Create pipeline.py

pipeline.py

@@ -0,0 +1,508 @@
+# Copyright 2022 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.
+
+
+import torch
+import numpy as np
+import inspect
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.image_processor import PipelineImageInput
+from typing import Any, Callable, Dict, List, Optional, Union
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+def pack_latents(latents, batch_size, num_channels_latents, height, width):
+    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 2, 4, 1, 3, 5)
+    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+    return latents
+
+
+def unpack_latents(latents, height, width):
+    batch_size, num_patches, channels = latents.shape
+
+    assert height % 2 == 0 and width % 2 == 0
+    latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
+    latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+    latents = latents.reshape(batch_size, channels // (2 * 2), height, width)
+
+    return latents
+
+
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+def prepare_latent_image_ids(batch_size, height, width, device, dtype):
+    latent_image_ids = torch.zeros(height, width, 3)
+    latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+    latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+
+    latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+    latent_image_ids = latent_image_ids.reshape(
+        latent_image_id_height * latent_image_id_width, latent_image_id_channels
+    )
+
+    return latent_image_ids.to(device=device, dtype=dtype)
+
+
+@torch.no_grad()
+def run(
+        self,
+        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,
+        timesteps: Optional[List[float]] = None,
+        scales: 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,
+):
+    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.
+        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):
+            Guidance scale as defined in [Classifier-Free Diffusion
+            Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+            of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
+        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`.
+
+    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,
+        )
+
+    # 4. 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,
+    )
+
+    # 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,
+    ) if timesteps is None else (timesteps, len(timesteps))
+    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
+    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
+            # 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 = self.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,
+            )[0]
+
+            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=latents,
+                    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_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+                noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            if scales is None:
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+            else:
+                latents_dtype = latents.dtype
+                sigma = sigmas[i]
+                sigma_next = sigmas[i + 1]
+                x0_pred = (latents - sigma * noise_pred)
+                x0_pred = unpack_latents(x0_pred, scales[i], scales[i])
+                if scales and i + 1 < len(scales):
+                    x0_pred = torch.nn.functional.interpolate(x0_pred, size=scales[i + 1], mode='bicubic')
+                    latent_image_ids = prepare_latent_image_ids(batch_size, scales[i + 1] // 2, scales[i + 1] // 2, device, prompt_embeds.dtype)
+                x0_pred = pack_latents(x0_pred, *x0_pred.shape)
+                noise = torch.randn(x0_pred.shape, generator=generator, dtype=x0_pred.dtype).to(x0_pred.device)
+                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)
+
+            # 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:
+        if scales is not None:
+            height, width = int(scales[-1] * 8), int(scales[-1] * 8)
+        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,)
+
+    return FluxPipelineOutput(images=image)