diffusers/pr_14312/en/api/pipelines/prx_pixel.md

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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	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 from_pretrained():

import torch
from diffusers import PRXPixelPipeline

pipe = PRXPixelPipeline.from_pretrained("Photoroom/prxpixel-t2i", 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:

import torch
from diffusers import PRXPixelPipeline

pipe = PRXPixelPipeline.from_pretrained("Photoroom/prxpixel-t2i", dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

# Or use sequential CPU offload for even lower memory
pipe.enable_sequential_cpu_offload()

PRXPixelPipeline[[diffusers.PRXPixelPipeline]]

• transformer (PRXTransformer2DModel) -- The ~7B-parameter PRX denoiser. For PRXPixel this is built with in_channels=3, a bottleneck img_in, and resolution_embeds=True, and it is trained to predict the clean image x0.
• scheduler (FlowMatchEulerDiscreteScheduler) -- Flow-matching scheduler used to denoise the (pixel-space) latents.
• text_encoder (PreTrainedModel) -- The Qwen3-VL text backbone used to encode prompts (the vision tower is discarded). Must return a last_hidden_state.
• tokenizer (PreTrainedTokenizerBase) -- Tokenizer for text_encoder (typically loaded via AutoTokenizer).
• default_sample_size (int, optional, defaults to 1024) -- Default height/width used when none is provided to __call__.
• prompt_max_tokens (int, optional, defaults to 256) -- Number of text tokens the prompt is padded/truncated to before encoding.
• noise_scale (float, optional, defaults to 2.0) -- Scale applied to the initial Gaussian noise. PRXPixel trains with a non-unit initial-noise scale, so sampling must start from randn * noise_scale.

Pipeline for text-to-image generation with the PRXPixel model.

PRXPixel is a standalone, pixel-space text-to-image pipeline. It denoises raw RGB directly with a ~7B-parameter PRXTransformer2DModel and has no VAE (generation happens entirely in pixel space, so the denoised output is the image). Prompts are encoded with a Qwen3-VL text encoder (the vision tower is discarded). Unlike PRXPipeline the transformer is trained with x-prediction: at every step it predicts the clean image x0, which is converted to a flow-matching velocity before the scheduler step. Sampling starts from randn * noise_scale (noise_scale=2.0 by default) and the default resolution is 1024px.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

Examples:

>>> 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")

• prompt (str or list[str], optional) -- The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds instead.
• negative_prompt (str, optional, defaults to "") -- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• height (int, optional, defaults to default_sample_size) -- The height in pixels of the generated image.
• width (int, optional, defaults to default_sample_size) -- The width in pixels of the generated image.
• num_inference_steps (int, optional, defaults to 28) -- The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
• timesteps (list[int], optional) -- Custom timesteps to use for the denoising process with schedulers which support a timesteps argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. Must be in descending order.
• guidance_scale (float, optional, defaults to 4.0) -- Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
• num_images_per_prompt (int, optional, defaults to 1) -- The number of images to generate per prompt.
• generator (torch.Generator or list[torch.Generator], optional) -- One or a list of torch generator(s) to make generation deterministic.
• latents (torch.Tensor, optional) -- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random generator.
• prompt_embeds (torch.FloatTensor, optional) -- Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.FloatTensor, optional) -- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided and guidance_scale > 1, negative embeddings will be generated from an empty string.
• prompt_attention_mask (torch.BoolTensor, optional) -- Pre-generated attention mask for prompt_embeds. If not provided, attention mask will be generated from prompt input argument.
• negative_prompt_attention_mask (torch.BoolTensor, optional) -- Pre-generated attention mask for negative_prompt_embeds. If not provided and guidance_scale > 1, attention mask will be generated from an empty string.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a PRXPipelineOutput instead of a plain tuple.
• use_resolution_binning (bool, optional, defaults to True) -- If set to True, the requested height and width are first mapped to the closest resolutions using predefined aspect ratio bins. After the produced latents are decoded into images, they are resized back to the requested resolution. Useful for generating non-square images at optimal resolutions.
• callback_on_step_end (Callable, optional) -- A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self, step, timestep, callback_kwargs). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
• callback_on_step_end_tensor_inputs (list, optional) -- The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include tensors that are listed in the ._callback_tensor_inputs attribute.PRXPipelineOutput or tuplePRXPipelineOutput if return_dict is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

>>> 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")

Check that all inputs are in correct format.

Encode text prompt using standard text encoder and tokenizer, or use precomputed embeddings.

Prepare initial latents for the diffusion process.

PRXPixel trains with a non-unit initial-noise scale, so the sampled noise is multiplied by self.config.noise_scale.

PRXPipelineOutput[[diffusers.pipelines.prx.PRXPipelineOutput]]

• images (list[PIL.Image.Image] or np.ndarray) -- list of denoised PIL images of length batch_size or numpy array of shape (batch_size, height, width, num_channels). PIL images or numpy array present the denoised images of the diffusion pipeline.

Output class for PRX pipelines.