Repository Analysis

huggingface/diffusers

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

22.1 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of huggingface/diffusers, a Python project with 34,059 GitHub stars. SynthScan v2.0 examined 1,094,968 lines of code across 2715 source files, recording 14441 pattern matches distributed across 24 syntactic categories. The overall adjusted score of 22.1 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

22.1
Adjusted Score
22.1
Raw Score
100%
Time Factor
2026-07-14
Last Push
34.1K
Stars
Python
Language
1.1M
Lines of Code
2.7K
Files
14.4K
Pattern Hits
2026-07-14
Scan Date
0.94
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 7HIGH 2550MEDIUM 466LOW 11418

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 14441 distinct pattern matches across 24 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Cross-File Repetition2156 hits · 10780 pts
SeverityFileLineSnippetContext
HIGHCONTRIBUTING.md0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGHdocs/source/zh/conceptual/contribution.md0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGHdocs/source/ko/conceptual/contribution.md0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGHdocs/source/en/conceptual/contribution.md0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGH…/pipelines/deprecated/alt_diffusion/pipeline_output.py0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGHsrc/diffusers/pipelines/ledits_pp/pipeline_output.py0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGH…iffusers/pipelines/stable_diffusion/pipeline_output.py0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGH…eprecated/semantic_stable_diffusion/pipeline_output.py0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGH…ble_diffusion_ldm3d/pipeline_stable_diffusion_ldm3d.py0output class for stable diffusion pipelines. args: rgb (`list[pil.image.image]` or `np.ndarray`) list of denoised pil imSTRING
HIGHtests/testing_utils.py0helper function for reproducible behavior during distributed training. see - https://pytorch.org/docs/stable/notes/randoSTRING
HIGHsrc/diffusers/utils/testing_utils.py0helper function for reproducible behavior during distributed training. see - https://pytorch.org/docs/stable/notes/randoSTRING
HIGHsrc/diffusers/utils/torch_utils.py0helper function for reproducible behavior during distributed training. see - https://pytorch.org/docs/stable/notes/randoSTRING
HIGH…sts/models/transformers/test_models_transformer_wan.py0bitsandbytes quantization tests for wan animate transformer 3d.STRING
HIGH…odels/transformers/test_models_transformer_wan_vace.py0bitsandbytes quantization tests for wan animate transformer 3d.STRING
HIGH…ls/transformers/test_models_transformer_wan_animate.py0bitsandbytes quantization tests for wan animate transformer 3d.STRING
HIGH…sts/models/transformers/test_models_transformer_wan.py0override to provide inputs matching the tiny wan animate model dimensions.STRING
HIGH…odels/transformers/test_models_transformer_wan_vace.py0override to provide inputs matching the tiny wan animate model dimensions.STRING
HIGH…ls/transformers/test_models_transformer_wan_animate.py0override to provide inputs matching the tiny wan animate model dimensions.STRING
HIGHtests/quantization/gguf/test_gguf.py0a simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. also ensures if inference works.STRING
HIGHtests/quantization/bnb/test_mixed_int8.py0a simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. also ensures if inference works.STRING
HIGHtests/quantization/bnb/test_4bit.py0a simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. also ensures if inference works.STRING
HIGHtests/quantization/quanto/test_quanto.py0a simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. also ensures if inference works.STRING
HIGHtests/quantization/bnb/test_mixed_int8.py0a simple test to check if the model conversion has been done correctly by checking on the memory footprint of the converSTRING
HIGHtests/quantization/bnb/test_4bit.py0a simple test to check if the model conversion has been done correctly by checking on the memory footprint of the converSTRING
HIGHtests/quantization/torchao/test_torchao.py0a simple test to check if the model conversion has been done correctly by checking on the memory footprint of the converSTRING
HIGHdocs/source/pt/stable_diffusion.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/zh/optimization/speed-memory-optims.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/en/stable_diffusion.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/en/quicktour.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/en/optimization/attention_backends.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/en/optimization/speed-memory-optims.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/en/training/distributed_inference.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/en/using-diffusers/loading.md0cinematic film still of a cat sipping a margarita in a pool in palm springs, california highly detailed, high budget holSTRING
HIGHdocs/source/zh/optimization/speed-memory-optims.md0steamboat willie style, golden era animation, the camera rushes from far to near in a low-angle shot, revealing a white STRING
HIGHdocs/source/en/optimization/speed-memory-optims.md0steamboat willie style, golden era animation, the camera rushes from far to near in a low-angle shot, revealing a white STRING
HIGHdocs/source/en/using-diffusers/text-img2vid.md0steamboat willie style, golden era animation, the camera rushes from far to near in a low-angle shot, revealing a white STRING
HIGHdocs/source/en/api/pipelines/wan.md0steamboat willie style, golden era animation, the camera rushes from far to near in a low-angle shot, revealing a white STRING
HIGHdocs/source/zh/optimization/speed-memory-optims.md0bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, woSTRING
HIGHdocs/source/en/optimization/speed-memory-optims.md0bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, woSTRING
HIGHdocs/source/en/using-diffusers/text-img2vid.md0bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, woSTRING
HIGHdocs/source/en/api/pipelines/wan.md0bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, woSTRING
HIGHdocs/source/en/api/pipelines/helios.md0bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, woSTRING
HIGH…/unconditional_image_generation/train_unconditional.py0extract values from a 1-d numpy array for a batch of indices. :param arr: the 1-d numpy array. :param timesteps: a tensoSTRING
HIGH…/unconditional_image_generation/train_unconditional.py0extract values from a 1-d numpy array for a batch of indices. :param arr: the 1-d numpy array. :param timesteps: a tensoSTRING
HIGH…ects/consistency_training/train_cm_ct_unconditional.py0extract values from a 1-d numpy array for a batch of indices. :param arr: the 1-d numpy array. :param timesteps: a tensoSTRING
HIGHexamples/controlnet/train_controlnet_flux.py0# cogview4-control-{repo_id} these are control weights trained on {base_model} with new type of conditioning. {img_str} STRING
HIGHexamples/flux-control/train_control_lora_flux.py0# cogview4-control-{repo_id} these are control weights trained on {base_model} with new type of conditioning. {img_str} STRING
HIGHexamples/flux-control/train_control_flux.py0# cogview4-control-{repo_id} these are control weights trained on {base_model} with new type of conditioning. {img_str} STRING
HIGHexamples/cogview4-control/train_control_cogview4.py0# cogview4-control-{repo_id} these are control weights trained on {base_model} with new type of conditioning. {img_str} STRING
HIGHexamples/controlnet/train_controlnet_sdxl.py0# controlnet-{repo_id} these are controlnet weights trained on {base_model} with new type of conditioning. {img_str}STRING
HIGHexamples/controlnet/train_controlnet.py0# controlnet-{repo_id} these are controlnet weights trained on {base_model} with new type of conditioning. {img_str}STRING
HIGH…rch_projects/controlnet/train_controlnet_webdataset.py0# controlnet-{repo_id} these are controlnet weights trained on {base_model} with new type of conditioning. {img_str}STRING
HIGH…research_projects/pixart/train_pixart_controlnet_hf.py0# controlnet-{repo_id} these are controlnet weights trained on {base_model} with new type of conditioning. {img_str}STRING
HIGH…andinsky2_2/text_to_image/train_text_to_image_prior.py0more information on all the cli arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).STRING
HIGH…dinsky2_2/text_to_image/train_text_to_image_decoder.py0more information on all the cli arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).STRING
HIGH…/wuerstchen/text_to_image/train_text_to_image_prior.py0more information on all the cli arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).STRING
HIGH…stchen/text_to_image/train_text_to_image_lora_prior.py0more information on all the cli arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).STRING
HIGH…ber_loss_training/text_to_image/train_text_to_image.py0more information on all the cli arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).STRING
HIGHexamples/text_to_image/train_text_to_image.py0more information on all the cli arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).STRING
HIGH…andinsky2_2/text_to_image/train_text_to_image_prior.py0returns either a context list that includes one that will disable zero.init or an empty context listSTRING
2096 more matches not shown…
Hyper-Verbose Identifiers4346 hits · 3914 pts
SeverityFileLineSnippetContext
LOWtests/testing_utils.py1566def backend_reset_peak_memory_stats(device: str):CODE
LOWtests/testing_utils.py1570def backend_reset_max_memory_allocated(device: str):CODE
LOWtests/testing_utils.py1574def backend_max_memory_allocated(device: str):CODE
LOWtests/testing_utils.py1580def backend_supports_training(device: str):CODE
LOWtests/testing_utils.py203def numpy_cosine_similarity_distance(a, b):CODE
LOWtests/testing_utils.py513def require_torch_version_greater_equal(torch_version):CODE
LOWtests/testing_utils.py526def require_torch_version_greater(torch_version):CODE
LOWtests/testing_utils.py543def require_torch_cuda_compatibility(expected_compute_capability):CODE
LOWtests/testing_utils.py557def require_torch_accelerator(test_case):CODE
LOWtests/testing_utils.py584def require_torch_multi_accelerator(test_case):CODE
LOWtests/testing_utils.py614def require_big_gpu_with_torch_cuda(test_case):CODE
LOWtests/testing_utils.py663def require_torch_accelerator_with_training(test_case):CODE
LOWtests/testing_utils.py763def require_peft_version_greater(peft_version):CODE
LOWtests/testing_utils.py780def require_transformers_version_greater(transformers_version):CODE
LOWtests/testing_utils.py798def require_accelerate_version_greater(accelerate_version):CODE
