle: Cycle,
+    node_memory: Mapping[NodeId, MemoryUsage],
+) -> ShardAssignments:
+    """Create shard assignments for pure pipeline execution (returns PipelineShardMetadata)."""
     if not cycle.node_ids:
         raise ValueError("Cannot create shard assignments for empty node cycle")
 
@@ -103,11 +216,10 @@ def get_shard_assignments_for_pipeline_parallel(
     )
 
     # Validate each node has sufficient memory for its assigned layers
-    memory_per_layer = model_card.storage_size.in_bytes / total_layers
-    for i, (node_id, node_layers) in enumerate(
-        zip(cycle.node_ids, layer_allocations, strict=True)
-    ):
-        required_memory = node_layers * memory_per_layer
+    total_storage_bytes = model_card.storage_size.in_bytes
+    for i, node_id in enumerate(cycle.node_ids):
+        node_layers = layer_allocations[i]
+        required_memory = (total_storage_bytes * node_layers) // total_layers
         available_memory = node_memory[node_id].ram_available.in_bytes
         if required_memory > available_memory:
             raise ValueError(
@@ -116,33 +228,30 @@ def get_shard_assignments_for_pipeline_parallel(
                 f"but only has {available_memory / (1024**3):.2f} GB available"
             )
 
-    layers_assigned = 0
-    for i, (node_id, node_layers) in enumerate(
-        zip(cycle.node_ids, layer_allocations, strict=True)
-    ):
+    for i, node_id in enumerate(cycle.node_ids):
+        layers_before = sum(layer_allocations[:i])
+        node_layers = layer_allocations[i]
+
         runner_id = RunnerId()
 
         shard = PipelineShardMetadata(
             model_card=model_card,
             device_rank=i,
             world_size=world_size,
-            start_layer=layers_assigned,
-            end_layer=layers_assigned + node_layers,
+            start_layer=layers_before,
+            end_layer=layers_before + node_layers,
             n_layers=total_layers,
         )
 
         runner_to_shard[runner_id] = shard
         node_to_runner[node_id] = runner_id
-        layers_assigned += node_layers
 
-    shard_assignments = ShardAssignments(
+    return ShardAssignments(
         model_id=model_card.model_id,
         runner_to_shard=runner_to_shard,
         node_to_runner=node_to_runner,
     )
 
-    return shard_assignments
-
 
 def get_shard_assignments_for_tensor_parallel(
     model_card: ModelCard,

src/exo/master/tests/test_placement_utils.py

@@ -5,6 +5,7 @@ from exo.master.placement_utils import (
     filter_cycles_by_memory,
     get_mlx_jaccl_coordinators,
     get_shard_assignments,
+    get_shard_assignments_for_pipeline_parallel,
     get_smallest_cycles,
 )
 from exo.master.tests.conftest import (
@@ -20,7 +21,11 @@ from exo.shared.types.profiling import (
     NodeNetworkInfo,
 )
 from exo.shared.types.topology import Connection, SocketConnection
-from exo.shared.types.worker.shards import Sharding
+from exo.shared.types.worker.shards import (
+    CfgShardMetadata,
+    PipelineShardMetadata,
+    Sharding,
+)
 
 
 def test_filter_cycles_by_memory():
@@ -487,3 +492,189 @@ def test_get_shard_assignments_insufficient_memory_raises():
         get_shard_assignments(
             model_card, selected_cycle, Sharding.Pipeline, node_memory
         )
+
+
+class TestCfgParallelPlacement:
+    def _create_ring_topology(self, node_ids: list[NodeId]) -> Topology:
+        topology = Topology()
+        for node_id in node_ids:
+            topology.add_node(node_id)
+
+        for i, node_id in enumerate(node_ids):
+            next_node = node_ids[(i + 1) % len(node_ids)]
+            conn = Connection(
+                source=node_id,
+                sink=next_node,
+                edge=create_socket_connection(i + 1),
+            )
+            topology.add_connection(conn)
+
+        return topology
+
+    def test_two_nodes_cfg_model_uses_cfg_parallel(self):
+        """Two nodes with CFG model should use CFG parallel (no pipeline)."""
+        node_a = NodeId()
+        node_b = NodeId()
+
+        topology = self._create_ring_topology([node_a, node_b])
+        cycles = [c for c in topology.get_cycles() if len(c) == 2]
+        cycle = cycles[0]
+
+        node_memory = {
+            node_a: create_node_memory(1000 * 1024),
+            node_b: create_node_memory(1000 * 1024),
+        }
+
+        model_card = ModelCard(
+            model_id=ModelId("qwen-image-test"),
+            n_layers=60,
+            storage_size=Memory.from_kb(1000),
+            hidden_size=1,
+            supports_tensor=False,
+            uses_cfg=True,
+            tasks=[ModelTask.TextToImage],
+        )
+
+        assignments = get_shard_assignments_for_pipeline_parallel(
+            model_card, cycle, node_memory
+        )
+
+        shards = list(assignments.runner_to_shard.values())
+        assert len(shards) == 2
+
+        # CFG models should get CfgShardMetadata
+        for shard in shards:
+            assert isinstance(shard, CfgShardMetadata)
+            # Both nodes should have all layers (no pipeline split)
+            assert shard.start_layer == 0
+            assert shard.end_layer == 60
+            assert shard.cfg_world_size == 2
+
+        cfg_ranks = sorted(
+            s.cfg_rank for s in shards if isinstance(s, CfgShardMetadata)
+        )
+        assert cfg_ranks == [0, 1]
+
+    def test_four_nodes_cfg_model_uses_hybrid(self):
+        """Four nodes with CFG model should use 2 CFG groups x 2 pipeline stages."""
+        nodes = [NodeId() for _ in range(4)]
+
+        topology = self._create_ring_topology(nodes)
+        cycles = [c for c in topology.get_cycles() if len(c) == 4]
+        cycle = cycles[0]
+
+        node_memory = {n: create_node_memory(1000 * 1024) for n in nodes}
+
+        model_card = ModelCard(
+            model_id=ModelId("qwen-image-test"),
+            n_layers=60,
+            storage_size=Memory.from_kb(1000),
+            hidden_size=1,
+            supports_tensor=False,
+            uses_cfg=True,
+            tasks=[ModelTask.TextToImage],
+        )
+
+        assignments = get_shard_assignments_for_pipeline_parallel(
+            model_card, cycle, node_memory
+        )
+
+        shards = list(assignments.runner_to_shard.values())
+        assert len(shards) == 4
+
+        # CFG models should get CfgShardMetadata
+        for shard in shards:
+            assert isinstance(shard, CfgShardMetadata)
+            assert shard.cfg_world_size == 2
+            assert shard.world_size // shard.cfg_world_size == 2
+
+        # Check we have 2 nodes in each CFG group
+        cfg_0_shards = [
+            s for s in shards if isinstance(s, CfgShardMetadata) and s.cfg_rank == 0
+        ]
+        cfg_1_shards = [
+            s for s in shards if isinstance(s, CfgShardMetadata) and s.cfg_rank == 1
+        ]
+        assert len(cfg_0_shards) == 2
+        assert len(cfg_1_shards) == 2
+
+        # Both CFG groups should have the same layer assignments
+        cfg_0_layers = [(s.start_layer, s.end_layer) for s in cfg_0_shards]
+        cfg_1_layers = [(s.start_layer, s.end_layer) for s in cfg_1_shards]
+        assert sorted(cfg_0_layers) == sorted(cfg_1_layers)
+
+    def test_three_nodes_cfg_model_uses_sequential_cfg(self):
+        """Three nodes (odd) with CFG model should use sequential CFG (PipelineShardMetadata)."""
+        nodes = [NodeId() for _ in range(3)]
+
+        topology = self._create_ring_topology(nodes)
+        cycles = [c for c in topology.get_cycles() if len(c) == 3]
+        cycle = cycles[0]
+
+        node_memory = {n: create_node_memory(1000 * 1024) for n in nodes}
+
+        model_card = ModelCard(
+            model_id=ModelId("qwen-image-test"),
+            n_layers=60,
+            storage_size=Memory.from_kb(1000),
+            hidden_size=1,
+            supports_tensor=False,
+            uses_cfg=True,
+            tasks=[ModelTask.TextToImage],
+        )
+
+        assignments = get_shard_assignments_for_pipeline_parallel(
+            model_card, cycle, node_memory
+        )
+
+        shards = list(assignments.runner_to_shard.values())
+        assert len(shards) == 3
+
+        # Odd node count with CFG model falls back to PipelineShardMetadata (sequential CFG)
+        for shard in shards:
+            assert isinstance(shard, PipelineShardMetadata)
+
+    def test_two_nodes_non_cfg_model_uses_pipeline(self):
+        """Two nodes with non-CFG model should use pure pipeline (PipelineShardMetadata)."""
+        node_a = NodeId()
+        node_b = NodeId()
+
+        topology = self._create_ring_topology([node_a, node_b])
+        cycles = [c for c in topology.get_cycles() if len(c) == 2]
+        cycle = cycles[0]
+
+        node_memory = {
+            node_a: create_node_memory(1000 * 1024),
+            node_b: create_node_memory(1000 * 1024),
+        }
+
+        model_card = ModelCard(
+            model_id=ModelId("flux-test"),
+            n_layers=57,
+            storage_size=Memory.from_kb(1000),
+            hidden_size=1,
+            supports_tensor=False,
+            uses_cfg=False,  # Non-CFG model
+            tasks=[ModelTask.TextToImage],
+        )
+
+        assignments = get_shard_assignments_for_pipeline_parallel(
+            model_card, cycle, node_memory
+        )
+
+        shards = list(assignments.runner_to_shard.values())
+        assert len(shards) == 2
+
+        # Non-CFG models should get PipelineShardMetadata
+        for shard in shards:
+            assert isinstance(shard, PipelineShardMetadata)
+
+        # Should have actual layer sharding (pipeline)
+        layer_ranges = sorted(
+            (s.start_layer, s.end_layer)
+            for s in shards
+            if isinstance(s, PipelineShardMetadata)
+        )
+        # First shard starts at 0, last shard ends at 57
+        assert layer_ranges[0][0] == 0
+        assert layer_ranges[-1][1] == 57

