Upload folder using huggingface_hub

.gitattributes

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.gitmodules

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+[submodule "newdream-sdxl-20"]
+	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

README.md

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+# edge-maxxing-newdream-sdxl
+
+This holds the baseline for the SDXL Nvidia GeForce RTX 4090 contest, which can be forked freely and optimized
+
+Some recommendations are as follows:
+- Installing dependencies should be done in pyproject.toml, including git dependencies
+- Compiled models should be included directly in the repository(rather than compiling during loading), loading time matters far more than file sizes
+- Avoid changing `src/main.py`, as that includes mostly protocol logic. Most changes should be in `models` and `src/pipeline.py`
+- Change `requirements.txt` to add extra arguments to be used when installing the package
+
+For testing, you need a docker container with pytorch and ubuntu 22.04,
+you can download your listed dependencies with `pip install -r requirements.txt -e .`, and then running `start_inference`

models/newdream-sdxl-20/.gitattributes

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models/newdream-sdxl-20/.gitignore

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+step_*
+epoch_*

models/newdream-sdxl-20/README.md

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+---
+license: creativeml-openrail-m
+tags:
+  - stablediffusionapi.com
+  - stable-diffusion-api
+  - text-to-image
+  - ultra-realistic
+pinned: true
+---
+
+# NewDream-SDXL 2.0 API Inference
+
+![generated from stablediffusionapi.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/8478583971702167737.png)
+## Get API Key
+
+Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. 
+
+Replace Key in below code, change **model_id**  to "newdream-sdxl-20"
+
+Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
+
+Try model for free: [Generate Images](https://stablediffusionapi.com/models/newdream-sdxl-20)
+
+Model link: [View model](https://stablediffusionapi.com/models/newdream-sdxl-20)
+
+Credits: [View credits](https://civitai.com/?query=NewDream-SDXL%202.0)
+
+View all models: [View Models](https://stablediffusionapi.com/models)
+
+    import requests  
+    import json  
+      
+    url =  "https://stablediffusionapi.com/api/v4/dreambooth"  
+      
+    payload = json.dumps({  
+    "key":  "your_api_key",  
+    "model_id":  "newdream-sdxl-20",  
+    "prompt":  "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",  
+    "negative_prompt":  "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",  
+    "width":  "512",  
+    "height":  "512",  
+    "samples":  "1",  
+    "num_inference_steps":  "30",  
+    "safety_checker":  "no",  
+    "enhance_prompt":  "yes",  
+    "seed":  None,  
+    "guidance_scale":  7.5,  
+    "multi_lingual":  "no",  
+    "panorama":  "no",  
+    "self_attention":  "no",  
+    "upscale":  "no",  
+    "embeddings":  "embeddings_model_id",  
+    "lora":  "lora_model_id",  
+    "webhook":  None,  
+    "track_id":  None  
+    })  
+      
+    headers =  {  
+    'Content-Type':  'application/json'  
+    }  
+      
+    response = requests.request("POST", url, headers=headers, data=payload)  
+      
+    print(response.text)
+
+> Use this coupon code to get 25% off **DMGG0RBN** 

models/newdream-sdxl-20/model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.21.2",
+  "force_zeros_for_empty_prompt": true,
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

models/newdream-sdxl-20/scheduler/scheduler_config.json

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+{
+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.21.2",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "use_karras_sigmas": false
+}

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+    "CLIPTextModel"
+  ],
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+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.33.1",
+  "vocab_size": 49408
+}

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models/newdream-sdxl-20/tokenizer/tokenizer_config.json

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+  }
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+  "_diffusers_version": "0.21.2",
+  "act_fn": "silu",
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+  ]
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models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/optimizer.bin

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+size 396043572

models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/pytorch_lora_weights.safetensors

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models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/random_states_0.pkl

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+size 14540

models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/scaler.pt

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+size 988

models/sdxl-lcmlora-1024-100k-3000steps/checkpoint-3000/scheduler.bin

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models/sdxl-lcmlora-1024-100k-3000steps/pytorch_lora_weights.safetensors

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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 = "1.0.0"
+dependencies = [
+    "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",
+]
+
+[project.scripts]
+start_inference = "main:main"

requirements.txt

@@ -0,0 +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

@@ -0,0 +1,48 @@
+from io import BytesIO
+from multiprocessing.connection import Listener
+from os import chmod
+from os.path import abspath
+from pathlib import Path
+
+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 main():
+    print(f"Loading pipeline")
+    pipeline = load_pipeline()
+
+    print(f"Pipeline loaded")
+
+    print(f"Creating socket at '{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

@@ -0,0 +1,33 @@
+import torch
+from PIL.Image import Image
+from diffusers import StableDiffusionXLPipeline, LCMScheduler
+from pipelines.models import TextToImageRequest
+from torch import Generator
+
+
+def load_pipeline() -> StableDiffusionXLPipeline:
+    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/sdxl-lcmlora-1024-100k-3000steps")
+
+    pipeline(prompt="")
+
+    return pipeline
+
+
+def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
+    generator = Generator(pipeline.device).manual_seed(request.seed) if request.seed else None
+
+    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]