Create pipeline_fill_sd_xl.py

pipeline_fill_sd_xl.py

@@ -0,0 +1,545 @@
+from typing import List, Optional, Union
+
+import cv2
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+
+from controlnet_union import ControlNetModel_Union
+
+
+def latents_to_rgb(latents):
+    weights = ((60, -60, 25, -70), (60, -5, 15, -50), (60, 10, -5, -35))
+
+    weights_tensor = torch.t(
+        torch.tensor(weights, dtype=latents.dtype).to(latents.device)
+    )
+    biases_tensor = torch.tensor((150, 140, 130), dtype=latents.dtype).to(
+        latents.device
+    )
+    rgb_tensor = torch.einsum(
+        "...lxy,lr -> ...rxy", latents, weights_tensor
+    ) + biases_tensor.unsqueeze(-1).unsqueeze(-1)
+    image_array = rgb_tensor.clamp(0, 255)[0].byte().cpu().numpy()
+    image_array = image_array.transpose(1, 2, 0)  # Change the order of dimensions
+
+    denoised_image = cv2.fastNlMeansDenoisingColored(image_array, None, 10, 10, 7, 21)
+    blurred_image = cv2.GaussianBlur(denoised_image, (5, 5), 0)
+    final_image = PIL.Image.fromarray(blurred_image)
+
+    width, height = final_image.size
+    final_image = final_image.resize(
+        (width * 8, height * 8), PIL.Image.Resampling.LANCZOS
+    )
+
+    return final_image
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    **kwargs,
+):
+    scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+    timesteps = scheduler.timesteps
+
+    return timesteps, num_inference_steps
+
+
+class StableDiffusionXLFillPipeline(DiffusionPipeline, StableDiffusionMixin):
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        controlnet: ControlNetModel_Union,
+        scheduler: KarrasDiffusionSchedulers,
+        force_zeros_for_empty_prompt: bool = True,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+        )
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor,
+            do_convert_rgb=True,
+            do_normalize=False,
+        )
+
+        self.register_to_config(
+            force_zeros_for_empty_prompt=force_zeros_for_empty_prompt
+        )
+
+    def encode_prompt(
+        self,
+        prompt: str,
+        device: Optional[torch.device] = None,
+        do_classifier_free_guidance: bool = True,
+    ):
+        device = device or self._execution_device
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+
+        # Define tokenizers and text encoders
+        tokenizers = (
+            [self.tokenizer, self.tokenizer_2]
+            if self.tokenizer is not None
+            else [self.tokenizer_2]
+        )
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2]
+            if self.text_encoder is not None
+            else [self.text_encoder_2]
+        )
+
+        prompt_2 = prompt
+        prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+        # textual inversion: process multi-vector tokens if necessary
+        prompt_embeds_list = []
+        prompts = [prompt, prompt_2]
+        for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+            text_inputs = tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            text_input_ids = text_inputs.input_ids
+
+            prompt_embeds = text_encoder(
+                text_input_ids.to(device), output_hidden_states=True
+            )
+
+            # We are only ALWAYS interested in the pooled output of the final text encoder
+            pooled_prompt_embeds = prompt_embeds[0]
+            prompt_embeds = prompt_embeds.hidden_states[-2]
+            prompt_embeds_list.append(prompt_embeds)
+
+        prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = True
+        negative_prompt_embeds = None
+        negative_pooled_prompt_embeds = None
+
+        if do_classifier_free_guidance and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = ""
+            negative_prompt_2 = negative_prompt
+
+            # normalize str to list
+            negative_prompt = (
+                batch_size * [negative_prompt]
+                if isinstance(negative_prompt, str)
+                else negative_prompt
+            )
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2]
+                if isinstance(negative_prompt_2, str)
+                else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(
+                uncond_tokens, tokenizers, text_encoders
+            ):
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, 1, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * 1, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(
+                    dtype=self.text_encoder_2.dtype, device=device
+                )
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(
+                    dtype=self.unet.dtype, device=device
+                )
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, 1, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(
+                batch_size * 1, seq_len, -1
+            )
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, 1).view(bs_embed * 1, -1)
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(
+                1, 1
+            ).view(bs_embed * 1, -1)
+
+        return (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        )
+
+    def check_inputs(
+        self,
+        prompt_embeds,
+        negative_prompt_embeds,
+        pooled_prompt_embeds,
+        negative_pooled_prompt_embeds,
+        image,
+        controlnet_conditioning_scale=1.0,
+    ):
+        if prompt_embeds is None:
+            raise ValueError(
+                "Provide `prompt_embeds`. Cannot leave `prompt_embeds` undefined."
+            )
+
+        if negative_prompt_embeds is None:
+            raise ValueError(
+                "Provide `negative_prompt_embeds`. Cannot leave `negative_prompt_embeds` undefined."
