Feature/sdxl lightning

README.md

@@ -107,7 +107,7 @@ datadreamer --save_dir <directory> --class_names <objects> --prompts_number <num
 - `--task`: Choose between `detection` and `classification`. Default is `detection`.
 - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.
 - `--prompt_generator`: Choose between `simple`, `lm` (language model) and `tiny` (tiny LM). Default is `simple`.
-- `--image_generator`: Choose image generator, e.g., `sdxl` or `sdxl-turbo`. Default is `sdxl-turbo`.
+- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.
 - `--image_annotator`: Specify the image annotator, like `owlv2`. Default is `owlv2`.
 - `--conf_threshold`: Confidence threshold for object detection. Default is 0.15.
 - `--use_tta`: Toggle test time augmentation for object detection. Default is True.
@@ -132,6 +132,7 @@ datadreamer --save_dir <directory> --class_names <objects> --prompts_number <num
 |                   | Simple random generator                                                               | Joins randomly chosen object names      |
 | Image Generation  | [SDXL-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)           | Slow and accurate (1024x1024 images)    |
 |                   | [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)                           | Fast and less accurate (512x512 images) |
+|                   | [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)                     | Fast and accurate (1024x1024 images)    |
 | Image Annotation  | [OWLv2](https://huggingface.co/google/owlv2-base-patch16-ensemble)                    | Open-Vocabulary object detector         |
 
 <a name="example"></a>

datadreamer/image_generation/__init__.py

@@ -1,4 +1,9 @@
 from .sdxl_image_generator import StableDiffusionImageGenerator
+from .sdxl_lightning_image_generator import StableDiffusionLightningImageGenerator
 from .sdxl_turbo_image_generator import StableDiffusionTurboImageGenerator
 
-__all__ = ["StableDiffusionImageGenerator", "StableDiffusionTurboImageGenerator"]
+__all__ = [
+    "StableDiffusionImageGenerator",
+    "StableDiffusionTurboImageGenerator",
+    "StableDiffusionLightningImageGenerator",
+]

datadreamer/image_generation/sdxl_image_generator.py

@@ -173,7 +173,7 @@ def release(self, empty_cuda_cache=False) -> None:
         "A photo of a bicycle pedaling alongside an aeroplane taking off, showcasing the harmony between human-powered and mechanical transportation.",
         "A photo of bicycles along a scenic mountain path, where the riders seem to have taken a moment to appreciate the stunning views.",
     ]
-    prompt_objects = [["aeroplane", "boat", "bicycle"], ["bicycle"]]
+    prompt_objects = [["aeroplane", "bicycle"], ["bicycle"]]
 
