Generalize CFG to allow for positive prompts (#25339)

* Generalize CFG to allow for positive prompts

* Add documentation, fix the correct class

src/transformers/generation/logits_process.py

@@ -1346,9 +1346,10 @@ class UnbatchedClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
 
     Args:
         guidance_scale (`float`):
-            The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
+            The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale != 1`.
             Higher guidance scale encourages the model to generate samples that are more closely linked to the input
-            prompt, usually at the expense of poorer quality.
+            prompt, usually at the expense of poorer quality. A value smaller than 1 has the opposite effect, while
+            making the negative prompt provided with negative_prompt_ids (if any) act as a positive prompt.
         unconditional_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Indices of input sequence tokens in the vocabulary for the unconditional branch. If unset, will default to
             the last token of the prompt.
@@ -1383,6 +1384,12 @@ class UnbatchedClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
     >>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
     The dragon flew over Paris, France, crashing into Notre Dame Cathedral in the French capital killing at least 127
     people and injuring more than 350.
+
+    >>> # with a positive prompt
+    >>> neg_inputs = tokenizer(["A very happy event happened,"], return_tensors="pt")
+    >>> out = model.generate(inputs["input_ids"], guidance_scale=0, negative_prompt_ids=neg_inputs["input_ids"])
+    >>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
+    Today, a dragon flew over Paris, France, and I'm very happy to be here.
     ```
     """
 

src/transformers/generation/utils.py

@@ -904,7 +904,7 @@ class GenerationMixin:
         # instantiate processors list
         processors = LogitsProcessorList()
 
-        if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
+        if generation_config.guidance_scale is not None and generation_config.guidance_scale != 1:
             processors.append(
                 UnbatchedClassifierFreeGuidanceLogitsProcessor(
                     generation_config.guidance_scale,