From 52257bb435162ff345fcc8cfde99c52c8923589d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 17 Jun 2026 15:42:29 -0700 Subject: [PATCH] Add negative prompt to boogu edit node and set min images to 0. (#14529) --- comfy/supported_models.py | 2 +- comfy_extras/nodes_boogu.py | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index d78b6ae87..cc05908ee 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1769,7 +1769,7 @@ class Boogu(Omnigen2): "shift": 3.16, } - memory_usage_factor = 1.95 #TODO + memory_usage_factor = 2.15 def get_model(self, state_dict, prefix="", device=None): out = model_base.Boogu(self, device=device) diff --git a/comfy_extras/nodes_boogu.py b/comfy_extras/nodes_boogu.py index 590623e12..f3951c290 100644 --- a/comfy_extras/nodes_boogu.py +++ b/comfy_extras/nodes_boogu.py @@ -26,13 +26,14 @@ class TextEncodeBooguEdit(io.ComfyNode): inputs=[ io.Clip.Input("clip"), io.String.Input("prompt", multiline=True, dynamic_prompts=True), + io.String.Input("negative_prompt", multiline=True, dynamic_prompts=True, advanced=True), io.Vae.Input("vae"), io.Autogrow.Input( "images", template=io.Autogrow.TemplateNames( io.Image.Input("image"), names=[f"image_{i}" for i in range(1, 17)], - min=1, + min=0, ), tooltip="Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.", ), @@ -44,7 +45,7 @@ class TextEncodeBooguEdit(io.ComfyNode): ) @classmethod - def execute(cls, clip, prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput: + def execute(cls, clip, prompt, negative_prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput: ref_latents = [] images_vl = [] @@ -75,7 +76,7 @@ class TextEncodeBooguEdit(io.ComfyNode): # positive: instruction + vision tokens; negative: empty (no vision). Ref latent on both. positive = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=images_vl)) - negative = clip.encode_from_tokens_scheduled(clip.tokenize("")) + negative = clip.encode_from_tokens_scheduled(clip.tokenize(negative_prompt)) if len(ref_latents) > 0: positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True)