feat: attempted a rewrite #2
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WalkthroughThis update introduces a comprehensive overhaul of the DiffUTE model's training infrastructure, emphasizing a modernized, MinIO-based data pipeline and improved code organization. Two new training scripts, Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant TrainingScript (train_vae_v2.py/train_diffute_v2.py)
participant MinioHandler
participant MinIO
participant Model (VAE/UNet/TrOCR)
participant Optimizer/Scheduler
User->>TrainingScript: Starts training with config & MinIO creds
TrainingScript->>MinioHandler: Initialize with credentials
MinioHandler->>MinIO: Connect & verify bucket
TrainingScript->>MinioHandler: Download data (images, OCR, etc.)
MinioHandler->>MinIO: Retrieve files
MinioHandler->>TrainingScript: Return image/JSON data
TrainingScript->>Model: Preprocess & forward pass
Model-->>TrainingScript: Output predictions
TrainingScript->>Optimizer/Scheduler: Compute loss, backprop, step
TrainingScript->>MinioHandler: (Optional) Upload checkpoints
MinioHandler->>MinIO: Store checkpoint
TrainingScript-->>User: Log progress, save models
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Actionable comments posted: 8
🔭 Outside diff range comments (1)
utils/minio_utils.py (1)
144-164: 🛠️ Refactor suggestionFix exception chaining in read_json method.
Use exception chaining for better error tracebacks.
- raise Exception(f"Failed to read/parse JSON {object_name}: {str(e)}") + raise Exception(f"Failed to read/parse JSON {object_name}: {str(e)}") from eMove the
import jsonstatement to the module level instead of inside the method for better performance, especially if the method is called frequently.+ import json + def read_json(self, object_name: str) -> dict: """ Read and parse a JSON file from MinIO storage. Args: object_name (str): Name of the JSON object Returns: dict: Parsed JSON content Raises: Exception: If reading or parsing fails """ - import json try: content = self.download_file(object_name) return json.loads(content.decode("utf-8"))🧰 Tools
🪛 Ruff (0.8.2)
163-163: Within an
exceptclause, raise exceptions withraise ... from errorraise ... from Noneto distinguish them from errors in exception handling(B904)
🧹 Nitpick comments (4)
requirements.txt (1)
1-12: Dependency updates look appropriate for the project modernization.The requirements have been updated with explicit versions that support the new MinIO-based data pipeline and modern training infrastructure. The torch version upgrade to 2.0.0+ and the addition of minio>=7.1.0 align well with the project's new direction.
However, consider pinning specific versions (e.g., torch==2.0.0 instead of torch>=2.0.0) to ensure reproducibility across different environments. This can help prevent unexpected behavior when dependencies release breaking changes.
train_diffute_v2.py (1)
397-399: Suppress unused loop variable to silence Ruff B007
stepis produced byenumeratebut never referenced. Rename to_to convey intent and pass linting:-for step, batch in enumerate(dataloader): +for _, batch in enumerate(dataloader):🧰 Tools
🪛 Ruff (0.8.2)
397-397: Loop control variable
stepnot used within loop bodyRename unused
stepto_step(B007)
train_vae_v2.py (1)
255-255: Rename unusedstepvariableSame Ruff B007 issue as in the DiffUTE script:
- for step, batch in enumerate(dataloader): + for _, batch in enumerate(dataloader):🧰 Tools
🪛 Ruff (0.8.2)
255-255: Loop control variable
stepnot used within loop bodyRename unused
stepto_step(B007)
README.md (1)
73-81: Specify language for fenced code block to satisfy MD040-``` +```bash🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
73-73: Fenced code blocks should have a language specified
null(MD040, fenced-code-language)
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📒 Files selected for processing (7)
README.md(1 hunks)requirements.txt(1 hunks)train_diffute_v1.py(1 hunks)train_diffute_v2.py(1 hunks)train_vae.py(1 hunks)train_vae_v2.py(1 hunks)utils/minio_utils.py(1 hunks)
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🧬 Code Graph Analysis (1)
train_vae_v2.py (1)
utils/minio_utils.py (2)
MinioHandler(15-163)download_image(68-89)
🪛 LanguageTool
README.md
[uncategorized] ~137-~137: You might be missing the article “the” here.
Context: ...# MinIO Setup 1. Install and configure MinIO server 2. Create a bucket for storing t...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
[uncategorized] ~173-~173: You might be missing the article “the” here.
Context: ...or velocity - Loss is calculated and model is updated - Checkpoints are saved p...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
[uncategorized] ~186-~186: You might be missing the article “the” here.
Context: ...rces ## Contributing All rights go to original authors
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
🪛 markdownlint-cli2 (0.17.2)
README.md
73-73: Fenced code blocks should have a language specified
null
(MD040, fenced-code-language)
🪛 Ruff (0.8.2)
utils/minio_utils.py
63-63: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
89-89: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
123-123: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
142-142: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
163-163: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
train_diffute_v2.py
326-326: Local variable processor is assigned to but never used
Remove assignment to unused variable processor
(F841)
371-371: Local variable vae_scale_factor is assigned to but never used
Remove assignment to unused variable vae_scale_factor
(F841)
397-397: Loop control variable step not used within loop body
Rename unused step to _step
(B007)
train_vae_v2.py
255-255: Loop control variable step not used within loop body
Rename unused step to _step
(B007)
🔇 Additional comments (5)
train_vae.py (1)
667-667: Code simplification looks good.The change consolidates the
Repositoryobject instantiation into a single line, which improves readability without altering functionality. This is a good style improvement that aligns with similar changes in other files.train_diffute_v1.py (1)
843-843: Code simplification looks good.The change consolidates the
Repositoryobject instantiation into a single line, which improves readability without altering functionality. This is a good style improvement that aligns with similar changes in other files.utils/minio_utils.py (1)
1-45: Good implementation of the MinIO client wrapper.The class is well-structured with a clear initialization method that validates the bucket existence, which is a good defensive programming practice. The docstrings are comprehensive and follow standard conventions.
train_diffute_v2.py (2)
430-433: Verify that visual embeddings, not textual embeddings, are really desiredHere the vision encoder of TrOCR is fed with the original image:
text_embeddings = trocr_model(batch["pixel_values"] ...)Yet the dataset already returns the recognised
text, which is unused.
