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[](https://pepy.tech/project/deepfloyd_if)
# IF by DeepFloyd Lab at [StabilityAI](https://stability.ai/)
We introduce DeepFloyd IF, a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. DeepFloyd IF is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules: a base model that generates 64x64 px image based on text prompt and two super-resolution models, each designed to generate images of increasing resolution: 256x256 px and 1024x1024 px. 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. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset. Our work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis.
*Inspired by* [*Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding*](https://arxiv.org/pdf/2205.11487.pdf)
## Minimum requirements to use all IF models:
- 16GB vRAM for IF-I-XL (4.3B text to 64x64 base module) & IF-II-L (1.2B to 256x256 upscaler module)
- 24GB vRAM for IF-I-XL (4.3B text to 64x64 base module) & IF-II-L (1.2B to 256x256 upscaler module) & Stable x4 (to 1024x1024 upscaler)
- `xformers` and set env variable `FORCE_MEM_EFFICIENT_ATTN=1`
## Quick Start
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/deepfloyd_if_free_tier_google_colab.ipynb)
[](https://huggingface.co/spaces/DeepFloyd/IF)
```shell
pip install deepfloyd_if==1.0.1
pip install xformers==0.0.16
pip install git+https://github.com/openai/CLIP.git --no-deps
```
## Local notebooks
[](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0/blob/main/notebooks/pipes-DeepFloyd-IF-v1.0.ipynb)
[](https://www.kaggle.com/code/shonenkov/deepfloyd-if-4-3b-generator-of-pictures)
The Dream, Style Transfer, Super Resolution or Inpainting modes are avaliable in a Jupyter Notebook [here](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0/blob/main/notebooks/pipes-DeepFloyd-IF-v1.0.ipynb).
## Integration with π€ Diffusers
IF is also integrated with the π€ Hugging Face [Diffusers library](https://github.com/huggingface/diffusers/).
Diffusers runs each stage individually allowing the user to customize the image generation process as well as allowing to inspect intermediate results easily.
### Example
Before you can use IF, you need to accept its usage conditions. To do so:
1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be loggin in
2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
3. Make sure to login locally. Install `huggingface_hub`
```sh
pip install huggingface_hub --upgrade
```
run the login function in a Python shell
```py
from huggingface_hub import login
login()
```
and enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens).
Next we install `diffusers` and dependencies:
```sh
pip install diffusers accelerate transformers safetensors
```
And we can now run the model locally.
By default `diffusers` makes use of [model cpu offloading](https://huggingface.co/docs/diffusers/optimization/fp16#model-offloading-for-fast-inference-and-memory-savings) 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 **delete all** `enable_xformers_memory_efficient_attention()`
functions.
```py
from diffusers import DiffusionPipeline
from diffusers.utils import pt_to_pil
import torch
# stage 1
stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-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-L-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()
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)
generator = torch.manual_seed(0)
# stage 1
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")
# 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")
# 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:
- π [Optimizing for inference time](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-speed)
- βοΈ [Optimizing for low memory during inference](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-memory)
For more in-detail information about how to use IF, please have a look at [the IF blog post](https://huggingface.co/blog/if) and [the documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/if) π.
## Run the code locally
### Loading the models into VRAM
```python
from deepfloyd_if.modules import IFStageI, IFStageII, StableStageIII
from deepfloyd_if.modules.t5 import T5Embedder
device = 'cuda:0'
if_I = IFStageI('IF-I-XL-v1.0', device=device)
if_II = IFStageII('IF-II-L-v1.0', device=device)
if_III = StableStageIII('stable-diffusion-x4-upscaler', device=device)
t5 = T5Embedder(device="cpu")
```
### I. Dream
Dream is the text-to-image mode of the IF model
```python
from deepfloyd_if.pipelines import dream
prompt = 'ultra close-up color photo portrait of rainbow owl with deer horns in the woods'
count = 4
result = dream(
t5=t5, if_I=if_I, if_II=if_II, if_III=if_III,
prompt=[prompt]*count,
seed=42,
if_I_kwargs={
"guidance_scale": 7.0,
"sample_timestep_respacing": "smart100",
},
if_II_kwargs={
"guidance_scale": 4.0,
"sample_timestep_respacing": "smart50",
},
if_III_kwargs={
"guidance_scale": 9.0,
"noise_level": 20,
"sample_timestep_respacing": "75",
},
)
if_III.show(result['III'], size=14)
```
## II. Zero-shot Image-to-Image Translation
In Style Transfer mode, the output of your prompt comes out at the style of the `support_pil_img`
```python
from deepfloyd_if.pipelines import style_transfer
result = style_transfer(
t5=t5, if_I=if_I, if_II=if_II,
support_pil_img=raw_pil_image,
style_prompt=[
'in style of professional origami',
'in style of oil art, Tate modern',
