forked from extern/easydiffusion
Fix a bug where Face Correction (GFPGAN) would fail on cuda:N (i.e. GPUs other than cuda:0), as well as fail on CPU if the system had an incompatible GPU.
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CHANGES.md
13
CHANGES.md
@ -1,9 +1,8 @@
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# What's new?
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### 2.4.6
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* 16 Nov 2022 - Fix a regression in VRAM usage during startup, which caused 'Out of Memory' errors when starting on GPUs with 4gb (or less) VRAM
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### 2.4.5
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* 16 Nov 2022 - Add checkbox for "Open browser on startup".
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* 16 Nov 2022 - Add a directory for core plugins that ship with Stable Diffusion UI by default.
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* 16 Nov 2022 - Add a "What's New?" tab as a core plugin, which fetches the contents of CHANGES.md from the app's release branch.
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### 2.4
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* 2.4.7 - 17 Nov 2022 - Fix a bug where Face Correction (GFPGAN) would fail on cuda:N (i.e. GPUs other than cuda:0), as well as fail on CPU if the system had an incompatible GPU.
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* 2.4.6 - 16 Nov 2022 - Fix a regression in VRAM usage during startup, which caused 'Out of Memory' errors when starting on GPUs with 4gb (or less) VRAM
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* 2.4.5 - 16 Nov 2022 - Add checkbox for "Open browser on startup".
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* 2.4.5 - 16 Nov 2022 - Add a directory for core plugins that ship with Stable Diffusion UI by default.
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* 2.4.5 - 16 Nov 2022 - Add a "What's New?" tab as a core plugin, which fetches the contents of CHANGES.md from the app's release branch.
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@ -20,7 +20,7 @@
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<div id="container">
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<div id="top-nav">
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<div id="logo">
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<h1>Stable Diffusion UI <small>v2.4.6 <span id="updateBranchLabel"></span></small></h1>
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<h1>Stable Diffusion UI <small>v2.4.7 <span id="updateBranchLabel"></span></small></h1>
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</div>
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<div id="server-status">
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<div id="server-status-color">●</div>
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@ -236,9 +236,14 @@ def wait_model_move_to(model, target_device): # Send to target_device and wait u
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def load_model_gfpgan():
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if thread_data.gfpgan_file is None: raise ValueError(f'Thread gfpgan_file is undefined.')
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# hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
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from facexlib.detection import retinaface
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retinaface.device = torch.device(thread_data.device)
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print('forced retinaface.device to', thread_data.device)
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model_path = thread_data.gfpgan_file + ".pth"
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device = 'cuda:0' if force_gfpgan_to_cuda0 else thread_data.device
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thread_data.model_gfpgan = GFPGANer(device=torch.device(device), model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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thread_data.model_gfpgan = GFPGANer(device=torch.device(thread_data.device), model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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print('loaded', thread_data.gfpgan_file, 'to', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
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def load_model_real_esrgan():
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@ -288,10 +293,10 @@ def apply_filters(filter_name, image_data, model_path=None):
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print(f'Applying filter {filter_name}...')
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gc() # Free space before loading new data.
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if filter_name == 'gfpgan':
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if isinstance(image_data, torch.Tensor):
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image_data.to('cuda:0' if force_gfpgan_to_cuda0 else thread_data.device)
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if isinstance(image_data, torch.Tensor):
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image_data.to(thread_data.device)
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if filter_name == 'gfpgan':
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if model_path is not None and model_path != thread_data.gfpgan_file:
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thread_data.gfpgan_file = model_path
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load_model_gfpgan()
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@ -303,9 +308,6 @@ def apply_filters(filter_name, image_data, model_path=None):
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image_data = output[:,:,::-1]
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if filter_name == 'real_esrgan':
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if isinstance(image_data, torch.Tensor):
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image_data.to(thread_data.device)
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if model_path is not None and model_path != thread_data.real_esrgan_file:
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thread_data.real_esrgan_file = model_path
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load_model_real_esrgan()
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@ -217,10 +217,6 @@ def thread_get_next_task():
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task = None
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try: # Select a render task.
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for queued_task in tasks_queue:
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if queued_task.request.use_face_correction and runtime.thread_data.device == 'cpu' and is_alive() == 1:
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queued_task.error = Exception('The CPU cannot be used to run this task currently. Please remove "Fix incorrect faces" from Image Settings and try again.')
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task = queued_task
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break
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if queued_task.render_device and runtime.thread_data.device != queued_task.render_device:
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# Is asking for a specific render device.
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if is_alive(queued_task.render_device) > 0:
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