LOWtests/testing_utils.py811def require_bitsandbytes_version_greater(bnb_version):CODE
LOWtests/testing_utils.py823def require_hf_hub_version_greater(hf_hub_version):CODE
LOWtests/testing_utils.py836def require_gguf_version_greater_or_equal(gguf_version):CODE
LOWtests/testing_utils.py848def require_torchao_version_greater_or_equal(torchao_version):CODE
LOWtests/testing_utils.py860def require_auto_round_version_greater_or_equal(auto_round_version):CODE
LOWtests/testing_utils.py873def require_kernels_version_greater_or_equal(kernels_version):CODE
LOWtests/testing_utils.py885def require_modelopt_version_greater_or_equal(modelopt_version):CODE
LOWtests/testing_utils.py895def deprecate_after_peft_backend(test_case):CODE
LOWtests/testing_utils.py1127def pytest_terminal_summary_main(tr, id):CODE
LOWtests/testing_utils.py1538def _device_agnostic_dispatch(device: str, dispatch_table: dict[str, Callable], *args, **kwargs):CODE
LOWtests/testing_utils.py1685 def _get_expected_safetensors_files(CODE
LOWtests/testing_utils.py1768 def _check_safetensors_serialization(CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py63 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py85 def test_single_file_components(self):CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py98 def test_single_file_components_local_files_only(self):CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py112 def test_single_file_components_with_original_config(self):CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py121 def test_single_file_components_with_original_config_local_files_only(self):CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py142 def test_single_file_components_with_diffusers_config(self):CODE
LOW…e_file/test_stable_diffusion_controlnet_single_file.py151 def test_single_file_components_with_diffusers_config_local_files_only(self):CODE
LOW…ts/single_file/test_stable_diffusion_xl_single_file.py52 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOWtests/single_file/test_model_vae_single_file.py52 def test_single_file_inference_same_as_pretrained(self):CODE
LOWtests/single_file/test_model_vae_single_file.py71 def test_single_file_arguments(self):CODE
LOW…ngle_file/test_stable_diffusion_upscale_single_file.py41 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…ngle_file/test_stable_diffusion_upscale_single_file.py75 def test_single_file_components_with_original_config(self):CODE
LOW…ngle_file/test_stable_diffusion_upscale_single_file.py83 def test_single_file_components_with_original_config_local_files_only(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py63 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py88 def test_single_file_components(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py102 def test_single_file_components_local_files_only(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py123 def test_single_file_components_with_original_config(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py141 def test_single_file_components_with_original_config_local_files_only(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py164 def test_single_file_components_with_diffusers_config(self):CODE
LOW…ile/test_stable_diffusion_xl_controlnet_single_file.py175 def test_single_file_components_with_diffusers_config_local_files_only(self):CODE
LOW…ngle_file/test_stable_diffusion_inpaint_single_file.py61 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…ngle_file/test_stable_diffusion_inpaint_single_file.py72 def test_single_file_components_with_original_config(self):CODE
LOW…ngle_file/test_stable_diffusion_inpaint_single_file.py76 def test_single_file_components_with_original_config_local_files_only(self):CODE
LOW…ngle_file/test_stable_diffusion_inpaint_single_file.py119 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…e_file/test_stable_diffusion_xl_img2img_single_file.py60 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…e_file/test_stable_diffusion_xl_img2img_single_file.py76 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…s/single_file/test_model_autoencoder_dc_single_file.py50 def test_single_file_inference_same_as_pretrained(self):CODE
LOW…s/single_file/test_model_autoencoder_dc_single_file.py67 def test_single_file_in_type_variant_components(self):CODE
LOW…s/single_file/test_model_autoencoder_dc_single_file.py86 def test_single_file_mix_type_variant_components(self):CODE
LOW…est_stable_diffusion_controlnet_inpaint_single_file.py70 def test_single_file_format_inference_is_same_as_pretrained(self):CODE
LOW…est_stable_diffusion_controlnet_inpaint_single_file.py89 def test_single_file_components(self):CODE
LOW…est_stable_diffusion_controlnet_inpaint_single_file.py102 def test_single_file_components_local_files_only(self):CODE
LOW…est_stable_diffusion_controlnet_inpaint_single_file.py117 def test_single_file_components_with_original_config(self):CODE
4286 more matches not shown…
Docstring Block Structure380 hits · 1900 pts
SeverityFileLineSnippetContext
HIGHutils/check_forward_call_docstrings.py15 Check that arguments of ``forward()`` (for models) and ``__call__()`` (for pipelines) match the method's docstring exacSTRING
HIGHexamples/research_projects/anytext/anytext.py1970 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…_projects/promptdiffusion/pipeline_prompt_diffusion.py938 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…ch_projects/pixart/pipeline_pixart_alpha_controlnet.py820 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_zero1to3.py624 Function invoked when calling the pipeline for generation. Args: input_imgs (`PIL` or `LisSTRING
HIGHexamples/community/mod_controlnet_tile_sr_sdxl.py1084 Calculates the number of tiles needed to cover an image, choosing the appropriate formula based on the STRING
HIGHexamples/community/mod_controlnet_tile_sr_sdxl.py1292 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…mmunity/stable_diffusion_controlnet_inpaint_img2img.py755 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…les/community/pipeline_flux_kontext_multiple_images.py851 Function invoked when calling the pipeline for generation. Args: image (`torch.Tensor`, `PSTRING
HIGH…munity/pipeline_stable_diffusion_3_instruct_pix2pix.py896 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…/community/stable_diffusion_xl_controlnet_reference.py375 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/mixture_tiling.py184 Function to run the diffusion pipeline with tiling support. Args: prompt: either a single STRING
HIGHexamples/community/pipeline_stg_hunyuan_video.py586 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/pipeline_flux_with_cfg.py632 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_stable_diffusion_xl_ipex.py759 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/stable_diffusion_repaint.py43 Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will beSTRING
HIGHexamples/community/stable_diffusion_repaint.py633 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[strSTRING
HIGH…/community/pipeline_hunyuandit_differential_img2img.py770 The call function to the pipeline for generation with HunyuanDiT. Args: prompt (`str` or `STRING
HIGHexamples/community/pipeline_stable_diffusion_boxdiff.py1353 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/adaptive_mask_inpainting.py206 Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will beSTRING
HIGHexamples/community/adaptive_mask_inpainting.py884 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/fresco_v2v.py2082 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…line_stable_diffusion_xl_controlnet_adapter_inpaint.py175 Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will beSTRING
HIGH…line_stable_diffusion_xl_controlnet_adapter_inpaint.py1240 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…ty/pipeline_stable_diffusion_3_differential_img2img.py699 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_demofusion_sdxl.py662 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/run_tensorrt_controlnet.py651 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_stg_ltx.py589 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_animatediff_ipex.py598 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…es/community/pipeline_faithdiff_stable_diffusion_xl.py1838 Function invoked when calling the pipeline for generation. Args: lr_img (PipelineImageInpuSTRING
HIGHexamples/community/pipeline_stable_diffusion_pag.py1126 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optiSTRING
HIGHexamples/community/stable_diffusion_reference.py286 Check the validity of the input arguments for the diffusion model. Args: prompt (Optional[STRING
HIGHexamples/community/stable_diffusion_reference.py849 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…unity/pipeline_stable_diffusion_xl_attentive_eraser.py326 Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will beSTRING
HIGH…unity/pipeline_stable_diffusion_xl_attentive_eraser.py1660 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…les/community/stable_diffusion_controlnet_reference.py135 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/stable_diffusion_xl_reference.py317 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…les/community/pipeline_controlnet_xl_kolors_img2img.py995 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_controlnet_xl_kolors.py821 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…y/pipeline_stable_diffusion_xl_differential_img2img.py978 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/matryoshka.py4278 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…les/community/pipeline_z_image_differential_img2img.py477 Function invoked when calling the pipeline for image-to-image generation. Args: prompt (`sSTRING
HIGHexamples/community/pipeline_stg_mochi.py592 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…s/community/pipeline_stable_diffusion_upscale_ldm3d.py533 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/rerender_a_video.py627 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…es/community/pipeline_stable_diffusion_xl_instantid.py586 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/lpw_stable_diffusion_xl.py1437 Function invoked when calling the pipeline for generation. Args: prompt (`str`): STRING
HIGHexamples/community/pipeline_kolors_inpainting.py123 Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will beSTRING
HIGHexamples/community/pipeline_kolors_inpainting.py1121 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/stable_diffusion_ipex.py583 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/mixture_tiling_sdxl.py803 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/gluegen.py607 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/instaflow_one_step.py522 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…mples/community/stable_diffusion_controlnet_inpaint.py767 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…ples/community/pipeline_kolors_differential_img2img.py819 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGHexamples/community/pipeline_fabric.py521 The call function to the pipeline for generation. Generate a trajectory of images with binary feedback. The STRING
