Refactor generation backend

invokeai/app/invocations/compel.py

@@ -16,7 +16,7 @@
 from ...backend.model_management import ModelType
 from ...backend.model_management.models import ModelNotFoundException
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
+from ...backend.stable_diffusion import InvokeAIDiffuserComponent, BasicConditioningInfo, SDXLConditioningInfo
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 from .model import ClipField
 from dataclasses import dataclass
@@ -29,28 +29,9 @@ class Config:
         schema_extra = {"required": ["conditioning_name"]}
 
 
-@dataclass
-class BasicConditioningInfo:
-    # type: Literal["basic_conditioning"] = "basic_conditioning"
-    embeds: torch.Tensor
-    extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
-    # weight: float
-    # mode: ConditioningAlgo
-
-
-@dataclass
-class SDXLConditioningInfo(BasicConditioningInfo):
-    # type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
-    pooled_embeds: torch.Tensor
-    add_time_ids: torch.Tensor
-
-
-ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")]
-
-
 @dataclass
 class ConditioningFieldData:
-    conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
+    conditionings: List[BasicConditioningInfo]
     # unconditioned: Optional[torch.Tensor]
 
 
@@ -176,7 +157,15 @@ def _lora_loader():
 
 
 class SDXLPromptInvocationBase:
-    def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
+    def run_clip_compel(
+        self,
+        context: InvocationContext,
+        clip_field: ClipField,
+        prompt: str,
+        get_pooled: bool,
+        lora_prefix: str,
+        zero_on_empty: bool,
+    ):
         tokenizer_info = context.services.model_manager.get_model(
             **clip_field.tokenizer.dict(),
             context=context,
@@ -186,82 +175,21 @@ def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
             context=context,
         )
 
-        def _lora_loader():
-            for lora in clip_field.loras:
-                lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
-
-        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
-
-        ti_list = []
-        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
-            name = trigger[1:-1]
-            try:
-                ti_list.append(
-                    (
-                        name,
-                        context.services.model_manager.get_model(
-                            model_name=name,
-                            base_model=clip_field.text_encoder.base_model,
-                            model_type=ModelType.TextualInversion,
-                            context=context,
-                        ).context.model,
-                    )
-                )
-            except ModelNotFoundException:
-                # print(e)
-                # import traceback
-                # print(traceback.format_exc())
-                print(f'Warn: trigger: "{trigger}" not found')
-
-        with ModelPatcher.apply_lora(
-            text_encoder_info.context.model, _lora_loader(), lora_prefix
-        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
-            tokenizer,
-            ti_manager,
-        ), ModelPatcher.apply_clip_skip(
-            text_encoder_info.context.model, clip_field.skipped_layers
-        ), text_encoder_info as text_encoder:
-            text_inputs = tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            prompt_embeds = text_encoder(
-                text_input_ids.to(text_encoder.device),
-                output_hidden_states=True,
+        # return zero on empty
+        if prompt == "" and zero_on_empty:
+            cpu_text_encoder = text_encoder_info.context.model
+            c = torch.zeros(
+                (1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
+                dtype=text_encoder_info.context.cache.precision,
             )
             if get_pooled:
-                c_pooled = prompt_embeds[0]
+                c_pooled = torch.zeros(
+                    (1, cpu_text_encoder.config.hidden_size),
+                    dtype=c.dtype,
+                )
             else:
                 c_pooled = None
-            c = prompt_embeds.hidden_states[-2]
-
-        del tokenizer
-        del text_encoder
-        del tokenizer_info
-        del text_encoder_info
-
-        c = c.detach().to("cpu")
-        if c_pooled is not None:
-            c_pooled = c_pooled.detach().to("cpu")
-
-        return c, c_pooled, None
-
-    def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix):
-        tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.dict(),
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.dict(),
-            context=context,
-        )
+            return c, c_pooled, None
 
         def _lora_loader():
             for lora in clip_field.loras:
@@ -366,11 +294,17 @@ class Config(InvocationConfig):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False, "lora_te1_")
+        c1, c1_pooled, ec1 = self.run_clip_compel(
+            context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
+        )
         if self.style.strip() == "":
-            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True, "lora_te2_")
+            c2, c2_pooled, ec2 = self.run_clip_compel(
+                context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True
+            )
         else:
-            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "lora_te2_")
+            c2, c2_pooled, ec2 = self.run_clip_compel(
+                context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True
+            )
 
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)
@@ -425,118 +359,7 @@ class Config(InvocationConfig):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
         # TODO: if there will appear lora for refiner - write proper prefix
-        c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>")
-
-        original_size = (self.original_height, self.original_width)
-        crop_coords = (self.crop_top, self.crop_left)
-
-        add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
-
-        conditioning_data = ConditioningFieldData(
-            conditionings=[
-                SDXLConditioningInfo(
-                    embeds=c2,
-                    pooled_embeds=c2_pooled,
-                    add_time_ids=add_time_ids,
-                    extra_conditioning=ec2,  # or None
-                )
-            ]
-        )
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-        context.services.latents.save(conditioning_name, conditioning_data)
-
-        return CompelOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-
-class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
-    """Pass unmodified prompt to conditioning without compel processing."""
-
-    type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt"
-
-    prompt: str = Field(default="", description="Prompt")
-    style: str = Field(default="", description="Style prompt")
-    original_width: int = Field(1024, description="")
-    original_height: int = Field(1024, description="")
-    crop_top: int = Field(0, description="")
-    crop_left: int = Field(0, description="")
-    target_width: int = Field(1024, description="")
-    target_height: int = Field(1024, description="")
-    clip: ClipField = Field(None, description="Clip to use")
-    clip2: ClipField = Field(None, description="Clip2 to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
-        }
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False, "lora_te1_")
-        if self.style.strip() == "":
-            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True, "lora_te2_")
-        else:
-            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "lora_te2_")
-
-        original_size = (self.original_height, self.original_width)
-        crop_coords = (self.crop_top, self.crop_left)
-        target_size = (self.target_height, self.target_width)
-
-        add_time_ids = torch.tensor([original_size + crop_coords + target_size])
-
-        conditioning_data = ConditioningFieldData(
-            conditionings=[
-                SDXLConditioningInfo(
-                    embeds=torch.cat([c1, c2], dim=-1),
-                    pooled_embeds=c2_pooled,
-                    add_time_ids=add_time_ids,
-                    extra_conditioning=ec1,
-                )
-            ]
-        )
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-        context.services.latents.save(conditioning_name, conditioning_data)
-
-        return CompelOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-
-class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
-    """Parse prompt using compel package to conditioning."""
-
-    type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
-
-    style: str = Field(default="", description="Style prompt")  # TODO: ?
-    original_width: int = Field(1024, description="")
-    original_height: int = Field(1024, description="")
-    crop_top: int = Field(0, description="")
-    crop_left: int = Field(0, description="")
-    aesthetic_score: float = Field(6.0, description="")
-    clip2: ClipField = Field(None, description="Clip to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "SDXL Refiner Prompt (Raw)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {"model": "model"},
-            },
-        }
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> CompelOutput:
-        # TODO: if there will appear lora for refiner - write proper prefix
-        c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "<NONE>")
+        c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
 
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)