git init

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.tar filter=lfs diff=lfs merge=lfs -text
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+cached_pipe/unet filter=lfs diff=lfs merge=lfs -text

README.md

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+Heban olla vogola
+

loss_params.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf0b76d04764575883fcd146c7ae8e0edb9e049ad55db61640e37deffa652fdb
+size 3120

pyproject.toml

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+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "edge-maxxing-4090-newdream"
+description = "An edge-maxxing model submission for the 4090 newdream contest"
+requires-python = ">=3.10,<3.11"
+version = "7"
+dependencies = [
+    "diffusers==0.28.2",
+    "onediff==1.2.0",
+    "onediffx==1.2.0",
+    "accelerate==0.31.0",
+    "numpy==1.26.4",
+    "xformers==0.0.25.post1",
+    "triton==2.2.0",
+    "transformers==4.41.2",
+    "accelerate==0.31.0",
+    "omegaconf==2.3.0",
+    "torch==2.2.2",
+    "torchvision==0.17.2",    
+    "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@8d8ff45863416484b5b4bc547782591bbdfc696a#subdirectory=pipelines",
+    "huggingface-hub==0.25.2",
+    "oneflow",
+    "setuptools>=75.2.0",
+    "bitsandbytes>=0.44.1",
+    "stable-fast",
+    "tomesd>=0.1.3",
+]
+
+[tool.edge-maxxing]
+models = [
+    "stablediffusionapi/newdream-sdxl-20",
+    "RobertML/cached-pipe-02"
+]
+
+[tool.uv.sources]
+oneflow = { url = "https://github.com/siliconflow/oneflow_releases/releases/download/community_cu118/oneflow-0.9.1.dev20240802%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" }
+stable-fast = { url = "https://github.com/chengzeyi/stable-fast/releases/download/v1.0.5/stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl" }
+
+[project.scripts]
+start_inference = "main:main"
+

requirements.txt

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+# Specify any extra options here, like --find-links, --pre, etc. Avoid specifying dependencies here and specify them in pyproject.toml instead
+https://github.com/siliconflow/oneflow_releases/releases/download/community_cu118/oneflow-0.9.1.dev20240802%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 

src/edge_maxxing_4090_newdream.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: edge-maxxing-4090-newdream
+Version: 7
+Summary: An edge-maxxing model submission for the 4090 newdream contest
+Requires-Python: <3.11,>=3.10
+Requires-Dist: diffusers==0.28.2
+Requires-Dist: onediff==1.2.0
+Requires-Dist: onediffx==1.2.0
+Requires-Dist: accelerate==0.31.0
+Requires-Dist: numpy==1.26.4
+Requires-Dist: xformers==0.0.25.post1
+Requires-Dist: triton==2.2.0
+Requires-Dist: transformers==4.41.2
+Requires-Dist: accelerate==0.31.0
+Requires-Dist: omegaconf==2.3.0
+Requires-Dist: torch==2.2.2
+Requires-Dist: torchvision==0.17.2
+Requires-Dist: edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing@8d8ff45863416484b5b4bc547782591bbdfc696a#subdirectory=pipelines
+Requires-Dist: huggingface-hub==0.25.2
+Requires-Dist: oneflow
+Requires-Dist: setuptools>=75.2.0
+Requires-Dist: bitsandbytes>=0.44.1
+Requires-Dist: stable-fast
+Requires-Dist: tomesd>=0.1.3

src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt

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+README.md
+pyproject.toml
+src/loss.py
+src/main.py
+src/pipeline.py
+src/edge_maxxing_4090_newdream.egg-info/PKG-INFO
+src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt
+src/edge_maxxing_4090_newdream.egg-info/dependency_links.txt
+src/edge_maxxing_4090_newdream.egg-info/entry_points.txt
+src/edge_maxxing_4090_newdream.egg-info/requires.txt
+src/edge_maxxing_4090_newdream.egg-info/top_level.txt

src/edge_maxxing_4090_newdream.egg-info/dependency_links.txt

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+

src/edge_maxxing_4090_newdream.egg-info/entry_points.txt

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+[console_scripts]
+start_inference = main:main

