IF II M V1.0

🚀 IF-II-M-v1.0

DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model. It can generate pictures with a new state-of-the-art level of 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.

Inspired by Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

🚀 Quick Start

Before using the model, you need to accept its usage conditions. The steps are as follows:

1. Make sure to have a Hugging Face account and be logged in.
2. Accept the license on the model card of DeepFloyd/IF-I-M-v1.0.
3. Make sure to login locally. Install huggingface_hub:

pip install huggingface_hub --upgrade

Run the login function in a Python shell:

from huggingface_hub import login

login()

And enter your Hugging Face Hub access token.

Next, install diffusers and dependencies:

pip install diffusers accelerate transformers safetensors sentencepiece

✨ Features

• High - performance Generation: Can generate pictures with a new state - of - the - art level of photorealism and language understanding.
• Efficient Model: Outperforms current state - of - the - art models, achieving a zero - shot FID - 30K score of 6.66 on the COCO dataset.

📦 Installation

Install the necessary libraries:

pip install diffusers accelerate transformers safetensors sentencepiece

💻 Usage Examples

Basic Usage

# Load all stages and offload to CPU
from diffusers import DiffusionPipeline
from diffusers.utils import pt_to_pil
import torch

# stage 1
stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-M-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_xformers_memory_efficient_attention()  # remove line if torch.__version__ >= 2.0.0
stage_1.enable_model_cpu_offload()

# stage 2
stage_2 = DiffusionPipeline.from_pretrained(
    "DeepFloyd/IF-II-M-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_xformers_memory_efficient_attention()  # remove line if torch.__version__ >= 2.0.0
stage_2.enable_model_cpu_offload()

# stage 3
safety_modules = {"feature_extractor": stage_1.feature_extractor, "safety_checker": stage_1.safety_checker, "watermarker": stage_1.watermarker}
stage_3 = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16)
stage_3.enable_xformers_memory_efficient_attention()  # remove line if torch.__version__ >= 2.0.0
stage_3.enable_model_cpu_offload()

# Retrieve Text Embeddings
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)

# Run stage 1
generator = torch.manual_seed(0)
image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt").images
pt_to_pil(image)[0].save("./if_stage_I.png")

# Run stage 2
image = stage_2(
    image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt"
).images
pt_to_pil(image)[0].save("./if_stage_II.png")

# Run stage 3
image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images
image[0].save("./if_stage_III.png")

Advanced Usage

There are multiple ways to speed up the inference time and lower the memory consumption even more with diffusers. To do so, please have a look at the Diffusers docs:

• 🚀 Optimizing for inference time
• ⚙️ Optimizing for low memory during inference

Diffusers dreambooth scripts also supports fine - tuning 🎨 IF. With parameter efficient finetuning, you can add new concepts to IF with a single GPU and ~28 GB VRAM.

📚 Documentation

For more in - detail information about how to use IF, please have a look at the IF blog post and the documentation 📖.

🔧 Technical Details

Model Details

Training Details

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 - II - M - v1.0 is a pixel - based diffusion cascade which uses T5 - Encoder embeddings (hidden states) to upscale image from 64px to 256px. During training:

• Images are cropped to square via shifted - center - crop augmentation (randomly shift from center up to 0.1 of size) and resized to 64px (low - res) and 256px (ground - truth) using Pillow==9.2.0 BICUBIC resampling with reducing_gap=None (it helps to avoid aliasing) and processed to tensor BxCxHxW.
• Low - res images are extra augmented by noise (q - sample methods) with the same diffusion configuration for cascade - I series. Uniform distributed randomised augmentation noising param (aug - level) is added to Unet as condition to process by trainable layers timestep embedding and linear projection with activation.
• Text prompts are encoded through open - sourced frozen T5 - v1_1 - xxl text - encoder (that completely was trained by Google team), random 10% of texts are dropped to empty string to add ability for classifier free guidance (CFG).
• The non - pooled output of the text encoder is fed into the projection (linear layer without activation) and is used in UNet backbone of the diffusion model via controlled hybrid self - and cross - attention.
• Also, the output of the text encode is pooled via attention - pooling (64 heads) and is used in time embed as additional features.
• Diffusion process is limited by 1000 discrete steps, with cosine beta schedule of noising image.
• The loss is a reconstruction objective between the noise that was added to the image and the prediction made by the UNet.
• The training process for checkpoint IF - II - M - v1.0 has 2_500_000 steps at resolution 256x256 on all datasets, OneCycleLR policy, few - bit backward GELU activations, optimizer AdamW8bit + DeepSpeed - Zero1, fully frozen T5 - Encoder.

Hardware: 24 x 8 x A100 GPUs Optimizer: AdamW8bit + DeepSpeed ZeRO - 1 Batch: 1536

📄 License

The model is licensed under the DeepFloyd IF License Agreement. The full license text is as follows:

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