     image_paths = []
     counter = 0

datadreamer/image_generation/sdxl_lightning_image_generator.py

@@ -0,0 +1,173 @@
+from typing import List, Optional
+
+import torch
+from compel import Compel, ReturnedEmbeddingsType
+from diffusers import (
+    EulerDiscreteScheduler,
+    StableDiffusionXLPipeline,
+    UNet2DConditionModel,
+)
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from safetensors.torch import load_file
+
+from datadreamer.image_generation.image_generator import ImageGenerator
+
+
+class StableDiffusionLightningImageGenerator(ImageGenerator):
+    """A subclass of ImageGenerator specifically designed to use the Stable Diffusion
+    Lightning model for faster image generation.
+
+    Attributes:
+        pipe (StableDiffusionXLPipeline): The Stable Diffusion Lightning model for image generation.
+
+    Methods:
+        _init_gen_model(): Initializes the Stable Diffusion Lightning model.
+        _init_compel(): Initializes the Compel model for text prompt weighting.
+        generate_images_batch(prompts, negative_prompt, prompt_objects): Generates a batch of images based on the provided prompts.
+        release(empty_cuda_cache): Releases resources and optionally empties the CUDA cache.
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initializes the StableDiffusionLightningImageGenerator with the given
+        arguments."""
+        super().__init__(*args, **kwargs)
+        self.pipe = self._init_gen_model()
+        self.compel = self._init_compel()
+
+    def _init_gen_model(self):
+        """Initializes the Stable Diffusion Lightning model for image generation.
+
+        Returns:
+            StableDiffusionXLPipeline: The initialized Stable Diffusion Lightning model.
+        """
+        base = "stabilityai/stable-diffusion-xl-base-1.0"
+        repo = "ByteDance/SDXL-Lightning"
+        ckpt = "sdxl_lightning_4step_unet.safetensors"  # Use the correct ckpt for your step setting!
+
+        # Load model.
+        if self.device == "cpu":
+            print("Loading SDXL Lightning on CPU...")
+            unet = UNet2DConditionModel.from_config(base, subfolder="unet")
+            unet.load_state_dict(load_file(hf_hub_download(repo, ckpt)))
+            pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet)
+        else:
+            print("Loading SDXL Lightning on GPU...")
+            unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(
+                self.device, torch.float16
+            )
+            unet.load_state_dict(
+                load_file(hf_hub_download(repo, ckpt), device=self.device)
+            )
+            pipe = StableDiffusionXLPipeline.from_pretrained(
+                base, unet=unet, torch_dtype=torch.float16, variant="fp16"
+            ).to(self.device)
+            pipe.enable_model_cpu_offload()
+
+        # Ensure sampler uses "trailing" timesteps.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(
+            pipe.scheduler.config, timestep_spacing="trailing"
+        )
+
+        return pipe
+
+    def _init_compel(self):
+        """Initializes the Compel model for text prompt weighting.
+
+        Returns:
+            Compel: The initialized Compel model.
+        """
+        compel = Compel(
+            tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2],
+            text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
+            returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+            requires_pooled=[False, True],
+        )
+        return compel
+
+    def generate_images_batch(
+        self,
+        prompts: List[str],
+        negative_prompt: str,
+        prompt_objects: Optional[List[List[str]]] = None,
+        batch_size: int = 1,
+    ) -> List[Image.Image]:
+        """Generates a batch of images using the Stable Diffusion Lightning model based
+        on the provided prompts.
+
+        Args:
+            prompts (List[str]): A list of positive prompts to guide image generation.
+            negative_prompt (str): The negative prompt to avoid certain features in the image.
+            prompt_objects (Optional[List[List[str]]]): Optional list of objects for each prompt for CLIP model testing.
+            batch_size (int): The number of images to generate in each batch.
+
+        Returns:
+            List[Image.Image]: A list of generated images.
+        """
+
+        if prompt_objects is not None:
+            for i in range(len(prompt_objects)):
+                for obj in prompt_objects[i]:
+                    prompts[i] = prompts[i].replace(obj, f"({obj})1.5", 1)
+
+        conditioning, pooled = self.compel(prompts)
+        conditioning_neg, pooled_neg = self.compel([negative_prompt] * len(prompts))
+        images = self.pipe(
+            prompt_embeds=conditioning,
+            pooled_prompt_embeds=pooled,
+            negative_prompt_embeds=conditioning_neg,
+            negative_pooled_prompt_embeds=pooled_neg,
+            guidance_scale=0.0,
+            num_inference_steps=4,
+        ).images
+
+        return images
+
+    def release(self, empty_cuda_cache=False) -> None:
+        """Releases the model and optionally empties the CUDA cache."""
+        self.pipe = self.pipe.to("cpu")
+        if self.use_clip_image_tester:
+            self.clip_image_tester.release()
+        if empty_cuda_cache:
+            with torch.no_grad():
+                torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    import os
+
+    # Create the generator
+    image_generator = StableDiffusionLightningImageGenerator(
+        seed=42,
+        use_clip_image_tester=False,
+        image_tester_patience=1,
+        batch_size=4,
+        device="cpu",
+    )
+    prompts = [
+        "A photo of a bicycle pedaling alongside an aeroplane.",
+        "A photo of a dragonfly flying in the sky.",
+        "A photo of a dog walking in the park.",
+        "A photo of an alien exploring the galaxy.",
+        "A photo of a robot working on a computer.",
+    ]
+    prompt_objects = [
+        ["aeroplane", "bicycle"],
+        ["dragonfly"],
+        ["dog"],
+        ["alien"],
+        ["robot", "computer"],
+    ]
+
+    image_paths = []
+    counter = 0
+    for generated_images_batch in image_generator.generate_images(
+        prompts, prompt_objects
+    ):
+        for generated_image in generated_images_batch:
+            image_path = os.path.join("./", f"image_lightning_{counter}.jpg")
+            generated_image.save(image_path)
+            image_paths.append(image_path)
+            counter += 1
+
+    image_generator.release(empty_cuda_cache=True)

datadreamer/image_generation/sdxl_turbo_image_generator.py

@@ -105,7 +105,7 @@ def release(self, empty_cuda_cache=False) -> None:
     prompts = [
         "A photo of a bicycle pedaling alongside an aeroplane taking off, showcasing the harmony between human-powered and mechanical transportation.",
     ] * 16
-    prompt_objects = [["aeroplane", "boat", "bicycle"]] * 16
+    prompt_objects = [["aeroplane", "bicycle"]] * 16
 
