VRAM optimisation via offloading

InstantIDNode.py

@@ -237,6 +237,7 @@ def INPUT_TYPES(cls):
                 "guidance_scale": ("FLOAT", {"default": 5, "min": 0, "max": 10, "display": "slider"}),
                 "enhance_face_region": ("BOOLEAN", {"default": True}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "vram_optimisation": (['off', 'level_1', 'level_2'], ),
             },
             "optional": {
                 "pose_image_optional": ("IMAGE",), 
@@ -247,7 +248,7 @@ def INPUT_TYPES(cls):
     FUNCTION = "id_generate_image"
     CATEGORY = "📷InstantID"
 
-    def id_generate_image(self, insightface, positive, negative, face_image, pipe, ip_adapter_scale, controlnet_conditioning_scale, steps, guidance_scale, seed, enhance_face_region, pose_image_optional=None):
+    def id_generate_image(self, insightface, positive, negative, face_image, pipe, ip_adapter_scale, controlnet_conditioning_scale, steps, guidance_scale, seed, enhance_face_region, vram_optimisation, pose_image_optional=None):
 
         face_image = resize_img(face_image)
 
@@ -297,6 +298,7 @@ def id_generate_image(self, insightface, positive, negative, face_image, pipe, i
             guidance_scale=guidance_scale,
             width=width,
             height=height,
+            vram_optimisation=vram_optimisation,
             return_dict=False
             )
 

ip_adapter/attention_processor.py

@@ -305,4 +305,148 @@ def __call__(
 
         hidden_states = hidden_states / attn.rescale_output_factor
 
-        return hidden_states
+        return hidden_states
+
+
+class IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        ip_hidden_states = F.scaled_dot_product_attention(
+            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+        )
+        with torch.no_grad():
+            self.attn_map = query @ ip_key.transpose(-2, -1).softmax(dim=-1)
+            #print(self.attn_map.shape)
+
+        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+        # region control
+        if len(region_control.prompt_image_conditioning) == 1:
+            region_mask = region_control.prompt_image_conditioning[0].get('region_mask', None)
+            if region_mask is not None:
+                query = query.reshape([-1, query.shape[-2], query.shape[-1]])
+                h, w = region_mask.shape[:2]
+                ratio = (h * w / query.shape[1]) ** 0.5
+                mask = F.interpolate(region_mask[None, None], scale_factor=1/ratio, mode='nearest').reshape([1, -1, 1])
+            else:
+                mask = torch.ones_like(ip_hidden_states)
+            ip_hidden_states = ip_hidden_states * mask
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+

pipeline_stable_diffusion_xl_instantid.py

@@ -44,6 +44,11 @@
 
 from .ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
 
+if is_torch2_available():
+    from .ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor
+else:
+    from .ip_adapter.attention_processor import IPAttnProcessor, AttnProcessor
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -132,13 +137,14 @@ def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,2
 
 class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
 
-    def cuda(self, dtype=torch.float16, use_xformers=False):
+    def cuda(self, dtype=torch.float16, use_xformers=True):
         self.to('cuda', dtype)
 
         if hasattr(self, 'image_proj_model'):
             self.image_proj_model.to(self.unet.device).to(self.unet.dtype)
 
         if use_xformers:
+#        if True:
             if is_xformers_available():
                 import xformers
                 from packaging import version
@@ -232,6 +238,20 @@ def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifie
         prompt_image_emb = self.image_proj_model(prompt_image_emb)
         return prompt_image_emb
 
+    def free_model_hooks(self):
+        r"""
+        Function that offloads all components, removes all model hooks that were added when using
+        `enable_model_cpu_offload` and then DOESN'T applies them again.
+        """
+        if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0:
+            # `enable_model_cpu_offload` has not be called, so silently do nothing
+            return
+
+        for hook in self._all_hooks:
+            # offload model and remove hook from model
+            hook.offload()
+            hook.remove()
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -255,6 +275,7 @@ def __call__(
         negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         image_embeds: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
+        vram_optimisation: bool = True,
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
@@ -457,6 +478,17 @@ def __call__(
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
 
+        # Level 1 VRAM optimisation
+        self.free_model_hooks()
+        # Move one model at once to the CPU - faster but needs more VRAM
+        if vram_optimisation == 'level_1':
+            self.enable_model_cpu_offload()
+
+        # Level 2 VRAM optimisation
+        # Slower but need minimal VRAM
+        if vram_optimisation == 'level_2' and self.device != torch.device("meta"):
+            self.enable_sequential_cpu_offload()
+
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
             batch_size = 1
@@ -745,7 +777,8 @@ def __call__(
             image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload all models
-        self.maybe_free_model_hooks()
+        # self.maybe_free_model_hooks()
+        self.free_model_hooks()
 
         if not return_dict:
             return (image,)