If the UNet should be conditioned on textual context, you likely need to:
- Tokenise
textwith theTrOCRProcessor(processor.tokenizer)- Obtain text embeddings from TrOCR’s decoder or a separate text encoder.
Passing images twice may dilute the intended guidance signal.
435-438: Potential channel mismatch: confirm UNetin_channels == 9
model_inputconcatenates noisy latents (4 ch) + mask (1 ch) + masked‑image latents (4 ch) → 9 channels.
The UNet loaded fromargs.pretrained_model_name_or_pathwill raise a shape error if itsin_channelsisn’t 9 (Stable‑Diffusion’s vanilla UNet expects 4). Make sure you export / load an in‑painting UNet or adapt the first convolution:assert unet.config.in_channels == model_input.shape[1], ( f"UNet expects {unet.config.in_channels} channels but got {model_input.shape[1]}" )
| def download_file(self, object_name: str) -> bytes: | ||
| """ | ||
| Download a file from MinIO storage. | ||
|
|
||
| Args: | ||
| object_name (str): Name of the object to download | ||
|
|
||
| Returns: | ||
| bytes: File contents as bytes | ||
|
|
||
| Raises: | ||
| Exception: If download fails | ||
| """ | ||
| try: | ||
| response = self.client.get_object(self.bucket_name, object_name) | ||
| return response.read() | ||
| except Exception as e: | ||
| raise Exception(f"Failed to download {object_name}: {str(e)}") | ||
| finally: | ||
| response.close() | ||
| response.release_conn() | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve exception handling by using exception chaining.
The exception handling is good, but you should use exception chaining with from e to maintain the original traceback, which is valuable for debugging.
- raise Exception(f"Failed to download {object_name}: {str(e)}")
+ raise Exception(f"Failed to download {object_name}: {str(e)}") from eAlso, make sure to handle the response variable properly as it might not be defined if an exception occurs before assignment.
- finally:
- response.close()
- response.release_conn()
+ finally:
+ if 'response' in locals():
+ response.close()
+ response.release_conn()📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def download_file(self, object_name: str) -> bytes: | |
| """ | |
| Download a file from MinIO storage. | |
| Args: | |
| object_name (str): Name of the object to download | |
| Returns: | |
| bytes: File contents as bytes | |
| Raises: | |
| Exception: If download fails | |
| """ | |
| try: | |
| response = self.client.get_object(self.bucket_name, object_name) | |
| return response.read() | |
| except Exception as e: | |
| raise Exception(f"Failed to download {object_name}: {str(e)}") | |
| finally: | |
| response.close() | |
| response.release_conn() | |
| def download_file(self, object_name: str) -> bytes: | |
| """ | |
| Download a file from MinIO storage. | |
| Args: | |
| object_name (str): Name of the object to download | |
| Returns: | |
| bytes: File contents as bytes | |
| Raises: | |
| Exception: If download fails | |
| """ | |
| try: | |
| response = self.client.get_object(self.bucket_name, object_name) | |
| return response.read() | |
| except Exception as e: | |
| raise Exception(f"Failed to download {object_name}: {str(e)}") from e | |
| finally: | |
| if 'response' in locals(): | |
| response.close() | |
| response.release_conn() |
🧰 Tools
🪛 Ruff (0.8.2)
63-63: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
| def list_objects(self, prefix: str = "", recursive: bool = True): | ||
| """ | ||
| List objects in the bucket with optional prefix filtering. | ||
|
|
||
| Args: | ||
| prefix (str): Filter objects by prefix | ||
| recursive (bool): Whether to list objects recursively in directories | ||
|
|
||
| Returns: | ||
| Generator yielding object names | ||
| """ | ||
| try: | ||
| objects = self.client.list_objects( | ||
| self.bucket_name, prefix=prefix, recursive=recursive | ||
| ) | ||
| return (obj.object_name for obj in objects) | ||
| except Exception as e: | ||
| raise Exception(f"Failed to list objects: {str(e)}") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Fix exception chaining in list_objects method.
Use exception chaining for better error tracebacks.
- raise Exception(f"Failed to list objects: {str(e)}")
+ raise Exception(f"Failed to list objects: {str(e)}") from eAlso, consider adding type hints for the return value to improve code clarity.
