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(requested) support AMD 8GB GPUs via Windows DirectML

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this update is requested by users
This commit is contained in:
lllyasviel
2023-12-30 06:30:59 -08:00
committed by GitHub
parent c0e11c3451
commit 8e62a72a63
31 changed files with 402 additions and 148 deletions
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ldm_patched/modules/model_base.py
+41 -5
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@@ -126,9 +126,15 @@ class BaseModel(torch.nn.Module):
cond_concat.append(blank_inpaint_image_like(noise))
data = torch.cat(cond_concat, dim=1)
out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data)
adm = self.encode_adm(**kwargs)
if adm is not None:
out['y'] = ldm_patched.modules.conds.CONDRegular(adm)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)
return out
def load_model_weights(self, sd, unet_prefix=""):
@@ -156,11 +162,7 @@ class BaseModel(torch.nn.Module):
def state_dict_for_saving(self, clip_state_dict, vae_state_dict):
clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict)
unet_sd = self.diffusion_model.state_dict()
unet_state_dict = {}
for k in unet_sd:
unet_state_dict[k] = ldm_patched.modules.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k)
unet_state_dict = self.diffusion_model.state_dict()
unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict)
if self.get_dtype() == torch.float16:
@@ -322,9 +324,43 @@ class SVD_img2vid(BaseModel):
out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)
if "time_conditioning" in kwargs:
out["time_context"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs["time_conditioning"])
out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device))
out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0])
return out
class Stable_Zero123(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
super().__init__(model_config, model_type, device=device)
self.cc_projection = ldm_patched.modules.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
self.cc_projection.weight.copy_(cc_projection_weight)
self.cc_projection.bias.copy_(cc_projection_bias)
def extra_conds(self, **kwargs):
out = {}
latent_image = kwargs.get("concat_latent_image", None)
noise = kwargs.get("noise", None)
if latent_image is None:
latent_image = torch.zeros_like(noise)
if latent_image.shape[1:] != noise.shape[1:]:
latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
if cross_attn.shape[-1] != 768:
cross_attn = self.cc_projection(cross_attn)
out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)
return out