✨ | Use SDXL from rupeshs instead
This commit is contained in:
58
main.py
58
main.py
@ -1,33 +1,30 @@
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from flask import Flask, request, jsonify
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from diffusers import DDPMScheduler, DiffusionPipeline
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import torch
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from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionXLPipeline
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import base64
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from io import BytesIO
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from PIL import Image
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import os
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app = Flask(__name__)
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scheduler = DDPMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base", subfolder="scheduler")
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", scheduler=scheduler)
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# Define paths for OpenVINO IR models
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OV_MODEL_DIR = "echarlaix/stable-diffusion-2-1-openvino" # New directory for the LCM model
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# --- Model Loading ---
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pipe = None
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print("Loading UNet weights...")
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ckpt_path = './models/model.bin'
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try:
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unet_state_dict = torch.load(ckpt_path)
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pipe.unet.load_state_dict(unet_state_dict)
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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pipe.to(torch.float16)
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print("UNet weights loaded successfully and model moved to CUDA.")
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else:
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print("CUDA is not available. Running on CPU (will be slow).")
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pipe = pipe.to("cpu")
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pipe.to(torch.float32)
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except FileNotFoundError:
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print(f"Error: Model not found at {ckpt_path}. Please ensure the file exists.")
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exit()
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pipe = OVStableDiffusionXLPipeline.from_pretrained(
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"rupeshs/hyper-sd-sdxl-1-step-openvino-int8",
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ov_config={"CACHE_DIR": ""},
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)
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print("Compiling OpenVINO pipeline...")
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pipe.compile() # Compile the pipeline for the target device (CPU by default)
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print("OpenVINO compiled successfully.")
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except Exception as e:
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print(f"An error occurred while loading UNet weights: {e}")
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print(f"An error occurred during OpenVINO LCM model loading or conversion: {e}")
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exit()
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@app.route('/api/generate', methods=['POST'])
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@ -36,23 +33,28 @@ def generate_image():
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if not prompt:
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return jsonify({"error": "Prompt is required in the request body."}), 400
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print(f"Generating image for prompt: '{prompt}'...")
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print(f"Generating image for prompt: '{prompt}' using OpenVINO LCM...")
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try:
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image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[999]).images[0]
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# Crucially, set num_inference_steps=1 for 1-step generation with LCMs
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# You might even omit guidance_scale for some LCMs, or use a very low value.
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image = pipe(
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prompt=prompt,
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width=768,
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height=768,
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num_inference_steps=1,
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guidance_scale=1.0,
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).images[0]
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image = image.resize((128, 128), Image.LANCZOS)
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# Convert image to base64
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buffered = BytesIO()
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image.save(buffered, format="JPEG") # You can choose JPEG for smaller size, but PNG is lossless
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image.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
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return jsonify({"image": img_str})
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except Exception as e:
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print(f"Error during image generation: {e}")
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print(f"Error during image generation with OpenVINO LCM: {e}")
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return jsonify({"error": "An error occurred during image generation."}), 500
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if __name__ == '__main__':
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# Make sure to run this with 'python app.py'
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# It will be accessible at http://127.0.0.1:5000/
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app.run(host='127.0.0.1', port=5000, debug=True) # debug=True is good for development
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app.run(host='127.0.0.1', port=5000, debug=True)
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@ -1,39 +1,107 @@
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about-time==4.2.1
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accelerate==1.8.1
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aiohappyeyeballs==2.6.1
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aiohttp==3.12.13
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aiosignal==1.4.0
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alive-progress==3.2.0
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attrs==25.3.0
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autograd==1.8.0
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blinker==1.9.0
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certifi==2025.6.15
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charset-normalizer==3.4.2
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click==8.2.1
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cma==4.2.0
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contourpy==1.3.2
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cycler==0.12.1
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datasets==3.6.0
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Deprecated==1.2.18
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diffusers==0.34.0
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dill==0.3.8
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filelock==3.18.0
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Flask==3.1.1
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fsspec==2025.5.1
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fonttools==4.58.5
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frozenlist==1.7.0
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fsspec==2025.3.0
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grapheme==0.6.0
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hf-xet==1.1.5
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huggingface-hub==0.33.2
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idna==3.10
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importlib_metadata==8.7.0
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itsdangerous==2.2.0
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Jinja2==3.1.6
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MarkupSafe==2.1.5
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joblib==1.5.1
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jsonschema==4.24.0
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jsonschema-specifications==2025.4.1
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kiwisolver==1.4.8
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markdown-it-py==3.0.0
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MarkupSafe==3.0.2
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matplotlib==3.10.3
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mdurl==0.1.2
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mpmath==1.3.0
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networkx==3.5
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numpy==2.3.1
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multidict==6.6.3
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multiprocess==0.70.16
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natsort==8.4.0
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networkx==3.4.2
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ninja==1.11.1.4
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nncf==2.17.0
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numpy==2.2.6
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nvidia-cublas-cu12==12.6.4.1
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nvidia-cuda-cupti-cu12==12.6.80
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nvidia-cuda-nvrtc-cu12==12.6.77
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nvidia-cuda-runtime-cu12==12.6.77
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nvidia-cudnn-cu12==9.5.1.17
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nvidia-cufft-cu12==11.3.0.4
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nvidia-cufile-cu12==1.11.1.6
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nvidia-curand-cu12==10.3.7.77
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nvidia-cusolver-cu12==11.7.1.2
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nvidia-cusparse-cu12==12.5.4.2
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nvidia-cusparselt-cu12==0.6.3
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nvidia-nccl-cu12==2.26.2
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nvidia-nvjitlink-cu12==12.6.85
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nvidia-nvtx-cu12==12.6.77
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onnx==1.18.0
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openvino==2025.2.0
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openvino-telemetry==2025.2.0
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openvino-tokenizers==2025.2.0.1
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optimum==1.26.1
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optimum-intel==1.24.0
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packaging==25.0
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pillow==11.2.1
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pandas==2.2.3
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pillow==11.3.0
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propcache==0.3.2
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protobuf==6.31.1
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psutil==7.0.0
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pytorch-triton-rocm==3.3.1+gitc8757738
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pyarrow==20.0.0
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pydot==3.0.4
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Pygments==2.19.2
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pymoo==0.6.1.5
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pyparsing==3.2.3
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python-dateutil==2.9.0.post0
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pytz==2025.2
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PyYAML==6.0.2
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referencing==0.36.2
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regex==2024.11.6
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requests==2.32.4
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rich==14.0.0
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rpds-py==0.26.0
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safetensors==0.5.3
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setuptools==78.1.0
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scikit-learn==1.7.0
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scipy==1.16.0
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setuptools==80.9.0
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six==1.17.0
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sympy==1.14.0
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tabulate==0.9.0
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threadpoolctl==3.6.0
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tokenizers==0.21.2
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torch==2.9.0.dev20250706+rocm6.4
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torchaudio==2.8.0.dev20250706+rocm6.4
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torchvision==0.24.0.dev20250706+rocm6.4
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torch==2.7.1
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tqdm==4.67.1
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transformers==4.53.1
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typing_extensions==4.14.0
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transformers==4.52.4
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triton==3.3.1
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typing_extensions==4.14.1
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tzdata==2025.2
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urllib3==2.5.0
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Werkzeug==3.1.3
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wrapt==1.17.2
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xxhash==3.5.0
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yarl==1.20.1
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zipp==3.23.0
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