Delete pipeline_flux_controlnet.py

pipeline_flux_controlnet.py

@@ -1,532 +0,0 @@
-# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any, Callable, Dict, List, Optional, Union
-import torch
-from transformers import (
-    T5EncoderModel,
-    T5TokenizerFast,
-)
-from diffusers.image_processor import PipelineImageInput
-
-from diffusers import AutoencoderKL  # Waiting for diffusers udpdate
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import logging
-from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
-from diffusers.pipelines.flux.pipeline_flux import retrieve_timesteps
-from .controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
-
-from .pipeline_bria import BriaPipeline
-from transformer_bria import BriaTransformer2DModel
-from bria_utils import get_original_sigmas
-
-XLA_AVAILABLE = False
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class FluxControlNetPipeline(BriaPipeline):
-    r"""
-    Args:
-        transformer ([`SD3Transformer2DModel`]):
-            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`T5EncoderModel`]):
-            Frozen text-encoder. Stable Diffusion 3 uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
-            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
-        tokenizer (`T5TokenizerFast`):
-            Tokenizer of class
-            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-    """
-
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder->transformer->vae"
-    _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
-
-    def __init__(  # EYAL - removed clip text encoder + tokenizer
-        self,
-        transformer: BriaTransformer2DModel,
-        scheduler: Union[FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers],
-        vae: AutoencoderKL,
-        text_encoder: T5EncoderModel,
-        tokenizer: T5TokenizerFast,
-        controlnet: FluxControlNetModel,
-    ):
-        super().__init__(
-            transformer=transformer, scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer
-        )
-        self.register_modules(controlnet=controlnet)
-
-    def prepare_image(
-        self,
-        image,
-        width,
-        height,
-        batch_size,
-        num_images_per_prompt,
-        device,
-        dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
-    ):
-        if isinstance(image, torch.Tensor):
-            pass
-        else:
-            image = self.image_processor.preprocess(image, height=height, width=width)
-
-        image_batch_size = image.shape[0]
-
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-
-        image = image.repeat_interleave(repeat_by, dim=0)
-
-        image = image.to(device=device, dtype=dtype)
-
-        if do_classifier_free_guidance and not guess_mode:
-            image = torch.cat([image] * 2)
-
-        return image
-
-    def prepare_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
-        num_channels_latents = self.transformer.config.in_channels // 4
-        control_image = self.prepare_image(
-            image=control_image,
-            width=width,
-            height=height,
-            batch_size=batch_size * num_images_per_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-            dtype=self.vae.dtype,
-        )
-        height, width = control_image.shape[-2:]
-
-        # vae encode
-        control_image = self.vae.encode(control_image).latent_dist.sample()
-        control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-        # pack
-        height_control_image, width_control_image = control_image.shape[2:]
-        control_image = self._pack_latents(
-            control_image,
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height_control_image,
-            width_control_image,
-        )
-
-        # Here we ensure that `control_mode` has the same length as the control_image.
-        if control_mode is not None:
-            if not isinstance(control_mode, int):
-                raise ValueError(" For `FluxControlNet`, `control_mode` should be an `int` or `None`")
-            control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
-            control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1)
-
-        return control_image, control_mode
-
-    def prepare_multi_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
-        num_channels_latents = self.transformer.config.in_channels // 4
-        control_images = []
-        for i, control_image_ in enumerate(control_image):
-            control_image_ = self.prepare_image(
-                image=control_image_,
-                width=width,
-                height=height,
-                batch_size=batch_size * num_images_per_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-                dtype=self.vae.dtype,
-            )
-            height, width = control_image_.shape[-2:]
-
-            # vae encode
-            control_image_ = self.vae.encode(control_image_).latent_dist.sample()
-            control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-            # pack
-            height_control_image, width_control_image = control_image_.shape[2:]
-            control_image_ = self._pack_latents(
-                control_image_,
-                batch_size * num_images_per_prompt,
-                num_channels_latents,
-                height_control_image,
-                width_control_image,
-            )
-            control_images.append(control_image_)
-
-        control_image = control_images
-
-        # Here we ensure that `control_mode` has the same length as the control_image.
-        if isinstance(control_mode, list) and len(control_mode) != len(control_image):
-            raise ValueError(
-                "For Multi-ControlNet, `control_mode` must be a list of the same "
-                + " length as the number of controlnets (control images) specified"
-            )
-        if not isinstance(control_mode, list):
-            control_mode = [control_mode] * len(control_image)
-        # set control mode
-        control_modes = []
-        for cmode in control_mode:
-            if cmode is None:
-                cmode = -1
-            control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long)
-            control_modes.append(control_mode)
-        control_mode = control_modes
-
-        return control_image, control_mode
-    
-    def get_controlnet_keep(self, timesteps, control_guidance_start, control_guidance_end):
-        controlnet_keep = []
-        for i in range(len(timesteps)):
-            keeps = [
-                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
-                for s, e in zip(control_guidance_start, control_guidance_end)
-            ]
-            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, FluxControlNetModel) else keeps)
-        return controlnet_keep
-
-    def get_control_start_end(self, control_guidance_start, control_guidance_end):
-        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
-            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
-        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
-            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
-        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
-            mult = 1  # TODO - why is this 1?
