Ensure unique model Id for each quant

src/exo/shared/models/model_cards.py

@@ -40,6 +40,7 @@ class ModelCard(CamelCaseModel):
     supports_tensor: bool
     tasks: list[ModelTask]
     components: list[ComponentInfo] | None = None
+    quantization: int | None = None
 
     @field_validator("tasks", mode="before")
     @classmethod
@@ -413,7 +414,7 @@ MODEL_CARDS: dict[str, ModelCard] = {
     ),
 }
 
-_IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
+_IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
     "flux1-schnell": ModelCard(
         model_id=ModelId("black-forest-labs/FLUX.1-schnell"),
         storage_size=Memory.from_bytes(23782357120 + 9524621312),
@@ -428,7 +429,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
                 storage_size=Memory.from_kb(0),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,  # Single file
+                safetensors_index_filename=None,
             ),
             ComponentInfo(
                 component_name="text_encoder_2",
@@ -442,7 +443,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
                 component_name="transformer",
                 component_path="transformer/",
                 storage_size=Memory.from_bytes(23782357120),
-                n_layers=57,  # 19 transformer_blocks + 38 single_transformer_blocks
+                n_layers=57,
                 can_shard=True,
                 safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
             ),
@@ -458,7 +459,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
     ),
     "flux1-dev": ModelCard(
         model_id=ModelId("black-forest-labs/FLUX.1-dev"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
+        storage_size=Memory.from_bytes(23802816640 + 9524621312),
         n_layers=57,
         hidden_size=1,
         supports_tensor=False,
@@ -470,7 +471,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
                 storage_size=Memory.from_kb(0),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,  # Single file
+                safetensors_index_filename=None,
             ),
             ComponentInfo(
                 component_name="text_encoder_2",
@@ -484,7 +485,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
                 component_name="transformer",
                 component_path="transformer/",
                 storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,  # 19 transformer_blocks + 38 single_transformer_blocks
+                n_layers=57,
                 can_shard=True,
                 safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
             ),
@@ -543,7 +544,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
     "qwen-image": ModelCard(
         model_id=ModelId("Qwen/Qwen-Image"),
         storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,  # Qwen has 60 transformer blocks (all joint-style)
+        n_layers=60,
         hidden_size=1,
         supports_tensor=False,
         tasks=[ModelTask.TextToImage],
@@ -551,10 +552,10 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
             ComponentInfo(
                 component_name="text_encoder",
                 component_path="text_encoder/",
-                storage_size=Memory.from_kb(16584333312),
+                storage_size=Memory.from_bytes(16584333312),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,  # Single file
+                safetensors_index_filename=None,
             ),
             ComponentInfo(
                 component_name="transformer",
@@ -577,7 +578,7 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
     "qwen-image-edit-2509": ModelCard(
         model_id=ModelId("Qwen/Qwen-Image-Edit-2509"),
         storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,  # Qwen has 60 transformer blocks (all joint-style)
+        n_layers=60,
         hidden_size=1,
         supports_tensor=False,
         tasks=[ModelTask.ImageToImage],
@@ -585,10 +586,10 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
             ComponentInfo(
                 component_name="text_encoder",
                 component_path="text_encoder/",
-                storage_size=Memory.from_kb(16584333312),
+                storage_size=Memory.from_bytes(16584333312),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,  # Single file
+                safetensors_index_filename=None,
             ),
             ComponentInfo(
                 component_name="transformer",
@@ -610,6 +611,93 @@ _IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
     ),
 }
 
+
+def _create_image_model_quant_variants(
+    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]
+
+    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),
+            quantization=None,
+        )
+    }
+
+    for quant in quantizations:
+        quant_transformer_bytes = Memory.from_bytes(
+            (num_transformer_bytes * quant) // 16
+        )
+        total_bytes = remaining_bytes + quant_transformer_bytes
+
+        model_id = base_card.model_id + f"-{quant}bit"
+
+        variants[f"{base_name}-{quant}bit"] = ModelCard(
+            model_id=ModelId(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),
+            quantization=quant,
+        )
+
+    return variants
+
+
+_image_model_cards: dict[str, ModelCard] = {}
+for _base_name, _base_card in _IMAGE_BASE_MODEL_CARDS.items():
+    _image_model_cards |= _create_image_model_quant_variants(_base_name, _base_card)
+_IMAGE_MODEL_CARDS = _image_model_cards
+
 if EXO_ENABLE_IMAGE_MODELS:
     MODEL_CARDS.update(_IMAGE_MODEL_CARDS)
 

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

@@ -71,8 +71,10 @@ class DistributedImageModel:
     def from_bound_instance(
         cls, bound_instance: BoundInstance
     ) -> "DistributedImageModel":
-        model_id = bound_instance.bound_shard.model_card.model_id
+        model_card = bound_instance.bound_shard.model_card
+        model_id = model_card.model_id
         model_path = build_model_path(model_id)
+        quantize = model_card.quantization
 
         shard_metadata = bound_instance.bound_shard
         if not isinstance(shard_metadata, PipelineShardMetadata):
@@ -93,6 +95,7 @@ class DistributedImageModel:
             local_path=model_path,
             shard_metadata=shard_metadata,
             group=group,
+            quantize=quantize,
         )
 
     def get_steps_for_quality(self, quality: Literal["low", "medium", "high"]) -> int: