diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 1379b770..573cea6a 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -52,9 +52,9 @@ def init_(tensor): # feedforward class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out, dtype=None): + def __init__(self, dim_in, dim_out, dtype=None, device=None): super().__init__() - self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype) + self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) @@ -62,19 +62,19 @@ class GEGLU(nn.Module): class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( - comfy.ops.Linear(dim, inner_dim, dtype=dtype), + comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device), nn.GELU() - ) if not glu else GEGLU(dim, inner_dim, dtype=dtype) + ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device) self.net = nn.Sequential( project_in, nn.Dropout(dropout), - comfy.ops.Linear(inner_dim, dim_out, dtype=dtype) + comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device) ) def forward(self, x): @@ -90,8 +90,8 @@ def zero_module(module): return module -def Normalize(in_channels, dtype=None): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype) +def Normalize(in_channels, dtype=None, device=None): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) class SpatialSelfAttention(nn.Module): @@ -148,7 +148,7 @@ class SpatialSelfAttention(nn.Module): class CrossAttentionBirchSan(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) @@ -156,12 +156,12 @@ class CrossAttentionBirchSan(nn.Module): self.scale = dim_head ** -0.5 self.heads = heads - self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype) - self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) - self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) + self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_out = nn.Sequential( - comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), + comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout) ) @@ -245,7 +245,7 @@ class CrossAttentionBirchSan(nn.Module): class CrossAttentionDoggettx(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) @@ -253,12 +253,12 @@ class CrossAttentionDoggettx(nn.Module): self.scale = dim_head ** -0.5 self.heads = heads - self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype) - self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) - self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) + self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_out = nn.Sequential( - comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), + comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout) ) @@ -343,7 +343,7 @@ class CrossAttentionDoggettx(nn.Module): return self.to_out(r2) class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) @@ -351,12 +351,12 @@ class CrossAttention(nn.Module): self.scale = dim_head ** -0.5 self.heads = heads - self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype) - self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) - self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) + self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_out = nn.Sequential( - comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), + comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout) ) @@ -399,7 +399,7 @@ class CrossAttention(nn.Module): class MemoryEfficientCrossAttention(nn.Module): # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None): super().__init__() print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " f"{heads} heads.") @@ -409,11 +409,11 @@ class MemoryEfficientCrossAttention(nn.Module): self.heads = heads self.dim_head = dim_head - self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype) - self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) - self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) + self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout)) + self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) self.attention_op: Optional[Any] = None def forward(self, x, context=None, value=None, mask=None): @@ -450,7 +450,7 @@ class MemoryEfficientCrossAttention(nn.Module): return self.to_out(out) class CrossAttentionPytorch(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) @@ -458,11 +458,11 @@ class CrossAttentionPytorch(nn.Module): self.heads = heads self.dim_head = dim_head - self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype) - self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) - self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype) + self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout)) + self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) self.attention_op: Optional[Any] = None def forward(self, x, context=None, value=None, mask=None): @@ -508,17 +508,17 @@ else: class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, - disable_self_attn=False, dtype=None): + disable_self_attn=False, dtype=None, device=None): super().