Support for control-lora

docs/source/en/api/models/controlnet.md

@@ -33,6 +33,21 @@ url = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/m
 pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)
 ```
 
+## Loading from Control LoRA
+
+Control-LoRA is introduced by Stability AI in [stabilityai/control-lora](https://huggingface.co/stabilityai/control-lora) by adding low-rank parameter efficient fine tuning to ControlNet. This approach offers a more efficient and compact method to bring model control to a wider variety of consumer GPUs.
+
+```py
+from diffusers import ControlNetModel, UNet2DConditionModel
+
+lora_id = "stabilityai/control-lora"
+lora_filename = "control-LoRAs-rank128/control-lora-canny-rank128.safetensors"
+
+unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", torch_dtype=torch.bfloat16).to("cuda")
+controlnet = ControlNetModel.from_unet(unet).to(device="cuda", dtype=torch.bfloat16)
+controlnet.load_lora_adapter(lora_id, weight_name=lora_filename, prefix=None, controlnet_config=controlnet.config)
+```
+
 ## ControlNetModel
 
 [[autodoc]] ControlNetModel

examples/research_projects/control_lora/README.md

@@ -0,0 +1,41 @@
+# Control-LoRA inference example
+
+Control-LoRA is introduced by Stability AI in [stabilityai/control-lora](https://huggingface.co/stabilityai/control-lora) by adding low-rank parameter efficient fine tuning to ControlNet. This approach offers a more efficient and compact method to bring model control to a wider variety of consumer GPUs.
+
+## Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install .
+```
+
+Then cd in the example folder  and run
+```bash
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+## Inference on SDXL
+
+[stabilityai/control-lora](https://huggingface.co/stabilityai/control-lora) provides a set of Control-LoRA weights for SDXL. Here we use the `canny` condition to generate an image from a text prompt and a reference image.
+
+```bash
+python control_lora.py
+```
+
+## Acknowledgements
+
+- [stabilityai/control-lora](https://huggingface.co/stabilityai/control-lora)
+- [comfyanonymous/ControlNet-v1-1_fp16_safetensors](https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors)
+- [HighCWu/control-lora-v2](https://github.com/HighCWu/control-lora-v2)

examples/research_projects/control_lora/control_lora.py

@@ -0,0 +1,53 @@
+import cv2
+import numpy as np
+from PIL import Image
+import torch
+
+from diffusers import (
+    StableDiffusionXLControlNetPipeline,
+    ControlNetModel,
+    UNet2DConditionModel,
+)
+from diffusers import AutoencoderKL
+from diffusers.utils import load_image, make_image_grid
+
+pipe_id = "stabilityai/stable-diffusion-xl-base-1.0"
+lora_id = "stabilityai/control-lora"
+lora_filename = "control-LoRAs-rank128/control-lora-canny-rank128.safetensors"
+
+unet = UNet2DConditionModel.from_pretrained(pipe_id, subfolder="unet", torch_dtype=torch.bfloat16).to("cuda")
+controlnet = ControlNetModel.from_unet(unet).to(device="cuda", dtype=torch.bfloat16)
+controlnet.load_lora_adapter(lora_id, weight_name=lora_filename, prefix=None, controlnet_config=controlnet.config)
+
+prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
+negative_prompt = "low quality, bad quality, sketches"
+
+image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
+
+controlnet_conditioning_scale = 1.0  # recommended for good generalization
+
+vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.bfloat16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    pipe_id,
+    unet=unet,
+    controlnet=controlnet,
+    vae=vae,
+    torch_dtype=torch.bfloat16,
+    safety_checker=None,
+).to("cuda")
+
+image = np.array(image)
+image = cv2.Canny(image, 100, 200)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+image = Image.fromarray(image)
+
+images = pipe(
+    prompt, negative_prompt=negative_prompt, image=image,
+    controlnet_conditioning_scale=controlnet_conditioning_scale,
+    num_images_per_prompt=4
+).images
+
+final_image = [image] + images
+grid = make_image_grid(final_image, 1, 5)
+grid.save("hf-logo_canny.png")

src/diffusers/loaders/peft.py

@@ -27,6 +27,7 @@
     MIN_PEFT_VERSION,
     USE_PEFT_BACKEND,
     check_peft_version,
+    convert_sai_sd_control_lora_state_dict_to_peft,
     convert_unet_state_dict_to_peft,
     delete_adapter_layers,
     get_adapter_name,
@@ -232,6 +233,13 @@ def load_lora_adapter(
             if "lora_A" not in first_key:
                 state_dict = convert_unet_state_dict_to_peft(state_dict)
 
+            # Control LoRA from SAI is different from BFL Control LoRA
+            # https://huggingface.co/stabilityai/control-lora
+            # https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors
+            is_sai_sd_control_lora = "lora_controlnet" in state_dict
+            if is_sai_sd_control_lora:
+                state_dict = convert_sai_sd_control_lora_state_dict_to_peft(state_dict)
+
             rank = {}
             for key, val in state_dict.items():
