Update version in pyproject.toml to 1.0.2

- Incremented the version number from 1.0.1 to 1.0.2 to reflect recent changes and improvements in the DSD package.

This update ensures users are informed of the latest version and can access new features and fixes.

.github/workflows/publish.yml

@@ -0,0 +1,28 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'irreveloper' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

LICENSE

@@ -0,0 +1,674 @@
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+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -10,6 +10,7 @@ DSD is a model for subject-preserving image generation that allows you to create
 - Gemini API prompt enhancement
 - Direct model download from Hugging Face
 - Fine-grained control over generation parameters
+- Multiple image resizing options
 
 ## Installation
 
@@ -39,31 +40,81 @@ pip install -r requirements.txt
 ## Available Nodes
 
 1. **DSD Model Downloader**: Automatically downloads the model from Hugging Face
+   - Supports downloading from custom repositories with the `repo_id` parameter
+   - Includes options for model precision (bfloat16, float16, float32)
+   - Provides memory optimization options (low_cpu_mem_usage, model_cpu_offload, sequential_cpu_offload)
+   - Optional Hugging Face token support via parameter or HF_TOKEN environment variable
 
 2. **DSD Model Loader**: Loads a pre-downloaded model
+   - Supports custom model and LoRA paths
+   - Multiple precision options (bfloat16, float16, float32)
+   - Memory optimization options for different hardware configurations
 
 3. **DSD Model Selector**: Helps select models from local directories
+   - Automatically finds models in the default ComfyUI model paths
+   - Verifies model existence and provides appropriate warnings
 
-4. **DSD Gemini Prompt Enhancer**: Uses Google's Gemini API to enhance prompts for better image generation results. The API key can be provided in two ways:
-   - As an input parameter to the node (not recommended for sharing workflows)
-   - Through the `GEMINI_API_KEY` environment variable (strongly recommended)
+4. **DSD Gemini Prompt Enhancer**: Uses Google's Gemini API to enhance prompts for better image generation results
+   - The API key can be provided in two ways:
+     - As an input parameter to the node (not recommended for sharing workflows)
+     - Through the `GEMINI_API_KEY` environment variable (strongly recommended)
+   - Analyzes both the input image and text prompt to generate improved prompts
    
-   Note: To use the enhanced prompts, make sure to enable the `use_gemini_prompt` option on the DSD Image Generator node. If you don't enter a API Key it will be skipped automatically.
+   Note: To use the enhanced prompts, connect this node's output to the DSD Image Generator's prompt input and enable the `use_gemini_prompt` option. If no API key is provided, the original prompt will be used.
 
 5. **DSD Image Generator**: Generates images with the DSD model
+   - Supports detailed parameter control:
+     - Guidance scale (overall, image-specific, and text-specific)
+     - Inference steps
+     - Resolution control
+     - Seed control (0 for random seed)
+   - Returns both the generated image and the reference image
+   - Displays progress during generation
+
+6. **DSD Resize Selector**: Provides flexible image resizing options for the DSD Image Generator:
+   - **resize_and_center_crop**: Resizes and center crops the image (default behavior)
+   - **center_crop**: Simple center crop and resize
+   - **pad**: Preserves aspect ratio and adds padding to reach target size
+   - **fit**: Resizes to target dimensions without preserving aspect ratio
+   - Additional customization:
+     - Interpolation method (LANCZOS, BICUBIC, BILINEAR, NEAREST)
+     - Padding color (RGB values for pad mode)
 
 ## Basic Workflow
 
-![Sample1](examples/screenshot.png)
+![Sample](examples/workflow.png)
 
-![Sample2](examples/screenshot-2.png)
+## Advanced Usage
 
+### Memory Optimization
+
+The DSD model can be memory-intensive. Several options are available to optimize memory usage:
+
+- **Precision**: Use `bfloat16` (default) for the best balance of speed and memory usage
+- **CPU Offloading**: Enable `model_cpu_offload` or `sequential_cpu_offload` for systems with limited VRAM
+- **Resolution**: Lower resolution and fewer inference steps can significantly reduce memory requirements
+
+### Gemini API Integration
+
+For optimal results with the Gemini API:
+1. Obtain a Gemini API key from Google AI Studio
+2. Set it as an environment variable: `GEMINI_API_KEY=your_key_here`
+3. Connect the DSD Gemini Prompt Enhancer to your workflow
+4. Enable `use_gemini_prompt` on the DSD Image Generator
+
+### Custom Model Loading
+
+If you have custom DSD models or want to use a different repository:
+1. Use the DSD Model Downloader with a custom `repo_id`
+2. Or manually download the model files and use DSD Model Loader with custom paths
 
 ## Troubleshooting
 
 - **Memory Issues**: Try reducing precision (use bfloat16), lower resolution, or fewer steps
 - **Gemini API**: Ensure you have a valid API key (can be set via GEMINI_API_KEY environment variable)
 - **Model Loading**: If you see errors, try using the Model Downloader node to re-download files
+- **Import Errors**: Make sure all dependencies are installed correctly
+- **CUDA Errors**: If you encounter CUDA out-of-memory errors, try enabling CPU offloading options
 
