Initial commit with folder contents

.gitattributes

@@ -36,5 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 cached_pipe/text_encoder filter=lfs diff=lfs merge=lfs -text
 cached_pipe/text_encoder_2 filter=lfs diff=lfs merge=lfs -text
 cached_pipe/vae.decoder filter=lfs diff=lfs merge=lfs -text
-cached_pipe/fast_unet filter=lfs diff=lfs merge=lfs -text
 cached_pipe/unet filter=lfs diff=lfs merge=lfs -text

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "newdream-sdxl-20"]
+	path = models/newdream-sdxl-20
+	url = https://huggingface.co/stablediffusionapi/newdream-sdxl-20
+	branch = main

cache_info.json

@@ -0,0 +1 @@
+{"repository": "https://huggingface.co/silencer107/poo3", "revision": "1a46d0d"}

cached_pipe/text_encoder

@@ -1,3 +1,3 @@
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-oid sha256:386b7cad4378861ad4fb7ecb4dee107bf7fe28c76668bea03a0dc084a210aced
-size 2728173
+oid sha256:5e05bb0bdc1e7bb9af8b5524fce5523d4ceadfc12037fce22cf224e9797f9c31
+size 2728178

cached_pipe/text_encoder_2

@@ -1,3 +1,3 @@
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-oid sha256:6f97e11242c00f86337bd9801ad4820b68c99918a922c8542922be505c2bb430
-size 9363341
+oid sha256:59f85126611ab3b28ea283da1b1a77eeae6937d3ed3c5fc9413c8b40418165fd
+size 9363012

cached_pipe/unet

@@ -1,3 +1,3 @@
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-oid sha256:e600471d0d62f0d13d24a2c2e79e49eae4709d02c8218912029e2b5eda7c457f
-size 676786352
+oid sha256:4ca58697e0025abc011c46f214df6e0850206b11287a91e79b4fe631d1a8adaa
+size 687271819

cached_pipe/vae.decoder

@@ -1,3 +1,3 @@
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-oid sha256:3986989d6743d07e59fb46dff1a13456b6fe41fe5bc9f635e194be7e01e73583
-size 187873926
+oid sha256:af3d1483221ecf9f753b862fed022729c3a793f1d85740d2cb93a9a8a8e995cf
+size 187873891

loss_params.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:27ba04dc09bfe8325c2b8d8acbfa5fbf746f61169cf1cdfe07d028ad697217f1
-size 3568
+oid sha256:8c8d58c214ba22a6aeacea98cef1a4b88fb88c8d0ed113f39b6a60b0165b3bdb
+size 3952

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "diffusers==0.28.2",
     "onediff==1.2.0",
     "onediffx==1.2.0",
-    "accelerate==0.31.0",
+    "oneflow",
     "numpy==1.26.4",
     "xformers==0.0.25.post1",
     "triton==2.2.0",
@@ -20,18 +20,13 @@ dependencies = [
     "omegaconf==2.3.0",
     "torch==2.2.2",
     "torchvision==0.17.2",
-    "huggingface-hub==0.25.2",
+    "huggingface_hub==0.24.7",
+    "setuptools==75.2.0",
     "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing#subdirectory=pipelines",
-    "oneflow",
-    "setuptools>=75.2.0",
-    "bitsandbytes>=0.44.1",
-    "stable-fast",
-    "tomesd>=0.1.3",
 ]
 
 [tool.uv.sources]
 oneflow = { url = "https://github.com/siliconflow/oneflow_releases/releases/download/community_cu118/oneflow-0.9.1.dev20240802%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" }
-stable-fast = { url = "https://github.com/chengzeyi/stable-fast/releases/download/v1.0.5/stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl" }
 