__init__() self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None, dtype=dtype) # is a self-attention if not self.disable_self_attn - self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype) + context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device) self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, - heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype) # is self-attn if context is none - self.norm1 = nn.LayerNorm(dim, dtype=dtype) - self.norm2 = nn.LayerNorm(dim, dtype=dtype) - self.norm3 = nn.LayerNorm(dim, dtype=dtype) + heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device) + self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device) + self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device) self.checkpoint = checkpoint self.n_heads = n_heads self.d_head = d_head @@ -648,34 +648,34 @@ class SpatialTransformer(nn.Module): def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False, use_linear=False, - use_checkpoint=True, dtype=None): + use_checkpoint=True, dtype=None, device=None): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] * depth self.in_channels = in_channels inner_dim = n_heads * d_head - self.norm = Normalize(in_channels, dtype=dtype) + self.norm = Normalize(in_channels, dtype=dtype, device=device) if not use_linear: self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, - padding=0, dtype=dtype) + padding=0, dtype=dtype, device=device) else: - self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype) + self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], - disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype) + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device) for d in range(depth)] ) if not use_linear: self.proj_out = nn.Conv2d(inner_dim,in_channels, kernel_size=1, stride=1, - padding=0, dtype=dtype) + padding=0, dtype=dtype, device=device) else: - self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype) + self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.use_linear = use_linear def forward(self, x, context=None, transformer_options={}): diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index 92f2438e..40060372 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -111,14 +111,14 @@ class Upsample(nn.Module): upsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None): + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: - self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype) + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) def forward(self, x, output_shape=None): assert x.shape[1] == self.channels @@ -160,7 +160,7 @@ class Downsample(nn.Module): downsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None): + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None): super().__init__() self.channels = channels self.out_channels = out_channels or channels @@ -169,7 +169,7 @@ class Downsample(nn.Module): stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device ) else: assert self.channels == self.out_channels @@ -208,7 +208,8 @@ class ResBlock(TimestepBlock): use_checkpoint=False, up=False, down=False, - dtype=None + dtype=None, + device=None, ): super().__init__() self.channels = channels @@ -220,19 +221,19 @@ class ResBlock(TimestepBlock): self.use_scale_shift_norm = use_scale_shift_norm self.in_layers = nn.Sequential( - nn.GroupNorm(32, channels, dtype=dtype), + nn.GroupNorm(32, channels, dtype=dtype, device=device), nn.SiLU(), - conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype), + conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device), ) self.updown = up or down if up: - self.h_upd = Upsample(channels, False, dims, dtype=dtype) - self.x_upd = Upsample(channels, False, dims, dtype=dtype) + self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) elif down: - self.h_upd = Downsample(channels, False, dims, dtype=dtype) - self.x_upd = Downsample(channels, False, dims, dtype=dtype) + self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) else: self.h_upd = self.x_upd = nn.Identity() @@ -240,15 +241,15 @@ class ResBlock(TimestepBlock): nn.SiLU(), linear( emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device ), ) self.out_layers = nn.Sequential( - nn.GroupNorm(32, self.out_channels, dtype=dtype), + nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device), nn.SiLU(), nn.Dropout(p=dropout), zero_module( - conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype) + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device) ), ) @@ -256,10 +257,10 @@ class ResBlock(TimestepBlock): self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( - dims, channels, self.out_channels, 3, padding=1, dtype=dtype + dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device ) else: - self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype) + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) def forward(self, x, emb): """ @@ -503,6 +504,7 @@ class UNetModel(nn.Module): use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, + device=None, ): super().__init__() if use_spatial_transformer: @@ -564,9 +566,9 @@ class UNetModel(nn.Module): time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( - linear(model_channels, time_embed_dim, dtype=self.dtype), + linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), - linear(time_embed_dim, time_embed_dim, dtype=self.dtype), + linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: @@ -579,9 +581,9 @@ class UNetModel(nn.Module): assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( - linear(adm_in_channels, time_embed_dim, dtype=self.dtype), + linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), - linear(time_embed_dim, time_embed_dim, dtype=self.dtype), + linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: @@ -590,7 +592,7 @@ class UNetModel(nn.Module): self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( - conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype) + conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) @@ -609,7 +611,8 @@ class UNetModel(nn.Module): dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype + dtype=self.dtype, + device=device, ) ] ch = mult * model_channels @@ -638,7 +641,7 @@ class UNetModel(nn.Module): ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) @@ -657,11 +660,12 @@ class UNetModel(nn.Module): use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, - dtype=self.dtype + dtype=self.dtype, + device=device, ) if resblock_updown else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device ) ) ) @@ -686,7 +690,8 @@ class UNetModel(nn.Module): dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype + dtype=self.dtype, + device=device, ), AttentionBlock( ch, @@ -697,7 +702,7 @@ class UNetModel(nn.Module): ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device ), ResBlock( ch, @@ -706,7 +711,8 @@ class UNetModel(nn.Module): dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype + dtype=self.dtype, + device=device, ), ) self._feature_size += ch @@ -724,7 +730,8 @@ class UNetModel(nn.Module): dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype + dtype=self.dtype, + device=device, ) ] ch = model_channels * mult @@ -753,7 +760,7 @@ class UNetModel(nn.Module): ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device ) ) if level and i == self.num_res_blocks[level]: @@ -768,24 +775,25 @@ class UNetModel(nn.Module): use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, - dtype=self.dtype + dtype=self.dtype, + device=device, ) if resblock_updown - else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype) + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( - nn.GroupNorm(32, ch, dtype=self.dtype), + nn.GroupNorm(32, ch, dtype=self.dtype, device=device), nn.SiLU(), - zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype)), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( - nn.GroupNorm(32, ch, dtype=self.dtype), - conv_nd(dims, model_channels, n_embed, 1), + nn.GroupNorm(32, ch, dtype=self.dtype, device=device), + conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) diff --git a/comfy/model_base.py b/comfy/model_base.py index d35f02a5..bf6983fc 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -12,14 +12,14 @@ class ModelType(Enum): V_PREDICTION = 2 class BaseModel(torch.nn.Module): - def __init__(self, model_config, model_type=ModelType.EPS): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__() unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) - self.diffusion_model = UNetModel(**unet_config) + self.diffusion_model = UNetModel(**unet_config, device=device) self.model_type = model_type self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: @@ -107,8 +107,8 @@ class BaseModel(torch.nn.Module): class SD21UNCLIP(BaseModel): - def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION): - super().__init__(model_config, model_type) + def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) def encode_adm(self, **kwargs): @@ -143,13 +143,13 @@ class SD21UNCLIP(BaseModel): return adm_out class SDInpaint(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS): - super().__init__(model_config, model_type) + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) self.concat_keys = ("mask", "masked_image") class SDXLRefiner(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS): - super().__init__(model_config, model_type) + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) def encode_adm(self, **kwargs): @@ -174,8 +174,8 @@ class SDXLRefiner(BaseModel): return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SDXL(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS): - super().__init__(model_config, model_type) + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) def encode_adm(self, **kwargs): diff --git a/comfy/sd.py b/comfy/sd.py index 70701ab6..922cbf21 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1169,8 +1169,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) offload_device = model_management.unet_offload_device() - model = model_config.get_model(sd, "model.diffusion_model.") - model = model.to(offload_device) + model = model_config.get_model(sd, "model.diffusion_model.", device=offload_device) model.load_model_weights(sd, "model.diffusion_model.") if output_vae: diff --git a/comfy/supported_models.py b/comfy/supported_models.py index b1c01fe8..95fc8f3f 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -109,8 +109,8 @@ class SDXLRefiner(supported_models_base.BASE): latent_format = latent_formats.SDXL - def get_model(self, state_dict, prefix=""): - return model_base.SDXLRefiner(self) + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXLRefiner(self, device=device) def process_clip_state_dict(self, state_dict): keys_to_replace = {} @@ -152,8 +152,8 @@ class SDXL(supported_models_base.BASE): else: return model_base.ModelType.EPS - def get_model(self, state_dict, prefix=""): - return model_base.SDXL(self, model_type=self.model_type(state_dict, prefix)) + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) def process_clip_state_dict(self, state_dict): keys_to_replace = {} diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index c5db6627..d0088bbd 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -53,13 +53,13 @@ class BASE: for x in self.unet_extra_config: self.unet_config[x] = self.unet_extra_config[x] - def get_model(self, state_dict, prefix=""): + def get_model(self, state_dict, prefix="", device=None): if self.inpaint_model(): - return model_base.SDInpaint(self, model_type=self.model_type(state_dict, prefix)) + return model_base.SDInpaint(self, model_type=self.model_type(state_dict, prefix), device=device) elif self.noise_aug_config is not None: - return model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix)) + return model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) else: - return model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix)) + return model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) def process_clip_state_dict(self, state_dict): return state_dict