- def list_objects(self, prefix: str = "", recursive: bool = True):
+ def list_objects(self, prefix: str = "", recursive: bool = True) -> 'Generator[str, None, None]':📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def list_objects(self, prefix: str = "", recursive: bool = True): | |
| """ | |
| List objects in the bucket with optional prefix filtering. | |
| Args: | |
| prefix (str): Filter objects by prefix | |
| recursive (bool): Whether to list objects recursively in directories | |
| Returns: | |
| Generator yielding object names | |
| """ | |
| try: | |
| objects = self.client.list_objects( | |
| self.bucket_name, prefix=prefix, recursive=recursive | |
| ) | |
| return (obj.object_name for obj in objects) | |
| except Exception as e: | |
| raise Exception(f"Failed to list objects: {str(e)}") | |
| def list_objects(self, prefix: str = "", recursive: bool = True) -> 'Generator[str, None, None]': | |
| """ | |
| List objects in the bucket with optional prefix filtering. | |
| Args: | |
| prefix (str): Filter objects by prefix | |
| recursive (bool): Whether to list objects recursively in directories | |
| Returns: | |
| Generator yielding object names | |
| """ | |
| try: | |
| objects = self.client.list_objects( | |
| self.bucket_name, prefix=prefix, recursive=recursive | |
| ) | |
| return (obj.object_name for obj in objects) | |
| except Exception as e: | |
| raise Exception(f"Failed to list objects: {str(e)}") from e |
🧰 Tools
🪛 Ruff (0.8.2)
142-142: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
| def upload_file( | ||
| self, | ||
| file_data: Union[bytes, BinaryIO], | ||
| object_name: str, | ||
| content_type: Optional[str] = None, | ||
| ) -> None: | ||
| """ | ||
| Upload a file to MinIO storage. | ||
|
|
||
| Args: | ||
| file_data: File contents as bytes or file-like object | ||
| object_name (str): Name to give the uploaded object | ||
| content_type (str, optional): Content type of the file | ||
|
|
||
| Raises: | ||
| Exception: If upload fails | ||
| """ | ||
| try: | ||
| if isinstance(file_data, bytes): | ||
| file_data = io.BytesIO(file_data) | ||
|
|
||
| file_size = file_data.seek(0, 2) | ||
| file_data.seek(0) | ||
|
|
||
| self.client.put_object( | ||
| bucket_name=self.bucket_name, | ||
| object_name=object_name, | ||
| data=file_data, | ||
| length=file_size, | ||
| content_type=content_type, | ||
| ) | ||
| except Exception as e: | ||
| raise Exception(f"Failed to upload {object_name}: {str(e)}") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Fix exception chaining and add validation in upload_file method.
Use exception chaining for better error tracebacks.
- raise Exception(f"Failed to upload {object_name}: {str(e)}")
+ raise Exception(f"Failed to upload {object_name}: {str(e)}") from eConsider adding validation to ensure file_data is not None and is either bytes or a file-like object.
+ if file_data is None:
+ raise ValueError("file_data cannot be None")
+
+ if not isinstance(file_data, (bytes, io.IOBase)):
+ raise TypeError("file_data must be bytes or a file-like object")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def upload_file( | |
| self, | |
| file_data: Union[bytes, BinaryIO], | |
| object_name: str, | |
| content_type: Optional[str] = None, | |
| ) -> None: | |
| """ | |
| Upload a file to MinIO storage. | |
| Args: | |
| file_data: File contents as bytes or file-like object | |
| object_name (str): Name to give the uploaded object | |
| content_type (str, optional): Content type of the file | |
| Raises: | |
| Exception: If upload fails | |
| """ | |
| try: | |
| if isinstance(file_data, bytes): | |
| file_data = io.BytesIO(file_data) | |
| file_size = file_data.seek(0, 2) | |
| file_data.seek(0) | |
| self.client.put_object( | |
| bucket_name=self.bucket_name, | |
| object_name=object_name, | |
| data=file_data, | |
| length=file_size, | |
| content_type=content_type, | |
| ) | |
| except Exception as e: | |
| raise Exception(f"Failed to upload {object_name}: {str(e)}") | |
| def upload_file( | |
| self, | |
| file_data: Union[bytes, BinaryIO], | |
| object_name: str, | |
| content_type: Optional[str] = None, | |
| ) -> None: | |
| """ | |
| Upload a file to MinIO storage. | |
| Args: | |
| file_data: File contents as bytes or file-like object | |
| object_name (str): Name to give the uploaded object | |
| content_type (str, optional): Content type of the file | |
| Raises: | |
| Exception: If upload fails | |
| """ | |
| # Validate inputs early | |
| if file_data is None: | |
| raise ValueError("file_data cannot be None") | |
| if not isinstance(file_data, (bytes, io.IOBase)): | |
| raise TypeError("file_data must be bytes or a file-like object") | |
| try: | |
| if isinstance(file_data, bytes): | |
| file_data = io.BytesIO(file_data) | |
| file_size = file_data.seek(0, 2) | |
| file_data.seek(0) | |
| self.client.put_object( | |
| bucket_name=self.bucket_name, | |
| object_name=object_name, | |
| data=file_data, | |
| length=file_size, | |
| content_type=content_type, | |
| ) | |
| except Exception as e: | |
| raise Exception(f"Failed to upload {object_name}: {str(e)}") from e |
🧰 Tools
🪛 Ruff (0.8.2)
123-123: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
| def download_image(self, object_name: str) -> np.ndarray: | ||
| """ | ||
| Download and decode an image from MinIO storage. | ||
|
|
||
| Args: | ||
| object_name (str): Name of the image object | ||
|
|
||
| Returns: | ||
| np.ndarray: Decoded image as numpy array | ||
|
|
||
| Raises: | ||
| Exception: If download or decoding fails | ||
| """ | ||
| try: | ||
| content = self.download_file(object_name) | ||
| img_array = np.frombuffer(content, dtype=np.uint8) | ||
| img = cv2.imdecode(img_array, cv2.IMREAD_COLOR) | ||
| if img is None: | ||
| raise ValueError("Failed to decode image") | ||
| return img | ||
| except Exception as e: | ||
| raise Exception(f"Failed to download/decode image {object_name}: {str(e)}") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve exception handling in download_image method.
Similar to the previous method, use exception chaining for better debugging.
- raise Exception(f"Failed to download/decode image {object_name}: {str(e)}")
+ raise Exception(f"Failed to download/decode image {object_name}: {str(e)}") from eAlso, consider adding color space conversion since OpenCV loads images in BGR format by default, which might be unexpected.
