mirror of
https://github.com/invoke-ai/InvokeAI
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Rewrite latent nodes to new model manager
This commit is contained in:
Sergey Borisov
parent
3b2a054f7a
commit
bc96727cbe
@ -7,6 +7,7 @@ import einops
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import torch
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from diffusers import DiffusionPipeline
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from diffusers.schedulers import SchedulerMixin as Scheduler
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from diffusers.image_processor import VaeImageProcessor
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from pydantic import BaseModel, Field
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from invokeai.app.util.misc import SEED_MAX, get_random_seed
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@ -26,6 +27,9 @@ from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
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from .compel import ConditioningField
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from .image import ImageField, ImageOutput, build_image_output
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from .model import ModelInfo, UNetField, VaeField
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from ...backend.model_management import SDModelType
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class LatentsField(BaseModel):
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"""A latents field used for passing latents between invocations"""
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@ -70,9 +74,21 @@ SAMPLER_NAME_VALUES = Literal[
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]
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def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
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def get_scheduler(
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context: InvocationContext,
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scheduler_info: ModelInfo,
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scheduler_name: str,
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) -> Scheduler:
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orig_scheduler_info = context.services.model_manager.get_model(
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model_name=scheduler_info.model_name,
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model_type=SDModelType[scheduler_info.model_type],
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submodel=SDModelType[scheduler_info.submodel],
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)
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with orig_scheduler_info.context as orig_scheduler:
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scheduler_config = orig_scheduler.config
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scheduler_class = scheduler_map.get(scheduler_name,'ddim')
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scheduler = scheduler_class.from_config(model.scheduler.config)
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scheduler = scheduler_class.from_config(scheduler_config)
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# hack copied over from generate.py
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if not hasattr(scheduler, 'uses_inpainting_model'):
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scheduler.uses_inpainting_model = lambda: False
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@ -102,12 +118,6 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c
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# x = (1 - self.perlin) * x + self.perlin * perlin_noise
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return x
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class ModelGetter:
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def get_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline:
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model_manager = context.services.model_manager
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model_info = model_manager.get_model(self.model,node=self,context=context)
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return model_info.context
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class NoiseInvocation(BaseInvocation):
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"""Generates latent noise."""
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@ -139,7 +149,7 @@ class NoiseInvocation(BaseInvocation):
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# Text to image
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class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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class TextToLatentsInvocation(BaseInvocation):
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"""Generates latents from conditionings."""
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type: Literal["t2l"] = "t2l"
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@ -152,9 +162,10 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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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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scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
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model: str = Field(default="", description="The model to use (currently ignored)")
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seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
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seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
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unet: UNetField = Field(default=None, description="UNet submodel")
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# fmt: on
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# Schema customisation
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@ -162,9 +173,6 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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schema_extra = {
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"ui": {
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"tags": ["latents", "image"],
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"type_hints": {
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"model": "model"
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}
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},
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}
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@ -179,7 +187,7 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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source_node_id=source_node_id,
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)
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def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
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def get_conditioning_data(self, context: InvocationContext, scheduler) -> ConditioningData:
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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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@ -194,9 +202,36 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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h_symmetry_time_pct=None,#h_symmetry_time_pct,
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v_symmetry_time_pct=None#v_symmetry_time_pct,
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),
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).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
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).add_scheduler_args_if_applicable(scheduler, eta=None)#ddim_eta)
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return conditioning_data
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def create_pipeline(self, unet, scheduler) -> StableDiffusionGeneratorPipeline:
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configure_model_padding(
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unet,
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self.seamless,
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self.seamless_axes,
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)
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class FakeVae:
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class FakeVaeConfig:
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def __init__(self):
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self.block_out_channels = [0]
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def __init__(self):
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self.config = FakeVae.FakeVaeConfig()
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return StableDiffusionGeneratorPipeline(
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vae=FakeVae(), # TODO: oh...
