diff --git a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
--- a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
+++ b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
@@ -230,6 +230,16 @@
   /// bufferized or not.
   bool bufferizeFunctionBoundaries = false;
 
+  /// Certain ops have aliasing OpOperand/OpResult invariants (e.g., scf.for).
+  /// If this flag is set to `false`, those invariants are no longer enforced
+  /// with buffer copies.
+  ///
+  /// Note: Deactivating this flag can lead to incorrect bufferization results
+  /// when used incorrectly. This flag is useful with
+  /// `AlwaysCopyBufferizationState` which bufferizes all writing tensor
+  /// OpOperands out-of-place.
+  bool enforceAliasingInvariants = true;
+
   /// This flag controls buffer types on function signatures.
   ///
   /// * InferLayoutMap: All function parameter types have a fully dynamic layout
diff --git a/mlir/lib/Dialect/SCF/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/SCF/Transforms/BufferizableOpInterfaceImpl.cpp
--- a/mlir/lib/Dialect/SCF/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/SCF/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -468,6 +468,54 @@
     return true;
   }
 
+  LogicalResult resolveConflicts(Operation *op, RewriterBase &rewriter,
+                                 const AnalysisState &state) const {
+    auto bufferizableOp = cast<BufferizableOpInterface>(op);
+    if (failed(bufferizableOp.resolveTensorOpOperandConflicts(rewriter, state)))
+      return failure();
+
+    if (!state.getOptions().enforceAliasingInvariants)
+      return success();
+
+    // According to the `getAliasing...` implementations, a bufferized OpResult
+    // may alias only with the corresponding bufferized init_arg and with no
+    // other buffers. I.e., the i-th OpResult may alias with the i-th init_arg;
+    // but not with any other OpOperand. If a corresponding OpResult/init_arg
+    // pair bufferizes to equivalent buffers, this aliasing requirement is
+    // satisfied. Otherwise, we cannot be sure and must yield a new buffer copy.
+    // (New buffer copies do not alias with any buffer.)
+    auto forOp = cast<scf::ForOp>(op);
+    auto yieldOp =
+        cast<scf::YieldOp>(forOp.getLoopBody().front().getTerminator());
+    OpBuilder::InsertionGuard g(rewriter);
+    rewriter.setInsertionPoint(yieldOp);
+
+    // Indices of all iter_args that have tensor type. These are the ones that
+    // are bufferized.
+    DenseSet<int64_t> indices = getTensorIndices(forOp.getInitArgs());
+    // For every yielded value, is the value equivalent to its corresponding
+    // bbArg?
+    DenseSet<int64_t> equivalentYields = getEquivalentBuffers(
+        forOp.getRegionIterArgs(), yieldOp.getResults(), state);
+    SmallVector<Value> yieldValues;
+    for (int64_t idx = 0;
+         idx < static_cast<int64_t>(yieldOp.getResults().size()); ++idx) {
+      Value value = yieldOp.getResults()[idx];
+      if (!indices.contains(idx) || equivalentYields.contains(idx)) {
+        yieldValues.push_back(value);
+        continue;
+      }
+      Value alloc = rewriter.create<bufferization::AllocTensorOp>(
+          yieldOp.getLoc(), value.getType().cast<RankedTensorType>(),
+          /*dynamicSizes=*/ValueRange(), value, /*escape=*/true);
+      yieldValues.push_back(alloc);
+    }
+
+    rewriter.updateRootInPlace(
+        yieldOp, [&]() { yieldOp.getResultsMutable().assign(yieldValues); });
+    return success();
+  }
+
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                           BufferizationState &state) const {
     auto forOp = cast<scf::ForOp>(op);
@@ -632,6 +680,82 @@
     return true;
   }
 
+  LogicalResult resolveConflicts(Operation *op, RewriterBase &rewriter,
+                                 const AnalysisState &state) const {
+    auto bufferizableOp = cast<BufferizableOpInterface>(op);
+    if (failed(bufferizableOp.resolveTensorOpOperandConflicts(rewriter, state)))
+      return failure();
+
+    if (!state.getOptions().enforceAliasingInvariants)
+      return success();
+
+    // According to the `getAliasing...` implementations, a bufferized OpResult
+    // may alias only with the corresponding bufferized init_arg and with no
+    // other buffers. I.e., the i-th OpResult may alias with the i-th init_arg;
+    // but not with any other OpOperand. If a corresponding OpResult/init_arg
+    // pair bufferizes to equivalent buffers, this aliasing requirement is
+    // satisfied. Otherwise, we cannot be sure and must yield a new buffer copy.
