src/SubjectGeniusPipeline.py

import ipdb
from accelerate import Accelerator
from diffusers.configuration_utils import register_to_config
from diffusers.pipelines import FluxPipeline
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from .condition import Condition
from diffusers.pipelines.flux.pipeline_flux import (
    FluxPipelineOutput,
    calculate_shift,
    retrieve_timesteps,
    np,
)
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.models import AutoencoderKL,FluxTransformer2DModel


class SubjectGeniusPipeline(FluxPipeline):
    @register_to_config
    def __init__(
        self,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        text_encoder_2: T5EncoderModel,
        tokenizer_2: T5TokenizerFast,
        transformer: FluxTransformer2DModel,
        image_encoder = None,
        feature_extractor = None,
    ):
        super().__init__(
            scheduler=scheduler,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            text_encoder_2=text_encoder_2,
            tokenizer_2=tokenizer_2,
            transformer=transformer,
            image_encoder = image_encoder,
            feature_extractor = feature_extractor,
        )
    @property
    def all_adapters(self):
        list_adapters = self.get_list_adapters()  # eg {"unet": ["adapter1", "adapter2"], "text_encoder": ["adapter2"]}
        # eg ["adapter1", "adapter2"]
        all_adapters = list({adapter for adapters in list_adapters.values() for adapter in adapters})
        return all_adapters

    @torch.no_grad()
    def __call__(self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        # additional begin
        conditions: List[Condition] = None,
        model_config: Optional[Dict[str, Any]] = {},
        condition_scale: float = 1.0,
        # additional over
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        num_inference_steps: int = 28,
        timesteps: List[int] = None,
        guidance_scale: float = 3.5,
        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,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: Optional[Dict[str, Any]] = 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 = 512,
        accelerator: Accelerator = None,
    ):
        # self.block_mask_routers = nn.ModuleList(
        #     [nn.Sequential(nn.Linear(self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads, 1, bias=False), nn.Tanh()) for _ in
        #      range(self.transformer.config.num_layers)]
        # ).to(accelerator.device,dtype=torch.bfloat16)
        # self.single_block_mask_routers = nn.ModuleList(
        #     [nn.Sequential(nn.Linear(self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads, 1, bias=False), nn.Tanh()) for _ in
        #      range(self.transformer.config.num_single_layers)]
        # ).to(accelerator.device,dtype=torch.bfloat16)

        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,
            height,
            width,
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=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._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,
            pooled_prompt_embeds,
            text_ids,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            max_sequence_length=max_sequence_length,
            lora_scale=lora_scale,
        )

        # 3. Prepare latent variables
        num_channels_latents = self.transformer.config.in_channels // 4
        latents, latent_image_ids = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )
        # 4. Prepare conditions
        condition_latents, condition_ids, condition_type_ids, condition_types = ([] for _ in range(4))
        use_condition = conditions is not None

        if use_condition:
            for condition in conditions:
                tokens,ids,type_id = condition.encode(self)
                condition_latents.append(tokens)
                condition_ids.append(ids)
                condition_type_ids.append(type_id)
                condition_types.append(condition.condition_type)

        # 5. Prepare timesteps
        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
        image_seq_len = latents.shape[1]
        mu = calculate_shift(
            image_seq_len,
            self.scheduler.config.base_image_seq_len,
            self.scheduler.config.max_image_seq_len,
            self.scheduler.config.base_shift,
            self.scheduler.config.max_shift,
        )
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            device,
            timesteps,
            sigmas,
            mu=mu,
        )
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        self._num_timesteps = len(timesteps)

        # handle guidance: Decide whether to enable guidance according to the configuration in base model's config file.
        # example: Flux-dev: True ; Flux-schnell: False.
        if self.transformer.config.guidance_embeds:
            guidance = torch.full([1], guidance_scale, device=device, dtype=latents.dtype)
            guidance = guidance.expand(latents.shape[0])
        else:
            guidance = None

        # 6. Denoising loop
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue
                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.expand(latents.shape[0]).to(latents.dtype)
                noise_pred, conditional_output = self.transformer(
                    model_config=model_config,
                    # Inputs of the condition (new feature)
                    condition_latents=condition_latents if use_condition else None,
                    condition_ids=condition_ids if use_condition else None,
                    condition_type_ids=condition_type_ids if use_condition else None, # the condition_type_ids is not used so far.
                    condition_types = condition_types if use_condition else None,
                    return_condition_latents = model_config.get("return_condition_latents",False),
                    # Inputs to the original transformer
                    hidden_states=latents,
                    timestep=timestep / 1000,
                    guidance=guidance,
                    pooled_projections=pooled_prompt_embeds,
                    encoder_hidden_states=prompt_embeds,
                    txt_ids=text_ids,
                    img_ids=latent_image_ids,
                    joint_attention_kwargs=self.joint_attention_kwargs,
                    return_dict=False,
                )
                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

                # prepare for callback
                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)

                # 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()

        # 7 finish denoising process
        if output_type == "latent":
            image = latents
        else:
            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
            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,conditional_output) if model_config.get("return_condition_latents",False) else (image,)

        return FluxPipelineOutput(images=image)