DeepFloyd IF By Stability AI - Is It Stable Diffusion XL or Version 3? We Review and Show How To Use

I review new amazing model DeepFloyd IF-I-XL by Stability AI and show how you can use it on a free Kaggle notebook step by step. #DeepFloyd IF is claimed to be the most advanced image generative model out there, with an FID-30K score of 6.66, beating DALL·E 2, Imagen, Parti & more.

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DeepFloyd IF GitHub repo ⤵️ https://github.com/deep-floyd/IF

DeepFloyd IF Official Website ⤵️ https://deepfloyd.ai/

DeepFloyd IF Kaggle NoteBook ⤵️ https://www.kaggle.com/furkangozukara/deepfloyd-if-4-3b-generator-of-pictures-video-vers

Generate your Hugging Face token ⤵️ https://huggingface.co/settings/tokens

DeepFloyd IF License Agreement To Accept ⤵️ https://huggingface.co/DeepFloyd/IF-I-XL-v1.0

Improved Kaggle Notebook file ⤵️ https://www.patreon.com/posts/enhanced-if-file-82253574

Kandinsky 2.1 Tutorial ⤵️ https://youtu.be/dYt9xJ7dnpU

0:00 Introduction to Stability AI DeepFloyd IF 0:29 How DeepFloyd IF is built and how does it work 0:51 Architecture of the DeepFloyd IF model 1:10 What makes DeepFloyd IF model better 1:55 Strongest part of DeepFloyd IF 2:17 Comparison between DeepFloyd IF and other models 3:16 More detailed architecture of DeepFloyd IF 3:39 Minimum requirements to use DeepFloyd IF 4:18 How to register a free Kaggle account 4:35 How to use DeepFloyd IF on a free Kaggle notebook step by step 5:23 How to contact Kaggle support to activate your Kaggle account for GPU usage 5:40 Other Kaggle notebook settings 5:50 Start Kaggle session and installation 7:50 How to get your Hugging Face token 9:07 How to accept DeepFloyd IF license agreement 9:41 Continuing the installation of the DeepFloyd IF libraries on Kaggle 11:09 Starting image generation with DeepFloyd IF 12:55 Seeing the first ourselves generated images by DeepFloyd IF 14:45 Where is saved generated images 15:15 DeepFloyd IF vs SD 1.5 Custom Model Rev Animated comparison 16:05 DeepFloyd IF vs Kandinsky 2.1 comparison 16:18 DeepFloyd IF vs Stable Diffusion 1.5 base model comparison 16:39 DeepFloyd IF vs Stable Diffusion 2.1 768px base model comparison 16:46 Text generation performance comparison of DeepFloyd IF with other models 17:16 How to disable IF watermark from generated images 17:43 Results of text written image generation 18:35 DeepFloyd IF vs other models text generation comparison 19:19 Experiments of 4 different prompts 20:45 How to download all of the images as a zip file. Utilize ChatGPT to get the code 22:00 Examples provided on DeepFloyd AI and testing them 22:16 How to generate multiple different images with same prompt by using random seeds 24:07 How to delete all generated images in the runtime folder of Kaggle 25:37 How to used downloaded enhanced Kaggle notebook

IF-I-XL-v1.0 DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for #photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of 6.66 on the COCO dataset.

Developed by: DeepFloyd, StabilityAI Model type: pixel-based text-to-image cascaded diffusion model Cascade Stage: I Num Parameters: 4.3B Language(s): primarily English and, to a lesser extent, other Romance languages License: DeepFloyd IF License Agreement Model Description: DeepFloyd-IF is modular composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024. All stages of the model utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling

Training Data:

1.2B text-image pairs (based on LAION-A and few additional internal datasets)

Test/Valid parts of datasets are not used at any cascade and stage of training. Valid part of COCO helps to demonstrate "online" loss behaviour during training (to catch incident and other problems), but dataset is never used for train.

Training Procedure: IF-I-XL-v1.0 is a pixel-based diffusion cascade which uses T5-Encoder embeddings (hidden states) to generate 64px image. During training,

thumbnail by twitter @artimindArt ... https://www.youtube.com/watch?v=R2fEocf-MU8