--- a
+++ b/Generation/custom_pipeline.py
@@ -0,0 +1,603 @@
+from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import *
+
+@torch.no_grad()
+@replace_example_docstring(EXAMPLE_DOC_STRING)
+def generate_ip_adapter_embeds(
+    self,
+    prompt: Union[str, List[str]] = None,
+    prompt_2: Optional[Union[str, List[str]]] = None,
+    height: Optional[int] = None,
+    width: Optional[int] = None,
+    num_inference_steps: int = 50,
+    timesteps: List[int] = None,
+    denoising_end: Optional[float] = None,
+    guidance_scale: float = 5.0,
+    negative_prompt: Optional[Union[str, List[str]]] = None,
+    negative_prompt_2: Optional[Union[str, List[str]]] = None,
+    num_images_per_prompt: Optional[int] = 1,
+    eta: float = 0.0,
+    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,
+    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ip_adapter_image: Optional[PipelineImageInput] = None,
+    ip_adapter_embeds: Optional[torch.FloatTensor] = None,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    guidance_rescale: float = 0.0,
+    original_size: Optional[Tuple[int, int]] = None,
+    crops_coords_top_left: Tuple[int, int] = (0, 0),
+    target_size: Optional[Tuple[int, int]] = None,
+    negative_original_size: Optional[Tuple[int, int]] = None,
+    negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+    negative_target_size: Optional[Tuple[int, int]] = None,
+    clip_skip: Optional[int] = None,
+    callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+    callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+    **kwargs,
+):
+    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 the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+            used in both text-encoders
+        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.
+            Anything below 512 pixels won't work well for
+            [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+            and checkpoints that are not specifically fine-tuned on low resolutions.
+        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.
+            Anything below 512 pixels won't work well for
+            [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+            and checkpoints that are not specifically fine-tuned on low resolutions.
+        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.
+        denoising_end (`float`, *optional*):
+            When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+            completed before it is intentionally prematurely terminated. As a result, the returned sample will
+            still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+            scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+            "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+            Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+        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`).
+        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 both text-encoders
+        num_images_per_prompt (`int`, *optional*, defaults to 1):
+            The number of images to generate per prompt.
+        eta (`float`, *optional*, defaults to 0.0):
+            Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+            [`schedulers.DDIMScheduler`], will be ignored for others.
+        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.
+        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.
+        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.
+        ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+        ip_adapter_embeds: (`FloatTensor`, *optional*): Optional image embeddings to work with IP Adapters.
+        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.
+        cross_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).
+        guidance_rescale (`float`, *optional*, defaults to 0.0):
+            Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+            [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+            Guidance rescale factor should fix overexposure when using zero terminal SNR.
+        original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+            If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+            `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+            explained in section 2.2 of
+            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+        crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+            `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+            `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+            `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+        target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+            For most cases, `target_size` should be set to the desired height and width of the generated image. If
+            not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+            section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+        negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+            To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+            micro-conditioning as explained in section 2.2 of
+            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+            information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+        negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+            To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+            micro-conditioning as explained in section 2.2 of
+            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+            information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+        negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+            To negatively condition the generation process based on a target image resolution. It should be as same
+            as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+            information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+        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.
+
+    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.
+    """
+
+    callback = kwargs.pop("callback", None)
+    callback_steps = kwargs.pop("callback_steps", None)
+
+    if callback is not None:
+        deprecate(
+            "callback",
+            "1.0.0",
+            "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+        )
+    if callback_steps is not None:
+        deprecate(
+            "callback_steps",
+            "1.0.0",
