Merge branch 'development' into sync-dev-with-main

README.md

@@ -65,14 +65,11 @@ requests. Be sure to use the provided templates. They will help aid diagnose iss
 
 ### Installation
 
-This fork is supported across multiple platforms. You can find individual installation instructions
+This fork is supported across Linux, Windows and Macintosh. Linux
-below.
+users can use either an Nvidia-based card (with CUDA support) or an
-
+AMD card (using the ROCm driver). For full installation and upgrade
-- #### [Linux](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_LINUX/)
+instructions, please see:
-
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
-- #### [Windows](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_WINDOWS/)
-
-- #### [Macintosh](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_MAC/)
 
 ### Hardware Requirements
 

configs/stable-diffusion/v1-inference.yaml

@@ -30,7 +30,7 @@ model:
       target: ldm.modules.embedding_manager.EmbeddingManager
       params:
         placeholder_strings: ["*"]
-        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        initializer_words: ['sculpture']
         per_image_tokens: false
         num_vectors_per_token: 1
         progressive_words: False

configs/stable-diffusion/v1-inpainting-inference.yaml

@@ -30,7 +30,7 @@ model:
       target: ldm.modules.embedding_manager.EmbeddingManager
       params:
         placeholder_strings: ["*"]
-        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        initializer_words: ['sculpture']
         per_image_tokens: false
         num_vectors_per_token: 1
         progressive_words: False

configs/stable-diffusion/v1-m1-finetune.yaml

@@ -22,7 +22,7 @@ model:
       target: ldm.modules.embedding_manager.EmbeddingManager
       params:
         placeholder_strings: ["*"]
-        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        initializer_words: ['sculpture']
         per_image_tokens: false
         num_vectors_per_token: 6
         progressive_words: False

docs/index.md

@@ -80,12 +80,11 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
 
 ## :octicons-package-dependencies-24: Installation
 
-This fork is supported across multiple platforms. You can find individual
+This fork is supported across Linux, Windows and Macintosh. Linux
-installation instructions below.
+users can use either an Nvidia-based card (with CUDA support) or an
-
+AMD card (using the ROCm driver). For full installation and upgrade
-- :fontawesome-brands-linux: [Linux](installation/INSTALL_LINUX.md)
+instructions, please see:
-- :fontawesome-brands-windows: [Windows](installation/INSTALL_WINDOWS.md)
+[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
-- :fontawesome-brands-apple: [Macintosh](installation/INSTALL_MAC.md)
 
 ## :fontawesome-solid-computer: Hardware Requirements
 
@@ -123,6 +122,14 @@ You wil need one of the following:
 
 ## :octicons-log-16: Latest Changes
 
+### v2.1.3 <small>(13 November 2022)</small>
+
+- A choice of installer scripts that automate installation and configuration. See [Installation](https://github.com/invoke-ai/InvokeAI/blob/2.1.3-rc6/docs/installation/INSTALL.md).
+- A streamlined manual installation process that works for both Conda and PIP-only installs. See [Manual Installation](https://github.com/invoke-ai/InvokeAI/blob/2.1.3-rc6/docs/installation/INSTALL_MANUAL.md).
+- The ability to save frequently-used startup options (model to load, steps, sampler, etc) in a `.invokeai` file. See [Client](https://github.com/invoke-ai/InvokeAI/blob/2.1.3-rc6/docs/features/CLI.md)
+- Support for AMD GPU cards (non-CUDA) on Linux machines.
+- Multiple bugs and edge cases squashed.
+
 ### v2.1.0 <small>(2 November 2022)</small>
 
 - [Inpainting](https://invoke-ai.github.io/InvokeAI/features/INPAINTING/)

installer/install.bat

@@ -19,7 +19,7 @@ set INSTALL_ENV_DIR=%cd%\installer_files\env
 @rem https://mamba.readthedocs.io/en/latest/installation.html
 set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
 set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-set RELEASE_SOURCEBALL=/archive/refs/heads/v2.1.3.tar.gz
+set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
 set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
 set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
 

installer/install.sh

@@ -74,7 +74,7 @@ fi
 INSTALL_ENV_DIR="$(pwd)/installer_files/env"
 MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
 RELEASE_URL=https://github.com/invoke-ai/InvokeAI
-RELEASE_SOURCEBALL=/archive/refs/heads/v2.1.3.tar.gz
+RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
 PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
 if [ "$OS_NAME" == "darwin" ]; then
     PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz

ldm/invoke/conditioning.py

@@ -14,7 +14,7 @@ import torch
 
 from .prompt_parser import PromptParser, Blend, FlattenedPrompt, \
     CrossAttentionControlledFragment, CrossAttentionControlSubstitute, Fragment, log_tokenization
-from ..models.diffusion.cross_attention_control import CrossAttentionControl
+from ..models.diffusion import cross_attention_control
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ..modules.encoders.modules import WeightedFrozenCLIPEmbedder
 
@@ -49,7 +49,7 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
     parsed_negative_prompt: FlattenedPrompt = pp.parse_conjunction(unconditioned_words).prompts[0]
 
     conditioning = None
-    cac_args:CrossAttentionControl.Arguments = None
+    cac_args:cross_attention_control.Arguments = None
 
     if type(parsed_prompt) is Blend:
         blend: Blend = parsed_prompt
@@ -120,7 +120,7 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
             conditioning = original_embeddings
             edited_conditioning = edited_embeddings
             #print('>> got edit_opcodes', edit_opcodes, 'options', edit_options)
-            cac_args = CrossAttentionControl.Arguments(
+            cac_args = cross_attention_control.Arguments(
                 edited_conditioning = edited_conditioning,
                 edit_opcodes = edit_opcodes,
                 edit_options = edit_options

ldm/models/diffusion/cross_attention_control.py

@@ -7,9 +7,6 @@ import torch
 # https://github.com/bloc97/CrossAttentionControl
 
 
-
-class CrossAttentionControl:
-
 class Arguments:
     def __init__(self, edited_conditioning: torch.Tensor, edit_opcodes: list[tuple], edit_options: dict):
         """
@@ -31,18 +28,30 @@ class CrossAttentionControl:
             self.edit_options = non_none_edit_options[0]
 
 
+class CrossAttentionType(enum.Enum):
+    SELF = 1
+    TOKENS = 2
+
+
 class Context:
 
+    cross_attention_mask: Optional[torch.Tensor]
+    cross_attention_index_map: Optional[torch.Tensor]
+
     class Action(enum.Enum):
         NONE = 0
         SAVE = 1,
         APPLY = 2
 
-        def __init__(self, arguments: 'CrossAttentionControl.Arguments', step_count: int):
+    def __init__(self, arguments: Arguments, step_count: int):
         """
         :param arguments: Arguments for the cross-attention control process
         :param step_count: The absolute total number of steps of diffusion (for img2img this is likely larger than the number of steps that will actually run)
         """
+        self.cross_attention_mask = None
+        self.cross_attention_index_map = None
+        self.self_cross_attention_action = Context.Action.NONE
+        self.tokens_cross_attention_action = Context.Action.NONE
         self.arguments = arguments
         self.step_count = step_count
 
@@ -54,58 +63,56 @@ class CrossAttentionControl:
         self.clear_requests(cleanup=True)
 
     def register_cross_attention_modules(self, model):
-            for name,module in CrossAttentionControl.get_attention_modules(model,
+        for name,module in get_attention_modules(model, CrossAttentionType.SELF):
-                                                                                 CrossAttentionControl.CrossAttentionType.SELF):
             self.self_cross_attention_module_identifiers.append(name)
-            for name,module in CrossAttentionControl.get_attention_modules(model,
+        for name,module in get_attention_modules(model, CrossAttentionType.TOKENS):
-                                                                                   CrossAttentionControl.CrossAttentionType.TOKENS):
             self.tokens_cross_attention_module_identifiers.append(name)
 
-        def request_save_attention_maps(self, cross_attention_type: 'CrossAttentionControl.CrossAttentionType'):
+    def request_save_attention_maps(self, cross_attention_type: CrossAttentionType):
-            if cross_attention_type == CrossAttentionControl.CrossAttentionType.SELF:
+        if cross_attention_type == CrossAttentionType.SELF:
-                self.self_cross_attention_action = CrossAttentionControl.Context.Action.SAVE
+            self.self_cross_attention_action = Context.Action.SAVE
         else:
-                self.tokens_cross_attention_action = CrossAttentionControl.Context.Action.SAVE
+            self.tokens_cross_attention_action = Context.Action.SAVE
 
