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167 lines
6.1 KiB
Python
167 lines
6.1 KiB
Python
from math import floor
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from typing import Callable, Optional, TypeAlias
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import torch
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from PIL import Image
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from invokeai.app.services.session_processor.session_processor_common import CanceledException
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from invokeai.backend.model_manager.config import BaseModelType
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from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
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# fast latents preview matrix for sdxl
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# generated by @StAlKeR7779
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SDXL_LATENT_RGB_FACTORS = [
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# R G B
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[0.3816, 0.4930, 0.5320],
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[-0.3753, 0.1631, 0.1739],
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[0.1770, 0.3588, -0.2048],
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[-0.4350, -0.2644, -0.4289],
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]
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SDXL_SMOOTH_MATRIX = [
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[0.0358, 0.0964, 0.0358],
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[0.0964, 0.4711, 0.0964],
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[0.0358, 0.0964, 0.0358],
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]
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# origingally adapted from code by @erucipe and @keturn here:
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# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
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# these updated numbers for v1.5 are from @torridgristle
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SD1_5_LATENT_RGB_FACTORS = [
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# R G B
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[0.3444, 0.1385, 0.0670], # L1
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[0.1247, 0.4027, 0.1494], # L2
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[-0.3192, 0.2513, 0.2103], # L3
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[-0.1307, -0.1874, -0.7445], # L4
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]
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SD3_5_LATENT_RGB_FACTORS = [
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[-0.05240681, 0.03251581, 0.0749016],
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[-0.0580572, 0.00759826, 0.05729818],
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[0.16144888, 0.01270368, -0.03768577],
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[0.14418615, 0.08460266, 0.15941818],
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[0.04894035, 0.0056485, -0.06686988],
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[0.05187166, 0.19222395, 0.06261094],
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[0.1539433, 0.04818359, 0.07103094],
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[-0.08601796, 0.09013458, 0.10893912],
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[-0.12398469, -0.06766567, 0.0033688],
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[-0.0439737, 0.07825329, 0.02258823],
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[0.03101129, 0.06382551, 0.07753657],
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[-0.01315361, 0.08554491, -0.08772475],
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[0.06464487, 0.05914605, 0.13262741],
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[-0.07863674, -0.02261737, -0.12761454],
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[-0.09923835, -0.08010759, -0.06264447],
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[-0.03392309, -0.0804029, -0.06078822],
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]
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FLUX_LATENT_RGB_FACTORS = [
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[-0.0412, 0.0149, 0.0521],
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[0.0056, 0.0291, 0.0768],
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[0.0342, -0.0681, -0.0427],
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[-0.0258, 0.0092, 0.0463],
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[0.0863, 0.0784, 0.0547],
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[-0.0017, 0.0402, 0.0158],
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[0.0501, 0.1058, 0.1152],
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[-0.0209, -0.0218, -0.0329],
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[-0.0314, 0.0083, 0.0896],
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[0.0851, 0.0665, -0.0472],
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[-0.0534, 0.0238, -0.0024],
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[0.0452, -0.0026, 0.0048],
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[0.0892, 0.0831, 0.0881],
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[-0.1117, -0.0304, -0.0789],
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[0.0027, -0.0479, -0.0043],
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[-0.1146, -0.0827, -0.0598],
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]
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def sample_to_lowres_estimated_image(
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samples: torch.Tensor, latent_rgb_factors: torch.Tensor, smooth_matrix: Optional[torch.Tensor] = None
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):
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latent_image = samples[0].permute(1, 2, 0) @ latent_rgb_factors
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if smooth_matrix is not None:
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latent_image = latent_image.unsqueeze(0).permute(3, 0, 1, 2)
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latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1, 1, 3, 3)), padding=1)
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latent_image = latent_image.permute(1, 2, 3, 0).squeeze(0)
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latents_ubyte = (
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((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte() # change scale from -1..1 to 0..1 # to 0..255
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).cpu()
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return Image.fromarray(latents_ubyte.numpy())
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def calc_percentage(intermediate_state: PipelineIntermediateState) -> float:
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"""Calculate the percentage of completion of denoising."""
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step = intermediate_state.step
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total_steps = intermediate_state.total_steps
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order = intermediate_state.order
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if total_steps == 0:
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return 0.0
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if order == 2:
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return floor(step / 2) / floor(total_steps / 2)
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# order == 1
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return step / total_steps
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SignalProgressFunc: TypeAlias = Callable[[str, float | None, Image.Image | None, tuple[int, int] | None], None]
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def stable_diffusion_step_callback(
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signal_progress: SignalProgressFunc,
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intermediate_state: PipelineIntermediateState,
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base_model: BaseModelType,
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is_canceled: Callable[[], bool],
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) -> None:
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if is_canceled():
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raise CanceledException
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# Some schedulers report not only the noisy latents at the current timestep,
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# but also their estimate so far of what the de-noised latents will be. Use
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# that estimate if it is available.
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if intermediate_state.predicted_original is not None:
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sample = intermediate_state.predicted_original
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else:
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sample = intermediate_state.latents
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if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
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sdxl_latent_rgb_factors = torch.tensor(SDXL_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
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sdxl_smooth_matrix = torch.tensor(SDXL_SMOOTH_MATRIX, dtype=sample.dtype, device=sample.device)
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image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
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elif base_model == BaseModelType.StableDiffusion3:
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sd3_latent_rgb_factors = torch.tensor(SD3_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
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image = sample_to_lowres_estimated_image(sample, sd3_latent_rgb_factors)
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else:
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v1_5_latent_rgb_factors = torch.tensor(SD1_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
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image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
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width = image.width * 8
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height = image.height * 8
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percentage = calc_percentage(intermediate_state)
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signal_progress("Denoising", percentage, image, (width, height))
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def flux_step_callback(
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signal_progress: SignalProgressFunc,
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intermediate_state: PipelineIntermediateState,
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is_canceled: Callable[[], bool],
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) -> None:
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if is_canceled():
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raise CanceledException
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sample = intermediate_state.latents
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latent_rgb_factors = torch.tensor(FLUX_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
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latent_image_perm = sample.permute(1, 2, 0).to(dtype=sample.dtype, device=sample.device)
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latent_image = latent_image_perm @ latent_rgb_factors
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latents_ubyte = (
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((latent_image + 1) / 2).clamp(0, 1).mul(0xFF) # change scale from -1..1 to 0..1 # to 0..255
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).to(device="cpu", dtype=torch.uint8)
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image = Image.fromarray(latents_ubyte.cpu().numpy())
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width = image.width * 8
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height = image.height * 8
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percentage = calc_percentage(intermediate_state)
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signal_progress("Denoising", percentage, image, (width, height))
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