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diffusers/docs/source/en/api/pipelines/blip_diffusion.md
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Ayush Mangal 157c9011d8 Add BLIP Diffusion (#4388)
* Add BLIP Diffusion skeleton

* Add other model components

* Add BLIP2, need to change it for now

* Fix pipeline imports

* Load pretrained ViT

* Make qformer fwd pass same

* Replicate fwd passes

* Fix device bug

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* Remove extra functions from Blip2

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* Add controlnet

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* Fix device

* Add fast tests

* Update conversion script

* Fix checkpoint conversion script

* Integrate review changes

* Integrate reivew changes

* Remove unused functions from test

* Reuse HF image processor in Cond image

* Create new BlipImageProcessor based on transfomers

* Fix image preprocessor

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* Fix controlnet preprocessing

* Fix blip diffusion test

* Add controlnet test

* Add initial doc strings

* Integrate review changes

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* Update examples

* Remove DDIM comments

* Add copied from for prepare_latents

* Add type anotations

* Add docstrings

* Do black formatting

* Add batch support

* Make tests pass

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* Black formatting

* Fix progress bar

* Fix some licensing comments

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* Refactor controlnet

* Make tests faster

* Edit examples

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* Add doc

* Minor

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Move controlnet pipeline

* Make tests faster

* Fix imports

* Fix formatting

* Fix make errors

* Fix make errors

* Minor

* Add suggested doc changes

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Edit docs

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* Update examples

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* Update docs/source/en/api/pipelines/blip_diffusion.md

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
2023-09-21 17:05:35 +01:00

2.4 KiB

Blip Diffusion

Blip Diffusion was proposed in BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing. It enables zero-shot subject-driven generation and control-guided zero-shot generation.

The abstract from the paper is:

Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications.

The original codebase can be found at salesforce/LAVIS. You can find the official BLIP Diffusion checkpoints under the hf.co/SalesForce organization.

BlipDiffusionPipeline and BlipDiffusionControlNetPipeline were contributed by ayushtues.

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

BlipDiffusionPipeline

autodoc BlipDiffusionPipeline - all - call

BlipDiffusionControlNetPipeline

autodoc BlipDiffusionControlNetPipeline - all - call