 [project.scripts]
-start_inference = "main:main"
+start_inference = "main:main"

src/edge_maxxing_4090_newdream.egg-info/PKG-INFO

@@ -0,0 +1,20 @@
+Metadata-Version: 2.1
+Name: edge-maxxing-4090-newdream
+Version: 6
+Summary: An edge-maxxing model submission for the 4090 newdream contest
+Requires-Python: <3.11,>=3.10
+Requires-Dist: diffusers==0.28.2
+Requires-Dist: onediff==1.2.0
+Requires-Dist: onediffx==1.2.0
+Requires-Dist: oneflow
+Requires-Dist: numpy==1.26.4
+Requires-Dist: xformers==0.0.25.post1
+Requires-Dist: triton==2.2.0
+Requires-Dist: transformers==4.41.2
+Requires-Dist: accelerate==0.31.0
+Requires-Dist: omegaconf==2.3.0
+Requires-Dist: torch==2.2.2
+Requires-Dist: torchvision==0.17.2
+Requires-Dist: huggingface_hub==0.24.7
+Requires-Dist: setuptools==75.2.0
+Requires-Dist: edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing#subdirectory=pipelines

src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt

@@ -0,0 +1,11 @@
+README.md
+pyproject.toml
+src/loss.py
+src/main.py
+src/pipeline.py
+src/edge_maxxing_4090_newdream.egg-info/PKG-INFO
+src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt
+src/edge_maxxing_4090_newdream.egg-info/dependency_links.txt
+src/edge_maxxing_4090_newdream.egg-info/entry_points.txt
+src/edge_maxxing_4090_newdream.egg-info/requires.txt
+src/edge_maxxing_4090_newdream.egg-info/top_level.txt

src/edge_maxxing_4090_newdream.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

src/edge_maxxing_4090_newdream.egg-info/entry_points.txt

@@ -0,0 +1,2 @@
+[console_scripts]
+start_inference = main:main

src/edge_maxxing_4090_newdream.egg-info/requires.txt

@@ -0,0 +1,15 @@
+diffusers==0.28.2
+onediff==1.2.0
+onediffx==1.2.0
+oneflow
+numpy==1.26.4
+xformers==0.0.25.post1
+triton==2.2.0
+transformers==4.41.2
+accelerate==0.31.0
+omegaconf==2.3.0
+torch==2.2.2
+torchvision==0.17.2
+huggingface_hub==0.24.7
+setuptools==75.2.0
+edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing#subdirectory=pipelines

src/edge_maxxing_4090_newdream.egg-info/top_level.txt

@@ -0,0 +1,3 @@
+loss
+main
+pipeline

src/loss.py

@@ -42,4 +42,4 @@ class SchedulerWrapper:
 		for E in A:F=torch.cat(C.catch_x[E],dim=0);B.append(F);G=torch.cat(C.catch_e[E],dim=0);D.append(G)
 		H=A[-1];I=torch.cat(C.catch_x_[H],dim=0);B.append(I);A=torch.tensor(A,dtype=torch.int32);B=torch.stack(B);D=torch.stack(D);return A,B,D
 	def load_loss_params(A):B,C,D=torch.load(A.loss_params_path,map_location='cpu');A.loss_model=LossSchedulerModel(C,D);A.loss_scheduler=LossScheduler(B,A.loss_model)
-	def prepare_loss(A,num_accelerate_steps=15):A.load_loss_params()
+	def prepare_loss(A,num_accelerate_steps=16):A.load_loss_params()

