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@@ -50,8 +50,9 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 |   IADB Pipeline                                                                                                    | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [IADB Pipeline](#iadb-pipeline)      | - |              [Thomas Chambon](https://github.com/tchambon)
 |   Zero1to3 Pipeline                                                                                                    | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Zero1to3 Pipeline](#zero1to3-pipeline)      | - |              [Xin Kong](https://github.com/kxhit) |
 | Stable Diffusion XL Long Weighted Prompt Pipeline | A pipeline support unlimited length of prompt and negative prompt, use A1111 style of prompt weighting | [Stable Diffusion XL Long Weighted Prompt Pipeline](#stable-diffusion-xl-long-weighted-prompt-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LsqilswLR40XLLcp6XFOl5nKb_wOe26W?usp=sharing) | [Andrew Zhu](https://xhinker.medium.com/) |
-| Stable Diffusion Mixture Tiling Pipeline SD 1.5 | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SD 1.5](#stable-diffusion-mixture-tiling-sd-15) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/albarji/mixture-of-diffusers) | [Álvaro B Jiménez](https://github.com/albarji/) |
-| Stable Diffusion Mixture Tiling Pipeline SDXL | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SDXL](#stable-diffusion-mixture-tiling-sdxl) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/elismasilva/mixture-of-diffusers-sdxl-tiling) | [Eliseu Silva](https://github.com/DEVAIEXP/) |
+| Stable Diffusion Mixture Tiling Pipeline SD 1.5 | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SD 1.5](#stable-diffusion-mixture-tiling-pipeline-sd-15) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/albarji/mixture-of-diffusers) | [Álvaro B Jiménez](https://github.com/albarji/) |
+| Stable Diffusion Mixture Canvas Pipeline SD 1.5 | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending. Works by defining a list of Text2Image region objects that detail the region of influence of each diffuser. | [Stable Diffusion Mixture Canvas Pipeline SD 1.5](#stable-diffusion-mixture-canvas-pipeline-sd-15) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/albarji/mixture-of-diffusers) | [Álvaro B Jiménez](https://github.com/albarji/) |
+| Stable Diffusion Mixture Tiling Pipeline SDXL | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SDXL](#stable-diffusion-mixture-tiling-pipeline-sdxl) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/elismasilva/mixture-of-diffusers-sdxl-tiling) | [Eliseu Silva](https://github.com/DEVAIEXP/) |
 | FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
 | sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
 | sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
@@ -2404,7 +2405,7 @@ pipe_images = mixing_pipeline(
 
 ![image_mixing_result](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir_gigachad.png)
 
-### Stable Diffusion Mixture Tiling SD 1.5
+### Stable Diffusion Mixture Tiling Pipeline SD 1.5
 
 This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.
 
@@ -2435,7 +2436,7 @@ image = pipeline(
 
 ![mixture_tiling_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/mixture_tiling.png)
 
-### Stable Diffusion Mixture Canvas
+### Stable Diffusion Mixture Canvas Pipeline SD 1.5
 
 This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.
 
@@ -2470,7 +2471,7 @@ output = pipeline(
 ![Input_Image](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/input_image.png)
 ![mixture_canvas_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/canvas.png)
 
-### Stable Diffusion Mixture Tiling SDXL
+### Stable Diffusion Mixture Tiling Pipeline SDXL
 
 This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.
 
