Update app.py

app.py

@@ -113,7 +113,35 @@ class AccDiffusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoade
     """
     Pipeline for text-to-image generation using Stable Diffusion XL.
 
-    [클래스 설명 생략 …]
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    In addition the pipeline inherits the following loading methods:
+        - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]
+        - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
+
+    as well as the following saving methods:
+        - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder.
+        text_encoder_2 ([`CLIPTextModelWithProjection`]):
+            Second frozen text-encoder.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer.
+        tokenizer_2 (`CLIPTokenizer`):
+            Second Tokenizer.
+        unet ([`UNet2DConditionModel`]):
+            Conditional U-Net architecture.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet`.
+        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+            Whether the negative prompt embeddings shall be forced to always be set to 0.
+        add_watermarker (`bool`, *optional*):
+            Whether to use the invisible watermark library.
     """
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
 
@@ -152,7 +180,469 @@ class AccDiffusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoade
         else:
             self.watermark = None
 
-    # (이하 기존 메서드들 생략 …)
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+    def enable_vae_slicing(self):
+        self.vae.enable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+    def disable_vae_slicing(self):
+        self.vae.disable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+    def enable_vae_tiling(self):
+        self.vae.enable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+    def disable_vae_tiling(self):
+        self.vae.disable_tiling()
+
+    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.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = 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, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+            adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+            adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+
+        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]
+
+        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_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,
+                )
+
+                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)
+
+        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
+
+            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 isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+            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,
+                )
+                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
+        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:
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.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
+            )
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        negative_pooled_prompt_embeds=None,
+        num_images_per_prompt=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            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 got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds` {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`."
+            )
+
+        if max(height, width) % 1024 != 0:
+            raise ValueError(f"the larger one of `height` and `width` has to be divisible by 1024 but are {height} and {width}.")
+
+        if num_images_per_prompt != 1:
+            warnings.warn("num_images_per_prompt != 1 is not supported by AccDiffusion and will be ignored.")
+            num_images_per_prompt = 1
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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 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)
+
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
+        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) + self.text_encoder_2.config.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 get_views(self, height, width, window_size=128, stride=64, random_jitter=False):
+        height //= self.vae_scale_factor
+        width //= self.vae_scale_factor
+        num_blocks_height = int((height - window_size) / stride - 1e-6) + 2 if height > window_size else 1
+        num_blocks_width = int((width - window_size) / stride - 1e-6) + 2 if width > window_size else 1
+        total_num_blocks = int(num_blocks_height * num_blocks_width)
+        views = []
+        for i in range(total_num_blocks):
+            h_start = int((i // num_blocks_width) * stride)
+            h_end = h_start + window_size
+            w_start = int((i % num_blocks_width) * stride)
+            w_end = w_start + window_size
+
+            if h_end > height:
+                h_start = int(h_start + height - h_end)
+                h_end = int(height)
+            if w_end > width:
+                w_start = int(w_start + width - w_end)
+                w_end = int(width)
+            if h_start < 0:
+                h_end = int(h_end - h_start)
+                h_start = 0
+            if w_start < 0:
+                w_end = int(w_end - w_start)
+                w_start = 0
+
+            if random_jitter:
+                jitter_range = (window_size - stride) // 4
+                w_jitter = 0
+                h_jitter = 0
+                if (w_start != 0) and (w_end != width):
+                    w_jitter = random.randint(-jitter_range, jitter_range)
+                elif (w_start == 0) and (w_end != width):
+                    w_jitter = random.randint(-jitter_range, 0)
