Update pyproject.toml

pyproject.toml

@@ -1,47 +1,37 @@
-#6
-from huggingface_hub.constants import HF_HUB_CACHE
-from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
-import torch
-import torch._dynamo
-import gc
-import os
-from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
-from PIL.Image import Image
-from pipelines.models import TextToImageRequest
-from torch import Generator
-from diffusers import FluxTransformer2DModel, DiffusionPipeline
-from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
+[build-system]
+requires = ["setuptools >= 75.0"]
+build-backend = "setuptools.build_meta"
 
-os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
-os.environ["TOKENIZERS_PARALLELISM"] = "True"
-torch._dynamo.config.suppress_errors = True
+[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",
+  "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@7c760ac54f6052803dadb3ade8ebfc9679a94589#subdirectory=pipelines",
+]
 
-Pipeline = None
-ids = "black-forest-labs/FLUX.1-schnell"
-Revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+[[tool.edge-maxxing.models]]
+repository = "black-forest-labs/FLUX.1-schnell"
+revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+exclude = ["transformer", "text_encoder_2"]
 
-def load_pipeline() -> Pipeline:
-    vae = AutoencoderTiny.from_pretrained("slobers/tt1",revision="ec746bf42d91e3335760895281f070df54f2196a", torch_dtype=torch.bfloat16,)
-    text_encoder_2 = T5EncoderModel.from_pretrained("city96/t5-v1_1-xxl-encoder-bf16", revision = "1b9c856aadb864af93c1dcdc226c2774fa67bc86", torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last)
-    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).to(memory_format=torch.channels_last)
-    pipeline = DiffusionPipeline.from_pretrained(ids, revision=Revision, vae=vae, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,)
-    pipeline.to("cuda")
+[[tool.edge-maxxing.models]]
+repository = "RobertML/FLUX.1-schnell-int8wo"
+revision = "307e0777d92df966a3c0f99f31a6ee8957a9857a"
 
-    for _ in range(3):
-        pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
-    return pipeline
+[[tool.edge-maxxing.models]]
+repository = "city96/t5-v1_1-xxl-encoder-bf16"
+revision = "1b9c856aadb864af93c1dcdc226c2774fa67bc86"
 
-@torch.no_grad()
-def infer(request: TextToImageRequest, pipeline: Pipeline) -> 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]
+[project.scripts]
+start_inference = "main:main"