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74 lines
2.5 KiB
74 lines
2.5 KiB
""" |
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This file is part of ComfyUI. |
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Copyright (C) 2024 Stability AI |
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This program is free software: you can redistribute it and/or modify |
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it under the terms of the GNU General Public License as published by |
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the Free Software Foundation, either version 3 of the License, or |
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(at your option) any later version. |
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This program is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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GNU General Public License for more details. |
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You should have received a copy of the GNU General Public License |
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along with this program. If not, see <https://www.gnu.org/licenses/>. |
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""" |
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import torch |
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import nodes |
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class StableCascade_EmptyLatentImage: |
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def __init__(self, device="cpu"): |
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self.device = device |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { |
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"width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), |
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"height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), |
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"compression": ("INT", {"default": 42, "min": 32, "max": 64, "step": 1}), |
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}) |
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}} |
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RETURN_TYPES = ("LATENT", "LATENT") |
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RETURN_NAMES = ("stage_c", "stage_b") |
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FUNCTION = "generate" |
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CATEGORY = "_for_testing/stable_cascade" |
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def generate(self, width, height, compression, batch_size=1): |
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c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) |
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b_latent = torch.zeros([batch_size, 4, height // 4, width // 4]) |
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return ({ |
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"samples": c_latent, |
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}, { |
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"samples": b_latent, |
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}) |
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class StableCascade_StageB_Conditioning: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { "conditioning": ("CONDITIONING",), |
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"stage_c": ("LATENT",), |
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}} |
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RETURN_TYPES = ("CONDITIONING",) |
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FUNCTION = "set_prior" |
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CATEGORY = "_for_testing/stable_cascade" |
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def set_prior(self, conditioning, stage_c): |
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c = [] |
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for t in conditioning: |
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d = t[1].copy() |
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d['stable_cascade_prior'] = stage_c['samples'] |
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n = [t[0], d] |
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c.append(n) |
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return (c, ) |
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NODE_CLASS_MAPPINGS = { |
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"StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, |
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"StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, |
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}
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