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text_encoder=None,
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tokenizer=None,
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unet=unet,
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scheduler=scheduler,
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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precision="float16" if unet.dtype == torch.float16 else "float32",
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#precision="float16", # TODO:
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)
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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noise = context.services.latents.get(self.noise.latents_name)
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@ -207,19 +242,33 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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def step_callback(state: PipelineIntermediateState):
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self.dispatch_progress(context, source_node_id, state)
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with self.get_model(context) as model:
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conditioning_data = self.get_conditioning_data(context, model)
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# TODO: Verify the noise is the right size
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result_latents, result_attention_map_saver = model.latents_from_embeddings(
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latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),
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noise=noise,
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num_inference_steps=self.steps,
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conditioning_data=conditioning_data,
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callback=step_callback
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#unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
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unet_info = context.services.model_manager.get_model(
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model_name=self.unet.unet.model_name,
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model_type=SDModelType[self.unet.unet.model_type],
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submodel=SDModelType[self.unet.unet.submodel] if self.unet.unet.submodel else None,
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)
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with unet_info.context as unet:
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scheduler = get_scheduler(
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context=context,
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scheduler_info=self.unet.scheduler,
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scheduler_name=self.scheduler,
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)
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pipeline = self.create_pipeline(unet, scheduler)
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conditioning_data = self.get_conditioning_data(context, scheduler)
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# TODO: Verify the noise is the right size
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result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
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latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
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noise=noise,
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num_inference_steps=self.steps,
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conditioning_data=conditioning_data,
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callback=step_callback
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)
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# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
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torch.cuda.empty_cache()
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@ -229,30 +278,8 @@ class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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latents=LatentsField(latents_name=name)
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)
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def get_model(self, context: InvocationContext) -> StableDiffusionGeneratorPipeline:
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model_ctx = super().get_model(context)
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with model_ctx as model:
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model.scheduler = get_scheduler(
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model=model,
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scheduler_name=self.scheduler
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)
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if isinstance(model, DiffusionPipeline):
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for component in [model.unet, model.vae]:
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configure_model_padding(component,
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self.seamless,
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self.seamless_axes
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)
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else:
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configure_model_padding(model,
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self.seamless,
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self.seamless_axes
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)
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return model_ctx
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class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter):
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class LatentsToLatentsInvocation(TextToLatentsInvocation):
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"""Generates latents using latents as base image."""
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type: Literal["l2l"] = "l2l"
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@ -266,9 +293,6 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter):
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schema_extra = {
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"ui": {
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"tags": ["latents"],
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"type_hints": {
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"model": "model"
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}
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},
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}
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@ -283,22 +307,35 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter):
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def step_callback(state: PipelineIntermediateState):
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self.dispatch_progress(context, source_node_id, state)
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with self.get_model(context) as model:
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conditioning_data = self.get_conditioning_data(model)
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#unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
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unet_info = context.services.model_manager.get_model(
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model_name=self.unet.unet.model_name,
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model_type=SDModelType[self.unet.unet.model_type],
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submodel=SDModelType[self.unet.unet.submodel] if self.unet.unet.submodel else None,
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)
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with unet_info.context as unet:
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scheduler = get_scheduler(
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context=context,
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scheduler_info=self.unet.scheduler,
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scheduler_name=self.scheduler,
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)
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pipeline = self.create_pipeline(unet, scheduler)
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conditioning_data = self.get_conditioning_data(context, scheduler)
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# TODO: Verify the noise is the right size
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initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
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latent, device=model.device, dtype=latent.dtype
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latent, device=unet.device, dtype=latent.dtype
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)
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timesteps, _ = model.get_img2img_timesteps(
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timesteps, _ = pipeline.get_img2img_timesteps(
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self.steps,
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self.strength,
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device=model.device,
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device=unet.device,
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)
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result_latents, result_attention_map_saver = model.latents_from_embeddings(
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result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
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latents=initial_latents,
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timesteps=timesteps,
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noise=noise,
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@ -318,23 +355,21 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation, ModelGetter):
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# Latent to image
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class LatentsToImageInvocation(BaseInvocation, ModelGetter):
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class LatentsToImageInvocation(BaseInvocation):
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"""Generates an image from latents."""