+            "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+        )
+
+    # 0. Default height and width to unet
+    height = height or self.default_sample_size * self.vae_scale_factor
+    width = width or self.default_sample_size * self.vae_scale_factor
+
+    original_size = original_size or (height, width)
+    target_size = target_size or (height, width)
+
+    # 1. Check inputs. Raise error if not correct
+    self.check_inputs(
+        prompt,
+        prompt_2,
+        height,
+        width,
+        callback_steps,
+        negative_prompt,
+        negative_prompt_2,
+        prompt_embeds,
+        negative_prompt_embeds,
+        pooled_prompt_embeds,
+        negative_pooled_prompt_embeds,
+        callback_on_step_end_tensor_inputs,
+    )
+
+    self._guidance_scale = guidance_scale
+    self._guidance_rescale = guidance_rescale
+    self._clip_skip = clip_skip
+    self._cross_attention_kwargs = cross_attention_kwargs
+    self._denoising_end = denoising_end
+
+    # 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
+
+    # 3. Encode input prompt
+    lora_scale = (
+        self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+    )
+
+    (
+        prompt_embeds,
+        negative_prompt_embeds,
+        pooled_prompt_embeds,
+        negative_pooled_prompt_embeds,
+    ) = self.encode_prompt(
+        prompt=prompt,
+        prompt_2=prompt_2,
+        device=device,
+        num_images_per_prompt=num_images_per_prompt,
+        do_classifier_free_guidance=self.do_classifier_free_guidance,
+        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,
+        lora_scale=lora_scale,
+        clip_skip=self.clip_skip,
+    )
+
+    # 4. Prepare timesteps
+    timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+
+    # 5. Prepare latent variables
+    num_channels_latents = self.unet.config.in_channels
+    latents = self.prepare_latents(
+        batch_size * num_images_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        prompt_embeds.dtype,
+        device,
+        generator,
+        latents,
+    )
+
+    # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+    extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+    # 7. Prepare added time ids & embeddings
+    add_text_embeds = pooled_prompt_embeds
+    if self.text_encoder_2 is None:
+        text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+    else:
+        text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+    add_time_ids = self._get_add_time_ids(
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        dtype=prompt_embeds.dtype,
+        text_encoder_projection_dim=text_encoder_projection_dim,
+    )
+    if negative_original_size is not None and negative_target_size is not None:
+        negative_add_time_ids = self._get_add_time_ids(
+            negative_original_size,
+            negative_crops_coords_top_left,
+            negative_target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+    else:
+        negative_add_time_ids = add_time_ids
+
+    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 * num_images_per_prompt, 1)
+
+    if ip_adapter_image is not None:
+        image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
+        if self.do_classifier_free_guidance:
+            image_embeds = torch.cat([negative_image_embeds, image_embeds])
+            image_embeds = image_embeds.to(device)
+    
+    if ip_adapter_embeds is not None:
+        image_embeds = ip_adapter_embeds.to(device=device, dtype=prompt_embeds.dtype)
+        if self.do_classifier_free_guidance:
+            negative_image_embeds = torch.zeros_like(image_embeds)
+            image_embeds = torch.cat([negative_image_embeds, image_embeds])
+            image_embeds = image_embeds.to(device)
+
+    # 8. Denoising loop
+    num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+    # 8.1 Apply denoising_end
+    if (
+        self.denoising_end is not None
+        and isinstance(self.denoising_end, float)
+        and self.denoising_end > 0
+        and self.denoising_end < 1
+    ):
+        discrete_timestep_cutoff = int(
+            round(
+                self.scheduler.config.num_train_timesteps
+                - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+            )
+        )
+        num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+        timesteps = timesteps[:num_inference_steps]
+
+    # 9. Optionally get Guidance Scale Embedding
+    timestep_cond = None
+    if self.unet.config.time_cond_proj_dim is not None:
+        guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+        timestep_cond = self.get_guidance_scale_embedding(
+            guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+        ).to(device=device, dtype=latents.dtype)
+
+    self._num_timesteps = len(timesteps)
+    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)
+
+            # predict the noise residual
+            added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+            if ip_adapter_image is not None or ip_adapter_embeds is not None:
+                added_cond_kwargs["image_embeds"] = image_embeds
+            noise_pred = self.unet(
+                latent_model_input,
+                t,
+                encoder_hidden_states=prompt_embeds,
+                timestep_cond=timestep_cond,
+                cross_attention_kwargs=self.cross_attention_kwargs,
+                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 + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+            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)
+                add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                negative_pooled_prompt_embeds = callback_outputs.pop(
+                    "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                )
+                add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
+
+            # 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 callback is not None and i % callback_steps == 0:
+                    step_idx = i // getattr(self.scheduler, "order", 1)
+                    callback(step_idx, t, latents)
+
+            if XLA_AVAILABLE:
+                xm.mark_step()
+
+    if not output_type == "latent":
+        # make sure the VAE is in float32 mode, as it overflows in float16
+        needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+        if needs_upcasting:
+            self.upcast_vae()
+            latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+        # cast back to fp16 if needed
+        if needs_upcasting:
