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93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
"""Flux2 Klein VAE Decode Invocation.
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Decodes latents to images using the FLUX.2 32-channel VAE (AutoencoderKLFlux2).
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"""
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import torch
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from einops import rearrange
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from PIL import Image
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from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
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from invokeai.app.invocations.fields import (
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FieldDescriptions,
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Input,
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InputField,
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LatentsField,
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WithBoard,
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WithMetadata,
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)
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from invokeai.app.invocations.model import VAEField
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from invokeai.app.invocations.primitives import ImageOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.model_manager.load.load_base import LoadedModel
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from invokeai.backend.util.devices import TorchDevice
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@invocation(
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"flux2_vae_decode",
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title="Latents to Image - FLUX2",
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tags=["latents", "image", "vae", "l2i", "flux2", "klein"],
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category="latents",
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version="1.0.0",
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classification=Classification.Prototype,
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)
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class Flux2VaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Generates an image from latents using FLUX.2 Klein's 32-channel VAE."""
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latents: LatentsField = InputField(
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description=FieldDescriptions.latents,
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input=Input.Connection,
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)
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vae: VAEField = InputField(
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description=FieldDescriptions.vae,
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input=Input.Connection,
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)
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def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
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"""Decode latents to image using FLUX.2 VAE.
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Input latents should already be in the correct space after BN denormalization
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was applied in the denoiser. The VAE expects (B, 32, H, W) format.
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"""
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with vae_info.model_on_device() as (_, vae):
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vae_dtype = next(iter(vae.parameters())).dtype
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device = TorchDevice.choose_torch_device()
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latents = latents.to(device=device, dtype=vae_dtype)
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# Decode using diffusers API
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decoded = vae.decode(latents, return_dict=False)[0]
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# Convert from [-1, 1] to [0, 1] then to [0, 255] PIL image
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img = (decoded / 2 + 0.5).clamp(0, 1)
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img = rearrange(img[0], "c h w -> h w c")
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img_np = (img * 255).byte().cpu().numpy()
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# Explicitly create RGB image (not grayscale)
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img_pil = Image.fromarray(img_np, mode="RGB")
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return img_pil
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> ImageOutput:
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latents = context.tensors.load(self.latents.latents_name)
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# Log latent statistics for debugging black image issues
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context.logger.debug(
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f"FLUX.2 VAE decode input: shape={latents.shape}, "
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f"min={latents.min().item():.4f}, max={latents.max().item():.4f}, "
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f"mean={latents.mean().item():.4f}"
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)
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# Warn if input latents are all zeros or very small (would cause black images)
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if latents.abs().max() < 1e-6:
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context.logger.warning(
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"FLUX.2 VAE decode received near-zero latents! This will cause black images. "
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"The latent cache may be corrupted - try clearing the cache."
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)
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vae_info = context.models.load(self.vae.vae)
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context.util.signal_progress("Running VAE")
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image = self._vae_decode(vae_info=vae_info, latents=latents)
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TorchDevice.empty_cache()
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image_dto = context.images.save(image=image)
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return ImageOutput.build(image_dto)
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