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type: Literal["l2i"] = "l2i"
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# Inputs
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latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
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model: str = Field(default="", description="The model to use")
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vae: VaeField = Field(default=None, description="Vae submodel")
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tiled: bool = Field(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
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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", "image"],
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"type_hints": {
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"model": "model"
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}
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},
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}
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@ -342,27 +377,45 @@ class LatentsToImageInvocation(BaseInvocation, ModelGetter):
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def invoke(self, context: InvocationContext) -> ImageOutput:
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latents = context.services.latents.get(self.latents.latents_name)
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# TODO: this only really needs the vae
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with self.get_model(context) as model:
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#vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
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vae_info = context.services.model_manager.get_model(
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model_name=self.vae.vae.model_name,
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model_type=SDModelType[self.vae.vae.model_type],
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submodel=SDModelType[self.vae.vae.submodel] if self.vae.vae.submodel else None,
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)
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with vae_info.context as vae:
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# TODO: check if it works
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if self.tiled:
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vae.enable_tiling()
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else:
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vae.disable_tiling()
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with torch.inference_mode():
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np_image = model.decode_latents(latents)
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image = model.numpy_to_pil(np_image)[0]
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# copied from diffusers pipeline
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latents = latents / vae.config.scaling_factor
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image = vae.decode(latents, return_dict=False)[0]
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image = (image / 2 + 0.5).clamp(0, 1) # denormalize
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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np_image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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image_type = ImageType.RESULT
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image_name = context.services.images.create_name(
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context.graph_execution_state_id, self.id
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)
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image = VaeImageProcessor.numpy_to_pil(np_image)[0]
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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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image_type = ImageType.RESULT
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image_name = context.services.images.create_name(
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context.graph_execution_state_id, self.id
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)
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torch.cuda.empty_cache()
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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(image_type, image_name, image, metadata)
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return build_image_output(
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image_type=image_type, image_name=image_name, image=image
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)
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torch.cuda.empty_cache()
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context.services.images.save(image_type, image_name, image, metadata)
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return build_image_output(
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image_type=image_type, image_name=image_name, image=image
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)
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LATENTS_INTERPOLATION_MODE = Literal[
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@ -430,21 +483,21 @@ class ScaleLatentsInvocation(BaseInvocation):
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return LatentsOutput(latents=LatentsField(latents_name=name))
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class ImageToLatentsInvocation(BaseInvocation, ModelGetter):
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class ImageToLatentsInvocation(BaseInvocation):
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"""Encodes an image into latents."""
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type: Literal["i2l"] = "i2l"
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# Inputs
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image: Union[ImageField, None] = Field(description="The image to encode")
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model: str = Field(default="", description="The model to use")
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vae: VaeField = Field(default=None, description="Vae submodel")
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tiled: bool = Field(default=False, description="Encode latents by overlaping tiles(less memory consumption)")
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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", "image"],
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"type_hints": {"model": "model"},
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},
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}
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@ -454,22 +507,38 @@ class ImageToLatentsInvocation(BaseInvocation, ModelGetter):
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self.image.image_type, self.image.image_name
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)
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# TODO: this only really needs the vae
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model_info = self.get_model(context)
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model: StableDiffusionGeneratorPipeline = model_info["model"]
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#vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
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vae_info = context.services.model_manager.get_model(
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model_name=self.vae.vae.model_name,
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model_type=SDModelType[self.vae.vae.model_type],
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submodel=SDModelType[self.vae.vae.submodel] if self.vae.vae.submodel else None,
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)
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image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
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if image_tensor.dim() == 3:
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image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
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latents = model.non_noised_latents_from_image(
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image_tensor,
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device=model._model_group.device_for(model.unet),
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dtype=model.unet.dtype,
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)
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with vae_info.context as vae:
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# TODO: check if it works
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if self.tiled:
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vae.enable_tiling()
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else:
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vae.disable_tiling()
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latents = self.non_noised_latents_from_image(vae, image_tensor)
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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.set(name, latents)
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return LatentsOutput(latents=LatentsField(latents_name=name))
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def non_noised_latents_from_image(self, vae, init_image):
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init_image = init_image.to(device=vae.device, dtype=vae.dtype)
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with torch.inference_mode():
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init_latent_dist = vae.encode(init_image).latent_dist
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init_latents = init_latent_dist.sample().to(
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dtype=vae.dtype
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) # FIXME: uses torch.randn. make reproducible!
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init_latents = 0.18215 * init_latents
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return init_latents
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