+                elif (w_start != 0) and (w_end == width):
+                    w_jitter = random.randint(0, jitter_range)
+
+                if (h_start != 0) and (h_end != height):
+                    h_jitter = random.randint(-jitter_range, jitter_range)
+                elif (h_start == 0) and (h_end != height):
+                    h_jitter = random.randint(-jitter_range, 0)
+                elif (h_start != 0) and (h_end == height):
+                    h_jitter = random.randint(0, jitter_range)
+                h_start = h_start + h_jitter + jitter_range
+                h_end = h_end + h_jitter + jitter_range
+                w_start = w_start + w_jitter + jitter_range
+                w_end = w_end + w_jitter + jitter_range
+            
+            views.append((h_start, h_end, w_start, w_end))
+        return views
+
+    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,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
+        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)
+
+    def register_attention_control(self, controller):
+        attn_procs = {}
+        cross_att_count = 0
+        ori_attn_processors = self.unet.attn_processors
+        for name in self.unet.attn_processors.keys():
+            if name.startswith("mid_block"):
+                place_in_unet = "mid"
+            elif name.startswith("up_blocks"):
+                place_in_unet = "up"
+            elif name.startswith("down_blocks"):
+                place_in_unet = "down"
+            else:
+                continue
+            cross_att_count += 1
+            attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)
+
+        self.unet.set_attn_processor(attn_procs)
+        controller.num_att_layers = cross_att_count
+        return ori_attn_processors
+
+    def recover_attention_control(self, ori_attn_processors):
+        self.unet.set_attn_processor(ori_attn_processors)
+
+    def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
+        else:
+            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
+
+        is_model_cpu_offload = False
+        is_sequential_cpu_offload = False
+        recursive = False
+        for _, component in self.components.items():
+            if isinstance(component, torch.nn.Module):
+                if hasattr(component, "_hf_hook"):
+                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
+                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
+                    logger.info(
+                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
+                    )
+                    recursive = is_sequential_cpu_offload
+                    remove_hook_from_module(component, recurse=recursive)
+        state_dict, network_alphas = self.lora_state_dict(
+            pretrained_model_name_or_path_or_dict,
+            unet_config=self.unet.config,
+            **kwargs,
+        )
+        self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
+
+        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
+        if len(text_encoder_state_dict) > 0:
+            self.load_lora_into_text_encoder(
+                text_encoder_state_dict,
+                network_alphas=network_alphas,
+                text_encoder=self.text_encoder,
+                prefix="text_encoder",
+                lora_scale=self.lora_scale,
+            )
+
+        text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
+        if len(text_encoder_2_state_dict) > 0:
+            self.load_lora_into_text_encoder(
+                text_encoder_2_state_dict,
+                network_alphas=network_alphas,
+                text_encoder=self.text_encoder_2,
+                prefix="text_encoder_2",
+                lora_scale=self.lora_scale,
+            )
+
+        if is_model_cpu_offload:
+            self.enable_model_cpu_offload()
+        elif is_sequential_cpu_offload:
+            self.enable_sequential_cpu_offload()
+
+    @classmethod
+    def save_lora_weights(
+        self,
+        save_directory: Union[str, os.PathLike],
+        unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        is_main_process: bool = True,
+        weight_name: str = None,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+    ):
+        state_dict = {}
+
+        def pack_weights(layers, prefix):
+            layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
+            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
+            return layers_state_dict
+
+        if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
+            raise ValueError(
+                "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
+            )
+
+        if unet_lora_layers:
+            state_dict.update(pack_weights(unet_lora_layers, "unet"))
+
+        if text_encoder_lora_layers and text_encoder_2_lora_layers:
+            state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
+            state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+
+        self.write_lora_layers(
+            state_dict=state_dict,
+            save_directory=save_directory,
+            is_main_process=is_main_process,
+            weight_name=weight_name,
+            save_function=save_function,
+            safe_serialization=safe_serialization,
+        )
+
+    def _remove_text_encoder_monkey_patch(self):
+        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
+        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
@@ -213,17 +703,554 @@ class AccDiffusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoade
         seed: Optional[int] = None,
         c : Optional[float] = 0.3,
     ):
-        r"""
-        [함수 설명 생략 …]
-        """
-        # (여기서는 기존 __call__ 함수 내부 구현을 그대로 유지합니다.)