                 # Cannot figure out rank from lora layers that don't have at least 2 dimensions.
@@ -263,6 +271,14 @@ def load_lora_adapter(
                 adapter_name=adapter_name,
             )
 
+            # Adjust LoRA config for Control LoRA
+            if is_sai_sd_control_lora:
+                lora_config.lora_alpha = lora_config.r
+                lora_config.alpha_pattern = lora_config.rank_pattern
+                lora_config.bias = "all"
+                lora_config.modules_to_save = lora_config.exclude_modules
+                lora_config.exclude_modules = None
+
             # <Unsafe code
             # We can be sure that the following works as it just sets attention processors, lora layers and puts all in the same dtype
             # Now we remove any existing hooks to `_pipeline`.

src/diffusers/models/controlnets/controlnet.py

@@ -19,6 +19,7 @@
 from torch.nn import functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import PeftAdapterMixin
 from ...loaders.single_file_model import FromOriginalModelMixin
 from ...utils import BaseOutput, logging
 from ..attention import AttentionMixin
@@ -106,7 +107,7 @@ def forward(self, conditioning):
         return embedding
 
 
-class ControlNetModel(ModelMixin, AttentionMixin, ConfigMixin, FromOriginalModelMixin):
+class ControlNetModel(ModelMixin, AttentionMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
     """
     A ControlNet model.
 

src/diffusers/utils/__init__.py

@@ -143,6 +143,7 @@
 from .remote_utils import remote_decode
 from .state_dict_utils import (
     convert_all_state_dict_to_peft,
+    convert_sai_sd_control_lora_state_dict_to_peft,
     convert_state_dict_to_diffusers,
     convert_state_dict_to_kohya,
     convert_state_dict_to_peft,

src/diffusers/utils/state_dict_utils.py

@@ -56,6 +56,36 @@ class StateDictType(enum.Enum):
     ".to_out.lora_magnitude_vector": ".to_out.0.lora_magnitude_vector",
 }
 
+CONTROL_LORA_TO_DIFFUSERS = {
+    ".to_q.down": ".to_q.lora_A.weight",
+    ".to_q.up": ".to_q.lora_B.weight",
+    ".to_k.down": ".to_k.lora_A.weight",
+    ".to_k.up": ".to_k.lora_B.weight",
+    ".to_v.down": ".to_v.lora_A.weight",
+    ".to_v.up": ".to_v.lora_B.weight",
+    ".to_out.0.down": ".to_out.0.lora_A.weight",
+    ".to_out.0.up": ".to_out.0.lora_B.weight",
+    ".ff.net.0.proj.down": ".ff.net.0.proj.lora_A.weight",
+    ".ff.net.0.proj.up": ".ff.net.0.proj.lora_B.weight",
+    ".ff.net.2.down": ".ff.net.2.lora_A.weight",
+    ".ff.net.2.up": ".ff.net.2.lora_B.weight",
+    ".proj_in.down": ".proj_in.lora_A.weight",
+    ".proj_in.up": ".proj_in.lora_B.weight",
+    ".proj_out.down": ".proj_out.lora_A.weight",
+    ".proj_out.up": ".proj_out.lora_B.weight",
+    ".conv.down": ".conv.lora_A.weight",
+    ".conv.up": ".conv.lora_B.weight",
+    **{f".conv{i}.down": f".conv{i}.lora_A.weight" for i in range(1, 3)},
+    **{f".conv{i}.up": f".conv{i}.lora_B.weight" for i in range(1, 3)},
+    "conv_in.down": "conv_in.lora_A.weight",
+    "conv_in.up": "conv_in.lora_B.weight",
+    ".conv_shortcut.down": ".conv_shortcut.lora_A.weight",
+    ".conv_shortcut.up": ".conv_shortcut.lora_B.weight",
+    **{f".linear_{i}.down": f".linear_{i}.lora_A.weight" for i in range(1, 3)},
+    **{f".linear_{i}.up": f".linear_{i}.lora_B.weight" for i in range(1, 3)},
+    "time_emb_proj.down": "time_emb_proj.lora_A.weight",
+    "time_emb_proj.up": "time_emb_proj.lora_B.weight",
+}
 
 DIFFUSERS_TO_PEFT = {
     ".q_proj.lora_linear_layer.up": ".q_proj.lora_B",
@@ -259,6 +289,155 @@ def convert_unet_state_dict_to_peft(state_dict):
     return convert_state_dict(state_dict, mapping)
 
 
+def convert_sai_sd_control_lora_state_dict_to_peft(state_dict):
