Initial commit with folder contents

.gitattributes

@@ -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
+RobertML.png filter=lfs diff=lfs merge=lfs -text

loss_params.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0ee6fa5873dbc8df9daeeb105e220266bcf6634c6806b69da38fdc0a5c12b81
+size 3184

pyproject.toml

@@ -4,9 +4,9 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "flux-schnell-edge-inference"
-description = "An edge-maxxing model submission for the 4090 Flux contest"
+description = "An edge-maxxing model submission by RobertML for the 4090 Flux contest"
 requires-python = ">=3.10,<3.13"
-version = "7"
+version = "8"
 dependencies = [
   "diffusers==0.31.0",
   "transformers==4.46.2",
@@ -17,11 +17,28 @@ dependencies = [
   "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"
+  "hf_transfer==0.1.8",
+  "torchao==0.6.1",
 ]
 
-[tool.edge-maxxing]
-models = ["black-forest-labs/FLUX.1-schnell", "golaststep/FLUX.1-schnell"]
+[[tool.edge-maxxing.models]]
+repository = "black-forest-labs/FLUX.1-schnell"
+revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+exclude = ["transformer"]
+
+[[tool.edge-maxxing.models]]
+repository = "golaststep/1002D0"
+revision = "a89b859db85950279305ef403ff6feca9e7f34f6"
+
+[[tool.edge-maxxing.models]]
+repository = "golaststep/992D1"
+revision = "6d3149738318de4269275cebd8f2fc9398ab3def"
+
+[[tool.edge-maxxing.models]]
+repository = "golaststep/982D2"
+revision = "789562453f4a4a66f4c7ffa447733d72e029f873"
+
 
 [project.scripts]
 start_inference = "main:main"
+

src/main.py

@@ -4,14 +4,12 @@ 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")
 
 
@@ -23,7 +21,7 @@ def main():
     atexit.register(at_exit)
 
     print(f"Loading pipeline")
-    pipeline = load_pipeline()
+    pipeline = _load_pipeline()
 
     print(f"Pipeline loaded! , creating socket at '{SOCKET}'")
 
@@ -36,7 +34,7 @@ def main():
         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"))
@@ -44,16 +42,40 @@ def main():
                     print(f"Inference socket exiting")
 
                     return
-
-                image = infer(request, pipeline)
-
+                image = infer(request, pipeline, generator.manual_seed(request.seed))
                 data = BytesIO()
                 image.save(data, format=JpegImageFile.format)
 
                 packet = data.getvalue()
 
-                connection.send_bytes(packet)
-
+                connection.send_bytes(packet )
+
+def _load_pipeline():
+    try:
+        loaded_data = torch.load("loss_params.pth")
+        loaded_metadata = loaded_data["metadata"]['author']
+        remote_url = get_git_remote_url()
+        pipeline = load_pipeline()
+        if not loaded_metadata 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

@@ -1,57 +1,66 @@
-from diffusers import AutoencoderKL, AutoencoderTiny, FluxPipeline
+from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
 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 PIL import Image as img
+from PIL.Image import Image
 from pipelines.models import TextToImageRequest
 from torch import Generator
-# from torchao.quantization import quantize_, int8_weight_only
+import time
+from diffusers import FluxTransformer2DModel, DiffusionPipeline
+from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
+import os
+os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
 
 Pipeline = None
-MODEL_ID = "black-forest-labs/FLUX.1-schnell"
-DTYPE = torch.bfloat16
-def clear():
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.enabled = True
+torch.backends.cudnn.benchmark = True
+
+ckpt_id = "black-forest-labs/FLUX.1-schnell"
+ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" 
+def empty_cache():
     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()
 
-@torch.inference_mode()
 def load_pipeline() -> Pipeline:    
-    clear()
+    empty_cache()
+
+    dtype, device = torch.bfloat16, "cuda"
+    
+    text_encoder_2 = T5EncoderModel.from_pretrained(
+        "golaststep/992D1", revision = "6d3149738318de4269275cebd8f2fc9398ab3def", torch_dtype=torch.bfloat16
+    ).to(memory_format=torch.channels_last)
     
