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Added support for launching on Apple Silicon
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@ -14,8 +14,11 @@ import modules.images
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def load_model(filename):
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# this code is adapted from https://github.com/xinntao/ESRGAN
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pretrained_net = torch.load(filename)
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if torch.has_mps:
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map_l = 'cpu'
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else:
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map_l = None
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pretrained_net = torch.load(filename, map_location=map_l)
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crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
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if 'conv_first.weight' in pretrained_net:
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@ -232,7 +232,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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z = outputs.last_hidden_state
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# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
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batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device)
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if torch.has_mps:
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batch_multipliers = torch.asarray(np.array(batch_multipliers).astype('float32')).to(device)
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else:
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batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device)
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original_mean = z.mean()
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z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
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new_mean = z.mean()
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@ -36,9 +36,12 @@ parser.add_argument("--opt-split-attention", action='store_true', help="enable o
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parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
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cmd_opts = parser.parse_args()
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cpu = torch.device("cpu")
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gpu = torch.device("cuda")
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device = gpu if torch.cuda.is_available() else cpu
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if torch.has_cuda:
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device = torch.device("cuda")
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elif torch.has_mps:
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device = torch.device("mps")
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else:
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device = torch.device("cpu")
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
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@ -10,5 +10,7 @@ omegaconf
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pytorch_lightning
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diffusers
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invisible-watermark
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einops
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taming-transformers-rom1504
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git+https://github.com/crowsonkb/k-diffusion.git
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git+https://github.com/TencentARC/GFPGAN.git
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