+            self.vae.to(dtype=torch.float16)
+    else:
+        image = latents
+
+    if not output_type == "latent":
+        # apply watermark if available
+        if self.watermark is not None:
+            image = self.watermark.apply_watermark(image)
+
+        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 StableDiffusionXLPipelineOutput(images=image)
+
+def encode_image(image, image_encoder, feature_extractor, num_images_per_prompt=1, device='cuda'):
+    dtype = next(image_encoder.parameters()).dtype
+
+    if not isinstance(image, torch.Tensor):
+        image = feature_extractor(image, return_tensors="pt").pixel_values # [1, 3, 224, 224]
+    
+    image = image.to(device=device, dtype=dtype)
+    image_embeds = image_encoder(image).image_embeds # (1, 1024)
+    image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) # (num_images_per_prompt, 1024)
+
+    return image_embeds
+
+class Generator4Embeds:
+
+    def __init__(self, num_inference_steps=1, device='cuda') -> None:
+        import os
+        os.environ['http_proxy'] = 'http://10.16.35.10:13390' 
+        os.environ['https_proxy'] = 'http://10.16.35.10:13390' 
+
+        self.num_inference_steps = num_inference_steps
+        self.dtype = torch.float16
+        self.device = device
+        
+        # path = '/home/weichen/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/f4b0486b498f84668e828044de1d0c8ba486e05b'
+        # path = "/home/ldy/Workspace/sdxl-turbo/f4b0486b498f84668e828044de1d0c8ba486e05b"
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
+        # pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16, variant="fp16")
+        pipe.to(device)
+        pipe.generate_ip_adapter_embeds = generate_ip_adapter_embeds.__get__(pipe)
+        # load ip adapter
+        pipe.load_ip_adapter(
+            "h94/IP-Adapter", subfolder="sdxl_models", 
+            weight_name="ip-adapter_sdxl_vit-h.safetensors", 
+            torch_dtype=torch.float16)
+        # set ip_adapter scale (defauld is 1)
+        pipe.set_ip_adapter_scale(1)
+        self.pipe = pipe
+
+    def generate(self, image_embeds, text_prompt='', generator=None):
+        image_embeds = image_embeds.to(device=self.device, dtype=self.dtype)
+        pipe = self.pipe
+
+        # generate image with image prompt - ip_adapter_embeds
+        image = pipe.generate_ip_adapter_embeds(
+            prompt=text_prompt, 
+            ip_adapter_embeds=image_embeds, 
+            num_inference_steps=self.num_inference_steps,
+            guidance_scale=0.0,
+            generator=generator,
+        ).images[0]
+
+        return image
+        
+        
+
+if __name__ == "__main__":
+    import os
+    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+
+    # import os
+    # os.environ['http_proxy'] = 'http://10.16.35.10:13390' 
+    # os.environ['https_proxy'] = 'http://10.16.35.10:13390' 
+    # # path = '/home/weichen/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/f4b0486b498f84668e828044de1d0c8ba486e05b'
+
+    # pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
+    # # pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16, variant="fp16")
+    # pipe.to("cuda")
+
+    # from IPython.display import Image, display
+
+    # pipe.generate_ip_adapter_embeds = generate_ip_adapter_embeds.__get__(pipe)
+
+    # prompt = ""
+
+    # # # 1. Generate image
+    # # image = pipe.generate_ip_adapter_embeds(
+    # #     prompt=prompt, 
+    # #     num_inference_steps=4,
+    # #     guidance_scale=0.0,
+    # # ).images[0]
+    # # # show this image
+    # # display(image)
+
+    # # load ip adapter
+    # pipe.load_ip_adapter(
+    #     "h94/IP-Adapter", subfolder="sdxl_models", 
+    #     weight_name="ip-adapter_sdxl_vit-h.safetensors", 
+    #     torch_dtype=torch.float16)
+    # pipe.set_ip_adapter_scale(0.5)
+
+
+    # # 2. Load image encoder
+    # from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
+
+    # image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    #     "h94/IP-Adapter", 
+    #     subfolder="models/image_encoder",
+    #     torch_dtype=torch.float16,
+    # ).to("cuda")
+    # feature_extractor = CLIPImageProcessor()
+
+    # from diffusers.utils import load_image
+    # image_prompt = load_image("/mnt/dataset0/weichen/projects/visobj/proposals/mise/data/things-images/THINGSplus/images/images_resized/apple.jpg")
+    # display(image_prompt)
+
+    # # encode image
+    # image_embeds = encode_image(image_prompt, image_encoder, feature_extractor, 1, "cuda")
+
+    # # 3. Generate image with image prompt - ip_adapter_image
+    # pipe.image_encoder = image_encoder
+    # image = pipe.generate_ip_adapter_embeds(
+    #     prompt=prompt, 
+    #     ip_adapter_image=image_prompt, 
+    #     num_inference_steps=4,
+    #     guidance_scale=0.0,
+    # ).images[0]
+    # # show this image
+    # display(image)
+
+
+    # # 4. Generate image with image prompt - ip_adapter_embeds
+    # image = pipe.generate_ip_adapter_embeds(
+    #     prompt=prompt, 
+    #     ip_adapter_embeds=image_embeds, 
+    #     num_inference_steps=4,
+    #     guidance_scale=0.0,
+    # ).images[0]
+    # # show this image
+    # display(image)
+
+    from IPython.display import Image, display
+    generator = Generator4Embeds(num_inference_steps=4)
+
+    # 2. Load image encoder
+    # from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
+
+    # image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    #     "h94/IP-Adapter", 
+    #     subfolder="models/image_encoder",
+    #     torch_dtype=torch.float16,
+    # ).to("cuda")
+    # feature_extractor = CLIPImageProcessor()
+
+    # 2.2 Load image encoder from open_clip
+    import open_clip
+    image_encoder, _, feature_extractor = open_clip.create_model_and_transforms(
+        'ViT-H-14', pretrained='laion2b_s32b_b79k', precision='fp16', device='cuda')
+
+    from diffusers.utils import load_image
+    image_prompt = load_image("/mnt/dataset0/weichen/projects/visobj/proposals/mise/data/things-images/THINGSplus/images/images_resized/apple.jpg")
+    # image_prompt = load_image("https://th.bing.com/th/id/OIP.BGo1V-YM46ZrqSo5N_edWAHaE7?rs=1&pid=ImgDetMain")
+
+    display(image_prompt)
+
+    # encode image
+    # image_embeds = encode_image(image_prompt, image_encoder, feature_extractor, 1, "cuda")
+    image_embeds = image_encoder.encode_image(feature_extractor(image_prompt)[None, ...].to("cuda", dtype=torch.float16))
+
+    image = generator.generate(image_embeds)
+    display(image)
\ No newline at end of file