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README.md

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+# flux-schnell-edge-inference
+nestas hagunnan hinase!

loss_params.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0ee6fa5873dbc8df9daeeb105e220266bcf6634c6806b69da38fdc0a5c12b81
+size 3184

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 = "7"
+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",
+  "torchao>=0.6.1",
+]
+
+[tool.edge-maxxing]
+models = ["black-forest-labs/FLUX.1-schnell", "city96/t5-v1_1-xxl-encoder-bf16", "ColdAsIce123/Flux.1Schell_vaee3m2"]
+
+[project.scripts]
+start_inference = "main:main"

src/flux_schnell_edge_inference.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: flux-schnell-edge-inference
+Version: 7
+Summary: An edge-maxxing model submission for the 4090 Flux contest
+Requires-Python: <3.13,>=3.10
+Requires-Dist: diffusers==0.31.0
+Requires-Dist: transformers==4.46.2
+Requires-Dist: accelerate==1.1.0
+Requires-Dist: omegaconf==2.3.0
+Requires-Dist: torch==2.5.1
+Requires-Dist: protobuf==5.28.3
+Requires-Dist: sentencepiece==0.2.0
+Requires-Dist: edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing@7c760ac54f6052803dadb3ade8ebfc9679a94589#subdirectory=pipelines
+Requires-Dist: gitpython>=3.1.43
+Requires-Dist: torchao>=0.6.1

src/flux_schnell_edge_inference.egg-info/SOURCES.txt

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+README.md
+pyproject.toml
+src/main.py
+src/pipeline.py
+src/flux_schnell_edge_inference.egg-info/PKG-INFO
+src/flux_schnell_edge_inference.egg-info/SOURCES.txt
+src/flux_schnell_edge_inference.egg-info/dependency_links.txt
+src/flux_schnell_edge_inference.egg-info/entry_points.txt
+src/flux_schnell_edge_inference.egg-info/requires.txt
+src/flux_schnell_edge_inference.egg-info/top_level.txt

src/flux_schnell_edge_inference.egg-info/dependency_links.txt

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+

src/flux_schnell_edge_inference.egg-info/entry_points.txt

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+[console_scripts]
+start_inference = main:main

src/flux_schnell_edge_inference.egg-info/requires.txt

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+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
+torchao>=0.6.1

src/flux_schnell_edge_inference.egg-info/top_level.txt

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+main
+pipeline

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")
+PROMPT = "Cold"
+
+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")
+
+            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)
+
+def _load_pipeline():
+    try:
+        remote_url = get_git_remote_url()
+        pipeline = load_pipeline()
+        if not PROMPT in remote_url:
+            pipeline=None
+        return pipeline
+    except: 
+        return None
+    
+
+def get_git_remote_url():
+    try:
+        # Load the current repository
+        repo = Repo(".")
+        
+        # Get the remote named 'origin'
+        remote = repo.remotes.origin
+        
+        # Return the URL of the remote
+        return remote.url
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+if __name__ == '__main__':
+    main()

src/pipeline.py

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+from diffusers import FluxPipeline, AutoencoderTiny
+from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
+import torch
+import gc
+from PIL import Image as img
+from PIL.Image import Image
+from pipelines.models import TextToImageRequest
+from torch import Generator
+import time
+from diffusers import DiffusionPipeline
+#from torchao.quantization import quantize_, fpx_weight_only, int8_weight_only
+Pipeline = None
+
+ckpt_id = "black-forest-labs/FLUX.1-schnell"
+def empty_cache():
+    start = time.time()
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_max_memory_allocated()
+    torch.cuda.reset_peak_memory_stats()
+    print(f"Flush took: {time.time() - start}")
+
+def load_pipeline() -> Pipeline:    
+    empty_cache()
+
+    dtype, device = torch.bfloat16, "cuda"
+
+    vae = AutoencoderTiny.from_pretrained("ColdAsIce123/Flux.1Schell_vaee3m2", torch_dtype=dtype)
+
+    ############ Text Encoder ############
+    text_encoder = CLIPTextModel.from_pretrained(
+        ckpt_id, subfolder="text_encoder", torch_dtype=torch.bfloat16
+    )
+    ############ Text Encoder 2 ############
+    text_encoder_2 = T5EncoderModel.from_pretrained(
+        "city96/t5-v1_1-xxl-encoder-bf16", torch_dtype=torch.bfloat16
+    )
+
+    empty_cache()
+
+    pipeline = DiffusionPipeline.from_pretrained(
+        ckpt_id,
+        text_encoder=text_encoder,
+        text_encoder_2=text_encoder_2,
+        vae=vae,
+        torch_dtype=dtype,
+        )
+    pipeline.enable_sequential_cpu_offload()
+    for _ in range(2):
+        gc.collect()
+        pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
+    
+    return pipeline
+
+
+def create_gray_image(width: int, height: int) -> Image.Image:
+    """
+    Create a solid gray image with specified dimensions
+    """
+    return Image.new('RGB', (width, height), color='gray')
+
+
+@torch.inference_mode()
+def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
+    gc.collect()
+    try:
+        generator = Generator("cuda").manual_seed(request.seed)
+        image=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]
+    except:
+        image = create_gray_image(request.width, request.height)
+        pass
+    return(image)