HIGHexamples/community/pipeline_animatediff_controlnet.py777 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGHexamples/community/pipeline_flux_semantic_guidance.py825 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
HIGH…nity/pipeline_stable_diffusion_xl_instandid_img2img.py590 The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optSTRING
HIGH…ples/community/pipline_flux_fill_controlnet_Inpaint.py865 Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[stSTRING
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Unused Imports2424 hits · 1690 pts
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LOWtests/models/testing_utils/__init__.py19CODE
LOWtests/models/testing_utils/__init__.py20CODE
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LOWtests/models/testing_utils/__init__.py21CODE
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LOWtests/models/testing_utils/__init__.py21CODE
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LOWtests/models/testing_utils/__init__.py21CODE
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LOWtests/models/testing_utils/__init__.py21CODE
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LOWexamples/community/pipeline_prompt2prompt.py15CODE
LOWscripts/convert_sana_to_diffusers.py2CODE
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Over-Commented Block1394 hits · 1387 pts
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LOW…/single_file/test_model_wan_autoencoder_single_file.py1# coding=utf-8COMMENT
LOWtests/single_file/test_lumina2_transformer.py1# coding=utf-8COMMENT
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LOW…s/single_file/test_model_motion_adapter_single_file.py1# coding=utf-8COMMENT
LOW…/modular_pipelines/test_conditional_pipeline_blocks.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
LOW…dular_pipelines/helios/test_modular_pipeline_helios.py1# coding=utf-8COMMENT
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LOW…ular_pipelines/cosmos/test_modular_pipeline_cosmos3.py1# coding=utf-8COMMENT
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LOWtests/others/test_utils.py1# coding=utf-8COMMENT
LOWtests/others/test_ema.py1# coding=utf-8COMMENT
LOWtests/others/test_hub_utils.py1# coding=utf-8COMMENT
LOWtests/others/test_dependencies.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
LOWtests/others/test_check_copies.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
LOWtests/others/test_check_dummies.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
LOWtests/others/test_flashpack.py1# coding=utf-8COMMENT
LOWtests/others/test_training.py1# coding=utf-8COMMENT
LOWtests/others/test_image_processor.py1# coding=utf-8COMMENT
LOWtests/others/test_config.py1# coding=utf-8COMMENT
LOWtests/others/test_video_processor.py1# coding=utf-8COMMENT
LOWtests/pipelines/test_pipelines.py1# coding=utf-8COMMENT
LOWtests/pipelines/test_pipelines_combined.py1# coding=utf-8COMMENT
LOWtests/pipelines/test_pipelines_auto.py1# coding=utf-8COMMENT
LOW…ble_diffusion_adapter/test_stable_diffusion_adapter.py1# coding=utf-8COMMENT
LOWtests/pipelines/qwenimage/test_qwenimage_controlnet.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/qwenimage/test_qwenimage_edit.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/qwenimage/test_qwenimage_inpaint.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/qwenimage/test_qwenimage_edit_plus.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/qwenimage/test_qwenimage.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/mochi/test_mochi.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/helios/test_helios.py1# Copyright 2025 The HuggingFace Team.COMMENT
LOWtests/pipelines/kolors/test_kolors.py1# coding=utf-8COMMENT
LOWtests/pipelines/kolors/test_kolors_img2img.py1# coding=utf-8COMMENT
LOWtests/pipelines/kandinsky/test_kandinsky_img2img.py1# coding=utf-8COMMENT
LOWtests/pipelines/kandinsky/test_kandinsky.py1# coding=utf-8COMMENT
LOWtests/pipelines/kandinsky/test_kandinsky_combined.py1# coding=utf-8COMMENT
LOWtests/pipelines/kandinsky/test_kandinsky_prior.py1# coding=utf-8COMMENT
LOWtests/pipelines/kandinsky/test_kandinsky_inpaint.py1# coding=utf-8COMMENT
LOWtests/pipelines/motif_video/test_motif_video.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
LOW…/pipelines/motif_video/test_motif_video_image2video.py1# Copyright 2026 The HuggingFace Team. All rights reserved.COMMENT
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Deep Nesting1424 hits · 1386 pts
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LOWtests/pipelines/test_pipelines_common.py1904CODE
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AI Structural Patterns1281 hits · 1248 pts
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LOWexamples/community/mod_controlnet_tile_sr_sdxl.py395CODE
LOWexamples/community/mod_controlnet_tile_sr_sdxl.py1253CODE
LOWexamples/community/pipeline_null_text_inversion.py194CODE
LOW…mmunity/stable_diffusion_controlnet_inpaint_img2img.py728CODE
LOWexamples/community/stable_diffusion_comparison.py88CODE
LOWexamples/community/stable_diffusion_comparison.py125CODE
LOWexamples/community/stable_diffusion_comparison.py162CODE
LOWexamples/community/stable_diffusion_comparison.py199CODE
LOWexamples/community/stable_diffusion_comparison.py236CODE
LOWexamples/community/sd_text2img_k_diffusion.py278CODE
LOWexamples/community/multilingual_stable_diffusion.py139CODE
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LOW…les/community/pipeline_flux_kontext_multiple_images.py817CODE
LOWexamples/community/unclip_image_interpolation.py38CODE
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Decorative Section Separators284 hits · 816 pts
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MEDIUMtests/pipelines/marigold/test_marigold_intrinsics.py18# --------------------------------------------------------------------------COMMENT
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MEDIUM…sts/models/autoencoders/test_models_autoencoder_rae.py47# ---------------------------------------------------------------------------COMMENT
MEDIUM…sts/models/autoencoders/test_models_autoencoder_rae.py86# ---------------------------------------------------------------------------COMMENT
MEDIUM…sts/models/autoencoders/test_models_autoencoder_rae.py88# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/models/testing_utils/attention.py34# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/models/testing_utils/attention.py36# ---------------------------------------------------------------------------COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md144# ── Experiment tracking (optional) ──────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md152# ── Model ───────────────────────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md159# ── Training schedule ───────────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md169# ── Data ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md186# ── Optimizer ───────────────────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md195# ── Learning rate scheduler ─────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md202# ── Flow matching ───────────────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md219# ── FSDP2 distributed training ──────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/training/nemo_automodel.md230# ── Checkpointing ──────────────────────────────────────────────────────────COMMENT
MEDIUMdocs/source/en/api/pipelines/control_flux_inpaint.md47# ---------------------------------------------------------------COMMENT
MEDIUMdocs/source/en/api/pipelines/control_flux_inpaint.md57# ---------------------------------------------------------------COMMENT
MEDIUM…cts/onnxruntime/textual_inversion/textual_inversion.py79# ------------------------------------------------------------------------------COMMENT
MEDIUM…/intel_opts/textual_inversion_dfq/textual_inversion.py46# ------------------------------------------------------------------------------COMMENT
MEDIUM…intel_opts/textual_inversion/textual_inversion_bf16.py49# ------------------------------------------------------------------------------COMMENT
MEDIUM…cts/multi_token_textual_inversion/textual_inversion.py74# ------------------------------------------------------------------------------COMMENT
MEDIUM…ulti_token_textual_inversion/textual_inversion_flax.py56# ------------------------------------------------------------------------------COMMENT
MEDIUMexamples/textual_inversion/textual_inversion.py81# ------------------------------------------------------------------------------COMMENT
MEDIUMexamples/textual_inversion/textual_inversion_sdxl.py76# ------------------------------------------------------------------------------COMMENT
MEDIUMexamples/textual_inversion/textual_inversion_flax.py56# ------------------------------------------------------------------------------COMMENT
MEDIUMexamples/community/lpw_stable_diffusion.py31# ------------------------------------------------------------------------------COMMENT
MEDIUMexamples/community/lpw_stable_diffusion_onnx.py54# ------------------------------------------------------------------------------COMMENT
MEDIUM…line_stable_diffusion_xl_controlnet_adapter_inpaint.py1642 # ----------------------------------------------------------------STRING
MEDIUMexamples/community/imagic_stable_diffusion.py46# ------------------------------------------------------------------------------COMMENT
MEDIUMexamples/community/README.md499 # ------------------------------------------------COMMENT
MEDIUMexamples/community/README.md504 # -------------------------------------------------COMMENT
MEDIUMexamples/community/lpw_stable_diffusion_xl.py1## ----------------------------------------------------------COMMENT
MEDIUMexamples/community/lpw_stable_diffusion_xl.py7## -----------------------------------------------------------COMMENT
MEDIUMexamples/community/lpw_stable_diffusion_xl.py467# ----------------------------------------------------------------------------------------------------------------------STRING
MEDIUMexamples/community/lpw_stable_diffusion_xl.py469# ----------------------------------------------------------------------------------------------------------------------STRING
MEDIUMexamples/community/marigold_depth_estimation.py14# --------------------------------------------------------------------------COMMENT
MEDIUMexamples/community/marigold_depth_estimation.py18# --------------------------------------------------------------------------COMMENT
