Update README.md
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README.md
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@@ -36,7 +36,7 @@ import torch
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import matplotlib.pyplot as plt
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import numpy as np
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model = ImageGPTForCausalImageModeling.from_pretrained('openai/imagegpt-small')
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@@ -48,8 +48,8 @@ context = torch.full((batch_size, 1), model.config.vocab_size - 1) #initialize w
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context = torch.tensor(context).to(device)
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output = model.generate(pixel_values=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40)
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clusters =
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n_px =
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samples = output[:,1:].cpu().detach().numpy()
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samples_img = [np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [n_px, n_px, 3]).astype(np.uint8) for s in samples] # convert color cluster tokens back to pixels
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import matplotlib.pyplot as plt
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import numpy as np
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processor = ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small')
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model = ImageGPTForCausalImageModeling.from_pretrained('openai/imagegpt-small')
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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context = torch.tensor(context).to(device)
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output = model.generate(pixel_values=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40)
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clusters = processor.clusters
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n_px = processor.size
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samples = output[:,1:].cpu().detach().numpy()
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samples_img = [np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [n_px, n_px, 3]).astype(np.uint8) for s in samples] # convert color cluster tokens back to pixels
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