fix(photomaker): drop explicit negative_prompt to fix CFG batch mismatch

Modal cert sweep #6 made it INTO the denoising loop and died with
"Sizes of tensors must match except in dimension 1. Expected size 2 but got size 1
for tensor number 1 in the list."

In the PhotoMaker pipeline's denoising loop, the per-step embeddings are built
as torch.cat([negative_prompt_embeds, prompt_embeds(_text_only)], dim=0). The
text-encoder + ID-encoder flow can leave the negative branch at batch=2 and the
ID-injected branch at batch=1 when a custom negative_prompt is passed, so the
cat fails. The upstream gradio demo just passes no negative_prompt and relies
on the pipeline's empty default; do the same.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/remove_ai_watermarks/photomaker_restore.py

@@ -318,9 +318,15 @@ def restore_faces_photomaker(
         id_crop_rgb = cv2.cvtColor(id_crop_bgr, cv2.COLOR_BGR2RGB)
         id_image_pil = Image.fromarray(id_crop_rgb)
 
+        # Don't pass negative_prompt: the PhotoMaker pipeline manages its own CFG by
+        # concatenating [negative_prompt_embeds, prompt_embeds]; if we pass a custom
+        # negative the upstream code splits text_only vs id-injected branches and
+        # the resulting embed batch dims can mismatch (we saw
+        # "Sizes of tensors must match except in dimension 1. Expected size 2 but got
+        # size 1" on a real run). The default empty negative is what the upstream
+        # gradio demo uses.
         out = pipeline(
             prompt=_PHOTOMAKER_PROMPT,
-            negative_prompt=_PHOTOMAKER_NEGATIVE,
             input_id_images=[id_image_pil],
             num_inference_steps=num_inference_steps,
             guidance_scale=guidance_scale,