MEDIUMexamples/community/pipeline_stable_diffusion_xl_t5.py144 # ------------------------------------------------------------------COMMENT
MEDIUMexamples/community/pipeline_stable_diffusion_xl_t5.py147 # ------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_prx_to_diffusers.py99# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_prx_to_diffusers.py101# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_prx_to_diffusers.py151# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_prx_to_diffusers.py153# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_prx_to_diffusers.py209# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_prx_to_diffusers.py211# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py129 # =========================================================================COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py131 # =========================================================================COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py196 # =========================================================================COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py198 # =========================================================================COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py304 # =========================================================================COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py323 # =========================================================================COMMENT
MEDIUMscripts/convert_ace_step_to_diffusers.py329 # =========================================================================COMMENT
MEDIUMsrc/diffusers/modular_pipelines/mellon_node_utils.py298 # =========================================================================COMMENT
MEDIUMsrc/diffusers/modular_pipelines/mellon_node_utils.py300 # =========================================================================COMMENT
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Self-Referential Comments129 hits · 298 pts
SeverityFileLineSnippetContext
MEDIUMtests/others/test_check_dummies.py96 expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.STRING
MEDIUMtests/pipelines/ace_step/test_ace_step.py160 # Create a tiny Qwen3Model for testing (matching the real Qwen3-Embedding-0.6B architecture)COMMENT
MEDIUMtests/pipelines/sana_video/test_sana_video_i2v.py131 # Create a dummy image input (PIL Image)COMMENT
MEDIUM…ines/visualcloze/test_pipeline_visualcloze_combined.py127 # Create an image list that conforms to the VisualCloze input formatCOMMENT
MEDIUM…es/visualcloze/test_pipeline_visualcloze_generation.py129 # Create an image list that conforms to the VisualCloze input formatCOMMENT
MEDIUMtests/models/testing_utils/attention.py243 # Create a dict of new processorsCOMMENT
MEDIUMtests/models/testing_utils/attention.py265 # Create a dict with wrong number of processorsCOMMENT
MEDIUMutils/consolidated_test_report.py503 # Create a list of failing tests for each moduleCOMMENT
MEDIUMutils/consolidated_test_report.py581 # Create a more compact summary sectionCOMMENT
MEDIUMutils/consolidated_test_report.py587 # Create the test suites table in markdown formatCOMMENT
MEDIUMutils/consolidated_test_report.py621 # Create the Slack payload with character limit enforcementCOMMENT
MEDIUMutils/check_support_list.py68 # Define the checks we need to performCOMMENT
MEDIUMutils/tests_fetcher.py938 # Create the map that will give us all impacted modules.COMMENT
MEDIUMutils/tests_fetcher.py980 # Create a map that maps test categories to test files, i.e. `models/bert` -> [...test_modeling_bert.py, ...]COMMENT
MEDIUMutils/check_dummies.py124 dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"CODE
MEDIUMexamples/instruct_pix2pix/train_instruct_pix2pix.py1008 # Create the pipeline using the trained modules and save it.COMMENT
MEDIUM…amples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py1219 # Create the pipeline using the trained modules and save it.COMMENT
MEDIUMexamples/controlnet/train_controlnet_flux.py1399 # Create the pipeline using using the trained modules and save it.STRING
MEDIUMexamples/controlnet/train_controlnet_sdxl.py1343 # Create the pipeline using using the trained modules and save it.STRING
MEDIUMexamples/controlnet/train_controlnet.py1156 # Create the pipeline using the trained modules and save it.STRING
MEDIUMexamples/controlnet/train_controlnet_sd3.py1434 # Create the pipeline using using the trained modules and save it.STRING
MEDIUMexamples/flux-control/train_control_lora_flux.py1111 # Create a pipeline for text encoding. We will move this pipeline to GPU/CPU as needed.STRING
MEDIUMexamples/flux-control/train_control_lora_flux.py1352 # Create the pipeline using using the trained modules and save it.STRING
MEDIUMexamples/flux-control/train_control_flux.py966 # Create a pipeline for text encoding. We will move this pipeline to GPU/CPU as needed.STRING
MEDIUMexamples/flux-control/train_control_flux.py1206 # Create the pipeline using using the trained modules and save it.STRING
MEDIUM…andinsky2_2/text_to_image/train_text_to_image_prior.py913 # Create the pipeline using the trained modules and save it.STRING
MEDIUM…dinsky2_2/text_to_image/train_text_to_image_decoder.py882 # Create the pipeline using the trained modules and save it.STRING
MEDIUMexamples/t2i_adapter/train_t2i_adapter_sdxl.py1301 # Create the pipeline using using the trained modules and save it.STRING
MEDIUMexamples/research_projects/gligen/train_gligen_text.py674 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…rch_projects/controlnet/train_controlnet_webdataset.py1445 # Create the pipeline using using the trained modules and save it.STRING
MEDIUM…rojects/dreambooth_inpaint/train_dreambooth_inpaint.py791 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…cts/onnxruntime/textual_inversion/textual_inversion.py920 # Create the pipeline using the trained modules and save it.STRING
MEDIUM…jects/onnxruntime/text_to_image/train_text_to_image.py927 # Create the pipeline using the trained modules and save it.COMMENT
MEDIUM…ts/instructpix2pix_lora/train_instruct_pix2pix_lora.py1116 # Create the pipeline using the trained modules and save it.STRING
MEDIUM…/wuerstchen/text_to_image/train_text_to_image_prior.py886 # Create the pipeline using the trained modules and save it.STRING
MEDIUM…stchen/text_to_image/train_text_to_image_lora_prior.py892 # Create the pipeline using the trained modules and save it.STRING
MEDIUM…research_projects/autoencoderkl/train_autoencoderkl.py1023 # Create the pipeline using using the trained modules and save it.STRING
MEDIUM…ber_loss_training/text_to_image/train_text_to_image.py1111 # Create the pipeline using the trained modules and save it.STRING
MEDIUM…led_huber_loss_training/dreambooth/train_dreambooth.py1463 # Create the pipeline using the trained modules and save it.COMMENT
MEDIUM…i_subject_dreambooth/train_multi_subject_dreambooth.py1171 # Create the pipeline using the trained modules and save it.COMMENT
MEDIUM…/intel_opts/textual_inversion_dfq/textual_inversion.py935 # Create the pipeline using using the trained modules and save it.STRING
MEDIUM…intel_opts/textual_inversion/textual_inversion_bf16.py612 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…cts/multi_token_textual_inversion/textual_inversion.py905 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…ulti_token_textual_inversion/textual_inversion_flax.py609 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…research_projects/sd3_lora_colab/compute_embeddings.py87 # Create a DataFrameCOMMENT
MEDIUM…_projects/flux_lora_quantization/compute_embeddings.py82 # Create a DataFrameCOMMENT
MEDIUMexamples/cogview4-control/train_control_cogview4.py976 # Create a pipeline for text encoding. We will move this pipeline to GPU/CPU as needed.STRING
MEDIUMexamples/cogview4-control/train_control_cogview4.py1199 # Create the pipeline using using the trained modules and save it.STRING
MEDIUM…s/consistency_distillation/train_lcm_distill_sd_wds.py1412 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…onsistency_distillation/train_lcm_distill_lora_sdxl.py1441 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…sistency_distillation/train_lcm_distill_lora_sd_wds.py1447 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…stency_distillation/train_lcm_distill_lora_sdxl_wds.py1510 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUM…consistency_distillation/train_lcm_distill_sdxl_wds.py1515 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUMexamples/model_search/pipeline_easy.py785 # Initialize additional variables with default valuesSTRING
MEDIUMexamples/textual_inversion/textual_inversion.py977 # Create the pipeline using the trained modules and save it.STRING
MEDIUMexamples/textual_inversion/textual_inversion_sdxl.py1066 # Create the pipeline using the trained modules and save it.STRING
MEDIUMexamples/textual_inversion/textual_inversion_flax.py636 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUMexamples/text_to_image/train_text_to_image.py1118 # Create the pipeline using the trained modules and save it.STRING
MEDIUMexamples/text_to_image/train_text_to_image_flax.py582 # Create the pipeline using using the trained modules and save it.COMMENT
MEDIUMexamples/dreambooth/train_dreambooth_flax.py630 # Create the pipeline using the trained modules and save it.COMMENT
69 more matches not shown…
Excessive Try-Catch Wrapping164 hits · 173 pts
SeverityFileLineSnippetContext
MEDIUMtests/testing_utils.py296def parse_flag_from_env(key, default=False):CODE
LOWtests/testing_utils.py1287 except Exception as err:CODE
LOWtests/testing_utils.py1339 except Exception as e:CODE
LOWtests/testing_utils.py1430 except Exception as e:CODE
LOWtests/testing_utils.py1452 except Exception as e:CODE
LOW…sts/modular_pipelines/test_modular_pipelines_common.py40 except Exception:CODE
LOWtests/others/test_dependencies.py81 except Exception:CODE
LOWtests/pipelines/test_pipelines.py135 except Exception:CODE
LOWtests/pipelines/diffusion_gemma/test_diffusion_gemma.py70 except Exception as e: # noqa: BLE001 - offline / hub errors should skip, not failCODE
MEDIUMtests/pipelines/diffusion_gemma/test_diffusion_gemma.py62def _load_pipeline(test):CODE
LOWtests/pipelines/prx/test_pipeline_prx.py32 except Exception:CODE
LOWtests/pipelines/prx/test_pipeline_prx.py130 except Exception:CODE
LOWtests/pipelines/prx/test_pipeline_prx.py149 except Exception:CODE
LOWtests/models/testing_utils/attention.py373 except Exception as e:CODE
LOWtests/models/testing_utils/attention.py443 except Exception as e:CODE
LOWtests/models/testing_utils/parallelism.py118 except Exception as e:CODE
LOWtests/models/testing_utils/parallelism.py176 except Exception as e:CODE
LOWtests/models/testing_utils/parallelism.py235 except Exception as e:CODE
LOWutils/label_issues.py118 except Exception:CODE
MEDIUMutils/label_issues.py86def main():CODE
LOWutils/remind_link_issue.py106 except Exception as e:CODE
LOWutils/remind_link_issue.py162 except Exception as e:CODE
LOWutils/remind_link_issue.py165 except Exception as e:CODE
MEDIUMutils/consolidated_test_report.py88 print(f"Error parsing {file_path}: {e}")CODE
MEDIUMutils/consolidated_test_report.py131 print(f"Error parsing durations file {file_path.replace('_stats.txt', '_durations.txt')}: {e}")CODE
MEDIUMutils/consolidated_test_report.py235 print(f"Error parsing failures in {file_path}: {e}")CODE
MEDIUMutils/consolidated_test_report.py301 print(f"Error parsing summary file: {e}")CODE
MEDIUMutils/consolidated_test_report.py727 print(f"Error: Reports directory '{args.reports_dir}' does not exist.")CODE
MEDIUMutils/consolidated_test_report.py784 print(f"Error sending report to Slack: {e}")CODE
LOWutils/consolidated_test_report.py87 except Exception as e:CODE
LOWutils/consolidated_test_report.py130 except Exception as e:CODE
LOWutils/consolidated_test_report.py170 except Exception:CODE
LOWutils/consolidated_test_report.py234 except Exception as e:CODE
LOWutils/consolidated_test_report.py300 except Exception as e:CODE
LOWutils/consolidated_test_report.py783 except Exception as e:CODE
LOWutils/tests_fetcher.py1115 except Exception as e:CODE
LOWutils/modular_auto_docstring.py92 except Exception as e:CODE
LOWutils/modular_auto_docstring.py110 except Exception as e:CODE
LOWutils/modular_auto_docstring.py230 except Exception as e:CODE
MEDIUMutils/generate_model_tests.py560 print(f"Error: File not found: {args.model_filepath}", file=sys.stderr)CODE
MEDIUMutils/generate_model_tests.py566 print(f"Error: No model classes found in {args.model_filepath}", file=sys.stderr)CODE
MEDIUMutils/generate_model_tests.py573 print(f"Error: Class '{args.class_name}' not found. Available: {available}", file=sys.stderr)CODE
LOWutils/fetch_torch_cuda_pipeline_test_matrix.py69 except Exception as e:CODE
MEDIUMutils/fetch_torch_cuda_pipeline_test_matrix.py66def fetch_pipeline_modules_to_test():CODE
LOWdocs/source/en/optimization/attention_backends.md112except Exception as e:STRING
LOWdocs/source/en/using-diffusers/create_a_server.md48 except Exception as e:CODE
LOW…rch_projects/controlnet/train_controlnet_webdataset.py189 except Exception:CODE
MEDIUM…rch_projects/controlnet/train_controlnet_webdataset.py178def __call__(self, x):CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py867 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1007 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1636 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1722 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1762 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1770 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1792 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1837 except Exception:CODE
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1850 except Exception:CODE
LOW…_xla/training/text_to_image/train_text_to_image_xla.py150 except Exception as e:STRING
LOWexamples/research_projects/rdm/retriever.py88 except Exception as e:CODE
LOWexamples/research_projects/intel_opts/inference_bf16.py38except Exception:CODE
104 more matches not shown…
Redundant / Tautological Comments91 hits · 96 pts
SeverityFileLineSnippetContext
LOW…sts/modular_pipelines/test_modular_pipelines_common.py290 # Check if cosine similarity is NaN (which can happen if vectors are zero or very small)COMMENT
LOWtests/pipelines/test_pipelines_common.py2802 # Check if warning is raise when no attention_weight_callback is providedCOMMENT
LOWtests/pipelines/test_pipelines_common.py2809 # Check if error raised when unsupported tensor format usedCOMMENT
LOWtests/pipelines/test_pipelines_common.py2885 # Check if faster_cache denoiser hook is attachedCOMMENT
LOWtests/pipelines/test_pipelines_common.py2893 # Check if all blocks have faster_cache block hook attachedCOMMENT
LOWtests/models/unets/test_models_unet_2d_condition.py559 # Check if input and output shapes are the sameCOMMENT
LOWtests/models/testing_utils/attention.py211 # Check if model has attention processorsCOMMENT
LOWtests/models/testing_utils/quantization.py304 # Check if this module is in the exclusion listCOMMENT
LOWtests/models/testing_utils/quantization.py318 # Check if this module is NOT in the exclusion listCOMMENT
LOWtests/models/testing_utils/common.py599 # Check if the right number of shards existsCOMMENT
LOWtests/models/testing_utils/common.py635 # Check if the right number of shards existsCOMMENT
LOWtests/models/testing_utils/common.py676 # Check if the right number of shards existsCOMMENT
LOWtests/quantization/bnb/test_mixed_int8.py552 # Check if inference works.STRING
LOWtests/quantization/bnb/test_4bit.py577 # Check if inference works.STRING
LOWutils/consolidated_test_report.py145 # Check if it's a short stack formatCOMMENT
LOWutils/consolidated_test_report.py733 # Check if we found any test resultsCOMMENT
LOWutils/check_inits.py100 # Check if the backend declaration is inside a try block:COMMENT
LOWutils/check_inits.py159 # Check if the backend declaration is inside a try block:COMMENT
LOWutils/custom_init_isort.py257 # Check if the block contains some `_import_structure`s thingy to sort.COMMENT
LOW…amples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py813 # Set UNet to trainable.COMMENT
LOWexamples/controlnet/train_controlnet_flux.py1339 # Check if the gradient of each model parameter contains NaNSTRING
LOW…andinsky2_2/text_to_image/train_text_to_image_prior.py533 # Set prior to trainable.STRING
LOW…dinsky2_2/text_to_image/train_text_to_image_decoder.py528 # Set unet to trainable.STRING
LOW…cts/onnxruntime/textual_inversion/textual_inversion.py658 # Check if initializer_token is a single token or a sequence of tokensSTRING
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1463 # Check if we have a SDXL or SD model and initialize default pipelineCOMMENT
LOW…es/research_projects/ip_adapter/tutorial_train_plus.py135 # Check if 'latents' exists in both the saved state_dict and the current model's state_dictCOMMENT
LOW…es/research_projects/ip_adapter/tutorial_train_plus.py138 # Check if the shapes are mismatchedCOMMENT
LOW…es/research_projects/ip_adapter/tutorial_train_plus.py153 # Verify if the weights have changedCOMMENT
LOW…earch_projects/ip_adapter/tutorial_train_ip-adapter.py143 # Verify if the weights have changedCOMMENT
LOW…/research_projects/ip_adapter/tutorial_train_faceid.py141 # Verify if the weights have changedCOMMENT
LOW…es/research_projects/ip_adapter/tutorial_train_sdxl.py203 # Verify if the weights have changedCOMMENT
LOW…/intel_opts/textual_inversion_dfq/textual_inversion.py500 # Check if initializer_token is a single token or a sequence of tokensSTRING
LOW…intel_opts/textual_inversion/textual_inversion_bf16.py415 # Check if initializer_token is a single token or a sequence of tokensCOMMENT
LOW…ulti_token_textual_inversion/textual_inversion_flax.py396 # Check if initializer_token is a single token or a sequence of tokensCOMMENT
LOW…ffusion_training/test_dreambooth_lora_flux_advanced.py316 # Check if the metadata was properly serialized.STRING
LOWexamples/server-async/Pipelines.py73 # Check if model exists in PresetModelsCOMMENT
LOWexamples/custom_diffusion/train_custom_diffusion.py861 # Check if initializer_token is a single token or a sequence of tokensCOMMENT
LOWexamples/model_search/pipeline_easy.py434 # Check if the keyword is an HTTP or HTTPS URLSTRING
LOWexamples/model_search/pipeline_easy.py437 # Check if the keyword is a fileSTRING
LOWexamples/model_search/pipeline_easy.py443 # Check if the keyword is a directorySTRING
LOWexamples/model_search/pipeline_easy.py451 # Check if the keyword is a Civitai URLSTRING
LOWexamples/model_search/pipeline_easy.py457 # Check if the keyword starts with any valid URL prefixesSTRING
LOWexamples/model_search/pipeline_easy.py469 # Check if the keyword matches a Hugging Face repository formatSTRING
LOWexamples/model_search/pipeline_easy.py527 # Check if the file already exists at the save pathSTRING
LOWexamples/model_search/pipeline_easy.py538 # Open the file in the appropriate mode (write or append)STRING
LOWexamples/model_search/pipeline_easy.py850 # Check if the file passes security scans and has a valid extensionSTRING
LOWexamples/model_search/pipeline_easy.py1030 # Check if tokenizer and text encoder are providedSTRING
LOWexamples/model_search/pipeline_easy.py1047 # Check if token is already in tokenizer vocabularySTRING
LOWexamples/textual_inversion/textual_inversion.py685 # Check if initializer_token is a single token or a sequence of tokensSTRING
LOWexamples/textual_inversion/textual_inversion_sdxl.py693 # Check if initializer_token is a single token or a sequence of tokensSTRING
LOWexamples/textual_inversion/textual_inversion_flax.py409 # Check if initializer_token is a single token or a sequence of tokensCOMMENT
LOWexamples/dreambooth/test_dreambooth_lora_qwenimage.py236 # Check if the metadata was properly serialized.STRING
LOWexamples/dreambooth/test_dreambooth_lora_flux2_klein.py250 # Check if the metadata was properly serialized.STRING
LOWexamples/dreambooth/test_dreambooth_lora_flux2.py250 # Check if the metadata was properly serialized.STRING
LOWexamples/dreambooth/test_dreambooth_lora_sana.py236 # Check if the metadata was properly serialized.STRING
LOWexamples/dreambooth/test_dreambooth_lora_flux.py269 # Check if the metadata was properly serialized.STRING
LOW…amples/dreambooth/test_dreambooth_lora_flux_kontext.py269 # Check if the metadata was properly serialized.STRING
LOW…unity/pipeline_stable_diffusion_xl_attentive_eraser.py116 >>> prompt = "" # Set prompt to nullCODE
LOWexamples/community/dps_pipeline.py345 # Assign weight to each pixel in the field of view. A matrix whose horizontal dim is the output pixeSTRING
LOWexamples/community/marigold_depth_estimation.py184 # Check if denoising step is reasonableCOMMENT
31 more matches not shown…
Slop Phrases92 hits · 95 pts
SeverityFileLineSnippetContext
MEDIUMtests/pipelines/test_pipelines_auto.py304 # make sure you can use pag with inpaint-specific pipelineCOMMENT
LOWtests/pipelines/test_pipelines_auto.py304 # make sure you can use pag with inpaint-specific pipelineCOMMENT
LOWutils/release.py128# print("Cleaning main README, don't forget to run `make fix-copies`.")COMMENT
LOWutils/release.py128# print("Cleaning main README, don't forget to run `make fix-copies`.")COMMENT
LOWutils/release.py148# print("Cleaning main README, don't forget to run `make fix-copies`.")COMMENT
LOWutils/release.py148# print("Cleaning main README, don't forget to run `make fix-copies`.")COMMENT
LOW…/unconditional_image_generation/train_unconditional.py331 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOWexamples/instruct_pix2pix/train_instruct_pix2pix.py560 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…amples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py616 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…amples/cogvideo/train_cogvideox_image_to_video_lora.py1119 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/cogvideo/train_cogvideox_lora.py1140 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/flux-control/train_control_lora_flux.py912 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/flux-control/train_control_flux.py836 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…andinsky2_2/text_to_image/train_text_to_image_prior.py552 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…dinsky2_2/text_to_image/train_text_to_image_decoder.py559 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…/unconditional_image_generation/train_unconditional.py322 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…jects/onnxruntime/text_to_image/train_text_to_image.py549 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…usion/convert_original_promptdiffusion_to_diffusers.py1020 # make sure to remove all keys > 22COMMENT
LOWexamples/research_projects/realfill/train_realfill.py654 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…ts/instructpix2pix_lora/train_instruct_pix2pix_lora.py651 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…/wuerstchen/text_to_image/train_text_to_image_prior.py551 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…stchen/text_to_image/train_text_to_image_lora_prior.py565 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…/research_projects/sana/train_sana_sprint_diffusers.py1151 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…s/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py771 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…jects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py664 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…ber_loss_training/text_to_image/train_text_to_image.py676 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…oss_training/text_to_image/train_text_to_image_sdxl.py780 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…raining/text_to_image/train_text_to_image_lora_sdxl.py724 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…led_huber_loss_training/dreambooth/train_dreambooth.py1003 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…uber_loss_training/dreambooth/train_dreambooth_lora.py1000 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…loss_training/dreambooth/train_dreambooth_lora_sdxl.py1311 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…research_projects/pixart/train_pixart_controlnet_hf.py679 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/research_projects/lpl/train_sdxl_lpl.py882 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…ects/consistency_training/train_cm_ct_unconditional.py963 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…npainting/train_multi_subject_dreambooth_inpainting.py498 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…rch_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py703 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…research_projects/diffusion_dpo/train_diffusion_dpo.py605 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…/sd3_lora_colab/train_dreambooth_lora_sd3_miniature.py746 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…a_quantization/train_dreambooth_lora_flux_miniature.py737 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/cogview4-control/train_control_cogview4.py846 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…fusion_training/train_dreambooth_lora_sd15_advanced.py1350 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…fusion_training/train_dreambooth_lora_flux_advanced.py1626 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…fusion_training/train_dreambooth_lora_sdxl_advanced.py1670 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOWexamples/amused/train_amused.py531 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…s/consistency_distillation/train_lcm_distill_sd_wds.py1003 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…onsistency_distillation/train_lcm_distill_lora_sdxl.py897 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…sistency_distillation/train_lcm_distill_lora_sd_wds.py1055 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…stency_distillation/train_lcm_distill_lora_sdxl_wds.py1070 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOW…consistency_distillation/train_lcm_distill_sdxl_wds.py1063 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOWexamples/text_to_image/train_text_to_image_lora.py726 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/text_to_image/train_text_to_image.py669 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/text_to_image/train_text_to_image_sdxl.py766 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/text_to_image/train_text_to_image_lora_sdxl.py750 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/dreambooth/train_dreambooth_lora_flux2.py1329 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…ples/dreambooth/train_dreambooth_lora_flux2_img2img.py1280 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…reambooth/train_dreambooth_lora_flux2_klein_img2img.py1274 # make sure to pop weight so that corresponding model is not saved againSTRING
LOW…mples/dreambooth/train_dreambooth_lora_flux_kontext.py1474 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/dreambooth/train_dreambooth_lora_hidream.py1192 # make sure to pop weight so that corresponding model is not saved againSTRING
LOWexamples/dreambooth/train_dreambooth.py960 # make sure to pop weight so that corresponding model is not saved againCOMMENT
LOWexamples/dreambooth/train_dreambooth_flux.py1236 # make sure to pop weight so that corresponding model is not saved againSTRING
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Structural Annotation Overuse63 hits · 80 pts
SeverityFileLineSnippetContext
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py256 # Step 1: Create an external model repo with custom code (requires trust_remote_code)COMMENT
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py271 # Step 2: Create a custom block that references the external repo.COMMENT
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py357 # Step 3: Load the pipeline from the saved directory.COMMENT
LOWtests/pipelines/glm_image/test_glm_image.py299 # Step 1: Run with prompt only to get prior_token_ids from AR modelCOMMENT
LOWtests/pipelines/glm_image/test_glm_image.py309 # Step 2: Run with both prompt and prior_token_ids — should not raiseCOMMENT
LOWtests/models/testing_utils/quantization.py395 # Step 1: freeze all parametersCOMMENT
LOWtests/models/testing_utils/quantization.py402 # Step 2: add adapters to attention layersCOMMENT
LOWtests/models/testing_utils/quantization.py419 # Step 3: run forward and backward passCOMMENT
LOWtests/models/testing_utils/quantization.py428 # Step 4: verify gradients are computedCOMMENT
LOWtests/quantization/bnb/test_mixed_int8.py390 # Step 1: freeze all parametersSTRING
LOWtests/quantization/bnb/test_mixed_int8.py397 # Step 2: add adaptersSTRING
LOWtests/quantization/bnb/test_mixed_int8.py404 # Step 3: dummy batchSTRING
LOWtests/quantization/bnb/test_mixed_int8.py411 # Step 4: Check if the gradient is not NoneSTRING
LOWtests/quantization/bnb/test_4bit.py419 # Step 1: freeze all parametersSTRING
LOWtests/quantization/bnb/test_4bit.py426 # Step 2: add adaptersSTRING
LOWtests/quantization/bnb/test_4bit.py433 # Step 3: dummy batchSTRING
LOWtests/quantization/bnb/test_4bit.py440 # Step 4: Check if the gradient is not NoneSTRING
LOWtests/hooks/test_mag_cache.py128 # Step 0: Input 10.0 -> Output 40.0 (2 blocks * 2x each)COMMENT
LOWtests/hooks/test_mag_cache.py134 # Step 1: Input 11.0.COMMENT
LOWtests/hooks/test_mag_cache.py162 # Step 1: Should COMPUTE (44.0) not SKIP (41.0) because of retentionCOMMENT
LOWtests/hooks/test_mag_cache.py181 # Step 0: Compute. Input 10.0 -> Output 20.0 (1 block * 2x)COMMENT
LOWtests/hooks/test_mag_cache.py188 # Step 1: Skip. Input 11.0.COMMENT
LOWexamples/research_projects/ip_adapter/README.md194 # Step 1: Project the image featuresCOMMENT
LOWexamples/research_projects/ip_adapter/README.md197 # Step 2: Pass the projected features through the IP AdapterCOMMENT
LOWexamples/profiling/README.md124### Step 3: Known Sync Issues to ValidateCOMMENT
LOWexamples/community/README.md1341# Step 1: Resize input image for CLIPSeg (224x224)STRING
LOWexamples/community/README.md1344# Step 2: Generate segmentation maskSTRING
LOWexamples/community/README.md1354# Step 3: Resize input image for Stable DiffusionSTRING
LOWexamples/community/README.md1357# Step 4: Run inpainting with Stable DiffusionSTRING
LOWexamples/community/checkpoint_merger.py124 # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoCOMMENT
LOWexamples/community/checkpoint_merger.py144 # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.COMMENT
LOWscripts/convert_dcae_to_diffusers.py31 # NOTE: The below two lines work because scales in the available configs only have a tuple length of 1COMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py137 # Step 1: Collect model inputsCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py145 # Step 2: Update guider stateCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py148 # Step 3: Prepare batched inputsCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py151 # Step 4: Run denoiser for each batchCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py170 # Step 5: Combine predictionsCOMMENT
LOW…c/diffusers/pipelines/flux2/pipeline_flux2_klein_kv.py794 # Step 0: include ref tokens, extract KV cacheSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py741 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py750 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py753 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py763 # Step 4: Run the denoiser for each batchSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py790 # Step 5: Combine predictions using the guidance methodSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py864 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py873 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py876 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py886 # Step 4: Run the denoiser for each batchSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py914 # Step 5: Combine predictions using the guidance methodSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py656 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py663 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py666 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py677 # Step 4: Run the denoiser for each batchSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py704 # Step 5: Combine predictions using the guidance methodSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py764 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py773 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py776 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py786 # Step 4: Run the denoiser for each batchSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py814 # Step 5: Combine predictions using the guidance methodSTRING
