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- model_index.json +33 -0
README.md
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---
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license: mit
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---
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license: mit
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- image-to-image
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- art
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widget:
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- src: >-
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https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png
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prompt: Cartoonize the following image
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datasets:
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- instruction-tuning-sd/cartoonization
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---
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# Instruction-tuned Stable Diffusion for Cartoonization (Fine-tuned)
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This pipeline is an 'instruction-tuned' version of [Stable Diffusion (v1.5)](https://huggingface.co/runwayml/stable-diffusion-v1-5). It was
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fine-tuned from the existing [InstructPix2Pix checkpoints](https://huggingface.co/timbrooks/instruct-pix2pix).
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## Pipeline description
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Motivation behind this pipeline partly comes from [FLAN](https://huggingface.co/papers/2109.01652) and partly
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comes from [InstructPix2Pix](https://huggingface.co/papers/2211.09800). The main idea is to first create an
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instruction prompted dataset (as described in [our blog](https://hf.co/blog/instruction-tuning-sd)) and then conduct InstructPix2Pix style
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training. The end objective is to make Stable Diffusion better at following specific instructions
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that entail image transformation related operations.
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<p align="center">
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<img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/instruction-tuning-sd.png" width=600/>
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</p>
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Follow [this post](https://hf.co/blog/instruction-tuning-sd) to know more.
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## Training procedure and results
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Training was conducted on [instruction-tuning-sd/cartoonization](https://huggingface.co/datasets/instruction-tuning-sd/cartoonization) dataset. Refer to
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[this repository](https://github.com/huggingface/instruction-tuned-sd) to know more. The training logs can be found [here](https://wandb.ai/sayakpaul/instruction-tuning-sd?workspace=user-sayakpaul).
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Here are some results dervied from the pipeline:
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<p align="center">
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<img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/cartoonization_results.jpeg" width=600/>
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</p>
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## Intended uses & limitations
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You can use the pipeline for performing cartoonization with an input image and an input prompt.
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### How to use
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Here is how to use this model:
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```python
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import torch
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from diffusers import StableDiffusionInstructPix2PixPipeline
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from diffusers.utils import load_image
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model_id = "instruction-tuning-sd/cartoonizer"
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pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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model_id, torch_dtype=torch.float16, use_auth_token=True
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).to("cuda")
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image_path = "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
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image = load_image(image_path)
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image = pipeline("Cartoonize the following image", image=image).images[0]
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image.save("image.png")
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```
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For notes on limitations, misuse, malicious use, out-of-scope use, please refer to the model card
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[here](https://huggingface.co/runwayml/stable-diffusion-v1-5).
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## Citation
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**FLAN**
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```bibtex
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@inproceedings{
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wei2022finetuned,
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title={Finetuned Language Models are Zero-Shot Learners},
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author={Jason Wei and Maarten Bosma and Vincent Zhao and Kelvin Guu and Adams Wei Yu and Brian Lester and Nan Du and Andrew M. Dai and Quoc V Le},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=gEZrGCozdqR}
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}
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```
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**InstructPix2Pix**
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```bibtex
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@InProceedings{
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brooks2022instructpix2pix,
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author = {Brooks, Tim and Holynski, Aleksander and Efros, Alexei A.},
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title = {InstructPix2Pix: Learning to Follow Image Editing Instructions},
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booktitle = {CVPR},
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year = {2023},
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}
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```
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**Instruction-tuning for Stable Diffusion blog**
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```bibtex
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@article{
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Paul2023instruction-tuning-sd,
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author = {Paul, Sayak},
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title = {Instruction-tuning Stable Diffusion with InstructPix2Pix},
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journal = {Hugging Face Blog},
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year = {2023},
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note = {https://huggingface.co/blog/instruction-tuning-sd},
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}
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```
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model_index.json
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{
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"_class_name": "StableDiffusionInstructPix2PixPipeline",
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"_diffusers_version": "0.15.0.dev0",
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"feature_extractor": [
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"transformers",
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"CLIPImageProcessor"
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],
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"requires_safety_checker": false,
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"safety_checker": [
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"stable_diffusion",
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"StableDiffusionSafetyChecker"
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],
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"scheduler": [
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"diffusers",
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"EulerAncestralDiscreteScheduler"
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],
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"text_encoder": [
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"transformers",
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"CLIPTextModel"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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],
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"unet": [
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"diffusers",
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"UNet2DConditionModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKL"
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]
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}
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