Initial commit with folder contents

src/pipeline.py

@@ -1,17 +1,20 @@
+import os
+from typing import Type
+
 import torch
 import torch._dynamo
-import os
 import torch.nn.functional as F
 from PIL import Image
-from pipelines.models import TextToImageRequest
 from torch import Generator
-from typing import Type
 from diffusers import DiffusionPipeline, FluxTransformer2DModel
 from huggingface_hub.constants import HF_HUB_CACHE
 from transformers import T5EncoderModel
+from pipelines.models import TextToImageRequest
 
-os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
+# Configure environment variables
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = "expandable_segments:True"
 os.environ["TOKENIZERS_PARALLELISM"] = "True"
+
 torch._dynamo.config.suppress_errors = True
 torch.backends.cuda.matmul.allow_tf32 = True
 torch.backends.cudnn.enabled = True
@@ -19,18 +22,81 @@ torch.backends.cudnn.enabled = True
 Pipeline = None
 
 def load_pipeline() -> Pipeline:
+    """
+    Load and initialize the Diffusion Pipeline with custom components.
+
+    Returns:
+        Pipeline: Initialized diffusion pipeline.
+    """
+    # Configuration for model checkpoints
     ckpt_id = "black-forest-labs/FLUX.1-schnell"
     ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
-    text_encoder_2 = T5EncoderModel.from_pretrained("VictorTn/extra0izer0", revision = "ea3cc69c8eba166304100f994e9b6ec9f5179a9d", subfolder="text_encoder_2",torch_dtype=torch.bfloat16)
-    path = os.path.join(HF_HUB_CACHE, "models--VictorTn--extra0izer0/snapshots/ea3cc69c8eba166304100f994e9b6ec9f5179a9d/transformer")
-    transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False)
-    pipeline = DiffusionPipeline.from_pretrained(ckpt_id, revision=ckpt_revision, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,)
+
+    # Load the secondary text encoder
+    text_encoder_2 = T5EncoderModel.from_pretrained(
+        "VictorTn/extra0izer0",
+        revision="ea3cc69c8eba166304100f994e9b6ec9f5179a9d",
+        subfolder="text_encoder_2",
+        torch_dtype=torch.bfloat16
+    )
+
+    # Load the transformer model
+    transformer_path = os.path.join(
+        HF_HUB_CACHE,
+        "models--VictorTn--extra0izer0/snapshots/ea3cc69c8eba166304100f994e9b6ec9f5179a9d/transformer"
+    )
+    transformer = FluxTransformer2DModel.from_pretrained(
+        transformer_path,
+        torch_dtype=torch.bfloat16,
+        use_safetensors=False
+    )
+
+    # Initialize the diffusion pipeline
+    pipeline = DiffusionPipeline.from_pretrained(
+        ckpt_id,
+        revision=ckpt_revision,
+        transformer=transformer,
+        text_encoder_2=text_encoder_2,
+        torch_dtype=torch.bfloat16
+    )
+
+    # Move pipeline to GPU and optimize memory format
     pipeline.to("cuda")
     pipeline.to(memory_format=torch.channels_last)
+
+    # Perform a warm-up run
     with torch.inference_mode():
-        pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
+        pipeline(
+            prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus",
+            width=1024,
+            height=1024,
+            guidance_scale=0.0,
+            num_inference_steps=4,
+            max_sequence_length=256
+        )
+
     return pipeline
 
 @torch.no_grad()
 def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image:
-    return pipeline(request.prompt,generator=generator, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, height=request.height, width=request.width, output_type="pil").images[0]
+    """
+    Perform inference using the provided diffusion pipeline.
+
+    Args:
+        request (TextToImageRequest): The text-to-image request containing prompt and image dimensions.
+        pipeline (Pipeline): The initialized diffusion pipeline.
+        generator (Generator): Random generator for reproducibility.
+
+    Returns:
+        Image: Generated PIL image.
+    """
+    return pipeline(
+        request.prompt,
+        generator=generator,
+        guidance_scale=0.0,
+        num_inference_steps=4,
+        max_sequence_length=256,
+        height=request.height,
+        width=request.width,
+        output_type="pil"
+    ).images[0]