src/edge_maxxing_4090_newdream.egg-info/requires.txt

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+diffusers==0.28.2
+onediff==1.2.0
+onediffx==1.2.0
+accelerate==0.31.0
+numpy==1.26.4
+xformers==0.0.25.post1
+triton==2.2.0
+transformers==4.41.2
+accelerate==0.31.0
+omegaconf==2.3.0
+torch==2.2.2
+torchvision==0.17.2
+edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing@8d8ff45863416484b5b4bc547782591bbdfc696a#subdirectory=pipelines
+huggingface-hub==0.25.2
+oneflow
+setuptools>=75.2.0
+bitsandbytes>=0.44.1
+stable-fast
+tomesd>=0.1.3

src/edge_maxxing_4090_newdream.egg-info/top_level.txt

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+loss
+main
+pipeline

src/loss.py

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+_A=None
+import torch
+from tqdm import tqdm
+class LossSchedulerModel(torch.nn.Module):
+	def __init__(A,wx,we):super(LossSchedulerModel,A).__init__();assert len(wx.shape)==1 and len(we.shape)==2;B=wx.shape[0];assert B==we.shape[0]and B==we.shape[1];A.register_parameter('wx',torch.nn.Parameter(wx));A.register_parameter('we',torch.nn.Parameter(we))
+	def forward(A,t,xT,e_prev):
+		B=e_prev;assert t-len(B)+1==0;C=xT*A.wx[t]
+		for(D,E)in zip(B,A.we[t]):C+=D*E
+		return C.to(xT.dtype)
+class LossScheduler:
+	def __init__(A,timesteps,model):A.timesteps=timesteps;A.model=model;A.init_noise_sigma=1.;A.order=1
+	@staticmethod
+	def load(path):A,B,C=torch.load(path,map_location='cpu');D=LossSchedulerModel(B,C);return LossScheduler(A,D)
+	def save(A,path):B,C,D=A.timesteps,A.model.wx,A.model.we;torch.save((B,C,D),path)
+	def set_timesteps(A,num_inference_steps,device='cuda'):B=device;A.xT=_A;A.e_prev=[];A.t_prev=-1;A.model=A.model.to(B);A.timesteps=A.timesteps.to(B)
+	def scale_model_input(A,sample,*B,**C):return sample
+	@torch.no_grad()
+	def step(self,model_output,timestep,sample,*D,**E):
+		A=self;B=A.timesteps.tolist().index(timestep);assert A.t_prev==-1 or B==A.t_prev+1
+		if A.t_prev==-1:A.xT=sample
+		A.e_prev.append(model_output);C=A.model(B,A.xT,A.e_prev)
+		if B+1==len(A.timesteps):A.xT=_A;A.e_prev=[];A.t_prev=-1
+		else:A.t_prev=B
+		return C,
+class SchedulerWrapper:
+	def __init__(A,scheduler,loss_params_path='loss_params.pth'):A.scheduler=scheduler;A.catch_x,A.catch_e,A.catch_x_={},{},{};A.loss_scheduler=_A;A.loss_params_path=loss_params_path
+	def set_timesteps(A,num_inference_steps,**C):
+		D=num_inference_steps
+		if A.loss_scheduler is _A:B=A.scheduler.set_timesteps(D,**C);A.timesteps=A.scheduler.timesteps;A.init_noise_sigma=A.scheduler.init_noise_sigma;A.order=A.scheduler.order;return B
+		else:B=A.loss_scheduler.set_timesteps(D,**C);A.timesteps=A.loss_scheduler.timesteps;A.init_noise_sigma=A.scheduler.init_noise_sigma;A.order=A.scheduler.order;return B
+	def step(B,model_output,timestep,sample,**F):
+		D=sample;E=model_output;A=timestep
+		if B.loss_scheduler is _A:
+			C=B.scheduler.step(E,A,D,**F);A=A.tolist()
+			if A not in B.catch_x:B.catch_x[A]=[];B.catch_e[A]=[];B.catch_x_[A]=[]
+			B.catch_x[A].append(D.clone().detach().cpu());B.catch_e[A].append(E.clone().detach().cpu());B.catch_x_[A].append(C[0].clone().detach().cpu());return C
+		else:C=B.loss_scheduler.step(E,A,D,**F);return C
+	def scale_model_input(A,sample,timestep):return sample
+	def add_noise(A,original_samples,noise,timesteps):B=A.scheduler.add_noise(original_samples,noise,timesteps);return B
+	def get_path(C):
+		A=sorted([A for A in C.catch_x],reverse=True);B,D=[],[]
+		for E in A:F=torch.cat(C.catch_x[E],dim=0);B.append(F);G=torch.cat(C.catch_e[E],dim=0);D.append(G)
+		H=A[-1];I=torch.cat(C.catch_x_[H],dim=0);B.append(I);A=torch.tensor(A,dtype=torch.int32);B=torch.stack(B);D=torch.stack(D);return A,B,D
+	def load_loss_params(A):B,C,D=torch.load(A.loss_params_path,map_location='cpu');A.loss_model=LossSchedulerModel(C,D);A.loss_scheduler=LossScheduler(B,A.loss_model)
+	def prepare_loss(A,num_accelerate_steps=15):A.load_loss_params()