+    // (New buffer copies do not alias with any buffer.)
+    OpBuilder::InsertionGuard g(rewriter);
+    auto whileOp = cast<scf::WhileOp>(op);
+    auto conditionOp = whileOp.getConditionOp();
+    auto yieldOp = whileOp.getYieldOp();
+
+    // Indices of all bbArgs that have tensor type. These are the ones that
+    // are bufferized. The "before" and "after" regions may have different args.
+    DenseSet<int64_t> indicesBefore = getTensorIndices(whileOp.getInits());
+    DenseSet<int64_t> indicesAfter =
+        getTensorIndices(whileOp.getAfterArguments());
+
+    // For every yielded value, is the value equivalent to its corresponding
+    // bbArg?
+    DenseSet<int64_t> equivalentYieldsBefore = getEquivalentBuffers(
+        whileOp.getBeforeArguments(), conditionOp.getArgs(), state);
+    DenseSet<int64_t> equivalentYieldsAfter = getEquivalentBuffers(
+        whileOp.getAfterArguments(), whileOp.getYieldOp().getResults(), state);
+
+    // Update "before" region.
+    rewriter.setInsertionPoint(conditionOp);
+    SmallVector<Value> beforeYieldValues;
+    for (int64_t idx = 0;
+         idx < static_cast<int64_t>(conditionOp.getArgs().size()); ++idx) {
+      Value value = conditionOp.getArgs()[idx];
+      if (!indicesBefore.contains(idx) ||
+          equivalentYieldsBefore.contains(idx)) {
+        beforeYieldValues.push_back(value);
+        continue;
+      }
+      Value alloc = rewriter.create<bufferization::AllocTensorOp>(
+          conditionOp.getLoc(), value.getType().cast<RankedTensorType>(),
+          /*dynamicSizes=*/ValueRange(), value, /*escape=*/true);
+      beforeYieldValues.push_back(alloc);
+    }
+    rewriter.updateRootInPlace(conditionOp, [&]() {
+      conditionOp.getArgsMutable().assign(beforeYieldValues);
+    });
+
+    // Update "after" region.
+    rewriter.setInsertionPoint(yieldOp);
+    SmallVector<Value> afterYieldValues;
+    for (int64_t idx = 0;
+         idx < static_cast<int64_t>(yieldOp.getResults().size()); ++idx) {
+      Value value = yieldOp.getResults()[idx];
+      if (!indicesAfter.contains(idx) || equivalentYieldsAfter.contains(idx)) {
+        afterYieldValues.push_back(value);
+        continue;
+      }
+      Value alloc = rewriter.create<bufferization::AllocTensorOp>(
+          yieldOp.getLoc(), value.getType().cast<RankedTensorType>(),
+          /*dynamicSizes=*/ValueRange(), value, /*escape=*/true);
+      afterYieldValues.push_back(alloc);
+    }
+    rewriter.updateRootInPlace(yieldOp, [&]() {
+      yieldOp.getResultsMutable().assign(afterYieldValues);
+    });
+
+    return success();
+  }
+
   LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                           BufferizationState &state) const {
     auto whileOp = cast<scf::WhileOp>(op);
@@ -855,6 +979,42 @@
     return BufferRelation::Equivalent;
   }
 
+  LogicalResult resolveConflicts(Operation *op, RewriterBase &rewriter,
+                                 const AnalysisState &state) const {
+    auto bufferizableOp = cast<BufferizableOpInterface>(op);
+    if (failed(bufferizableOp.resolveTensorOpOperandConflicts(rewriter, state)))
+      return failure();
+
+    OpBuilder::InsertionGuard g(rewriter);
+    auto foreachThreadOp = cast<ForeachThreadOp>(op);
+    for (OpResult opResult : foreachThreadOp->getOpResults()) {
+      SmallVector<OpOperand *> destOperands =
+          state.getAliasingOpOperand(opResult);
+      assert(destOperands.size() == 1 &&
+             "expected exactly one aliasing OpOperand");
+      assert(isa<ParallelInsertSliceOp>(destOperands.front()->getOwner()) &&
+             "expected ParallelInsertSliceOp");
+
+      // Nothing to do if there is no conflict.
