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f5c9e68dbf
Co-authored-by: kent <kent@invoke.ai> Revert unnecessary validation changes in multi-diffusion Fix in python instead of graphbuilder tidy(ui): remove extraneous comment
183 lines
6.5 KiB
Python
183 lines
6.5 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.taxonomy import BaseModelType
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from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
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# See scripts/generate_vae_linear_approximation.py for generating these factors.
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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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COGVIEW4_LATENT_RGB_FACTORS = [
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[0.00408832, -0.00082485, -0.00214816],
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[0.00084172, 0.00132241, 0.00842067],
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[-0.00466737, -0.00983181, -0.00699561],
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[0.03698397, -0.04797235, 0.03585809],
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[0.00234701, -0.00124326, 0.00080869],
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[-0.00723903, -0.00388422, -0.00656606],
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[-0.00970917, -0.00467356, -0.00971113],
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[0.17292486, -0.03452463, -0.1457515],
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[0.02330308, 0.02942557, 0.02704329],
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[-0.00903131, -0.01499841, -0.01432564],
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[0.01250298, 0.0019407, -0.02168986],
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[0.01371188, 0.00498283, -0.01302135],
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[0.42396525, 0.4280575, 0.42148206],
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[0.00983825, 0.00613302, 0.00610316],
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[0.00473307, -0.00889551, -0.00915924],
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[-0.00955853, -0.00980067, -0.00977842],
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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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if samples.dim() == 4:
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samples = samples[0]
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latent_image = samples.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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# Prevent division by zero when total_steps is 1 or 2
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denominator = floor(total_steps / 2)
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if denominator == 0:
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return 0.0
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return floor(step / 2) / denominator
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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 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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smooth_matrix: list[list[float]] | None = None
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if base_model in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
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latent_rgb_factors = SD1_5_LATENT_RGB_FACTORS
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elif base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
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latent_rgb_factors = SDXL_LATENT_RGB_FACTORS
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smooth_matrix = SDXL_SMOOTH_MATRIX
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elif base_model == BaseModelType.StableDiffusion3:
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latent_rgb_factors = SD3_5_LATENT_RGB_FACTORS
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elif base_model == BaseModelType.CogView4:
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latent_rgb_factors = COGVIEW4_LATENT_RGB_FACTORS
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elif base_model == BaseModelType.Flux:
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latent_rgb_factors = FLUX_LATENT_RGB_FACTORS
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else:
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raise ValueError(f"Unsupported base model: {base_model}")
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latent_rgb_factors_torch = torch.tensor(latent_rgb_factors, dtype=sample.dtype, device=sample.device)
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smooth_matrix_torch = (
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torch.tensor(smooth_matrix, dtype=sample.dtype, device=sample.device) if smooth_matrix else None
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)
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image = sample_to_lowres_estimated_image(
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samples=sample, latent_rgb_factors=latent_rgb_factors_torch, smooth_matrix=smooth_matrix_torch
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)
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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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