Multi-modal recommenders (MRs) focus on leveraging the item modality features to facilitate user preferences modeling. Previous research mainly suffers from two limitations: (1) The pre-trained modality features are usually extracted by the encoders trained on general tasks (e.g., text classification), and thus inevitably contain the recommendation-irrelevant features. (2) Existing modality fusion mechanisms often diminish the contribution of features from weaker modalities, leading to biased fused representations. To address these challenges, we propose a novel Behavior Conditional Difussion model for Multi-Modal recommendation (BCDMM). Specifically, we first design a Behavior Multi-modal Diffusion (BMD) module to filter the recommendation-irrelevant noise within the pre-trained modality features. Then, we iteratively denoise the modality features with the guidance of user behavior signals to reconstruct the recommendation-related features. Next, we apply a Multi-modal Graph Fusion (MGF) module to explore the item modality latent structures. Moreover, we construct a modality fusion graph to capture the cross-modal complementary features for comprehensively modeling user preferences. Finally, a set of adversarial loss functions is used to balance the preservation of modality-specific and modality-shared features. Extensive experiments on three real-world datasets demonstrate the superiority of our method. We release our code at https://github.com/fanko79/BCDMM2025.