Remove unneeded dimensions arguments

Author: saddam213

OnnxStack.StableDiffusion/Helpers/TensorHelper.cs

@@ -25,24 +25,6 @@ public static DenseTensor<T> CreateTensor<T>(T[] data, ReadOnlySpan<int> dimensi
         }
 
 
-        /// <summary>
-        /// Divides the tensor by float.
-        /// </summary>
-        /// <param name="tensor">The data.</param>
-        /// <param name="value">The value.</param>
-        /// <param name="dimensions">The dimensions.</param>
-        /// <returns></returns>
-        public static DenseTensor<float> DivideTensorByFloat(this DenseTensor<float> tensor, float value, ReadOnlySpan<int> dimensions)
-        {
-            var divTensor = new DenseTensor<float>(dimensions);
-            for (int i = 0; i < tensor.Length; i++)
-            {
-                divTensor.SetValue(i, tensor.GetValue(i) / value);
-            }
-            return divTensor;
-        }
-
-
         /// <summary>
         /// Divides the tensor by float.
         /// </summary>
@@ -131,29 +113,15 @@ public static DenseTensor<float> SumTensors(this DenseTensor<float>[] tensors, R
         }
 
 
-        /// <summary>
-        /// Duplicates the specified tensor.
-        /// </summary>
-        /// <param name="tensor">The data.</param>
-        /// <param name="dimensions">The dimensions.</param>
-        /// <returns></returns>
-        public static DenseTensor<float> Duplicate(this DenseTensor<float> tensor, ReadOnlySpan<int> dimensions)
-        {
-            var dupTensor = tensor.Concat(tensor).ToArray();
-            return CreateTensor(dupTensor, dimensions);
-        }
-
-
         /// <summary>
         /// Subtracts the tensors.
         /// </summary>
-        /// <param name="tensor">The tensor.</param>
+        /// <param name="tensor">The sample.</param>
         /// <param name="subTensor">The sub tensor.</param>
-        /// <param name="dimensions">The dimensions.</param>
         /// <returns></returns>
-        public static DenseTensor<float> SubtractTensors(this DenseTensor<float> tensor, DenseTensor<float> subTensor, ReadOnlySpan<int> dimensions)
+        public static DenseTensor<float> SubtractTensors(this DenseTensor<float> tensor, DenseTensor<float> subTensor)
         {
-            var result = new DenseTensor<float>(dimensions);
+            var result = new DenseTensor<float>(tensor.Dimensions);
             for (var i = 0; i < tensor.Length; i++)
             {
                 result.SetValue(i, tensor.GetValue(i) - subTensor.GetValue(i));
@@ -162,18 +130,6 @@ public static DenseTensor<float> SubtractTensors(this DenseTensor<float> tensor,
         }
 
 
-        /// <summary>
-        /// Subtracts the tensors.
-        /// </summary>
-        /// <param name="tensor">The sample.</param>
-        /// <param name="subTensor">The sub tensor.</param>
-        /// <returns></returns>
-        public static DenseTensor<float> SubtractTensors(this DenseTensor<float> tensor, DenseTensor<float> subTensor)
-        {
-            return tensor.SubtractTensors(subTensor, tensor.Dimensions);
-        }
-
-
         /// <summary>
         /// Reorders the tensor.
         /// </summary>
@@ -196,7 +152,6 @@ public static DenseTensor<float> ReorderTensor(this DenseTensor<float> tensor, R
         }
 
 
-
         /// <summary>
         /// Clips the specified Tensor valuse to the specified minimum/maximum.
         /// </summary>
@@ -215,10 +170,6 @@ public static DenseTensor<float> Clip(this DenseTensor<float> tensor, float minV
         }
 
 
-
-
-
-
         /// <summary>
         /// Computes the absolute values of the Tensor
         /// </summary>

OnnxStack.StableDiffusion/Schedulers/DDPMScheduler.cs

@@ -174,7 +174,7 @@ private DenseTensor<float> GetPredictedSample(DenseTensor<float> modelOutput, De
             {
                 //pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
                 var sampleBeta = sample.SubtractTensors(modelOutput.MultipleTensorByFloat((float)Math.Sqrt(betaProdT)));
-                predOriginalSample = sampleBeta.DivideTensorByFloat((float)Math.Sqrt(alphaProdT), sampleBeta.Dimensions);
+                predOriginalSample = sampleBeta.DivideTensorByFloat((float)Math.Sqrt(alphaProdT));
             }
             else if (Options.PredictionType == PredictionType.Sample)
             {

OnnxStack.StableDiffusion/Schedulers/EulerAncestralScheduler.cs

@@ -91,7 +91,7 @@ public override DenseTensor<float> ScaleInput(DenseTensor<float> sample, int tim
             sigma = (float)Math.Sqrt(Math.Pow(sigma, 2) + 1);
 
             // Divide sample tensor shape {2,4,(H/8),(W/8)} by sigma
-            return sample.DivideTensorByFloat(sigma, sample.Dimensions);
+            return sample.DivideTensorByFloat(sigma);
         }
 
 
@@ -124,7 +124,7 @@ public override DenseTensor<float> Step(DenseTensor<float> modelOutput, int time
             // 2. Convert to an ODE derivative
             var derivative = sample
                 .SubtractTensors(predOriginalSample)
-                .DivideTensorByFloat(sigma, predOriginalSample.Dimensions);
+                .DivideTensorByFloat(sigma);
 
             var delta = sigmaDown - sigma;
             var prevSample = sample.AddTensors(derivative.MultipleTensorByFloat(delta));

OnnxStack.StableDiffusion/Schedulers/LMSScheduler.cs

@@ -95,7 +95,7 @@ public override DenseTensor<float> ScaleInput(DenseTensor<float> sample, int tim
             sigma = (float)Math.Sqrt(Math.Pow(sigma, 2) + 1);
 
             // Divide sample tensor shape {2,4,(H/8),(W/8)} by sigma
-            return sample.DivideTensorByFloat(sigma, sample.Dimensions);
+            return sample.DivideTensorByFloat(sigma);
         }
 
 
@@ -118,7 +118,7 @@ public override DenseTensor<float> Step(DenseTensor<float> modelOutput, int time
             // 2. Convert to an ODE derivative
             var derivativeSample = sample
                 .SubtractTensors(predOriginalSample)
-                .DivideTensorByFloat(sigma, sample.Dimensions);
+                .DivideTensorByFloat(sigma);
 
             _derivatives.Enqueue(derivativeSample);
             if (_derivatives.Count > order)