 ## Examples
 

__init__.py

@@ -4,4 +4,6 @@ Diffusion Self-Distillation ComfyUI nodes
 
 from .dsd_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] 
+WEB_DIRECTORY = "./web"
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] 

core/pipeline.py

@@ -393,8 +393,8 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
                 prompt=prompt_2,
                 num_images_per_prompt=num_images_per_prompt,
                 max_sequence_length=max_sequence_length,
-                device=device,
-            )
+                device=device
+                )
 
         if self.text_encoder is not None:
             if isinstance(self, SD3LoraLoaderMixin) and USE_PEFT_BACKEND:
@@ -615,9 +615,7 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
         negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         no_cfg_until_timestep: int = 2,
         image: Optional[torch.FloatTensor] = None,
-        image_path = None,
-        cut_output = True,
-        gemini_prompt = True
+        cut_output = True
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -782,7 +780,7 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
         image = torch.nn.functional.interpolate(image, size=(height, width // 2))
         black_image = torch.full((1, 3, height, width // 2), -1.0)
         image = torch.cat([image, black_image], dim=3)
-        latents_cond = self.vae.encode(image.to(self.vae.dtype).to(self.vae.device)).latent_dist.sample()
+        latents_cond = self.vae.encode(image.to(dtype=self.vae.dtype).to(device)).latent_dist.sample()
         latents_cond = (
             latents_cond - self.vae.config.shift_factor
         ) * self.vae.config.scaling_factor
@@ -829,11 +827,6 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
         )
         self._num_timesteps = len(timesteps)
 
-        latents = latents.to(self.transformer.device)
-        latent_image_ids = latent_image_ids.to(self.transformer.device)
-        timesteps = timesteps.to(self.transformer.device)
-        text_ids = text_ids.to(self.transformer.device)
-
         # 6. Denoising loop
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
@@ -846,7 +839,7 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
                 # handle guidance
                 if self.transformer.config.guidance_embeds:
                     guidance = torch.tensor(
-                        [guidance_scale], device=self.transformer.device
+                        [guidance_scale], device=device
                     )
                     guidance = guidance.expand(latents.shape[0])
                 else:
@@ -859,79 +852,58 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
                     )
 
                 noise_pred = self.transformer(
-                    hidden_states=latents.to(
-                        device=self.transformer.device, dtype=self.transformer.dtype
-                    ),
+                    hidden_states=latents,
                     # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                     timestep=timestep / 1000,
                     guidance=guidance,
-                    pooled_projections=pooled_prompt_embeds.to(
-                        device=self.transformer.device, dtype=self.transformer.dtype
-                    ),
-                    encoder_hidden_states=prompt_embeds.to(
-                        device=self.transformer.device, dtype=self.transformer.dtype
-                    ),
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
                     txt_ids=text_ids,
                     img_ids=latent_image_ids,
                     joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
-                    condition_hidden_states=latents_cond.to(
-                        device=self.transformer.device, dtype=self.transformer.dtype
-                    ),
+                    condition_hidden_states=latents_cond,
                     **extra_transformer_args,
                 )[0]
 
                 # TODO optionally use batch prediction to speed this up.
                 if guidance_scale_real_i > 1.0 and i >= no_cfg_until_timestep:
                     noise_pred_uncond = self.transformer(
-                        hidden_states=latents.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
+                        hidden_states=latents,
                         # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                         timestep=timestep / 1000,
                         guidance=guidance,
-                        pooled_projections=negative_pooled_prompt_embeds.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
-                        encoder_hidden_states=negative_prompt_embeds.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
-                        txt_ids=negative_text_ids.to(device=self.transformer.device),
-                        img_ids=latent_image_ids.to(device=self.transformer.device),
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        txt_ids=negative_text_ids,
+                        img_ids=latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,
-                        condition_hidden_states=torch.zeros_like(latents_cond).to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
+                        condition_hidden_states=torch.zeros_like(latents_cond),
                     )[0]
                     noise_pred_uncond_t = self.transformer(
-                        hidden_states=latents.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
+                        hidden_states=latents,
                         # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
                         timestep=timestep / 1000,
                         guidance=guidance,
-                        pooled_projections=negative_pooled_prompt_embeds.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
-                        encoder_hidden_states=negative_prompt_embeds.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
-                        txt_ids=negative_text_ids.to(device=self.transformer.device),
-                        img_ids=latent_image_ids.to(device=self.transformer.device),
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        txt_ids=negative_text_ids,
+                        img_ids=latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,
-                        condition_hidden_states=latents_cond.to(
-                            device=self.transformer.device, dtype=self.transformer.dtype
-                        ),
+                        condition_hidden_states=latents_cond,
                     )[0]
 
                     # noise_pred = noise_pred_uncond + guidance_scale_real * (
                     #     noise_pred - noise_pred_uncond
                     # )
-                    noise_pred = noise_pred_uncond + \
-                        guidance_scale_real_i * (noise_pred_uncond_t - noise_pred_uncond) + \
-                        guidance_scale_real_t * (noise_pred - noise_pred_uncond_t)
+                    noise_pred = (
+                        noise_pred_uncond
+                        + guidance_scale_real_i
+                        * (noise_pred_uncond_t - noise_pred_uncond)
+                        + guidance_scale_real_t * (noise_pred - noise_pred_uncond_t)
+                    )
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents.dtype
@@ -976,7 +948,7 @@ class FluxConditionalPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
             ) + self.vae.config.shift_factor
 
             image = self.vae.decode(
-                latents.to(device=self.vae.device, dtype=self.vae.dtype),
+                latents,
                 return_dict=False,
             )[0]
             

dsd_nodes.py

@@ -10,7 +10,7 @@ from pathlib import Path
 from tqdm import tqdm
 from huggingface_hub import hf_hub_download, snapshot_download
 
