Add PRXPixelPipeline: pixel-space PRX text-to-image pipeline (#13928)

Adds a pixel-space variant of PRX that denoises raw RGB directly
(no VAE), conditioned on a Qwen3-VL text encoder:

- PRXTransformer2DModel: new optional config args `bottleneck_size`
  (two-layer img_in projection for large patch dims) and
  `resolution_embeds` (PRXResolutionEmbedder conditions the timestep
  modulation on the latent resolution)
- PRXPipeline: support for subclass-tuned tokenizer max length, light
  text cleaning, x-prediction flow matching (x0 -> velocity conversion),
  and non-unit initial noise scale
- PRXPixelPipeline: thin subclass wiring the above together
  (vae optional/None, vae_scale_factor=1, 1024px default)
- conversion script support for the pixel checkpoint format
- registration in __init__ files + dummy objects, docs autodoc entry,
  fast pipeline tests

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

Author: DavidBert

docs/source/en/_toctree.yml

@@ -591,6 +591,8 @@
         title: PixArt-Σ
       - local: api/pipelines/prx
         title: PRX
+      - local: api/pipelines/prx_pixel
+        title: PRX Pixel
       - local: api/pipelines/qwenimage
         title: QwenImage
       - local: api/pipelines/sana

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

@@ -0,0 +1,67 @@
+<!-- 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 Pixel
+
+PRXPixel is a pixel-space text-to-image generation model by Photoroom. A ~7B [`PRXTransformer2DModel`]
+denoises raw RGB images directly — no VAE is needed. The model is conditioned on a Qwen3-VL text encoder
+and uses flow matching where the transformer predicts the clean image at each step (x-prediction). The
+generation resolution is fed into the timestep modulation so the model is aware of the target size.
+
+## Available models
+
+| Model | Resolution | Description | Suggested parameters | Recommended dtype |
+|:-----:|:---------:|:----------:|:----------:|:----------:|
+| [`Photoroom/prxpixel-t2i`](https://huggingface.co/Photoroom/prxpixel-t2i) | 1024 | Pixel-space ~7B model with Qwen3-VL text encoder | 28 steps, cfg=5.0 | `torch.bfloat16` |
+
+## Loading the pipeline
+
+[`PRXPixelPipeline`] requires `transformers >= 4.57` (the version that introduced `Qwen3VLTextModel`). Load it with [`~DiffusionPipeline.from_pretrained`]:
+
+```py
+import torch
+from diffusers import PRXPixelPipeline
+
+pipe = PRXPixelPipeline.from_pretrained("Photoroom/prxpixel-t2i", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+prompt = "A front-facing portrait of a lion in the golden savanna at sunset."
+image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0]
+image.save("prxpixel_output.png")
+```
+
+## Memory Optimization
+
+For memory-constrained environments:
+
+```py
+import torch
+from diffusers import PRXPixelPipeline
+
+pipe = PRXPixelPipeline.from_pretrained("Photoroom/prxpixel-t2i", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+# Or use sequential CPU offload for even lower memory
+pipe.enable_sequential_cpu_offload()
+```
+
+## PRXPixelPipeline
+
+[[autodoc]] PRXPixelPipeline
+  - all
+  - __call__
+
+## PRXPipelineOutput
+
+[[autodoc]] pipelines.prx.pipeline_output.PRXPipelineOutput