Upload folder using huggingface_hub

pyproject.toml

@@ -15,23 +15,19 @@ dependencies = [
   "torch==2.5.1",
   "protobuf==5.28.3",
   "sentencepiece==0.2.0",
+  "torchao==0.6.1",
   "hf_transfer==0.1.8",
   "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@7c760ac54f6052803dadb3ade8ebfc9679a94589#subdirectory=pipelines",
-  "torchao>=0.6.1",
-  "ipython>=8.29.0",
-  "setuptools >= 75.0",
-  "optimum-quanto>=0.2.6",
-  "torch-tensorrt>=2.5.0",
-  "torchvision>=0.20.1",
 ]
 
 [[tool.edge-maxxing.models]]
 repository = "black-forest-labs/FLUX.1-schnell"
 revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+exclude = ["transformer"]
 
 [[tool.edge-maxxing.models]]
-repository = "madebyollin/taef1"
-revision = "2d552378e58c9c94201075708d7de4e1163b2689"
+repository = "RobertML/FLUX.1-schnell-int8wo"
+revision = "307e0777d92df966a3c0f99f31a6ee8957a9857a"
 
 [project.scripts]
 start_inference = "main:main"

src/pipeline.py

@@ -4,45 +4,65 @@ from typing import TypeAlias
 
 import torch
 from PIL.Image import Image
-from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, AutoencoderTiny
+from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL
 from huggingface_hub.constants import HF_HUB_CACHE
 from pipelines.models import TextToImageRequest
 from torch import Generator
-from torchao.quantization import quantize_, int8_weight_only
 from transformers import T5EncoderModel, CLIPTextModel
-from time import perf_counter
-
 
 Pipeline: TypeAlias = FluxPipeline
 
-
 CHECKPOINT = "black-forest-labs/FLUX.1-schnell"
 REVISION = "741f7c3ce8b383c54771c7003378a50191e9efe9"
 
-TinyVAE = "madebyollin/taef1"
-TinyVAE_REV = "2d552378e58c9c94201075708d7de4e1163b2689"
 
+def load_pipeline() -> Pipeline:
+    text_encoder = CLIPTextModel.from_pretrained(
+        CHECKPOINT,
+        revision=REVISION,
+        subfolder="text_encoder",
+        local_files_only=True,
+        torch_dtype=torch.bfloat16,
+    )
 
+    text_encoder_2 = T5EncoderModel.from_pretrained(
+        CHECKPOINT,
+        revision=REVISION,
+        subfolder="text_encoder_2",
+        local_files_only=True,
+        torch_dtype=torch.bfloat16,
+    )
 
-def load_pipeline() -> Pipeline:
+    vae = AutoencoderKL.from_pretrained(
+        CHECKPOINT,
+        revision=REVISION,
+        subfolder="vae",
+        local_files_only=True,
+        torch_dtype=torch.bfloat16,
+    )
+
+    path = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a")
+
+    transformer = FluxTransformer2DModel.from_pretrained(
+        path,
+        torch_dtype=torch.bfloat16,
+        use_safetensors=False,
+    )
 
     pipeline = FluxPipeline.from_pretrained(
         CHECKPOINT,
         revision=REVISION,
         local_files_only=True,
+        text_encoder=text_encoder,
+        text_encoder_2=text_encoder_2,
+        transformer=transformer,
+        vae=vae,
         torch_dtype=torch.bfloat16,
-    )
-    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) 
-    quantize_(pipeline.transformer, int8_weight_only(), device="cuda")
-    pipeline.to("cuda")
-    pipeline("cat")
-#    for _ in range(1):
-#        start = perf_counter()
-#        pipeline("cat", num_inference_steps=4)
-#        stop = perf_counter()
-#        print(f"Time: {stop - start}s")
+    ).to("cuda")
+    pipeline.enable_vae_slicing()
+    pipeline.enable_vae_tiling()
+
+    pipeline("")
 