src/exo/shared/models/model_cards.py

@@ -65,9 +65,9 @@ class ComponentInfo(CamelCaseModel):
     component_name: str
     component_path: str
     storage_size: Memory
-    n_layers: PositiveInt | None
+    n_layers: PositiveInt | None = None
     can_shard: bool
-    safetensors_index_filename: str | None
+    safetensors_index_filename: str | None = None
 
 
 class ModelCard(CamelCaseModel):
@@ -82,6 +82,7 @@ class ModelCard(CamelCaseModel):
     quantization: str = ""
     base_model: str = ""
     capabilities: list[str] = []
+    uses_cfg: bool = False
 
     @field_validator("tasks", mode="before")
     @classmethod
@@ -155,87 +156,6 @@ def is_custom_card(model_id: ModelId) -> bool:
     return os.path.isfile(str(card_path))
 
 
-# TODO: quantizing and dynamically creating model cards
-def _generate_image_model_quant_variants(  # pyright: ignore[reportUnusedFunction]
-    base_name: str,
-    base_card: ModelCard,
-) -> dict[str, ModelCard]:
-    """Create quantized variants of an image model card.
-
-    Only the transformer component is quantized; text encoders stay at bf16.
-    Sizes are calculated exactly from the base card's component sizes.
-    """
-    if base_card.components is None:
-        raise ValueError(f"Image model {base_name} must have components defined")
-
-    # quantizations = [8, 6, 5, 4, 3]
-    quantizations = [8, 4]
-
-    num_transformer_bytes = next(
-        c.storage_size.in_bytes
-        for c in base_card.components
-        if c.component_name == "transformer"
-    )
-
-    transformer_bytes = Memory.from_bytes(num_transformer_bytes)
-
-    remaining_bytes = Memory.from_bytes(
-        sum(
-            c.storage_size.in_bytes
-            for c in base_card.components
-            if c.component_name != "transformer"
-        )
-    )
-
-    def with_transformer_size(new_size: Memory) -> list[ComponentInfo]:
-        assert base_card.components is not None
-        return [
-            ComponentInfo(
-                component_name=c.component_name,
-                component_path=c.component_path,
-                storage_size=new_size
-                if c.component_name == "transformer"
-                else c.storage_size,
-                n_layers=c.n_layers,
-                can_shard=c.can_shard,
-                safetensors_index_filename=c.safetensors_index_filename,
-            )
-            for c in base_card.components
-        ]
-
-    variants = {
-        base_name: ModelCard(
-            model_id=base_card.model_id,
-            storage_size=transformer_bytes + remaining_bytes,
-            n_layers=base_card.n_layers,
-            hidden_size=base_card.hidden_size,
-            supports_tensor=base_card.supports_tensor,
-            tasks=base_card.tasks,
-            components=with_transformer_size(transformer_bytes),
-        )
-    }
-
-    for quant in quantizations:
-        quant_transformer_bytes = Memory.from_bytes(
-            (num_transformer_bytes * quant) // 16
-        )
-        total_bytes = remaining_bytes + quant_transformer_bytes
-
-        model_id = ModelId(base_card.model_id + f"-{quant}bit")
-
-        variants[f"{base_name}-{quant}bit"] = ModelCard(
-            model_id=model_id,
-            storage_size=total_bytes,
-            n_layers=base_card.n_layers,
-            hidden_size=base_card.hidden_size,
-            supports_tensor=base_card.supports_tensor,
-            tasks=base_card.tasks,
-            components=with_transformer_size(quant_transformer_bytes),
-        )
-
-    return variants
-
-
 class ConfigData(BaseModel):
     model_config = {"extra": "ignore"}  # Allow unknown fields
 

src/exo/shared/types/worker/shards.py

@@ -1,4 +1,5 @@
 from enum import Enum
+from typing import TypeAlias, final
 
 from pydantic import Field
 
@@ -51,6 +52,7 @@ class BaseShardMetadata(TaggedModel):
         )
 
 
+@final
 class PipelineShardMetadata(BaseShardMetadata):
     """
     Pipeline parallelism shard meta.
@@ -60,8 +62,23 @@ class PipelineShardMetadata(BaseShardMetadata):
     """
 