src/pipeline.py

@@ -1,982 +1,47 @@
 import torch
-from PIL import Image
+from PIL.Image import Image
+from onediffx.deep_cache import StableDiffusionXLPipeline
 from pipelines.models import TextToImageRequest
 from torch import Generator
-import json
-from diffusers import StableDiffusionXLPipeline, DDIMScheduler
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import oneflow as flow
+from onediff.infer_compiler import oneflow_compile
+from onediffx import compile_pipe, save_pipe, load_pipe
+from diffusers import DDIMScheduler
 from loss import SchedulerWrapper
-from onediffx import compile_pipe,load_pipe
-# Import necessary components
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionModelWithProjection,
-)
-
-
-from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
-from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.loaders import (
-    FromSingleFileMixin,
-    IPAdapterMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    TextualInversionLoaderMixin,
-)
-from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
-from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    deprecate,
-    is_invisible_watermark_available,
-    is_torch_xla_available,
-    logging,
-    replace_example_docstring,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
-from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
-
-# Import watermark if available
-if is_invisible_watermark_available():
-    from .watermark import StableDiffusionXLWatermarker
-
-# Check for XLA availability
-if is_torch_xla_available():
-    import torch_xla.core.xla_model as xm
-    XLA_AVAILABLE = True
-else:
-    XLA_AVAILABLE = False
-
-logger = logging.get_logger(__name__)
-
-# Constants
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import StableDiffusionXLPipeline
-
-        >>> pipe = StableDiffusionXLPipeline.from_pretrained(
-        >>>     "stabilityai/stable-diffusion-xl-base-1.0", 
-        >>>     torch_dtype=torch.float16
-        >>> )
-        >>> pipe = pipe.to("cuda")
-
-        >>> prompt = "a photo of an astronaut riding a horse on mars"
-        >>> image = pipe(prompt).images[0]
-        ```
-"""
-
-# Helper functions
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """Rescale noise configuration."""
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-# Utils functions
-import numpy as np
-def max_pixel_filter(image: Image) -> Image:
-    try:
-        # Convert the image to a numpy array
-        img_array = np.array(image)
-        # Find the maximum pixel value in the image
-        max_val = img_array.max()
-        # Reduce the maximum value to 1
-        img_array[img_array == max_val] -= 2
-        # Convert the numpy array back to an image
-        filtered_image = Image.fromarray(img_array)
-        return filtered_image
-    except:
-        return image
-    
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-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,
-):
-    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
-
-
-class StableDiffusionXLPipeline_new(
-    DiffusionPipeline,
-    StableDiffusionMixin,
-    FromSingleFileMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    TextualInversionLoaderMixin,
-    IPAdapterMixin,
-):
-
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
-    _optional_components = [
-        "tokenizer",
-        "tokenizer_2",
-        "text_encoder",
-        "text_encoder_2",
-        "image_encoder",
-        "feature_extractor",
-    ]
-    _callback_tensor_inputs = [
-        "latents",
-        "prompt_embeds",
-        "negative_prompt_embeds",
-        "add_text_embeds",
-        "add_time_ids",
-        "negative_pooled_prompt_embeds",
-        "negative_add_time_ids",
-    ]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        text_encoder_2: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        tokenizer_2: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        image_encoder: CLIPVisionModelWithProjection = None,
-        feature_extractor: CLIPImageProcessor = None,
-        force_zeros_for_empty_prompt: bool = True,
-        add_watermarker: Optional[bool] = None,
-    ):
-        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,
-            scheduler=scheduler,
-            image_encoder=image_encoder,
-            feature_extractor=feature_extractor,
-        )
-        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-        self.default_sample_size = self.unet.config.sample_size
-
-        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
-
-        if add_watermarker:
-            self.watermark = StableDiffusionXLWatermarker()
-        else:
-            self.watermark = None
-
-    def encode_prompt(
-        self,
-        prompt: str,
-        prompt_2: Optional[str] = None,
-        device: Optional[torch.device] = None,
-        num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[str] = None,
-        negative_prompt_2: Optional[str] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        device = device or self._execution_device