-from .utils import get_model_path, get_lora_path, comfy_to_pil, pil_to_comfy, center_crop_and_resize
+from .utils import get_model_path, get_lora_path, comfy_to_pil, pil_to_comfy, resize_and_center_crop, center_crop, pad_resize, fit_resize
 from .dsd_imports import FluxConditionalPipeline, FluxTransformer2DConditionalModel, enhance_prompt, IMPORTS_AVAILABLE
 from comfy.utils import ProgressBar
 try:
@@ -42,7 +42,10 @@ class DSDModelLoader:
                 "model_path": ("STRING", {"default": ""}),
                 "lora_path": ("STRING", {"default": ""}),
                 "device": (["cuda", "cpu"], {"default": "cuda"}),
-                "dtype": (["bfloat16", "float16", "float32"], {"default": "bfloat16"})
+                "dtype": (["bfloat16", "float16", "float32"], {"default": "bfloat16"}),
+                "low_cpu_mem_usage": ("BOOLEAN", {"default": True, "tooltip": "Reduces CPU memory usage during model loading. Recommended for faster loading."}),
+                "model_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Offloads state dict to reduce memory usage during loading. May slow down inference speed."}), 
+                "sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enables sequential CPU offloading. Only use if low on VRAM. Significantly impacts inference speed."})
             }
         }
     
@@ -51,7 +54,7 @@ class DSDModelLoader:
     FUNCTION = "load_model"
     CATEGORY = "DSD"
     
-    def load_model(self, model_path, lora_path, device, dtype):
+    def load_model(self, model_path, lora_path, device, dtype, low_cpu_mem_usage, model_cpu_offload, sequential_cpu_offload):
         if not IMPORTS_AVAILABLE:
             raise ImportError("Could not import DSD modules. Make sure DSD project files (pipeline.py, transformer.py) are properly installed in the parent directory.")
         
@@ -83,24 +86,30 @@ class DSDModelLoader:
         print("Loading transformer...")
 
         model_folder = os.path.dirname(model_path)
-        # Load model with optimized parameters
+        # Load model with user-specified parameters
         transformer = FluxTransformer2DConditionalModel.from_pretrained(
             model_folder,
             torch_dtype=torch_dtype,
-            low_cpu_mem_usage=True,  # Changed to True for faster loading
+            low_cpu_mem_usage=low_cpu_mem_usage,
             ignore_mismatched_sizes=True,
-            use_safetensors=True,    # Added for faster loading
-            offload_state_dict=True  # Added to reduce memory usage during loading
+            use_safetensors=True,
         )
+        
+        
 
         print("Loading pipeline...")
         
         # Use the optimized from_pretrained method (which was monkey-patched in pipeline.py)
+            # Use the optimized from_pretrained method
         pipe = FluxConditionalPipeline.from_pretrained(
-            "black-forest-labs/FLUX.1-dev",
+            "black-forest-labs/FLUX.1-schnell",
             transformer=transformer,
             torch_dtype=torch_dtype
-        )
+            )
+        
+        # Access and modify scheduler configs
+        pipe.scheduler.config.shift = 3
+        pipe.scheduler.config.use_dynamic_shifting = True
 
         print("Loading LoRA weights...")
         
@@ -110,8 +119,16 @@ class DSDModelLoader:
 
         print("Moving to device...")
         
-        # Move to device
-        pipe.to(device)
+        # Apply sequential CPU offloading if requested and device is CPU
+        if model_cpu_offload:
+            pipe.enable_model_cpu_offload()
+        if sequential_cpu_offload:
+            pipe.enable_sequential_cpu_offload()
+        if not model_cpu_offload and not sequential_cpu_offload:
+            pipe.to(device)
+            
+            
+
 
         print("Model loaded successfully")
         
@@ -135,14 +152,25 @@ class DSDGeminiPromptEnhancer:
     RETURN_NAMES = ("enhanced_prompt",)
     FUNCTION = "enhance_prompt"
     CATEGORY = "DSD"
+    OUTPUT_NODE = True  # This ensures that UI data is sent to the node
+    
+    def __init__(self):
+        self.enhanced_prompt = None
+    
+    def get_state(self):
+        return {
+            "enhanced_prompt": self.enhanced_prompt
+        }
     
     def enhance_prompt(self, image, prompt, api_key):
         if not IMPORTS_AVAILABLE:
             print("Warning: DSD modules not available. Using original prompt.")
+            self.enhanced_prompt = None
             return (prompt,)
             
         if not GEMINI_AVAILABLE:
             print("Warning: Google Gemini API not available. Returning original prompt.")
+            self.enhanced_prompt = None
             return (prompt,)
         
         if not api_key:
@@ -150,6 +178,7 @@ class DSDGeminiPromptEnhancer:
             api_key = os.getenv("GEMINI_API_KEY")
             if not api_key:
                 print("Warning: No API key provided for Gemini. Returning original prompt.")
+                self.enhanced_prompt = None
                 return (prompt,)
             
         # Convert from ComfyUI image to PIL
@@ -159,14 +188,20 @@ class DSDGeminiPromptEnhancer:
         try:
             
             # Call the imported enhance_prompt function
-            enhanced_prompt = enhance_prompt(pil_image, prompt,api_key)
+            enhanced_prompt = enhance_prompt(pil_image, prompt, api_key)
             
             print("Original prompt:", prompt)
             print("Enhanced prompt:", enhanced_prompt)
             
-            return (enhanced_prompt,)
+            # Store the enhanced prompt for UI display
+            self.enhanced_prompt = enhanced_prompt
+            
+            # Return the enhanced prompt and explicitly include it in the UI data
+            # Make sure enhanced_prompt is a proper string, not an array/list of characters
+            return {"ui": {"enhanced_prompt": str(enhanced_prompt)}, "result": (enhanced_prompt,)}
         except Exception as e:
             print(f"Error enhancing prompt: {e}")
+            self.enhanced_prompt = None
             return (prompt,)
 
 
@@ -186,9 +221,12 @@ class DSDImageGenerator:
                 "image_guidance_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 20.0, "step": 0.1}),
                 "text_guidance_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 20.0, "step": 0.1}),
                 "num_inference_steps": ("INT", {"default": 28, "min": 1, "max": 100}),
-                "width": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 8}),
-                "height": ("INT", {"default": 512, "min": 512, "max": 2048, "step": 8}),
+                "width": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 64}),
+                "height": ("INT", {"default": 512, "min": 512, "max": 2048, "step": 64}),
                 "use_gemini_prompt": ("BOOLEAN", {"default": False})
+            },
+            "optional": {
+                "resize_params": ("RESIZE_PARAMS",),
             }
         }
     
