inference.py

import inspect
import torch

from typing import Any, Callable, Dict, List, Optional, Union
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.utils import (
    USE_PEFT_BACKEND,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.pipelines.stable_diffusion_3.pipeline_output import StableDiffusion3PipelineOutput

if is_torch_xla_available():
    import torch_xla.core.xla_model as xm

    XLA_AVAILABLE = True
else:
    XLA_AVAILABLE = False


@torch.no_grad()
def run(
        self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        prompt_3: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 28,
        sigmas: Optional[List[float]] = None,
        timesteps: Optional[List[float]] = None,
        scales: List[float] = None,
        guidance_scale: float = 7.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        negative_prompt_3: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        ip_adapter_image: Optional[PipelineImageInput] = None,
        ip_adapter_image_embeds: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        clip_skip: Optional[int] = None,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 256,
        skip_guidance_layers: List[int] = None,
        skip_layer_guidance_scale: float = 2.8,
        skip_layer_guidance_stop: float = 0.2,
        skip_layer_guidance_start: float = 0.01,
        mu: Optional[float] = None,
):
    height = height or self.default_sample_size * self.vae_scale_factor
    width = width or self.default_sample_size * self.vae_scale_factor

    # 1. Check inputs. Raise error if not correct
    self.check_inputs(
        prompt,
        prompt_2,
        prompt_3,
        height,
        width,
        negative_prompt=negative_prompt,
        negative_prompt_2=negative_prompt_2,
        negative_prompt_3=negative_prompt_3,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        max_sequence_length=max_sequence_length,
    )

    self._guidance_scale = guidance_scale
    self._skip_layer_guidance_scale = skip_layer_guidance_scale
    self._clip_skip = clip_skip
    self._joint_attention_kwargs = joint_attention_kwargs
    self._interrupt = False

    # 2. Define call parameters
    if prompt is not None and isinstance(prompt, str):
        batch_size = 1
    elif prompt is not None and isinstance(prompt, list):
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

    device = self._execution_device

    lora_scale = (
        self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
    )
    (
        prompt_embeds,
        negative_prompt_embeds,
        pooled_prompt_embeds,
        negative_pooled_prompt_embeds,
    ) = self.encode_prompt(
        prompt=prompt,
        prompt_2=prompt_2,
        prompt_3=prompt_3,
        negative_prompt=negative_prompt,
        negative_prompt_2=negative_prompt_2,
        negative_prompt_3=negative_prompt_3,
        do_classifier_free_guidance=self.do_classifier_free_guidance,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        device=device,
        clip_skip=self.clip_skip,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )

    if self.do_classifier_free_guidance:
        if skip_guidance_layers is not None:
            original_prompt_embeds = prompt_embeds
            original_pooled_prompt_embeds = pooled_prompt_embeds
        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
        pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)

    # 4. Prepare latent variables
    num_channels_latents = self.transformer.config.in_channels
    latents = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        device,
        generator,
        latents,
    )

    # 5. Prepare timesteps
    scheduler_kwargs = {}
    if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
        _, _, height, width = latents.shape
        image_seq_len = (height // self.transformer.config.patch_size) * (
                width // self.transformer.config.patch_size
        )
        mu = calculate_shift(
            image_seq_len,
            self.scheduler.config.get("base_image_seq_len", 256),
            self.scheduler.config.get("max_image_seq_len", 4096),
            self.scheduler.config.get("base_shift", 0.5),
            self.scheduler.config.get("max_shift", 1.16),
        )
        scheduler_kwargs["mu"] = mu
    elif mu is not None:
        scheduler_kwargs["mu"] = mu
    num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
    self._num_timesteps = len(timesteps)

    # 6. Prepare image embeddings
    if (ip_adapter_image is not None and self.is_ip_adapter_active) or ip_adapter_image_embeds is not None:
        ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
            ip_adapter_image,
            ip_adapter_image_embeds,
            device,
            batch_size * num_images_per_prompt,
            self.do_classifier_free_guidance,
        )

        if self.joint_attention_kwargs is None:
            self._joint_attention_kwargs = {"ip_adapter_image_embeds": ip_adapter_image_embeds}
        else:
            self._joint_attention_kwargs.update(ip_adapter_image_embeds=ip_adapter_image_embeds)

    # 7. Denoising loop
    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            if self.interrupt:
                continue

            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            timestep = t.expand(latent_model_input.shape[0])

            noise_pred = self.transformer(
                hidden_states=latent_model_input,
                timestep=timestep,
                encoder_hidden_states=prompt_embeds,
                pooled_projections=pooled_prompt_embeds,
                joint_attention_kwargs=self.joint_attention_kwargs,
                return_dict=False,
            )[0]

            # perform guidance
            if self.do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
                should_skip_layers = (
                    True
                    if i > num_inference_steps * skip_layer_guidance_start
                       and i < num_inference_steps * skip_layer_guidance_stop
                    else False
                )
                if skip_guidance_layers is not None and should_skip_layers:
                    timestep = t.expand(latents.shape[0])
                    latent_model_input = latents
                    noise_pred_skip_layers = self.transformer(
                        hidden_states=latent_model_input,
                        timestep=timestep,
                        encoder_hidden_states=original_prompt_embeds,
                        pooled_projections=original_pooled_prompt_embeds,
                        joint_attention_kwargs=self.joint_attention_kwargs,
                        return_dict=False,
                        skip_layers=skip_guidance_layers,
                    )[0]
                    noise_pred = (
                            noise_pred + (noise_pred_text - noise_pred_skip_layers) * self._skip_layer_guidance_scale
                    )

            # compute the previous noisy sample x_t -> x_t-1
            latents_dtype = latents.dtype
            sigma = sigmas[i]
            sigma_next = sigmas[i + 1]
            x0_pred = (latents - sigma * noise_pred)
            try:
                x0_pred = torch.nn.functional.interpolate(x0_pred, size=scales[i + 1], mode='bicubic')
            except IndexError:
                x0_pred = x0_pred
            noise = torch.randn(x0_pred.shape, generator=generator).to('cuda').half()
            latents = (1 - sigma_next) * x0_pred + sigma_next * noise

            if latents.dtype != latents_dtype:
                if torch.backends.mps.is_available():
                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                    latents = latents.to(latents_dtype)

            if callback_on_step_end is not None:
                callback_kwargs = {}
                for k in callback_on_step_end_tensor_inputs:
                    callback_kwargs[k] = locals()[k]
                callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                latents = callback_outputs.pop("latents", latents)
                prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                negative_pooled_prompt_embeds = callback_outputs.pop(
                    "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
                )

            # call the callback, if provided
            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                progress_bar.update()

            if XLA_AVAILABLE:
                xm.mark_step()

    if output_type == "latent":
        image = latents

    else:
        latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor

        image = self.vae.decode(latents, return_dict=False)[0]
        image = self.image_processor.postprocess(image, output_type=output_type)

    # Offload all models
    self.maybe_free_model_hooks()

    if not return_dict:
        return (image,)

    return StableDiffusion3PipelineOutput(images=image)