 
+@final
+class CfgShardMetadata(BaseShardMetadata):
+    """
+    Shard metadata for CFG-enabled models.
+
+    Effectively two separate instances of pipeline parallel
+    """
+
+    cfg_rank: int  # 0 = positive branch, 1 = negative branch
+    cfg_world_size: int = 2
+
+
+@final
 class TensorShardMetadata(BaseShardMetadata):
     pass
 
 
-ShardMetadata = PipelineShardMetadata | TensorShardMetadata
+ShardMetadata: TypeAlias = (
+    PipelineShardMetadata | CfgShardMetadata | TensorShardMetadata
+)

src/exo/worker/engines/image/distributed_model.py

@@ -9,7 +9,7 @@ from PIL import Image
 from exo.download.download_utils import build_model_path
 from exo.shared.types.api import AdvancedImageParams
 from exo.shared.types.worker.instances import BoundInstance
-from exo.shared.types.worker.shards import PipelineShardMetadata
+from exo.shared.types.worker.shards import CfgShardMetadata, PipelineShardMetadata
 from exo.worker.engines.image.config import ImageModelConfig
 from exo.worker.engines.image.models import (
     create_adapter_for_model,
@@ -30,14 +30,19 @@ class DistributedImageModel:
         self,
         model_id: str,
         local_path: Path,
-        shard_metadata: PipelineShardMetadata,
+        shard_metadata: PipelineShardMetadata | CfgShardMetadata,
         group: Optional[mx.distributed.Group] = None,
         quantize: int | None = None,
     ):
         config = get_config_for_model(model_id)
         adapter = create_adapter_for_model(config, model_id, local_path, quantize)
 
-        if group is not None:
+        has_layer_sharding = (
+            shard_metadata.start_layer != 0
+            or shard_metadata.end_layer != shard_metadata.n_layers
+        )
+
+        if group is not None and has_layer_sharding:
             adapter.slice_transformer_blocks(
                 start_layer=shard_metadata.start_layer,
                 end_layer=shard_metadata.end_layer,
@@ -75,8 +80,10 @@ class DistributedImageModel:
         model_path = build_model_path(model_id)
 
         shard_metadata = bound_instance.bound_shard
-        if not isinstance(shard_metadata, PipelineShardMetadata):
-            raise ValueError("Expected PipelineShardMetadata for image generation")
+        if not isinstance(shard_metadata, (PipelineShardMetadata, CfgShardMetadata)):
+            raise ValueError(
+                "Expected PipelineShardMetadata or CfgShardMetadata for image generation"
+            )
 
         is_distributed = (
             len(bound_instance.instance.shard_assignments.node_to_runner) > 1

src/exo/worker/engines/image/models/base.py

@@ -86,6 +86,27 @@ class PromptData(ABC):
         """
         ...
 
+    @abstractmethod
+    def get_cfg_branch_data(
+        self, positive: bool
+    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
+        """Get embeddings for a single CFG branch (positive or negative).
+
+        Used for sequential CFG and CFG parallel modes where we process
+        one branch at a time instead of batching.
+
+        Args:
+            positive: True for positive prompt, False for negative prompt
+
+        Returns:
+            Tuple of:
+            - embeds: [1, seq, hidden] prompt embeddings
+            - mask: [1, seq] attention mask or None
+            - pooled: [1, hidden] pooled embeddings or None
+            - conditioning_latents: [1, latent_seq, latent_dim] or None
+        """
+        ...
+
 
 class ModelAdapter(ABC, Generic[ModelT, TransformerT]):
     _config: ImageModelConfig

src/exo/worker/engines/image/models/flux/adapter.py

@@ -64,6 +64,12 @@ class FluxPromptData(PromptData):
     ) -> tuple[mx.array, mx.array, mx.array | None, mx.array | None] | None:
         return None
 
+    def get_cfg_branch_data(
+        self, positive: bool
+    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
+        """Flux doesn't use CFG, but we return positive data for compatibility."""
+        return (self._prompt_embeds, None, self._pooled_prompt_embeds, None)
+
 
 class FluxModelAdapter(ModelAdapter[Flux1, Transformer]):
     def __init__(

src/exo/worker/engines/image/models/qwen/adapter.py

@@ -133,6 +133,24 @@ class QwenPromptData(PromptData):
 
         return batched_embeds, batched_mask, None, cond_latents
 
+    def get_cfg_branch_data(
+        self, positive: bool
+    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
+        if positive:
+            return (
+                self._prompt_embeds,
+                self._prompt_mask,
+                None,
+                self.conditioning_latents,
+            )
+        else:
+            return (
+                self._negative_prompt_embeds,
+                self._negative_prompt_mask,
+                None,
+                self.conditioning_latents,
+            )
+
 
 class QwenModelAdapter(ModelAdapter[QwenImage, QwenTransformer]):
     """Adapter for Qwen-Image model.

src/exo/worker/engines/image/models/qwen/config.py

@@ -12,7 +12,7 @@ QWEN_IMAGE_CONFIG = ImageModelConfig(
         ),
     ),
     default_steps={"low": 10, "medium": 25, "high": 50},
-    num_sync_steps_factor=0.125,  # ~3 sync steps for medium (30 steps)
+    num_sync_steps_factor=0.25,
     guidance_scale=3.5,  # Set to None or < 1.0 to disable CFG
 )
 
@@ -24,6 +24,6 @@ QWEN_IMAGE_EDIT_CONFIG = ImageModelConfig(
         ),
     ),
     default_steps={"low": 10, "medium": 25, "high": 50},
-    num_sync_steps_factor=0.125,
+    num_sync_steps_factor=0.25,
     guidance_scale=3.5,
 )

src/exo/worker/engines/image/models/qwen/edit_adapter.py

@@ -153,6 +153,24 @@ class QwenEditPromptData(PromptData):
 
         return batched_embeds, batched_mask, None, batched_cond_latents
 
+    def get_cfg_branch_data(
+        self, positive: bool
+    ) -> tuple[mx.array, mx.array | None, mx.array | None, mx.array | None]:
+        if positive:
+            return (
+                self._prompt_embeds,
+                self._prompt_mask,
+                None,
+                self._conditioning_latents,
+            )
+        else:
+            return (
+                self._negative_prompt_embeds,
+                self._negative_prompt_mask,
+                None,
+                self._conditioning_latents,
+            )
+
 
 class QwenEditModelAdapter(ModelAdapter[QwenImageEdit, QwenTransformer]):
     """Adapter for Qwen-Image-Edit model.

src/exo/worker/engines/image/pipeline/runner.py

@@ -1,5 +1,7 @@
+from collections.abc import Iterator
+from dataclasses import dataclass
 from math import ceil
-from typing import Any, Optional
+from typing import Any, Optional, final
 
 import mlx.core as mx
 from mflux.models.common.config.config import Config
@@ -11,7 +13,7 @@ from exo.shared.tracing import (
     clear_trace_buffer,
     trace,
 )
-from exo.shared.types.worker.shards import PipelineShardMetadata
+from exo.shared.types.worker.shards import CfgShardMetadata, PipelineShardMetadata
 from exo.worker.engines.image.config import ImageModelConfig
 from exo.worker.engines.image.models.base import (
     ModelAdapter,
@@ -25,6 +27,16 @@ from exo.worker.engines.image.pipeline.block_wrapper import (
 )
 