+      if (state.isInPlace(*destOperands.front()))
+        continue;
+
+      // Create AllocTensorOp.
+      bool isYielded = state.isTensorYielded(opResult);
+      auto resultType = opResult.getType().cast<RankedTensorType>();
+      Value alloc = rewriter.create<bufferization::AllocTensorOp>(
+          op->getLoc(), resultType, /*dynamicDims=*/ValueRange(),
+          /*copy=*/destOperands.front()->get(),
+          /*escape=*/isYielded);
+
+      // Update terminator operand.
+      rewriter.updateRootInPlace(destOperands.front()->getOwner(),
+                                 [&]() { destOperands.front()->set(alloc); });
+    }
+
+    return success();
+  }
+
   LogicalResult bufferize(Operation *op, RewriterBase &b,
                           BufferizationState &state) const {
     OpBuilder::InsertionGuard g(b);
@@ -1010,6 +1170,11 @@
     return BufferRelation::Equivalent;
   }
 
+  LogicalResult resolveConflicts(Operation *op, RewriterBase &rewriter,
+                                 const AnalysisState &state) const {
+    return success();
+  }
+
   LogicalResult bufferize(Operation *op, RewriterBase &b,
                           BufferizationState &state) const {
     // Will be bufferized as part of ForeachThreadOp.
diff --git a/mlir/test/Dialect/SCF/one-shot-bufferize-tensor-copy-insertion.mlir b/mlir/test/Dialect/SCF/one-shot-bufferize-tensor-copy-insertion.mlir
new file mode 100644
--- /dev/null
+++ b/mlir/test/Dialect/SCF/one-shot-bufferize-tensor-copy-insertion.mlir
@@ -0,0 +1,135 @@
+// RUN: mlir-opt %s -tensor-copy-insertion="allow-return-allocs" -allow-unregistered-dialect -split-input-file | FileCheck %s
+// RUN: mlir-opt %s -tensor-copy-insertion="bufferize-function-boundaries allow-return-allocs" -split-input-file | FileCheck %s --check-prefix=CHECK-FUNC
+
+// CHECK-LABEL: func @scf_for(
+//  CHECK-SAME:     %[[A:.*]]: tensor<?xf32>, %[[B:.*]]: tensor<?xf32>
+func.func @scf_for(%A : tensor<?xf32>, %B : tensor<?xf32>,
+                   %lb : index, %ub : index, %step : index)
+  -> (tensor<?xf32>, tensor<?xf32>)
+{
+  // CHECK: %[[A_copy:.*]] = bufferization.alloc_tensor() copy(%[[A]]) {escape = false} : tensor<?xf32>
+  // CHECK: %[[B_copy:.*]] = bufferization.alloc_tensor() copy(%[[B]]) {escape = false} : tensor<?xf32>
+  // CHECK:   %[[for:.*]]:2 = scf.for {{.*}} iter_args(%[[iter1:.*]] = %[[A_copy]], %[[iter2:.*]] = %[[B_copy]])
+  %r0:2 = scf.for %i = %lb to %ub step %step iter_args(%tA = %A, %tB = %B)
+      -> (tensor<?xf32>, tensor<?xf32>)
+  {
+    // CHECK: scf.yield %[[iter1]], %[[iter2]]
+    scf.yield %tA, %tB : tensor<?xf32>, tensor<?xf32>
+  }
+
+  return %r0#0, %r0#1 : tensor<?xf32>, tensor<?xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @scf_for_swapping_yields(
+//  CHECK-SAME:     %[[A:.*]]: tensor<?xf32>, %[[B:.*]]: tensor<?xf32>
+func.func @scf_for_swapping_yields(%A : tensor<?xf32>, %B : tensor<?xf32>,
+                                   %lb : index, %ub : index, %step : index)
+  -> (tensor<?xf32>, tensor<?xf32>)
+{
+  // CHECK: %[[A_copy:.*]] = bufferization.alloc_tensor() copy(%[[A]]) {escape = false} : tensor<?xf32>
+  // CHECK: %[[B_copy:.*]] = bufferization.alloc_tensor() copy(%[[B]]) {escape = false} : tensor<?xf32>
+  // CHECK:   %[[for:.*]]:2 = scf.for {{.*}} iter_args(%[[iter1:.*]] = %[[A_copy]], %[[iter2:.*]] = %[[B_copy]])
+  %r0:2 = scf.for %i = %lb to %ub step %step iter_args(%tA = %A, %tB = %B)
+      -> (tensor<?xf32>, tensor<?xf32>)
+  {
+    // Yield tensors in different order.
