Merge branch 'main' into xformers_flux

Author: SahilCarterr

examples/server-async/Pipelines.py

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+import logging
+import os
+from dataclasses import dataclass, field
+from typing import List
+
+import torch
+from pydantic import BaseModel
+
+from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3 import StableDiffusion3Pipeline
+
+
+logger = logging.getLogger(__name__)
+
+
+class TextToImageInput(BaseModel):
+    model: str
+    prompt: str
+    size: str | None = None
+    n: int | None = None
+
+
+@dataclass
+class PresetModels:
+    SD3: List[str] = field(default_factory=lambda: ["stabilityai/stable-diffusion-3-medium"])
+    SD3_5: List[str] = field(
+        default_factory=lambda: [
+            "stabilityai/stable-diffusion-3.5-large",
+            "stabilityai/stable-diffusion-3.5-large-turbo",
+            "stabilityai/stable-diffusion-3.5-medium",
+        ]
+    )
+
+
+class TextToImagePipelineSD3:
+    def __init__(self, model_path: str | None = None):
+        self.model_path = model_path or os.getenv("MODEL_PATH")
+        self.pipeline: StableDiffusion3Pipeline | None = None
+        self.device: str | None = None
+
+    def start(self):
+        if torch.cuda.is_available():
+            model_path = self.model_path or "stabilityai/stable-diffusion-3.5-large"
+            logger.info("Loading CUDA")
+            self.device = "cuda"
+            self.pipeline = StableDiffusion3Pipeline.from_pretrained(
+                model_path,
+                torch_dtype=torch.float16,
+            ).to(device=self.device)
+        elif torch.backends.mps.is_available():
+            model_path = self.model_path or "stabilityai/stable-diffusion-3.5-medium"
+            logger.info("Loading MPS for Mac M Series")
+            self.device = "mps"
+            self.pipeline = StableDiffusion3Pipeline.from_pretrained(
+                model_path,
+                torch_dtype=torch.bfloat16,
+            ).to(device=self.device)
+        else:
+            raise Exception("No CUDA or MPS device available")
+
+
+class ModelPipelineInitializer:
+    def __init__(self, model: str = "", type_models: str = "t2im"):
+        self.model = model
+        self.type_models = type_models
+        self.pipeline = None
+        self.device = "cuda" if torch.cuda.is_available() else "mps"
+        self.model_type = None
+
+    def initialize_pipeline(self):
+        if not self.model:
+            raise ValueError("Model name not provided")
+
+        # Check if model exists in PresetModels
+        preset_models = PresetModels()
+
+        # Determine which model type we're dealing with
+        if self.model in preset_models.SD3:
+            self.model_type = "SD3"
+        elif self.model in preset_models.SD3_5:
+            self.model_type = "SD3_5"
+
+        # Create appropriate pipeline based on model type and type_models
+        if self.type_models == "t2im":
+            if self.model_type in ["SD3", "SD3_5"]:
+                self.pipeline = TextToImagePipelineSD3(self.model)
+            else:
+                raise ValueError(f"Model type {self.model_type} not supported for text-to-image")
+        elif self.type_models == "t2v":
+            raise ValueError(f"Unsupported type_models: {self.type_models}")
+
+        return self.pipeline

