Model pushed

.gitattributes

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models/lora/pytorch_lora_weights.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d0d20f48219b393264e82870b4e86ec51f8fc4f9d100bd572af7061449eb216
+size 787239704

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/edge_maxxing_4090_newdream.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: edge-maxxing-4090-newdream
+Version: 1.0.0
+Summary: An edge-maxxing model submission for the 4090 newdream contest
+Requires-Python: <3.11,>=3.10
+Requires-Dist: diffusers==0.30.2
+Requires-Dist: transformers==4.41.2
+Requires-Dist: accelerate==0.31.0
+Requires-Dist: omegaconf==2.3.0
+Requires-Dist: edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing#subdirectory=pipelines

src/edge_maxxing_4090_newdream.egg-info/SOURCES.txt

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+README.md
+pyproject.toml
+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==0.30.2
+transformers==4.41.2
+accelerate==0.31.0
+omegaconf==2.3.0
+edge-maxxing-pipelines@ git+https://github.com/womboai/edge-maxxing#subdirectory=pipelines

src/edge_maxxing_4090_newdream.egg-info/top_level.txt

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+main
+pipeline

src/pipeline.py

@@ -1,16 +1,40 @@
 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
 
 
 def load_pipeline() -> StableDiffusionXLPipeline:
-    pipeline = StableDiffusionXLPipeline.from_pretrained(
+    pipeline: StableDiffusionXLPipeline = StableDiffusionXLPipeline.from_pretrained(
         "./models/newdream-sdxl-20",
         torch_dtype=torch.float16,
         local_files_only=True,
     ).to("cuda")
+    adapter_id = "./models/lora"
+    pipeline.scheduler = LCMScheduler.from_config({
+        "_class_name": "LCMScheduler",
+        "_diffusers_version": "0.29.0",
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "beta_start": 0.00085,
+        "clip_sample": False,
+        "clip_sample_range": 1.0,
+        "num_train_timesteps": 1000,
+        "original_inference_steps": 300,
+        "prediction_type": "epsilon",
+        "rescale_betas_zero_snr": False,
+        "sample_max_value": 1.0,
+        "set_alpha_to_one": False,
+        "steps_offset": 1,
+        "thresholding": False,
+        "timestep_scaling": 10.0,
+        "timestep_spacing": "leading",
+        "trained_betas": None,
+        })
+
+    # # LCMScheduler.from_config(pipeline.scheduler.config)
+    pipeline.load_lora_weights(adapter_id)
 
     pipeline(prompt="")
 
@@ -26,4 +50,6 @@ def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> I
         width=request.width,
         height=request.height,
         generator=generator,
+        num_inference_steps=4,
+        guidance_scale=1.5,
     ).images[0]