@@ -216,15 +254,33 @@ class DSDImageGenerator:
     
     def generate(self, dsd_model, image, prompt, negative_prompt, seed,
                 guidance_scale, image_guidance_scale, text_guidance_scale, num_inference_steps,
-                width, height, use_gemini_prompt):
+                width, height, use_gemini_prompt, resize_params=None):
         # Initialize progress bar
         pbar = ProgressBar(num_inference_steps)
         # Reset progress value
         self.progress_value = 0.0
         
-        # Convert from ComfyUI image format to PIL and prepare
+        # Convert from ComfyUI image format to PIL
         pil_image = comfy_to_pil(image)
-        pil_image = center_crop_and_resize(pil_image, width//2)  # DSD expects 512x512 input images
+        
+        # Process the image based on resize_params
+        if resize_params is not None:
+            method = resize_params.get("method", "center_crop")
+            interpolation = resize_params.get("interpolation", "LANCZOS")
+            pad_color = resize_params.get("pad_color", (0, 0, 0))
+            
+            # Apply the selected resize method
+            if method == "resize_and_center_crop":
+                pil_image = resize_and_center_crop(pil_image, height, width//2)
+            elif method == "center_crop":
+                pil_image = center_crop(pil_image, height, width//2, interpolation)
+            elif method == "pad":
+                pil_image = pad_resize(pil_image, height, width//2, pad_color, interpolation)
+            elif method == "fit":
+                pil_image = fit_resize(pil_image, height, width//2, interpolation)
+        else:
+            # Use the default center_crop_and_resize if no resize_params provided
+            pil_image = resize_and_center_crop(pil_image, height, width//2)
         
         # Clean prompt
         prompt = prompt.strip().replace("\n", "").replace("\r", "")
@@ -271,7 +327,6 @@ class DSDImageGenerator:
             image=pil_image,
             guidance_scale_real_i=image_guidance_scale,
             guidance_scale_real_t=text_guidance_scale,
-            gemini_prompt=use_gemini_prompt,
             callback_on_step_end=progress_callback,
             generator=generator
         ).images
@@ -367,10 +422,10 @@ class DSDModelDownloader:
                 "repo_id": ("STRING", {"default": "primecai/dsd_model"}),
                 "force_download": ("BOOLEAN", {"default": False}),
                 "device": (["cuda", "cpu"], {"default": "cuda"}),
-                "dtype": (["bfloat16", "float16", "float32"], {"default": "bfloat16"})
-            },
-            "optional": {
-                "hf_token": ("STRING", {"default": "", "multiline": False, "tooltip": "Enter your Hugging Face token here or use the environment variable HF_TOKEN."})
+                "dtype": (["bfloat16", "float16", "float32"], {"default": "bfloat16", "tooltip": "bfloat16 provides best speed/memory tradeoff"}),
+                "low_cpu_mem_usage": ("BOOLEAN", {"default": True, "tooltip": "Reduces CPU memory usage during model loading. Recommended for faster loading."}),
+                "model_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Offloads state dict to reduce memory usage during loading. May slow down loading speed."}), 
+                "sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enables sequential CPU offloading. Only use if low on VRAM. Significantly impacts loading speed."})
             }
         }
     
@@ -389,17 +444,10 @@ class DSDModelDownloader:
             "status_text": self.status_text
         }
     
-    def download_and_load_model(self, repo_id, force_download, device, dtype, hf_token=""):
+    def download_and_load_model(self, repo_id, force_download, device, dtype, low_cpu_mem_usage, model_cpu_offload, sequential_cpu_offload):
         if not IMPORTS_AVAILABLE:
             raise ImportError("Could not import DSD modules. Make sure DSD project files (pipeline.py, transformer.py) are properly installed in the parent directory.")
         
-        # Get token from environment variable if not provided
-        if not hf_token:
-            hf_token = os.getenv("HF_TOKEN")
-            if hf_token:
-                self.status_text = "Using HF_TOKEN from environment variable"
-                print(self.status_text)
-        
         # Create the dsd_model directory in ComfyUI models folder if it doesn't exist
         os.makedirs(dsd_model_path, exist_ok=True)
         transformer_path = os.path.join(dsd_model_path, "transformer")
@@ -427,8 +475,7 @@ class DSDModelDownloader:
                     repo_id=repo_id,
                     local_dir=dsd_model_path,
                     local_dir_use_symlinks=False,
-                    resume_download=True,
-                    token=hf_token
+                    resume_download=True
                 )
                 
                 self.progress_value = 0.5
@@ -466,28 +513,34 @@ class DSDModelDownloader:
         self.progress_value = 0.6
         
         try:
-            # Load model with optimized parameters
+            # Load model with user-specified parameters
             transformer = FluxTransformer2DConditionalModel.from_pretrained(
                 transformer_path,
                 torch_dtype=torch_dtype,
-                low_cpu_mem_usage=True,
+                low_cpu_mem_usage=low_cpu_mem_usage,
                 ignore_mismatched_sizes=True,
-                use_safetensors=True,
-                offload_state_dict=True,
-                token=hf_token
+                use_safetensors=True
             )
             
             self.status_text = "Loading pipeline..."
             print(self.status_text)
             self.progress_value = 0.7
             