+    def _convert_controlnet_to_diffusers(state_dict):
+        is_sdxl = "input_blocks.11.0.in_layers.0.weight" not in state_dict
+        logger.info(f"Using ControlNet lora ({'SDXL' if is_sdxl else 'SD15'})")
+
+        # Retrieves the keys for the input blocks only
+        num_input_blocks = len(
+            {".".join(layer.split(".")[:2]) for layer in state_dict if "input_blocks" in layer}
+        )
+        input_blocks = {
+            layer_id: [key for key in state_dict if f"input_blocks.{layer_id}" in key]
+            for layer_id in range(num_input_blocks)
+        }
+        layers_per_block = 2
+
+        # op blocks
+        op_blocks = [key for key in state_dict if "0.op" in key]
+
+        converted_state_dict = {}
+        # Conv in layers
+        for key in input_blocks[0]:
+            diffusers_key = key.replace("input_blocks.0.0", "conv_in")
+            converted_state_dict[diffusers_key] = state_dict.get(key)
+
+        # controlnet time embedding blocks
+        time_embedding_blocks = [key for key in state_dict if "time_embed" in key]
+        for key in time_embedding_blocks:
+            diffusers_key = (key.replace("time_embed.0", "time_embedding.linear_1")
+                .replace("time_embed.2", "time_embedding.linear_2")
+            )
+            converted_state_dict[diffusers_key] = state_dict.get(key)
+
+        # controlnet label embedding blocks
+        label_embedding_blocks = [key for key in state_dict if "label_emb" in key]
+        for key in label_embedding_blocks:
+            diffusers_key = (key.replace("label_emb.0.0", "add_embedding.linear_1")
+                .replace("label_emb.0.2", "add_embedding.linear_2")
+            )
+            converted_state_dict[diffusers_key] = state_dict.get(key)
+
+        # Down blocks
+        for i in range(1, num_input_blocks):
+            block_id = (i - 1) // (layers_per_block + 1)
+            layer_in_block_id = (i - 1) % (layers_per_block + 1)
+
+            resnets = [
+                key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+            ]
+            for key in resnets:
+                diffusers_key = (key.replace("in_layers.0", "norm1")
+                    .replace("in_layers.2", "conv1")
+                    .replace("out_layers.0", "norm2")
+                    .replace("out_layers.3", "conv2")
+                    .replace("emb_layers.1", "time_emb_proj")
+                    .replace("skip_connection", "conv_shortcut")
+                )
+                diffusers_key = diffusers_key.replace(
+                    f"input_blocks.{i}.0", f"down_blocks.{block_id}.resnets.{layer_in_block_id}"
+                )
+                converted_state_dict[diffusers_key] = state_dict.get(key)
+
+            if f"input_blocks.{i}.0.op.bias" in state_dict:
+                for key in [key for key in op_blocks if f"input_blocks.{i}.0.op" in key]:
+                    diffusers_key = key.replace(f"input_blocks.{i}.0.op", f"down_blocks.{block_id}.downsamplers.0.conv")
+                    converted_state_dict[diffusers_key] = state_dict.get(key)
+
+            attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+            if attentions:
+                for key in attentions:
+                    diffusers_key = key.replace(
+                        f"input_blocks.{i}.1", f"down_blocks.{block_id}.attentions.{layer_in_block_id}"
+                    )
+                    converted_state_dict[diffusers_key] = state_dict.get(key)
+
+        # controlnet down blocks
+        for i in range(num_input_blocks):
+            converted_state_dict[f"controlnet_down_blocks.{i}.weight"] = state_dict.get(f"zero_convs.{i}.0.weight")
