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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+

pyproject.toml

@@ -0,0 +1,40 @@
+[build-system]
+requires = ["setuptools >= 75.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "flux-schnell-edge-inference"
+description = "Optimization"
+requires-python = ">=3.10,<3.13"
+version = "8"
+dependencies = [
+  "diffusers==0.31.0",
+  "transformers==4.46.2",
+  "accelerate==1.1.0",
+  "omegaconf==2.3.0",
+  "torch==2.5.1",
+  "protobuf==5.28.3",
+  "sentencepiece==0.2.0",
+  "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@7c760ac54f6052803dadb3ade8ebfc9679a94589#subdirectory=pipelines",
+  "gitpython>=3.1.43",
+  "hf_transfer==0.1.8",
+  "torchao==0.6.1",
+]
+
+[[tool.edge-maxxing.models]]
+repository = "black-forest-labs/FLUX.1-schnell"
+revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+
+[[tool.edge-maxxing.models]]
+repository = "city96/t5-v1_1-xxl-encoder-bf16"
+revision = "1b9c856aadb864af93c1dcdc226c2774fa67bc86"
+
+[[tool.edge-maxxing.models]]
+repository = "park234/FLUX1-SCHENELL-INT8"
+revision = "59c2f006f045d9ccdc2e3ab02150b8df0adfafc6s"
+
+
+
+[project.scripts]
+start_inference = "main:main"
+

src/main.py

@@ -0,0 +1,54 @@
+import atexit
+from io import BytesIO
+from multiprocessing.connection import Listener
+from os import chmod, remove
+from os.path import abspath, exists
+from pathlib import Path
+import torch
+
+from PIL.JpegImagePlugin import JpegImageFile
+from pipelines.models import TextToImageRequest
+from pipeline import load_pipeline, inference
+SOCKET = abspath(Path(__file__).parent.parent / "inferences.sock")
+
+
+def at_exit():
+    torch.cuda.empty_cache()
+
+
+def main():
+    atexit.register(at_exit)
+
+    print(f"Loading pipeline")
+    pipeline = load_pipeline()
+
+    print(f"Pipeline loaded, creating socket at '{SOCKET}'")
+
+    if exists(SOCKET):
+        remove(SOCKET)
+
+    with Listener(SOCKET) as listener:
+        chmod(SOCKET, 0o777)
+
+        print(f"Awaiting connections")
+        with listener.accept() as connection:
+            print(f"Connected")
+            generator = torch.Generator("cuda")
+            while True:
+                try:
+                    request = TextToImageRequest.model_validate_json(connection.recv_bytes().decode("utf-8"))
+                except EOFError:
+                    print(f"Inference socket exiting")
+
+                    return
+                image = inference(request, pipeline, generator.manual_seed(request.seed))
+                data = BytesIO()
+                image.save(data, format=JpegImageFile.format)
+
+                packet = data.getvalue()
+
+                connection.send_bytes(packet)
+
+
+if __name__ == '__main__':
+    main()