+            )
+
+        if prompt_embeds.shape != negative_prompt_embeds.shape:
+            raise ValueError(
+                "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                f" {negative_prompt_embeds.shape}."
+            )
+
+        if prompt_embeds is not None and pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+
+        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        # Check `image`
+        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
+            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
+        )
+        if (
+            isinstance(self.controlnet, ControlNetModel_Union)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, ControlNetModel_Union)
+        ):
+            if not isinstance(image, PIL.Image.Image):
+                raise TypeError(
+                    f"image must be passed and has to be a PIL image, but is {type(image)}"
+                )
+
+        else:
+            assert False
+
+        # Check `controlnet_conditioning_scale`
+        if (
+            isinstance(self.controlnet, ControlNetModel_Union)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, ControlNetModel_Union)
+        ):
+            if not isinstance(controlnet_conditioning_scale, float):
+                raise TypeError(
+                    "For single controlnet: `controlnet_conditioning_scale` must be type `float`."
+                )
+        else:
+            assert False
+
+    def prepare_image(self, image, device, dtype, do_classifier_free_guidance=False):
+        image = self.control_image_processor.preprocess(image).to(dtype=torch.float32)
+
+        image_batch_size = image.shape[0]
+
+        image = image.repeat_interleave(image_batch_size, dim=0)
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    def prepare_latents(
+        self, batch_size, num_channels_latents, height, width, dtype, device
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+
+        latents = randn_tensor(shape, device=device, dtype=dtype)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt_embeds: torch.Tensor,
+        negative_prompt_embeds: torch.Tensor,
+        pooled_prompt_embeds: torch.Tensor,
+        negative_pooled_prompt_embeds: torch.Tensor,
+        image: PipelineImageInput = None,
+        num_inference_steps: int = 8,
+        guidance_scale: float = 1.5,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+    ):
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            image,
+            controlnet_conditioning_scale,
+        )
+
+        self._guidance_scale = guidance_scale
+
+        # 2. Define call parameters
+        batch_size = 1
+        device = self._execution_device
+
+        # 4. Prepare image
+        if isinstance(self.controlnet, ControlNetModel_Union):
+            image = self.prepare_image(
+                image=image,
+                device=device,
+                dtype=self.controlnet.dtype,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+            )
+            height, width = image.shape[-2:]
+        else:
+            assert False
+
+        # 5. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+        )
+
+        # 7 Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+
+        add_time_ids = negative_add_time_ids = torch.tensor(
+            image.shape[-2:] + torch.Size([0, 0]) + image.shape[-2:]
+        ).unsqueeze(0)
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat(
+                [negative_pooled_prompt_embeds, add_text_embeds], dim=0
+            )
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size, 1)
+
+        controlnet_image_list = [0, 0, 0, 0, 0, 0, image, 0]
+        union_control_type = (
+            torch.Tensor([0, 0, 0, 0, 0, 0, 1, 0])
+            .to(device, dtype=prompt_embeds.dtype)
+            .repeat(batch_size * 2, 1)
+        )
+
+        added_cond_kwargs = {
+            "text_embeds": add_text_embeds,
+            "time_ids": add_time_ids,
+            "control_type": union_control_type,
+        }
+
+        controlnet_prompt_embeds = prompt_embeds
+        controlnet_added_cond_kwargs = added_cond_kwargs
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # 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
+                )
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                # controlnet(s) inference
+                control_model_input = latent_model_input
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    control_model_input,
+                    t,
+                    encoder_hidden_states=controlnet_prompt_embeds,
+                    controlnet_cond_list=controlnet_image_list,
+                    conditioning_scale=controlnet_conditioning_scale,
+                    guess_mode=False,
+                    added_cond_kwargs=controlnet_added_cond_kwargs,
+                    return_dict=False,
+                )
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=None,
+                    cross_attention_kwargs={},
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, return_dict=False
+                )[0]
+
+                if i == 2:
+                    prompt_embeds = prompt_embeds[-1:]
+                    add_text_embeds = add_text_embeds[-1:]
+                    add_time_ids = add_time_ids[-1:]
+                    union_control_type = union_control_type[-1:]
+
+                    added_cond_kwargs = {
+                        "text_embeds": add_text_embeds,
+                        "time_ids": add_time_ids,
+                        "control_type": union_control_type,
+                    }
+
+                    controlnet_prompt_embeds = prompt_embeds
+                    controlnet_added_cond_kwargs = added_cond_kwargs
+
+                    image = image[-1:]
+                    controlnet_image_list = [0, 0, 0, 0, 0, 0, image, 0]
+
+                    self._guidance_scale = 0.0
+
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    yield latents_to_rgb(latents)
+
+        latents = latents / self.vae.config.scaling_factor
+        image = self.vae.decode(latents, return_dict=False)[0]
+        image = self.image_processor.postprocess(image)[0]
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        yield image