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.gitattributes

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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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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 by RobertML 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",
+  "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",
+  "setuptools>=75.3.0",
+]
+[[tool.edge-maxxing.models]]
+repository = "manbeast3b/Flux.1.Schnell-full-quant1"
+revision = "e7ddf488a4ea8a3cba05db5b8d06e7e0feb826a2"
+
+[[tool.edge-maxxing.models]]
+repository = "manbeast3b/flux.1-schnell-full1"
+revision = "cb1b599b0d712b9aab2c4df3ad27b050a27ec146"
+
+[project.scripts]
+start_inference = "main:main"
+

src/main.py

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+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
+from git import Repo
+import torch
+
+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 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 = infer(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

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+from diffusers import (
+    DiffusionPipeline,
+    AutoencoderKL,
+    FluxPipeline,
+    FluxTransformer2DModel
+)
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from huggingface_hub.constants import HF_HUB_CACHE
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+    CLIPTokenizer,
+    CLIPTextModel
+)
+import torch
+import torch._dynamo
+import gc
+from PIL import Image
+from pipelines.models import TextToImageRequest
+from torch import Generator
+import time
+import math
+from typing import Type, Dict, Any, Tuple, Callable, Optional, Union
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torchao.quantization import quantize_, float8_weight_only, int8_dynamic_activation_int4_weight
+
+# preconfigs
+import os
+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
+# torch.backends.cudnn.benchmark = True
+
+# globals
+Pipeline = None
+ckpt_id = "manbeast3b/flux.1-schnell-full1"
+ckpt_revision = "cb1b599b0d712b9aab2c4df3ad27b050a27ec146"
+
+def load_pipeline() -> Pipeline:
+    model_name = "manbeast3b/Flux.1.Schnell-full-quant1"
+    revision = "e7ddf488a4ea8a3cba05db5b8d06e7e0feb826a2"
+
+    # text_encoder_2 = T5EncoderModel.from_pretrained(
+    #     model_name,
+    #     revision=text_enc_revision,
+    #     subfolder="text_encoder_2",
+    #     torch_dtype=torch.bfloat16
+    # ).to(memory_format=torch.channels_last)
+    
+    # vae = AutoencoderKL.from_pretrained(
+    #     ckpt_id,
+    #     revision=ckpt_revision,
+    #     subfolder="vae",
+    #     local_files_only=True,
+    #     torch_dtype=torch.bfloat16
+    # ).to(memory_format=torch.channels_last)
+    
+    hub_model_dir = os.path.join(
+        HF_HUB_CACHE,
+        f"models--{model_name.replace('/', '--')}",
+        "snapshots",
+        revision,
+        "transformer"
+    )
+    transformer = FluxTransformer2DModel.from_pretrained(
+        hub_model_dir,
+        torch_dtype=torch.bfloat16,
+        use_safetensors=False
+    ).to(memory_format=torch.channels_last)
+    
+    pipeline = FluxPipeline.from_pretrained(
+        ckpt_id,
+        revision=ckpt_revision,
+        # text_encoder_2=text_encoder_2,
+        transformer=transformer,
+        # vae=vae,
+        torch_dtype=torch.bfloat16
+    )
+    # pipeline.vae = torch.compile(vae)
+    pipeline.to("cuda")
+    pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
+    quantize_(pipeline.vae, int8_dynamic_activation_int4_weight())
+    
+    warmup_ = "controllable varied focus thai warriors entertainment blue golden pink soft tough padthai"
+    for _ in range(1):
+        pipeline(
+            prompt=warmup_,
+            width=1024,
+            height=1024,
+            guidance_scale=0.0,
+            num_inference_steps=4,
+            max_sequence_length=256
+        )
+    return pipeline
+
+sample = 1
+@torch.no_grad()
+def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image:
+    global sample
+    if not sample:
+        sample=1
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+        
+    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]