mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
402cf9b0ee
Refactor services folder/module structure.
**Motivation**
While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward.
**Services**
Services are now in their own folder with a few files:
- `services/{service_name}/__init__.py`: init as needed, mostly empty now
- `services/{service_name}/{service_name}_base.py`: the base class for the service
- `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory`
- `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc
Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename.
There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`.
**Shared**
Things that are used across disparate services are in `services/shared/`:
- `default_graphs.py`: previously in `services/`
- `graphs.py`: previously in `services/`
- `paginatation`: generic pagination models used in a few services
- `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
import torch
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from PIL import Image
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from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException, ProgressImage
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from ...backend.model_management.models import BaseModelType
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from ...backend.stable_diffusion import PipelineIntermediateState
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from ...backend.util.util import image_to_dataURL
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from ..invocations.baseinvocation import InvocationContext
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def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
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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 stable_diffusion_step_callback(
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context: InvocationContext,
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intermediate_state: PipelineIntermediateState,
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node: dict,
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source_node_id: str,
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base_model: BaseModelType,
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):
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if context.services.queue.is_canceled(context.graph_execution_state_id):
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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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# TODO: This does not seem to be needed any more?
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# # txt2img provides a Tensor in the step_callback
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# # img2img provides a PipelineIntermediateState
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# if isinstance(sample, PipelineIntermediateState):
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# # this was an img2img
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# print('img2img')
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# latents = sample.latents
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# step = sample.step
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# else:
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# print('txt2img')
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# latents = sample
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# step = intermediate_state.step
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# TODO: only output a preview image when requested
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if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
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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 = torch.tensor(
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[
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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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dtype=sample.dtype,
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device=sample.device,
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)
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sdxl_smooth_matrix = torch.tensor(
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[
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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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dtype=sample.dtype,
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device=sample.device,
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)
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image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
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else:
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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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v1_5_latent_rgb_factors = torch.tensor(
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[
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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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dtype=sample.dtype,
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device=sample.device,
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)
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image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
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(width, height) = image.size
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width *= 8
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height *= 8
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dataURL = image_to_dataURL(image, image_format="JPEG")
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context.services.events.emit_generator_progress(
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queue_id=context.queue_id,
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queue_item_id=context.queue_item_id,
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queue_batch_id=context.queue_batch_id,
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graph_execution_state_id=context.graph_execution_state_id,
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node=node,
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source_node_id=source_node_id,
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progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
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step=intermediate_state.step,
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order=intermediate_state.order,
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total_steps=intermediate_state.total_steps,
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)
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