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
+
+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")
+
+
+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)
+
+
+if __name__ == '__main__':
+    main()

src/pipeline.py

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+import torch
+from PIL.Image import Image
+from diffusers import StableDiffusionXLPipeline
+
+from pipelines.models import TextToImageRequest
+from diffusers import DDIMScheduler
+from torch import Generator
+from loss import SchedulerWrapper
+
+from onediffx import compile_pipe, save_pipe, load_pipe
+
+def callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):
+  if step_index == int(pipe.num_timesteps * 0.78):
+    callback_kwargs['prompt_embeds'] = callback_kwargs['prompt_embeds'].chunk(2)[-1]
+    callback_kwargs['add_text_embeds'] = callback_kwargs['add_text_embeds'].chunk(2)[-1]
+    callback_kwargs['add_time_ids'] = callback_kwargs['add_time_ids'].chunk(2)[-1]
+    pipe._guidance_scale = 0.1
+
+  return callback_kwargs
+
+def load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
+    if not pipeline:
+        pipeline = StableDiffusionXLPipeline.from_pretrained(
+            "stablediffusionapi/newdream-sdxl-20",
+            torch_dtype=torch.float16,
+        ).to("cuda")
+    
+    pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
+
+    pipeline = compile_pipe(pipeline)
+    load_pipe(pipeline, dir="/home/sandbox/.cache/huggingface/hub/models--RobertML--cached-pipe-02/snapshots/58d70deae87034cce351b780b48841f9746d4ad7")
+
+    for _ in range(1):
+        deepcache_output = pipeline(prompt="telestereography, unstrengthen, preadministrator, copatroness, hyperpersonal, paramountness, paranoid, guaniferous", output_type="pil", num_inference_steps=20)
+    pipeline.scheduler.prepare_loss()
+    for _ in range(2):
+        pipeline(prompt="telestereography, unstrengthen, preadministrator, copatroness, hyperpersonal, paramountness, paranoid, guaniferous", output_type="pil", num_inference_steps=20)
+    return pipeline
+
+def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
+    if request.seed is None:
+        generator = None
+    else:
+        generator = Generator(pipeline.device).manual_seed(request.seed)
+
+    return pipeline(
+        prompt=request.prompt,
+        negative_prompt=request.negative_prompt,
+        width=request.width,
+        height=request.height,
+        generator=generator,
+        num_inference_steps=13,
+        cache_interval=1,
+        cache_layer_id=1,
+        cache_block_id=0,
+        eta=1.0,
+        guidance_scale = 5.0,
+        guidance_rescale = 0.0,
+        callback_on_step_end=callback_dynamic_cfg,
+        callback_on_step_end_tensor_inputs=['prompt_embeds', 'add_text_embeds', 'add_time_ids'],
+    ).images[0]
+
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src/scheduler_config.json

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+{
+  "_by": "RobertML"
+}

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+    { name = "numpy" },
+    { name = "packaging" },
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+    { name = "torch" },
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