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https://github.com/huggingface/diffusers.git
synced 2026-01-18 17:45:52 +08:00
eda9ff8300
* Basic implementation of request scheduling * Basic editing in SD and Flux Pipelines * Small Fix * Fix * Update for more pipelines * Add examples/server-async * Add examples/server-async * Updated RequestScopedPipeline to handle a single tokenizer lock to avoid race conditions * Fix * Fix _TokenizerLockWrapper * Fix _TokenizerLockWrapper * Delete _TokenizerLockWrapper * Fix tokenizer * Update examples/server-async * Fix server-async * Optimizations in examples/server-async * We keep the implementation simple in examples/server-async * Update examples/server-async/README.md * Update examples/server-async/README.md for changes to tokenizer locks and backward-compatible retrieve_timesteps * The changes to the diffusers core have been undone and all logic is being moved to exmaples/server-async * Update examples/server-async/utils/* * Fix BaseAsyncScheduler * Rollback in the core of the diffusers * Update examples/server-async/README.md * Complete rollback of diffusers core files * Simple implementation of an asynchronous server compatible with SD3-3.5 and Flux Pipelines * Update examples/server-async/README.md * Fixed import errors in 'examples/server-async/serverasync.py' * Flux Pipeline Discard * Update examples/server-async/README.md * Apply style fixes --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import gc
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import logging
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import os
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import tempfile
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import uuid
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import torch
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logger = logging.getLogger(__name__)
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class Utils:
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def __init__(self, host: str = "0.0.0.0", port: int = 8500):
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self.service_url = f"http://{host}:{port}"
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self.image_dir = os.path.join(tempfile.gettempdir(), "images")
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if not os.path.exists(self.image_dir):
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os.makedirs(self.image_dir)
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self.video_dir = os.path.join(tempfile.gettempdir(), "videos")
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if not os.path.exists(self.video_dir):
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os.makedirs(self.video_dir)
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def save_image(self, image):
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if hasattr(image, "to"):
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try:
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image = image.to("cpu")
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except Exception:
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pass
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if isinstance(image, torch.Tensor):
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from torchvision import transforms
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to_pil = transforms.ToPILImage()
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image = to_pil(image.squeeze(0).clamp(0, 1))
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filename = "img" + str(uuid.uuid4()).split("-")[0] + ".png"
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image_path = os.path.join(self.image_dir, filename)
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logger.info(f"Saving image to {image_path}")
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image.save(image_path, format="PNG", optimize=True)
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del image
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return os.path.join(self.service_url, "images", filename)
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