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* rename photon to prx

* rename photon into prx

* Revert .gitignore to state before commit b7fb0fe9d6

* rename photon to prx

* rename photon into prx

* Revert .gitignore to state before commit b7fb0fe9d6

* make fix-copies
This commit is contained in:
David Bertoin
2025-10-22 02:09:22 +02:00
committed by GitHub
parent 57636ad4f4
commit dd07b19e27
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docs/source/en/_toctree.yml
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title: PAG
- local: api/pipelines/paint_by_example
title: Paint by Example
- local: api/pipelines/photon
title: Photon
- local: api/pipelines/pixart
title: PixArt-α
- local: api/pipelines/pixart_sigma
title: PixArt-Σ
- local: api/pipelines/prx
title: PRX
- local: api/pipelines/qwenimage
title: QwenImage
- local: api/pipelines/sana
docs/source/en/api/pipelines/photon.md
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<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License. -->
# Photon
Photon generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing.
## Available models
Photon offers multiple variants with different VAE configurations, each optimized for specific resolutions. Base models excel with detailed prompts, capturing complex compositions and subtle details. Fine-tuned models trained on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) improve aesthetic quality, especially with simpler prompts.
| Model | Resolution | Fine-tuned | Distilled | Description | Suggested prompts | Suggested parameters | Recommended dtype |
|:-----:|:-----------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
| [`Photoroom/photon-256-t2i`](https://huggingface.co/Photoroom/photon-256-t2i)| 256 | No | No | Base model pre-trained at 256 with Flux VAE|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/photon-256-t2i-sft`](https://huggingface.co/Photoroom/photon-256-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/photon-512-t2i`](https://huggingface.co/Photoroom/photon-512-t2i)| 512 | No | No | Base model pre-trained at 512 with Flux VAE |Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/photon-512-t2i-sft`](https://huggingface.co/Photoroom/photon-512-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/photon-512-t2i-sft-distilled`](https://huggingface.co/Photoroom/photon-512-t2i-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/photon-512-t2i-sft`](https://huggingface.co/Photoroom/photon-512-t2i-sft) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |
| [`Photoroom/photon-512-t2i-dc-ae`](https://huggingface.co/Photoroom/photon-512-t2i-dc-ae)| 512 | No | No | Base model pre-trained at 512 with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae)|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/photon-512-t2i-dc-ae-sft`](https://huggingface.co/Photoroom/photon-512-t2i-dc-ae-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae) | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/photon-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/photon-512-t2i-dc-ae-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/photon-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/photon-512-t2i-dc-ae-sft-distilled) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |s
Refer to [this](https://huggingface.co/collections/Photoroom/photon-models-68e66254c202ebfab99ad38e) collection for more information.
## Loading the pipeline
Load the pipeline with [`~DiffusionPipeline.from_pretrained`].
```py
from diffusers.pipelines.photon import PhotonPipeline
# Load pipeline - VAE and text encoder will be loaded from HuggingFace
pipe = PhotonPipeline.from_pretrained("Photoroom/photon-512-t2i-sft", torch_dtype=torch.bfloat16)
pipe.to("cuda")
prompt = "A front-facing portrait of a lion the golden savanna at sunset."
image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0]
image.save("photon_output.png")
```
### Manual Component Loading
Load components individually to customize the pipeline for instance to use quantized models.
```py
import torch
from diffusers.pipelines.photon import PhotonPipeline
from diffusers.models import AutoencoderKL, AutoencoderDC
from diffusers.models.transformers.transformer_photon import PhotonTransformer2DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from transformers import T5GemmaModel, GemmaTokenizerFast
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as BitsAndBytesConfig
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
# Load transformer
transformer = PhotonTransformer2DModel.from_pretrained(
"checkpoints/photon-512-t2i-sft",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.bfloat16,
)
# Load scheduler
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
"checkpoints/photon-512-t2i-sft", subfolder="scheduler"
)
# Load T5Gemma text encoder
t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2",
quantization_config=quant_config,
torch_dtype=torch.bfloat16)
text_encoder = t5gemma_model.encoder.to(dtype=torch.bfloat16)
tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
tokenizer.model_max_length = 256
