Add Photon model and pipeline support

docs/source/en/_toctree.yml

@@ -544,6 +544,8 @@
         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

docs/source/en/api/pipelines/photon.md

@@ -0,0 +1,131 @@
+<!-- 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