Tidy variable management and dtype handling in FluxTextToImageInvocation.

Author: RyanJDick

invokeai/app/invocations/flux_text_to_image.py

@@ -58,26 +58,28 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-
-        latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
+        latents = self._run_diffusion(context)
         image = self._run_vae_decoding(context, latents)
         image_dto = context.images.save(image=image)
         return ImageOutput.build(image_dto)
 
     def _run_diffusion(
         self,
         context: InvocationContext,
-        clip_embeddings: torch.Tensor,
-        t5_embeddings: torch.Tensor,
     ):
-        transformer_info = context.models.load(self.transformer.transformer)
         inference_dtype = torch.bfloat16
 
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+
+        transformer_info = context.models.load(self.transformer.transformer)
+
         # Prepare input noise.
         x = get_noise(
             num_samples=1,
@@ -88,13 +90,13 @@ def _run_diffusion(
             seed=self.seed,
         )
 
-        img, img_ids = prepare_latent_img_patches(x)
+        x, img_ids = prepare_latent_img_patches(x)
 
         is_schnell = "schnell" in transformer_info.config.config_path
 
         timesteps = get_schedule(
             num_steps=self.num_steps,
-            image_seq_len=img.shape[1],
+            image_seq_len=x.shape[1],
             shift=not is_schnell,
         )
 
@@ -135,7 +137,7 @@ def step_callback() -> None:
 
             x = denoise(
                 model=transformer,
-                img=img,
+                img=x,
                 img_ids=img_ids,
                 txt=t5_embeddings,
                 txt_ids=txt_ids,

invokeai/backend/flux/sampling.py

@@ -111,16 +111,7 @@ def denoise(
     step_callback: Callable[[], None],
     guidance: float = 4.0,
 ):
-    dtype = model.txt_in.bias.dtype
-
-    # TODO(ryand): This shouldn't be necessary if we manage the dtypes properly in the caller.
-    img = img.to(dtype=dtype)
-    img_ids = img_ids.to(dtype=dtype)
-    txt = txt.to(dtype=dtype)
-    txt_ids = txt_ids.to(dtype=dtype)
-    vec = vec.to(dtype=dtype)
-
-    # this is ignored for schnell
+    # guidance_vec is ignored for schnell.
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
     for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
@@ -168,9 +159,9 @@ def prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor,
         img = repeat(img, "1 ... -> bs ...", bs=bs)
 
     # Generate patch position ids.
-    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device)[None, :]
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device, dtype=img.dtype)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device, dtype=img.dtype)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device, dtype=img.dtype)[None, :]
     img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
     return img, img_ids

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -43,6 +43,11 @@ class FLUXConditioningInfo:
     clip_embeds: torch.Tensor
     t5_embeds: torch.Tensor
 
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        self.clip_embeds = self.clip_embeds.to(device=device, dtype=dtype)
+        self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
+        return self
+
 
 @dataclass
 class ConditioningFieldData: