'diff' 검색 결과

Automate Diffbot operations through Rube MCP, a connection for agent tools. The skill discovers available actions and their required inputs, checks the Diffbot connection, and runs selected tools using Composio's toolkit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding available Diffbot operations
• Checking the connection before running tasks
• Automating repeated Diffbot operations

Automate Diffbot operations through Rube MCP, a connection for agent tools. The skill discovers available actions and their required inputs, checks the Diffbot connection, and runs selected tools using Composio's toolkit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding available Diffbot operations
• Checking the connection before running tasks
• Automating repeated Diffbot operations

Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new diffusion model to SGLang
• Porting a Diffusers generation pipeline
• Adding a variant by reusing existing model components

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing ModelOpt FP8 weights for SGLang Diffusion
• Checking quality after diffusion model quantization
• Comparing BF16 and quantized generation speed

Evaluate policy effects with difference-in-differences, a method comparing changes in treated and untreated groups. Guides assumption checks, regression models, staggered adoption analysis, robustness checks, and reporting with event study plots.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating policy effects with comparison groups
• Checking outcome trends before treatment
• Analyzing policies adopted at different times

Review a diffusers contribution before opening a pull request. Check the full change against project rules, trace potentially unused code, and receive a severity-based report separating required fixes from points to discuss with reviewers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a diffusers contribution before opening a pull request
• Investigating potentially unused code in a change
• Reviewing fixes again and preparing notes for reviewers

Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ComfyUI 워크플로로 이미지, 영상, 음성, 3D 콘텐츠를 생성합니다. 모델과 추가 노드 준비, 조건을 바꾼 일괄 생성, 진행 상황 확인도 다룹니다.

• Stable Diffusion이나 Flux로 이미지 생성
• 기존 이미지 변환과 지정 영역 다시 그리기
• 생성 조건을 바꿔 여러 후보 일괄 제작

Compare software binaries before and after a security patch to identify the underlying flaw. Guide creation of a proof of concept, a small vulnerability demonstration, and compare its behavior across patched and unpatched versions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a known CVE without a public proof of concept
• Analyzing Windows Patch Tuesday changes
• Checking Linux LTS branches for missing fixes

Generate commit messages from git diffs, staged changes, or written descriptions. Explain the reason and problem behind each change using Conventional Commits, a structured message format, and suggest separate messages for unrelated changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing a commit message from staged changes
• Recording why a bug fix was needed
• Splitting unrelated changes into separate commit messages

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Integrate a model or pipeline into diffusers, from file structure and weight conversion to tests and documentation. Verify numerical agreement with the original implementation, with modular pipelines as the preferred starting point for new integrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new model and pipeline to diffusers
• Converting a pipeline to modular format
• Converting weights for a new version of a supported model

Locate BotProfile::Difficulty in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::Difficulty" (symbol BotProfile_Difficulty). This is a VIRTUAL function - resolve the BotProfile::Difficulty vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_Difficulty, BotProfile::Difficulty

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate candidate positions and orientations for small molecules binding to proteins with DiffDock and DiffDock-L. Prepare single or batch docking runs, organize predicted poses by model confidence, and review validation needs without treating confidence as binding affinity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting binding poses for a protein and small molecule
• Preparing and validating batch docking inputs
• Comparing poses across receptor conformations

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Package an existing custom diffusers block into a directory ready for Hub upload. Select a class from a Python file and save its source, configuration, and associated files so others can load the block.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a custom block for Hub distribution
• Selecting one block class from a Python file
• Creating the directory layout needed to load a shared block

Review local code changes with git diff and update the matching pages under docs/. Keep commands, settings, examples, and navigation aligned with current behavior so readers can understand the changes without reading the code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Documenting uncommitted feature changes
• Updating setup instructions after a refactor
• Replacing outdated commands and configuration examples

Predict how a small molecule may sit within a protein using structural inputs. Generate ranked 3D poses without defining a search region, then use those poses as starting points for further evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting ligand poses from a protein structure and SMILES
• Generating ranked poses for a small fragment library
• Preparing starting poses for downstream rescoring