-            # Use the optimized from_pretrained method
+                        
+
+                        
+            # Using black-forest-labs/FLUX.1-schnell,so we don't need to login to Hugging Face
             pipe = FluxConditionalPipeline.from_pretrained(
-                "black-forest-labs/FLUX.1-dev",
+                "black-forest-labs/FLUX.1-schnell",
                 transformer=transformer,
-                torch_dtype=torch_dtype,
-                token=hf_token
-            )
+                torch_dtype=torch_dtype
+                )
+            
+            # Access and modify scheduler configs
+            pipe.scheduler.config.shift = 3
+            pipe.scheduler.config.use_dynamic_shifting = True
+
+            
             
             self.status_text = "Loading LoRA weights..."
             print(self.status_text)
@@ -496,12 +549,15 @@ class DSDModelDownloader:
             # Load LoRA weights
             pipe.load_lora_weights(lora_file)
             
-            self.status_text = f"Moving to {device}..."
-            print(self.status_text)
-            self.progress_value = 0.9
+            # Apply sequential CPU offloading if requested and device is CPU
+            if model_cpu_offload:
+                pipe.enable_model_cpu_offload()
+            if sequential_cpu_offload:
+                pipe.enable_sequential_cpu_offload()
+            if not model_cpu_offload and not sequential_cpu_offload:
+                pipe.to(device)
             
-            # Move to device
-            pipe.to(device)
+            self.progress_value = 0.9
             
             self.status_text = "Model loaded successfully"
             print(self.status_text)
@@ -515,13 +571,45 @@ class DSDModelDownloader:
             raise
 
 
+class DSDResizeSelector:
+    """Selects image resize options for DSD Image Generator"""
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "resize_method": (["resize_and_center_crop", "center_crop", "pad", "fit"], {"default": "resize_and_center_crop"}),
+                "interpolation": (["LANCZOS", "BICUBIC", "BILINEAR", "NEAREST"], {"default": "LANCZOS"}),
+                "pad_r": ("INT", {"default": 0, "min": 0, "max": 255}),
+                "pad_g": ("INT", {"default": 0, "min": 0, "max": 255}),
+                "pad_b": ("INT", {"default": 0, "min": 0, "max": 255}),
+            }
+        }
+    
+    RETURN_TYPES = ("RESIZE_PARAMS",)
+    RETURN_NAMES = ("resize_params",)
+    FUNCTION = "select_resize_options"
+    CATEGORY = "DSD"
+    
+    def select_resize_options(self, resize_method, interpolation, pad_r, pad_g, pad_b):
+        # Create a JSON object with the resize parameters
+        resize_params = {
+            "method": resize_method,
+            "interpolation": interpolation,
+            "pad_color": (pad_r, pad_g, pad_b)
+        }
+        
+        return (resize_params,)
+
+
 # Register nodes
 NODE_CLASS_MAPPINGS = {
     "DSDModelLoader": DSDModelLoader,
     "DSDGeminiPromptEnhancer": DSDGeminiPromptEnhancer,
     "DSDImageGenerator": DSDImageGenerator,
     "DSDModelSelector": DSDModelSelector,
-    "DSDModelDownloader": DSDModelDownloader
+    "DSDModelDownloader": DSDModelDownloader,
+    "DSDResizeSelector": DSDResizeSelector
 }
 
 # Node display names
@@ -530,5 +618,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "DSDGeminiPromptEnhancer": "DSD Gemini Prompt Enhancer",
     "DSDImageGenerator": "DSD Image Generator",
     "DSDModelSelector": "DSD Model Selector",
-    "DSDModelDownloader": "DSD Model Downloader"
+    "DSDModelDownloader": "DSD Model Downloader",
+    "DSDResizeSelector": "DSD Resize Selector"
 } 