+            converted_state_dict[f"controlnet_down_blocks.{i}.bias"] = state_dict.get(f"zero_convs.{i}.0.bias")
+
+        # Retrieves the keys for the middle blocks only
+        num_middle_blocks = len(
+            {".".join(layer.split(".")[:2]) for layer in state_dict if "middle_block" in layer}
+        )
+        middle_blocks = {
+            layer_id: [key for key in state_dict if f"middle_block.{layer_id}" in key]
+            for layer_id in range(num_middle_blocks)
+        }
+
+        # Mid blocks
+        for key in middle_blocks.keys():
+            diffusers_key = max(key - 1, 0)
+            if key % 2 == 0:
+                for k in middle_blocks[key]:
+                    diffusers_key_hf = (k.replace("in_layers.0", "norm1")
+                        .replace("in_layers.2", "conv1")
+                        .replace("out_layers.0", "norm2")
+                        .replace("out_layers.3", "conv2")
+                        .replace("emb_layers.1", "time_emb_proj")
+                        .replace("skip_connection", "conv_shortcut")
+                    )
+                    diffusers_key_hf = diffusers_key_hf.replace(
+                        f"middle_block.{key}", f"mid_block.resnets.{diffusers_key}"
+                    )
+                    converted_state_dict[diffusers_key_hf] = state_dict.get(k)
+            else:
+                for k in middle_blocks[key]:
+                    diffusers_key_hf = k.replace(
+                        f"middle_block.{key}", f"mid_block.attentions.{diffusers_key}"
+                    )
+                    converted_state_dict[diffusers_key_hf] = state_dict.get(k)
+
+        # mid block
+        converted_state_dict["controlnet_mid_block.weight"] = state_dict.get("middle_block_out.0.weight")
+        converted_state_dict["controlnet_mid_block.bias"] = state_dict.get("middle_block_out.0.bias")
+
+        # controlnet cond embedding blocks
+        cond_embedding_blocks = {
+            ".".join(layer.split(".")[:2])
+            for layer in state_dict
+            if "input_hint_block" in layer and ("input_hint_block.0" not in layer) and ("input_hint_block.14" not in layer)
+        }
+        num_cond_embedding_blocks = len(cond_embedding_blocks)
+
+        for idx in range(1, num_cond_embedding_blocks + 1):
+            diffusers_idx = idx - 1
+            cond_block_id = 2 * idx
+
+            converted_state_dict[f"controlnet_cond_embedding.blocks.{diffusers_idx}.weight"] = state_dict.get(
+                f"input_hint_block.{cond_block_id}.weight"
+            )
+            converted_state_dict[f"controlnet_cond_embedding.blocks.{diffusers_idx}.bias"] = state_dict.get(
+                f"input_hint_block.{cond_block_id}.bias"
+            )
+
+        for key in [key for key in state_dict if "input_hint_block.0" in key]:
+            diffusers_key = key.replace("input_hint_block.0", "controlnet_cond_embedding.conv_in")
+            converted_state_dict[diffusers_key] = state_dict.get(key)
+
+        for key in [key for key in state_dict if "input_hint_block.14" in key]:
+            diffusers_key = key.replace(f"input_hint_block.14", "controlnet_cond_embedding.conv_out")
+            converted_state_dict[diffusers_key] = state_dict.get(key)
+
+        return converted_state_dict
+
+    state_dict = _convert_controlnet_to_diffusers(state_dict)
+    mapping = CONTROL_LORA_TO_DIFFUSERS
+    return convert_state_dict(state_dict, mapping)
+
+
 def convert_all_state_dict_to_peft(state_dict):
     r"""
     Attempts to first `convert_state_dict_to_peft`, and if it doesn't detect `lora_linear_layer` for a valid