-        # ... (중략)
-        output_images = []
+        if debug:
+            num_inference_steps = 1
+        
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        x1_size = self.default_sample_size * self.vae_scale_factor
+
+        height_scale = height / x1_size
+        width_scale = width / x1_size
+        scale_num = int(max(height_scale, width_scale))
+        aspect_ratio = min(height_scale, width_scale) / max(height_scale, width_scale)
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        if attn_res is None:
+            attn_res = int(np.ceil(self.default_sample_size * self.vae_scale_factor / 32)), int(np.ceil(self.default_sample_size * self.vae_scale_factor / 32))
+        self.attn_res = attn_res
+
+        if lowvram:
+            attention_map_device = torch.device("cpu")
+        else:
+            attention_map_device = self.device
+
+        self.controller = create_controller(
+            prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=attention_map_device, attn_res=self.attn_res
+        )
+
+        if save_attention_map or use_md_prompt:
+            ori_attn_processors = self.register_attention_control(self.controller)
+
+        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,
+            num_images_per_prompt,
+        )
+
+        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
+        self.lowvram = lowvram
+        if self.lowvram:
+            self.vae.cpu()
+            self.unet.cpu()
+            self.text_encoder.to(device)
+            self.text_encoder_2.to(device)
+
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if 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=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=text_encoder_lora_scale,
+        )
+
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height // scale_num,
+            width // scale_num,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        add_text_embeds = pooled_prompt_embeds
+
+        add_time_ids = self._get_add_time_ids(
+            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
+        )
+
+        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,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
 
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0).to(device)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0).to(device)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0).to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        del negative_prompt_embeds, negative_pooled_prompt_embeds, negative_add_time_ids
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps - (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]
+
+        output_images = []
+        
         ###################################################### Phase Initialization ########################################################
-        # (중략) 실제 denoising 및 upscaling 부분
 
-        # 마지막에 이미지 저장 및 반환
+        if self.lowvram:
+            self.text_encoder.cpu()
+            self.text_encoder_2.cpu()
+
+        if image_lr == None:
+            print("### Phase 1 Denoising ###")
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+    
+                    if self.lowvram:
+                        self.vae.cpu()
+                        self.unet.to(device)
+    
+                    latents_for_view = latents
+    
+                    latent_model_input = (
+                        latents.repeat_interleave(2, dim=0)
+                        if do_classifier_free_guidance
+                        else latents
+                    )
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                    added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                    noise_pred = self.unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        added_cond_kwargs=added_cond_kwargs,
+                        return_dict=False,
+                    )[0]
+    
+                    if do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+    
+                    if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+    
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+    
+                    if t == 1 and use_md_prompt:
+                        md_prompts, views_attention = get_multidiffusion_prompts(tokenizer=self.tokenizer, prompts=[prompt], threthod=c, attention_store=self.controller, height=height//scale_num, width=width//scale_num, from_where=["up","down"], random_jitter=True, scale_num=scale_num)
+    
+                    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)
+
+            del latents_for_view, latent_model_input, noise_pred, noise_pred_text, noise_pred_uncond
+            if use_md_prompt or save_attention_map:
+                self.recover_attention_control(ori_attn_processors=ori_attn_processors)
+                del self.controller
+            torch.cuda.empty_cache()
+        else:
+            print("### Encoding Real Image ###")
+            latents = self.vae.encode(image_lr)
+            latents = latents.latent_dist.sample() * self.vae.config.scaling_factor
+
+        anchor_mean = latents.mean()
+        anchor_std = latents.std()
+        if self.lowvram:
+            latents = latents.cpu()
+            torch.cuda.empty_cache()
+        if not output_type == "latent":
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            
+            if self.lowvram:
+                needs_upcasting = False
+                self.unet.cpu()
+                self.vae.to(device)
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            if self.lowvram and multi_decoder:
+                current_width_height = self.unet.config.sample_size * self.vae_scale_factor
+                image = self.tiled_decode(latents, current_width_height, current_width_height)
+            else:
+                image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+    
+        image = self.image_processor.postprocess(image, output_type=output_type)