examples/example_workflow.json

@@ -1,17 +1,46 @@
 {
-  "last_node_id": 38,
-  "last_link_id": 116,
+  "last_node_id": 39,
+  "last_link_id": 117,
   "nodes": [
     {
-      "id": 11,
-      "type": "PrimitiveNode",
+      "id": 18,
+      "type": "PreviewImage",
       "pos": [
-        20.652568817138672,
-        635.3331909179688
+        1214.10986328125,
+        -74.78528594970703
       ],
       "size": [
-        210,
-        88
+        406.026123046875,
+        409.0712585449219
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 115
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 3,
+      "type": "LoadImage",
+      "pos": [
+        -119.63543701171875,
+        -71.78013610839844
+      ],
+      "size": [
+        366.44219970703125,
+        419.337890625
       ],
       "flags": {},
       "order": 0,
@@ -19,34 +48,74 @@
       "inputs": [],
       "outputs": [
         {
-          "name": "STRING",
-          "type": "STRING",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "shape": 3,
           "links": [
-            111
-          ]
+            4,
+            109
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "shape": 3,
+          "links": []
         }
       ],
-      "title": "negative_prompt",
       "properties": {
-        "Run widget replace on values": false
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "LoadImage"
       },
       "widgets_values": [
-        "text, watermark, blurry"
+        "DALL·E 2024-08-18 18.34.08 - A 2D anime-style character concept art in the style of Porco Rosso. The character is a young, male airplane mechanic in his early 20s, with messy brow.webp",
+        "image"
       ]
     },
+    {
+      "id": 19,
+      "type": "PreviewImage",
+      "pos": [
+        1271.379638671875,
+        431.7550964355469
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 113
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.26",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
     {
       "id": 5,
       "type": "DSDGeminiPromptEnhancer",
       "pos": [
-        299.8087463378906,
-        312.0589294433594
+        299.41937255859375,
+        386.8332824707031
       ],
       "size": [
-        404.98046875,
-        125.29296875
+        334.03948974609375,
+        137.88925170898438
       ],
       "flags": {},
-      "order": 4,
+      "order": 5,
       "mode": 0,
       "inputs": [
         {
@@ -75,28 +144,67 @@
         }
       ],
       "properties": {
-        "aux_id": "irreveloper/ComfyUI-DSD-Node",
+        "cnr_id": "dsd",
         "ver": "4642def54ab46095a128cc2f8d37abde99a0f099",
-        "Node name for S&R": "DSDGeminiPromptEnhancer"
+        "Node name for S&R": "DSDGeminiPromptEnhancer",
+        "aux_id": "irreveloper/ComfyUI-DSD-Node"
       },
       "widgets_values": [
-        "Superhero fights with evil fish underwater.",
+        "Side view of anime character.",
         ""
       ]
     },
+    {
+      "id": 39,
+      "type": "DSDResizeSelector",
+      "pos": [
+        314.8365478515625,
+        171.76687622070312
+      ],
+      "size": [
+        315,
+        154
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "resize_params",
+          "type": "RESIZE_PARAMS",
+          "links": [
+            117
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "cnr_id": "dsd",
+        "ver": "48d020ac58d05c8a14223dac597a645ee908684a",
+        "Node name for S&R": "DSDResizeSelector"
+      },
+      "widgets_values": [
+        "resize_and_center_crop",
+        "LANCZOS",
+        0,
+        0,
+        0
+      ]
+    },
     {
       "id": 38,
       "type": "DSDModelDownloader",
       "pos": [
-        317.01739501953125,
-        13.488250732421875
+        328.3520812988281,
+        -165.09207153320312
       ],
       "size": [
         315,
-        194
+        266
       ],
       "flags": {},
-      "order": 1,
+      "order": 2,
       "mode": 0,
       "inputs": [],
       "outputs": [
@@ -119,125 +227,28 @@
         }
       ],
       "properties": {
-        "aux_id": "irreveloper/ComfyUI-DSD-Node",
+        "cnr_id": "dsd",
         "ver": "4642def54ab46095a128cc2f8d37abde99a0f099",
-        "Node name for S&R": "DSDModelDownloader"
+        "Node name for S&R": "DSDModelDownloader",
+        "aux_id": "irreveloper/ComfyUI-DSD-Node"
       },
       "widgets_values": [
         "primecai/dsd_model",
         false,
         "cuda",
         "bfloat16",
+        true,
+        false,
+        false,
         ""
       ]
     },
-    {
-      "id": 19,
-      "type": "PreviewImage",
-      "pos": [
-        1260.008544921875,
-        416.4699401855469
-      ],
-      "size": [
-        210,
-        246
-      ],
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 113
-        }
-      ],
-      "outputs": [],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.26",
-        "Node name for S&R": "PreviewImage"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 18,
-      "type": "PreviewImage",
-      "pos": [
-        1214.10986328125,
-        -74.78528594970703
-      ],
-      "size": [
-        406.026123046875,
-        409.0712585449219
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 115
-        }
-      ],
-      "outputs": [],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.26",
-        "Node name for S&R": "PreviewImage"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 3,
-      "type": "LoadImage",
-      "pos": [
-        -94.37196350097656,
-        -36.992637634277344
-      ],
-      "size": [
-        366.44219970703125,
-        419.337890625
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "shape": 3,
-          "links": [
-            4,
-            109
-          ],
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "shape": 3,
-          "links": []
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.26",
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "image (17).webp",
-        "image"
-      ]
-    },
     {
       "id": 4,
       "type": "PrimitiveNode",
       "pos": [
-        -77.04129791259766,
-        436.9585266113281
+        -117.78815460205078,
+        407.2402648925781
       ],
       "size": [
         315,
@@ -262,22 +273,54 @@
         "Run widget replace on values": false
       },
       "widgets_values": [
-        "Superhero fights with evil fish underwater."
+        "Side view of anime character."
+      ]
+    },
+    {
+      "id": 11,
+      "type": "PrimitiveNode",
+      "pos": [
+        -106.84088897705078,
+        607.5902099609375
+      ],
+      "size": [
+        301.3577880859375,
+        88
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            111
+          ]
+        }
+      ],
+      "title": "negative_prompt",
+      "properties": {
+        "Run widget replace on values": false
+      },
+      "widgets_values": [
+        "text, watermark, blurry"
       ]
     },
     {
       "id": 37,
       "type": "DSDImageGenerator",
       "pos": [
-        754.3903198242188,
-        41.60234832763672
+        721.5396728515625,
+        86.6509017944336
       ],
       "size": [
         400,
         398
       ],
       "flags": {},
-      "order": 5,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
@@ -305,6 +348,12 @@
             "name": "negative_prompt"
           },
           "link": 111
+        },
+        {
+          "name": "resize_params",
+          "type": "RESIZE_PARAMS",
+          "shape": 7,
+          "link": 117
         }
       ],
       "outputs": [
@@ -329,9 +378,10 @@
         }
       ],
       "properties": {
-        "aux_id": "irreveloper/ComfyUI-DSD-Node",
+        "cnr_id": "dsd",
         "ver": "4642def54ab46095a128cc2f8d37abde99a0f099",
-        "Node name for S&R": "DSDImageGenerator"
+        "Node name for S&R": "DSDImageGenerator",
+        "aux_id": "irreveloper/ComfyUI-DSD-Node"
       },
       "widgets_values": [
         "",
@@ -342,8 +392,8 @@
         1,
         1,
         28,
-        1536,
-        768,
+        1024,
+        512,
         true
       ]
     }
@@ -412,16 +462,24 @@
       37,
       0,
       "DSD_MODEL"
+    ],
+    [
+      117,
+      39,
+      0,
+      37,
+      4,
+      "RESIZE_PARAMS"
     ]
   ],
   "groups": [],
   "config": {},
   "extra": {
     "ds": {
-      "scale": 1.3310000000000017,
+      "scale": 0.8768324088719838,
       "offset": [
-        145.57362443895767,
-        125.29861875515397
+        773.2185529985686,
+        450.555653889974
       ]
     }
   },