-            control_guidance_start, control_guidance_end = (
-                mult * [control_guidance_start],
-                mult * [control_guidance_end],
-            )
-
-        return control_guidance_start, control_guidance_end
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 30,
-        timesteps: List[int] = None,
-        guidance_scale: float = 3.5,
-        control_guidance_start: Union[float, List[float]] = 0.0,
-        control_guidance_end: Union[float, List[float]] = 1.0,
-        control_image: Optional[PipelineImageInput] = None,
-        control_mode: Optional[Union[int, List[int]]] = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
-        negative_prompt: 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,
-        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 = 128,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            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.
-            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.
-            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 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). 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.
-            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 `guidance_scale` is
-                less than `1`).
-            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.
-            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.
-            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.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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 256): Maximum sequence length to use with the `prompt`.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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
-        control_guidance_start, control_guidance_end = self.get_control_start_end(
-            control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end
-        )
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_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, negative_prompt_embeds, text_ids) = self.encode_prompt(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            max_sequence_length=max_sequence_length,
-            lora_scale=lora_scale,
-        )
-
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-
-        # 3. Prepare control image
-        if control_image is not None:
-            if isinstance(self.controlnet, FluxControlNetModel):
-                control_image, control_mode = self.prepare_control(
-                    control_image=control_image,
-                    width=width,
-                    height=height,
-                    batch_size=batch_size,
-                    num_images_per_prompt=num_images_per_prompt,
-                    device=device,
-                    control_mode=control_mode,
-                )
-            elif isinstance(self.controlnet, FluxMultiControlNetModel):
-                control_image, control_mode = self.prepare_multi_control(
-                    control_image=control_image,
-                    width=width,
-                    height=height,
-                    batch_size=batch_size,
-                    num_images_per_prompt=num_images_per_prompt,
-                    device=device,
-                    control_mode=control_mode,
-                )                
-
-        # 4. Prepare timesteps
-        # Sample from training sigmas
-        sigmas = get_original_sigmas(
-            num_train_timesteps=self.scheduler.config.num_train_timesteps, num_inference_steps=num_inference_steps
-        )
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas
-        )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels // 4  # due to patch=2, we devide by 4
-        latents, latent_image_ids = self.prepare_latents(
-            batch_size=batch_size * num_images_per_prompt,
-            num_channels_latents=num_channels_latents,
-            height=height,
-            width=width,
-            dtype=prompt_embeds.dtype,
-            device=device,
-            generator=generator,
-            latents=latents,
-        )
-
-        # 6. Create tensor stating which controlnets to keep
-        if control_image is not None:
-            controlnet_keep = self.get_controlnet_keep(
-                timesteps=timesteps,
-                control_guidance_start=control_guidance_start,
-                control_guidance_end=control_guidance_end,
-            )
-
-        # EYAL - added the CFG loop
-        # 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
-                # if type(self.scheduler) != FlowMatchEulerDiscreteScheduler:
-                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
-                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0])
-
-                # Handling ControlNet
-                if control_image is not None:
-                    if isinstance(controlnet_keep[i], list):
-                        if isinstance(controlnet_conditioning_scale, list):
-                            cond_scale = controlnet_conditioning_scale
-                        else:
-                            cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
-                    else:
-                        controlnet_cond_scale = controlnet_conditioning_scale
-                        if isinstance(controlnet_cond_scale, list):
-                            controlnet_cond_scale = controlnet_cond_scale[0]
-                        cond_scale = controlnet_cond_scale * controlnet_keep[i]
-
-                    # controlnet
-                    controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
-                        hidden_states=latents,
-                        controlnet_cond=control_image,
-                        controlnet_mode=control_mode,
-                        conditioning_scale=cond_scale,
-                        timestep=timestep,
-                        # 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,
-                    )
-                else:
-                    controlnet_block_samples, controlnet_single_block_samples = None, None
-
-                # This is predicts "v" from flow-matching
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                    txt_ids=text_ids,
-                    img_ids=latent_image_ids,
-                    controlnet_block_samples=controlnet_block_samples,
-                    controlnet_single_block_samples=controlnet_single_block_samples,
-                )[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)
-
-                # 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)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_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 = 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.to(dtype=self.vae.dtype), 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)