We propose a novel flame generation algorithm, called fire-GAN, based on the HistoGAN algorithm to solve the issues of low quality and complex color control of flame images produced by a generative adversarial network. First, flame image segmentation is introduced in the image preprocessing link to remove background interference from the network, reduce the flame shape distortion and color distortion. Second, the roundness loss function is suggested to increase the focus of the network during training on the intricacy of the flame contour. Finally, data enhancement is implemented in the generator and discriminator to maintain the network stability during training and prevent gradient explosion. The experimental results demonstrate that the average RGB error between the flame generated by fire-GAN and the target flame is 2.6%, the Fréchet inception distance (FID) is 59.23, and the inception score (IS) is 2.81. The outcomes demonstrate the feasibility of the fire-GAN to produce a flame image with color, definition, and authenticity levels quite comparable to the target flame image.