-
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the LoRA scale
-            if self.text_encoder is not None:
-                if not USE_PEFT_BACKEND:
-                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
-                else:
-                    scale_lora_layers(self.text_encoder, lora_scale)
-
-            if self.text_encoder_2 is not None:
-                if not USE_PEFT_BACKEND:
-                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
-                else:
-                    scale_lora_layers(self.text_encoder_2, lora_scale)
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # 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]
-        )
-
-        if prompt_embeds is None:
-            prompt_2 = prompt_2 or 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):
-                if isinstance(self, TextualInversionLoaderMixin):
-                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
-
-                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
-                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                    text_input_ids, untruncated_ids
-                ):
-                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
-                    logger.warning(
-                        "The following part of your input was truncated because CLIP can only handle sequences up to"
-                        f" {tokenizer.model_max_length} tokens: {removed_text}"
-                    )
-
-                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]
-                if clip_skip is None:
-                    prompt_embeds = prompt_embeds.hidden_states[-2]
-                else:
-                    # "2" because SDXL always indexes from the penultimate layer.
-                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 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 = negative_prompt is None and self.config.force_zeros_for_empty_prompt
-        if do_classifier_free_guidance and negative_prompt_embeds is None 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 or ""
-            negative_prompt_2 = negative_prompt_2 or 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):
-                if isinstance(self, TextualInversionLoaderMixin):
-                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
-
-                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)
-
-        if self.text_encoder_2 is not None:
-            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
-        else:
-            prompt_embeds = prompt_embeds.to(dtype=self.unet.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, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, 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, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-            bs_embed * num_images_per_prompt, -1
-        )
-        if do_classifier_free_guidance:
-            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-                bs_embed * num_images_per_prompt, -1
-            )
-
-        if self.text_encoder is not None:
-            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
-                # Retrieve the original scale by scaling back the LoRA layers
-                unscale_lora_layers(self.text_encoder, lora_scale)
-
-        if self.text_encoder_2 is not None:
-            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
-                # Retrieve the original scale by scaling back the LoRA layers
-                unscale_lora_layers(self.text_encoder_2, lora_scale)
-
-        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        if output_hidden_states:
-            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_enc_hidden_states = self.image_encoder(
-                torch.zeros_like(image), output_hidden_states=True
-            ).hidden_states[-2]
-            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
-                num_images_per_prompt, dim=0
-            )
-            return image_enc_hidden_states, uncond_image_enc_hidden_states
-        else:
-            image_embeds = self.image_encoder(image).image_embeds
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_embeds = torch.zeros_like(image_embeds)
-
-            return image_embeds, uncond_image_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
-    ):
-        image_embeds = []
-        if do_classifier_free_guidance:
-            negative_image_embeds = []
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-
-                image_embeds.append(single_image_embeds[None, :])
-                if do_classifier_free_guidance:
-                    negative_image_embeds.append(single_negative_image_embeds[None, :])
-        else:
-            for single_image_embeds in ip_adapter_image_embeds:
-                if do_classifier_free_guidance:
-                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
-                    negative_image_embeds.append(single_negative_image_embeds)
