Merge branch 'main' into fix/npu-rmsnorm-and-attn-mask-main

Author: mengchengTang

.ai/AGENTS.md

@@ -37,6 +37,8 @@ Strive to write code as simple and explicit as possible.
 Task-specific guides live in `.ai/skills/` and are loaded on demand by AI agents. Available skills include:
 
 - [model-integration](./skills/model-integration/SKILL.md) (adding/converting pipelines)
+- [custom-blocks](./skills/custom-blocks/SKILL.md) (packaging a `ModularPipelineBlocks` subclass for the Hub)
+- [diffusers-cli](./skills/diffusers-cli/SKILL.md) (running pipelines, inspecting schemas, and using the Diffusers CLI)
 - [self-review](./skills/self-review/SKILL.md) (pre-PR self-review against the project rules)
 
 ## Self-review before a PR

.ai/modular.md

@@ -235,6 +235,19 @@ OutputParam(
 
 If a template's predefined description doesn't fit (e.g. the `"latents"` output template means "Denoised latents", which is wrong for the noisy latents out of a prepare-latents step) — drop the template and declare the field directly with an accurate description. See gotcha #5.
 
+**Declare defaults in the `InputParam`, not inside `__call__`.**
+
+```python
+# yes
+InputParam(name="num_frames", type_hint=int, default=189)
+
+# no — works, but the assembled pipeline is not aware of it
+if block_state.num_frames is None:
+    block_state.num_frames = 189
+```
+
+A declared default is part of the block's contract, so the assembled pipeline is aware of it: the generated docstring shows it and `default_call_parameters` reports it. Resolved inside the body instead, the input renders as `*optional*` with no default, and nothing at the pipeline level can report what the block will actually do. Don't worry about branches of a conditional blockset declaring different defaults for the same input — each branch resolves its own at runtime. Resolve inside `__call__` only when the default is *computed* — derived from other inputs or component config (`height = components.default_sample_size * components.vae_scale_factor`). And when several blocks in a sequence share an input, declare the same default on each (or only on the first block that reads it): in a sequence the input is one shared value, so disagreeing declarations are silently resolved first-block-wins.
+
 ## ComponentSpec patterns
 
 ```python

.github/workflows/new_model_request_reply.yml

@@ -0,0 +1,43 @@
+name: New Model Request Reply
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  reply:
+    name: Point new model requests at Modular Diffusers
+    # Match the heading the issue form renders for its first field rather than a label: template
+    # labels are applied after the issue is created, so `github.event.issue.labels` is empty here.
+    # Keep this string in sync with .github/ISSUE_TEMPLATE/new-model-addition.yml.
+    if: >-
+      github.repository == 'huggingface/diffusers' &&
+      contains(github.event.issue.body, '### Model/Pipeline/Scheduler description')
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+    steps:
+      - name: Post Modular Diffusers guidance
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GH_REPO: ${{ github.repository }}
+          ISSUE_NUMBER: ${{ github.event.issue.number }}
+          BODY: |
+            Thanks for the request!
+
+            **How new model support works in Diffusers**
+
+            We're a small team, and our review queue shouldn't be what decides whether a model is usable in Diffusers. With [Modular Diffusers](https://huggingface.co/docs/diffusers/modular_diffusers/overview), a pipeline can live as remote code in any Hub repo and load straight from there with `from_pretrained`.
+
+            🛠️ **Want to bring this model to Diffusers?**
+
+            Please start with a Hub repo — you don't need anything from us to do that, and people can use it immediately. From there we decide how to support it: we might work with the authors, upstream an existing community version, or just point people at the one on the Hub. The pipelines we integrate are usually the ones people are already running.
+
+            Tag `@asomoza` when you have something to share — we'll give feedback on the implementation, help get it in front of people, and add the ones we like to our hand-picked [Modular Pipelines](https://huggingface.co/collections/diffusers/modular-pipelines) collection. Tell us where you hit friction along the way, too: confusing APIs, missing docs, bugs. That feedback is worth as much to us as the pipeline.
+
+            👋 **Are you an author of the model?** We'd love to hear from you — comment here and we'll help you pick the path that fits.
+
+            📚 [Quickstart](https://huggingface.co/docs/diffusers/modular_diffusers/quickstart) · [Building custom blocks](https://huggingface.co/docs/diffusers/modular_diffusers/custom_blocks) — template repo, and how to publish to the Hub · [Modular Pipelines](https://huggingface.co/collections/diffusers/modular-pipelines) and [Custom Blocks](https://huggingface.co/collections/diffusers/modular-diffusers-custom-blocks) — examples to crib from
+
+            *This is an automated message.*
+        run: gh issue comment "$ISSUE_NUMBER" --body "$BODY"

README.md

@@ -65,7 +65,7 @@ Generating outputs is super easy with 🤗 Diffusers. To generate an image from
 from diffusers import DiffusionPipeline
 import torch
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.float16)
 pipeline.to("cuda")
 pipeline("An image of a squirrel in Picasso style").images[0]
 ```

