
# 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.

*Inspired by* [*Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding*](https://arxiv.org/pdf/2205.11487.pdf)

![](./pics/deepfloyd_if_scheme.jpg)

## Model Details

- **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:** <span style="color:blue"><a href="https://huggingface.co/spaces/DeepFloyd/deepfloyd-if-license">DeepFloyd IF License Agreement</a></span>
- **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 
- **Resources for more information:** [GitHub](https://github.com/deep-floyd/IF), [deepfloyd.ai](https://deepfloyd.ai), [All Links](https://linktr.ee/deepfloyd)
- **Cite as (Soon):** -

## Using with `diffusers`

IF is integrated with the 🤗 Hugging Face [🧨 diffusers library](https://github.com/huggingface/diffusers/), 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](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 sentencepiece
```

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 **remove all** `enable_xformers_memory_efficient_attention()` functions.

* **Load all stages and offload to CPU**

```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()
```

* **Retrieve Text Embeddings**

```py
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**

```py
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**

```py
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**

```py
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](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-speed)
- ⚙️ [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) 📖.

Diffusers dreambooth scripts also supports fine-tuning 🎨 [IF](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#if).
With parameter efficient finetuning, you can add new concepts to IF with a single GPU and ~28 GB VRAM.

## 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-XL-v1.0 is a 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-XL-v1.0 has 2_420_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder

![](./pics/loss.jpg)

**Hardware:** 64 x 8 x A100 GPUs

**Optimizer:** [AdamW8bit](https://arxiv.org/abs/2110.02861) + [DeepSpeed ZeRO-1](https://www.deepspeed.ai/tutorials/zero/)

**Batch:** 3072

**Learning rate**: [one-cycle](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.OneCycleLR.html) cosine strategy, warmup 10000 steps, start_lr=2e-6, max_lr=5e-5, final_lr=5e-9


![](./pics/lr.jpg)


## Evaluation Results

`FID-30K: 6.66`

![](./pics/fid30k_if.jpg)

# Uses

## Direct Use 
The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model.

Possible research areas and tasks include:
- Generation of artistic imagery and use in design and other artistic processes.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Applications in educational or creative tools.
- Research on generative models.

Excluded uses are described below.

 ### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion but applies in the same way for IF_.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.

## Limitations and Bias

### Limitations

- The model does not achieve perfect photorealism
- The model was trained mainly with English captions and will not work as well in other languages.
- The model was trained on a subset of the large-scale dataset
  [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section).

### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
IF was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), 
which consists of images that are limited to English descriptions. 
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.


## Citation (Soon)

*This model card was written by: DeepFloyd-Team and is based on the [StableDiffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).*
{"license": "deepfloyd-if-license", "tags": ["if", "text-to-image"], "extra_gated_prompt": "DeepFloyd LICENSE AGREEMENT\nThis License Agreement (as may be amended in accordance with this License Agreement, \u201cLicense\u201d), between you, or your employer or other entity (if you are entering into this agreement on behalf of your employer or other entity) (\u201cLicensee\u201d or \u201cyou\u201d) and Stability AI Ltd.. (\u201cStability AI\u201d or \u201cwe\u201d) applies to your use of any computer program, algorithm, source code, object code, or software that is made available by Stability AI under this License (\u201cSoftware\u201d) and any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software (\u201cDocumentation\u201d).\nBy clicking \u201cI Accept\u201d below or by using the Software, you agree to the terms of this License. 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The following sections survive termination of this License: 2 (Restrictions), 3 (Attribution), 4 (Disclaimers), 5 (Limitation on Liability), 6 (Indemnification) 7 (Termination; Survival), 8 (Third Party Materials), 9 (Trademarks), 10 (Applicable Law; Dispute Resolution), and 11 (Miscellaneous).\u2028\n8. THIRD PARTY MATERIALS\n  The Software Products may contain third-party software or other components (including free and open source software) (all of the foregoing, \u201cThird Party Materials\u201d), which are subject to the license terms of the respective third-party licensors. Your dealings or correspondence with third parties and your use of or interaction with any Third Party Materials are solely between you and the third party. Stability AI does not control or endorse, and makes no representations or warranties regarding, any Third Party Materials, and your access to and use of such Third Party Materials are at your own risk.\u2028\n9. TRADEMARKS\n  Licensee has not been granted any trademark license as part of this License and may not use any name or mark associated with Stability AI without the prior written permission of Stability AI, except to the extent necessary to make the reference required by the \u201cATTRIBUTION\u201d section of this Agreement.\u2028\n10. APPLICABLE LAW; DISPUTE RESOLUTION\n  This License will be governed and construed under the laws of the State of California without regard to conflicts of law provisions. Any suit or proceeding arising out of or relating to this License will be brought in the federal or state courts, as applicable, in San Mateo County, California, and each party irrevocably submits to the jurisdiction and venue of such courts.\u2028\n11. MISCELLANEOUS\n  If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Stability AI to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Stability AI regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Stability AI regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Stability AI.", "extra_gated_fields": {"Organization /\u00a0Affiliation": "text", "Previously related publications": "text", "I accept the above license agreement, and will use the Software non-commercially and for research purposes only": "checkbox"}, "inference": false}