 
+@final
+@dataclass(frozen=True)
+class CfgBranch:
+    positive: bool
+    embeds: mx.array
+    mask: mx.array | None
+    pooled: mx.array | None
+    cond_latents: mx.array | None
+
+
 def calculate_patch_heights(
     latent_height: int, num_patches: int
 ) -> tuple[list[int], int]:
@@ -70,29 +82,18 @@ class DiffusionRunner:
         config: ImageModelConfig,
         adapter: ModelAdapter[Any, Any],
         group: Optional[mx.distributed.Group],
-        shard_metadata: PipelineShardMetadata,
+        shard_metadata: PipelineShardMetadata | CfgShardMetadata,
         num_patches: Optional[int] = None,
     ):
         self.config = config
         self.adapter = adapter
         self.group = group
 
-        if group is None:
-            self.rank = 0
-            self.world_size = 1
-            self.next_rank = 0
-            self.prev_rank = 0
-            self.start_layer = 0
-            self.end_layer = config.total_blocks
-        else:
-            self.rank = shard_metadata.device_rank
-            self.world_size = shard_metadata.world_size
-            self.next_rank = (self.rank + 1) % self.world_size
-            self.prev_rank = (self.rank - 1 + self.world_size) % self.world_size
-            self.start_layer = shard_metadata.start_layer
-            self.end_layer = shard_metadata.end_layer
+        self._init_cfg_topology(shard_metadata)
 
-        self.num_patches = num_patches if num_patches else max(1, self.world_size)
+        self.num_patches = (
+            num_patches if num_patches else max(1, self.pipeline_world_size)
+        )
 
         self.total_joint = config.joint_block_count
         self.total_single = config.single_block_count
@@ -102,6 +103,111 @@ class DiffusionRunner:
 
         self._compute_assigned_blocks()
 
+    def _init_cfg_topology(
+        self, shard_metadata: PipelineShardMetadata | CfgShardMetadata
+    ) -> None:
+        """Initialize CFG and pipeline topology from shard metadata.
+
+        Handles both CfgShardMetadata (for CFG-enabled models) and
+        PipelineShardMetadata (for non-CFG models with sequential CFG).
+        """
+        if self.group is None:
+            self.rank = 0
+            self.world_size = 1
+            self.start_layer = 0
+            self.end_layer = self.config.total_blocks
+
+            self.cfg_rank = 0
+            self.cfg_world_size = 1
+            self.cfg_parallel = False
+
+            self.pipeline_world_size = 1
+            self.pipeline_rank = 0
+
+            self.next_pipeline_rank: int | None = None
+            self.prev_pipeline_rank: int | None = None
+            self.cfg_peer_rank: int | None = None
+            self.first_pipeline_rank: int = 0
+            self.last_pipeline_rank: int = 0
+        elif isinstance(shard_metadata, CfgShardMetadata):
+            self.rank = shard_metadata.device_rank
+            self.world_size = shard_metadata.world_size
+            self.start_layer = shard_metadata.start_layer
+            self.end_layer = shard_metadata.end_layer
+            self.cfg_rank = shard_metadata.cfg_rank
+            self.cfg_world_size = shard_metadata.cfg_world_size
+
+            self.cfg_parallel = self.cfg_world_size > 1
+            self.pipeline_world_size = self.world_size // self.cfg_world_size
+
+            # Ring topology: CFG group 0 at positions [0..pipeline_world_size-1]
+            #                CFG group 1 at positions [world_size-1..pipeline_world_size] (reversed)
+            if self.cfg_rank == 0:
+                self.pipeline_rank = self.rank
+            else:
+                self.pipeline_rank = self.world_size - 1 - self.rank
+
+            is_first = self.pipeline_rank == 0
+            is_last = self.pipeline_rank == self.pipeline_world_size - 1
+
+            position_to_cfg_pipeline = [
+                (0, r) for r in range(self.pipeline_world_size)
+            ] + [(1, r) for r in reversed(range(self.pipeline_world_size))]
+            cfg_pipeline_to_device: dict[tuple[int, int], int] = {
+                (cfg_r, pipe_r): i
+                for i, (cfg_r, pipe_r) in enumerate(position_to_cfg_pipeline)
+            }
+
+            if is_last:
+                self.next_pipeline_rank = None
+            else:
+                self.next_pipeline_rank = cfg_pipeline_to_device[
+                    (self.cfg_rank, self.pipeline_rank + 1)
+                ]
+
+            if is_first:
+                self.prev_pipeline_rank = None
+            else:
+                self.prev_pipeline_rank = cfg_pipeline_to_device[
+                    (self.cfg_rank, self.pipeline_rank - 1)
+                ]
+
+            if is_last:
+                other_cfg_rank = 1 - self.cfg_rank
+                self.cfg_peer_rank = cfg_pipeline_to_device[
+                    (other_cfg_rank, self.pipeline_rank)
+                ]
+            else:
+                self.cfg_peer_rank = None
+
+            if self.cfg_rank == 0:
+                self.first_pipeline_rank = 0
+                self.last_pipeline_rank = self.pipeline_world_size - 1
+            else:
+                self.first_pipeline_rank = self.world_size - 1
+                self.last_pipeline_rank = self.pipeline_world_size
+        else:
+            self.rank = shard_metadata.device_rank
+            self.world_size = shard_metadata.world_size
+            self.start_layer = shard_metadata.start_layer
+            self.end_layer = shard_metadata.end_layer
+
+            self.cfg_rank = 0
+            self.cfg_world_size = 1
+            self.cfg_parallel = False
+
+            self.pipeline_world_size = self.world_size
+            self.pipeline_rank = self.rank
+
+            is_first = self.pipeline_rank == 0
+            is_last = self.pipeline_rank == self.pipeline_world_size - 1
+            self.next_pipeline_rank = None if is_last else self.pipeline_rank + 1
+            self.prev_pipeline_rank = None if is_first else self.pipeline_rank - 1
+            self.cfg_peer_rank = None
+
+            self.first_pipeline_rank = 0
+            self.last_pipeline_rank = self.pipeline_world_size - 1
+
     def _compute_assigned_blocks(self) -> None:
         """Determine which joint/single blocks this stage owns."""
         start = self.start_layer
@@ -138,11 +244,11 @@ class DiffusionRunner:
 
     @property
     def is_first_stage(self) -> bool:
-        return self.rank == 0
+        return self.pipeline_rank == 0
 
     @property
     def is_last_stage(self) -> bool:
-        return self.rank == self.world_size - 1
+        return self.pipeline_rank == self.pipeline_world_size - 1
 
     @property
     def is_distributed(self) -> bool:
@@ -153,6 +259,97 @@ class DiffusionRunner:
             return self._guidance_override
         return self.config.guidance_scale
 
