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Merge pull request #16078 from huchenlei/fix_sd2 

Fix SD2 loading
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AUTOMATIC1111 2024-07-06 09:59:29 +03:00 committed by GitHub
parent 50514ce414 c3ef381cd8
commit 4cc3add770
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2 changed files with 8 additions and 1 deletions
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2
modules/sd_hijack_unet.py
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@ -138,6 +138,7 @@ CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_s
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond) CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond) CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
# Always make sure inputs to unet are in correct dtype
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model) CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model) CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
@ -150,5 +151,6 @@ def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
return orig_func(timesteps, *args, **kwargs).to(dtype=dtype) return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
# Always make sure timestep calculation is in correct dtype
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result) CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result) CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)

7
modules/sd_models_config.py
View File

@ -56,14 +56,19 @@ def is_using_v_parameterization_for_sd2(state_dict):
unet.eval() unet.eval()
with torch.no_grad(): with torch.no_grad():
unet_dtype = torch.float
original_unet_dtype = devices.dtype_unet
unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k} unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
unet.load_state_dict(unet_sd, strict=True) unet.load_state_dict(unet_sd, strict=True)
unet.to(device=device, dtype=torch.float) unet.to(device=device, dtype=unet_dtype)
devices.dtype_unet = unet_dtype
test_cond = torch.ones((1, 2, 1024), device=device) * 0.5 test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5 x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item() out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
devices.dtype_unet = original_unet_dtype
return out < -1 return out < -1