- img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+ img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def download_image(self, object_name: str) -> np.ndarray: | |
| """ | |
| Download and decode an image from MinIO storage. | |
| Args: | |
| object_name (str): Name of the image object | |
| Returns: | |
| np.ndarray: Decoded image as numpy array | |
| Raises: | |
| Exception: If download or decoding fails | |
| """ | |
| try: | |
| content = self.download_file(object_name) | |
| img_array = np.frombuffer(content, dtype=np.uint8) | |
| img = cv2.imdecode(img_array, cv2.IMREAD_COLOR) | |
| if img is None: | |
| raise ValueError("Failed to decode image") | |
| return img | |
| except Exception as e: | |
| raise Exception(f"Failed to download/decode image {object_name}: {str(e)}") | |
| def download_image(self, object_name: str) -> np.ndarray: | |
| """ | |
| Download and decode an image from MinIO storage. | |
| Args: | |
| object_name (str): Name of the image object | |
| Returns: | |
| np.ndarray: Decoded image as numpy array | |
| Raises: | |
| Exception: If download or decoding fails | |
| """ | |
| try: | |
| content = self.download_file(object_name) | |
| img_array = np.frombuffer(content, dtype=np.uint8) | |
| img = cv2.imdecode(img_array, cv2.IMREAD_COLOR) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB | |
| if img is None: | |
| raise ValueError("Failed to decode image") | |
| return img | |
| except Exception as e: | |
| raise Exception(f"Failed to download/decode image {object_name}: {str(e)}") from e |
🧰 Tools
🪛 Ruff (0.8.2)
89-89: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
| vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Delete vae_scale_factor (unused) or integrate it where intended
vae_scale_factor is calculated but never referenced, triggering F841 and signaling either left‑over experimentation or a missing piece in the pipeline (e.g. latent scaling when decoding for visualisation).
If it is genuinely unnecessary, please drop it; otherwise apply it consistently.
🧰 Tools
🪛 Ruff (0.8.2)
371-371: Local variable vae_scale_factor is assigned to but never used
Remove assignment to unused variable vae_scale_factor
(F841)
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-printed") | ||
| trocr_model = VisionEncoderDecoderModel.from_pretrained( | ||
| "microsoft/trocr-large-printed" | ||
| ).encoder |
There was a problem hiding this comment.
Remove unused variable to avoid confusion and lint warnings
processor is instantiated but never used. Keeping dead code obscures intent and will fail ruff F841.
-processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-printed")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-printed") | |
| trocr_model = VisionEncoderDecoderModel.from_pretrained( | |
| "microsoft/trocr-large-printed" | |
| ).encoder | |
| trocr_model = VisionEncoderDecoderModel.from_pretrained( | |
| "microsoft/trocr-large-printed" | |
| ).encoder |
🧰 Tools
🪛 Ruff (0.8.2)
326-326: Local variable processor is assigned to but never used
Remove assignment to unused variable processor
(F841)
| # Freeze VAE and TrOCR | ||
| vae.requires_grad_(False) | ||
| trocr_model.requires_grad_(False) | ||
|
|
There was a problem hiding this comment.
Call .eval() on frozen modules to disable dropout / norm updates
Although the VAE and TrOCR weights are frozen, they remain in train mode, so layers such as BatchNorm, LayerNorm, or Dropout will still update running statistics or behave stochastically, introducing non‑determinism and slowing convergence.
-vae.requires_grad_(False)
-trocr_model.requires_grad_(False)
+vae.requires_grad_(False).eval()
+trocr_model.requires_grad_(False).eval()| with accelerator.accumulate(vae): | ||
| # Get VAE loss | ||
| loss = vae(batch["pixel_values"], return_dict=False)[0] | ||
| accelerator.backward(loss) |
There was a problem hiding this comment.
Loss computation is wrong – back‑propagating on latents, not reconstruction error
AutoencoderKL.forward() returns (sample, posterior) when return_dict=False; sample is the latent representation, not a scalar loss. Using it as loss will back‑propagate but make optimisation meaningless and quickly diverge.
A minimal, correct objective is the L2 reconstruction error plus KL term:
- loss = vae(batch["pixel_values"], return_dict=False)[0]
+ recon_latents, posterior = vae.encode(
+ batch["pixel_values"]
+ ).latent_dist.sample(), None
+ recon_images = vae.decode(recon_latents).sample
+ mse = torch.nn.functional.mse_loss(
+ recon_images, batch["pixel_values"]
+ )
+ kl = posterior.kl().mean() if posterior is not None else torch.tensor(0.0, device=mse.device)
+ loss = mse + 0.001 * kl # β‑VAE weightingAdjust the weighting/metric to your research needs.
Committable suggestion skipped: line range outside the PR's diff.
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Actionable comments posted: 5
🧹 Nitpick comments (16)
stable_diffusion_text_inpaint/utils/style_utils.py (4)
3-7: Remove unused importsThere are imports that aren't being used in this file.
-from PIL import Image +# No need to import Image as it's not used directly in this file import numpy as np from transformers import TrOCRProcessor, VisionEncoderDecoderModel -import torch +# Remove torch import as it's not used🧰 Tools
🪛 Ruff (0.8.2)
3-3:
PIL.Imageimported but unusedRemove unused import:
PIL.Image(F401)
6-6:
torchimported but unusedRemove unused import:
torch(F401)
35-42: Unused variable in exception handlingThe
featuresvariable is assigned but never used after capturing it from the encoder.try: # Get text features from TrOCR pixel_values = self.processor(text_region, return_tensors="pt").pixel_values - features = self.model.encoder(pixel_values).last_hidden_state + # Either use the features or remove the assignment + _ = self.model.encoder(pixel_values).last_hidden_state except Exception as e: print(f"Warning: Style analysis failed ({str(e)}), using basic analysis only") - features = None + # No need to set features to None if it's not usedAlternatively, if you intended to use these features for more sophisticated style analysis, consider extending the implementation to make use of them.
