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Parameters:

a = -1.24458; b = -1.25191; c = -1.815908; d = -1.90866;

Compiled function used for iteration:

cf = Compile[{{pt, _Real, 1}},
   {Sin[a pt[[2]]] + c Cos[pt[[1]]],
    Sin[b pt[[1]]] + d Cos[pt[[2]]]},
   CompilationOptions -> {"InlineExternalDefinitions" -> True}
   ];

Iterate, rescale the result to the box {{0,1},{0,1}}, histogram it, convert to an image, and finally apply a gamma adjustment (^0.5) for better visibility.

im = ImageAdjust@Image@BinCounts[
     Rescale@NestList[cf, {0., 0.}, 1000000],
     1/500., 1/500.
     ];
ColorNegate[im^0.5]

You may want to throw in a Colorize too.

With 10 million points and $\gamma = 1/3$, I get

Here's a LibraryLink implementation with LTemplate.

Create class and initialize with the the desired $a,b,c,d$ parameters and image width.

clifford = Make[CliffordAttractor];    
clifford@"init"[{-1.8, -2.0, -0.5, -0.9}, 600]

Compute 50 million iterations. If the image is not of sufficient quality, more iterations can be computed without losing the old data.

clifford@"compute"[50000000] // AbsoluteTiming
(* {3.1645, Null} *)

Visualize with a custom colour function:

Colorize[
 ImageAdjust[clifford@"image"[]]^0.1,
 ColorFunction -> (Blend[{White, RGBColor[0.87, 0.94, 1], RGBColor[0.48, 0.33333, 0.66667], Red}, #] &)
 ]

Check the current $(x,y)$ value:

clifford@"state"[]
(* {-0.773525, 1.1536} *)

The library code follows. Admittedly, this could have been done with a single function that takes the parameters and returns an image. To refine the result, we could have averaged multiple returned images.

Here I wanted to demonstrate how to maintain a state within the library and update it or retrieve information about it as needed.

Needs["LTemplate`"]
SetDirectory[$TemporaryDirectory];

template =
  LClass["CliffordAttractor",
   {LFun["init", {{Real, 1, "Constant"} (* {a,b,c,d} *), Integer (* image width *)}, "Void"],
    LFun["setState", {{Real, 1, "Constant"} (* {x,y} *)}, "Void"],
    LFun["state", {}, {Real, 1}], (* get {x,y} *)
    
    LFun["compute", {Integer (* iterations *)}, "Void"],
    LFun["image", {}, Image]}
   ];

code = "
  using namespace mma;
  
  class CliffordAttractor {
    double a = 0.0, b = 0.0, c = 0.0, d = 0.0;
    double x = 0.0, y = 0.0;
  
    ImageRef<float> *im = nullptr;
  
    double w, h; // image half-width and half-height in real coordinates
  
    void free() { 
        if (im) {
            im->free();
            delete im;
        }
    }
  
  public:
    ~CliffordAttractor() { free(); }
  
    void init(RealTensorRef param, mint size) {
        massert(param.size() == 4);
  
        a = param[0]; b = param[1]; c = param[2]; d = param[3];
        w = 1+std::abs(c);
        h = 1+std::abs(d);
  
        free();     
        im = new ImageRef<float>(makeImage<float>(size, std::ceil(size * (h/w))));
        std::fill(im->begin(), im->end(), 0.0);
    }
  
    void setState(mma::RealTensorRef state) {
        massert(state.size() == 2);
        x = state[0]; y = state[1];
    } 
  
    mma::RealTensorRef state() const { return mma::makeVector<double>({x,y}); }
  
    void compute(mint n) {
        massert(im);
        for (mint i=0; i < n; ++i) {
            double newx, newy;
            newx = std::sin(a*y) + c*std::cos(a*x);
            newy = std::sin(b*x) + d*std::cos(b*y);
            x = newx; y = newy;
            (*im)( (im->rows()-1) * (y+h)/(2*h), (im->cols()-1) * (x+w)/(2*w) ) += 1;           
        }
    }
  
    GenericImageRef image() const { massert(im); return im->clone(); }
  };
  ";
Export["CliffordAttractor.h", code, "String"];

CompileTemplate[template, "CompileOptions" -> {"-O3 -ffast-math"}]

LoadTemplate[template]