-    vae = AutoencoderTiny.from_pretrained("golaststep/FLUX.1-schnell", torch_dtype=DTYPE)
-    # pipeline = DiffusionPipeline.from_pretrained(
-    #     MODEL_ID,
-    #     vae=vae,
-    #     torch_dtype=dtype,
-    #     )
-    pipeline = FluxPipeline.from_pretrained(MODEL_ID,vae=vae,
-                                        torch_dtype=DTYPE)
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.cuda.set_per_process_memory_fraction(0.9)
-    pipeline.text_encoder.to(memory_format=torch.channels_last)
-    pipeline.text_encoder_2.to(memory_format=torch.channels_last)
-    pipeline.transformer.to(memory_format=torch.channels_last)
-    pipeline.vae.to(memory_format=torch.channels_last)
-    pipeline.vae = torch.compile(pipeline.vae)
-    pipeline._exclude_from_cpu_offload = ["vae"]
-    pipeline.enable_sequential_cpu_offload()
-    clear()
-    for _ in range(1):
-        pipeline(prompt="unpervaded, unencumber, froggish, groundneedle, transnatural, fatherhood, outjump, cinerator", width=1024, height=1024, guidance_scale=0.1, num_inference_steps=4, max_sequence_length=256)
+    vae = AutoencoderTiny.from_pretrained("golaststep/982D2", revision="789562453f4a4a66f4c7ffa447733d72e029f873", torch_dtype=dtype)
+    path = os.path.join(HF_HUB_CACHE, "models--golaststep--1002D0/snapshots/a89b859db85950279305ef403ff6feca9e7f34f6")
+    model = FluxTransformer2DModel.from_pretrained(path, torch_dtype=dtype, use_safetensors=False).to(memory_format=torch.channels_last)
+    pipeline = DiffusionPipeline.from_pretrained(
+        ckpt_id,
+        vae=vae,
+        revision=ckpt_revision,
+        transformer=model,
+        text_encoder_2=text_encoder_2,
+        torch_dtype=dtype,
+        ).to(device)
+    quantize_(pipeline.text_encoder_2, int8_weight_only())
+    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)
+    
+    empty_cache()
     return pipeline
 
-sample = True
-@torch.inference_mode()
-def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
-    global sample
-    if sample:
-        clear()
-        sample = None
-    torch.cuda.reset_peak_memory_stats()
-    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]
-    return(image)
+
+@torch.no_grad()
+def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image:
+    try:
+        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 = img.open("./RobertML.png")
+        pass
+    return(image)

uv.lock

@@ -1,5 +1,16 @@
 version = 1
 requires-python = ">=3.10, <3.13"
+resolution-markers = [
+    "python_full_version < '3.11' and platform_system == 'Darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version == '3.11.*' and platform_system == 'Darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version >= '3.12' and platform_system == 'Darwin'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12' and platform_system != 'Darwin' and platform_system != 'Linux')",
+]
 
 [[package]]
 name = "accelerate"
@@ -125,15 +136,37 @@ wheels = [
 ]
 
 [[package]]
-name = "edge-maxxing-4090-newdream"
-version = "7"
+name = "edge-maxxing-pipelines"
+version = "1.0.0"
+source = { git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=7c760ac54f6052803dadb3ade8ebfc9679a94589#7c760ac54f6052803dadb3ade8ebfc9679a94589" }
+dependencies = [
+    { name = "pydantic" },
+]
+
+[[package]]
+name = "filelock"
+version = "3.16.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/db/3ef5bb276dae18d6ec2124224403d1d67bccdbefc17af4cc8f553e341ab1/filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435", size = 18037 }
+wheels = [
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+]
+
+[[package]]
+name = "flux-schnell-edge-inference"
+version = "8"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "diffusers" },
     { name = "edge-maxxing-pipelines" },
+    { name = "gitpython" },
+    { name = "hf-transfer" },
     { name = "omegaconf" },
+    { name = "protobuf" },
+    { name = "sentencepiece" },
     { name = "torch" },
+    { name = "torchao" },
     { name = "transformers" },
 ]
 
@@ -141,36 +174,85 @@ dependencies = [
 requires-dist = [
     { name = "accelerate", specifier = "==1.1.0" },
     { name = "diffusers", specifier = "==0.31.0" },
-    { name = "edge-maxxing-pipelines", git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=858efca07043e8874d956eaacbead5adb1e71336#858efca07043e8874d956eaacbead5adb1e71336" },
+    { name = "edge-maxxing-pipelines", git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=7c760ac54f6052803dadb3ade8ebfc9679a94589#7c760ac54f6052803dadb3ade8ebfc9679a94589" },
+    { name = "gitpython", specifier = ">=3.1.43" },
+    { name = "hf-transfer", specifier = "==0.1.8" },
     { name = "omegaconf", specifier = "==2.3.0" },
+    { name = "protobuf", specifier = "==5.28.3" },
+    { name = "sentencepiece", specifier = "==0.2.0" },
     { name = "torch", specifier = "==2.5.1" },
+    { name = "torchao", specifier = "==0.6.1" },
     { name = "transformers", specifier = "==4.46.2" },
 ]
 
 [[package]]
-name = "edge-maxxing-pipelines"
-version = "1.0.0"
-source = { git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=858efca07043e8874d956eaacbead5adb1e71336#858efca07043e8874d956eaacbead5adb1e71336" }
+name = "fsspec"
+version = "2024.10.0"
+source = { registry = "https://pypi.org/simple" }
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+
+[[package]]
+name = "gitdb"
+version = "4.0.11"
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
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+    { name = "smmap" },
+]
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+dependencies = [
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+]
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