examples/server-async/README.md

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+# Asynchronous server and parallel execution of models
+
+> Example/demo server that keeps a single model in memory while safely running parallel inference requests by creating per-request lightweight views and cloning only small, stateful components (schedulers, RNG state, small mutable attrs). Works with StableDiffusion3 pipelines.
+> We recommend running 10 to 50 inferences in parallel for optimal performance, averaging between 25 and 30 seconds to 1 minute and 1 minute and 30 seconds. (This is only recommended if you have a GPU with 35GB of VRAM or more; otherwise, keep it to one or two inferences in parallel to avoid decoding or saving errors due to memory shortages.)
+
+## ⚠️ IMPORTANT
+
+* The example demonstrates how to run pipelines like `StableDiffusion3-3.5` concurrently while keeping a single copy of the heavy model parameters on GPU.
+
+## Necessary components
+
+All the components needed to create the inference server are in the current directory:
+
+```
+server-async/
+├── utils/
+├─────── __init__.py
+├─────── scheduler.py              # BaseAsyncScheduler wrapper and async_retrieve_timesteps for secure inferences
+├─────── requestscopedpipeline.py  # RequestScoped Pipeline for inference with a single in-memory model
+├─────── utils.py                  # Image/video saving utilities and service configuration
+├── Pipelines.py                   # pipeline loader classes (SD3)
+├── serverasync.py                 # FastAPI app with lifespan management and async inference endpoints
+├── test.py                        # Client test script for inference requests
+├── requirements.txt               # Dependencies
+└── README.md                      # This documentation
+```
+
+## What `diffusers-async` adds / Why we needed it
+
+Core problem: a naive server that calls `pipe.__call__` concurrently can hit **race conditions** (e.g., `scheduler.set_timesteps` mutates shared state) or explode memory by deep-copying the whole pipeline per-request.
+
+`diffusers-async` / this example addresses that by:
+
+* **Request-scoped views**: `RequestScopedPipeline` creates a shallow copy of the pipeline per request so heavy weights (UNet, VAE, text encoder) remain shared and *are not duplicated*.
+* **Per-request mutable state**: stateful small objects (scheduler, RNG state, small lists/dicts, callbacks) are cloned per request. The system uses `BaseAsyncScheduler.clone_for_request(...)` for scheduler cloning, with fallback to safe `deepcopy` or other heuristics.
+* **Tokenizer concurrency safety**: `RequestScopedPipeline` now manages an internal tokenizer lock with automatic tokenizer detection and wrapping. This ensures that Rust tokenizers are safe to use under concurrency — race condition errors like `Already borrowed` no longer occur.
+* **`async_retrieve_timesteps(..., return_scheduler=True)`**: fully retro-compatible helper that returns `(timesteps, num_inference_steps, scheduler)` without mutating the shared scheduler. For users not using `return_scheduler=True`, the behavior is identical to the original API.
+* **Robust attribute handling**: wrapper avoids writing to read-only properties (e.g., `components`) and auto-detects small mutable attributes to clone while avoiding duplication of large tensors. Configurable tensor size threshold prevents cloning of large tensors.
+* **Enhanced scheduler wrapping**: `BaseAsyncScheduler` automatically wraps schedulers with improved `__getattr__`, `__setattr__`, and debugging methods (`__repr__`, `__str__`).
+
+## How the server works (high-level flow)
+
+1. **Single model instance** is loaded into memory (GPU/MPS) when the server starts.
+2. On each HTTP inference request:
+
+   * The server uses `RequestScopedPipeline.generate(...)` which:
+
+     * automatically wraps the base scheduler in `BaseAsyncScheduler` (if not already wrapped),
+     * obtains a *local scheduler* (via `clone_for_request()` or `deepcopy`),
+     * does `local_pipe = copy.copy(base_pipe)` (shallow copy),
+     * sets `local_pipe.scheduler = local_scheduler` (if possible),
+     * clones only small mutable attributes (callbacks, rng, small latents) with auto-detection,
+     * wraps tokenizers with thread-safe locks to prevent race conditions,
