Merge branch 'bria_3_2_pipeline' of https://github.com/galbria/diffusers into bria_3_2_pipeline

Author: galbria

docs/source/en/_toctree.yml

@@ -5,7 +5,7 @@
   - local: installation
     title: Installation
   - local: quicktour
-    title: Quicktour
+    title: Quickstart
   - local: stable_diffusion
     title: Basic performance
 
@@ -340,7 +340,7 @@
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
-      - local: api/models/transformer_bria
+      - local: api/models/bria_transformer
         title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
         title: ChromaTransformer2DModel

docs/source/en/quicktour.md

@@ -22,14 +22,17 @@ This guide recommends some basic performance tips for using the [`DiffusionPipel
 
 Reducing the amount of memory used indirectly speeds up generation and can help a model fit on device.
 
+The [`~DiffusionPipeline.enable_model_cpu_offload`] method moves a model to the CPU when it is not in use to save GPU memory.
+
 ```py
 import torch
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
   "stabilityai/stable-diffusion-xl-base-1.0",
-  torch_dtype=torch.bfloat16
-).to("cuda")
+  torch_dtype=torch.bfloat16,
+  device_map="cuda"
+)
 pipeline.enable_model_cpu_offload()
 
 prompt = """
@@ -44,7 +47,7 @@ print(f"Max memory reserved: {torch.cuda.max_memory_allocated() / 1024**3:.2f} G
 
 Denoising is the most computationally demanding process during diffusion. Methods that optimizes this process accelerates inference speed. Try the following methods for a speed up.
 
-- Add `.to("cuda")` to place the pipeline on a GPU. Placing a model on an accelerator, like a GPU, increases speed because it performs computations in parallel.
+- Add `device_map="cuda"` to place the pipeline on a GPU. Placing a model on an accelerator, like a GPU, increases speed because it performs computations in parallel.
 - Set `torch_dtype=torch.bfloat16` to execute the pipeline in half-precision. Reducing the data type precision increases speed because it takes less time to perform computations in a lower precision.
 
