Adding better way to define multiple concepts and also validation capabilities.

examples/research_projects/multi_subject_dreambooth/README.md

@@ -86,6 +86,53 @@ This example shows training for 2 subjects, but please note that the model can b
 
 Note also that in this script, `sks` and `t@y` were used as tokens to learn the new subjects ([this thread](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/71) inspired the use of `t@y` as our second identifier). However, there may be better rare tokens to experiment with, and results also seemed to be good when more intuitive words are used.
 
+**Important**: New parameters are added to the script, making possible to validate the progress of the training by 
+generating images at specified steps. Taking also into account that a comma separated list in a text field for a prompt
+it's never a good idea (simply because it is very common in prompts to have them as part of a regular text) we 
+introduce the `concept_list` parameter: allowing to specify a json-like file where you can define the different 
+configuration for each subject that you want to train.
+
+An example of how to generate the file:
+```python
+import json
+
+# here we are using parameters for prior-preservation and validation as well. 
+concepts_list = [
+    {
+        "instance_prompt":      "drawing of a t@y meme",
+        "class_prompt":         "drawing of a meme",
+        "instance_data_dir":    "/some_folder/meme_toy",
+        "class_data_dir":       "/data/meme",
+        "validation_prompt":    "drawing of a t@y meme about football in Uruguay",
+        "validation_negative_prompt": "black and white"
+    },
+    {
+        "instance_prompt":      "drawing of a sks sir",
+        "class_prompt":         "drawing of a sir",
+        "instance_data_dir":    "/some_other_folder/sir_sks",
+        "class_data_dir":       "/data/sir",
+        "validation_prompt":    "drawing of a sks sir with the Uruguayan sun in his chest",
+        "validation_negative_prompt": "an old man",
+        "validation_guidance_scale": 20,
+        "validation_number_images": 3,
+        "validation_inference_steps": 10
+    }
+]
+
+with open("concepts_list.json", "w") as f:
+    json.dump(concepts_list, f, indent=4)
+```
+And then just point to the file when executing the script:
+
+```bash
+# exports...
+accelerate launch train_multi_subject_dreambooth.py \
+# more parameters...
+--concepts_list="concepts_list.json"
+```
+
+You can use the helper from the script to get a better sense of each parameter.
+
 ### Inference
 
 Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.