     image_paths = []
     counter = 0

datadreamer/pipelines/generate_dataset_from_scratch.py

@@ -12,6 +12,7 @@
 from datadreamer.dataset_annotation import OWLv2Annotator
 from datadreamer.image_generation import (
     StableDiffusionImageGenerator,
+    StableDiffusionLightningImageGenerator,
     StableDiffusionTurboImageGenerator,
 )
 from datadreamer.prompt_generation import (
@@ -30,6 +31,7 @@
 image_generators = {
     "sdxl": StableDiffusionImageGenerator,
     "sdxl-turbo": StableDiffusionTurboImageGenerator,
+    "sdxl-lightning": StableDiffusionLightningImageGenerator,
 }
 
 annotators = {"owlv2": OWLv2Annotator}
@@ -84,7 +86,7 @@ def parse_args():
         "--image_generator",
         type=str,
         default="sdxl-turbo",
-        choices=["sdxl", "sdxl-turbo"],
+        choices=["sdxl", "sdxl-turbo", "sdxl-lightning"],
         help="Image generator to use",
     )
     parser.add_argument(

tests/integration/test_pipeline.py

@@ -318,6 +318,50 @@ def test_cuda_simple_sdxl_detection_pipeline():
     _check_detection_pipeline(cmd, target_folder)
 
 
+@pytest.mark.skipif(
+    total_memory < 16 or total_disk_space < 35,
+    reason="Test requires at least 16GB of RAM and 35GB of HDD",
+)
+def test_cpu_simple_sdxl_lightning_detection_pipeline():
+    # Define target folder
+    target_folder = "data/data-det-cpu-simple-sdxl-lightning/"
+    # Define the command to run the datadreamer
+    cmd = (
+        f"datadreamer --save_dir {target_folder} "
+        f"--class_names alien mars cat "
+        f"--prompts_number 1 "
+        f"--prompt_generator simple "
+        f"--num_objects_range 1 2 "
+        f"--image_generator sdxl-lightning "
+        f"--use_image_tester "
+        f"--device cpu"
+    )
+    # Check the run of the pipeline
+    _check_detection_pipeline(cmd, target_folder)
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35,
+    reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD",
+)
+def test_cuda_simple_sdxl_lightning_detection_pipeline():
+    # Define target folder
+    target_folder = "data/data-det-cuda-simple-sdxl-lightning/"
+    # Define the command to run the datadreamer
+    cmd = (
+        f"datadreamer --save_dir {target_folder} "
+        f"--class_names alien mars cat "
+        f"--prompts_number 1 "
+        f"--prompt_generator simple "
+        f"--num_objects_range 1 2 "
+        f"--image_generator sdxl-lightning "
+        f"--use_image_tester "
+        f"--device cuda"
+    )
+    # Check the run of the pipeline
+    _check_detection_pipeline(cmd, target_folder)
+
+
 # =========================================================
 # DETECTION - LLM
 # =========================================================

tests/unittests/test_image_generation.py

@@ -6,13 +6,12 @@
 import torch
 from PIL import Image
 
-from datadreamer.image_generation.clip_image_tester import ClipImageTester
-from datadreamer.image_generation.sdxl_image_generator import (
+from datadreamer.image_generation import (
     StableDiffusionImageGenerator,
-)
-from datadreamer.image_generation.sdxl_turbo_image_generator import (
+    StableDiffusionLightningImageGenerator,
     StableDiffusionTurboImageGenerator,
 )
+from datadreamer.image_generation.clip_image_tester import ClipImageTester
 
 # Get the total memory in GB
 total_memory = psutil.virtual_memory().total / (1024**3)
@@ -55,7 +54,11 @@ def test_cpu_clip_image_tester():
 
 def _check_image_generator(
     image_generator_class: Type[
-        Union[StableDiffusionImageGenerator, StableDiffusionTurboImageGenerator]
+        Union[
+            StableDiffusionImageGenerator,
+            StableDiffusionTurboImageGenerator,
+            StableDiffusionLightningImageGenerator,
+        ]
     ],
     device: str,
 ):
@@ -101,3 +104,19 @@ def test_cuda_sdxl_turbo_image_generator():
 )
 def test_cpu_sdxl_turbo_image_generator():
     _check_image_generator(StableDiffusionTurboImageGenerator, "cpu")
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25,
+    reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD",
+)
+def test_cuda_sdxl_lightning_image_generator():
+    _check_image_generator(StableDiffusionLightningImageGenerator, "cuda")
+
+
+@pytest.mark.skipif(
+    total_memory < 16 or total_disk_space < 25,
+    reason="Test requires at least 16GB of RAM and 25GB of HDD",
+)
+def test_cpu_sdxl_lightning_image_generator():
+    _check_image_generator(StableDiffusionLightningImageGenerator, "cpu")