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Fix errors in T2I-Adapter input image sizing logic.

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This commit is contained in:
Ryan Dick
2023-09-20 17:15:34 -04:00
parent ffcb7d3895
commit a07f77ef7c
1 changed files with 21 additions and 5 deletions
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26
invokeai/app/invocations/latent.py
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@ -8,7 +8,8 @@ import numpy as np
import torch
import torchvision.transforms as T
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import MultiAdapter, T2IAdapter, UNet2DConditionModel
from diffusers.models import UNet2DConditionModel
from diffusers.models.adapter import FullAdapterXL
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
@ -482,13 +483,28 @@ class DenoiseLatentsInvocation(BaseInvocation):
)
image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
# The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
max_unet_downscale = 8
elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
max_unet_downscale = 4
else:
raise ValueError(
f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
)
t2i_adapter_data = []
with t2i_adapter_model_info as t2i_adapter_model:
total_downscale_factor = t2i_adapter_model.total_downscale_factor
if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
# HACK(ryand): Work around a bug in FullAdapterXL. This will be fixed upstream in diffusers.
total_downscale_factor = total_downscale_factor // 2
# Resize the T2I-Adapter input image.
# We select the resize dimensions so that after the T2I-Adapter's downsampling factor is applied, the result
# will match the latents_shape.
t2i_input_height = latents_shape[2] * t2i_adapter_model.total_downscale_factor
t2i_input_width = latents_shape[3] * t2i_adapter_model.total_downscale_factor
# We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
# result will match the latent image's dimensions after max_unet_downscale is applied.
t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
# Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
# a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the