Adds Boogu image support

docs/Model Support.md

@@ -23,6 +23,7 @@
 [Lens](#lens) | MMDiT | 2026 | Microsoft | 4B | Minimal | Modern, lightweight, eh quality |
 [Ideogram 4](#ideogram-4) | DiT | 2026 | Ideogram AI | 9B | Yes | Modern, advanced on input understanding |
 [Krea 2](#krea-2) | DiT | 2026 | Krea AI | 12B | Yes | Modern, extremely smart and great quality |
+[Boogu](#boogu) | MMDiT | 2026 | Boogu | 10B | Minimal | Modern, fast |
 
 Old or bad options also tracked listed via [Obscure Model Support](/docs/Obscure%20Model%20Support.md):
 
@@ -693,6 +694,24 @@ For upscaling with SD3, the `Refiner Do Tiling` parameter is highly recommended
     - **Scheduler:** Default is fine.
     - **Sigma Shift:** Defaults to `1.15`
 
+# Boogu
+
+- [Boogu](<https://huggingface.co/Boogu>) is supported in SwarmUI!
+- It is a 10B model, derived from OmniGen, that comes in three separate variants - **Base**, **Edit**, and **Turbo**:
+    - **Base**: [Comfy-Org/Boogu-Image - Base FP8](<https://huggingface.co/Comfy-Org/Boogu-Image/resolve/main/diffusion_models/boogu_image_base_fp8_scaled.safetensors>)
+    - **Edit** - image editing, takes a prompt image as a reference: [Comfy-Org/Boogu-Image - Edit FP8](<https://huggingface.co/Comfy-Org/Boogu-Image/resolve/main/diffusion_models/boogu_image_edit_fp8_scaled.safetensors>)
+        - Or the NVFP4 (6 gigs) here: [Comfy-Org/Boogu-Image - Edit nvfp4](<https://huggingface.co/Comfy-Org/Boogu-Image/resolve/main/diffusion_models/boogu_image_edit_nvfp4.safetensors>)
+    - **Turbo** - very fast, low step counts: [Comfy-Org/Boogu-Image - Turbo FP8](<https://huggingface.co/Comfy-Org/Boogu-Image/resolve/main/diffusion_models/boogu_image_turbo_fp8_scaled.safetensors>)
+        - Or the NVFP4 (6 gigs) here: [Comfy-Org/Boogu-Image - Turbo nvfp4](<https://huggingface.co/Comfy-Org/Boogu-Image/resolve/main/diffusion_models/boogu_image_turbo_nvfp4.safetensors>)
+- Uses the Flux.1 VAE and a Qwen3-VL8B text encoder, both downloaded and handled automatically.
+- For editing, use the `Edit` model and give it a prompt image - SwarmUI automatically wires it in as the reference.
+- **Parameters:** recommendations differ between the Base, Edit, and Turbo models:
+    - **Sampler**: For Turbo, use LCM, otherwise default is fine, DPM++ 2M is recommended as a bit better than Euler for these models.
+    - **Scheduler:** For Turbo, use SGM Uniform, otherwise default is fine.
+    - **CFG Scale:** For Turbo, `1`, otherwise normal CFG ranges (around `4`).
+    - **Steps:** For Turbo, `4` is recommended, otherwise `20` as normal.
+    - **Resolution:** Side length `1024` is the default.
+    - **Sigma Shift:** Default is `3.16`.
 
 # Video Models
 

src/BuiltinExtensions/ComfyUIBackend/ExtraNodes/SwarmComfyCommon/SwarmTextHandling.py

@@ -70,7 +70,7 @@ def encode(self, clip, steps: int, prompt: str, width: int, height: int, target_
         def tokenize(text: str):
             nonlocal images
             if clip_vision_output is not None:
-                return clip.tokenize(text, llama_template=llama_template, image_embeds=clip_vision_output.mm_projected)
+                return clip.tokenize(text, llama_template=llama_template if llama_template else None, image_embeds=clip_vision_output.mm_projected)
             elif images is not None:
                 if append_images:
                     image_prompt = ""
@@ -83,7 +83,7 @@ def tokenize(text: str):
                         text = image_prompt + text
                     else:
                         text = text + image_prompt
-                return clip.tokenize(text, llama_template=llama_template, images=images)
+                return clip.tokenize(text, llama_template=llama_template if llama_template else None, images=images)
             else:
                 return clip.tokenize(text)
 