+        if not os.path.exists(f'{result_path}'):
+            os.makedirs(f'{result_path}')
+
+        image_lr_save_path = f'{result_path}/{image[0].size[0]}_{image[0].size[1]}.png'
+        image[0].save(image_lr_save_path)
+        output_images.append(image[0])
+
+        ####################################################### Phase Upscaling #####################################################
+        if use_progressive_upscaling:
+            if image_lr == None:
+                starting_scale = 2
+            else:
+                starting_scale = 1
+        else:
+            starting_scale = scale_num
+
+        for current_scale_num in range(starting_scale, scale_num + 1):
+            if self.lowvram:
+                latents = latents.to(device)
+                self.unet.to(device)
+                torch.cuda.empty_cache()
+            
+            current_height = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num
+            current_width = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num
+
+            if height > width:
+                current_width = int(current_width * aspect_ratio)
+            else:
+                current_height = int(current_height * aspect_ratio)
+
+            if upscale_mode == "bicubic_latent" or debug:
+                latents = F.interpolate(latents.to(device), size=(int(current_height / self.vae_scale_factor), int(current_width / self.vae_scale_factor)), mode='bicubic')
+            else:
+                raise NotImplementedError
+            
+            print("### Phase {} Denoising ###".format(current_scale_num))
+            noise_latents = []
+            noise = torch.randn_like(latents)
+            for timestep in timesteps:
+                noise_latent = self.scheduler.add_noise(latents, noise, timestep.unsqueeze(0))
+                noise_latents.append(noise_latent)
+            latents = noise_latents[0]
+
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    count = torch.zeros_like(latents)
+                    value = torch.zeros_like(latents)
+                    cosine_factor = 0.5 * (1 + torch.cos(torch.pi * (self.scheduler.config.num_train_timesteps - t) / self.scheduler.config.num_train_timesteps)).cpu()
+                    if use_skip_residual:
+                        c1 = cosine_factor ** cosine_scale_1
+                        latents = latents * (1 - c1) + noise_latents[i] * c1
+                    
+                    if use_multidiffusion:
+                        if use_md_prompt:
+                            md_prompt_embeds_list = []
+                            md_add_text_embeds_list = []
+                            for md_prompt in md_prompts[current_scale_num]:
+                                (  
+                                    md_prompt_embeds,
+                                    md_negative_prompt_embeds,
+                                    md_pooled_prompt_embeds,
+                                    md_negative_pooled_prompt_embeds,
+                                ) = self.encode_prompt(
+                                    prompt=md_prompt,
+                                    prompt_2=prompt_2,
+                                    device=device,
+                                    num_images_per_prompt=num_images_per_prompt,
+                                    do_classifier_free_guidance=do_classifier_free_guidance,
+                                    negative_prompt=negative_prompt,
+                                    negative_prompt_2=negative_prompt_2,
+                                    prompt_embeds=None,
+                                    negative_prompt_embeds=None,
+                                    pooled_prompt_embeds=None,
+                                    negative_pooled_prompt_embeds=None,
+                                    lora_scale=text_encoder_lora_scale,
+                                )
+                                md_prompt_embeds_list.append(torch.cat([md_negative_prompt_embeds, md_prompt_embeds], dim=0).to(device))
+                                md_add_text_embeds_list.append(torch.cat([md_negative_pooled_prompt_embeds, md_pooled_prompt_embeds], dim=0).to(device))
+                                del md_negative_prompt_embeds, md_negative_pooled_prompt_embeds
+
+                        if use_md_prompt:
+                            random_jitter = True
+                            views = [(h_start*4, h_end*4, w_start*4, w_end*4) for h_start, h_end, w_start, w_end in views_attention[current_scale_num]]
+                        else:
+                            random_jitter = True
+                            views = self.get_views(current_height, current_width, stride=stride, window_size=self.unet.config.sample_size, random_jitter=random_jitter)
+
+                        views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
+
+                        if use_md_prompt:
+                            views_prompt_embeds_input = [md_prompt_embeds_list[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
+                            views_add_text_embeds_input = [md_add_text_embeds_list[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
+
+                        if random_jitter:
+                            jitter_range = int((self.unet.config.sample_size - stride) // 4)
+                            latents_ = F.pad(latents, (jitter_range, jitter_range, jitter_range, jitter_range), 'constant', 0)
+                        else:
+                            latents_ = latents
+
+                        count_local = torch.zeros_like(latents_)
+                        value_local = torch.zeros_like(latents_)
+                        
+                        for j, batch_view in enumerate(views_batch):
+                            vb_size = len(batch_view)
+                            latents_for_view = torch.cat(
+                                [