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "dsd"
+description = "An Unofficial ComfyUI custom node package that integrates [a/Diffusion Self-Distillation (DSD)](https://github.com/primecai/diffusion-self-distillation) for zero-shot customized image generation.\nDSD is a model for subject-preserving image generation that allows you to create images of a specific subject in novel contexts without per-instance tuning."
+version = "1.0.2"
+license = {file = "LICENSE"}
+dependencies = ["torch>=2.0.0", "diffusers>=0.24.0", "transformers>=4.36.0", "sentencepiece>=0.1.99", "accelerate>=0.27.0", "google-genai>=0.1.0", "Pillow>=9.5.0", "protobuf>=4.25.0", "peft>=0.7.0", "hf_transfer"]
+
+[project.urls]
+Repository = "https://github.com/irreveloper/ComfyUI-DSD"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "irreveloper"
+DisplayName = "ComfyUI-DSD"
+Icon = ""

utils.py

@@ -92,13 +92,14 @@ def pil_to_comfy(image: Union[Image.Image, List[Image.Image], None]) -> Optional
     # Convert to torch tensor and add batch dimension
     return torch.from_numpy(image_np)[None,]
 
-def center_crop_and_resize(image: Union[torch.Tensor, Image.Image], target_size: int = 512) -> Union[torch.Tensor, Image.Image]:
+def resize_and_center_crop(image: Union[torch.Tensor, Image.Image], target_height: int = 512,target_width: int = 512) -> Union[torch.Tensor, Image.Image]:
     """
     Center crop and resize an image.
     
     Args:
         image: Image to process (ComfyUI tensor or PIL Image)
-        target_size: Target size for width and height
+        target_height: Target height
+        target_width: Target width
         
     Returns:
         Processed image in the same format as input
@@ -106,15 +107,176 @@ def center_crop_and_resize(image: Union[torch.Tensor, Image.Image], target_size:
     # Handle ComfyUI tensor
     if isinstance(image, torch.Tensor):
         pil_image = comfy_to_pil(image)
-        result = center_crop_and_resize(pil_image, target_size)
+        result = resize_and_center_crop(pil_image, target_height, target_width)
         return pil_to_comfy(result)
     
     # Handle PIL Image
     w, h = image.size
     min_size = min(w, h)
-    cropped = image.crop(((w - min_size) // 2, 
-                         (h - min_size) // 2, 
-                         (w + min_size) // 2, 
-                         (h + min_size) // 2))
-    resized = cropped.resize((target_size, target_size), Image.LANCZOS)
-    return resized 
+
+    # Calculate target aspect ratio
+    target_ratio = target_width / target_height
+    # Calculate current aspect ratio
+    current_ratio = w / h
+
+    # Resize to match target width or height while preserving aspect ratio
+    if current_ratio > target_ratio:
+        # Image is wider than target - resize by height
+        new_height = target_height
+        new_width = int(w * (target_height / h))
+    else:
+        # Image is taller than target - resize by width
+        new_width = target_width
+        new_height = int(h * (target_width / w))
+
+    image = image.resize((new_width, new_height), Image.BILINEAR)
+
+    # Center crop the image to the target size
+    cropped = image.crop(((new_width - target_width) // 2, 
+                         (new_height - target_height) // 2, 
+                         (new_width + target_width) // 2, 
+                         (new_height + target_height) // 2))
+
+    return cropped
+
+def center_crop(image: Union[torch.Tensor, Image.Image], target_height: int = 512, target_width: int = 512, 
+                interpolation: str = "LANCZOS") -> Union[torch.Tensor, Image.Image]:
+    """
+    Center crop an image to target size. If image is smaller than target size,
+    resize first before cropping.
+    
+    Args:
+        image: Image to process (ComfyUI tensor or PIL Image)
+        target_height: Target height
+        target_width: Target width
+        interpolation: Interpolation method (NEAREST, BILINEAR, BICUBIC, LANCZOS)
+        
+    Returns:
+        Processed image in the same format as input
+    """
+    # Handle ComfyUI tensor
+    if isinstance(image, torch.Tensor):
+        pil_image = comfy_to_pil(image)
+        result = center_crop(pil_image, target_height, target_width, interpolation)
+        return pil_to_comfy(result)
+    
+    # Handle PIL Image
+    w, h = image.size
+    
+    # Get interpolation method
+    interp_method = {
+        "NEAREST": Image.NEAREST,
+        "BILINEAR": Image.BILINEAR,
+        "BICUBIC": Image.BICUBIC,
+        "LANCZOS": Image.LANCZOS
+    }.get(interpolation, Image.LANCZOS)
+
+    # If image is smaller than target in either dimension, resize first
+    if w < target_width or h < target_height:
+        # Calculate scale factor needed
+        scale = max(target_width / w, target_height / h)
+        new_w = int(w * scale)
+        new_h = int(h * scale)
+        image = image.resize((new_w, new_h), interp_method)
+        w, h = new_w, new_h
+
+    # Calculate crop coordinates
+    left = (w - target_width) // 2
+    top = (h - target_height) // 2
+    right = left + target_width
+    bottom = top + target_height
+
+    # Perform center crop
+    cropped = image.crop((left, top, right, bottom))
+    return cropped
+
+def pad_resize(image: Union[torch.Tensor, Image.Image], target_height: int = 512, target_width: int = 512, 
+               pad_color: tuple = (0, 0, 0), interpolation: str = "LANCZOS") -> Union[torch.Tensor, Image.Image]:
+    """
+    Resize image preserving aspect ratio and pad to target size.
+    
+    Args:
+        image: Image to process (ComfyUI tensor or PIL Image)
+        target_height: Target height
+        target_width: Target width
+        pad_color: RGB color tuple for padding (default: black)
+        interpolation: Interpolation method (NEAREST, BILINEAR, BICUBIC, LANCZOS)
+        
+    Returns:
+        Processed image in the same format as input
+    """
+    # Handle ComfyUI tensor
+    if isinstance(image, torch.Tensor):
+        pil_image = comfy_to_pil(image)
+        result = pad_resize(pil_image, target_height, target_width, pad_color, interpolation)
+        return pil_to_comfy(result)
+    
+    # Handle PIL Image
+    w, h = image.size
+    
+    # Get interpolation method
+    interp_method = {
+        "NEAREST": Image.NEAREST,
+        "BILINEAR": Image.BILINEAR,
+        "BICUBIC": Image.BICUBIC,
+        "LANCZOS": Image.LANCZOS
+    }.get(interpolation, Image.LANCZOS)
+    
+    # Calculate target aspect ratio
+    target_ratio = target_width / target_height
+    # Calculate current aspect ratio
+    current_ratio = w / h
+    
+    # Create a new image with the target size and fill with the pad color
+    new_image = Image.new("RGB", (target_width, target_height), pad_color)
+    
+    # Resize the original image preserving aspect ratio
+    if current_ratio > target_ratio:
+        # Image is wider than target - resize by width
+        new_w = target_width
+        new_h = int(h * (target_width / w))
+        resized = image.resize((new_w, new_h), interp_method)
+        # Paste in the center (horizontally)
+        new_image.paste(resized, (0, (target_height - new_h) // 2))
+    else:
+        # Image is taller than target - resize by height
+        new_h = target_height
+        new_w = int(w * (target_height / h))
+        resized = image.resize((new_w, new_h), interp_method)
+        # Paste in the center (vertically)
+        new_image.paste(resized, ((target_width - new_w) // 2, 0))
+    
+    return new_image
+
+def fit_resize(image: Union[torch.Tensor, Image.Image], target_height: int = 512, target_width: int = 512,
+               interpolation: str = "LANCZOS") -> Union[torch.Tensor, Image.Image]:
+    """
+    Resize image to target size without preserving aspect ratio.
+    
+    Args:
+        image: Image to process (ComfyUI tensor or PIL Image)
+        target_height: Target height
+        target_width: Target width
+        interpolation: Interpolation method (NEAREST, BILINEAR, BICUBIC, LANCZOS)
+        
+    Returns:
+        Processed image in the same format as input
+    """
+    # Handle ComfyUI tensor
+    if isinstance(image, torch.Tensor):
+        pil_image = comfy_to_pil(image)
+        result = fit_resize(pil_image, target_height, target_width, interpolation)
+        return pil_to_comfy(result)
+    
+    # Handle PIL Image
+    # Get interpolation method
+    interp_method = {
+        "NEAREST": Image.NEAREST,
+        "BILINEAR": Image.BILINEAR,
+        "BICUBIC": Image.BICUBIC,
+        "LANCZOS": Image.LANCZOS
+    }.get(interpolation, Image.LANCZOS)
+    
+    # Resize directly to target size (no aspect ratio preservation)
+    resized = image.resize((target_width, target_height), interp_method)
+    return resized

