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Add support for the stable diffusion x4 upscaling model.

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This is an old model.

Load the checkpoint like a regular one and use the new
SD_4XUpscale_Conditioning node.
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comfyanonymous
2024-01-03 03:30:39 -05:00
parent 2c4e92a98b
commit a7874d1a8b
6 changed files with 103 additions and 1 deletions
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28
comfy/supported_models.py
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@@ -278,6 +278,32 @@ class Stable_Zero123(supported_models_base.BASE):
def clip_target(self):
return None
class SD_X4Upscaler(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 256,
'in_channels': 7,
"use_linear_in_transformer": True,
"adm_in_channels": None,
"use_temporal_attention": False,
}
models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega]
unet_extra_config = {
"disable_self_attentions": [True, True, True, False],
"num_heads": 8,
"num_head_channels": -1,
}
latent_format = latent_formats.SD_X4
sampling_settings = {
"linear_start": 0.0001,
"linear_end": 0.02,
}
def get_model(self, state_dict, prefix="", device=None):
out = model_base.SD_X4Upscaler(self, device=device)
return out
models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler]
models += [SVD_img2vid]