 ```py
@@ -54,8 +57,9 @@ from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 
 pipeline = DiffusionPipeline.from_pretrained(
   "stabilityai/stable-diffusion-xl-base-1.0",
-  torch_dtype=torch.bfloat16
-).to("cuda")
+  torch_dtype=torch.bfloat16,
+  device_map="cuda
+)
 ```
 
 - Use a faster scheduler, such as [`DPMSolverMultistepScheduler`], which only requires ~20-25 steps.
@@ -88,8 +92,9 @@ Many modern diffusion models deliver high-quality images out-of-the-box. However
 
     pipeline = DiffusionPipeline.from_pretrained(
         "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.bfloat16
-    ).to("cuda")
+        torch_dtype=torch.bfloat16,
+        device_map="cuda"
+    )
 
     prompt = """
     cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
@@ -109,8 +114,9 @@ Many modern diffusion models deliver high-quality images out-of-the-box. However
 
     pipeline = DiffusionPipeline.from_pretrained(
         "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.bfloat16
-    ).to("cuda")
+        torch_dtype=torch.bfloat16,
+        device_map="cuda"
+    )
     pipeline.scheduler = HeunDiscreteScheduler.from_config(pipeline.scheduler.config)
 
     prompt = """

docs/source/en/stable_diffusion.md

@@ -494,6 +494,7 @@
             "QwenImageImg2ImgPipeline",
             "QwenImageInpaintPipeline",
             "QwenImagePipeline",
+            "QwenImageEditPipeline",
             "ReduxImageEncoder",
             "SanaControlNetPipeline",
             "SanaPAGPipeline",
@@ -1127,6 +1128,7 @@
             PixArtAlphaPipeline,
             PixArtSigmaPAGPipeline,
             PixArtSigmaPipeline,
+            QwenImageEditPipeline,
             QwenImageImg2ImgPipeline,
             QwenImageInpaintPipeline,
             QwenImagePipeline,

src/diffusers/__init__.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import functools
 import math
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -161,17 +160,17 @@ def __init__(self, theta: int, axes_dim: List[int], scale_rope=False):
         super().__init__()
         self.theta = theta
         self.axes_dim = axes_dim
-        pos_index = torch.arange(1024)
-        neg_index = torch.arange(1024).flip(0) * -1 - 1
-        pos_freqs = torch.cat(
+        pos_index = torch.arange(4096)
+        neg_index = torch.arange(4096).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat(
             [
                 self.rope_params(pos_index, self.axes_dim[0], self.theta),
                 self.rope_params(pos_index, self.axes_dim[1], self.theta),
                 self.rope_params(pos_index, self.axes_dim[2], self.theta),
             ],
             dim=1,
         )
-        neg_freqs = torch.cat(
+        self.neg_freqs = torch.cat(
             [
                 self.rope_params(neg_index, self.axes_dim[0], self.theta),
                 self.rope_params(neg_index, self.axes_dim[1], self.theta),
@@ -180,10 +179,8 @@ def __init__(self, theta: int, axes_dim: List[int], scale_rope=False):
             dim=1,
         )
         self.rope_cache = {}
-        self.register_buffer("pos_freqs", pos_freqs, persistent=False)
-        self.register_buffer("neg_freqs", neg_freqs, persistent=False)
 
-        # 是否使用 scale rope
+        # DO NOT USING REGISTER BUFFER HERE, IT WILL CAUSE COMPLEX NUMBERS LOSE ITS IMAGINARY PART
         self.scale_rope = scale_rope
 
     def rope_params(self, index, dim, theta=10000):
@@ -201,35 +198,47 @@ def forward(self, video_fhw, txt_seq_lens, device):
         Args: video_fhw: [frame, height, width] a list of 3 integers representing the shape of the video Args:
         txt_length: [bs] a list of 1 integers representing the length of the text
         """
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
         if isinstance(video_fhw, list):
             video_fhw = video_fhw[0]
-        frame, height, width = video_fhw
-        rope_key = f"{frame}_{height}_{width}"
-
-        if not torch.compiler.is_compiling():
-            if rope_key not in self.rope_cache:
-                self.rope_cache[rope_key] = self._compute_video_freqs(frame, height, width)
-            vid_freqs = self.rope_cache[rope_key]
-        else:
-            vid_freqs = self._compute_video_freqs(frame, height, width)
+        if not isinstance(video_fhw, list):
+            video_fhw = [video_fhw]
+
+        vid_freqs = []
+        max_vid_index = 0
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            rope_key = f"{idx}_{height}_{width}"
+
+            if not torch.compiler.is_compiling():
+                if rope_key not in self.rope_cache:
+                    self.rope_cache[rope_key] = self._compute_video_freqs(frame, height, width, idx)
+                video_freq = self.rope_cache[rope_key]
+            else:
+                video_freq = self._compute_video_freqs(frame, height, width, idx)
+            vid_freqs.append(video_freq)
 
-        if self.scale_rope:
-            max_vid_index = max(height // 2, width // 2)
-        else:
-            max_vid_index = max(height, width)
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
 
         max_len = max(txt_seq_lens)
         txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
 
         return vid_freqs, txt_freqs
 
     @functools.lru_cache(maxsize=None)
-    def _compute_video_freqs(self, frame, height, width):
+    def _compute_video_freqs(self, frame, height, width, idx=0):
         seq_lens = frame * height * width
         freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
         freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
 
-        freqs_frame = freqs_pos[0][:frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+        freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
         if self.scale_rope:
             freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0)
             freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)

src/diffusers/models/transformers/transformer_qwenimage.py

@@ -392,6 +392,7 @@
         "QwenImagePipeline",
         "QwenImageImg2ImgPipeline",
         "QwenImageInpaintPipeline",
+        "QwenImageEditPipeline",
     ]
 try:
     if not is_onnx_available():
@@ -710,7 +711,12 @@
         from .paint_by_example import PaintByExamplePipeline
         from .pia import PIAPipeline
         from .pixart_alpha import PixArtAlphaPipeline, PixArtSigmaPipeline
-        from .qwenimage import QwenImageImg2ImgPipeline, QwenImageInpaintPipeline, QwenImagePipeline
+        from .qwenimage import (
+            QwenImageEditPipeline,
+            QwenImageImg2ImgPipeline,
+            QwenImageInpaintPipeline,
+            QwenImagePipeline,
+        )
         from .sana import SanaControlNetPipeline, SanaPipeline, SanaSprintImg2ImgPipeline, SanaSprintPipeline
         from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
         from .shap_e import ShapEImg2ImgPipeline, ShapEPipeline

src/diffusers/pipelines/__init__.py

@@ -26,6 +26,7 @@
     _import_structure["pipeline_qwenimage"] = ["QwenImagePipeline"]
     _import_structure["pipeline_qwenimage_img2img"] = ["QwenImageImg2ImgPipeline"]
     _import_structure["pipeline_qwenimage_inpaint"] = ["QwenImageInpaintPipeline"]
+    _import_structure["pipeline_qwenimage_edit"] = ["QwenImageEditPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -35,6 +36,7 @@
         from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
     else:
         from .pipeline_qwenimage import QwenImagePipeline
+        from .pipeline_qwenimage_edit import QwenImageEditPipeline
         from .pipeline_qwenimage_img2img import QwenImageImg2ImgPipeline
         from .pipeline_qwenimage_inpaint import QwenImageInpaintPipeline
 else:

src/diffusers/pipelines/qwenimage/__init__.py

@@ -253,6 +253,9 @@ def encode_prompt(
         if prompt_embeds is None:
             prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, device)
 
+        prompt_embeds = prompt_embeds[:, :max_sequence_length]
+        prompt_embeds_mask = prompt_embeds_mask[:, :max_sequence_length]
+
         _, seq_len, _ = prompt_embeds.shape
         prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
         prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
@@ -316,20 +319,6 @@ def check_inputs(
         if max_sequence_length is not None and max_sequence_length > 1024:
             raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
 
-    @staticmethod
-    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3)
-        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
-        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
-
-        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
-
-        latent_image_ids = latent_image_ids.reshape(
-            latent_image_id_height * latent_image_id_width, latent_image_id_channels
-        )
-
-        return latent_image_ids.to(device=device, dtype=dtype)
-
     @staticmethod
     def _pack_latents(latents, batch_size, num_channels_latents, height, width):
         latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
@@ -402,8 +391,7 @@ def prepare_latents(
         shape = (batch_size, 1, num_channels_latents, height, width)
 
         if latents is not None:
-            latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
-            return latents.to(device=device, dtype=dtype), latent_image_ids
+            return latents.to(device=device, dtype=dtype)
 
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
@@ -414,9 +402,7 @@ def prepare_latents(
         latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
         latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
 
-        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
-
-        return latents, latent_image_ids
+        return latents
 
     @property
     def guidance_scale(self):
@@ -594,7 +580,7 @@ def __call__(
 
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels // 4
-        latents, latent_image_ids = self.prepare_latents(
+        latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,
             height,
@@ -604,7 +590,7 @@ def __call__(
             generator,
             latents,
         )
-        img_shapes = [(1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2)] * batch_size
+        img_shapes = [[(1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2)]] * batch_size
 
         # 5. Prepare timesteps
         sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas