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main/lpw_stable_diffusion.py

@@ -13,13 +13,17 @@ from diffusers.configuration_utils import FrozenDict
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.loaders import FromSingleFileMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import (
     PIL_INTERPOLATION,
+    USE_PEFT_BACKEND,
     deprecate,
     logging,
+    scale_lora_layers,
+    unscale_lora_layers,
 )
 from diffusers.utils.torch_utils import randn_tensor
 
@@ -199,6 +203,7 @@ def get_unweighted_text_embeddings(
     text_input: torch.Tensor,
     chunk_length: int,
     no_boseos_middle: Optional[bool] = True,
+    clip_skip: Optional[int] = None,
 ):
     """
     When the length of tokens is a multiple of the capacity of the text encoder,
@@ -214,7 +219,20 @@ def get_unweighted_text_embeddings(
             # cover the head and the tail by the starting and the ending tokens
             text_input_chunk[:, 0] = text_input[0, 0]
             text_input_chunk[:, -1] = text_input[0, -1]
-            text_embedding = pipe.text_encoder(text_input_chunk)[0]
+            if clip_skip is None:
+                prompt_embeds = pipe.text_encoder(text_input_chunk.to(pipe.device))
+                text_embedding = prompt_embeds[0]
+            else:
+                prompt_embeds = pipe.text_encoder(text_input_chunk.to(pipe.device), output_hidden_states=True)
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                text_embedding = pipe.text_encoder.text_model.final_layer_norm(prompt_embeds)
 
             if no_boseos_middle:
                 if i == 0:
@@ -230,7 +248,10 @@ def get_unweighted_text_embeddings(
             text_embeddings.append(text_embedding)
         text_embeddings = torch.concat(text_embeddings, axis=1)
     else:
-        text_embeddings = pipe.text_encoder(text_input)[0]
+        if clip_skip is None:
+            clip_skip = 0
+        prompt_embeds = pipe.text_encoder(text_input, output_hidden_states=True)[-1][-(clip_skip + 1)]
+        text_embeddings = pipe.text_encoder.text_model.final_layer_norm(prompt_embeds)
     return text_embeddings
 
 
@@ -242,6 +263,8 @@ def get_weighted_text_embeddings(
     no_boseos_middle: Optional[bool] = False,
     skip_parsing: Optional[bool] = False,
     skip_weighting: Optional[bool] = False,
+    clip_skip=None,
+    lora_scale=None,
 ):
     r"""
     Prompts can be assigned with local weights using brackets. For example,
@@ -268,6 +291,16 @@ def get_weighted_text_embeddings(
         skip_weighting (`bool`, *optional*, defaults to `False`):
             Skip the weighting. When the parsing is skipped, it is forced True.
     """
+    # set lora scale so that monkey patched LoRA
+    # function of text encoder can correctly access it
+    if lora_scale is not None and isinstance(pipe, StableDiffusionLoraLoaderMixin):
+        pipe._lora_scale = lora_scale
+
+        # dynamically adjust the LoRA scale
+        if not USE_PEFT_BACKEND:
+            adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
+        else:
+            scale_lora_layers(pipe.text_encoder, lora_scale)
     max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
     if isinstance(prompt, str):
         prompt = [prompt]
@@ -334,10 +367,7 @@ def get_weighted_text_embeddings(
 
     # get the embeddings
     text_embeddings = get_unweighted_text_embeddings(
-        pipe,
-        prompt_tokens,
-        pipe.tokenizer.model_max_length,
-        no_boseos_middle=no_boseos_middle,
+        pipe, prompt_tokens, pipe.tokenizer.model_max_length, no_boseos_middle=no_boseos_middle, clip_skip=clip_skip
     )
     prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device)
     if uncond_prompt is not None:
@@ -346,6 +376,7 @@ def get_weighted_text_embeddings(
             uncond_tokens,
             pipe.tokenizer.model_max_length,
             no_boseos_middle=no_boseos_middle,
+            clip_skip=clip_skip,
         )
         uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device)
 
@@ -362,6 +393,11 @@ def get_weighted_text_embeddings(
             current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
             uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
 
+    if pipe.text_encoder is not None:
+        if isinstance(pipe, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(pipe.text_encoder, lora_scale)
+
     if uncond_prompt is not None:
         return text_embeddings, uncond_embeddings
     return text_embeddings, None
@@ -549,6 +585,8 @@ class StableDiffusionLongPromptWeightingPipeline(
         max_embeddings_multiples=3,
         prompt_embeds: Optional[torch.Tensor] = None,
         negative_prompt_embeds: Optional[torch.Tensor] = None,
+        clip_skip: Optional[int] = None,
+        lora_scale: Optional[float] = None,
     ):
         r"""
         Encodes the prompt into text encoder hidden states.
@@ -597,6 +635,8 @@ class StableDiffusionLongPromptWeightingPipeline(
                 prompt=prompt,
                 uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
                 max_embeddings_multiples=max_embeddings_multiples,
+                clip_skip=clip_skip,
+                lora_scale=lora_scale,
             )
             if prompt_embeds is None:
                 prompt_embeds = prompt_embeds1
@@ -790,6 +830,7 @@ class StableDiffusionLongPromptWeightingPipeline(
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
         is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        clip_skip: Optional[int] = None,
         callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
     ):
@@ -865,6 +906,9 @@ class StableDiffusionLongPromptWeightingPipeline(
             is_cancelled_callback (`Callable`, *optional*):
                 A function that will be called every `callback_steps` steps during inference. If the function returns
                 `True`, the inference will be cancelled.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
@@ -903,6 +947,7 @@ class StableDiffusionLongPromptWeightingPipeline(
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
+        lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
 