🧰 Tools
🪛 Ruff (0.8.2)
41-41: Local variable
featuresis assigned to but never usedRemove assignment to unused variable
features(F841)
101-117: Consider expanding color vocabularyThe color mapping is quite limited, recognizing only 6 basic colors. This may not be sufficient for accurately describing text styles in various design contexts.
Consider using a more comprehensive color mapping approach, such as:
def _rgb_to_description(rgb): """Convert RGB values to color description with expanded vocabulary.""" r, g, b = rgb # Check for black, white, and gray first if max(r, g, b) < 50: return "black" elif min(r, g, b) > 200: return "white" elif abs(r - g) < 30 and abs(r - b) < 30 and abs(g - b) < 30: if r < 100: return "dark gray" elif r > 150: return "light gray" return "gray" # Calculate hue for more precise color naming max_val = max(r, g, b) min_val = min(r, g, b) if max_val == 0: return "black" diff = max_val - min_val # Richer color vocabulary if max_val == r: hue = 60 * ((g - b) / diff % 6) elif max_val == g: hue = 60 * ((b - r) / diff + 2) else: hue = 60 * ((r - g) / diff + 4) saturation = 0 if max_val == 0 else diff / max_val value = max_val / 255 # Map HSV to color names if saturation < 0.1: return "gray" if value < 0.3: return "dark " + _hue_to_color_name(hue) elif value > 0.7: return "light " + _hue_to_color_name(hue) return _hue_to_color_name(hue) def _hue_to_color_name(hue): """Map hue value to color name.""" color_ranges = [ (0, 30, "red"), (30, 60, "orange"), (60, 90, "yellow"), (90, 150, "green"), (150, 210, "cyan"), (210, 270, "blue"), (270, 330, "purple"), (330, 360, "red") ] for low, high, name in color_ranges: if low <= hue < high: return name return "unknown"
9-16: Consider model loading optimizationLoading the TrOCR model every time the class is instantiated can be resource-intensive. For production use, you might want to optimize this.
Consider implementing a lazy loading pattern or a singleton pattern to avoid reloading the model multiple times:
class TextStyleAnalyzer: _instance = None _initialized = False def __new__(cls): if cls._instance is None: cls._instance = super(TextStyleAnalyzer, cls).__new__(cls) return cls._instance def __init__(self): if not self._initialized: print("Loading TrOCR model (this happens only once)...") self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-printed") self.model = VisionEncoderDecoderModel.from_pretrained( "microsoft/trocr-large-printed" ) TextStyleAnalyzer._initialized = Truestable_diffusion_text_inpaint/utils/region_finder.py (5)
3-8: Remove unused importsSeveral imported modules are not used in this file.
import cv2 -import numpy as np from PIL import Image, ImageDraw import matplotlib.pyplot as plt -from transformers import TrOCRProcessor, VisionEncoderDecoderModel -import torch🧰 Tools
🪛 Ruff (0.8.2)
4-4:
numpyimported but unusedRemove unused import:
numpy(F401)
7-7:
transformers.TrOCRProcessorimported but unusedRemove unused import
(F401)
7-7:
transformers.VisionEncoderDecoderModelimported but unusedRemove unused import
(F401)
8-8:
torchimported but unusedRemove unused import:
torch(F401)
50-58: Update docstring to match actual return valueThe docstring incorrectly states that the function returns a list of (text, box) tuples, but it actually returns a list of bounding boxes only.
def detect_text_regions(image_path): """Automatically detect text regions using OCR. Args: image_path (str): Path to the image Returns: - list: List of (text, box) tuples where box is (x1, y1, x2, y2) + list: List of bounding boxes as (x1, y1, x2, y2) tuples """
63-65: Improve code comments to match implementationThe comment mentions using EAST text detector or Tesseract OCR, but the actual implementation uses edge detection. This comment could be misleading.
- # Use EAST text detector or Tesseract OCR - # For now, using simple edge detection as placeholder + # Using simple edge detection to find potential text regions + # Note: This is a basic approach and might not detect all text accurately + # Future enhancement: Consider using EAST text detector or Tesseract OCR
86-87: Remove unused variableThe
imgvariable is assigned but never used.- img = Image.open(image_path) + # No need to open the image here as it's opened in visualize_region🧰 Tools
🪛 Ruff (0.8.2)
86-86: Local variable
imgis assigned to but never usedRemove assignment to unused variable
img(F841)
107-111: Simplify coordinate validation logicThe coordinate validation code can be simplified for better readability.
- if not (0 <= x1 < size[0] and 0 <= x2 < size[0] and - 0 <= y1 < size[1] and 0 <= y2 < size[1]): + width, height = size + if not all(0 <= x < width for x in (x1, x2)) or not all(0 <= y < height for y in (y1, y2)): print(f"Coordinates must be within image bounds: width={size[0]}, height={size[1]}") continuestable_diffusion_text_inpaint/find_regions.py (1)
12-18: Consider batch visualization option for multiple regionsIn automatic mode, the current implementation visualizes each region individually, which might be cumbersome for images with many detected regions.
