import inspect import os from typing import Any, Callable, Dict, List, Optional, Union import json import sys sys.path.append((os.path.dirname(__file__))) import PIL import PIL.Image import numpy as np import torch from accelerate import load_checkpoint_in_model from diffusers.utils.torch_utils import randn_tensor from diffusers import FluxKontextPipeline from diffusers.image_processor import PipelineImageInput from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput from diffusers.pipelines.flux.pipeline_flux_kontext import calculate_shift, retrieve_timesteps from model.utils import compute_vae_encodings from utils import prepare_image class FluxKontextImg2ImgLoRAPipeline(FluxKontextPipeline): def get_base_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): # VAE applies 8x compression on images but we must also account for packing which requires # latent height and width to be divisible by 2. height = 2 * (int(height) // (self.vae_scale_factor * 2)) width = 2 * (int(width) // (self.vae_scale_factor * 2)) shape = (batch_size, num_channels_latents, height, width) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) return latents @torch.no_grad() def __call__(self, image: Union[PIL.Image.Image, torch.Tensor], condition_images: List[Union[PIL.Image.Image, torch.Tensor]], prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, negative_prompt: Union[str, List[str]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, true_cfg_scale: float = 1.0, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 28, sigmas: Optional[List[float]] = 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, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, negative_ip_adapter_image: Optional[PipelineImageInput] = None, negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_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, max_area: int = 1024**2, _auto_resize: bool = True, ): r""" Function invoked when calling the pipeline for generation. Args: image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again. 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. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is will be used instead. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is not greater than `1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders. true_cfg_scale (`float`, *optional*, defaults to 1.0): When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. guidance_scale (`float`, *optional*, defaults to 3.5): Embedded guidance scale is enabled by setting `guidance_scale` > 1. Higher `guidance_scale` encourages a model to generate images more aligned with prompt at the expense of lower image quality. Guidance-distilled models approximates true classifier-free guidance for `guidance_scale` > 1. Refer to the [paper](https://huggingface.co/papers/2210.03142) to learn more. 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)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *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 ge 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. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. negative_ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` 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, negative_prompt_embeds will be generated from `negative_prompt` input argument. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple. joint_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). 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: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `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 variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. max_area (`int`, defaults to `1024 ** 2`): The maximum area of the generated image in pixels. The height and width will be adjusted to fit this area while maintaining the aspect ratio. Examples: Returns: [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ 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, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, 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._joint_attention_kwargs = joint_attention_kwargs self._current_timestep = None 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 ) has_neg_prompt = negative_prompt is not None or ( negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None ) do_true_cfg = true_cfg_scale > 1 and has_neg_prompt ( 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, ) if do_true_cfg: ( negative_prompt_embeds, negative_pooled_prompt_embeds, negative_text_ids, ) = self.encode_prompt( prompt=negative_prompt, prompt_2=negative_prompt_2, prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=negative_pooled_prompt_embeds, device=device, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, lora_scale=lora_scale, ) # image, condition_image, mask = self.check_inputs(image, condition_image, mask, width, height) image = prepare_image(image).to(self.transformer.device, dtype=self.transformer.dtype) # condition_image_1 = prepare_image(condition_image_1).to(self.transformer.device, dtype=self.transformer.dtype) # condition_image_2 = prepare_image(condition_image_2).to(self.transformer.device, dtype=self.transformer.dtype) condition_images = [prepare_image(ci).to(self.transformer.device, dtype=self.transformer.dtype) for ci in condition_images] # VAE encoding # condition_1_latent = compute_vae_encodings(condition_image_1, self.vae) # condition_2_latent = compute_vae_encodings(condition_image_2, self.vae) # condition_latent = torch.cat([condition_1_latent, condition_2_latent], dim=2) condition_latents = [compute_vae_encodings(ci, self.vae, sample_mode="argmax") for ci in condition_images] # image_latent = compute_vae_encodings(image, self.vae) del condition_images # # Concatenate latents # cond_latents = torch.cat([ # image_latent, # dp_latent, # # condition_latent # ], dim=2) # 4. Prepare latent variables num_channels_latents = self.transformer.config.in_channels // 4 latents, image_latents, latent_ids, image_ids = self.prepare_latents( image, batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # packed_dp_latent = self._pack_latents( # dp_latent, # dp_latent.shape[0], # dp_latent.shape[1], # dp_latent.shape[2], # dp_latent.shape[3] # ) # dp_latents, _, dp_latent_ids, _ = self.prepare_latents( # None, # dp_latent.shape[0], # dp_latent.shape[1], # dp_latent.shape[2] * self.vae_scale_factor, # dp_latent.shape[3] * self.vae_scale_factor, # prompt_embeds.dtype, # device, # generator, # packed_dp_latent, # ) # dp_latent_ids[:, 1] = dp_latent_ids[:, 1] + (int(height*2) // (self.vae_scale_factor * 2)) # dp_ids = dp_latent_ids.clone() # dp_ids[..., 0] = 1 packed_conds_latents = [] cond_ids = [] cond_latents = [] cond_latents_ids = [] for idx, condition_latent in enumerate(condition_latents): packed_conds_latent = self._pack_latents( condition_latent, condition_latent.shape[0], condition_latent.shape[1], condition_latent.shape[2], condition_latent.shape[3] ) packed_conds_latents.append(packed_conds_latent) cond_latent, _, cond_latent_ids, _ = self.prepare_latents( None, condition_latent.shape[0], condition_latent.shape[1], condition_latent.shape[2] * self.vae_scale_factor, condition_latent.shape[3] * self.vae_scale_factor, prompt_embeds.dtype, device, generator, packed_conds_latent, ) cond_latents.append(cond_latent) cond_id = cond_latent_ids.clone() # shift cond ids by condition image size cond_id[:, 1] = cond_id[:, 1] * (idx + 1) + (int(height) // (self.vae_scale_factor * 2)) cond_id[:, 2] = cond_id[:, 2] * (idx + 1) + (int(width) // (self.vae_scale_factor * 2)) cond_id[..., 0] = 1 # noise_latent is 0, image_latent is 1, cond_latents start from 2 cond_ids.append(cond_id) cond_latent_ids[:, 1] = cond_latent_ids[:, 1] * (idx + 1) + (int(height) // (self.vae_scale_factor * 2)) cond_latents_ids.append(cond_latent_ids) # concat all latent ids & image ids # latent_ids = torch.cat([ # latent_ids, # dp_latent_ids, # cond_latent_ids, # ], dim=0 # ) image_ids = torch.cat([ image_ids, # dp_ids, *cond_ids ], dim=0 ) # latent_ids = torch.cat([latent_ids, *cond_latents_ids], dim=0) latent_ids = torch.cat([latent_ids, image_ids], dim=0) # dim 0 is sequence dimension n_out_tokens = latents.shape[1] # latents = torch.cat([latents, *cond_latents], dim=1) # latent_id_len = latents.shape[1]//4 # for i in range(1,4): # w_offset = (int(width) // (self.vae_scale_factor * 2)) * i # latent_image_ids[:,(latent_id_len*i) : int(latent_id_len*(i+1)), 2] = \ # latent_image_ids[:,(latent_id_len*i) : int(latent_id_len*(i+1)), 2] + w_offset # h_offset = (int(height) // (self.vae_scale_factor * 2)) * i # latent_image_ids[:,(latent_id_len*i) : int(latent_id_len*(i+1)), 1] = \ # latent_image_ids[:,(latent_id_len*i) : int(latent_id_len*(i+1)), 1] - h_offset # 5. Prepare timesteps sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas image_seq_len = latents.shape[1] 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", 1.15), ) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, mu=mu, ) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # handle guidance if self.transformer.config.guidance_embeds: guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32) guidance = guidance.expand(latents.shape[0]) else: guidance = None if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and ( negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None ): negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8) negative_ip_adapter_image = [negative_ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and ( negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None ): ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8) ip_adapter_image = [ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters if self.joint_attention_kwargs is None: self._joint_attention_kwargs = {} image_embeds = None negative_image_embeds = None if ip_adapter_image is not None or ip_adapter_image_embeds is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, ) if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None: negative_image_embeds = self.prepare_ip_adapter_image_embeds( negative_ip_adapter_image, negative_ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, ) # 6. Denoising loop # We set the index here to remove DtoH sync, helpful especially during compilation. # Check out more details here: https://github.com/huggingface/diffusers/pull/11696 self.scheduler.set_begin_index(0) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue self._current_timestep = t if image_embeds is not None: self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds # latent_model_input = latents # if image_latents is not None: latent_model_input = torch.cat( [latents, image_latents, # dp_latents, *cond_latents], dim=1) timestep = t.expand(latents.shape[0]).to(latents.dtype) noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep / 1000, guidance=guidance, pooled_projections=pooled_prompt_embeds, encoder_hidden_states=prompt_embeds, txt_ids=text_ids, img_ids=latent_ids, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, )[0] noise_pred = noise_pred[:, : latents.size(1)] if do_true_cfg: if negative_image_embeds is not None: self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds neg_noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep / 1000, guidance=guidance, pooled_projections=negative_pooled_prompt_embeds, encoder_hidden_states=negative_prompt_embeds, txt_ids=negative_text_ids, img_ids=latent_ids, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, )[0] neg_noise_pred = neg_noise_pred[:, : latents.size(1)] noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred) # else: # if i < 1: # noise_pred = noise_pred * 0 # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] 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) # 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() self._current_timestep = None if output_type == "latent": image = latents else: latents = self._unpack_latents(latents[:,:n_out_tokens], 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 return FluxPipelineOutput(images=image)