'in style of plastic building bricks',
'in style of classic anime from 1990',
],
seed=42,
if_I_kwargs={
"guidance_scale": 10.0,
"sample_timestep_respacing": "10,10,10,10,10,10,10,10,0,0",
'support_noise_less_qsample_steps': 5,
},
if_II_kwargs={
"guidance_scale": 4.0,
"sample_timestep_respacing": 'smart50',
"support_noise_less_qsample_steps": 5,
},
)
if_I.show(result['II'], 1, 20)
```
## III. Super Resolution
For super-resolution, users can run `IF-II` and `IF-III` or 'Stable x4' on an image that was not necessarely generated by IF (two cascades):
```python
from deepfloyd_if.pipelines import super_resolution
middle_res = super_resolution(
t5,
if_III=if_II,
prompt=['woman with a blue headscarf and a blue sweaterp, detailed picture, 4k dslr, best quality'],
support_pil_img=raw_pil_image,
img_scale=4.,
img_size=64,
if_III_kwargs={
'sample_timestep_respacing': 'smart100',
'aug_level': 0.5,
'guidance_scale': 6.0,
},
)
high_res = super_resolution(
t5,
if_III=if_III,
prompt=[''],
support_pil_img=middle_res['III'][0],
img_scale=4.,
img_size=256,
if_III_kwargs={
"guidance_scale": 9.0,
"noise_level": 20,
"sample_timestep_respacing": "75",
},
)
show_superres(raw_pil_image, high_res['III'][0])
```
### IV. Zero-shot Inpainting
```python
from deepfloyd_if.pipelines import inpainting
result = inpainting(
t5=t5, if_I=if_I,
if_II=if_II,
if_III=if_III,
support_pil_img=raw_pil_image,
inpainting_mask=inpainting_mask,
prompt=[
'oil art, a man in a hat',
],
seed=42,
if_I_kwargs={
"guidance_scale": 7.0,
"sample_timestep_respacing": "10,10,10,10,10,0,0,0,0,0",
'support_noise_less_qsample_steps': 0,
},
if_II_kwargs={
"guidance_scale": 4.0,
'aug_level': 0.0,
"sample_timestep_respacing": '100',
},
if_III_kwargs={
"guidance_scale": 9.0,
"noise_level": 20,
"sample_timestep_respacing": "75",
},
)
if_I.show(result['I'], 2, 3)
if_I.show(result['II'], 2, 6)
if_I.show(result['III'], 2, 14)
```
### π€ Model Zoo π€
The link to download the weights as well as the model cards will be available soon on each model of the model zoo
#### Original
| Name | Cascade | Params | FID | Batch size | Steps |
|:----------------------------------------------------------|:-------:|:------:|:----:|:----------:|:-----:|
| [IF-I-M](https://huggingface.co/DeepFloyd/IF-I-M-v1.0) | I | 400M | 8.86 | 3072 | 2.5M |
| [IF-I-L](https://huggingface.co/DeepFloyd/IF-I-L-v1.0) | I | 900M | 8.06 | 3200 | 3.0M |
| [IF-I-XL](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)* | I | 4.3B | 6.66 | 3072 | 2.42M |
| [IF-II-M](https://huggingface.co/DeepFloyd/IF-II-M-v1.0) | II | 450M | - | 1536 | 2.5M |
| [IF-II-L](https://huggingface.co/DeepFloyd/IF-II-L-v1.0)* | II | 1.2B | - | 1536 | 2.5M |
| IF-III-L* _(soon)_ | III | 700M | - | 3072 | 1.25M |
*best modules
### Quantitative Evaluation
`FID = 6.66`
## License
The code in this repository is released under the bespoke license (see added [point two](https://github.com/deep-floyd/IF/blob/main/LICENSE#L13)).
The weights will be available soon via [the DeepFloyd organization at Hugging Face](https://huggingface.co/DeepFloyd) and have their own LICENSE.
**Disclaimer:** *The initial release of the IF model is under a restricted research-purposes-only license temporarily to gather feedback, and after that we intend to release a fully open-source model in line with other Stability AI models.*
## Limitations and Biases
The models available in this codebase have known limitations and biases. Please refer to [the model card](https://huggingface.co/DeepFloyd/IF-I-L-v1.0) for more information.
## π DeepFloyd IF creators:
- [Alex Shonenkov](https://github.com/shonenkov)
- [Misha Konstantinov](https://github.com/zeroshot-ai)
- [Daria Bakshandaeva](https://github.com/Gugutse)
- [Christoph Schuhmann](https://github.com/christophschuhmann)
- [Ksenia Ivanova](https://github.com/ivksu)
- [Nadiia Klokova](https://github.com/vauimpuls)
## π Research Paper (Soon)
## Acknowledgements
Special thanks to [StabilityAI](http://stability.ai) and its CEO [Emad Mostaque](https://twitter.com/emostaque) for invaluable support, providing GPU compute and infrastructure to train the models (our gratitude goes to [Richard Vencu](https://github.com/rvencu)); thanks to [LAION](https://laion.ai) and [Christoph Schuhmann](https://github.com/christophschuhmann) in particular for contribution to the project and well-prepared datasets; thanks to [Huggingface](https://huggingface.co) teams for optimizing models' speed and memory consumption during inference, creating demos and giving cool advice!
## π External Contributors π
- The Biggest Thanks [@ApolinΓ‘rio](https://github.com/apolinario), for ideas, consultations, help and support on all stages to make IF available in open-source; for writing a lot of documentation and instructions; for creating a friendly atmosphere in difficult moments π¦;
- Thanks, [@patrickvonplaten](https://github.com/patrickvonplaten), for improving loading time of unet models by 80%;
for integration Stable-Diffusion-x4 as native pipeline πͺ;
- Thanks, [@williamberman](https://github.com/williamberman) and [@patrickvonplaten](https://github.com/patrickvonplaten) for diffusers integration π;
- Thanks, [@hysts](https://github.com/hysts) and [@ApolinΓ‘rio](https://github.com/apolinario) for creating [the best gradio demo with IF](https://huggingface.co/spaces/DeepFloyd/IF) π;
- Thanks, [@Dango233](https://github.com/Dango233), for adapting IF with xformers memory efficient attention πͺ;