+    def _get_cfg_branches(self, prompt_data: PromptData) -> Iterator[CfgBranch]:
+        """Yield the CFG branches this node should process.
+
+        - No CFG: yields one branch (positive)
+        - CFG parallel: yields one branch (our assigned branch)
+        - Sequential CFG: yields two branches (positive, then negative)
+        """
+        if not self.adapter.needs_cfg:
+            embeds, mask, pooled, cond = prompt_data.get_cfg_branch_data(positive=True)
+            yield CfgBranch(
+                positive=True,
+                embeds=embeds,
+                mask=mask,
+                pooled=pooled,
+                cond_latents=cond,
+            )
+        elif self.cfg_parallel:
+            positive = self.cfg_rank == 0
+            embeds, mask, pooled, cond = prompt_data.get_cfg_branch_data(positive)
+            yield CfgBranch(
+                positive=positive,
+                embeds=embeds,
+                mask=mask,
+                pooled=pooled,
+                cond_latents=cond,
+            )
+        else:
+            pos_embeds, pos_mask, pos_pooled, pos_cond = (
+                prompt_data.get_cfg_branch_data(positive=True)
+            )
+            yield CfgBranch(
+                positive=True,
+                embeds=pos_embeds,
+                mask=pos_mask,
+                pooled=pos_pooled,
+                cond_latents=pos_cond,
+            )
+            neg_embeds, neg_mask, neg_pooled, neg_cond = (
+                prompt_data.get_cfg_branch_data(positive=False)
+            )
+            yield CfgBranch(
+                positive=False,
+                embeds=neg_embeds,
+                mask=neg_mask,
+                pooled=neg_pooled,
+                cond_latents=neg_cond,
+            )
+
+    def _combine_cfg_results(self, results: list[tuple[bool, mx.array]]) -> mx.array:
+        if len(results) == 1:
+            positive, noise = results[0]
+            if self.cfg_parallel and self.is_last_stage:
+                # TODO(ciaran): try to remove
+                mx.eval(noise)
+                return self._exchange_and_apply_guidance(noise, positive)
+            return noise
+
+        noise_neg = next(n for p, n in results if not p)
+        noise_pos = next(n for p, n in results if p)
+        return self._apply_guidance(noise_pos, noise_neg)
+
+    def _exchange_and_apply_guidance(
+        self, noise: mx.array, is_positive: bool
+    ) -> mx.array:
+        assert self.group is not None
+        assert self.cfg_peer_rank is not None
+
+        if is_positive:
+            noise = mx.distributed.send(noise, self.cfg_peer_rank, group=self.group)
+            mx.async_eval(noise)
+            noise_neg = mx.distributed.recv_like(
+                noise, self.cfg_peer_rank, group=self.group
+            )
+            mx.eval(noise_neg)
+            noise_pos = noise
+        else:
+            noise_pos = mx.distributed.recv_like(
+                noise, self.cfg_peer_rank, group=self.group
+            )
+            mx.eval(noise_pos)
+            noise = mx.distributed.send(noise, self.cfg_peer_rank, group=self.group)
+            mx.async_eval(noise)
+            noise_neg = noise
+
+        return self._apply_guidance(noise_pos, noise_neg)
+
+    def _apply_guidance(self, noise_pos: mx.array, noise_neg: mx.array) -> mx.array:
+        scale = self._get_effective_guidance_scale()
+        assert scale is not None
+        return self.adapter.apply_guidance(noise_pos, noise_neg, scale)
+
     def _ensure_wrappers(
         self,
         text_seq_len: int,
@@ -470,7 +667,9 @@ class DiffusionRunner:
     ) -> mx.array:
         if self.group is None:
             return self._single_node_step(t, config, latents, prompt_data)
-        elif t < config.init_time_step + num_sync_steps:
+        elif (
+            self.pipeline_world_size == 1 or t < config.init_time_step + num_sync_steps
+        ):
             with trace(name=f"sync {t}", rank=self.rank, category="sync"):
                 return self._sync_pipeline_step(
                     t,
@@ -496,42 +695,29 @@ class DiffusionRunner:
         prompt_data: PromptData,
     ) -> mx.array:
         cond_image_grid = prompt_data.cond_image_grid
-        needs_cfg = self.adapter.needs_cfg
+        results: list[tuple[bool, mx.array]] = []
+
+        for branch in self._get_cfg_branches(prompt_data):
+            # Reset caches before each branch to ensure no state contamination
+            self._reset_all_caches()
 
-        if needs_cfg:
-            batched_data = prompt_data.get_batched_cfg_data()
-            assert batched_data is not None, "CFG model must provide batched data"
-            prompt_embeds, encoder_mask, batched_pooled, cond_latents = batched_data
             pooled_embeds = (
-                batched_pooled if batched_pooled is not None else prompt_embeds
-            )
-            step_latents = mx.concatenate([latents, latents], axis=0)
-        else:
-            prompt_embeds = prompt_data.prompt_embeds
-            pooled_embeds = prompt_data.pooled_prompt_embeds
-            encoder_mask = prompt_data.get_encoder_hidden_states_mask(positive=True)
-            cond_latents = prompt_data.conditioning_latents
-            step_latents = latents
-
-        noise = self._forward_pass(
-            step_latents,
-            prompt_embeds,
-            pooled_embeds,
-            t=t,
-            config=config,
-            encoder_hidden_states_mask=encoder_mask,
-            cond_image_grid=cond_image_grid,
-            conditioning_latents=cond_latents,
-        )
-
-        if needs_cfg:
-            noise_pos, noise_neg = mx.split(noise, 2, axis=0)
-            guidance_scale = self._get_effective_guidance_scale()
-            assert guidance_scale is not None
-            noise = self.adapter.apply_guidance(
-                noise_pos, noise_neg, guidance_scale=guidance_scale
+                branch.pooled if branch.pooled is not None else branch.embeds
             )
 
+            noise = self._forward_pass(
+                latents,
+                branch.embeds,
+                pooled_embeds,
+                t=t,
+                config=config,
+                encoder_hidden_states_mask=branch.mask,
+                cond_image_grid=cond_image_grid,
+                conditioning_latents=branch.cond_latents,
+            )
+            results.append((branch.positive, noise))
+
+        noise = self._combine_cfg_results(results)
         return config.scheduler.step(noise=noise, timestep=t, latents=latents)  # pyright: ignore[reportAny]
 
     def _create_patches(
@@ -582,7 +768,7 @@ class DiffusionRunner:
             )
 
         text_embeddings = self.adapter.compute_text_embeddings(
-            t, config, pooled_prompt_embeds
+            t, config, pooled_prompt_embeds, hidden_states=hidden_states
         )
         image_rotary_embeddings = self.adapter.compute_rotary_embeddings(
             prompt_embeds,
@@ -594,19 +780,22 @@ class DiffusionRunner:
 