benchmarks/README.md

@@ -55,7 +55,7 @@ BenchmarkScenario(
     model_cls=FluxTransformer2DModel,
     model_init_kwargs={
         "pretrained_model_name_or_path": CKPT_ID,
-        "torch_dtype": torch.bfloat16,
+        "dtype": torch.bfloat16,
         "subfolder": "transformer",
         "quantization_config": BitsAndBytesConfig(load_in_8bit=True),
     },

docs/source/en/_toctree.yml

@@ -184,6 +184,8 @@
     title: NVIDIA ModelOpt
   - local: quantization/autoround
     title: AutoRound
+  - local: quantization/sdnq
+    title: SDNQ
   title: Quantization
 - isExpanded: false
   sections:
@@ -232,8 +234,6 @@
     title: How to contribute?
   - local: conceptual/ethical_guidelines
     title: Diffusers' Ethical Guidelines
-  - local: conceptual/evaluation
-    title: Evaluating Diffusion Models
   title: Resources
 - isExpanded: false
   sections:

docs/source/en/advanced_inference/outpaint.md

@@ -111,15 +111,15 @@ Load the inpainting ControlNet, ZoeDepth model, VAE and pass them to the [`Stabl
 ```py
 controlnets = [
     ControlNetModel.from_pretrained(
-        "destitech/controlnet-inpaint-dreamer-sdxl", torch_dtype=torch.float16, variant="fp16"
+        "destitech/controlnet-inpaint-dreamer-sdxl", dtype=torch.float16, variant="fp16"
     ),
     ControlNetModel.from_pretrained(
-        "diffusers/controlnet-zoe-depth-sdxl-1.0", torch_dtype=torch.float16
+        "diffusers/controlnet-zoe-depth-sdxl-1.0", dtype=torch.float16
     ),
 ]
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to("cuda")
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", dtype=torch.float16).to("cuda")
 pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
-    "SG161222/RealVisXL_V4.0", torch_dtype=torch.float16, variant="fp16", controlnet=controlnets, vae=vae
+    "SG161222/RealVisXL_V4.0", dtype=torch.float16, variant="fp16", controlnet=controlnets, vae=vae
 ).to("cuda")
 
 def generate_image(prompt, negative_prompt, inpaint_image, zoe_image, seed: int = None):
@@ -173,7 +173,7 @@ Now that you have an initial outpainted image, load the [`StableDiffusionXLInpai
 ```py
 pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
     "OzzyGT/RealVisXL_V4.0_inpainting",
-    torch_dtype=torch.float16,
+    dtype=torch.float16,
     variant="fp16",
     vae=vae,
 ).to("cuda")

docs/source/en/api/models/allegro_transformer3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AllegroTransformer3DModel
 
-transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", dtype=torch.bfloat16).to("cuda")
 ```
 
 ## AllegroTransformer3DModel

docs/source/en/api/models/autoencoder_dc.md

@@ -36,7 +36,7 @@ Load a model in Diffusers format with [`~ModelMixin.from_pretrained`].
 ```python
 from diffusers import AutoencoderDC
 
-ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", torch_dtype=torch.float32).to("cuda")
+ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", dtype=torch.float32).to("cuda")
 ```
 
 ## Load a model in Diffusers via `from_single_file`

docs/source/en/api/models/autoencoder_kl_hunyuan_video.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLHunyuanVideo
 
-vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="vae", torch_dtype=torch.float16)
+vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="vae", dtype=torch.float16)
 ```
 
 ## AutoencoderKLHunyuanVideo