-                image_embeds.append(single_image_embeds)
-
-        ip_adapter_image_embeds = []
-        for i, single_image_embeds in enumerate(image_embeds):
-            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
-            if do_classifier_free_guidance:
-                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
-                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
-
-            single_image_embeds = single_image_embeds.to(device=device)
-            ip_adapter_image_embeds.append(single_image_embeds)
-
-        return ip_adapter_image_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            int(height) // self.vae_scale_factor,
-            int(width) // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def _get_add_time_ids(
-        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
-    ):
-        add_time_ids = list(original_size + crops_coords_top_left + target_size)
-
-        passed_add_embed_dim = (
-            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
-        )
-        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
-
-        if expected_add_embed_dim != passed_add_embed_dim:
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
-            )
-
-        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
-        return add_time_ids
-
-    def upcast_vae(self):
-        dtype = self.vae.dtype
-        self.vae.to(dtype=torch.float32)
-        use_torch_2_0_or_xformers = isinstance(
-            self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                FusedAttnProcessor2_0,
-            ),
-        )
-        # if xformers or torch_2_0 is used attention block does not need
-        # to be in float32 which can save lots of memory
-        if use_torch_2_0_or_xformers:
-            self.vae.post_quant_conv.to(dtype)
-            self.vae.decoder.conv_in.to(dtype)
-            self.vae.decoder.mid_block.to(dtype)
-
-    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(
-        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
-    ) -> torch.Tensor:
-        """
-        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
-
-        Args:
-            w (`torch.Tensor`):
-                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
-            embedding_dim (`int`, *optional*, defaults to 512):
-                Dimension of the embeddings to generate.
-            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
-                Data type of the generated embeddings.
-
-        Returns:
-            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
-        """
-        assert len(w.shape) == 1
-        w = w * 1000.0
-
-        half_dim = embedding_dim // 2
-        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
-        emb = w.to(dtype)[:, None] * emb[None, :]
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-        if embedding_dim % 2 == 1:  # zero pad
-            emb = torch.nn.functional.pad(emb, (0, 1))
-        assert emb.shape == (w.shape[0], embedding_dim)
-        return emb
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def guidance_rescale(self):
-        return self._guidance_rescale
-
-    @property
-    def clip_skip(self):
-        return self._clip_skip
-
-    # 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 cross_attention_kwargs(self):
-        return self._cross_attention_kwargs
-
-    @property
-    def denoising_end(self):
-        return self._denoising_end
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    def __call__(
-        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,
-        sigmas: List[float] = 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.Tensor] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        end_cfg: float = 0.9,
-        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[
-            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
-        ] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        **kwargs,
-    ):
-        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`",
-            )
-
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
-            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
-        # 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)
-
-        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
-        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
-
-        # 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, sigmas
-        )
-
-        # 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 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,
-                self.do_classifier_free_guidance,
-            )
-
-        # 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:
-            do_classifier_free_guidance = self.do_classifier_free_guidance
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-                if end_cfg is not None and i / num_inference_steps > end_cfg and do_classifier_free_guidance:
-                    do_classifier_free_guidance = False