Consider adding an option to visualize all regions on a single image:
if args.auto: print("Detecting text regions automatically...") regions = detect_text_regions(args.image_path) print(f"\nFound {len(regions)} regions:") + + # First show all regions on one image + if len(regions) > 1: + import matplotlib.pyplot as plt + from PIL import Image, ImageDraw + + img = Image.open(args.image_path) + draw_img = img.copy() + draw = ImageDraw.Draw(draw_img) + + # Draw all regions with different colors for clarity + colors = ['red', 'blue', 'green', 'yellow', 'purple', 'cyan', 'magenta', 'orange'] + for i, region in enumerate(regions): + color = colors[i % len(colors)] + draw.rectangle(region, outline=color, width=2) + x1, y1, x2, y2 = region + draw.text((x1, y1-15), f'{i+1}', fill=color) + + plt.figure(figsize=(12, 8)) + plt.imshow(draw_img) + plt.title(f"All {len(regions)} detected regions") + plt.axis('off') + plt.show() + + # Then show individual regions if requested + detail_view = input("\nShow detailed view of each region? (y/n): ").lower() == 'y' + if detail_view: for i, region in enumerate(regions, 1): print(f"Region {i}: {region}") visualize_region(args.image_path, region)stable_diffusion_text_inpaint/README.md (1)
7-9: Consider expanding the requirements section.The requirements section currently only lists three core packages, but the implementation likely requires additional dependencies such as Pillow, numpy, opencv-python, and click which are mentioned in the AI summary.
-pip install diffusers transformers torch +pip install diffusers transformers torch pillow numpy opencv-python clickstable_diffusion_text_inpaint/example.py (2)
41-44: Avoid hardcoded file paths.The function always saves to "example.png" in the current directory, which could lead to unexpected file creation depending on where the script is executed from. Consider using a dedicated output directory or allowing customization of the file path.
- # Save the image - if not os.path.exists("example.png"): - image.save("example.png") + # Save the image to a dedicated output directory + output_dir = os.path.join(os.path.dirname(__file__), "output") + os.makedirs(output_dir, exist_ok=True) + output_path = os.path.join(output_dir, "example.png") + if not os.path.exists(output_path): + image.save(output_path) return image
59-60: Use configurable output paths.The function saves the output to hardcoded file paths, which could cause issues if the script is run from different locations. Consider using a configurable output directory.
+ # Define output directory + output_dir = os.path.join(os.path.dirname(__file__), "output") + os.makedirs(output_dir, exist_ok=True) + # Simple inpainting result = inpainter.inpaint_text(image=image, text="Hello World", text_box=text_box) - result.save("single_text_result.png") + result.save(os.path.join(output_dir, "single_text_result.png"))Apply similar changes to the other output file paths in the script.
stable_diffusion_text_inpaint/text_inpainter.py (2)
81-94: Add progress feedback for batch inpainting.The
batch_inpaint_textmethod processes multiple regions sequentially without providing any progress feedback. Consider adding a progress indicator using tqdm, similar to what's used in theinpaint_textmethod.def batch_inpaint_text(self, image, text_regions): """Inpaint multiple text regions in an image. Args: image (PIL.Image): Input image text_regions (list): List of (text, box) tuples Returns: PIL.Image: Image with all text regions inpainted """ result = image.copy() - for text, box in text_regions: + for text, box in tqdm(text_regions, desc="Inpainting text regions"): result = self.inpaint_text(result, text, box) return result
97-119: Improve themainfunction with better file handling.The main function uses hardcoded file paths, making it less reliable when run from different directories. Consider using configurable paths or creating a dedicated output directory.
def main(): """Example usage of TextInpainter.""" + import os + + # Create output directory + output_dir = os.path.join(os.path.dirname(__file__), "output") + os.makedirs(output_dir, exist_ok=True) + + # Example image path + example_path = os.path.join(os.path.dirname(__file__), "example.png") + # Initialize inpainter inpainter = TextInpainter() # Load image - image = Image.open("example.png") + # Create example image if it doesn't exist + if not os.path.exists(example_path): + # Create a sample image (similar to example.py) + image = Image.new("RGB", (512, 512), "white") + draw = ImageDraw.Draw(image) + draw.rectangle((50, 50, 462, 462), outline="gray", width=2) + image.save(example_path) + else: + image = Image.open(example_path) # Define text region text_box = (100, 100, 300, 150) # Inpaint text result = inpainter.inpaint_text( image=image, text="Hello World", text_box=text_box, num_attempts=3 ) # Save results if isinstance(result, list): for i, img in enumerate(result): - img.save(f"result_{i}.png") + img.save(os.path.join(output_dir, f"result_{i}.png")) else: - result.save("result.png") + result.save(os.path.join(output_dir, "result.png"))stable_diffusion_text_inpaint/cli.py (1)
93-108: Simplify the result handling logic.The current code checks
attempts == 1twice and has a redundant conversion to a list. This can be simplified for better readability.- if attempts == 1: - results = [results] # Make it a list for consistent handling - - # Save all variations - for i, result in enumerate(results): - try: - if attempts > 1: - base, ext = os.path.splitext(output) - save_path = f"{base}_{i+1}{ext}" - else: - save_path = output - result.save(save_path) - click.echo(f"Saved result to: {save_path}") - except Exception as e: - click.echo(f"Error saving result {i+1}: {str(e)}", err=True) - continue + # Ensure results is always a list for consistent handling + results_list = results if isinstance(results, list) else [results] + + # Save all variations + for i, result in enumerate(results_list): + try: + # If multiple attempts, add index to filename + if len(results_list) > 1: + base, ext = os.path.splitext(output) + save_path = f"{base}_{i+1}{ext}" + else: + save_path = output + + result.save(save_path) + click.echo(f"Saved result to: {save_path}") + except Exception as e: + click.echo(f"Error saving result {i+1}: {str(e)}", err=True) + continue
