enable HQT

Makefile

@@ -53,8 +53,8 @@ slow_tests_deepspeed: test_installs
 	python -m pytest tests/test_examples.py -v -s -k "deepspeed"
 
 slow_tests_diffusers: test_installs
-	python -m pip install git+https://github.com/huggingface/diffusers.git
 	python -m pytest tests/test_diffusers.py -v -s -k "test_no_"
+	python -m pytest tests/test_diffusers.py -v -s -k "test_textual_inversion"
 
 # Run text-generation non-regression tests
 slow_tests_text_generation_example: test_installs

docs/source/tutorials/stable_diffusion.mdx

@@ -105,33 +105,13 @@ There are two different checkpoints for Stable Diffusion 2:
 </Tip>
 
 
-## Tips
-
-To accelerate your Stable Diffusion pipeline, you can run it in full *bfloat16* precision.
-This will also save memory.
-You just need to pass `torch_dtype=torch.bfloat16` to `from_pretrained` when instantiating your pipeline.
-Here is how to do it:
-
-```py
-import torch
-
-pipeline = GaudiStableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    scheduler=scheduler,
-    use_habana=True,
-    use_hpu_graphs=True,
-    gaudi_config="Habana/stable-diffusion",
-    torch_dtype=torch.bfloat16
-)
-```
-
-# Super-resolution
+## Super-resolution
 
 The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from CompVis, Stability AI, and LAION. It is used to enhance the resolution of input images by a factor of 4.
 
 See [here](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale) for more information.
 
-## How to upscale low resolution images?
+### How to upscale low resolution images?
 
 To generate RGB and depth images with Stable Diffusion Upscale on Gaudi, you need to instantiate two instances:
 - A pipeline with [`GaudiStableDiffusionUpscalePipeline`](../package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiStableDiffusionUpscalePipeline).
@@ -172,4 +152,32 @@ pipeline = GaudiStableDiffusionUpscalePipeline.from_pretrained(
 upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
 upscaled_image.save("upsampled_cat.png")
 
-```
+```
+
+
+## Tips
+
+To accelerate your Stable Diffusion pipeline, you can run it in full *bfloat16* precision.
+This will also save memory.
+You just need to pass `torch_dtype=torch.bfloat16` to `from_pretrained` when instantiating your pipeline.
+Here is how to do it:
+
+```py
+import torch
+
+pipeline = GaudiStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    scheduler=scheduler,
+    use_habana=True,
+    use_hpu_graphs=True,
+    gaudi_config="Habana/stable-diffusion",
+    torch_dtype=torch.bfloat16
+)
+```
+
+
+## Textual Inversion Fine-Tuning
+
+[Textual Inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like Stable Diffusion on your own images using just 3-5 examples.
+
+You can find [here](https://github.com/huggingface/optimum-habana/blob/main/examples/stable-diffusion/textual_inversion.py) an example script that implements this training method.

examples/stable-diffusion/README.md

@@ -115,3 +115,98 @@ python text_to_image_generation.py \
 > - use [the latest checkpoint](https://huggingface.co/Intel/ldm3d-4c) for generating improved results
 > - use [the pano checkpoint](https://huggingface.co/Intel/ldm3d-pano) to generate panoramic view
 
+
+## Textual Inversion
+
+[Textual Inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like Stable Diffusion on your own images using just 3-5 examples.
+The `textual_inversion.py` script shows how to implement the training procedure on Habana Gaudi.
+
+
+### Cat toy example
+
+Let's get our dataset. For this example, we will use some cat images: https://huggingface.co/datasets/diffusers/cat_toy_example .
+
+Let's first download it locally:
+
+```py
+from huggingface_hub import snapshot_download
+
+local_dir = "./cat"
+snapshot_download("diffusers/cat_toy_example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes")
+```
+
+This will be our training data.
+Now we can launch the training using:
+
+```bash
+python textual_inversion.py \
+  --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 \
+  --train_data_dir ./cat \
+  --learnable_property object \
+  --placeholder_token "<cat-toy>" \
+  --initializer_token toy \
+  --resolution 512 \
+  --train_batch_size 4 \
+  --max_train_steps 3000 \
+  --learning_rate 5.0e-04 \
+  --scale_lr \
+  --lr_scheduler constant \
+  --lr_warmup_steps 0 \
+  --output_dir /tmp/textual_inversion_cat \
+  --save_as_full_pipeline \
+  --gaudi_config_name Habana/stable-diffusion \
+  --throughput_warmup_steps 3
+```
+
+> Change `--resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.
+
+> As described in [the official paper](https://arxiv.org/abs/2208.01618), only one embedding vector is used for the placeholder token, *e.g.* `"<cat-toy>"`. However, one can also add multiple embedding vectors for the placeholder token to increase the number of fine-tuneable parameters. This can help the model to learn more complex details. To use multiple embedding vectors, you can define `--num_vectors` to a number larger than one, *e.g.*: `--num_vectors 5`. The saved textual inversion vectors will then be larger in size compared to the default case.
+
+
+### Multi-card Run
+
+You can run this fine-tuning script in a distributed fashion as follows:
+```bash
+python ../gaudi_spawn.py --use_mpi --world_size 8 textual_inversion.py \
+  --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 \
+  --train_data_dir ./cat \
+  --learnable_property object \
+  --placeholder_token '"<cat-toy>"' \
+  --initializer_token toy \
+  --resolution 512 \
+  --train_batch_size 4 \
+  --max_train_steps 375 \
+  --learning_rate 5.0e-04 \
+  --scale_lr \
+  --lr_scheduler constant \
+  --lr_warmup_steps 0 \
+  --output_dir /tmp/textual_inversion_cat \
+  --save_as_full_pipeline \
+  --gaudi_config_name Habana/stable-diffusion \
+  --throughput_warmup_steps 3
+```
+
+
+### Inference
+
+Once you have trained a model as described right above, inference can be done simply using the `GaudiStableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.
+
+```python
+import torch
+from optimum.habana.diffusers import GaudiStableDiffusionPipeline
+
+model_id = "path-to-your-trained-model"
+pipe = GaudiStableDiffusionPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.bfloat16,
+  use_habana=True,
+  use_hpu_graphs=True,
+  gaudi_config="Habana/stable-diffusion",
+)
+
+prompt = "A <cat-toy> backpack"
+
+image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
+
+image.save("cat-backpack.png")
+```