+                                    latents_[:, :, h_start:h_end, w_start:w_end]
+                                    for h_start, h_end, w_start, w_end in batch_view
+                                ]
+                            )
+
+                            latent_model_input = latents_for_view
+                            latent_model_input = (
+                                latent_model_input.repeat_interleave(2, dim=0)
+                                if do_classifier_free_guidance
+                                else latent_model_input
+                            )
+                            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                            
+                            add_time_ids_input = []
+                            for h_start, h_end, w_start, w_end in batch_view:
+                                add_time_ids_ = add_time_ids.clone()
+                                add_time_ids_[:, 2] = h_start * self.vae_scale_factor
+                                add_time_ids_[:, 3] = w_start * self.vae_scale_factor
+                                add_time_ids_input.append(add_time_ids_)
+                            add_time_ids_input = torch.cat(add_time_ids_input)
+
+                            if not use_md_prompt:
+                                prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
+                                add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
+                                added_cond_kwargs = {"text_embeds": add_text_embeds_input, "time_ids": add_time_ids_input}
+                                noise_pred = self.unet(
+                                    latent_model_input,
+                                    t,
+                                    encoder_hidden_states=prompt_embeds_input,
+                                    added_cond_kwargs=added_cond_kwargs,
+                                    return_dict=False,
+                                )[0]
+                            else:
+                                md_prompt_embeds_input = torch.cat(views_prompt_embeds_input[j])
+                                md_add_text_embeds_input = torch.cat(views_add_text_embeds_input[j])
+                                md_added_cond_kwargs = {"text_embeds": md_add_text_embeds_input, "time_ids": add_time_ids_input}
+                                noise_pred = self.unet(
+                                    latent_model_input,
+                                    t,
+                                    encoder_hidden_states=md_prompt_embeds_input,
+                                    added_cond_kwargs=md_added_cond_kwargs,
+                                    return_dict=False,
+                                )[0]
+
+                            if do_classifier_free_guidance:
+                                noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
+                                noise_pred = noise_pred_uncond + multi_guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                            if do_classifier_free_guidance and guidance_rescale > 0.0:
+                                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                            self.scheduler._init_step_index(t)
+                            latents_denoised_batch = self.scheduler.step(
+                                noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False)[0]
+                            
+                            for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(
+                                latents_denoised_batch.chunk(vb_size), batch_view
+                            ):
+                                value_local[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
+                                count_local[:, :, h_start:h_end, w_start:w_end] += 1
+
+                        if random_jitter:
+                            value_local = value_local[: ,:, jitter_range: jitter_range + current_height // self.vae_scale_factor, jitter_range: jitter_range + current_width // self.vae_scale_factor]
+                            count_local = count_local[: ,:, jitter_range: jitter_range + current_height // self.vae_scale_factor, jitter_range: jitter_range + current_width // self.vae_scale_factor]
+
+                        noise_index = i + 1 if i != (len(timesteps) - 1) else i
+
+                        value_local = torch.where(count_local == 0, noise_latents[noise_index], value_local)
+                        count_local = torch.where(count_local == 0, torch.ones_like(count_local), count_local)
+                        if use_dilated_sampling:
+                            c2 = cosine_factor ** cosine_scale_2
+                            value += value_local / count_local * (1 - c2)
+                            count += torch.ones_like(value_local) * (1 - c2)
+                        else:
+                            value += value_local / count_local
+                            count += torch.ones_like(value_local) 
+
+                    if use_dilated_sampling:
+                        views = [[h, w] for h in range(current_scale_num) for w in range(current_scale_num)]
+                        views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
+                        
+                        h_pad = (current_scale_num - (latents.size(2) % current_scale_num)) % current_scale_num
+                        w_pad = (current_scale_num - (latents.size(3) % current_scale_num)) % current_scale_num
+                        latents_ = F.pad(latents, (w_pad, 0, h_pad, 0), 'constant', 0)
+                        
+                        count_global = torch.zeros_like(latents_)
+                        value_global = torch.zeros_like(latents_)
+
+                        if use_guassian:
+                            c3 = 0.99 * cosine_factor ** cosine_scale_3 + 1e-2
