unet: get rid of clip_embedding attribute for SD1

It is implicitly defined by the underlying cross-attention layer. This
also makes it consistent with SDXL.

scripts/conversion/convert_diffusers_controlnet.py

@@ -21,7 +21,7 @@ class Args(argparse.Namespace):
 @torch.no_grad()
 def convert(args: Args) -> dict[str, torch.Tensor]:
     controlnet_src: nn.Module = ControlNetModel.from_pretrained(pretrained_model_name_or_path=args.source_path)  # type: ignore
-    unet = SD1UNet(in_channels=4, clip_embedding_dim=768)
+    unet = SD1UNet(in_channels=4)
     adapter = SD1ControlnetAdapter(unet, name="mycn").inject()
     controlnet = unet.Controlnet
 

scripts/conversion/convert_diffusers_lora.py

@@ -44,7 +44,7 @@ def process(args: Args) -> None:
     diffusers_sd = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=args.base_model)  # type: ignore
     diffusers_model = cast(fl.Module, diffusers_sd.unet)  # type: ignore
 
-    refiners_model = SD1UNet(in_channels=4, clip_embedding_dim=768)
+    refiners_model = SD1UNet(in_channels=4)
     target = LoraTarget.CrossAttention
     metadata = {"unet_targets": "CrossAttentionBlock2d"}
     rank = diffusers_state_dict[

scripts/conversion/convert_diffusers_unet.py

@@ -22,9 +22,7 @@ def setup_converter(args: Args) -> ModelConverter:
     source_clip_embedding_dim: int = source.config.cross_attention_dim  # type: ignore
     source_has_time_ids: bool = source.config.addition_embed_type == "text_time"  # type: ignore
     target = (
-        SDXLUNet(in_channels=source_in_channels)
-        if source_has_time_ids
-        else SD1UNet(in_channels=source_in_channels, clip_embedding_dim=source_clip_embedding_dim)
+        SDXLUNet(in_channels=source_in_channels) if source_has_time_ids else SD1UNet(in_channels=source_in_channels)
     )
 
     x = torch.randn(1, source_in_channels, 32, 32)

scripts/conversion/convert_refiners_lora_to_sdwebui.py

@@ -62,7 +62,7 @@ def main() -> None:
     for meta_key, meta_value in metadata.items():
         match meta_key:
             case "unet_targets":
-                model = SD1UNet(in_channels=4, clip_embedding_dim=768)
+                model = SD1UNet(in_channels=4)
                 create_mapping = partial(get_unet_mapping, source_path=args.sd15)
                 key_prefix = "unet."
                 lora_prefix = "lora_unet_"

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py

@@ -24,7 +24,7 @@ class StableDiffusion_1(LatentDiffusionModel):
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
-        unet = unet or SD1UNet(in_channels=4, clip_embedding_dim=768)
+        unet = unet or SD1UNet(in_channels=4)
         lda = lda or LatentDiffusionAutoencoder()
         clip_text_encoder = clip_text_encoder or CLIPTextEncoderL()
         scheduler = scheduler or DPMSolver(num_inference_steps=30)

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py

@@ -243,17 +243,10 @@ class ResidualConcatenator(fl.Chain):
 
 
 class SD1UNet(fl.Chain):
-    structural_attrs = ["in_channels", "clip_embedding_dim"]
+    structural_attrs = ["in_channels"]
 
-    def __init__(
-        self,
-        in_channels: int,
-        clip_embedding_dim: int,
-        device: Device | str | None = None,
-        dtype: DType | None = None,
-    ):
+    def __init__(self, in_channels: int, device: Device | str | None = None, dtype: DType | None = None) -> None:
         self.in_channels = in_channels
-        self.clip_embedding_dim = clip_embedding_dim
         super().__init__(
             TimestepEncoder(device=device, dtype=dtype),
             DownBlocks(in_channels=in_channels, device=device, dtype=dtype),

src/refiners/training_utils/latent_diffusion.py

@@ -128,7 +128,7 @@ class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
     @cached_property
     def unet(self) -> SD1UNet:
         assert self.config.models["unet"] is not None, "The config must contain a unet entry."
-        return SD1UNet(in_channels=4, clip_embedding_dim=768, device=self.device).to(device=self.device)
+        return SD1UNet(in_channels=4, device=self.device).to(device=self.device)
 
     @cached_property
     def text_encoder(self) -> CLIPTextEncoderL:

tests/e2e/test_diffusion.py

@@ -224,7 +224,7 @@ def sd15_inpainting(
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
+    unet = SD1UNet(in_channels=9)
     sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device)
 
     sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights))
@@ -242,7 +242,7 @@ def sd15_inpainting_float16(
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
+    unet = SD1UNet(in_channels=9)
     sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device, dtype=torch.float16)
 
     sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights))

tests/foundationals/latent_diffusion/test_controlnet.py

@@ -11,7 +11,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet impor
 @pytest.fixture(scope="module", params=[True, False])
 def unet(request: pytest.FixtureRequest) -> Iterator[SD1UNet]:
     with_parent: bool = request.param
-    unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
+    unet = SD1UNet(in_channels=9)
     if with_parent:
         fl.Chain(unet)
     yield unet

tests/foundationals/latent_diffusion/test_reference_only_control.py

@@ -14,7 +14,7 @@ from refiners.foundationals.latent_diffusion.cross_attention import CrossAttenti
 
 @torch.no_grad()
 def test_sai_inject_eject() -> None:
-    unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
+    unet = SD1UNet(in_channels=9)
     sai = ReferenceOnlyControlAdapter(unet)
 
     nb_cross_attention_blocks = len(list(unet.walk(CrossAttentionBlock)))

tests/foundationals/latent_diffusion/test_unet.py

@@ -9,7 +9,7 @@ def test_unet_context_flush():
     timestep = torch.randint(0, 999, size=(1, 1))
     x = torch.randn(1, 4, 32, 32)
 
-    unet = SD1UNet(in_channels=4, clip_embedding_dim=768)
+    unet = SD1UNet(in_channels=4)
     unet.set_clip_text_embedding(clip_text_embedding=text_embedding)  # not flushed between forward-s
 
     with torch.no_grad():