         if self.has_joint_blocks:
             if not self.is_first_stage:
+                assert self.prev_pipeline_rank is not None
                 with trace(
-                    name=f"recv {self.prev_rank}", rank=self.rank, category="comms"
+                    name=f"recv {self.prev_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     hidden_states = mx.distributed.recv(
                         (batch_size, num_img_tokens, hidden_dim),
                         dtype,
-                        self.prev_rank,
+                        self.prev_pipeline_rank,
                         group=self.group,
                     )
                     encoder_hidden_states = mx.distributed.recv(
                         (batch_size, text_seq_len, hidden_dim),
                         dtype,
-                        self.prev_rank,
+                        self.prev_pipeline_rank,
                         group=self.group,
                     )
                     mx.eval(hidden_states, encoder_hidden_states)
@@ -639,34 +828,45 @@ class DiffusionRunner:
             if self.has_single_blocks or self.is_last_stage:
                 hidden_states = concatenated
             else:
+                assert self.next_pipeline_rank is not None
                 with trace(
-                    name=f"send {self.next_rank}", rank=self.rank, category="comms"
+                    name=f"send {self.next_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     concatenated = mx.distributed.send(
-                        concatenated, self.next_rank, group=self.group
+                        concatenated, self.next_pipeline_rank, group=self.group
                     )
                     mx.async_eval(concatenated)
 
         elif self.has_joint_blocks and not self.is_last_stage:
             assert encoder_hidden_states is not None
-            with trace(name=f"send {self.next_rank}", rank=self.rank, category="comms"):
+            assert self.next_pipeline_rank is not None
+            with trace(
+                name=f"send {self.next_pipeline_rank}",
+                rank=self.rank,
+                category="comms",
+            ):
                 hidden_states = mx.distributed.send(
-                    hidden_states, self.next_rank, group=self.group
+                    hidden_states, self.next_pipeline_rank, group=self.group
                 )
                 encoder_hidden_states = mx.distributed.send(
-                    encoder_hidden_states, self.next_rank, group=self.group
+                    encoder_hidden_states, self.next_pipeline_rank, group=self.group
                 )
                 mx.async_eval(hidden_states, encoder_hidden_states)
 
         if self.has_single_blocks:
             if not self.owns_concat_stage and not self.is_first_stage:
+                assert self.prev_pipeline_rank is not None
                 with trace(
-                    name=f"recv {self.prev_rank}", rank=self.rank, category="comms"
+                    name=f"recv {self.prev_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     hidden_states = mx.distributed.recv(
                         (batch_size, text_seq_len + num_img_tokens, hidden_dim),
                         dtype,
-                        self.prev_rank,
+                        self.prev_pipeline_rank,
                         group=self.group,
                     )
                     mx.eval(hidden_states)
@@ -689,11 +889,14 @@ class DiffusionRunner:
                     mx.eval(hidden_states)
 
             if not self.is_last_stage:
+                assert self.next_pipeline_rank is not None
                 with trace(
-                    name=f"send {self.next_rank}", rank=self.rank, category="comms"
+                    name=f"send {self.next_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     hidden_states = mx.distributed.send(
-                        hidden_states, self.next_rank, group=self.group
+                        hidden_states, self.next_pipeline_rank, group=self.group
                     )
                     mx.async_eval(hidden_states)
 
@@ -716,83 +919,67 @@ class DiffusionRunner:
         kontext_image_ids: mx.array | None = None,
     ) -> mx.array:
         prev_latents = hidden_states
-        needs_cfg = self.adapter.needs_cfg
         cond_image_grid = prompt_data.cond_image_grid
 
         scaled_hidden_states = config.scheduler.scale_model_input(hidden_states, t)  # pyright: ignore[reportAny]
         original_latent_tokens: int = scaled_hidden_states.shape[1]  # pyright: ignore[reportAny]
 
-        if needs_cfg:
-            batched_data = prompt_data.get_batched_cfg_data()
-            assert batched_data is not None, "CFG model must provide batched data"
-            prompt_embeds, encoder_mask, batched_pooled, cond_latents = batched_data
+        results: list[tuple[bool, mx.array]] = []
+
+        for branch in self._get_cfg_branches(prompt_data):
             pooled_embeds = (
-                batched_pooled if batched_pooled is not None else prompt_embeds
+                branch.pooled if branch.pooled is not None else branch.embeds
             )
-            step_latents = mx.concatenate(
-                [scaled_hidden_states, scaled_hidden_states], axis=0
+
+            cond_latents = branch.cond_latents
+            if cond_latents is not None:
+                num_img_tokens: int = original_latent_tokens + cond_latents.shape[1]
+            else:
+                num_img_tokens = original_latent_tokens
+
+            step_latents: mx.array = scaled_hidden_states  # pyright: ignore[reportAny]
+            if self.is_first_stage and cond_latents is not None:
+                step_latents = mx.concatenate([step_latents, cond_latents], axis=1)
+
+            text_seq_len = branch.embeds.shape[1]
+            self._ensure_wrappers(text_seq_len, branch.mask)
+
+            noise = self._run_sync_pass(
+                t,
+                config,
+                step_latents,
+                branch.embeds,
+                pooled_embeds,
+                branch.mask,
+                cond_image_grid,
+                kontext_image_ids,
+                num_img_tokens,
+                original_latent_tokens,
+                cond_latents,
             )
-        else:
-            prompt_embeds = prompt_data.prompt_embeds
-            pooled_embeds = prompt_data.pooled_prompt_embeds
-            encoder_mask = prompt_data.get_encoder_hidden_states_mask(positive=True)
-            cond_latents = prompt_data.conditioning_latents
-            step_latents = scaled_hidden_states  # pyright: ignore[reportAny]
 
-        if cond_latents is not None:
-            num_img_tokens: int = original_latent_tokens + cond_latents.shape[1]
-        else:
-            num_img_tokens = original_latent_tokens
-
-        if self.is_first_stage and cond_latents is not None:
-            step_latents = mx.concatenate([step_latents, cond_latents], axis=1)
-
-        text_seq_len = prompt_embeds.shape[1]
-        self._ensure_wrappers(text_seq_len, encoder_mask)
-
-        noise = self._run_sync_pass(
-            t,
-            config,
-            step_latents,
-            prompt_embeds,
-            pooled_embeds,
-            encoder_mask,
-            cond_image_grid,
-            kontext_image_ids,
-            num_img_tokens,
-            original_latent_tokens,
-            cond_latents,
-        )
+            if self.is_last_stage:
+                assert noise is not None
+                results.append((branch.positive, noise))
 
         if self.is_last_stage:
-            assert noise is not None
-            if needs_cfg:
-                noise_pos, noise_neg = mx.split(noise, 2, axis=0)
-                guidance_scale = self._get_effective_guidance_scale()
-                assert guidance_scale is not None
-                noise = self.adapter.apply_guidance(
-                    noise_pos, noise_neg, guidance_scale
-                )
+            noise = self._combine_cfg_results(results)
 
             hidden_states = config.scheduler.step(  # pyright: ignore[reportAny]
                 noise=noise, timestep=t, latents=prev_latents
             )
 
             if not self.is_first_stage:
-                with trace(name="send 0", rank=self.rank, category="comms"):
-                    hidden_states = mx.distributed.send(
-                        hidden_states, 0, group=self.group
-                    )
-                    mx.async_eval(hidden_states)
+                hidden_states = mx.distributed.send(
+                    hidden_states, self.first_pipeline_rank, group=self.group
+                )
+                mx.async_eval(hidden_states)
 
         elif self.is_first_stage:
-            with trace(
-                name=f"recv {self.world_size - 1}", rank=self.rank, category="comms"
-            ):
-                hidden_states = mx.distributed.recv_like(
-                    prev_latents, src=self.world_size - 1, group=self.group
-                )
-                mx.eval(hidden_states)
+            hidden_states = mx.distributed.recv_like(
+                prev_latents, src=self.last_pipeline_rank, group=self.group
+            )
+            mx.eval(hidden_states)
 
         else:
             hidden_states = prev_latents
@@ -809,39 +996,10 @@ class DiffusionRunner:
         kontext_image_ids: mx.array | None = None,
     ) -> mx.array:
         patch_latents, token_indices = self._create_patches(latents, config)
-        needs_cfg = self.adapter.needs_cfg
         cond_image_grid = prompt_data.cond_image_grid
 