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📒 Files selected for processing (11)
stable_diffusion_text_inpaint/README.md(1 hunks)stable_diffusion_text_inpaint/__init__.py(1 hunks)stable_diffusion_text_inpaint/cli.py(1 hunks)stable_diffusion_text_inpaint/example.py(1 hunks)stable_diffusion_text_inpaint/find_regions.py(1 hunks)stable_diffusion_text_inpaint/requirements.txt(1 hunks)stable_diffusion_text_inpaint/text_inpainter.py(1 hunks)stable_diffusion_text_inpaint/utils/__init__.py(1 hunks)stable_diffusion_text_inpaint/utils/mask_utils.py(1 hunks)stable_diffusion_text_inpaint/utils/region_finder.py(1 hunks)stable_diffusion_text_inpaint/utils/style_utils.py(1 hunks)
✅ Files skipped from review due to trivial changes (3)
- stable_diffusion_text_inpaint/utils/init.py
- stable_diffusion_text_inpaint/requirements.txt
- stable_diffusion_text_inpaint/init.py
🧰 Additional context used
🧬 Code Graph Analysis (3)
stable_diffusion_text_inpaint/find_regions.py (1)
stable_diffusion_text_inpaint/utils/region_finder.py (3)
interactive_region_select(77-122)detect_text_regions(50-75)visualize_region(10-48)
stable_diffusion_text_inpaint/example.py (1)
stable_diffusion_text_inpaint/text_inpainter.py (3)
TextInpainter(12-94)inpaint_text(32-79)batch_inpaint_text(81-94)
stable_diffusion_text_inpaint/text_inpainter.py (3)
stable_diffusion_text_inpaint/utils/mask_utils.py (2)
create_context_mask(22-40)validate_text_box(58-76)stable_diffusion_text_inpaint/utils/style_utils.py (3)
TextStyleAnalyzer(9-74)generate_style_prompt(77-98)analyze_text_region(17-51)stable_diffusion_text_inpaint/find_regions.py (1)
main(6-23)
🪛 LanguageTool
stable_diffusion_text_inpaint/README.md
[misspelling] ~66-~66: This word is normally spelled as one.
Context: ...htly larger than the text area - Use anti-aliasing on mask edges for smoother blending ...
(EN_COMPOUNDS_ANTI_ALIASING)
[uncategorized] ~167-~167: You might be missing the article “the” here.
Context: ...editing - Text needs to perfectly match surrounding context - Working with complex backgrou...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
🪛 Ruff (0.8.2)
stable_diffusion_text_inpaint/utils/mask_utils.py
55-55: Undefined name ImageFilter
(F821)
stable_diffusion_text_inpaint/utils/region_finder.py
4-4: numpy imported but unused
Remove unused import: numpy
(F401)
7-7: transformers.TrOCRProcessor imported but unused
Remove unused import
(F401)
7-7: transformers.VisionEncoderDecoderModel imported but unused
Remove unused import
(F401)
8-8: torch imported but unused
Remove unused import: torch
(F401)
86-86: Local variable img is assigned to but never used
Remove assignment to unused variable img
(F841)
stable_diffusion_text_inpaint/utils/style_utils.py
3-3: PIL.Image imported but unused
Remove unused import: PIL.Image
(F401)
6-6: torch imported but unused
Remove unused import: torch
(F401)
41-41: Local variable features is assigned to but never used
Remove assignment to unused variable features
(F841)
🔇 Additional comments (6)
stable_diffusion_text_inpaint/utils/mask_utils.py (3)
6-19: LGTM! Clean implementation of text mask creationThe function correctly creates a binary mask by drawing a white rectangle on a black background for the text region.
22-40: LGTM! Context mask implementation handles boundaries correctlyThe implementation properly creates a padded mask while ensuring the boundaries stay within the image dimensions.
58-76: LGTM! Robust validation functionThe
validate_text_boxfunction correctly clamps coordinates to ensure they remain within image bounds.stable_diffusion_text_inpaint/find_regions.py (3)
3-4: LGTM! Clean importsThe imports are concise and directly related to the functionality of the script.
6-24: LGTM! Well-structured main functionThe main function is well-structured with clear argument parsing and logical flow between automatic and interactive modes.
25-26: LGTM! Standard Python script entry pointFollowing the standard pattern for script execution.
| def create_antialiased_mask(image, text_box, blur_radius=2): | ||
| """Create an anti-aliased mask for smoother blending. | ||
|
|
||
| Args: | ||
| image (PIL.Image): Input image | ||
| text_box (tuple): (x1, y1, x2, y2) coordinates for text region | ||
| blur_radius (int): Radius for Gaussian blur | ||
|
|
||
| Returns: | ||
| PIL.Image: Anti-aliased mask | ||
| """ | ||
| mask = create_text_mask(image, text_box) | ||
| return mask.filter(ImageFilter.GaussianBlur(blur_radius)) |
There was a problem hiding this comment.
Fix missing import for ImageFilter
The create_antialiased_mask function uses ImageFilter.GaussianBlur() but the ImageFilter module is not imported.
Add the missing import at the top of the file:
-from PIL import Image, ImageDraw
+from PIL import Image, ImageDraw, ImageFilter📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def create_antialiased_mask(image, text_box, blur_radius=2): | |
| """Create an anti-aliased mask for smoother blending. | |
| Args: | |
| image (PIL.Image): Input image | |
| text_box (tuple): (x1, y1, x2, y2) coordinates for text region | |
| blur_radius (int): Radius for Gaussian blur | |
| Returns: | |
| PIL.Image: Anti-aliased mask | |
| """ | |
| mask = create_text_mask(image, text_box) | |
| return mask.filter(ImageFilter.GaussianBlur(blur_radius)) | |
| -from PIL import Image, ImageDraw | |
| +from PIL import Image, ImageDraw, ImageFilter |
🧰 Tools
🪛 Ruff (0.8.2)
55-55: Undefined name ImageFilter
(F821)
| ```python | ||
| def match_text_style(image, text_region): | ||
| """Analyze existing text style in the image""" | ||
| # Add OCR or style analysis here | ||
| return "style_description" | ||
|
|
||
| style = match_text_style(image, text_region) | ||
| prompt = f"Text saying 'Hello World' in style: {style}" | ||
| ``` |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace placeholder function with actual implementation.