-        def request_apply_saved_attention_maps(self, cross_attention_type: 'CrossAttentionControl.CrossAttentionType'):
+    def request_apply_saved_attention_maps(self, cross_attention_type: CrossAttentionType):
-            if cross_attention_type == CrossAttentionControl.CrossAttentionType.SELF:
+        if cross_attention_type == CrossAttentionType.SELF:
-                self.self_cross_attention_action = CrossAttentionControl.Context.Action.APPLY
+            self.self_cross_attention_action = Context.Action.APPLY
         else:
-                self.tokens_cross_attention_action = CrossAttentionControl.Context.Action.APPLY
+            self.tokens_cross_attention_action = Context.Action.APPLY
 
     def is_tokens_cross_attention(self, module_identifier) -> bool:
         return module_identifier in self.tokens_cross_attention_module_identifiers
 
     def get_should_save_maps(self, module_identifier: str) -> bool:
         if module_identifier in self.self_cross_attention_module_identifiers:
-                return self.self_cross_attention_action == CrossAttentionControl.Context.Action.SAVE
+            return self.self_cross_attention_action == Context.Action.SAVE
         elif module_identifier in self.tokens_cross_attention_module_identifiers:
-                return self.tokens_cross_attention_action == CrossAttentionControl.Context.Action.SAVE
+            return self.tokens_cross_attention_action == Context.Action.SAVE
         return False
 
     def get_should_apply_saved_maps(self, module_identifier: str) -> bool:
         if module_identifier in self.self_cross_attention_module_identifiers:
-                return self.self_cross_attention_action == CrossAttentionControl.Context.Action.APPLY
+            return self.self_cross_attention_action == Context.Action.APPLY
         elif module_identifier in self.tokens_cross_attention_module_identifiers:
-                return self.tokens_cross_attention_action == CrossAttentionControl.Context.Action.APPLY
+            return self.tokens_cross_attention_action == Context.Action.APPLY
         return False
 
     def get_active_cross_attention_control_types_for_step(self, percent_through:float=None)\
-                -> list['CrossAttentionControl.CrossAttentionType']:
+            -> list[CrossAttentionType]:
         """
         Should cross-attention control be applied on the given step?
         :param percent_through: How far through the step sequence are we (0.0=pure noise, 1.0=completely denoised image). Expected range 0.0..<1.0.
         :return: A list of attention types that cross-attention control should be performed for on the given step. May be [].
         """
         if percent_through is None:
-                return [CrossAttentionControl.CrossAttentionType.SELF, CrossAttentionControl.CrossAttentionType.TOKENS]
+            return [CrossAttentionType.SELF, CrossAttentionType.TOKENS]
 
         opts = self.arguments.edit_options
         to_control = []
-            if opts['s_start'] <= percent_through and percent_through < opts['s_end']:
+        if opts['s_start'] <= percent_through < opts['s_end']:
-                to_control.append(CrossAttentionControl.CrossAttentionType.SELF)
+            to_control.append(CrossAttentionType.SELF)
-            if opts['t_start'] <= percent_through and percent_through < opts['t_end']:
+        if opts['t_start'] <= percent_through < opts['t_end']:
-                to_control.append(CrossAttentionControl.CrossAttentionType.TOKENS)
+            to_control.append(CrossAttentionType.TOKENS)
         return to_control
 
     def save_slice(self, identifier: str, slice: torch.Tensor, dim: Optional[int], offset: int,
@@ -132,7 +139,7 @@ class CrossAttentionControl:
                     f"slice_size mismatch: expected slice_size={slice_size}, have {saved_attention_dict['slice_size']}")
             return saved_attention_dict['slices'][requested_offset]
 
-            if saved_attention_dict['dim'] == None:
+        if saved_attention_dict['dim'] is None:
             whole_saved_attention = saved_attention_dict['slices'][0]
             if requested_dim == 0:
                 return whole_saved_attention[requested_offset:requested_offset + slice_size]
@@ -141,15 +148,15 @@ class CrossAttentionControl:
 
         raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
 
-        def get_slicing_strategy(self, identifier: str) -> Optional[tuple[int, int]]:
+    def get_slicing_strategy(self, identifier: str) -> tuple[Optional[int], Optional[int]]:
         saved_attention = self.saved_cross_attention_maps.get(identifier, None)
         if saved_attention is None:
             return None, None
         return saved_attention['dim'], saved_attention['slice_size']
 
     def clear_requests(self, cleanup=True):
-            self.tokens_cross_attention_action = CrossAttentionControl.Context.Action.NONE
+        self.tokens_cross_attention_action = Context.Action.NONE
-            self.self_cross_attention_action = CrossAttentionControl.Context.Action.NONE
+        self.self_cross_attention_action = Context.Action.NONE
         if cleanup:
             self.saved_cross_attention_maps = {}
 
