Move IP Adapter Scripts to research project (#9960)

* Move files to research-projects.

* docs: add IP Adapter training instructions

* Delete venv

* Update examples/ip_adapter/tutorial_train_sdxl.py

Co-authored-by: Sayak Paul <[email protected]>

* Cherry-picked commits and re-moved files
to research_projects.

* make style.

* Update toctree and delete ip_adapter.

* Nit Fix

* Fix nit.

* Fix nit.

* Create training script for single GPU and set
model format to .safetensors

* Add sample inference script and restore _toctree

* Restore toctree.yaml

* fix spacing.

* Update toctree.yaml

---------

Co-authored-by: AMohamedAakhil <[email protected]>
Co-authored-by: BootesVoid <[email protected]>
Co-authored-by: Sayak Paul <[email protected]>

Author: 3 people

examples/research_projects/ip_adapter/README.md

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+# IP Adapter Training Example 
+
+[IP Adapter](https://arxiv.org/abs/2308.06721) is a novel approach designed to enhance text-to-image models such as Stable Diffusion by enabling them to generate images based on image prompts rather than text prompts alone. Unlike traditional methods that rely solely on complex text prompts, IP Adapter introduces the concept of using image prompts, leveraging the idea that "an image is worth a thousand words." By decoupling cross-attention layers for text and image features, IP Adapter effectively integrates image prompts into the generation process without the need for extensive fine-tuning or large computing resources.
+
+## Training locally with PyTorch
+
+### 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 -e .
+```
+
+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
+```
+
+Or for a default accelerate configuration without answering questions about your environment
+
+```bash
+accelerate config default
+```
+
+Or if your environment doesn't support an interactive shell e.g. a notebook
+
+```python
+from accelerate.utils import write_basic_config
+write_basic_config()
+```
+
+Certainly! Below is the documentation in pure Markdown format:
+
+### Accelerate Launch Command Documentation
+
+#### Description:
+The Accelerate launch command is used to train a model using multiple GPUs and mixed precision training. It launches the training script `tutorial_train_ip-adapter.py` with specified parameters and configurations.
+
+#### Usage Example:
+
+```
+accelerate launch --mixed_precision "fp16" \
+tutorial_train_ip-adapter.py \
+--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
+--image_encoder_path="{image_encoder_path}" \
+--data_json_file="{data.json}" \
+--data_root_path="{image_path}" \
+--mixed_precision="fp16" \
+--resolution=512 \
+--train_batch_size=8 \
+--dataloader_num_workers=4 \
+--learning_rate=1e-04 \
+--weight_decay=0.01 \
+--output_dir="{output_dir}" \
+--save_steps=10000
+```
+
+### Multi-GPU Script:
+```
+accelerate launch --num_processes 8 --multi_gpu --mixed_precision "fp16" \
+  tutorial_train_ip-adapter.py \
+  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
+  --image_encoder_path="{image_encoder_path}" \
+  --data_json_file="{data.json}" \
+  --data_root_path="{image_path}" \
+  --mixed_precision="fp16" \
+  --resolution=512 \
+  --train_batch_size=8 \
+  --dataloader_num_workers=4 \
+  --learning_rate=1e-04 \
+  --weight_decay=0.01 \
+  --output_dir="{output_dir}" \
+  --save_steps=10000
+```
+
+#### Parameters:
+- `--num_processes`: Number of processes to launch for distributed training (in this example, 8 processes).
+- `--multi_gpu`: Flag indicating the usage of multiple GPUs for training.
+- `--mixed_precision "fp16"`: Enables mixed precision training with 16-bit floating-point precision.
+- `tutorial_train_ip-adapter.py`: Name of the training script to be executed.
+- `--pretrained_model_name_or_path`: Path or identifier for a pretrained model.
+- `--image_encoder_path`: Path to the CLIP image encoder.
+- `--data_json_file`: Path to the training data in JSON format.
+- `--data_root_path`: Root path where training images are located.
+- `--resolution`: Resolution of input images (512x512 in this example).
+- `--train_batch_size`: Batch size for training data (8 in this example).
+- `--dataloader_num_workers`: Number of subprocesses for data loading (4 in this example).
+- `--learning_rate`: Learning rate for training (1e-04 in this example).
