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https://github.com/invoke-ai/InvokeAI
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Review changes
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StAlKeR7779
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@ -30,35 +30,24 @@ class CompelOutput(BaseInvocationOutput):
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#fmt: off
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type: Literal["compel_output"] = "compel_output"
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# name + loras -> pipeline + loras
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# model: ModelField = Field(default=None, description="Model")
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# src? + loras -> tokenizer + text_encoder + loras
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# clip: ClipField = Field(default=None, description="Text encoder(clip)")
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positive: ConditioningField = Field(default=None, description="Positive conditioning")
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negative: ConditioningField = Field(default=None, description="Negative conditioning")
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conditioning: ConditioningField = Field(default=None, description="Conditioning")
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#fmt: on
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class CompelInvocation(BaseInvocation):
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"""Parse prompt using compel package to conditioning."""
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type: Literal["compel"] = "compel"
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positive_prompt: str = Field(default="", description="Positive prompt")
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negative_prompt: str = Field(default="", description="Negative prompt")
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prompt: str = Field(default="", description="Prompt")
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model: str = Field(default="", description="Model to use")
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truncate_long_prompts: bool = Field(default=False, description="Whether or not to truncate long prompt to 77 tokens")
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# name + loras -> pipeline + loras
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# model: ModelField = Field(default=None, description="Model to use")
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# src? + loras -> tokenizer + text_encoder + loras
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# clip: ClipField = Field(default=None, description="Text encoder(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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"tags": ["latents", "noise"],
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"tags": ["prompt", "compel"],
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"type_hints": {
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"model": "model"
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}
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@ -88,14 +77,8 @@ class CompelInvocation(BaseInvocation):
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pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
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# apply the concepts library to the prompt
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positive_prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
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self.positive_prompt,
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lambda concepts: load_huggingface_concepts(concepts),
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pipeline.textual_inversion_manager.get_all_trigger_strings(),
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)
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negative_prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
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self.negative_prompt,
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prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
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self.prompt,
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lambda concepts: load_huggingface_concepts(concepts),
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pipeline.textual_inversion_manager.get_all_trigger_strings(),
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)
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@ -103,7 +86,7 @@ class CompelInvocation(BaseInvocation):
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# lazy-load any deferred textual inversions.
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# this might take a couple of seconds the first time a textual inversion is used.
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pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
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positive_prompt_str + "[" + negative_prompt_str + "]"
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prompt_str
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)
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compel = Compel(
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@ -111,43 +94,35 @@ class CompelInvocation(BaseInvocation):
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text_encoder=text_encoder,
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textual_inversion_manager=pipeline.textual_inversion_manager,
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dtype_for_device_getter=torch_dtype,
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truncate_long_prompts=self.truncate_long_prompts,
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truncate_long_prompts=True, # TODO:
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)
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# TODO: support legacy blend?
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positive_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(positive_prompt_str)
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negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(negative_prompt_str)
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prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
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if getattr(Globals, "log_tokenization", False):
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log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
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log_tokenization_for_prompt_object(prompt, tokenizer)
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# TODO: add lora(with model and clip field types)
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c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
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uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
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c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
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if not self.truncate_long_prompts:
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[c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
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# TODO: long prompt support
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#if not self.truncate_long_prompts:
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# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
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ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
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tokens_count_including_eos_bos=get_max_token_count(tokenizer, positive_prompt),
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tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
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cross_attention_control_args=options.get("cross_attention_control", None),
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)
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name_prefix = f'{context.graph_execution_state_id}__{self.id}'
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name_positive = f"{name_prefix}_positive"
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name_negative = f"{name_prefix}_negative"
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conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
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# TODO: hacky but works ;D maybe rename latents somehow?
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context.services.latents.set(name_positive, (c, ec))
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context.services.latents.set(name_negative, (uc, None))
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context.services.latents.set(conditioning_name, (c, ec))
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return CompelOutput(
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positive=ConditioningField(
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conditioning_name=name_positive,
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),
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negative=ConditioningField(
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conditioning_name=name_negative,
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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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@ -195,20 +170,6 @@ def get_tokens_for_prompt_object(
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return tokens
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def log_tokenization(
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positive_prompt: Union[Blend, FlattenedPrompt],
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negative_prompt: Union[Blend, FlattenedPrompt],
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tokenizer,
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):
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print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
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print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
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log_tokenization_for_prompt_object(positive_prompt, tokenizer)
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log_tokenization_for_prompt_object(
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negative_prompt, tokenizer, display_label_prefix="(negative prompt)"
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)
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def log_tokenization_for_prompt_object(
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p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
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):
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@ -138,14 +138,14 @@ class NoiseInvocation(BaseInvocation):
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# Text to image
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class TextToLatentsInvocation(BaseInvocation):
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"""Generates latents from a prompt."""
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"""Generates latents from conditionings."""
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type: Literal["t2l"] = "t2l"
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# Inputs
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# fmt: off
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positive: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
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negative: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
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positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
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negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
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noise: Optional[LatentsField] = Field(description="The noise to use")
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steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
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cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
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@ -204,8 +204,8 @@ class TextToLatentsInvocation(BaseInvocation):
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def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
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c, extra_conditioning_info = context.services.latents.get(self.positive.conditioning_name)
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uc, _ = context.services.latents.get(self.negative.conditioning_name)
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c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
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uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
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conditioning_data = ConditioningData(
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uc,
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@ -20,28 +20,29 @@ def create_text_to_image() -> LibraryGraph:
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'seed': ParamIntInvocation(id='seed', a=-1),
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'3': NoiseInvocation(id='3'),
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'4': CompelInvocation(id='4'),
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'5': TextToLatentsInvocation(id='5'),
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'6': LatentsToImageInvocation(id='6'),
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'5': CompelInvocation(id='5'),
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'6': TextToLatentsInvocation(id='6'),
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'7': LatentsToImageInvocation(id='7'),
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},
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edges=[
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Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
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Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
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Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
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Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='5', field='noise')),
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Edge(source=EdgeConnection(node_id='5', field='latents'), destination=EdgeConnection(node_id='6', field='latents')),
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Edge(source=EdgeConnection(node_id='4', field='positive'), destination=EdgeConnection(node_id='5', field='positive')),
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Edge(source=EdgeConnection(node_id='4', field='negative'), destination=EdgeConnection(node_id='5', field='negative')),
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Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
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Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
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Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
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Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
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]
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),
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exposed_inputs=[
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ExposedNodeInput(node_path='4', field='positive_prompt', alias='positive_prompt'),
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ExposedNodeInput(node_path='4', field='negative_prompt', alias='negative_prompt'),
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ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
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ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
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ExposedNodeInput(node_path='width', field='a', alias='width'),
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ExposedNodeInput(node_path='height', field='a', alias='height'),
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ExposedNodeInput(node_path='seed', field='a', alias='seed'),
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],
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exposed_outputs=[
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ExposedNodeOutput(node_path='6', field='image', alias='image')
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ExposedNodeOutput(node_path='7', field='image', alias='image')
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])
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@ -463,16 +463,16 @@ def test_graph_subgraph_t2i():
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n4 = ShowImageInvocation(id = "4")
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g.add_node(n4)
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g.add_edge(create_edge("1.6","image","4","image"))
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g.add_edge(create_edge("1.7","image","4","image"))
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# Validate
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dg = g.nx_graph_flat()
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assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '2', '3', '4'])
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assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '2', '3', '4'])
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expected_edges = [(f'1.{e.source.node_id}',f'1.{e.destination.node_id}') for e in lg.graph.edges]
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expected_edges.extend([
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('2','1.width'),
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('3','1.height'),
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('1.6','4')
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('1.7','4')
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])
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print(expected_edges)
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print(list(dg.edges))
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