@@ -158,12 +165,12 @@ class CrossAttentionControl:
             for offset, slice in map_dict['slices'].items():
                 map_dict[offset] = slice.to('cpu')
 
-    @classmethod
-    def remove_cross_attention_control(cls, model):
-        cls.remove_attention_function(model)
 
-    @classmethod
+def remove_cross_attention_control(model):
-    def setup_cross_attention_control(cls, model, context: Context):
+    remove_attention_function(model)
+
+
+def setup_cross_attention_control(model, context: Context):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -191,22 +198,16 @@ class CrossAttentionControl:
     context.register_cross_attention_modules(model)
     context.cross_attention_mask = mask.to(device)
     context.cross_attention_index_map = indices.to(device)
-        cls.inject_attention_function(model, context)
+    inject_attention_function(model, context)
 
 
-    class CrossAttentionType(enum.Enum):
+def get_attention_modules(model, which: CrossAttentionType):
-        SELF = 1
+    which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
-        TOKENS = 2
-
-    @classmethod
-    def get_attention_modules(cls, model, which: CrossAttentionType):
-        which_attn = "attn1" if which is cls.CrossAttentionType.SELF else "attn2"
     return [(name,module) for name, module in model.named_modules() if
                   type(module).__name__ == "CrossAttention" and which_attn in name]
 
 
-    @classmethod
+def inject_attention_function(unet, context: Context):
-    def inject_attention_function(cls, unet, context: 'CrossAttentionControl.Context'):
     # ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
 
     def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim, offset, slice_size):
@@ -251,12 +252,11 @@ class CrossAttentionControl:
             module.set_slicing_strategy_getter(lambda module, module_identifier=name: \
                                                    context.get_slicing_strategy(module_identifier))
 
-    @classmethod
+
-    def remove_attention_function(cls, unet):
+def remove_attention_function(unet):
     # clear wrangler callback
     for name, module in unet.named_modules():
         module_name = type(module).__name__
         if module_name == "CrossAttention":
             module.set_attention_slice_wrangler(None)
             module.set_slicing_strategy_getter(None)
-

ldm/models/diffusion/shared_invokeai_diffusion.py

@@ -4,7 +4,8 @@ from typing import Callable, Optional, Union
 
 import torch
 
-from ldm.models.diffusion.cross_attention_control import CrossAttentionControl
+from ldm.models.diffusion.cross_attention_control import Arguments, \
+    remove_cross_attention_control, setup_cross_attention_control, Context
 from ldm.modules.attention import get_mem_free_total
 
 
@@ -20,7 +21,7 @@ class InvokeAIDiffuserComponent:
 
 
     class ExtraConditioningInfo:
-        def __init__(self, cross_attention_control_args: Optional[CrossAttentionControl.Arguments]):
+        def __init__(self, cross_attention_control_args: Optional[Arguments]):
             self.cross_attention_control_args = cross_attention_control_args
 
         @property
@@ -40,16 +41,16 @@ class InvokeAIDiffuserComponent:
 
     def setup_cross_attention_control(self, conditioning: ExtraConditioningInfo, step_count: int):
         self.conditioning = conditioning
-        self.cross_attention_control_context = CrossAttentionControl.Context(
+        self.cross_attention_control_context = Context(
             arguments=self.conditioning.cross_attention_control_args,
             step_count=step_count
         )
-        CrossAttentionControl.setup_cross_attention_control(self.model, self.cross_attention_control_context)
+        setup_cross_attention_control(self.model, self.cross_attention_control_context)
 
     def remove_cross_attention_control(self):
         self.conditioning = None
         self.cross_attention_control_context = None
-        CrossAttentionControl.remove_cross_attention_control(self.model)
+        remove_cross_attention_control(self.model)
 
 
 
@@ -71,7 +72,7 @@ class InvokeAIDiffuserComponent:
 
 
         cross_attention_control_types_to_do = []
-        context: CrossAttentionControl.Context = self.cross_attention_control_context
+        context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
             percent_through = self.estimate_percent_through(step_index, sigma)
             cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
@@ -133,7 +134,7 @@ class InvokeAIDiffuserComponent:
         # representing batched uncond + cond, but then when it comes to applying the saved attention, the
         # wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
         # todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
-        context:CrossAttentionControl.Context = self.cross_attention_control_context
+        context:Context = self.cross_attention_control_context
 
         try:
             unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)