src/BuiltinExtensions/ComfyUIBackend/WorkflowGenerator.cs

@@ -769,9 +769,9 @@ public List<JArray> LoadPromptImagesForMainRef(List<Image> images)
     public (JArray, JArray, JArray, JArray) BuildInputImageHandling(List<JArray> images, JArray pos, JArray neg, JArray latent)
     {
         JArray imgNeg = null;
-        if (IsKontext() || IsOmniGen() || IsQwenImage() || IsAnyFlux2())
+        if (IsKontext() || IsOmniGen() || IsQwenImage() || IsAnyFlux2() || IsBoogu())
         {
-            if (IsOmniGen() || IsQwenImageEditPlus())
+            if (IsOmniGen() || IsQwenImageEditPlus() || IsBoogu())
             {
                 imgNeg = neg;
             }
@@ -803,7 +803,7 @@ void makeRefLatent(JArray image)
             }
             if (img is not null)
             {
-                if (IsQwenImageEditPlus())
+                if (IsQwenImageEditPlus() || IsBoogu())
                 {
                     neg = imgNeg;
                 }
@@ -970,6 +970,10 @@ public string CreateKSampler(JArray model, JArray pos, JArray neg, JArray latent
             defsampler ??= "lcm";
             defscheduler ??= "simple";
         }
+        else if (IsBoogu())
+        {
+            defscheduler ??= "simple";
+        }
         // TODO: Registry of model default preferences instead of this
         else if (IsFlux() || IsWanVideo() || IsWanVideo22() || IsOmniGen() || IsQwenImage() || IsZImage() || IsZetaChroma() || IsErnie() || IsHiDreamO1() || IsLens() || IsPixelDiT() || IsKrea2())
         {
@@ -1301,7 +1305,7 @@ public JArray GetPromptImage(bool fixSize, bool promptSize = false, int index =
                     doesFit = false;
                 }
             }
-            else if (IsQwenImageEditPlus() && promptSize)
+            else if ((IsBoogu() || IsQwenImageEditPlus()) && promptSize)
             {
                 target = 384;
                 doesFit = false;
@@ -2345,7 +2349,7 @@ public JArray CreateConditioningDirect(string prompt, JArray clip, T2IModel mode
                 ["text"] = prompt
             }, id);
         }
-        else if (IsIdeogram4() || IsKrea2())
+        else if (IsIdeogram4() || IsKrea2() || (IsBoogu() && isPositive))
         {
             JArray imageNode = GetPromptImage(true, true, 0);
             for (int i = 1; i < 10; i++)
@@ -2373,7 +2377,7 @@ public JArray CreateConditioningDirect(string prompt, JArray clip, T2IModel mode
                 ["target_height"] = height,
                 ["guidance"] = UserInput.Get(T2IParamTypes.FluxGuidanceScale, defaultGuidance),
                 ["images"] = imageNode,
-                ["llama_template"] = "krea2" // TODO: Ideogram preferred template?
+                ["llama_template"] = IsBoogu() ? null : "krea2" // TODO: Ideogram preferred template?
             }, id);
         }
         else if (IsQwenImageEdit() && (isPositive || IsQwenImageEditPlus()) && (qwenImage = GetPromptImage(true, true)) is not null)

src/BuiltinExtensions/ComfyUIBackend/WorkflowGeneratorModelSupport.cs

@@ -157,6 +157,9 @@ public bool IsQwenImageEditPlus()
     /// <summary>Returns true if the current model is Krea 2.</summary>
     public bool IsKrea2() => IsModelCompatClass(T2IModelClassSorter.CompatKrea2);
 
+    /// <summary>Returns true if the current model is Boogu.</summary>
+    public bool IsBoogu() => IsModelCompatClass(T2IModelClassSorter.CompatBoogu);
+
     /// <summary>Returns true if the current model is Hunyuan Video (original / v1).</summary>
     public bool IsHunyuanVideo() => IsModelCompatClass(T2IModelClassSorter.CompatHunyuanVideo);
 
@@ -1136,6 +1139,11 @@ public void LoadClip3(string type, string modelA, string modelB, string modelC)
             helpers.LoadClip("krea2", helpers.GetQwen3vl_4bModel());
             helpers.DoVaeLoader(UserInput.SourceSession?.User?.Settings?.VAEs?.DefaultQwenVAE, "qwen-image", "qwen-image-vae");
         }
+        else if (IsBoogu())
+        {
+            helpers.LoadClip("boogu", helpers.GetQwen3vl_8bModel());
+            helpers.DoVaeLoader(UserInput.SourceSession?.User?.Settings?.VAEs?.DefaultFluxVAE, "flux-1", "flux-ae");
+        }
         else if (IsFlux() && (LoadingClip is null || LoadingVAE is null || UserInput.Get(T2IParamTypes.T5XXLModel) is not null || UserInput.Get(T2IParamTypes.ClipLModel) is not null))
         {
             helpers.LoadClip2("flux", helpers.GetT5XXLModel(), helpers.GetClipLModel());
@@ -1419,7 +1427,7 @@ public void LoadClip3(string type, string modelA, string modelB, string modelC)
                 });
                 LoadingModel = [samplingNode, 0];
             }
-            else if (IsZImage() || IsAceStep15() || IsAnima() || IsKrea2())
+            else if (IsZImage() || IsAceStep15() || IsAnima() || IsKrea2() || IsBoogu())
             {
                 string samplingNode = CreateNode("ModelSamplingAuraFlow", new JObject()
                 {