     return pipeline
 
@@ -62,21 +82,3 @@ def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
         height=request.height,
         width=request.width,
     ).images[0]
-
-
-if __name__ == "__main__":
-    from time import perf_counter
-    PROMPT = 'martyr, semiconformity, peregrination, quip, twineless, emotionless, tawa, depickle'
-    request = TextToImageRequest(prompt=PROMPT, 
-            height=None,
-            width=None,
-            seed=666)
-    start_time = perf_counter()
-    pipe_ = load_pipeline()
-    stop_time = perf_counter()
-    print(f"Pipeline is loaded in {stop_time - start_time}s")
-    for _ in range(4):
-        start_time = perf_counter()
-        infer(request, pipe_)
-        stop_time = perf_counter()
-        print(f"Request in {stop_time - start_time}s")

uv.lock

@@ -37,18 +37,6 @@ version = "4.9.3"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
 
-[[package]]
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-version = "2.4.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "six" },
-]
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-
 [[package]]
 name = "certifi"
 version = "2024.8.30"
@@ -121,15 +109,6 @@ wheels = [
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 ]
 
-[[package]]
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-version = "5.1.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/66/0c/8d907af351aa16b42caae42f9d6aa37b900c67308052d10fdce809f8d952/decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330", size = 35016 }
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-]
-
 [[package]]
 name = "diffusers"
 version = "0.31.0"
@@ -157,24 +136,6 @@ dependencies = [
     { name = "pydantic" },
 ]
 
-[[package]]
-name = "exceptiongroup"
-version = "1.2.2"
-source = { registry = "https://pypi.org/simple" }
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-]
-
-[[package]]
-name = "executing"
-version = "2.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/8c/e3/7d45f492c2c4a0e8e0fad57d081a7c8a0286cdd86372b070cca1ec0caa1e/executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab", size = 977485 }
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-]
-
 [[package]]
 name = "filelock"
 version = "3.16.1"
@@ -193,12 +154,9 @@ dependencies = [
     { name = "diffusers" },
     { name = "edge-maxxing-pipelines" },
     { name = "hf-transfer" },
-    { name = "ipython" },
     { name = "omegaconf" },
-    { name = "optimum-quanto" },
     { name = "protobuf" },
     { name = "sentencepiece" },
-    { name = "setuptools" },
     { name = "torch" },
     { name = "torchao" },
     { name = "transformers" },
@@ -210,14 +168,11 @@ requires-dist = [
     { name = "diffusers", specifier = "==0.31.0" },
     { name = "edge-maxxing-pipelines", git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=7c760ac54f6052803dadb3ade8ebfc9679a94589#7c760ac54f6052803dadb3ade8ebfc9679a94589" },
     { name = "hf-transfer", specifier = "==0.1.8" },
-    { name = "ipython", specifier = ">=8.29.0" },
     { name = "omegaconf", specifier = "==2.3.0" },
-    { name = "optimum-quanto", specifier = ">=0.2.6" },
     { name = "protobuf", specifier = "==5.28.3" },
     { name = "sentencepiece", specifier = "==0.2.0" },
-    { name = "setuptools", specifier = ">=75.0" },
     { name = "torch", specifier = "==2.5.1" },
-    { name = "torchao", specifier = ">=0.6.1" },
+    { name = "torchao", specifier = "==0.6.1" },
     { name = "transformers", specifier = "==4.46.2" },
 ]
 
@@ -306,40 +261,6 @@ wheels = [
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-[[package]]
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-version = "8.29.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
-    { name = "decorator" },
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "jedi" },
-    { name = "matplotlib-inline" },
-    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "prompt-toolkit" },
-    { name = "pygments" },
-    { name = "stack-data" },
-    { name = "traitlets" },
-    { name = "typing-extensions", marker = "python_full_version < '3.12'" },
-]
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-[[package]]
-name = "matplotlib-inline"
-version = "0.1.7"
-source = { registry = "https://pypi.org/simple" }
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