mirror of
https://github.com/invoke-ai/InvokeAI
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88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import numpy as np
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import torch
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from PIL import Image
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import (
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FieldDescriptions,
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ImageField,
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InputField,
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UIType,
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WithBoard,
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WithMetadata,
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)
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from invokeai.app.invocations.model import ModelIdentifierField
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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.spandrel_image_to_image_model import SpandrelImageToImageModel
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def pil_to_tensor(image: Image.Image) -> torch.Tensor:
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"""Convert PIL Image to torch.Tensor.
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Args:
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image (Image.Image): A PIL Image with shape (H, W, C) and values in the range [0, 255].
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Returns:
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torch.Tensor: A torch.Tensor with shape (N, C, H, W) and values in the range [0, 1].
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"""
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image_np = np.array(image)
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# (H, W, C) -> (C, H, W)
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image_np = np.transpose(image_np, (2, 0, 1))
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image_np = image_np / 255
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image_tensor = torch.from_numpy(image_np).float()
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# (C, H, W) -> (N, C, H, W)
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image_tensor = image_tensor.unsqueeze(0)
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return image_tensor
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def tensor_to_pil(tensor: torch.Tensor) -> Image.Image:
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"""Convert torch.Tensor to PIL Image.
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Args:
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tensor (torch.Tensor): A torch.Tensor with shape (N, C, H, W) and values in the range [0, 1].
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Returns:
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Image.Image: A PIL Image with shape (H, W, C) and values in the range [0, 255].
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"""
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# (N, C, H, W) -> (C, H, W)
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tensor = tensor.squeeze(0)
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# (C, H, W) -> (H, W, C)
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tensor = tensor.permute(1, 2, 0)
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tensor = tensor.clamp(0, 1)
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tensor = (tensor * 255).cpu().detach().numpy().astype(np.uint8)
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image = Image.fromarray(tensor)
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return image
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@invocation("upscale_spandrel", title="Upscale (spandrel)", tags=["upscale"], category="upscale", version="1.0.0")
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class UpscaleSpandrelInvocation(BaseInvocation, WithMetadata, WithBoard):
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"""Upscales an image using any upscaler supported by spandrel (https://github.com/chaiNNer-org/spandrel)."""
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image: ImageField = InputField(description="The input image")
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spandrel_image_to_image_model: ModelIdentifierField = InputField(
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description=FieldDescriptions.spandrel_image_to_image_model, ui_type=UIType.LoRAModel
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)
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@torch.inference_mode()
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.images.get_pil(self.image.image_name)
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# Load the model.
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spandrel_model_info = context.models.load(self.spandrel_image_to_image_model)
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with spandrel_model_info as spandrel_model:
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assert isinstance(spandrel_model, SpandrelImageToImageModel)
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# Prepare input image for inference.
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image_tensor = pil_to_tensor(image)
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image_tensor = image_tensor.to(device=spandrel_model.device, dtype=spandrel_model.dtype)
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# Run inference.
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image_tensor = spandrel_model.run(image_tensor)
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# Convert the output tensor to a PIL image.
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pil_image = tensor_to_pil(image_tensor)
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image_dto = context.images.save(image=pil_image)
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return ImageOutput.build(image_dto)
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