         # 3. Encode input prompt
         prompt_embeds = self._encode_prompt(
@@ -914,6 +959,8 @@ class StableDiffusionLongPromptWeightingPipeline(
             max_embeddings_multiples,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
+            clip_skip=clip_skip,
+            lora_scale=lora_scale,
         )
         dtype = prompt_embeds.dtype
 
@@ -1044,6 +1091,7 @@ class StableDiffusionLongPromptWeightingPipeline(
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
         is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        clip_skip=None,
         callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
     ):
@@ -1101,6 +1149,9 @@ class StableDiffusionLongPromptWeightingPipeline(
             is_cancelled_callback (`Callable`, *optional*):
                 A function that will be called every `callback_steps` steps during inference. If the function returns
                 `True`, the inference will be cancelled.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
@@ -1135,6 +1186,7 @@ class StableDiffusionLongPromptWeightingPipeline(
             return_dict=return_dict,
             callback=callback,
             is_cancelled_callback=is_cancelled_callback,
+            clip_skip=clip_skip,
             callback_steps=callback_steps,
             cross_attention_kwargs=cross_attention_kwargs,
         )

main/lpw_stable_diffusion_xl.py

@@ -25,21 +25,25 @@ from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import (
     FromSingleFileMixin,
     IPAdapterMixin,
-    StableDiffusionLoraLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
 from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
+from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import (
+    USE_PEFT_BACKEND,
     deprecate,
     is_accelerate_available,
     is_accelerate_version,
     is_invisible_watermark_available,
     logging,
     replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
 )
 from diffusers.utils.torch_utils import randn_tensor
 
@@ -261,6 +265,7 @@ def get_weighted_text_embeddings_sdxl(
     num_images_per_prompt: int = 1,
     device: Optional[torch.device] = None,
     clip_skip: Optional[int] = None,
+    lora_scale: Optional[int] = None,
 ):
     """
     This function can process long prompt with weights, no length limitation
@@ -281,6 +286,24 @@ def get_weighted_text_embeddings_sdxl(
     """
     device = device or pipe._execution_device
 
+    # set lora scale so that monkey patched LoRA
+    # function of text encoder can correctly access it
+    if lora_scale is not None and isinstance(pipe, StableDiffusionXLLoraLoaderMixin):
+        pipe._lora_scale = lora_scale
+
+        # dynamically adjust the LoRA scale
+        if pipe.text_encoder is not None:
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
+            else:
+                scale_lora_layers(pipe.text_encoder, lora_scale)
+
+        if pipe.text_encoder_2 is not None:
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(pipe.text_encoder_2, lora_scale)
+            else:
+                scale_lora_layers(pipe.text_encoder_2, lora_scale)
+
     if prompt_2:
         prompt = f"{prompt} {prompt_2}"
 
@@ -429,6 +452,16 @@ def get_weighted_text_embeddings_sdxl(
         bs_embed * num_images_per_prompt, -1
     )
 
+    if pipe.text_encoder is not None:
+        if isinstance(pipe, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(pipe.text_encoder, lora_scale)
+
+    if pipe.text_encoder_2 is not None:
+        if isinstance(pipe, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(pipe.text_encoder_2, lora_scale)
+
     return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
 
 
@@ -549,7 +582,7 @@ class SDXLLongPromptWeightingPipeline(
     StableDiffusionMixin,
     FromSingleFileMixin,
     IPAdapterMixin,
-    StableDiffusionLoraLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
     TextualInversionLoaderMixin,
 ):
     r"""
@@ -561,8 +594,8 @@ class SDXLLongPromptWeightingPipeline(
     The pipeline also inherits the following loading methods:
         - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
         - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
-        - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
         - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
 
     Args:
@@ -743,7 +776,7 @@ class SDXLLongPromptWeightingPipeline(
 
         # set lora scale so that monkey patched LoRA
         # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
             self._lora_scale = lora_scale
 
         if prompt is not None and isinstance(prompt, str):
@@ -1612,7 +1645,9 @@ class SDXLLongPromptWeightingPipeline(
                 image_embeds = torch.cat([negative_image_embeds, image_embeds])
 
         # 3. Encode input prompt
-        (self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None)
+        lora_scale = (
+            self._cross_attention_kwargs.get("scale", None) if self._cross_attention_kwargs is not None else None
+        )
 
         negative_prompt = negative_prompt if negative_prompt is not None else ""
 
@@ -1627,6 +1662,7 @@ class SDXLLongPromptWeightingPipeline(
             neg_prompt=negative_prompt,
             num_images_per_prompt=num_images_per_prompt,
             clip_skip=clip_skip,
+            lora_scale=lora_scale,
         )
         dtype = prompt_embeds.dtype