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Merge branch 'main' into feat/onnx

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Brandon Rising
2023-07-26 10:42:31 -04:00
parent 78750042f5 f060e321eb
commit c16da75ac7
465 changed files with 14417 additions and 8732 deletions
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27
invokeai/app/invocations/latent.py
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@ -22,8 +22,8 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_torch_device, torch_dtype
from ...backend.model_management import ModelPatcher
from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
@ -31,6 +31,7 @@ from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .image import ImageOutput
from .model import ModelInfo, UNetField, VaeField
from invokeai.app.util.controlnet_utils import prepare_control_image
from diffusers.models.attention_processor import (
AttnProcessor2_0,
@ -40,6 +41,9 @@ from diffusers.models.attention_processor import (
)
DEFAULT_PRECISION = choose_precision(choose_torch_device())
class LatentsField(BaseModel):
"""A latents field used for passing latents between invocations"""
@ -286,7 +290,7 @@ class TextToLatentsInvocation(BaseInvocation):
# and add in batch_size, num_images_per_prompt?
# and do real check for classifier_free_guidance?
# prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
control_image = model.prepare_control_image(
control_image = prepare_control_image(
image=input_image,
do_classifier_free_guidance=do_classifier_free_guidance,
width=control_width_resize,
@ -296,13 +300,18 @@ class TextToLatentsInvocation(BaseInvocation):
device=control_model.device,
dtype=control_model.dtype,
control_mode=control_info.control_mode,
resize_mode=control_info.resize_mode,
)
control_item = ControlNetData(
model=control_model, image_tensor=control_image,
model=control_model,
image_tensor=control_image,
weight=control_info.control_weight,
begin_step_percent=control_info.begin_step_percent,
end_step_percent=control_info.end_step_percent,
control_mode=control_info.control_mode,
# any resizing needed should currently be happening in prepare_control_image(),
# but adding resize_mode to ControlNetData in case needed in the future
resize_mode=control_info.resize_mode,
)
control_data.append(control_item)
# MultiControlNetModel has been refactored out, just need list[ControlNetData]
@ -493,8 +502,8 @@ class LatentsToImageInvocation(BaseInvocation):
vae: VaeField = Field(default=None, description="Vae submodel")
tiled: bool = Field(
default=False,
description="Decode latents by overlaping tiles(less memory consumption)")
fp32: bool = Field(False, description="Decode in full precision")
description="Decode latents by overlapping tiles(less memory consumption)")
fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
# Schema customisation
@ -599,7 +608,7 @@ class ResizeLatentsInvocation(BaseInvocation):
antialias: bool = Field(
default=False,
description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
class Config(InvocationConfig):
schema_extra = {
"ui": {
@ -645,7 +654,7 @@ class ScaleLatentsInvocation(BaseInvocation):
antialias: bool = Field(
default=False,
description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
class Config(InvocationConfig):
schema_extra = {
"ui": {
@ -688,7 +697,7 @@ class ImageToLatentsInvocation(BaseInvocation):
tiled: bool = Field(
default=False,
description="Encode latents by overlaping tiles(less memory consumption)")
fp32: bool = Field(False, description="Decode in full precision")
fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
# Schema customisation
@ -756,7 +765,7 @@ class ImageToLatentsInvocation(BaseInvocation):
dtype=vae.dtype
) # FIXME: uses torch.randn. make reproducible!
latents = 0.18215 * latents
latents = vae.config.scaling_factor * latents
latents = latents.to(dtype=orig_dtype)
name = f"{context.graph_execution_state_id}__{self.id}"