+     * optionally enters a `model_cpu_offload_context()` for memory offload hooks,
+     * calls the pipeline on the local view (`local_pipe(...)`).
+3. **Result**: inference completes, images are moved to CPU & saved (if requested), internal buffers freed (GC + `torch.cuda.empty_cache()`).
+4. Multiple requests can run in parallel while sharing heavy weights and isolating mutable state.
+
+## How to set up and run the server
+
+### 1) Install dependencies
+
+Recommended: create a virtualenv / conda environment.
+
+```bash
+pip install diffusers
+pip install -r requirements.txt
+```
+
+### 2) Start the server
+
+Using the `serverasync.py` file that already has everything you need:
+
+```bash
+python serverasync.py
+```
+
+The server will start on `http://localhost:8500` by default with the following features:
+- FastAPI application with async lifespan management
+- Automatic model loading and pipeline initialization
+- Request counting and active inference tracking
+- Memory cleanup after each inference
+- CORS middleware for cross-origin requests
+
+### 3) Test the server
+
+Use the included test script:
+
+```bash
+python test.py
+```
+
+Or send a manual request:
+
+`POST /api/diffusers/inference` with JSON body:
+
+```json
+{
+  "prompt": "A futuristic cityscape, vibrant colors",
+  "num_inference_steps": 30,
+  "num_images_per_prompt": 1
+}
+```
+
+Response example:
+
+```json
+{
+  "response": ["http://localhost:8500/images/img123.png"]
+}
+```
+
+### 4) Server endpoints
+
+- `GET /` - Welcome message
+- `POST /api/diffusers/inference` - Main inference endpoint
+- `GET /images/{filename}` - Serve generated images
+- `GET /api/status` - Server status and memory info
+
+## Advanced Configuration
+
+### RequestScopedPipeline Parameters
+
+```python
+RequestScopedPipeline(
+    pipeline,                        # Base pipeline to wrap
+    mutable_attrs=None,             # Custom list of attributes to clone
+    auto_detect_mutables=True,      # Enable automatic detection of mutable attributes
+    tensor_numel_threshold=1_000_000, # Tensor size threshold for cloning
+    tokenizer_lock=None,            # Custom threading lock for tokenizers
+    wrap_scheduler=True             # Auto-wrap scheduler in BaseAsyncScheduler
+)
+```
+
+### BaseAsyncScheduler Features
+
+* Transparent proxy to the original scheduler with `__getattr__` and `__setattr__`
+* `clone_for_request()` method for safe per-request scheduler cloning
+* Enhanced debugging with `__repr__` and `__str__` methods
+* Full compatibility with existing scheduler APIs
+
+### Server Configuration
+
+The server configuration can be modified in `serverasync.py` through the `ServerConfigModels` dataclass:
+
+```python
+@dataclass
+class ServerConfigModels:
+    model: str = 'stabilityai/stable-diffusion-3.5-medium'  
+    type_models: str = 't2im'  
+    host: str = '0.0.0.0' 
+    port: int = 8500
+```
+
+## Troubleshooting (quick)
+
+* `Already borrowed` — previously a Rust tokenizer concurrency error.
+  ✅ This is now fixed: `RequestScopedPipeline` automatically detects and wraps tokenizers with thread locks, so race conditions no longer happen.
+
+* `can't set attribute 'components'` — pipeline exposes read-only `components`.
+  ✅ The RequestScopedPipeline now detects read-only properties and skips setting them automatically.
+
+* Scheduler issues:
+  * If the scheduler doesn't implement `clone_for_request` and `deepcopy` fails, we log and fallback — but prefer `async_retrieve_timesteps(..., return_scheduler=True)` to avoid mutating the shared scheduler.
+  ✅ Note: `async_retrieve_timesteps` is fully retro-compatible — if you don't pass `return_scheduler=True`, the behavior is unchanged.
+
+* Memory issues with large tensors:
+  ✅ The system now has configurable `tensor_numel_threshold` to prevent cloning of large tensors while still cloning small mutable ones.
+
+* Automatic tokenizer detection:
+  ✅ The system automatically identifies tokenizer components by checking for tokenizer methods, class names, and attributes, then applies thread-safe wrappers.

examples/server-async/requirements.txt

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+torch 
+torchvision 
+transformers 
+sentencepiece 
+fastapi 
+uvicorn 
+ftfy
+accelerate
+xformers
+protobuf