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Make the tile_overlap input to MultiDiffusion *strictly* control the amount of overlap rather than being a lower bound.

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This commit is contained in:
Ryan Dick 2024-06-25 09:57:40 -04:00 committed by Kent Keirsey
parent c5588e1ff7
commit e1af78c702
2 changed files with 33 additions and 24 deletions
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11
invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
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@ -86,11 +86,11 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
)
tile_height: int = InputField(default=64, gt=0, description="Height of the tiles in latent space.")
tile_width: int = InputField(default=64, gt=0, description="Width of the tiles in latent space.")
tile_min_overlap: int = InputField(
tile_overlap: int = InputField(
default=16,
gt=0,
description="The minimum overlap between adjacent tiles in latent space. The actual overlap may be larger than "
"this to evenly cover the entire image.",
description="The overlap between adjacent tiles in latent space. Tiles will be cropped during merging "
"(if necessary) to ensure that they overlap by exactly this amount.",
)
steps: int = InputField(default=18, gt=0, description=FieldDescriptions.steps)
cfg_scale: float | list[float] = InputField(default=6.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
@ -167,7 +167,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
image_width=latent_width,
tile_height=self.tile_height,
tile_width=self.tile_width,
min_overlap=self.tile_min_overlap,
min_overlap=self.tile_overlap,
)
# Get the unet's config so that we can pass the base to sd_step_callback().
@ -234,7 +234,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
for tile, tile_controlnet_data in zip(tiles, controlnet_data_tiles, strict=True):
multi_diffusion_conditioning.append(
MultiDiffusionRegionConditioning(
region=tile.coords,
region=tile,
text_conditioning_data=conditioning_data,
control_data=tile_controlnet_data,
)
@ -252,6 +252,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
# Run Multi-Diffusion denoising.
result_latents = pipeline.multi_diffusion_denoise(
multi_diffusion_conditioning=multi_diffusion_conditioning,
target_overlap=self.tile_overlap,
latents=latents,
scheduler_step_kwargs=scheduler_step_kwargs,
noise=noise,

46
invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py
View File

@ -13,13 +13,13 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import (
StableDiffusionGeneratorPipeline,
)
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import TextConditioningData
from invokeai.backend.tiles.utils import TBLR
from invokeai.backend.tiles.utils import Tile
@dataclass
class MultiDiffusionRegionConditioning:
# Region coords in latent space.
region: TBLR
region: Tile
text_conditioning_data: TextConditioningData
control_data: list[ControlNetData]
@ -39,6 +39,7 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
def multi_diffusion_denoise(
self,
multi_diffusion_conditioning: list[MultiDiffusionRegionConditioning],
target_overlap: int,
latents: torch.Tensor,
scheduler_step_kwargs: dict[str, Any],
noise: Optional[torch.Tensor],
@ -66,15 +67,6 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
# cropping into regions.
self._adjust_memory_efficient_attention(latents)
# Populate a weighted mask that will be used to combine the results from each region after every step.
# For now, we assume that each region has the same weight (1.0).
region_weight_mask = torch.zeros(
(1, 1, latent_height, latent_width), device=latents.device, dtype=latents.dtype
)
for region_conditioning in multi_diffusion_conditioning:
region = region_conditioning.region
region_weight_mask[:, :, region.top : region.bottom, region.left : region.right] += 1.0
# Many of the diffusers schedulers are stateful (i.e. they update internal state in each call to step()). Since
# we are calling step() multiple times at the same timestep (once for each region batch), we must maintain a
# separate scheduler state for each region batch.
@ -101,6 +93,9 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
batched_t = t.expand(batch_size)
merged_latents = torch.zeros_like(latents)
merged_latents_weights = torch.zeros(
(1, 1, latent_height, latent_width), device=latents.device, dtype=latents.dtype
)
merged_pred_original: torch.Tensor | None = None
for region_idx, region_conditioning in enumerate(multi_diffusion_conditioning):
# Switch to the scheduler for the region batch.
@ -110,8 +105,8 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
region_latents = latents[
:,
:,
region_conditioning.region.top : region_conditioning.region.bottom,
region_conditioning.region.left : region_conditioning.region.right,
region_conditioning.region.coords.top : region_conditioning.region.coords.bottom,
region_conditioning.region.coords.left : region_conditioning.region.coords.right,
]
# Run the denoising step on the region.
@ -129,24 +124,37 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
)
# Store the results from the region.
# If two tiles overlap by more than the target overlap amount, crop the left and top edges of the
# affected tiles to achieve the target overlap.
region = region_conditioning.region
merged_latents[:, :, region.top : region.bottom, region.left : region.right] += step_output.prev_sample
top_adjustment = max(0, region.overlap.top - target_overlap)
left_adjustment = max(0, region.overlap.left - target_overlap)
region_height_slice = slice(region.coords.top + top_adjustment, region.coords.bottom)
region_width_slice = slice(region.coords.left + left_adjustment, region.coords.right)
merged_latents[:, :, region_height_slice, region_width_slice] += step_output.prev_sample[
:, :, top_adjustment:, left_adjustment:
]
# For now, we treat every region as having the same weight.
merged_latents_weights[:, :, region_height_slice, region_width_slice] += 1.0
pred_orig_sample = getattr(step_output, "pred_original_sample", None)
if pred_orig_sample is not None:
# If one region has pred_original_sample, then we can assume that all regions will have it, because
# they all use the same scheduler.
if merged_pred_original is None:
merged_pred_original = torch.zeros_like(latents)
merged_pred_original[:, :, region.top : region.bottom, region.left : region.right] += (
pred_orig_sample
)
merged_pred_original[:, :, region_height_slice, region_width_slice] += pred_orig_sample[
:, :, top_adjustment:, left_adjustment:
]
# Normalize the merged results.
latents = torch.where(region_weight_mask > 0, merged_latents / region_weight_mask, merged_latents)
latents = torch.where(merged_latents_weights > 0, merged_latents / merged_latents_weights, merged_latents)
# For debugging, uncomment this line to visualize the region seams:
# latents = torch.where(merged_latents_weights > 1, 0.0, latents)
predicted_original = None
if merged_pred_original is not None:
predicted_original = torch.where(
region_weight_mask > 0, merged_pred_original / region_weight_mask, merged_pred_original
merged_latents_weights > 0, merged_pred_original / merged_latents_weights, merged_pred_original
)
callback(