IF I M V1.0

🚀 IF-I-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

📚 Documentation

Model Details

💻 Usage Examples

Basic Usage

IF is integrated with the 🤗 Hugging Face 🧨 diffusers library, which is optimized to run on GPUs with as little as 14 GB of VRAM.

Before you can use IF, you need to accept its usage conditions. To do so:

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 we install diffusers and dependencies:

pip install diffusers accelerate transformers safetensors sentencepiece

And we can now run the model locally.

By default diffusers makes use of model cpu offloading to run the whole IF pipeline with as little as 14 GB of VRAM.

If you are using torch>=2.0.0, make sure to remove all enable_xformers_memory_efficient_attention() functions.

Advanced 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"'

# text embeds
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")

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

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

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.

🔧 Technical Details

Training

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-M-v1.0 is the smallest (from IF series) pixel-based diffusion cascade which uses T5-Encoder embeddings (hidden states) to generate 64px image. 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 using Pillow==9.2.0 BICUBIC resampling with reducing_gap=None (it helps to avoid aliasing) and processed to tensor BxCxHxW

• 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-I-M-v1.0 has 2_500_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, SiLU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder

Hardware

12 x 8 x A100 GPUs

Optimizer

AdamW8bit + DeepSpeed ZeRO-1

Batch

3072

Learning rate

one-cycle cosine strategy, warmup 10000 steps, start_lr=4e-6, max_lr=1e-4, final_lr=1e-8

Evaluation Results

FID-30K: 8.86

📄 License

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

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