+                            std_, mean_ = latents_.std(), latents_.mean()
+                            latents_gaussian = gaussian_filter(latents_, kernel_size=(2*current_scale_num-1), sigma=sigma*c3)
+                            latents_gaussian = (latents_gaussian - latents_gaussian.mean()) / latents_gaussian.std() * std_ + mean_
+                        else:
+                            latents_gaussian = latents_
+
+                        for j, batch_view in enumerate(views_batch):
+                            
+                            latents_for_view = torch.cat(
+                                [
+                                    latents_[:, :, h::current_scale_num, w::current_scale_num]
+                                    for h, w in batch_view
+                                ]
+                            )
+
+                            latents_for_view_gaussian = torch.cat(
+                                [
+                                    latents_gaussian[:, :, h::current_scale_num, w::current_scale_num]
+                                    for h, w in batch_view
+                                ]
+                            )
+
+                            if shuffle:
+                                shape = latents_for_view.shape
+                                shuffle_index = torch.stack([torch.randperm(shape[0]) for _ in range(latents_for_view.reshape(-1).shape[0]//shape[0]])
+                                shuffle_index = shuffle_index.view(shape[1],shape[2],shape[3],shape[0])
+                                original_index = torch.zeros_like(shuffle_index).scatter_(3, shuffle_index, torch.arange(shape[0]).repeat(shape[1], shape[2], shape[3], 1))
+                                shuffle_index = shuffle_index.permute(3,0,1,2).to(device)
+                                original_index = original_index.permute(3,0,1,2).to(device)
+                                latents_for_view_gaussian = latents_for_view_gaussian.gather(0, shuffle_index)
+                        
+                            vb_size = latents_for_view.size(0)
+
+                            latent_model_input = latents_for_view_gaussian
+                            latent_model_input = (
+                                latent_model_input.repeat_interleave(2, dim=0)
+                                if do_classifier_free_guidance
+                                else latent_model_input
+                            )
+                            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                            prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
+                            add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
+                            add_time_ids_input = torch.cat([add_time_ids] * vb_size)
+
+                            added_cond_kwargs = {"text_embeds": add_text_embeds_input, "time_ids": add_time_ids_input}
+                            noise_pred = self.unet(
+                                latent_model_input,
+                                t,
+                                encoder_hidden_states=prompt_embeds_input,
+                                added_cond_kwargs=added_cond_kwargs,
+                                return_dict=False,
+                            )[0]
+
+                            if do_classifier_free_guidance:
+                                noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
+                                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                            if do_classifier_free_guidance and guidance_rescale > 0.0:
+                                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                            if shuffle:
+                                noise_pred = noise_pred.gather(0, original_index)
+
+                            self.scheduler._init_step_index(t)
+                            latents_denoised_batch = self.scheduler.step(noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False)[0]
+
+                            for latents_view_denoised, (h, w) in zip(
+                                latents_denoised_batch.chunk(vb_size), batch_view
+                            ):
+                                value_global[:, :, h::current_scale_num, w::current_scale_num] += latents_view_denoised
+                                count_global[:, :, h::current_scale_num, w::current_scale_num] += 1
+                                
+                        value_global = value_global[: ,:, h_pad:, w_pad:]
+
+                        if use_multidiffusion:
+                            c2 = cosine_factor ** cosine_scale_2
+                            value += value_global * c2
+                            count += torch.ones_like(value_global) * c2
+                        else:
+                            value += value_global
+                            count += torch.ones_like(value_global)
+                                
+                    latents = torch.where(count > 0, value / count, value)
+
+                    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)
+
+                latents = (latents - latents.mean()) / latents.std() * anchor_std + anchor_mean
+                if self.lowvram:
+                    latents = latents.cpu()
+                    torch.cuda.empty_cache()
+                if not output_type == "latent":
+                    needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+                    if self.lowvram:
+                        needs_upcasting = False
+                        self.unet.cpu()
+                        self.vae.to(device)
+                    
+                    if needs_upcasting:
+                        self.upcast_vae()
+                        latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+                    print("### Phase {} Decoding ###".format(current_scale_num))
+                    if current_height > 2048 or current_width > 2048:
