mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
0f733c42fc
- Restore calculation of step percentage but in the backend instead of client - Simplify signatures for denoise progress event callbacks - Clean up `step_callback.py` (types, do not recreate constant matrix on every step, formatting)
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
from typing import TYPE_CHECKING, Callable, Optional
|
|
|
|
import torch
|
|
from PIL import Image
|
|
|
|
from invokeai.app.services.session_processor.session_processor_common import CanceledException, ProgressImage
|
|
from invokeai.backend.model_manager.config import BaseModelType
|
|
|
|
from ...backend.stable_diffusion import PipelineIntermediateState
|
|
from ...backend.util.util import image_to_dataURL
|
|
|
|
if TYPE_CHECKING:
|
|
from invokeai.app.services.events.events_base import EventServiceBase
|
|
from invokeai.app.services.shared.invocation_context import InvocationContextData
|
|
|
|
# fast latents preview matrix for sdxl
|
|
# generated by @StAlKeR7779
|
|
SDXL_LATENT_RGB_FACTORS = [
|
|
# R G B
|
|
[0.3816, 0.4930, 0.5320],
|
|
[-0.3753, 0.1631, 0.1739],
|
|
[0.1770, 0.3588, -0.2048],
|
|
[-0.4350, -0.2644, -0.4289],
|
|
]
|
|
SDXL_SMOOTH_MATRIX = [
|
|
[0.0358, 0.0964, 0.0358],
|
|
[0.0964, 0.4711, 0.0964],
|
|
[0.0358, 0.0964, 0.0358],
|
|
]
|
|
|
|
# origingally adapted from code by @erucipe and @keturn here:
|
|
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
|
|
# these updated numbers for v1.5 are from @torridgristle
|
|
SD1_5_LATENT_RGB_FACTORS = [
|
|
# R G B
|
|
[0.3444, 0.1385, 0.0670], # L1
|
|
[0.1247, 0.4027, 0.1494], # L2
|
|
[-0.3192, 0.2513, 0.2103], # L3
|
|
[-0.1307, -0.1874, -0.7445], # L4
|
|
]
|
|
|
|
|
|
def sample_to_lowres_estimated_image(
|
|
samples: torch.Tensor, latent_rgb_factors: torch.Tensor, smooth_matrix: Optional[torch.Tensor] = None
|
|
):
|
|
latent_image = samples[0].permute(1, 2, 0) @ latent_rgb_factors
|
|
|
|
if smooth_matrix is not None:
|
|
latent_image = latent_image.unsqueeze(0).permute(3, 0, 1, 2)
|
|
latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1, 1, 3, 3)), padding=1)
|
|
latent_image = latent_image.permute(1, 2, 3, 0).squeeze(0)
|
|
|
|
latents_ubyte = (
|
|
((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte() # change scale from -1..1 to 0..1 # to 0..255
|
|
).cpu()
|
|
|
|
return Image.fromarray(latents_ubyte.numpy())
|
|
|
|
|
|
def stable_diffusion_step_callback(
|
|
context_data: "InvocationContextData",
|
|
intermediate_state: PipelineIntermediateState,
|
|
base_model: BaseModelType,
|
|
events: "EventServiceBase",
|
|
is_canceled: Callable[[], bool],
|
|
) -> None:
|
|
if is_canceled():
|
|
raise CanceledException
|
|
|
|
# Some schedulers report not only the noisy latents at the current timestep,
|
|
# but also their estimate so far of what the de-noised latents will be. Use
|
|
# that estimate if it is available.
|
|
if intermediate_state.predicted_original is not None:
|
|
sample = intermediate_state.predicted_original
|
|
else:
|
|
sample = intermediate_state.latents
|
|
|
|
if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
|
|
sdxl_latent_rgb_factors = torch.tensor(SDXL_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
|
|
sdxl_smooth_matrix = torch.tensor(SDXL_SMOOTH_MATRIX, dtype=sample.dtype, device=sample.device)
|
|
image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
|
|
else:
|
|
v1_5_latent_rgb_factors = torch.tensor(SD1_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
|
|
image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
|
|
|
|
(width, height) = image.size
|
|
width *= 8
|
|
height *= 8
|
|
|
|
dataURL = image_to_dataURL(image, image_format="JPEG")
|
|
|
|
events.emit_invocation_denoise_progress(
|
|
context_data.queue_item,
|
|
context_data.invocation,
|
|
intermediate_state,
|
|
ProgressImage(dataURL=dataURL, width=width, height=height),
|
|
)
|