Initial commit with folder contents

pyproject.toml

@@ -17,11 +17,11 @@ 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",
+  "torchao>=0.6.1"
 ]
 
 [tool.edge-maxxing]
-models = ["black-forest-labs/FLUX.1-schnell", "RobertML/FLUX.1-schnell-int8wo"]
+models = ["black-forest-labs/FLUX.1-schnell"]
 
 [project.scripts]
 start_inference = "main:main"

src/main.py

@@ -4,7 +4,7 @@ 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
@@ -23,7 +23,7 @@ def main():
     atexit.register(at_exit)
 
     print(f"Loading pipeline")
-    pipeline = _load_pipeline()
+    pipeline = load_pipeline()
 
     print(f"Pipeline loaded! , creating socket at '{SOCKET}'")
 
@@ -54,32 +54,6 @@ def main():
 
                 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

@@ -1,56 +1,71 @@
-from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
+from diffusers import AutoencoderKL
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-
-from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
 import torch
 import torch._dynamo
 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 FluxTransformer2DModel, DiffusionPipeline
+from diffusers import DiffusionPipeline
 from torchao.quantization import quantize_, int8_weight_only
-#from torchao.quantization import autoquant
-Pipeline = None
 
-ckpt_id = "black-forest-labs/FLUX.1-schnell"
-def empty_cache():
-    start = time.time()
+Pipeline = None
+MODEL_ID = "black-forest-labs/FLUX.1-schnell"
+def clear():
     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()
+class BasicQuantization:
+    def __init__(self, bits=1):
+        self.bits = bits
+        self.qmin = -(2**(bits-1))
+        self.qmax = 2**(bits-1) - 1
+    def quantize_tensor(self, tensor):
+        scale = (tensor.max() - tensor.min()) / (self.qmax - self.qmin)
+        zero_point = self.qmin - torch.round(tensor.min() / scale)
+        qtensor = torch.round(tensor / scale + zero_point) #q
+        qtensor = torch.clamp(qtensor, self.qmin, self.qmax)
+        tensor_q = (qtensor - zero_point) * scale #d
+        return tensor_q, scale, zero_point
+class SDXLQuantization:
+    def __init__(self, model, bit_number=16):
+        self.model = model
+        self.quant = BasicQuantization(bit_number) 
+    def quantize_model(self, save_name=None):
+        quantized_layers_state = {}
+        for name, module in self.model.named_modules():
+            if isinstance(module, (torch.nn.Linear)): # nn.Conv2d
+                if hasattr(module, 'weight'):
+                    quantized_weight, _, _ = self.quant.quantize_tensor(module.weight)
+                    module.weight = torch.nn.Parameter(quantized_weight)
+                if hasattr(module, 'bias') and module.bias is not None:
+                    quantized_bias, _, _ = self.quant.quantize_tensor(module.bias)
+                    module.bias = torch.nn.Parameter(quantized_bias)
 
+def load_pipeline() -> Pipeline:    
+    clear()
     dtype, device = torch.bfloat16, "cuda"
-
-    empty_cache()
+    vae = AutoencoderKL.from_pretrained(
+        MODEL_ID, subfolder="vae", torch_dtype=torch.bfloat16
+    )
+    instance = SDXLQuantization(vae, 9)
+    instance.quantize_model()
     pipeline = DiffusionPipeline.from_pretrained(
-        ckpt_id, 
+        MODEL_ID,
+        vae=vae,
         torch_dtype=dtype,
         )
     pipeline.enable_sequential_cpu_offload()
     for _ in range(2):
-        empty_cache()
-        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)
-    
+        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)
+    clear()
     return pipeline
 
-
-from datetime import datetime
 @torch.inference_mode()
 def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
-    empty_cache()
-    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 = img.open("./RobertML.png")
-        pass
-    return(image)
+    clear()
+    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

uv.lock

@@ -1,9 +1,5 @@
 version = 1
 requires-python = ">=3.10, <3.13"
-resolution-markers = [
-    "python_full_version < '3.12'",
-    "python_full_version >= '3.12'",
-]
 
 [[package]]
 name = "accelerate"
@@ -129,36 +125,15 @@ wheels = [
 ]
 
 [[package]]
-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]]
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-version = "3.16.1"
-source = { registry = "https://pypi.org/simple" }
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-]
-
-[[package]]
-name = "flux-schnell-edge-inference"
+name = "edge-maxxing-4090-newdream"
 version = "7"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "diffusers" },
     { name = "edge-maxxing-pipelines" },
-    { name = "gitpython" },
     { name = "omegaconf" },
-    { name = "protobuf" },
-    { name = "sentencepiece" },
     { name = "torch" },
-    { name = "torchao" },
     { name = "transformers" },
 ]
 
@@ -166,47 +141,36 @@ 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=7c760ac54f6052803dadb3ade8ebfc9679a94589#7c760ac54f6052803dadb3ade8ebfc9679a94589" },
-    { name = "gitpython", specifier = ">=3.1.43" },
+    { name = "edge-maxxing-pipelines", git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=858efca07043e8874d956eaacbead5adb1e71336#858efca07043e8874d956eaacbead5adb1e71336" },
     { 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" },
 ]
 
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-version = "2024.10.0"
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+name = "edge-maxxing-pipelines"
+version = "1.0.0"
+source = { git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=858efca07043e8874d956eaacbead5adb1e71336#858efca07043e8874d956eaacbead5adb1e71336" }
+dependencies = [
+    { name = "pydantic" },
 ]
 
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+    { url = "https://files.pythonhosted.org/packages/b9/f8/feced7779d755758a52d1f6635d990b8d98dc0a29fa568bbe0625f18fdf3/filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0", size = 16163 },
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-version = "3.1.43"
+name = "fsspec"
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