mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Fixes, zero tensor for empty negative prompt, remove raw prompt node
This commit is contained in:
Sergey Borisov
parent
9aaf67c5b4
commit
b0738b7f70
@ -185,7 +185,7 @@ class CompelInvocation(BaseInvocation):
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class SDXLPromptInvocationBase:
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def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
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def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix, zero_on_empty):
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tokenizer_info = context.services.model_manager.get_model(
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**clip_field.tokenizer.dict(),
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context=context,
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@ -195,83 +195,22 @@ class SDXLPromptInvocationBase:
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context=context,
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)
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def _lora_loader():
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for lora in clip_field.loras:
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lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
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yield (lora_info.context.model, lora.weight)
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del lora_info
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return
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# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
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ti_list = []
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for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
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name = trigger[1:-1]
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try:
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ti_list.append(
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(
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name,
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context.services.model_manager.get_model(
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model_name=name,
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base_model=clip_field.text_encoder.base_model,
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model_type=ModelType.TextualInversion,
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context=context,
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).context.model,
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)
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)
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except ModelNotFoundException:
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# print(e)
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# import traceback
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# print(traceback.format_exc())
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print(f'Warn: trigger: "{trigger}" not found')
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with ModelPatcher.apply_lora(
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text_encoder_info.context.model, _lora_loader(), lora_prefix
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), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
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tokenizer,
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ti_manager,
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), ModelPatcher.apply_clip_skip(
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text_encoder_info.context.model, clip_field.skipped_layers
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), text_encoder_info as text_encoder:
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text_inputs = tokenizer(
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prompt,
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padding="max_length",
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max_length=tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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prompt_embeds = text_encoder(
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text_input_ids.to(text_encoder.device),
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output_hidden_states=True,
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# return zero on empty
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if prompt == "" and zero_on_empty:
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cpu_text_encoder = text_encoder_info.context.model
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c = torch.zeros(
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(1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
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dtype=text_encoder_info.context.cache.precision,
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)
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if get_pooled:
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c_pooled = prompt_embeds[0]
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c_pooled = torch.zeros(
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(1, cpu_text_encoder.config.hidden_size),
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dtype=c.dtype,
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)
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else:
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c_pooled = None
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c = prompt_embeds.hidden_states[-2]
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del tokenizer
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del text_encoder
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del tokenizer_info
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del text_encoder_info
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c = c.detach().to("cpu")
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if c_pooled is not None:
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c_pooled = c_pooled.detach().to("cpu")
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return c, c_pooled, None
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def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix):
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tokenizer_info = context.services.model_manager.get_model(
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**clip_field.tokenizer.dict(),
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context=context,
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)
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text_encoder_info = context.services.model_manager.get_model(
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**clip_field.text_encoder.dict(),
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context=context,
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)
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def _lora_loader():
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for lora in clip_field.loras:
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lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
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@ -375,11 +314,13 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False, "lora_te1_")
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c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=False)
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if self.style.strip() == "":
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True, "lora_te2_")
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True)
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else:
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "lora_te2_")
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True)
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print(f"{c1.shape=} {c2.shape=} {c2_pooled.shape=} {self.prompt=}")
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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@ -434,118 +375,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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# TODO: if there will appear lora for refiner - write proper prefix
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>")
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
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conditioning_data = ConditioningFieldData(
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conditionings=[
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SDXLConditioningInfo(
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embeds=c2,
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pooled_embeds=c2_pooled,
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add_time_ids=add_time_ids,
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extra_conditioning=ec2, # or None
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)
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]
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)
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conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
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context.services.latents.save(conditioning_name, conditioning_data)
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return CompelOutput(
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conditioning=ConditioningField(
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conditioning_name=conditioning_name,
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),
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)
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class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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"""Pass unmodified prompt to conditioning without compel processing."""
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type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt"
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prompt: str = Field(default="", description="Prompt")
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style: str = Field(default="", description="Style prompt")
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original_width: int = Field(1024, description="")
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original_height: int = Field(1024, description="")
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crop_top: int = Field(0, description="")
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crop_left: int = Field(0, description="")
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target_width: int = Field(1024, description="")
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target_height: int = Field(1024, description="")
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clip: ClipField = Field(None, description="Clip to use")
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clip2: ClipField = Field(None, description="Clip2 to use")
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
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}
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False, "lora_te1_")
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if self.style.strip() == "":
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c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True, "lora_te2_")
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else:
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c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "lora_te2_")
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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target_size = (self.target_height, self.target_width)
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add_time_ids = torch.tensor([original_size + crop_coords + target_size])
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conditioning_data = ConditioningFieldData(
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conditionings=[
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SDXLConditioningInfo(
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embeds=torch.cat([c1, c2], dim=-1),
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pooled_embeds=c2_pooled,
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add_time_ids=add_time_ids,
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extra_conditioning=ec1,
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)
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]
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)
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conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
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context.services.latents.save(conditioning_name, conditioning_data)
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return CompelOutput(
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conditioning=ConditioningField(
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conditioning_name=conditioning_name,
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),
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)
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class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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"""Parse prompt using compel package to conditioning."""
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type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
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style: str = Field(default="", description="Style prompt") # TODO: ?
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original_width: int = Field(1024, description="")
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original_height: int = Field(1024, description="")
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crop_top: int = Field(0, description="")
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crop_left: int = Field(0, description="")
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aesthetic_score: float = Field(6.0, description="")
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clip2: ClipField = Field(None, description="Clip to use")
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "SDXL Refiner Prompt (Raw)",
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"tags": ["prompt", "compel"],
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"type_hints": {"model": "model"},
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},
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}
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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# TODO: if there will appear lora for refiner - write proper prefix
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c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "<NONE>")
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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@ -386,8 +386,7 @@ class InvokeAIDiffuserComponent:
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self,
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x: torch.Tensor,
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sigma,
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unconditioning: torch.Tensor,
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conditioning: torch.Tensor,
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conditioning_data,
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**kwargs,
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):
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# low-memory sequential path
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@ -444,8 +443,7 @@ class InvokeAIDiffuserComponent:
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self,
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x: torch.Tensor,
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sigma,
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unconditioning,
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conditioning,
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conditioning_data,
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cross_attention_control_types_to_do,
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**kwargs,
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):
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