-        if needs_cfg:
-            batched_data = prompt_data.get_batched_cfg_data()
-            assert batched_data is not None, "CFG model must provide batched data"
-            prompt_embeds, encoder_mask, batched_pooled, _ = batched_data
-            pooled_embeds = (
-                batched_pooled if batched_pooled is not None else prompt_embeds
-            )
-        else:
-            prompt_embeds = prompt_data.prompt_embeds
-            pooled_embeds = prompt_data.pooled_prompt_embeds
-            encoder_mask = prompt_data.get_encoder_hidden_states_mask(positive=True)
-
-        text_seq_len = prompt_embeds.shape[1]
-        self._ensure_wrappers(text_seq_len, encoder_mask)
-        self._set_text_seq_len(text_seq_len)
-
-        if self.joint_block_wrappers:
-            for wrapper in self.joint_block_wrappers:
-                wrapper.set_encoder_mask(encoder_mask)
-
-        text_embeddings = self.adapter.compute_text_embeddings(t, config, pooled_embeds)
-        image_rotary_embeddings = self.adapter.compute_rotary_embeddings(
-            prompt_embeds,
-            config,
-            encoder_hidden_states_mask=encoder_mask,
-            cond_image_grid=cond_image_grid,
-            kontext_image_ids=kontext_image_ids,
-        )
-
         prev_patch_latents = [p for p in patch_latents]
+
         encoder_hidden_states: mx.array | None = None
 
         for patch_idx in range(len(patch_latents)):
@@ -853,34 +1011,57 @@ class DiffusionRunner:
                 and not is_first_async_step
             ):
                 with trace(
-                    name=f"recv {self.prev_rank}", rank=self.rank, category="comms"
+                    name=f"recv {self.last_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     patch = mx.distributed.recv_like(
-                        patch, src=self.prev_rank, group=self.group
+                        patch, src=self.last_pipeline_rank, group=self.group
                     )
                     mx.eval(patch)
 
-            step_patch = mx.concatenate([patch, patch], axis=0) if needs_cfg else patch
+            results: list[tuple[bool, mx.array]] = []
 
-            noise, encoder_hidden_states = self._run_single_patch_pass(
-                patch=step_patch,
-                patch_idx=patch_idx,
-                token_indices=token_indices[patch_idx],
-                prompt_embeds=prompt_embeds,
-                text_embeddings=text_embeddings,
-                image_rotary_embeddings=image_rotary_embeddings,
-                encoder_hidden_states=encoder_hidden_states,
-            )
+            for branch in self._get_cfg_branches(prompt_data):
+                pooled_embeds = (
+                    branch.pooled if branch.pooled is not None else branch.embeds
+                )
+
+                text_seq_len = branch.embeds.shape[1]
+                self._ensure_wrappers(text_seq_len, branch.mask)
+                self._set_text_seq_len(text_seq_len)
+
+                if self.joint_block_wrappers:
+                    for wrapper in self.joint_block_wrappers:
+                        wrapper.set_encoder_mask(branch.mask)
+
+                text_embeddings = self.adapter.compute_text_embeddings(
+                    t, config, pooled_embeds
+                )
+                image_rotary_embeddings = self.adapter.compute_rotary_embeddings(
+                    branch.embeds,
+                    config,
+                    encoder_hidden_states_mask=branch.mask,
+                    cond_image_grid=cond_image_grid,
+                    kontext_image_ids=kontext_image_ids,
+                )
+
+                noise, encoder_hidden_states = self._run_single_patch_pass(
+                    patch=patch,
+                    patch_idx=patch_idx,
+                    token_indices=token_indices[patch_idx],
+                    prompt_embeds=branch.embeds,
+                    text_embeddings=text_embeddings,
+                    image_rotary_embeddings=image_rotary_embeddings,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+                if self.is_last_stage:
+                    assert noise is not None
+                    results.append((branch.positive, noise))
 
             if self.is_last_stage:
-                assert noise is not None
-                if needs_cfg:
-                    noise_pos, noise_neg = mx.split(noise, 2, axis=0)
-                    guidance_scale = self._get_effective_guidance_scale()
-                    assert guidance_scale is not None
-                    noise = self.adapter.apply_guidance(
-                        noise_pos, noise_neg, guidance_scale
-                    )
+                noise = self._combine_cfg_results(results)
 
                 patch_latents[patch_idx] = config.scheduler.step(  # pyright: ignore[reportAny]
                     noise=noise,
@@ -890,10 +1071,14 @@ class DiffusionRunner:
 
                 if not self.is_first_stage and t != config.num_inference_steps - 1:
                     with trace(
-                        name=f"send {self.next_rank}", rank=self.rank, category="comms"
+                        name=f"send {self.first_pipeline_rank}",
+                        rank=self.rank,
+                        category="comms",
                     ):
                         patch_latents[patch_idx] = mx.distributed.send(
-                            patch_latents[patch_idx], self.next_rank, group=self.group
+                            patch_latents[patch_idx],
+                            self.first_pipeline_rank,
+                            group=self.group,
                         )
                         mx.async_eval(patch_latents[patch_idx])
 
@@ -933,26 +1118,31 @@ class DiffusionRunner:
 
         if self.has_joint_blocks:
             if not self.is_first_stage:
+                assert self.prev_pipeline_rank is not None
                 patch_len = patch.shape[1]
                 with trace(
-                    name=f"recv {self.prev_rank}", rank=self.rank, category="comms"
+                    name=f"recv {self.prev_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     patch = mx.distributed.recv(
                         (batch_size, patch_len, hidden_dim),
                         patch.dtype,
-                        self.prev_rank,
+                        self.prev_pipeline_rank,
                         group=self.group,
                     )
                     mx.eval(patch)
 
                 if patch_idx == 0:
                     with trace(
-                        name=f"recv {self.prev_rank}", rank=self.rank, category="comms"
+                        name=f"recv {self.prev_pipeline_rank}",
+                        rank=self.rank,
+                        category="comms",
                     ):
                         encoder_hidden_states = mx.distributed.recv(
                             (batch_size, text_seq_len, hidden_dim),
                             patch.dtype,
-                            self.prev_rank,
+                            self.prev_pipeline_rank,
                             group=self.group,
                         )
                         mx.eval(encoder_hidden_states)
@@ -988,39 +1178,54 @@ class DiffusionRunner:
             if self.has_single_blocks or self.is_last_stage:
                 patch = patch_concat
             else:
+                assert self.next_pipeline_rank is not None
                 with trace(
-                    name=f"send {self.next_rank}", rank=self.rank, category="comms"
+                    name=f"send {self.next_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     patch_concat = mx.distributed.send(
-                        patch_concat, self.next_rank, group=self.group
+                        patch_concat, self.next_pipeline_rank, group=self.group
                     )
                     mx.async_eval(patch_concat)
 
