mirror of
https://github.com/invoke-ai/InvokeAI
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108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
import torch
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models.attention_processor import (
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AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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LoRAXFormersAttnProcessor,
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XFormersAttnProcessor,
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)
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from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
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from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
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from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.denoise_latents import DEFAULT_PRECISION
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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.stable_diffusion import set_seamless
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from invokeai.backend.util.devices import TorchDevice
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@invocation(
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"l2i",
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title="Latents to Image",
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tags=["latents", "image", "vae", "l2i"],
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category="latents",
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version="1.2.2",
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)
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class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Generates an image from latents."""
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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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tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
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fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
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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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vae_info = context.models.load(self.vae.vae)
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assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL, AutoencoderTiny))
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with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
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assert isinstance(vae, torch.nn.Module)
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latents = latents.to(vae.device)
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if self.fp32:
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vae.to(dtype=torch.float32)
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use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
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vae.decoder.mid_block.attentions[0].processor,
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(
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AttnProcessor2_0,
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XFormersAttnProcessor,
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LoRAXFormersAttnProcessor,
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LoRAAttnProcessor2_0,
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),
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)
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# if xformers or torch_2_0 is used attention block does not need
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# to be in float32 which can save lots of memory
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if use_torch_2_0_or_xformers:
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vae.post_quant_conv.to(latents.dtype)
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vae.decoder.conv_in.to(latents.dtype)
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vae.decoder.mid_block.to(latents.dtype)
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else:
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latents = latents.float()
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else:
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vae.to(dtype=torch.float16)
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latents = latents.half()
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if self.tiled or context.config.get().force_tiled_decode:
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vae.enable_tiling()
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else:
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vae.disable_tiling()
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# clear memory as vae decode can request a lot
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TorchDevice.empty_cache()
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with torch.inference_mode():
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# copied from diffusers pipeline
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latents = latents / vae.config.scaling_factor
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image = vae.decode(latents, return_dict=False)[0]
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image = (image / 2 + 0.5).clamp(0, 1) # denormalize
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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np_image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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image = VaeImageProcessor.numpy_to_pil(np_image)[0]
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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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