@@ -2516,14 +2517,13 @@ image = pipe(
     tile_col_overlap=256,
     guidance_scale_tiles=[[7, 7, 7]], # or guidance_scale=7 if is the same for all prompts
     height=1024,
-    width=3840,
-    target_size=(1024, 3840),
+    width=3840,    
     generator=generator,
     num_inference_steps=30,
 )["images"][0]
 ```
 
-![mixture_tiling_results](https://huggingface.co/datasets/elismasilva/results/resolve/main/mixture_sdxl.png)
+![mixture_tiling_results](https://huggingface.co/datasets/elismasilva/results/resolve/main/mixture_of_diffusers_sdxl_1.png)
 
 ### TensorRT Inpainting Stable Diffusion Pipeline
 

main/mixture_tiling_sdxl.py

@@ -1,4 +1,4 @@
-# Copyright 2024 The HuggingFace Team. All rights reserved.
+# Copyright 2025 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -151,6 +151,51 @@ def _tile2latent_exclusive_indices(
     return row_segment[0], row_segment[1], col_segment[0], col_segment[1]
 
 
+def _get_crops_coords_list(num_rows, num_cols, output_width):
+    """
+    Generates a list of lists of `crops_coords_top_left` tuples for focusing on
+    different horizontal parts of an image, and repeats this list for the specified
+    number of rows in the output structure.
+
+    This function calculates `crops_coords_top_left` tuples to create horizontal
+    focus variations (like left, center, right focus) based on `output_width`
+    and `num_cols` (which represents the number of horizontal focus points/columns).
+    It then repeats the *list* of these horizontal focus tuples `num_rows` times to
+    create the final list of lists output structure.
+
+    Args:
+        num_rows (int): The desired number of rows in the output list of lists.
+                          This determines how many times the list of horizontal
+                          focus variations will be repeated.
+        num_cols (int): The number of horizontal focus points (columns) to generate.
+                          This determines how many horizontal focus variations are
+                          created based on dividing the `output_width`.
+        output_width (int): The desired width of the output image.
+
+    Returns:
+        list[list[tuple[int, int]]]: A list of lists of tuples. Each inner list
+                                     contains `num_cols` tuples of `(ctop, cleft)`,
+                                     representing horizontal focus points. The outer list
+                                     contains `num_rows` such inner lists.
+    """
+    crops_coords_list = []
+    if num_cols <= 0:
+        crops_coords_list = []
+    elif num_cols == 1:
+        crops_coords_list = [(0, 0)]
+    else:
+        section_width = output_width / num_cols
+        for i in range(num_cols):
+            cleft = int(round(i * section_width))
+            crops_coords_list.append((0, cleft))
+
+    result_list = []
+    for _ in range(num_rows):
+        result_list.append(list(crops_coords_list))
+
+    return result_list
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     r"""
@@ -757,10 +802,10 @@ class StableDiffusionXLTilingPipeline(
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         original_size: Optional[Tuple[int, int]] = None,
-        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
         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_crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
         negative_target_size: Optional[Tuple[int, int]] = None,
         clip_skip: Optional[int] = None,
         tile_height: Optional[int] = 1024,
@@ -826,7 +871,7 @@ class StableDiffusionXLTilingPipeline(
                 `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
                 explained in section 2.2 of
                 [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+            crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
                 `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
                 `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
                 `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
@@ -840,7 +885,7 @@ class StableDiffusionXLTilingPipeline(
                 micro-conditioning as explained in section 2.2 of
                 [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                 information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+            negative_crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
                 To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
                 micro-conditioning as explained in section 2.2 of
                 [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
@@ -883,6 +928,8 @@ class StableDiffusionXLTilingPipeline(
 
         original_size = original_size or (height, width)
         target_size = target_size or (height, width)
+        negative_original_size = negative_original_size or (height, width)
+        negative_target_size = negative_target_size or (height, width)
 
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
@@ -914,6 +961,11 @@ class StableDiffusionXLTilingPipeline(
 
         device = self._execution_device
 
+        # update crops coords list
+        crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
+
         # update height and width tile size and tile overlap size
         height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)
         width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)
@@ -1020,7 +1072,7 @@ class StableDiffusionXLTilingPipeline(
                     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,
+                        crops_coords_top_left[row][col],
                         target_size,
                         dtype=prompt_embeds.dtype,
                         text_encoder_projection_dim=text_encoder_projection_dim,
@@ -1028,7 +1080,7 @@ class StableDiffusionXLTilingPipeline(
                     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_crops_coords_top_left[row][col],
                             negative_target_size,
                             dtype=prompt_embeds.dtype,
                             text_encoder_projection_dim=text_encoder_projection_dim,