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* rename * restore * mappings * unedited tests+docs * docs * fixes * fix auto-sync breakage * cleanup * wip * wip * add fetch_images * remove einops dependency * update * fix * fix * fix * fix * fix * re-add * add batching * rework * fix * improve * add Leo as I am extending his work * cleanup * fix * cleanup * slow-test * fix * fix * fixes * deal with warning * rename modified llama classes * rework fetch_images * alternative implementation * cleanup * strict version * cleanup * [`IDEFICS`] Fix idefics ci (#25056) * Fix IDEFICS CI * fix test file * fixup * some changes to make tests pass * fix * fixup * Update src/transformers/models/idefics/configuration_idefics.py Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> --------- Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * remove compat checks * style * explain that Idefics is not for training from scratch * require pt>=2.0 * fix idefics vision config (#25092) * fix idefics vision config * fixup * clean * Update src/transformers/models/idefics/configuration_idefics.py --------- Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * cleanup * style * cleanup * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * upcase * sequence of images * handle the case with no images * Update src/transformers/image_processing_utils.py Co-authored-by: Victor SANH <victorsanh@gmail.com> * support pure lm take 2 * support tokenizer options * parameterize num_channels * fix upcase * s|IdeficsForCausalLM|IdeficsForVisionText2Text|g * manual to one line * addressing review * unbreak * remove clip dependency * fix test * consistency * PIL import * Idefics prefix * Idefics prefix * hack to make tests work * style * fix * fix * revert * try/finally * cleanup * clean up * move * [`IDEFICS`] Fix idefics config refactor (#25149) * refactor config * nuke init weights * more refactor * oops * remove visual question answering pipeline support * Update src/transformers/models/idefics/clip.py Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * Update src/transformers/models/idefics/modeling_idefics.py * cleanup * mv clip.py vision.py * tidyup --------- Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Stas Bekman <stas@stason.org> * fix * license * condition on pt * fix * style * fix * rm torchvision dependency, allow custom transforms * address review * rework device arg * add_eos_token * s/transforms/transform/ * fix top level imports * fix return value * cleanup * cleanup * fix * style * license * license * Update src/transformers/models/idefics/image_processing_idefics.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add a wrapper to freeze vision layears * tidyup * use the correct std/mean settings * parameterize values from config * add tests/models/idefics/test_image_processing_idefics.py * add test_processor_idefics.py * cleanup * cleanups * fix * fix * move to the right group * style * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add perceiver config * reset * missing arg docs * Apply suggestions from code review Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com> * address review comments * inject automatic end of utterance tokens (#25218) * inject automatic end of utterance tokens * fix * fix * fix * rework to not use the config * not end_of_utterance_token at the end * Update src/transformers/models/idefics/processing_idefics.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * address review * Apply suggestions from code review Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/image_processing_utils.py Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com> * [`Idefics`] add image_embeddings option in generate-related methods (#25442) * add image_embeddings option in generate-related methods * style * rename image_embeddings and allow perceiver embeddings precomputation * compute embeddings within generate * make is_encoder_decoder= True the default in config * nested if else fix * better triple check * switch if elif order for pixel values / img embeds * update model_kwargs perceiver only at the end * use _prepare_model_inputs instead of encoder_decoder logic * fix comment typo * fix config default for is_encoder_decoder * style * add typehints * precompute in forward * doc builder * style * pop instead of get image hidden states * Trigger CI * Update src/transformers/models/idefics/modeling_idefics.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/idefics/modeling_idefics.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * + indentation + style * simplify a bit the use_resampler logic using comments * update diocstrings * Trigger CI --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix rebase changes * unbreak #25237 - to be fixed in follow up PRs * is_composition = False * no longer needed --------- Co-authored-by: leot13 <leo.tronchon@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Victor SANH <victorsanh@gmail.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
2.9 KiB
IDEFICS
Overview
The IDEFICS model was proposed in OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh
The abstract from the paper is the following:
Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks that require reasoning over one or multiple images to generate a text. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELISC, we train an 80 billion parameters vision and language model on the dataset and obtain competitive performance on various multimodal benchmarks. We release the code to reproduce the dataset along with the dataset itself.
This model was contributed by HuggingFaceM4. The original code can be found here. (TODO: don't have a public link yet).
Idefics modeling code in Transformers is for finetuning and inferencing the pre-trained Idefics models.
To train a new Idefics model from scratch use the m4 codebase (a link will be provided once it's made public)
IdeficsConfig
autodoc IdeficsConfig
IdeficsModel
autodoc IdeficsModel - forward
IdeficsForVisionText2Text
autodoc IdeficsForVisionText2Text - forward
IdeficsImageProcessor
autodoc IdeficsImageProcessor - preprocess
IdeficsProcessor
autodoc IdeficsProcessor - call