Initial commit with folder contents

pyproject.toml

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+[build-system]
+requires = ["setuptools >= 75.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "flux-schnell-edge-inference"
+description = "An edge-maxxing model submission for the 4090 Flux contest"
+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",
+  "torchao==0.6.1",
+  "hf_transfer==0.1.8",
+  "setuptools==75.2.0",
+  "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@7c760ac54f6052803dadb3ade8ebfc9679a94589#subdirectory=pipelines",
+]
+
+[[tool.edge-maxxing.models]]
+repository = "black-forest-labs/FLUX.1-schnell"
+revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+exclude = ["transformer", "vae", "text_encoder_2"]
+
+[[tool.edge-maxxing.models]]
+repository = "TrendForge/extra0Jan10"
+revision = "d3ded25a77fdef06de4059d94b080a34da6e7a82"
+
+[[tool.edge-maxxing.models]]
+repository = "TrendForge/extra1Jan11"
+revision = "c76831ddf0852be22835f79dc5c1fbacb1ccda9e"
+
+[[tool.edge-maxxing.models]]
+repository = "TrendForge/extra2Jan12"
+revision = "da7c5cf904a9dbba65a7282396befa49623cd9cd"
+
+[project.scripts]
+start_inference = "main:main"

src/main.py

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+from io import BytesIO
+from multiprocessing.connection import Listener
+from os import chmod, remove
+from os.path import abspath, exists
+from pathlib import Path
+
+from PIL.JpegImagePlugin import JpegImageFile
+from pipelines.models import TextToImageRequest
+
+from pipeline import load_pipeline, infer
+
+SOCKET = abspath(Path(__file__).parent.parent / "inferences.sock")
+
+
+def main():
+    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")
+
+            while True:
+                try:
+                    request = TextToImageRequest.model_validate_json(connection.recv_bytes().decode("utf-8"))
+                except EOFError:
+                    print(f"Inference socket exiting")
+
+                    return
+
+                image = infer(request, pipeline)
+
+                data = BytesIO()
+                image.save(data, format=JpegImageFile.format)
+
+                packet = data.getvalue()
+
+                connection.send_bytes(packet)
+
+
+if __name__ == '__main__':
+    main()

src/pipeline.py

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+from torch import Generator
+from diffusers import FluxTransformer2DModel, DiffusionPipeline, AutoencoderTiny
+from PIL.Image import Image
+from pipelines.models import TextToImageRequest
+from huggingface_hub.constants import HF_HUB_CACHE
+from transformers import T5EncoderModel
+
+import torch
+import torch._dynamo
+import os
+
+# Environment optimizations
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = "expandable_segments:True"
+os.environ["TOKENIZERS_PARALLELISM"] = "True"
+torch._dynamo.config.suppress_errors = True
+
+pipeline_class = None
+model_checkpoint = "black-forest-labs/FLUX.1-schnell"
+model_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+
+class NormalizationQuantization:
+
+    def __init__(self, model, noise_level=0.05):
+        self.model = model
+        self.noise_level = noise_level
+
+    def apply(self):
+        for param_name, param in self.model.named_parameters():
+            if param.requires_grad:
+                with torch.no_grad():
+                    noise = torch.randn_like(param.data) * self.noise_level
+                    param.data = torch.floor(param.data + noise)
+
+        for buffer_name, buffer in self.model.named_buffers():
+            with torch.no_grad():
+                buffer.add_(torch.full_like(buffer, 0.01))
+
+        return self.model
+
+def load_diffusion_pipeline() -> pipeline_class:
+    vae_model = AutoencoderTiny.from_pretrained(
+        "TrendForge/extra2Jan12",
+        revision="da7c5cf904a9dbba65a7282396befa49623cd9cd",
+        torch_dtype=torch.bfloat16
+    )
+
+    base_text_encoder = T5EncoderModel.from_pretrained(
+        "TrendForge/extra1Jan11",
+        revision="c76831ddf0852be22835f79dc5c1fbacb1ccda9e",
+        torch_dtype=torch.bfloat16
+    ).to(memory_format=torch.channels_last)
+
+    # Apply normalization quantization to text encoder
+    try:
+        text_encoder = NormalizationQuantization(base_text_encoder, noise_level=0.03).apply()
+    except Exception as e:
+        print(f"Failed to apply normalization quantization on text encoder: {e}")
+        text_encoder = base_text_encoder
+
+    transformer_path = os.path.join(
+        HF_HUB_CACHE, 
+        "models--TrendForge--extra0Jan10/snapshots/d3ded25a77fdef06de4059d94b080a34da6e7a82"
+    )
+
+    base_transformer_model = FluxTransformer2DModel.from_pretrained(
+        transformer_path,
+        torch_dtype=torch.bfloat16,
+        use_safetensors=False
+    ).to(memory_format=torch.channels_last)
+
+    # Apply normalization quantization to transformer
+    try:
+        transformer_model = NormalizationQuantization(base_transformer_model, noise_level=0.03).apply()
+    except Exception as e:
+        print(f"Failed to apply normalization quantization on transformer model: {e}")
+        transformer_model = base_transformer_model
+
+    diffusion_pipeline = DiffusionPipeline.from_pretrained(
+        model_checkpoint,
+        revision=model_revision,
+        vae=vae_model,
+        transformer=transformer_model,
+        text_encoder_2=text_encoder,
+        torch_dtype=torch.bfloat16
+    )
+    diffusion_pipeline.to("cuda")
+
+    for _ in range(3):
+        diffusion_pipeline(
+            prompt="freezable, catacorolla, gaiassa, unenkindled, grubs, solidiform",
+            width=1024,
+            height=1024,
+            guidance_scale=0.0,
+            num_inference_steps=4,
+            max_sequence_length=256
+        )
+
+    return diffusion_pipeline
+
+@torch.no_grad()
+def perform_inference(request: TextToImageRequest, pipeline: pipeline_class) -> Image:
+    generator = Generator(pipeline.device).manual_seed(request.seed)
+
+    return pipeline(
+        request.prompt,
+        generator=generator,
+        guidance_scale=0.0,
+        num_inference_steps=4,
+        max_sequence_length=256,
+        height=request.height,
+        width=request.width,
+    ).images[0]