         elif self.has_joint_blocks and not self.is_last_stage:
-            with trace(name=f"send {self.next_rank}", rank=self.rank, category="comms"):
-                patch = mx.distributed.send(patch, self.next_rank, group=self.group)
+            assert self.next_pipeline_rank is not None
+            with trace(
+                name=f"send {self.next_pipeline_rank}",
+                rank=self.rank,
+                category="comms",
+            ):
+                patch = mx.distributed.send(
+                    patch, self.next_pipeline_rank, group=self.group
+                )
                 mx.async_eval(patch)
 
             if patch_idx == 0:
                 assert encoder_hidden_states is not None
                 with trace(
-                    name=f"send {self.next_rank}", rank=self.rank, category="comms"
+                    name=f"send {self.next_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     encoder_hidden_states = mx.distributed.send(
-                        encoder_hidden_states, self.next_rank, group=self.group
+                        encoder_hidden_states, self.next_pipeline_rank, group=self.group
                     )
                     mx.async_eval(encoder_hidden_states)
 
         if self.has_single_blocks:
             if not self.owns_concat_stage and not self.is_first_stage:
+                assert self.prev_pipeline_rank is not None
                 patch_len = patch.shape[1]
                 with trace(
-                    name=f"recv {self.prev_rank}", rank=self.rank, category="comms"
+                    name=f"recv {self.prev_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
                     patch = mx.distributed.recv(
                         (batch_size, text_seq_len + patch_len, hidden_dim),
                         patch.dtype,
-                        self.prev_rank,
+                        self.prev_pipeline_rank,
                         group=self.group,
                     )
                     mx.eval(patch)
@@ -1043,15 +1248,20 @@ class DiffusionRunner:
                     mx.eval(patch)
 
             if not self.is_last_stage:
+                assert self.next_pipeline_rank is not None
                 with trace(
-                    name=f"send {self.next_rank}", rank=self.rank, category="comms"
+                    name=f"send {self.next_pipeline_rank}",
+                    rank=self.rank,
+                    category="comms",
                 ):
-                    patch = mx.distributed.send(patch, self.next_rank, group=self.group)
+                    patch = mx.distributed.send(
+                        patch, self.next_pipeline_rank, group=self.group
+                    )
                     mx.async_eval(patch)
 
         noise: mx.array | None = None
         if self.is_last_stage:
-            patch = patch[:, text_seq_len:, :]
-            noise = self.adapter.final_projection(patch, text_embeddings)
+            patch_img_only = patch[:, text_seq_len:, :]
+            noise = self.adapter.final_projection(patch_img_only, text_embeddings)
 
         return noise, encoder_hidden_states

src/exo/worker/engines/mlx/utils_mlx.py

@@ -48,6 +48,7 @@ from exo.shared.types.worker.instances import (
     MlxRingInstance,
 )
 from exo.shared.types.worker.shards import (
+    CfgShardMetadata,
     PipelineShardMetadata,
     ShardMetadata,
     TensorShardMetadata,
@@ -274,6 +275,11 @@ def shard_and_load(
             logger.info(f"loading model from {model_path} with pipeline parallelism")
             model = pipeline_auto_parallel(model, group, shard_metadata)
             eval_with_timeout(model.parameters(), timeout_seconds, on_timeout)
+        case CfgShardMetadata():
+            raise ValueError(
+                "CfgShardMetadata is not supported for text model loading - "
+                "this metadata type is only for image generation models"
+            )
 
     # TODO: Do we need this?
     mx.eval(model)

src/exo/worker/runner/runner.py

@@ -66,7 +66,11 @@ from exo.shared.types.worker.runners import (
     RunnerStatus,
     RunnerWarmingUp,
 )
-from exo.shared.types.worker.shards import ShardMetadata
+from exo.shared.types.worker.shards import (
+    CfgShardMetadata,
+    PipelineShardMetadata,
+    ShardMetadata,
+)
 from exo.utils.channels import MpReceiver, MpSender
 from exo.worker.engines.image import (
     DistributedImageModel,
@@ -87,6 +91,28 @@ from exo.worker.engines.mlx.utils_mlx import (
 from exo.worker.runner.bootstrap import logger
 
 
+def _is_primary_output_node(shard_metadata: ShardMetadata) -> bool:
+    """Check if this node is the primary output node for image generation.
+
+    For CFG models: the node with cfg_rank == 0 and is_pipeline_last.
+    For non-CFG models: the node with device_rank == world_size - 1.
+    """
+    if isinstance(shard_metadata, CfgShardMetadata):
+        pipeline_world_size = shard_metadata.world_size // shard_metadata.cfg_world_size
+        # Ring topology: CFG group 0 at positions [0..pipeline_world_size-1]
+        #                CFG group 1 at positions [world_size-1..pipeline_world_size] (reversed)
+        if shard_metadata.cfg_rank == 0:
+            pipeline_rank = shard_metadata.device_rank
+        else:
+            pipeline_rank = shard_metadata.world_size - 1 - shard_metadata.device_rank
+        is_pipeline_last = pipeline_rank == pipeline_world_size - 1
+        return is_pipeline_last and shard_metadata.cfg_rank == 0
+    elif isinstance(shard_metadata, PipelineShardMetadata):
+        # For pipeline metadata: device_rank == world_size - 1 means last stage
+        return shard_metadata.device_rank == shard_metadata.world_size - 1
+    return False
+
+
 def main(
     bound_instance: BoundInstance,
     event_sender: MpSender[Event],
@@ -371,10 +397,10 @@ def main(
                         # Track image_index for final images only
                         image_index = 0
                         for response in generate_image(model=model, task=task_params):
-                            if (
-                                shard_metadata.device_rank
-                                == shard_metadata.world_size - 1
-                            ):
+                            # Only the primary output node (last pipeline stage, CFG rank 0) sends results
+                            is_primary_output = _is_primary_output_node(shard_metadata)
+
+                            if is_primary_output:
                                 match response:
                                     case PartialImageResponse():
                                         logger.info(
@@ -399,7 +425,7 @@ def main(
                                         image_index += 1
                     # can we make this more explicit?
                     except Exception as e:
-                        if shard_metadata.device_rank == shard_metadata.world_size - 1:
+                        if _is_primary_output_node(shard_metadata):
                             event_sender.send(
                                 ChunkGenerated(
                                     command_id=command_id,
@@ -434,10 +460,7 @@ def main(
                     try:
                         image_index = 0
                         for response in generate_image(model=model, task=task_params):
-                            if (
-                                shard_metadata.device_rank
-                                == shard_metadata.world_size - 1
-                            ):
+                            if _is_primary_output_node(shard_metadata):
                                 match response:
                                     case PartialImageResponse():
                                         logger.info(
@@ -461,7 +484,7 @@ def main(
                                         )
                                         image_index += 1
                     except Exception as e:
-                        if shard_metadata.device_rank == shard_metadata.world_size - 1:
+                        if _is_primary_output_node(shard_metadata):
                             event_sender.send(
                                 ChunkGenerated(
                                     command_id=command_id,