Add support for PRX Pixel T2I

extensions_built_in/diffusion_models/prx_pixel_t2i/README.md

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+# PRXPixel (Photoroom PRX-7B, pixel-space text-to-image)
+
+ai-toolkit integration for [`Photoroom/prxpixel-t2i`](https://huggingface.co/Photoroom/prxpixel-t2i),
+a ~7B pixel-space diffusion transformer.
+
+It is implemented **from scratch** so ai-toolkit does not depend on the (still
+unmerged) diffusers PR [huggingface/diffusers#13928](https://github.com/huggingface/diffusers/pull/13928):
+the transformer is vendored in [src/transformer_prx.py](src/transformer_prx.py)
+and a minimal preview sampler lives in [src/pipeline.py](src/pipeline.py).
+
+## What makes this model unusual
+
+PRXPixel differs from a typical latent flow-matching model in three ways, each
+handled in [prx_pixel_t2i.py](prx_pixel_t2i.py):
+
+| Property | What it means | How it's handled |
+|---|---|---|
+| **Pixel space** | No VAE — the transformer denoises raw RGB (`in_channels=3`, `patch_size=16`) | A `FakeVAE` (identity, scaling 1) so encode/decode are no-ops; "latents" are the image in `[-1, 1]` |
+| **x-prediction** | The model predicts the clean image `x0`, not the flow velocity | `get_noise_prediction` returns `x0`; `get_loss_target` is the clean latents. The `x0 → velocity` conversion only happens at sampling time |
+| **noise_scale = 2.0** | Trains/samples from `randn * 2.0`, not unit noise | `get_latent_noise_from_latents` scales the training noise; the pipeline scales the starting noise |
+
+Text is encoded by the Qwen3-VL text tower (`Qwen3VLTextModel`, hidden size
+2048 → the transformer's `context_in_dim`), padded to 256 tokens.
+
+The x-prediction objective follows *"Back to Basics: Let Denoising Generative
+Models Denoise"* (https://arxiv.org/abs/2511.13720).
+
+## Architecture (released checkpoint)
+
+`depth=24`, `hidden_size=3584`, `num_heads=28`, `mlp_ratio=3.5`,
+`in_channels=3`, `patch_size=16`, `context_in_dim=2048`, `bottleneck_size=768`,
+`axes_dim=[64, 64]`, `resolution_embeds=True`, flow-matching scheduler with
+`shift=3.0`.
+
+## Train it
+
+```yaml
+model:
+  arch: "prx_pixel"
+  name_or_path: "/path/to/prxpixel-t2i"   # diffusers folder: transformer/,
+                                          # text_encoder/, tokenizer/, scheduler/
+  quantize: true        # optional: qfloat8 the transformer
+  quantize_te: true     # optional: qfloat8 the Qwen3-VL text encoder
+train:
+  gradient_checkpointing: true
+sample:
+  guidance_scale: 5.0
+  sample_steps: 28
+```
+
+Datasets bucket to multiples of 16px (`vae_scale_factor * patch_size`).
+See [../example_model/README.md](../example_model/README.md) for the generic
+lifecycle, registration and LoRA conventions.