-                    prompt_embeds = 1.5*torch.chunk(prompt_embeds, 2, dim=0)[-1]
-                    add_text_embeds = 1.5*torch.chunk(add_text_embeds, 2, dim=0)[-1]
-                    add_time_ids = 1.25*torch.chunk(add_time_ids, 2, dim=0)[-1]
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if 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_image_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 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 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_dtype = latents.dtype
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, 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)
-                    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)
-            elif latents.dtype != self.vae.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
-                    self.vae = self.vae.to(latents.dtype)
-
-            # unscale/denormalize the latents
-            # denormalize with the mean and std if available and not None
-            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
-            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
-            if has_latents_mean and has_latents_std:
-                latents_mean = (
-                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
-                )
-                latents_std = (
-                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
-                )
-                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
-            else:
-                latents = latents / self.vae.config.scaling_factor
-
-            image = self.vae.decode(latents, 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 load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
-    """Load and prepare the pipeline."""
     if not pipeline:
-        pipeline = StableDiffusionXLPipeline_new.from_pretrained(
+        pipeline = StableDiffusionXLPipeline.from_pretrained(
             "./models/newdream-sdxl-20",
             torch_dtype=torch.float16,
             local_files_only=True,
-        ).to("cuda")
-    
+        )
+    pipeline.to("cuda")
     pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
     pipeline = compile_pipe(pipeline)
-    load_pipe(pipeline, dir="cached_pipe")
+    pipeline.unet = oneflow_compile(pipeline.unet)
     
-    # Warm-up runs
+    load_pipe(pipeline,dir="cached_pipe")
     for _ in range(4):
-        pipeline(
-            prompt="a cat and a dog",
-            num_inference_steps=18
-        )
+        deepcache_output = pipeline(prompt="make submissions great again", cache_interval=1, cache_layer_id=0, cache_block_id=0, num_inference_steps=20)
     pipeline.scheduler.prepare_loss()
     return pipeline
 
-
 def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
-    """Generate image from text prompt."""
-    generator = Generator(pipeline.device).manual_seed(request.seed) if request.seed else None
-    
-    image_0 = pipeline(
+    if request.seed is None:
+        generator = None
+    else:
+        generator = Generator(pipeline.device).manual_seed(request.seed)
+
+    return pipeline(
         prompt=request.prompt,
         negative_prompt=request.negative_prompt,
         width=request.width,
         height=request.height,
         generator=generator,
-        num_inference_steps=18,
+        num_inference_steps=15,
+        cache_interval=1,
+        cache_layer_id=0,
+        cache_block_id=0,
     ).images[0]
 
-    filter_image = max_pixel_filter(image_0)
-    return filter_image
-
-

uv.lock

@@ -34,19 +34,6 @@ version = "4.9.3"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
 
-[[package]]
-name = "bitsandbytes"
-version = "0.44.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "torch" },
-]
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e4/e6/ccb84da7ffaf208a71c2c3c8e1120b34759df640db959660be9a98505eb4/bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:b2f24c6cbf11fc8c5d69b3dcecee9f7011451ec59d6ac833e873c9f105259668", size = 122419627 },
-    { url = "https://files.pythonhosted.org/packages/5f/f5/11bddebb5addc0a005b0c1cecc6e4c6e4055ad7b860bdcbf6374e12a51f5/bitsandbytes-0.44.1-py3-none-win_amd64.whl", hash = "sha256:8e68e12aa25d2cf9a1730ad72890a5d1a19daa23f459a6a4679331f353d58cb4", size = 121451331 },
-]
-
 [[package]]
 name = "certifi"
 version = "2024.8.30"
@@ -114,7 +101,6 @@ version = "6"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
-    { name = "bitsandbytes" },
     { name = "diffusers" },
     { name = "edge-maxxing-pipelines" },
     { name = "huggingface-hub" },
@@ -124,8 +110,6 @@ dependencies = [
     { name = "onediffx" },
     { name = "oneflow" },
     { name = "setuptools" },
-    { name = "stable-fast" },
-    { name = "tomesd" },
     { name = "torch" },
     { name = "torchvision" },
     { name = "transformers" },
@@ -136,18 +120,15 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "accelerate", specifier = "==0.31.0" },
-    { name = "bitsandbytes", specifier = ">=0.44.1" },
