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--- |
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license: creativeml-openrail-m |
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tags: |
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- text-to-image |
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--- |
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# Karlo v1 alpha |
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Karlo is a text-conditional image generation model based on OpenAI's unCLIP architecture with the improvement over the standard super-resolution model from 64px to 256px, recovering high-frequency details only in the small number of denoising steps. |
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* [Original codebase](https://github.com/kakaobrain/karlo) |
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## Usage |
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Karlo is available in diffusers! |
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```python |
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pip install diffusers transformers accelerate safetensors |
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``` |
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### Text to image |
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```python |
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from diffusers import UnCLIPPipeline |
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import torch |
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pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16) |
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pipe = pipe.to('cuda') |
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prompt = "a high-resolution photograph of a big red frog on a green leaf." |
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image = pipe([prompt]).images[0] |
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image.save("./frog.png") |
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``` |
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### Image variation |
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```python |
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from diffusers import UnCLIPImageVariationPipeline |
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import torch |
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from PIL import Image |
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pipe = UnCLIPImageVariationPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=torch.float16) |
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pipe = pipe.to('cuda') |
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image = Image.open("./frog.png") |
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image = pipe(image).images[0] |
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image.save("./frog-variation.png") |
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``` |
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## Model Architecture |
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### Overview |
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Karlo is a text-conditional diffusion model based on unCLIP, composed of prior, decoder, and super-resolution modules. In this repository, we include the improved version of the standard super-resolution module for upscaling 64px to 256px only in 7 reverse steps, as illustrated in the figure below: |
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<p float="left"> |
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<img src="https://raw.githubusercontent.com/kakaobrain/karlo/main/assets/improved_sr_arch.jpg"/> |
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</p> |
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In specific, the standard SR module trained by DDPM objective upscales 64px to 256px in the first 6 denoising steps based on the respacing technique. Then, the additional fine-tuned SR module trained by [VQ-GAN](https://compvis.github.io/taming-transformers/)-style loss performs the final reverse step to recover high-frequency details. We observe that this approach is very effective to upscale the low-resolution in a small number of reverse steps. |
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### Details |
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We train all components from scratch on 115M image-text pairs including COYO-100M, CC3M, and CC12M. In the case of Prior and Decoder, we use ViT-L/14 provided by OpenAI’s [CLIP repository](https://github.com/openai/CLIP). Unlike the original implementation of unCLIP, we replace the trainable transformer in the decoder into the text encoder in ViT-L/14 for efficiency. In the case of the SR module, we first train the model using the DDPM objective in 1M steps, followed by additional 234K steps to fine-tune the additional component. The table below summarizes the important statistics of our components: |
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| | Prior | Decoder | SR | |
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|:------|----:|----:|----:| |
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| CLIP | ViT-L/14 | ViT-L/14 | - | |
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| #param | 1B | 900M | 700M + 700M | |
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| #optimization steps | 1M | 1M | 1M + 0.2M | |
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| #sampling steps | 25 | 50 (default), 25 (fast) | 7 | |
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|Checkpoint links| [ViT-L-14](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt), [ViT-L-14 stats](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th), [model](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt) | [model](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt) | [model](https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt) | |
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In the checkpoint links, ViT-L-14 is equivalent to the original version, but we include it for convenience. We also remark that ViT-L-14-stats is required to normalize the outputs of the prior module. |
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### Evaluation |
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We quantitatively measure the performance of Karlo-v1.0.alpha in the validation split of CC3M and MS-COCO. The table below presents CLIP-score and FID. To measure FID, we resize the image of the shorter side to 256px, followed by cropping it at the center. We set classifier-free guidance scales for prior and decoder to 4 and 8 in all cases. We observe that our model achieves reasonable performance even with 25 sampling steps of decoder. |
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CC3M |
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| Sampling step | CLIP-s (ViT-B/16) | FID (13k from val)| |
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|:------|----:|----:| |
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| Prior (25) + Decoder (25) + SR (7) | 0.3081 | 14.37 | |
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| Prior (25) + Decoder (50) + SR (7) | 0.3086 | 13.95 | |
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MS-COCO |
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| Sampling step | CLIP-s (ViT-B/16) | FID (30k from val)| |
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|:------|----:|----:| |
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| Prior (25) + Decoder (25) + SR (7) | 0.3192 | 15.24 | |
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| Prior (25) + Decoder (50) + SR (7) | 0.3192 | 14.43 | |
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For more information, please refer to the upcoming technical report. |
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### Training Details |
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This alpha version of Karlo is trained on 115M image-text pairs, |
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including [COYO](https://github.com/kakaobrain/coyo-dataset)-100M high-quality subset, CC3M, and CC12M. |
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For those who are interested in a better version of Karlo trained on more large-scale high-quality datasets, |
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please visit the landing page of our application [B^DISCOVER](https://bdiscover.kakaobrain.com/). |
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## BibTex |
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If you find this repository useful in your research, please cite: |
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``` |
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@misc{kakaobrain2022karlo-v1-alpha, |
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title = {Karlo-v1.0.alpha on COYO-100M and CC15M}, |
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author = {Donghoon Lee, Jiseob Kim, Jisu Choi, Jongmin Kim, Minwoo Byeon, Woonhyuk Baek and Saehoon Kim}, |
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year = {2022}, |
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howpublished = {\url{https://github.com/kakaobrain/karlo}}, |
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} |
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``` |