+    // CHECK-DAG: %[[yield1:.*]] = bufferization.alloc_tensor() copy(%[[iter2]]) {escape = true} : tensor<?xf32>
+    // CHECK-DAG: %[[yield2:.*]] = bufferization.alloc_tensor() copy(%[[iter1]]) {escape = true} : tensor<?xf32>
+    // CHECK: scf.yield %[[yield1]], %[[yield2]]
+    scf.yield %tB, %tA : tensor<?xf32>, tensor<?xf32>
+  }
+
+  return %r0#0, %r0#1 : tensor<?xf32>, tensor<?xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @scf_while(
+//  CHECK-SAME:     %[[A:.*]]: tensor<5xi1>, %[[B:.*]]: tensor<5xi1>
+func.func @scf_while(%A: tensor<5xi1>, %B: tensor<5xi1>, %idx: index)
+  -> (tensor<5xi1>, tensor<5xi1>)
+{
+  // CHECK: %[[A_copy:.*]] = bufferization.alloc_tensor() copy(%[[A]]) {escape = false} : tensor<5xi1>
+  // CHECK: %[[B_copy:.*]] = bufferization.alloc_tensor() copy(%[[B]]) {escape = false} : tensor<5xi1>
+  // CHECK: %[[loop:.*]]:2 = scf.while (%[[w0:.*]] = %[[A_copy]], %[[w1:.*]] = %[[B_copy]]) {{.*}} {
+  %r0, %r1 = scf.while (%w0 = %A, %w1 = %B)
+      : (tensor<5xi1>, tensor<5xi1>) -> (tensor<5xi1>, tensor<5xi1>) {
+    // CHECK: %[[condition:.*]] = tensor.extract %[[w0]]
+    %condition = tensor.extract %w0[%idx] : tensor<5xi1>
+    // Yield tensors in different order.