The match_text_style function is defined but only contains a placeholder comment. Consider implementing this function with a concrete example that uses the TextStyleAnalyzer from the related utils.style_utils module, as referenced in the relevant code snippets.
def match_text_style(image, text_region):
"""Analyze existing text style in the image"""
- # Add OCR or style analysis here
- return "style_description"
+ from stable_diffusion_text_inpaint.utils.style_utils import TextStyleAnalyzer, generate_style_prompt
+
+ # Initialize style analyzer
+ analyzer = TextStyleAnalyzer()
+
+ # Analyze the region
+ style_props = analyzer.analyze_text_region(image, text_region)
+
+ # Generate a descriptive prompt
+ return generate_style_prompt(style_props)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ```python | |
| def match_text_style(image, text_region): | |
| """Analyze existing text style in the image""" | |
| # Add OCR or style analysis here | |
| return "style_description" | |
| style = match_text_style(image, text_region) | |
| prompt = f"Text saying 'Hello World' in style: {style}" | |
| ``` | |
| def match_text_style(image, text_region): | |
| """Analyze existing text style in the image""" | |
| - # Add OCR or style analysis here | |
| - return "style_description" | |
| + from stable_diffusion_text_inpaint.utils.style_utils import TextStyleAnalyzer, generate_style_prompt | |
| + | |
| + # Initialize style analyzer | |
| + analyzer = TextStyleAnalyzer() | |
| + | |
| + # Analyze the region | |
| + style_props = analyzer.analyze_text_region(image, text_region) | |
| + | |
| + # Generate a descriptive prompt | |
| + return generate_style_prompt(style_props) | |
| style = match_text_style(image, text_region) | |
| prompt = f"Text saying 'Hello World' in style: {style}" |
| """ | ||
|
|
||
| from PIL import Image, ImageDraw | ||
| from text_inpainter import TextInpainter |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use absolute imports for better reliability.
The current import assumes that text_inpainter is in the current directory or Python path. For better reliability and to avoid potential import errors, use a fully-qualified import from the package.
-from text_inpainter import TextInpainter
+from stable_diffusion_text_inpaint.text_inpainter import TextInpainter📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from text_inpainter import TextInpainter | |
| from stable_diffusion_text_inpaint.text_inpainter import TextInpainter |
| # Generate prompt | ||
| if match_style: | ||
| style_props = self.style_analyzer.analyze_text_region(image, text_box) | ||
| style_prompt = generate_style_prompt(style_props) | ||
| prompt = f"{style_prompt}, text saying '{text}'" | ||
| else: | ||
| prompt = f"Clear text saying '{text}'" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling for style analysis.
The style matching logic could fail if the style analyzer encounters issues when analyzing the text region. Add try-except blocks to gracefully handle potential style analysis failures.
# Generate prompt
if match_style:
- style_props = self.style_analyzer.analyze_text_region(image, text_box)
- style_prompt = generate_style_prompt(style_props)
- prompt = f"{style_prompt}, text saying '{text}'"
+ try:
+ style_props = self.style_analyzer.analyze_text_region(image, text_box)
+ style_prompt = generate_style_prompt(style_props)
+ prompt = f"{style_prompt}, text saying '{text}'"
+ except Exception as e:
+ print(f"Warning: Style matching failed ({str(e)}), falling back to default prompt")
+ prompt = f"Clear text saying '{text}'"
else:
prompt = f"Clear text saying '{text}'"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Generate prompt | |
| if match_style: | |
| style_props = self.style_analyzer.analyze_text_region(image, text_box) | |
| style_prompt = generate_style_prompt(style_props) | |
| prompt = f"{style_prompt}, text saying '{text}'" | |
| else: | |
| prompt = f"Clear text saying '{text}'" | |
| # Generate prompt | |
| if match_style: | |
| try: | |
| style_props = self.style_analyzer.analyze_text_region(image, text_box) | |
| style_prompt = generate_style_prompt(style_props) | |
| prompt = f"{style_prompt}, text saying '{text}'" | |
| except Exception as e: | |
| print(f"Warning: Style matching failed ({str(e)}), falling back to default prompt") | |
| prompt = f"Clear text saying '{text}'" | |
| else: | |
| prompt = f"Clear text saying '{text}'" |
| from text_inpainter import TextInpainter | ||
| from utils.region_finder import interactive_region_select, detect_text_regions, visualize_region |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use absolute imports for better package compatibility.
The current imports assume a specific directory structure. For better reliability across different execution contexts, use fully-qualified imports from the package.
-from text_inpainter import TextInpainter
-from utils.region_finder import interactive_region_select, detect_text_regions, visualize_region
+from stable_diffusion_text_inpaint.text_inpainter import TextInpainter
+from stable_diffusion_text_inpaint.utils.region_finder import interactive_region_select, detect_text_regions, visualize_region📝 Committable suggestion
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| from text_inpainter import TextInpainter | |
| from utils.region_finder import interactive_region_select, detect_text_regions, visualize_region | |
| from stable_diffusion_text_inpaint.text_inpainter import TextInpainter | |
| from stable_diffusion_text_inpaint.utils.region_finder import interactive_region_select, detect_text_regions, visualize_region |
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