mirror of
https://github.com/invoke-ai/InvokeAI
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476 lines
18 KiB
Python
476 lines
18 KiB
Python
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Literal, Optional, Union
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import diffusers
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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 pydantic import BaseModel, Field
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from invokeai.app.util.misc import SEED_MAX, get_random_seed
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from invokeai.app.util.step_callback import stable_diffusion_step_callback
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from ...backend.image_util.seamless import configure_model_padding
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from ...backend.stable_diffusion import PipelineIntermediateState
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from ...backend.stable_diffusion.diffusers_pipeline import (
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ConditioningData, StableDiffusionGeneratorPipeline,
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image_resized_to_grid_as_tensor)
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from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
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PostprocessingSettings
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from ...backend.util.devices import choose_torch_device, torch_dtype
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from ..services.image_storage import ImageType
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from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
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InvocationConfig, InvocationContext)
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from .compel import ConditioningField
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from .image import ImageField, ImageOutput, build_image_output
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class LatentsField(BaseModel):
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"""A latents field used for passing latents between invocations"""
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latents_name: Optional[str] = Field(default=None, description="The name of the latents")
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class Config:
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schema_extra = {"required": ["latents_name"]}
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class LatentsOutput(BaseInvocationOutput):
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"""Base class for invocations that output latents"""
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#fmt: off
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type: Literal["latent_output"] = "latent_output"
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latents: LatentsField = Field(default=None, description="The output latents")
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#fmt: on
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class NoiseOutput(BaseInvocationOutput):
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"""Invocation noise output"""
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#fmt: off
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type: Literal["noise_output"] = "noise_output"
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noise: LatentsField = Field(default=None, description="The output noise")
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#fmt: on
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# TODO: this seems like a hack
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scheduler_map = dict(
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ddim=diffusers.DDIMScheduler,
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dpmpp_2=diffusers.DPMSolverMultistepScheduler,
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k_dpm_2=diffusers.KDPM2DiscreteScheduler,
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k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
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k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
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k_euler=diffusers.EulerDiscreteScheduler,
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k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
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k_heun=diffusers.HeunDiscreteScheduler,
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k_lms=diffusers.LMSDiscreteScheduler,
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plms=diffusers.PNDMScheduler,
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)
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SAMPLER_NAME_VALUES = Literal[
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tuple(list(scheduler_map.keys()))
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]
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def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
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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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# 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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return scheduler
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def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_channels:int=4, use_mps_noise:bool=False, downsampling_factor:int = 8):
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# limit noise to only the diffusion image channels, not the mask channels
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input_channels = min(latent_channels, 4)
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use_device = "cpu" if (use_mps_noise or device.type == "mps") else device
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generator = torch.Generator(device=use_device).manual_seed(seed)
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x = torch.randn(
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[
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1,
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input_channels,
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height // downsampling_factor,
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width // downsampling_factor,
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],
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dtype=torch_dtype(device),
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device=use_device,
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generator=generator,
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).to(device)
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# if self.perlin > 0.0:
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# perlin_noise = self.get_perlin_noise(
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# width // self.downsampling_factor, height // self.downsampling_factor
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# )
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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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type: Literal["noise"] = "noise"
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# Inputs
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seed: int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
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width: int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
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height: int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
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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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},
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}
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def invoke(self, context: InvocationContext) -> NoiseOutput:
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device = torch.device(choose_torch_device())
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noise = get_noise(self.width, self.height, device, self.seed)
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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.set(name, noise)
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return NoiseOutput(
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noise=LatentsField(latents_name=name)
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)
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# Text to image
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class TextToLatentsInvocation(BaseInvocation, ModelGetter):
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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_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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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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# fmt: on
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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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# TODO: pass this an emitter method or something? or a session for dispatching?
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def dispatch_progress(
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self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
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) -> None:
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stable_diffusion_step_callback(
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context=context,
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intermediate_state=intermediate_state,
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node=self.dict(),
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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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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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c,
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self.cfg_scale,
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extra_conditioning_info,
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postprocessing_settings=PostprocessingSettings(
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threshold=0.0,#threshold,
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warmup=0.2,#warmup,
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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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return conditioning_data
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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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# Get the source node id (we are invoking the prepared node)
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graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
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source_node_id = graph_execution_state.prepared_source_mapping[self.id]
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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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)
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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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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.set(name, result_latents)
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return LatentsOutput(
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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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"""Generates latents using latents as base image."""
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type: Literal["l2l"] = "l2l"
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# Inputs
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latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
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strength: float = Field(default=0.5, description="The strength of the latents 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"],
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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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def invoke(self, context: InvocationContext) -> LatentsOutput:
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noise = context.services.latents.get(self.noise.latents_name)
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latent = context.services.latents.get(self.latents.latents_name)
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# Get the source node id (we are invoking the prepared node)
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graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
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source_node_id = graph_execution_state.prepared_source_mapping[self.id]
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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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# 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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)
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timesteps, _ = model.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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)
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result_latents, result_attention_map_saver = model.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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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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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.set(name, result_latents)
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return LatentsOutput(
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latents=LatentsField(latents_name=name)
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)
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# Latent to image
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class LatentsToImageInvocation(BaseInvocation, ModelGetter):
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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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# 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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@torch.no_grad()
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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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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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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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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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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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"nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"
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]
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class ResizeLatentsInvocation(BaseInvocation):
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"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
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type: Literal["lresize"] = "lresize"
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# Inputs
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latents: Optional[LatentsField] = Field(description="The latents to resize")
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width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
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height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
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mode: Optional[LATENTS_INTERPOLATION_MODE] = Field(default="bilinear", description="The interpolation mode")
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antialias: Optional[bool] = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents = context.services.latents.get(self.latents.latents_name)
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resized_latents = torch.nn.functional.interpolate(
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latents,
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size=(self.height // 8, self.width // 8),
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mode=self.mode,
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antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
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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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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.set(name, resized_latents)
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return LatentsOutput(latents=LatentsField(latents_name=name))
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class ScaleLatentsInvocation(BaseInvocation):
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"""Scales latents by a given factor."""
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type: Literal["lscale"] = "lscale"
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# Inputs
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latents: Optional[LatentsField] = Field(description="The latents to scale")
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scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
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mode: Optional[LATENTS_INTERPOLATION_MODE] = Field(default="bilinear", description="The interpolation mode")
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antialias: Optional[bool] = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents = context.services.latents.get(self.latents.latents_name)
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# resizing
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resized_latents = torch.nn.functional.interpolate(
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latents,
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scale_factor=self.scale_factor,
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mode=self.mode,
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antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
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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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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.set(name, resized_latents)
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return LatentsOutput(latents=LatentsField(latents_name=name))
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class ImageToLatentsInvocation(BaseInvocation, ModelGetter):
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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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# 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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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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image = context.services.images.get(
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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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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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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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