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import torch |
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def load_torch_file(ckpt): |
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if ckpt.lower().endswith(".safetensors"): |
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import safetensors.torch |
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sd = safetensors.torch.load_file(ckpt, device="cpu") |
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else: |
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pl_sd = torch.load(ckpt, map_location="cpu") |
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if "global_step" in pl_sd: |
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print(f"Global Step: {pl_sd['global_step']}") |
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if "state_dict" in pl_sd: |
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sd = pl_sd["state_dict"] |
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else: |
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sd = pl_sd |
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return sd |
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def transformers_convert(sd, prefix_from, prefix_to, number): |
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resblock_to_replace = { |
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"ln_1": "layer_norm1", |
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"ln_2": "layer_norm2", |
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"mlp.c_fc": "mlp.fc1", |
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"mlp.c_proj": "mlp.fc2", |
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"attn.out_proj": "self_attn.out_proj", |
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} |
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for resblock in range(number): |
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for x in resblock_to_replace: |
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for y in ["weight", "bias"]: |
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k = "{}.transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) |
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k_to = "{}.encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) |
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if k in sd: |
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sd[k_to] = sd.pop(k) |
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for y in ["weight", "bias"]: |
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k_from = "{}.transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) |
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if k_from in sd: |
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weights = sd.pop(k_from) |
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shape_from = weights.shape[0] // 3 |
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for x in range(3): |
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p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] |
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k_to = "{}.encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) |
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sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] |
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return sd |
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def common_upscale(samples, width, height, upscale_method, crop): |
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if crop == "center": |
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old_width = samples.shape[3] |
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old_height = samples.shape[2] |
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old_aspect = old_width / old_height |
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new_aspect = width / height |
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x = 0 |
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y = 0 |
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if old_aspect > new_aspect: |
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x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) |
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elif old_aspect < new_aspect: |
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y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) |
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s = samples[:,:,y:old_height-y,x:old_width-x] |
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else: |
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s = samples |
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return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) |
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@torch.inference_mode() |
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def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3): |
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output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu") |
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for b in range(samples.shape[0]): |
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s = samples[b:b+1] |
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out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") |
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out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") |
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for y in range(0, s.shape[2], tile_y - overlap): |
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for x in range(0, s.shape[3], tile_x - overlap): |
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s_in = s[:,:,y:y+tile_y,x:x+tile_x] |
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ps = function(s_in).cpu() |
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mask = torch.ones_like(ps) |
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feather = round(overlap * upscale_amount) |
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for t in range(feather): |
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mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) |
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mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) |
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mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) |
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mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) |
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out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask |
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out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask |
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output[b:b+1] = out/out_div |
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return output |
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