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.gitattributes

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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+models/newdream-sdxl-20/text_encoder/model.safetensors filter=lfs diff=lfs merge=lfs -text
+models/newdream-sdxl-20/text_encoder/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+models/newdream-sdxl-20/text_encoder_2/model.safetensors filter=lfs diff=lfs merge=lfs -text
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+models/newdream-sdxl-20/unet/diffusion_pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+models/newdream-sdxl-20/unet/diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text
+models/newdream-sdxl-20/vae/diffusion_pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+models/newdream-sdxl-20/vae/diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text
+models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/optimizer.bin filter=lfs diff=lfs merge=lfs -text
+models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/pytorch_lora_weights.safetensors filter=lfs diff=lfs merge=lfs -text
+models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/random_states_0.pkl filter=lfs diff=lfs merge=lfs -text
+models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/scaler.pt filter=lfs diff=lfs merge=lfs -text
+models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/scheduler.bin filter=lfs diff=lfs merge=lfs -text
+models/sdxl-lcmlora-1024-100k-3000steps/pytorch_lora_weights.safetensors filter=lfs diff=lfs merge=lfs -text
+models/model/checkpoint-3000/optimizer.bin filter=lfs diff=lfs merge=lfs -text
+models/model/checkpoint-3000/pytorch_lora_weights.safetensors filter=lfs diff=lfs merge=lfs -text
+models/model/checkpoint-3000/random_states_0.pkl filter=lfs diff=lfs merge=lfs -text
+models/model/checkpoint-3000/scaler.pt filter=lfs diff=lfs merge=lfs -text
+models/model/checkpoint-3000/scheduler.bin filter=lfs diff=lfs merge=lfs -text
+models/model/pytorch_lora_weights.safetensors filter=lfs diff=lfs merge=lfs -text

.gitmodules

@@ -2,3 +2,7 @@
 	path = models/newdream-sdxl-20
 	url = https://huggingface.co/stablediffusionapi/newdream-sdxl-20
 	branch = main
+[submodule "sdxl-lcmlora-1024-100k-3000steps"]
+	path = models/sdxl-lcmlora-1024-100k-3000steps
+	url = https://huggingface.co/mhussainahmad/sdxl-lcmlora-1024-100k-3000steps
+	branch = main

models/model/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.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
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

models/model/checkpoint-3000/optimizer.bin

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+size 396043572

models/model/checkpoint-3000/pytorch_lora_weights.safetensors

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models/model/checkpoint-3000/random_states_0.pkl

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+size 14540

models/model/checkpoint-3000/scaler.pt

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+size 988

models/model/checkpoint-3000/scheduler.bin

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models/model/pytorch_lora_weights.safetensors

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requirements.txt

@@ -1 +1,2 @@
 # Specify any extra options here, like --find-links, --pre, etc. Avoid specifying dependencies here and specify them in pyproject.toml instead
+peft

src/main.py

@@ -1,7 +1,7 @@
 from io import BytesIO
 from multiprocessing.connection import Listener
-from os import chmod, remove
-from os.path import abspath, exists
+from os import chmod
+from os.path import abspath
 from pathlib import Path
 
 from PIL.JpegImagePlugin import JpegImageFile
@@ -16,11 +16,9 @@ def main():
     print(f"Loading pipeline")
     pipeline = load_pipeline()
 
-    print(f"Pipeline loaded, creating socket at '{SOCKET}'")
-
-    if exists(SOCKET):
-        remove(SOCKET)
+    print(f"Pipeline loaded")
 
+    print(f"Creating socket at '{SOCKET}'")
     with Listener(SOCKET) as listener:
         chmod(SOCKET, 0o777)
 

src/pipeline.py

@@ -1,48 +1,33 @@
 import torch
 from PIL.Image import Image
-from diffusers import StableDiffusionXLPipeline
+from diffusers import StableDiffusionXLPipeline, LCMScheduler
 from pipelines.models import TextToImageRequest
 from torch import Generator
-import gc
 
 
-# Efficiently load the pipeline and apply mixed precision if possible.
 def load_pipeline() -> StableDiffusionXLPipeline:
-    print("Loading model pipeline...")
+    pipeline = StableDiffusionXLPipeline.from_pretrained(
+        "./models/newdream-sdxl-20",
+        torch_dtype=torch.float16,
+        local_files_only=True,
+    ).to("cuda")
+    pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
+    pipeline.load_lora_weights("./models/model")
 
-    # Enable mixed precision for better memory usage
-    with torch.cuda.amp.autocast():
-        pipeline = StableDiffusionXLPipeline.from_pretrained(
-            "./models/newdream-sdxl-20",
-            torch_dtype=torch.float16,  # Mixed precision to speed up inference
-            local_files_only=True,
-        ).to("cuda")
-
-    # Prime the pipeline to reduce initial inference time by running a dummy prompt
     pipeline(prompt="")
 
     return pipeline
 
 
-# Inference function with better resource management and memory handling
 def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
-    # Use the provided seed for reproducibility, if available
     generator = Generator(pipeline.device).manual_seed(request.seed) if request.seed else None
 
-    print(f"Generating image with prompt: {request.prompt}")
-    
-    # Inference with mixed precision
-    with torch.cuda.amp.autocast():
-        image = pipeline(
-            prompt=request.prompt,
-            negative_prompt=request.negative_prompt,
-            width=request.width,
-            height=request.height,
-            generator=generator,
-        ).images[0]
-
-    # Clear memory after inference to avoid CUDA OOM errors
-    torch.cuda.empty_cache()
-    gc.collect()
-
-    return image
+    return pipeline(
+        prompt=request.prompt,
+        negative_prompt=request.negative_prompt,
+        width=request.width,
+        height=request.height,
+        generator=generator,
+        num_inference_steps=4,
+        guidance_scale=1.5,
+    ).images[0]