LOWsrc/diffusers/models/modeling_flax_utils.py412 # Step 1: Get the pytorch fileSTRING
LOWsrc/diffusers/models/modeling_flax_utils.py415 # Step 2: Convert the weightsSTRING
3 more matches not shown…
Modern Structural Boilerplate82 hits · 73 pts
SeverityFileLineSnippetContext
LOWtests/models/testing_utils/attention.py31logger = logging.getLogger(__name__)CODE
LOWtests/models/testing_utils/__init__.py50__all__ = [CODE
LOWutils/remind_link_issue.py37logger = logging.getLogger(__name__)CODE
LOWutils/fetch_torch_cuda_pipeline_test_matrix.py26logger = logging.getLogger(__name__)CODE
LOWexamples/controlnet/train_controlnet_flax.py66logger = logging.getLogger(__name__)CODE
LOW…ulti_token_textual_inversion/textual_inversion_flax.py61logger = logging.getLogger(__name__)CODE
LOWexamples/cosmos/train_cosmos_predict25_lora.py316 def _setup_caption_format(self) -> None:CODE
LOWexamples/server/server.py19logger = logging.getLogger(__name__)CODE
LOWexamples/server-async/Pipelines.py12logger = logging.getLogger(__name__)CODE
LOWexamples/server-async/utils/utils.py10logger = logging.getLogger(__name__)CODE
LOWexamples/profiling/profiling_utils.py13logger = logging.getLogger(__name__)CODE
LOWexamples/profiling/profiling_pipelines.py26logger = logging.getLogger(__name__)CODE
LOWexamples/textual_inversion/textual_inversion_flax.py61logger = logging.getLogger(__name__)CODE
LOWexamples/text_to_image/train_text_to_image_flax.py54logger = logging.getLogger(__name__)CODE
LOWexamples/dreambooth/train_dreambooth_flax.py43logger = logging.getLogger(__name__)CODE
LOWbenchmarks/benchmarking_utils.py20logger = logging.getLogger(__name__)CODE
LOWbenchmarks/run_all.py10logger = logging.getLogger(__name__)CODE
LOWscripts/convert_ltx2_to_diffusers.py134def update_state_dict_inplace(state_dict: dict[str, Any], old_key: str, new_key: str) -> None:CODE
LOWscripts/convert_hunyuan_image_to_diffusers.py10logger = logging.getLogger(__name__) # pylint: disable=invalid-nameCODE
LOWscripts/convert_anyflow_to_diffusers.py51logger = logging.getLogger(__name__)CODE
LOWscripts/convert_flux2_to_diffusers.py383def update_state_dict(state_dict: Dict[str, Any], old_key: str, new_key: str) -> None:CODE
LOWsrc/diffusers/modular_pipelines/mellon_node_utils.py22logger = logging.getLogger(__name__)CODE
LOWsrc/diffusers/quantizers/nunchaku/utils.py323def _set_submodule(model: nn.Module, path: str, module: nn.Module) -> None:CODE
LOWsrc/diffusers/guiders/guider_utils.py100 def set_state(self, step: int, num_inference_steps: int, timestep: torch.LongTensor) -> None:CODE
LOWsrc/diffusers/pipelines/pipeline_utils.py2028 def set_use_memory_efficient_attention_xformers(self, valid: bool, attention_op: Callable | None = None) -> None:STRING
LOWsrc/diffusers/pipelines/ddim/pipeline_ddim.py26logger = logging.getLogger(__name__)CODE
LOWsrc/diffusers/pipelines/llada2/pipeline_llada2.py563__all__ = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"]CODE
LOW…/pipelines/diffusion_gemma/pipeline_diffusion_gemma.py460__all__ = ["DiffusionGemmaPipeline", "DiffusionGemmaPipelineOutput"]CODE
LOWsrc/diffusers/utils/logging.py167def set_verbosity(verbosity: int) -> None:CODE
LOWsrc/diffusers/utils/logging.py186def set_verbosity_info() -> None:CODE
LOWsrc/diffusers/utils/logging.py191def set_verbosity_warning() -> None:CODE
LOWsrc/diffusers/utils/logging.py196def set_verbosity_debug() -> None:CODE
LOWsrc/diffusers/utils/logging.py201def set_verbosity_error() -> None:CODE
LOWsrc/diffusers/models/attention.py127 def set_processor(self, processor: AttentionProcessor) -> None:STRING
LOWsrc/diffusers/models/attention.py171 def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None:STRING
LOWsrc/diffusers/models/attention.py340 def set_attention_slice(self, slice_size: int) -> None:STRING
LOWsrc/diffusers/models/attention.py1551 def set_chunk_feed_forward(self, chunk_size: int | None, dim: int = 0) -> None:STRING
LOWsrc/diffusers/models/attention_processor.py344 def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None:STRING
LOWsrc/diffusers/models/attention_processor.py507 def set_attention_slice(self, slice_size: int) -> None:STRING
LOWsrc/diffusers/models/attention_processor.py535 def set_processor(self, processor: "AttnProcessor") -> None:STRING
LOWsrc/diffusers/models/modeling_utils.py323 def set_use_npu_flash_attention(self, valid: bool) -> None:STRING
LOWsrc/diffusers/models/modeling_utils.py382 def set_use_memory_efficient_attention_xformers(self, valid: bool, attention_op: Callable | None = None) -> None:STRING
LOWsrc/diffusers/models/modeling_utils.py588 def set_attention_backend(self, backend: str) -> None:STRING
LOWsrc/diffusers/models/unets/unet_motion_model.py1801 def set_default_attn_processor(self) -> None:STRING
LOWsrc/diffusers/models/unets/unet_3d_condition.py290 def set_attention_slice(self, slice_size: str | int | list[int]) -> None:STRING
LOWsrc/diffusers/models/unets/unet_dreamlite.py2032__all__ = [STRING
LOWsrc/diffusers/models/controlnets/controlnet.py537 def set_attention_slice(self, slice_size: str | int | list[int]) -> None:STRING
LOW…/diffusers/models/controlnets/controlnet_sparsectrl.py469 def set_attention_slice(self, slice_size: str | int | list[int]) -> None:STRING
LOWsrc/diffusers/models/controlnets/controlnet_union.py476 def set_attention_slice(self, slice_size: str | int | list[int]) -> None:STRING
LOWsrc/diffusers/hooks/hooks.py49 def set_context(self, name: str) -> None:CODE
LOWsrc/diffusers/hooks/hooks.py135 def _set_context(self, module: torch.nn.Module, name: str) -> None:STRING
LOWsrc/diffusers/hooks/hooks.py268 def _set_context(self, name: str | None = None) -> None:STRING
LOWsrc/diffusers/loaders/peft.py546 def set_adapter(self, adapter_name: str | list[str]) -> None:STRING
LOWsrc/diffusers/loaders/lora_base.py938 def set_lora_device(self, adapter_names: list[str], device: torch.device | str | int) -> None:STRING
LOWsrc/diffusers/loaders/single_file_utils.py3916 def update_state_dict(state_dict: dict[str, object], old_key: str, new_key: str) -> None:CODE
LOWsrc/diffusers/loaders/single_file_utils.py3976 def update_state_dict(state_dict: dict[str, object], old_key: str, new_key: str) -> None:CODE
LOWsrc/diffusers/loaders/single_file_utils.py4040 def update_state_dict_inplace(state_dict, old_key: str, new_key: str) -> None:CODE
LOWsrc/diffusers/loaders/single_file_utils.py4119 def update_state_dict_inplace(state_dict, old_key: str, new_key: str) -> None:CODE
LOWsrc/diffusers/loaders/single_file_utils.py4159 def update_state_dict_inplace(state_dict, old_key: str, new_key: str) -> None:CODE
LOWsrc/diffusers/loaders/ip_adapter.py1066 def set_ip_adapter_scale(self, scale: float) -> None:CODE
22 more matches not shown…
Verbosity Indicators55 hits · 70 pts
SeverityFileLineSnippetContext
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py256 # Step 1: Create an external model repo with custom code (requires trust_remote_code)COMMENT
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py271 # Step 2: Create a custom block that references the external repo.COMMENT
LOW…ular_pipelines/test_modular_pipelines_custom_blocks.py357 # Step 3: Load the pipeline from the saved directory.COMMENT
LOWtests/pipelines/glm_image/test_glm_image.py299 # Step 1: Run with prompt only to get prior_token_ids from AR modelCOMMENT
LOWtests/pipelines/glm_image/test_glm_image.py309 # Step 2: Run with both prompt and prior_token_ids — should not raiseCOMMENT
LOWtests/models/testing_utils/quantization.py395 # Step 1: freeze all parametersCOMMENT
LOWtests/models/testing_utils/quantization.py402 # Step 2: add adapters to attention layersCOMMENT
LOWtests/models/testing_utils/quantization.py419 # Step 3: run forward and backward passCOMMENT
LOWtests/models/testing_utils/quantization.py428 # Step 4: verify gradients are computedCOMMENT
LOWtests/quantization/bnb/test_mixed_int8.py390 # Step 1: freeze all parametersSTRING
LOWtests/quantization/bnb/test_mixed_int8.py397 # Step 2: add adaptersSTRING
LOWtests/quantization/bnb/test_mixed_int8.py404 # Step 3: dummy batchSTRING
LOWtests/quantization/bnb/test_mixed_int8.py411 # Step 4: Check if the gradient is not NoneSTRING
LOWtests/quantization/bnb/test_4bit.py419 # Step 1: freeze all parametersSTRING
LOWtests/quantization/bnb/test_4bit.py426 # Step 2: add adaptersSTRING
LOWtests/quantization/bnb/test_4bit.py433 # Step 3: dummy batchSTRING
LOWtests/quantization/bnb/test_4bit.py440 # Step 4: Check if the gradient is not NoneSTRING
LOWtests/hooks/test_mag_cache.py128 # Step 0: Input 10.0 -> Output 40.0 (2 blocks * 2x each)COMMENT
LOWtests/hooks/test_mag_cache.py134 # Step 1: Input 11.0.COMMENT
LOWtests/hooks/test_mag_cache.py162 # Step 1: Should COMPUTE (44.0) not SKIP (41.0) because of retentionCOMMENT
LOWtests/hooks/test_mag_cache.py181 # Step 0: Compute. Input 10.0 -> Output 20.0 (1 block * 2x)COMMENT
LOWtests/hooks/test_mag_cache.py188 # Step 1: Skip. Input 11.0.COMMENT
LOWexamples/community/checkpoint_merger.py124 # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoCOMMENT
LOWexamples/community/checkpoint_merger.py144 # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.COMMENT
LOWexamples/community/checkpoint_merger.py176 # Step 3:-COMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py137 # Step 1: Collect model inputsCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py145 # Step 2: Update guider stateCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py148 # Step 3: Prepare batched inputsCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py151 # Step 4: Run denoiser for each batchCOMMENT
LOW…iffusers/modular_pipelines/hunyuan_video1_5/denoise.py170 # Step 5: Combine predictionsCOMMENT
LOW…c/diffusers/pipelines/flux2/pipeline_flux2_klein_kv.py794 # Step 0: include ref tokens, extract KV cacheSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py741 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py750 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py753 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py763 # Step 4: Run the denoiser for each batchSTRING
LOW…ipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py790 # Step 5: Combine predictions using the guidance methodSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py864 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py873 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py876 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py886 # Step 4: Run the denoiser for each batchSTRING
