{"url":"/method/film-module","slug":"film-module","name":"FiLM Module","full_name":"FiLM Module","full_name_withheld":false,"description_markdown":"The **Feature-wise linear modulation** (**FiLM**) module combines information from both noisy waveform and input mel-spectrogram. It is used in the [WaveGrad](https://paperswithcode.com/method/wavegrad) model. The authors also added iteration index $n$ which indicates the noise level of the input waveform by using the [Transformer](https://paperswithcode.com/method/transformer) sinusoidal positional embedding. To condition on the noise level directly, $n$ is replaced by $\\sqrt{\\bar{\\alpha}}$ and a linear scale $C = 5000$ is applied. The FiLM module produces both scale and bias vectors given inputs, which are used in a UBlock for feature-wise affine transformation as:\r\n\r\n$$ \\gamma\\left(D, \\sqrt{\\bar{\\alpha}}\\right) \\odot U + \\zeta\\left(D, \\sqrt{\\bar{\\alpha}}\\right) $$\r\n\r\nwhere $\\gamma$ and $\\zeta$ correspond to the scaling and shift vectors from the FiLM module, $D$ is the output from corresponding [DBlock](https://paperswithcode.com/method/dblock), $U$ is an intermediate output in the UBlock.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2009.00713v2","title":"WaveGrad: Estimating Gradients for Waveform Generation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Audio","area_id":"audio","collection":"Audio Model Blocks","url":"/methods/category/audio-model-blocks","pwc_aliases":[]}],"n_papers_tagged":7,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"GLA-Grad: A Griffin-Lim Extended Waveform Generation Diffusion Model","date":"2024-02-09","arxiv_id":"2402.15516","n_code_links":0,"syntology":null},{"paper":"/paper/bddm-bilateral-denoising-diffusion-models-for-1","title":"BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis","date":"2022-03-25","arxiv_id":"2203.13508","n_code_links":1,"syntology":{"ran":5,"of":6,"unverified":1,"pointer_only":0}},{"paper":null,"title":"InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training","date":"2022-02-08","arxiv_id":"2202.03751","n_code_links":0,"syntology":null},{"paper":null,"title":"Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives","date":"2021-12-26","arxiv_id":"2112.13339","n_code_links":0,"syntology":null},{"paper":"/paper/vocbench-a-neural-vocoder-benchmark-for","title":"VocBench: A Neural Vocoder Benchmark for Speech Synthesis","date":"2021-12-06","arxiv_id":"2112.03099","n_code_links":1,"syntology":null},{"paper":"/paper/wavegrad-2-iterative-refinement-for-text-to","title":"WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis","date":"2021-06-17","arxiv_id":"2106.09660","n_code_links":3,"syntology":{"ran":2,"of":2,"unverified":0,"pointer_only":0}},{"paper":"/paper/wavegrad-estimating-gradients-for-waveform","title":"WaveGrad: Estimating Gradients for Waveform Generation","date":"2020-09-02","arxiv_id":"2009.00713","n_code_links":7,"syntology":{"ran":1,"of":3,"unverified":2,"pointer_only":1}}],"papers_shown":7,"tasks":[{"task":"/task/speech-synthesis","name":"Speech Synthesis","papers":5},{"task":"/task/denoising","name":"Denoising","papers":2},{"task":"/task/image-generation","name":"Image Generation","papers":2},{"task":"/task/text-to-speech-synthesis","name":"Text-To-Speech Synthesis","papers":2},{"task":"/task/text-to-speech","name":"Text to Speech","papers":1},{"task":"/task/text-to-speech-1","name":"text-to-speech","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":3},{"year":"2022","papers":2},{"year":"2024","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/film-module"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}