src/Text2Image/T2IModelClassSorter.cs

@@ -94,6 +94,7 @@ public static T2IModelCompatClass
         CompatPixelDiT = RegisterCompat(new() { ID = "pixeldit", ShortCode = "PixDiT", LorasTargetTextEnc = false }),
         CompatIdeogram4 = RegisterCompat(new() { ID = "ideogram-4", ShortCode = "Ideo4", LorasTargetTextEnc = false, VaeFamily = VaeFlux2 }),
         CompatKrea2 = RegisterCompat(new() { ID = "krea-2", ShortCode = "Krea2", LorasTargetTextEnc = false, VaeFamily = VaeQwenImage }),
+        CompatBoogu = RegisterCompat(new() { ID = "boogu", ShortCode = "Boogu", LorasTargetTextEnc = false, VaeFamily = VaeFlux1 }),
         // Audio models
         CompatAceStep15 = RegisterCompat(new() { ID = "ace-step-1_5", ShortCode = "Ace15", IsAudioModel = true }),
         // Obscure old random ones
@@ -236,7 +237,8 @@ bool isZImageLora(JObject h) => (hasLoraKey(h, "layers.0.adaLN_modulation.0") &&
         bool isChromaRadiance(JObject h) => hasKey(h, "nerf_image_embedder.embedder.0.bias");
         bool isPiD(JObject h) => h.ContainsKey("net.lq_proj.latent_proj.0.weight") && h.ContainsKey("net.pixel_blocks.0.attn.q_norm.weight") && h.ContainsKey("net.pixel_blocks.0.compress_to_attn.weight");
         bool isPixelDiT(JObject h) => h.ContainsKey("core.pixel_embedder.proj.weight") && h.ContainsKey("core.pixel_blocks.0.attn.q_norm.weight") && h.ContainsKey("core.pixel_blocks.0.compress_to_attn.weight") && !isPiD(h);
-        bool isOmniGen(JObject h) => h.ContainsKey("time_caption_embed.timestep_embedder.linear_2.weight") && h.ContainsKey("context_refiner.0.attn.norm_k.weight");
+        bool isOmniGen(JObject h) => h.ContainsKey("time_caption_embed.timestep_embedder.linear_2.weight") && h.ContainsKey("context_refiner.0.attn.norm_k.weight") && !isBoogu(h);
+        bool isBoogu(JObject h) => hasKey(h, "double_stream_layers.0.img_instruct_attn.processor.img_to_q.weight") && hasKey(h, "double_stream_layers.0.img_instruct_attn.processor.instruct_to_q.weight");
         bool isQwenImage(JObject h) => (h.ContainsKey("time_text_embed.timestep_embedder.linear_1.bias") && h.ContainsKey("img_in.bias") && (h.ContainsKey("transformer_blocks.0.attn.add_k_proj.bias") || h.ContainsKey("transformer_blocks.0.attn.add_qkv_proj.bias")))
             || (h.ContainsKey("model.diffusion_model.time_text_embed.timestep_embedder.linear_1.bias") && h.ContainsKey("model.diffusion_model.img_in.bias") && (h.ContainsKey("model.diffusion_model.transformer_blocks.0.attn.add_k_proj.bias") || h.ContainsKey("model.diffusion_model.transformer_blocks.0.attn.add_qkv_proj.bias")));
         bool isQwenImageEdit2511(JObject h) => h.ContainsKey("__index_timestep_zero__");
@@ -865,6 +867,11 @@ JToken GetEmbeddingKey(JObject h)
         {
             return isLongcat(h);
         }});
+        // ====================== Boogu ======================
+        Register(new() { ID = "boogu", CompatClass = CompatBoogu, Name = "Boogu", StandardWidth = 1024, StandardHeight = 1024, IsThisModelOfClass = (m, h) =>
+        {
+            return isBoogu(h);
+        }});
         // ====================== Audio Models ======================
         Register(new() { ID = "ace-step-1_5", CompatClass = CompatAceStep15, Name = "Ace Step 1.5", IsThisModelOfClass = (m, h) =>
         {