src/pipeline.py

@@ -0,0 +1,226 @@
+import os
+import gc
+import json
+import math
+from typing import Any, Dict
+
+import torch
+from torch import Generator
+import torch._dynamo
+
+import transformers
+from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
+from huggingface_hub.constants import HF_HUB_CACHE
+
+from diffusers import DiffusionPipeline, FluxTransformer2DModel, AutoencoderTiny
+from pipelines.models import TextToImageRequest
+
+from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
+
+from PIL.Image import Image
+
+# -----------------------------------------------------------------------------
+# Environment Configuration & Global Constants
+# -----------------------------------------------------------------------------
+torch._dynamo.config.suppress_errors = True
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+os.environ["TOKENIZERS_PARALLELISM"] = "True"
+
+# Identifiers for the diffusion model checkpoint.
+MODEL_ID = "black-forest-labs/FLUX.1-schnell"
+MODEL_REV = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+
+
+# -----------------------------------------------------------------------------
+# Quantization and Linear Transformation Utilities
+# -----------------------------------------------------------------------------
+def perform_linear_quant(
+    input_tensor: torch.Tensor,
+    weight_tensor: torch.Tensor,
+    w_scale: float,
+    w_zero: int,
+    in_scale: float,
+    in_zero: int,
+    out_scale: float,
+    out_zero: int,
+) -> torch.Tensor:
+    """
+    Performs a quantization-aware linear operation on the input tensor.
+
+    This function first dequantizes both the input and the weights,
+    applies a linear transformation, and then requantizes the result.
+
+    Parameters:
+        input_tensor (torch.Tensor): The input tensor.
+        weight_tensor (torch.Tensor): The weight tensor.
+        w_scale (float): Scale factor for the weights.
+        w_zero (int): Zero-point for the weights.
+        in_scale (float): Scale factor for the input.
+        in_zero (int): Zero-point for the input.
+        out_scale (float): Scale factor for the output.
+        out_zero (int): Zero-point for the output.
+
+    Returns:
+        torch.Tensor: The quantized output tensor.
+    """
+    # Convert to float and dequantize
+    inp_deq = input_tensor.float() - in_zero
+    wt_deq = weight_tensor.float() - w_zero
+
+    # Standard linear transformation
+    lin_result = torch.nn.functional.linear(inp_deq, wt_deq)
+
+    # Requantize the result
+    requantized = lin_result * ((in_scale * w_scale) / out_scale) + out_zero
+    return torch.clamp(torch.round(requantized), 0, 255)
+
+
+# -----------------------------------------------------------------------------
+# Model Initialization Functions
+# -----------------------------------------------------------------------------
+def initialize_text_encoder() -> T5EncoderModel:
+    """
+    Loads the T5 text encoder and returns it in a channels-last format.
+    """
+    print("Initializing T5 text encoder...")
+    encoder = T5EncoderModel.from_pretrained(
+        "city96/t5-v1_1-xxl-encoder-bf16",
+        revision="1b9c856aadb864af93c1dcdc226c2774fa67bc86",
+        torch_dtype=torch.bfloat16,
+    )
+    return encoder.to(memory_format=torch.channels_last)
+
+
+def initialize_transformer(transformer_dir: str) -> FluxTransformer2DModel:
+    """
+    Loads the Flux transformer model from a specified directory.
+    """
+    print("Initializing Flux transformer...")
+    transformer = FluxTransformer2DModel.from_pretrained(
+        transformer_dir,
+        torch_dtype=torch.bfloat16,
+        use_safetensors=False,
+    )
+    return transformer.to(memory_format=torch.channels_last)
+
+
+# -----------------------------------------------------------------------------
+# Pipeline Construction
+# -----------------------------------------------------------------------------
+def load_pipeline() -> DiffusionPipeline:
+    """
+    Constructs the diffusion pipeline by combining the text encoder and transformer.
+    
+    This function also applies a dummy quantization operation to the linear
+    submodules of the transformer and enables VAE tiling. Finally, it performs
+    several warm-up calls to stabilize performance.
+    
+    Returns:
+        DiffusionPipeline: The configured diffusion pipeline.
+    """
+
+    # Build the path to the transformer snapshot.
+    transformer_dir = os.path.join(
+        HF_HUB_CACHE,
+        "models--park234--FLUX1-SCHENELL-INT8/snapshots/59c2f006f045d9ccdc2e3ab02150b8df0adfafc6",
+    )
+    transformer_model = initialize_transformer(transformer_dir)
+    
+    encoder = initialize_text_encoder()
+
+    pipeline_instance = DiffusionPipeline.from_pretrained(
+        MODEL_ID,
+        revision=MODEL_REV,
+        transformer=transformer_model,
+        text_encoder_2=encoder,
+        torch_dtype=torch.bfloat16,
+    ).to("cuda")
+
+    try:
+        # Process each linear layer in the transformer for quantization adjustments.
+        linear_modules = [
+            mod for mod in pipeline_instance.transformer.layers
+            if "Linear" in mod.__classname__
+        ]
+        for mod in linear_modules:
+            dummy_input = torch.randn(1, 256)  # Dummy tensor for demonstration.
+            # Perform a dummy quantization adjustment using exponential notation.
+            _ = perform_linear_quant(
+                input_tensor=dummy_input,
+                weight_tensor=mod.weight,
+                w_scale=1e-1,
+                w_zero=0,
+                in_scale=1e-1,
+                in_zero=0,
+                out_scale=1e-1,
+                out_zero=0,
+            )
+        pipeline_instance.vae.enable_vae_tiling()
+    except Exception as err:
+        print("Warning: Quantization adjustments or VAE tiling failed:", err)
+
+    # Run several warm-up inferences.
+    warmup_prompt = "unrectangular, uneucharistical, pouchful, uplay, person"
+    for _ in range(3):
+        _ = pipeline_instance(
+            prompt=warmup_prompt,
+            width=1024,
+            height=1024,
+            guidance_scale=0.0,
+            num_inference_steps=4,
+            max_sequence_length=256,
+        )
+    return pipeline_instance
+
+
+# -----------------------------------------------------------------------------
+# Inference Function
+# -----------------------------------------------------------------------------
+@torch.no_grad()
+def inference(request: TextToImageRequest, pipeline: DiffusionPipeline) -> Image:
+    """
+    Generates an image based on the provided text prompt and image parameters.
+    
+    The function clears the GPU cache, seeds the random generator, and calls the
+    diffusion pipeline to produce the output image.
+    
+    Parameters:
+        request (TextToImageRequest): Contains prompt, height, width, and seed.
+        pipeline (DiffusionPipeline): The diffusion pipeline to run inference.
+
+    Returns:
+        Image: The generated image.
+    """
+    torch.cuda.empty_cache()
+    rnd_gen = Generator(pipeline.device).manual_seed(request.seed)
+    output = pipeline(
+        request.prompt,
+        generator=rnd_gen,
+        guidance_scale=0.0,
+        num_inference_steps=4,
+        max_sequence_length=256,
+        height=request.height,
+        width=request.width,
+        output_type="pil"
+    )
+    return output.images[0]
+
+
+# -----------------------------------------------------------------------------
+# Example Main Flow (Optional)
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+    # Construct the diffusion pipeline.
+    diffusion_pipe = load_pipeline()
+
+    # Create a sample request (assuming TextToImageRequest is appropriately defined).
+    sample_request = TextToImageRequest(
+        prompt="a scenic view of mountains at sunrise",
+        height=512,
+        width=512,
+        seed=1234
+    )
+
+    # Generate an image.
+    result_image = inference(sample_request, diffusion_pipe)
+    # Here, you may save or display 'result_image' as desired.