+                        self.enable_vae_tiling()
+                        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+                    else:
+                        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+                    
+                    image = self.image_processor.postprocess(image, output_type=output_type)
+                    image[0].save(f'{result_path}/AccDiffusion_{current_scale_num}.png')
+                    output_images.append(image[0])
+
+                    if needs_upcasting:
+                        self.vae.to(dtype=torch.float16)
+                else:
+                    image = latents
+        
+        self.maybe_free_model_hooks()
+
         return output_images
 
 
@@ -260,36 +1287,8 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
-    # 파이프라인 불러오기 (필요한 모델 체크포인트 사용)
     pipe = AccDiffusionSDXLPipeline.from_pretrained(args.model_ckpt, torch_dtype=torch.float16).to("cuda")
     
-    
-    # ----------------------- GRADIO INTERFACE (개선된 UI) -----------------------
-
-    # 사용자 인터페이스에 적용할 CSS (배경, 폰트, 카드 스타일 등)
-    css = """
-    body {
-        background: linear-gradient(135deg, #2c3e50, #4ca1af);
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        color: #ffffff;
-    }
-    #col-container {
-        margin: 20px auto;
-        padding: 20px;
-        max-width: 900px;
-        background-color: rgba(0, 0, 0, 0.5);
-        border-radius: 12px;
-        box-shadow: 0 4px 12px rgba(0,0,0,0.5);
-    }
-    h1, h2 {
-        text-align: center;
-        margin-bottom: 10px;
-    }
-    footer {
-        visibility: hidden;
-    }
-    """
-
     @spaces.GPU(duration=200)
     def infer(prompt, resolution, num_inference_steps, guidance_scale, seed, use_multidiffusion, use_skip_residual, use_dilated_sampling, use_progressive_upscaling, shuffle, use_md_prompt, progress=gr.Progress(track_tqdm=True)):
         set_seed(seed)
@@ -319,8 +1318,7 @@ if __name__ == "__main__":
             cosine_scale_1=args.cosine_scale_1, 
             cosine_scale_2=args.cosine_scale_2, 
             cosine_scale_3=args.cosine_scale_3,
-            sigma=args.sigma, 
-            use_guassian=args.use_guassian,
+            sigma=args.sigma, use_guassian=args.use_guassian,
             multi_decoder=args.multi_decoder, 
             upscale_mode=args.upscale_mode, 
             use_multidiffusion=use_multidiffusion, 
@@ -329,18 +1327,37 @@ if __name__ == "__main__":
             use_dilated_sampling=use_dilated_sampling,
             shuffle=shuffle, 
             result_path=result_path, 
-            debug=args.debug, 
-            save_attention_map=args.save_attention_map, 
-            use_md_prompt=use_md_prompt, 
-            c=args.c
+            debug=args.debug, save_attention_map=args.save_attention_map, use_md_prompt=use_md_prompt, c=args.c
         )
         print(images)
 
         return images
-
-
+    
     MAX_SEED = np.iinfo(np.int32).max
 
+    css = """
+    body {
+        background: linear-gradient(135deg, #2c3e50, #4ca1af);
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        color: #ffffff;
+    }
+    #col-container {
+        margin: 20px auto;
+        padding: 20px;
+        max-width: 900px;
+        background-color: rgba(0, 0, 0, 0.5);
+        border-radius: 12px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.5);
+    }
+    h1, h2 {
+        text-align: center;
+        margin-bottom: 10px;
+    }
+    footer {
+        visibility: hidden;
+    }
+    """
+
     with gr.Blocks(css=css) as demo:
         with gr.Column(elem_id="col-container"):
             gr.Markdown("<h1>AccDiffusion: Advanced AI Art Generator</h1>")
@@ -355,10 +1372,10 @@ if __name__ == "__main__":
                 with gr.Row():
                     resolution = gr.Radio(
                         label="Resolution",
-                        choices=[
+                        choices = [
                             "1024,1024", "2048,2048", "2048,1024", "1536,3072", "3072,3072", "4096,4096", "4096,2048"
                         ],
-                        value="1024,1024",
+                        value = "1024,1024",
                         interactive=True
                     )
                     with gr.Column():
@@ -377,7 +1394,7 @@ if __name__ == "__main__":
             output_images = gr.Gallery(label="Output Images", format="png").style(grid=[2], height="auto")
             gr.Markdown("### Example Prompts")
             gr.Examples(
-                examples=[
+                examples = [
                     ["A surreal landscape with floating islands and vibrant colors."],
                     ["Cyberpunk cityscape at night with neon lights and futuristic architecture."],
                     ["A majestic dragon soaring over a medieval castle amidst stormy skies."],
@@ -385,14 +1402,14 @@ if __name__ == "__main__":
                     ["Abstract geometric patterns in vivid, pulsating colors."],
                     ["A mystical forest illuminated by bioluminescent plants under a starry sky."]
                 ],
-                inputs=[prompt],
+                inputs = [prompt],
                 label="Click an example to populate the prompt box."
             )
         submit_btn.click(
-            fn=infer,
-            inputs=[prompt, resolution, num_inference_steps, guidance_scale, seed, 
-                    use_multidiffusion, use_skip_residual, use_dilated_sampling, use_progressive_upscaling, shuffle, use_md_prompt],
-            outputs=[output_images],
+            fn = infer,
+            inputs = [prompt, resolution, num_inference_steps, guidance_scale, seed, 
+                      use_multidiffusion, use_skip_residual, use_dilated_sampling, use_progressive_upscaling, shuffle, use_md_prompt],
+            outputs = [output_images],
             show_api=False
         )
     demo.launch(show_api=False, show_error=True)