+- `--weight_decay`: Weight decay for regularization (0.01 in this example).
+- `--output_dir`: Directory to save model checkpoints and predictions.
+- `--save_steps`: Frequency of saving checkpoints during training (10000 in this example).
+
+### Inference
+
+#### Description:
+The provided inference code is used to load a trained model checkpoint and extract the components related to image projection and IP (Image Processing) adapter. These components are then saved into a binary file for later use in inference.
+
+#### Usage Example:
+```python
+from safetensors.torch import load_file, save_file
+
+# Load the trained model checkpoint in safetensors format
+ckpt = "checkpoint-50000/pytorch_model.safetensors"
+sd = load_file(ckpt)  # Using safetensors load function
+
+# Extract image projection and IP adapter components
+image_proj_sd = {}
+ip_sd = {}
+
+for k in sd:
+    if k.startswith("unet"):
+        pass  # Skip unet-related keys
+    elif k.startswith("image_proj_model"):
+        image_proj_sd[k.replace("image_proj_model.", "")] = sd[k]
+    elif k.startswith("adapter_modules"):
+        ip_sd[k.replace("adapter_modules.", "")] = sd[k]
+
+# Save the components into separate safetensors files
+save_file(image_proj_sd, "image_proj.safetensors")
+save_file(ip_sd, "ip_adapter.safetensors")
+```
+
+### Sample Inference Script using the CLIP Model
+
+```python
+
+import torch
+from safetensors.torch import load_file
+from transformers import CLIPProcessor, CLIPModel  # Using the Hugging Face CLIP model 
+
+# Load model components from safetensors
+image_proj_ckpt = "image_proj.safetensors"
+ip_adapter_ckpt = "ip_adapter.safetensors"
+
+# Load the saved weights
+image_proj_sd = load_file(image_proj_ckpt)
+ip_adapter_sd = load_file(ip_adapter_ckpt)
+
+# Define the model Parameters
+class ImageProjectionModel(torch.nn.Module):
+    def __init__(self, input_dim=768, output_dim=512):  # CLIP's default embedding size is 768
+        super().__init__()
+        self.model = torch.nn.Linear(input_dim, output_dim)
+
+    def forward(self, x):
+        return self.model(x)
+
+class IPAdapterModel(torch.nn.Module):
+    def __init__(self, input_dim=512, output_dim=10):  # Example for 10 classes
+        super().__init__()
+        self.model = torch.nn.Linear(input_dim, output_dim)
+
+    def forward(self, x):
+        return self.model(x)
+
+# Initialize models
+image_proj_model = ImageProjectionModel()
+ip_adapter_model = IPAdapterModel()
+
+# Load weights into models
+image_proj_model.load_state_dict(image_proj_sd)
+ip_adapter_model.load_state_dict(ip_adapter_sd)
+
+# Set models to evaluation mode
+image_proj_model.eval()
+ip_adapter_model.eval()
+
+#Inference pipeline
+def inference(image_tensor):
+    """
+    Run inference using the loaded models.
+
+    Args:
+        image_tensor: Preprocessed image tensor from CLIPProcessor
+
+    Returns:
+        Final inference results
+    """
+    with torch.no_grad():
+        # Step 1: Project the image features
+        image_proj = image_proj_model(image_tensor)
+
+        # Step 2: Pass the projected features through the IP Adapter
+        result = ip_adapter_model(image_proj)
+
+    return result
+
+# Using CLIP for image preprocessing
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+
+#Image file path
+image_path = "path/to/image.jpg"
+
+# Preprocess the image
+inputs = processor(images=image_path, return_tensors="pt")
+image_features = clip_model.get_image_features(inputs["pixel_values"])
+
+# Normalize the image features as per CLIP's recommendations
+image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+
+# Run inference
+output = inference(image_features)
+print("Inference output:", output)
+```
+
+#### Parameters:
+- `ckpt`: Path to the trained model checkpoint file.
+- `map_location="cpu"`: Specifies that the model should be loaded onto the CPU.
+- `image_proj_sd`: Dictionary to store the components related to image projection.
+- `ip_sd`: Dictionary to store the components related to the IP adapter.
+- `"unet"`, `"image_proj_model"`, `"adapter_modules"`: Prefixes indicating components of the model.

examples/research_projects/ip_adapter/requirements.txt

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+accelerate
+torchvision
+transformers>=4.25.1
+ip_adapter