diffusers pytorch safetensors if text-to-image arxiv:2205.11487 arxiv:2110.02861 license:deepfloyd-if-license has_space diffusers:IFPipeline region:us
TAGS
#diffusers #pytorch #safetensors #if #text-to-image #arxiv-2205.11487 #arxiv-2110.02861 #license-deepfloyd-if-license #has_space #diffusers-IFPipeline #region-us 


# 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.

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

![](./pics/deepfloyd_if_scheme.jpg)

## Model Details

- 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: <span style="color:blue"><a href="URL IF License Agreement</a></span>
- 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 
- Resources for more information: GitHub, URL, All Links
- Cite as (Soon): -

## Using with 'diffusers'

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 loggin in
2. Accept the license on the model card of DeepFloyd/IF-I-XL-v1.0
3. Make sure to login locally. Install 'huggingface_hub'


run the login function in a Python shell



and enter your Hugging Face Hub access token.

Next we install 'diffusers' and dependencies:



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.

* Load all stages and offload to CPU



* Retrieve Text Embeddings



* Run stage 1



* Run stage 2



* Run stage 3



 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.

## 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-XL-v1.0 is a 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-XL-v1.0 has 2_420_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder

![](./pics/URL)

Hardware: 64 x 8 x A100 GPUs

Optimizer: AdamW8bit + DeepSpeed ZeRO-1

Batch: 3072

Learning rate: one-cycle cosine strategy, warmup 10000 steps, start_lr=2e-6, max_lr=5e-5, final_lr=5e-9


![](./pics/URL)


## Evaluation Results

'FID-30K: 6.66'

![](./pics/fid30k_if.jpg)

# Uses

## Direct Use 
The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model.

Possible research areas and tasks include:
- Generation of artistic imagery and use in design and other artistic processes.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Applications in educational or creative tools.
- Research on generative models.

Excluded uses are described below.

 ### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion but applies in the same way for IF_.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.

## Limitations and Bias

### Limitations

- The model does not achieve perfect photorealism
- The model was trained mainly with English captions and will not work as well in other languages.
- The model was trained on a subset of the large-scale dataset
  LAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section).

### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
IF was primarily trained on subsets of LAION-2B(en), 
which consists of images that are limited to English descriptions. 
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.


(Soon)

*This model card was written by: DeepFloyd-Team and is based on the StableDiffusion model card.*
# 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.

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

![](./pics/deepfloyd_if_scheme.jpg) ## Model Details

- 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: <span style="color:blue"><a href="URL IF License Agreement</a></span>
- 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 
- Resources for more information: GitHub, URL, All Links
- Cite as (Soon): - ## Using with 'diffusers'

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 loggin in
2. Accept the license on the model card of DeepFloyd/IF-I-XL-v1.0
3. Make sure to login locally. Install 'huggingface_hub'


run the login function in a Python shell



and enter your Hugging Face Hub access token.

Next we install 'diffusers' and dependencies:



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.

* Load all stages and offload to CPU



* Retrieve Text Embeddings



* Run stage 1



* Run stage 2



* Run stage 3



 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. ## 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-XL-v1.0 is a 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-XL-v1.0 has 2_420_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder

![](./pics/URL)

Hardware: 64 x 8 x A100 GPUs

Optimizer: AdamW8bit + DeepSpeed ZeRO-1

Batch: 3072

Learning rate: one-cycle cosine strategy, warmup 10000 steps, start_lr=2e-6, max_lr=5e-5, final_lr=5e-9


![](./pics/URL) ## Evaluation Results

'FID-30K: 6.66'

![](./pics/fid30k_if.jpg) # Uses ## Direct Use 
The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model.

Possible research areas and tasks include:
- Generation of artistic imagery and use in design and other artistic processes.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Applications in educational or creative tools.
- Research on generative models.

Excluded uses are described below.

 ### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion but applies in the same way for IF_.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations

- The model does not achieve perfect photorealism
- The model was trained mainly with English captions and will not work as well in other languages.
- The model was trained on a subset of the large-scale dataset
  LAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section). ### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
IF was primarily trained on subsets of LAION-2B(en), 
which consists of images that are limited to English descriptions. 
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.


(Soon)

*This model card was written by: DeepFloyd-Team and is based on the StableDiffusion model card.*
TAGS
#diffusers #pytorch #safetensors #if #text-to-image #arxiv-2205.11487 #arxiv-2110.02861 #license-deepfloyd-if-license #has_space #diffusers-IFPipeline #region-us 
 # 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.

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

![](./pics/deepfloyd_if_scheme.jpg) ## Model Details

- 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: <span style="color:blue"><a href="URL IF License Agreement</a></span>
- 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 
- Resources for more information: GitHub, URL, All Links
- Cite as (Soon): - ## Using with 'diffusers'

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 loggin in
2. Accept the license on the model card of DeepFloyd/IF-I-XL-v1.0
3. Make sure to login locally. Install 'huggingface_hub'


run the login function in a Python shell



and enter your Hugging Face Hub access token.

Next we install 'diffusers' and dependencies:



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.

* Load all stages and offload to CPU



* Retrieve Text Embeddings



* Run stage 1



* Run stage 2



* Run stage 3



 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. ## 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-XL-v1.0 is a 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-XL-v1.0 has 2_420_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder

![](./pics/URL)

Hardware: 64 x 8 x A100 GPUs

Optimizer: AdamW8bit + DeepSpeed ZeRO-1

Batch: 3072

Learning rate: one-cycle cosine strategy, warmup 10000 steps, start_lr=2e-6, max_lr=5e-5, final_lr=5e-9


![](./pics/URL) ## Evaluation Results

'FID-30K: 6.66'

![](./pics/fid30k_if.jpg) # Uses ## Direct Use 
The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model.

Possible research areas and tasks include:
- Generation of artistic imagery and use in design and other artistic processes.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Applications in educational or creative tools.
- Research on generative models.

Excluded uses are described below.

 ### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion but applies in the same way for IF_.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations

- The model does not achieve perfect photorealism
- The model was trained mainly with English captions and will not work as well in other languages.
- The model was trained on a subset of the large-scale dataset
  LAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section). ### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
IF was primarily trained on subsets of LAION-2B(en), 
which consists of images that are limited to English descriptions. 
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.


(Soon)

*This model card was written by: DeepFloyd-Team and is based on the StableDiffusion model card.*
TAGS
#diffusers #pytorch #safetensors #if #text-to-image #arxiv-2205.11487 #arxiv-2110.02861 #license-deepfloyd-if-license #has_space #diffusers-IFPipeline #region-us 
# 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.

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

![](./pics/deepfloyd_if_scheme.jpg)## Model Details

- 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: <span style="color:blue"><a href="URL IF License Agreement</a></span>
- 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 
- Resources for more information: GitHub, URL, All Links
- Cite as (Soon): -## Using with 'diffusers'

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 loggin in
2. Accept the license on the model card of DeepFloyd/IF-I-XL-v1.0
3. Make sure to login locally. Install 'huggingface_hub'


run the login function in a Python shell



and enter your Hugging Face Hub access token.

Next we install 'diffusers' and dependencies:



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.

* Load all stages and offload to CPU



* Retrieve Text Embeddings



* Run stage 1



* Run stage 2



* Run stage 3



 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.## 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-XL-v1.0 is a 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-XL-v1.0 has 2_420_000 steps at resolution 64x64 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder

![](./pics/URL)

Hardware: 64 x 8 x A100 GPUs

Optimizer: AdamW8bit + DeepSpeed ZeRO-1

Batch: 3072

Learning rate: one-cycle cosine strategy, warmup 10000 steps, start_lr=2e-6, max_lr=5e-5, final_lr=5e-9


![](./pics/URL)## Evaluation Results

'FID-30K: 6.66'

![](./pics/fid30k_if.jpg)# Uses## Direct Use 
The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model.

Possible research areas and tasks include:
- Generation of artistic imagery and use in design and other artistic processes.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Applications in educational or creative tools.
- Research on generative models.

Excluded uses are described below.

 ### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion but applies in the same way for IF_.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.## Limitations and Bias### Limitations

- The model does not achieve perfect photorealism
- The model was trained mainly with English captions and will not work as well in other languages.
- The model was trained on a subset of the large-scale dataset
  LAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section).### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
IF was primarily trained on subsets of LAION-2B(en), 
which consists of images that are limited to English descriptions. 
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.


(Soon)

*This model card was written by: DeepFloyd-Team and is based on the StableDiffusion model card.*