     { name = "diffusers", specifier = "==0.28.2" },
     { name = "edge-maxxing-pipelines", git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines" },
-    { name = "huggingface-hub", specifier = "==0.25.2" },
+    { name = "huggingface-hub", specifier = "==0.24.7" },
     { name = "numpy", specifier = "==1.26.4" },
     { name = "omegaconf", specifier = "==2.3.0" },
     { name = "onediff", specifier = "==1.2.0" },
     { name = "onediffx", specifier = "==1.2.0" },
     { name = "oneflow", url = "https://github.com/siliconflow/oneflow_releases/releases/download/community_cu118/oneflow-0.9.1.dev20240802%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" },
-    { name = "setuptools", specifier = ">=75.2.0" },
-    { name = "stable-fast", url = "https://github.com/chengzeyi/stable-fast/releases/download/v1.0.5/stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl" },
-    { name = "tomesd", specifier = ">=0.1.3" },
+    { name = "setuptools", specifier = "==75.2.0" },
     { name = "torch", specifier = "==2.2.2" },
     { name = "torchvision", specifier = "==0.17.2" },
     { name = "transformers", specifier = "==4.41.2" },
@@ -183,7 +164,7 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.25.2"
+version = "0.24.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
@@ -194,9 +175,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/df/fd/5f81bae67096c5ab50d29a0230b8374f0245916cca192f8ee2fada51f4f6/huggingface_hub-0.25.2.tar.gz", hash = "sha256:a1014ea111a5f40ccd23f7f7ba8ac46e20fa3b658ced1f86a00c75c06ec6423c", size = 365806 }
+sdist = { url = "https://files.pythonhosted.org/packages/af/33/d252098a3b8d910065ad09cf318efb5dbe6c8bb586269bdfb47b7e021020/huggingface_hub-0.24.7.tar.gz", hash = "sha256:0ad8fb756e2831da0ac0491175b960f341fe06ebcf80ed6f8728313f95fc0207", size = 349211 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/64/09/a535946bf2dc88e61341f39dc507530411bb3ea4eac493e5ec833e8f35bd/huggingface_hub-0.25.2-py3-none-any.whl", hash = "sha256:1897caf88ce7f97fe0110603d8f66ac264e3ba6accdf30cd66cc0fed5282ad25", size = 436575 },
+    { url = "https://files.pythonhosted.org/packages/57/28/a0b0dd3cca63908045edc300360d6cd8758d4d86eee3fd2b08f00c5a41c4/huggingface_hub-0.24.7-py3-none-any.whl", hash = "sha256:a212c555324c8a7b1ffdd07266bb7e7d69ca71aa238d27b7842d65e9a26ac3e5", size = 417514 },
 ]
 
 [[package]]
@@ -777,31 +758,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/31/2d/90165d51ecd38f9a02c6832198c13a4e48652485e2ccf863ebb942c531b6/setuptools-75.2.0-py3-none-any.whl", hash = "sha256:a7fcb66f68b4d9e8e66b42f9876150a3371558f98fa32222ffaa5bced76406f8", size = 1249825 },
 ]
 
-[[package]]
-name = "stable-fast"
-version = "1.0.5+torch222cu121"
-source = { url = "https://github.com/chengzeyi/stable-fast/releases/download/v1.0.5/stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl" }
-dependencies = [
-    { name = "torch" },
-]
-wheels = [
-    { url = "https://github.com/chengzeyi/stable-fast/releases/download/v1.0.5/stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:03d193666c52016096ecfdf0e8e2183fb5aa8ea51e99d5132b353a1e9a6c1264" },
-]
-
-[package.metadata]
-requires-dist = [
-    { name = "diffusers", marker = "extra == 'diffusers'", specifier = ">=0.19.0" },
-    { name = "numpy", marker = "extra == 'dev'" },
-    { name = "opencv-python", marker = "extra == 'dev'" },
-    { name = "pillow", marker = "extra == 'dev'" },
-    { name = "prettytable", marker = "extra == 'dev'" },
-    { name = "pytest", marker = "extra == 'dev'" },
-    { name = "torch" },
-    { name = "transformers", marker = "extra == 'diffusers'" },
-    { name = "triton", marker = "extra == 'triton'", specifier = ">=2.1.0" },
-    { name = "xformers", marker = "extra == 'xformers'", specifier = ">=0.0.20" },
-]
-
 [[package]]
 name = "sympy"
 version = "1.13.3"
@@ -844,18 +800,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/45/b6/36c1bb106bbe96012c9367df89ed01599cada036c0b96d38fbbdbeb75c9f/tokenizers-0.19.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:43350270bfc16b06ad3f6f07eab21f089adb835544417afda0f83256a8bf8b75", size = 9945103 },
 ]
 
-[[package]]
-name = "tomesd"
-version = "0.1.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "torch" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/29/37/ed74c7449fe5a8a4726be3dab4d879d000babf6ea538658171933b1f000e/tomesd-0.1.3.tar.gz", hash = "sha256:15bba2e952f4643c8355951e892fda918ddccbdff2238dc368d42bd078fcedc9", size = 14032 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0c/02/367c67c8f510313f143a7818e92254a5f861c7d94c98ad6a08d25db52fee/tomesd-0.1.3-py3-none-any.whl", hash = "sha256:3d5aa0857fe2c2aab253891050601ca13a87d8d7a99b6760b9ca0856aa0c6355", size = 11467 },
-]
-
 [[package]]
 name = "torch"
 version = "2.2.2"
@@ -988,4 +932,4 @@ source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/54/bf/5c0000c44ebc80123ecbdddba1f5dcd94a5ada602a9c225d84b5aaa55e86/zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29", size = 24199 }
 wheels = [
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-]
+]