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)