LOW…yuan_video1_5/pipeline_hunyuan_video1_5_image2video.py914 # Step 5: Combine predictions using the guidance methodSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py656 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py663 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py666 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py677 # Step 4: Run the denoiser for each batchSTRING
LOW…pelines/hunyuan_image/pipeline_hunyuanimage_refiner.py704 # Step 5: Combine predictions using the guidance methodSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py764 # Step 1: Collect model inputs needed for the guidance methodSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py773 # Step 2: Update guider's internal state for this denoising stepSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py776 # Step 3: Prepare batched model inputs based on the guidance methodSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py786 # Step 4: Run the denoiser for each batchSTRING
LOW…users/pipelines/hunyuan_image/pipeline_hunyuanimage.py814 # Step 5: Combine predictions using the guidance methodSTRING
LOWsrc/diffusers/models/modeling_flax_utils.py412 # Step 1: Get the pytorch fileSTRING
LOWsrc/diffusers/models/modeling_flax_utils.py415 # Step 2: Convert the weightsSTRING
LOWsrc/diffusers/models/modeling_flax_pytorch_utils.py108 # Step 1: Convert pytorch tensor to numpyCOMMENT
LOWsrc/diffusers/models/modeling_flax_pytorch_utils.py111 # Step 2: Since the model is stateless, get random Flax paramsCOMMENT
Magic Placeholder Names9 hits · 68 pts
SeverityFileLineSnippetContext
HIGHexamples/dreambooth/README_qwen.md109* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_flux2.md261* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_lumina2.md109* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_flux.md121* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_sana.md109* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_sdxl.md102* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_hidream.md103* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_sd3.md108* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use itCOMMENT
HIGHexamples/dreambooth/README_z_image.md156* `report_to="wandb"` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use iCOMMENT
Hallucination Indicators7 hits · 50 pts
SeverityFileLineSnippetContext
CRITICALutils/check_repo.py673 model_types = list(diffusers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys())CODE
CRITICALsrc/diffusers/models/lora.py124 w_up = self.lora_linear_layer.up.weight.data.float()CODE
CRITICALsrc/diffusers/models/lora.py125 w_down = self.lora_linear_layer.down.weight.data.float()CODE
CRITICALsrc/diffusers/models/lora.py322 w_up = self.lora_layer.up.weight.data.float()STRING
CRITICALsrc/diffusers/models/lora.py323 w_down = self.lora_layer.down.weight.data.float()STRING
CRITICALsrc/diffusers/models/lora.py408 w_up = self.lora_layer.up.weight.data.float()STRING
CRITICALsrc/diffusers/models/lora.py409 w_down = self.lora_layer.down.weight.data.float()STRING
AI Slop Vocabulary37 hits · 46 pts
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LOWtests/single_file/single_file_testing_utils.py212 # we just pass it in here otherwise this test will failCOMMENT
LOWtests/single_file/single_file_testing_utils.py232 # we just pass it in here otherwise this test will failCOMMENT
LOWtests/single_file/single_file_testing_utils.py400 # we just pass it in here otherwise this test will failCOMMENT
LOWtests/single_file/single_file_testing_utils.py421 # we just pass it in here otherwise this test will failCOMMENT
LOWutils/consolidated_test_report.py171 pass # If we can't read the line file, just use what we haveCODE
MEDIUMutils/consolidated_test_report.py351 """Generate a comprehensive markdown report from consolidated data."""STRING
LOWutils/check_inits.py71 # If this is a traditional init, just return.COMMENT
LOW…uber_loss_training/dreambooth/train_dreambooth_lora.py912 # IF does not have a VAE so let's just set it to NoneCOMMENT
LOWexamples/research_projects/lpl/train_sdxl_lpl.py1342 # If no SNR weighting, just use meanSTRING
MEDIUMexamples/research_projects/sdxl_flax/sdxl_single.py1# Show best practices for SDXL JAXCOMMENT
MEDIUM…fusion_training/train_dreambooth_lora_flux_advanced.py1718 if args.enable_t5_ti: # whether to do pivotal tuning/textual inversion for T5 as wellSTRING
MEDIUM…fusion_training/train_dreambooth_lora_flux_advanced.py1753 else: # regular te training or regular pivotal for clipSTRING
MEDIUM…fusion_training/train_dreambooth_lora_flux_advanced.py1757 # pivotal tuning of clip & t5STRING
MEDIUM…fusion_training/train_dreambooth_lora_flux_advanced.py1769 else: # regular pivotal tuning of clip & t5STRING
MEDIUM…fusion_training/train_dreambooth_lora_flux_advanced.py2116 elif args.train_text_encoder_ti: # textual inversion / pivotal tuningSTRING
MEDIUM…ffusion_training/test_dreambooth_lora_flux_advanced.py146 # when performing pivotal tuning, all the parameters in the state dict should startSTRING
MEDIUM…ffusion_training/test_dreambooth_lora_flux_advanced.py189 # when performing pivotal tuning, all the parameters in the state dict should startSTRING
LOWexamples/dreambooth/train_dreambooth_lora_flux2.py1235 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOW…ples/dreambooth/train_dreambooth_lora_flux2_img2img.py1186 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOW…reambooth/train_dreambooth_lora_flux2_klein_img2img.py1181 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOWexamples/dreambooth/train_dreambooth_lora_hidream.py1134 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOWexamples/dreambooth/train_dreambooth_lora.py893 # IF does not have a VAE so let's just set it to NoneCOMMENT
LOWexamples/dreambooth/train_dreambooth_lora_z_image.py1224 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOWexamples/dreambooth/train_dreambooth_lora_ideogram4.py1390 # we never offload the transformer to CPU, so we can just use the accelerator device.STRING
LOW…amples/dreambooth/train_dreambooth_lora_flux2_klein.py1229 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOWexamples/dreambooth/train_dreambooth_lora_krea2.py1202 # we never offload the transformer to CPU, so we can just use the accelerator deviceSTRING
LOWexamples/dreambooth/train_dreambooth_lora_qwen_image.py1138 # we never offload the transformer to CPU, so we can just use the accelerator deviceCOMMENT
MEDIUMscripts/convert_diffusers_sdxl_lora_to_webui.py13# Dreambooth Advanced Training Script - state of the art techniques such as pivotal tuning and prodigy optimizer:COMMENT
MEDIUMsrc/diffusers/modular_pipelines/components_manager.py774 """Get comprehensive information about a component.STRING
LOWsrc/diffusers/modular_pipelines/modular_pipeline.py2108 # if it is not set yet, just set it and skip the process to check and warn belowCOMMENT
MEDIUMsrc/diffusers/pipelines/prx/pipeline_prx.py129 """Clean text using comprehensive text processing logic."""STRING
MEDIUMsrc/diffusers/utils/state_dict_utils.py155 # This is not a comprehensive dict as kohya format requires replacing `.` with `_` in keys,COMMENT
LOWsrc/diffusers/models/attention_processor.py593 # here we simply pass along all tensors to the selected processor classSTRING
MEDIUMsrc/diffusers/models/modeling_utils.py501 # If users want to apply layerwise casting on peft layers as well, they can utilize theSTRING
LOWsrc/diffusers/models/autoencoders/vae.py631 inds[inds >= self.used.shape[0]] = 0 # simply set to zeroCODE
MEDIUMsrc/diffusers/loaders/lora_conversion_utils.py363 # not utilize it yet.COMMENT
LOW…fusers/schedulers/scheduling_ltx_euler_ancestral_rf.py320 # Already at the end; simply return the current sample.COMMENT
Cross-Language Confusion5 hits · 18 pts
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HIGHsetup.py51 python setup.py bdist_wheel && python setup.py sdistSTRING
HIGHsetup.py64 pip install diffusers && pip uninstall diffusersSTRING
HIGHtests/quantization/test_pipeline_level_quantization.py281 "llm_int8_skip_modules": null,CODE
HIGHutils/label_issues.py43Example: {"labels": ["bug", "models", "needs-code-example"], "model_name": null}CODE
HIGHsrc/diffusers/pipelines/ace_step/pipeline_ace_step.py1171 # Batched guidance: stack (cond, null) on batch dim and run the DiT once.STRING
Modern AI Meta-Vocabulary6 hits · 16 pts
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MEDIUMCONTRIBUTING.md590### AI-assisted and agentic contributionsCOMMENT
MEDIUMdocs/source/en/conceptual/contribution.md590### AI-assisted and agentic contributionsCOMMENT
MEDIUMscripts/convert_original_audioldm2_to_diffusers.py637 # extract state dict for CLAP text embedding model, discarding the audio componentCOMMENT
MEDIUMscripts/convert_original_audioldm_to_diffusers.py612 # extract state dict for CLAP text embedding model, discarding the audio componentCOMMENT
MEDIUMscripts/convert_original_musicldm_to_diffusers.py617 # extract state dict for CLAP text embedding model, discarding the audio componentCOMMENT
MEDIUMsrc/diffusers/pipelines/dreamlite/pipeline_dreamlite.py215 # (system + chat-template scaffolding) that must be dropped from the encoder hidden states so the cross-STRING
Dead Code4 hits · 8 pts
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MEDIUM…research_projects/pixart/train_pixart_controlnet_hf.py686CODE
MEDIUM…rs/modular_pipelines/z_image/modular_blocks_z_image.py260CODE
MEDIUM…rs/modular_pipelines/z_image/modular_blocks_z_image.py261CODE
MEDIUM…rs/modular_pipelines/z_image/modular_blocks_z_image.py262CODE
Example Usage Blocks3 hits · 4 pts
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LOWexamples/profiling/run_profiling.sh4# Usage:COMMENT
LOWscripts/convert_longcat_audio_dit_to_diffusers.py16# Usage:COMMENT
LOWscripts/convert_ace_step_to_diffusers.py3# Usage:COMMENT
Fake / Example Data3 hits · 3 pts
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LOW…_gligen/pipeline_stable_diffusion_gligen_text_image.py144 ... "placeholder",CODE
LOW…elines/deprecated/unidiffuser/modeling_text_decoder.py153 dummy_token = self.get_dummy_token(input_ids.shape[0], input_ids.device)CODE
LOW…elines/deprecated/unidiffuser/modeling_text_decoder.py154 labels = torch.cat((dummy_token, input_ids), dim=1)CODE
TODO Padding2 hits · 2 pts
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LOWsrc/diffusers/modular_pipelines/flux/inputs.py102 # TODO: consider adding negative embeddings?COMMENT
LOWsrc/diffusers/models/transformers/transformer_ltx2.py1023 # TODO: consider implementing this as a utility and reuse in `connectors.py`.STRING