# Load VAE - choose either Flux VAE or DC-AE
# Flux VAE
vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev",
subfolder="vae",
quantization_config=quant_config,
torch_dtype=torch.bfloat16)
pipe = PhotonPipeline(
transformer=transformer,
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae
)
pipe.to("cuda")
```
## Memory Optimization
For memory-constrained environments:
```py
import torch
from diffusers.pipelines.photon import PhotonPipeline
pipe = PhotonPipeline.from_pretrained("Photoroom/photon-512-t2i-sft", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() # Offload components to CPU when not in use
# Or use sequential CPU offload for even lower memory
pipe.enable_sequential_cpu_offload()
```
## PhotonPipeline
[[autodoc]] PhotonPipeline
- all
- __call__
## PhotonPipelineOutput
[[autodoc]] pipelines.photon.pipeline_output.PhotonPipelineOutput
docs/source/en/api/pipelines/prx.md
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<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License. -->
# PRX
PRX generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing.
## Available models
PRX offers multiple variants with different VAE configurations, each optimized for specific resolutions. Base models excel with detailed prompts, capturing complex compositions and subtle details. Fine-tuned models trained on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) improve aesthetic quality, especially with simpler prompts.
| Model | Resolution | Fine-tuned | Distilled | Description | Suggested prompts | Suggested parameters | Recommended dtype |
|:-----:|:-----------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
| [`Photoroom/prx-256-t2i`](https://huggingface.co/Photoroom/prx-256-t2i)| 256 | No | No | Base model pre-trained at 256 with Flux VAE|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/prx-256-t2i-sft`](https://huggingface.co/Photoroom/prx-256-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/prx-512-t2i`](https://huggingface.co/Photoroom/prx-512-t2i)| 512 | No | No | Base model pre-trained at 512 with Flux VAE |Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/prx-512-t2i-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |
| [`Photoroom/prx-512-t2i-dc-ae`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae)| 512 | No | No | Base model pre-trained at 512 with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae)|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/prx-512-t2i-dc-ae-sft`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae) | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
| [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |s
Refer to [this](https://huggingface.co/collections/Photoroom/prx-models-68e66254c202ebfab99ad38e) collection for more information.
## Loading the pipeline
Load the pipeline with [`~DiffusionPipeline.from_pretrained`].
```py
from diffusers.pipelines.prx import PRXPipeline
# Load pipeline - VAE and text encoder will be loaded from HuggingFace
pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
pipe.to("cuda")
prompt = "A front-facing portrait of a lion the golden savanna at sunset."
image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0]
image.save("prx_output.png")
```
### Manual Component Loading
Load components individually to customize the pipeline for instance to use quantized models.
```py
import torch
from diffusers.pipelines.prx import PRXPipeline
from diffusers.models import AutoencoderKL, AutoencoderDC
from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from transformers import T5GemmaModel, GemmaTokenizerFast
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as BitsAndBytesConfig
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
# Load transformer
transformer = PRXTransformer2DModel.from_pretrained(
"checkpoints/prx-512-t2i-sft",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.bfloat16,
)
# Load scheduler
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
"checkpoints/prx-512-t2i-sft", subfolder="scheduler"
)
# Load T5Gemma text encoder
t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2",
quantization_config=quant_config,
torch_dtype=torch.bfloat16)
text_encoder = t5gemma_model.encoder.to(dtype=torch.bfloat16)
tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
tokenizer.model_max_length = 256
# Load VAE - choose either Flux VAE or DC-AE
# Flux VAE
vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev",
subfolder="vae",
quantization_config=quant_config,
torch_dtype=torch.bfloat16)
pipe = PRXPipeline(
transformer=transformer,
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae
)
pipe.to("cuda")
```
## Memory Optimization
For memory-constrained environments:
```py
import torch
from diffusers.pipelines.prx import PRXPipeline
pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() # Offload components to CPU when not in use
# Or use sequential CPU offload for even lower memory
pipe.enable_sequential_cpu_offload()
```
## PRXPipeline
[[autodoc]] PRXPipeline
- all
- __call__
## PRXPipelineOutput
[[autodoc]] pipelines.prx.pipeline_output.PRXPipelineOutput