web/js/showEnhancedPrompt.js

@@ -0,0 +1,77 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+// Displays enhanced prompt on DSDGeminiPromptEnhancer node
+app.registerExtension({
+    name: "comfyui.dsd.showEnhancedPrompt",
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "DSDGeminiPromptEnhancer") {
+            function normalizePrompt(prompt) {
+                if (!prompt) return "";
+                if (Array.isArray(prompt)) {
+                    return prompt.join("");
+                }
+                return prompt;
+            }
+
+            function populateEnhancedPrompt(enhancedPrompt) {
+                enhancedPrompt = normalizePrompt(enhancedPrompt);
+                if (!enhancedPrompt) {
+                    return;
+                }
+
+                try {
+                    if (this.widgets) {
+                        for (let i = 0; i < this.widgets.length; i++) {
+                            if (this.widgets[i].name === "enhanced_prompt_display") {
+                                this.widgets[i].onRemove?.();
+                                this.widgets.splice(i, 1);
+                                i--;
+                            }
+                        }
+                    }
+
+                    const textWidgetResult = ComfyWidgets["STRING"](this, "enhanced_prompt_text", ["STRING", { multiline: true }], app);
+                    if (!textWidgetResult || !textWidgetResult.widget || !textWidgetResult.widget.inputEl) {
+                        return;
+                    }
+                    const w = textWidgetResult.widget;
+                    w.inputEl.readOnly = true;
+                    w.inputEl.style.opacity = 0.8;
+                    w.inputEl.style.backgroundColor = "#1e2124";
+                    w.inputEl.style.color = "#9eec51";
+                    w.name = "enhanced_prompt_display";
+                    w.value = enhancedPrompt;
+
+                    requestAnimationFrame(() => {
+                        const sz = this.computeSize();
+                        if (sz[0] < this.size[0]) sz[0] = this.size[0];
+                        if (sz[1] < this.size[1]) sz[1] = this.size[1];
+                        this.onResize?.(sz);
+                        app.graph.setDirtyCanvas(true, false);
+                    });
+                } catch (error) {
+                    // Silent error handling
+                }
+            }
+
+            const onExecuted = nodeType.prototype.onExecuted;
+            nodeType.prototype.onExecuted = function (message) {
+                try {
+                    onExecuted?.apply(this, arguments);
+                    if (message.enhanced_prompt) {
+                        populateEnhancedPrompt.call(this, message.enhanced_prompt);
+                    } else if (message.ui && message.ui.enhanced_prompt) {
+                        populateEnhancedPrompt.call(this, message.ui.enhanced_prompt);
+                    } else if (message.text) {
+                        populateEnhancedPrompt.call(this, message.text);
+                    }
+                } catch (error) {
+                    // Silent error handling
+                }
+            };
+        }
+    },
+});
+
+window.DSD_ENHANCED_PROMPT_LOADED = true;