+    // CHECK: scf.condition(%[[condition]]) %[[w0]], %[[w1]]
+    scf.condition(%condition) %w0, %w1 : tensor<5xi1>, tensor<5xi1>
+  } do {
+  ^bb0(%b0: tensor<5xi1>, %b1: tensor<5xi1>):
+    // CHECK: } do {
+    // CHECK: ^bb0(%[[b0:.*]]: tensor<5xi1>, %[[b1:.*]]: tensor<5xi1>):
+    // CHECK: scf.yield %[[b0]], %[[b1]]
+    // CHECK: }
+    scf.yield %b0, %b1 : tensor<5xi1>, tensor<5xi1>
+  }
+
+  return %r0, %r1 : tensor<5xi1>, tensor<5xi1>
+}
+
+// -----
+
+// CHECK-LABEL: func @scf_while_non_equiv_condition_and_body(
+//  CHECK-SAME:     %[[A:.*]]: tensor<5xi1>, %[[B:.*]]: tensor<5xi1>
+func.func @scf_while_non_equiv_condition_and_body(%A: tensor<5xi1>,
+                                                  %B: tensor<5xi1>,
+                                                  %idx: index)
+  -> (tensor<5xi1>, tensor<5xi1>)
+{
+  // CHECK: %[[A_copy:.*]] = bufferization.alloc_tensor() copy(%[[A]]) {escape = false} : tensor<5xi1>
+  // CHECK: %[[B_copy:.*]] = bufferization.alloc_tensor() copy(%[[B]]) {escape = false} : tensor<5xi1>
+  // CHECK: %[[loop:.*]]:2 = scf.while (%[[w0:.*]] = %[[A_copy]], %[[w1:.*]] = %[[B_copy]]) {{.*}} {
+  %r0, %r1 = scf.while (%w0 = %A, %w1 = %B)
+      : (tensor<5xi1>, tensor<5xi1>) -> (tensor<5xi1>, tensor<5xi1>) {
+    // CHECK: %[[condition:.*]] = tensor.extract %[[w0]]
+    %condition = tensor.extract %w0[%idx] : tensor<5xi1>
+    // Yield tensors in different order.
+    // CHECK-DAG: %[[yield0:.*]] = bufferization.alloc_tensor() copy(%[[w1]]) {escape = true} : tensor<5xi1>
+    // CHECK-DAG: %[[yield1:.*]] = bufferization.alloc_tensor() copy(%[[w0]]) {escape = true} : tensor<5xi1>
+    // CHECK: scf.condition(%[[condition]]) %[[yield0]], %[[yield1]]
+    scf.condition(%condition) %w1, %w0 : tensor<5xi1>, tensor<5xi1>
+  } do {
+  ^bb0(%b0: tensor<5xi1>, %b1: tensor<5xi1>):
+    // CHECK: } do {
+    // CHECK: ^bb0(%[[b0:.*]]: tensor<5xi1>, %[[b1:.*]]: tensor<5xi1>):
+    // CHECK-DAG: %[[yield2:.*]] = bufferization.alloc_tensor() copy(%[[b1]]) {escape = true} : tensor<5xi1>
+    // CHECK-DAG: %[[yield3:.*]] = bufferization.alloc_tensor() copy(%[[b0]]) {escape = true} : tensor<5xi1>
+    // CHECK: scf.yield %[[yield2]], %[[yield3]]
+    // CHECK: }
+    scf.yield %b1, %b0 : tensor<5xi1>, tensor<5xi1>
+  }
+
+  return %r0, %r1 : tensor<5xi1>, tensor<5xi1>
+}
+
+// -----
+
+// CHECK-LABEL: func @scf_foreach_thread_out_of_place(
+//  CHECK-SAME:     %[[arg0:.*]]: tensor<100xf32>, %[[arg1:.*]]: tensor<100xf32>
+// CHECK-FUNC-LABEL: func @scf_foreach_thread_out_of_place(
+func.func @scf_foreach_thread_out_of_place(%in: tensor<100xf32>,
+                                           %out: tensor<100xf32>) {
+  %c1 = arith.constant 1 : index
+  %num_threads = arith.constant 100 : index
+
+  // CHECK-FUNC-NOT: alloc_tensor
+  // CHECK: %[[alloc:.*]] = bufferization.alloc_tensor() copy(%[[arg1]]) {escape = false} : tensor<100xf32>
+  // CHECK: scf.foreach_thread
+  %result = scf.foreach_thread (%thread_idx) in (%num_threads) -> tensor<100xf32> {
+      // CHECK: tensor.extract_slice
+      // CHECK: scf.foreach_thread.perform_concurrently
+      // CHECK: scf.foreach_thread.parallel_insert_slice %{{.*}} into %[[alloc]]
+      %1 = tensor.extract_slice %in[%thread_idx][1][1] : tensor<100xf32> to tensor<1xf32>
+      scf.foreach_thread.perform_concurrently {
+        scf.foreach_thread.parallel_insert_slice %1 into %out[%thread_idx][1][1] :
+          tensor<1xf32> into tensor<100xf32>
+      }
+  }
+  return
+}