diff --git a/.github/instructions/v3.example.instructions.md b/.github/instructions/v3.example.instructions.md new file mode 100644 index 0000000..1f637ae --- /dev/null +++ b/.github/instructions/v3.example.instructions.md @@ -0,0 +1,605 @@ +--- +applyTo: "**/*.py" +description: "ComfyUI v3 Node Examples" +--- + +# ComfyUI v3 Node Examples + +Real-world examples of v3 nodes demonstrating various features and patterns. + +## Basic Examples + +### Simple Image Processor + +```python +from comfy_api.latest import io, ui +import torch + +class ImageInvertV3(io.ComfyNode): + """Simple node that inverts image colors.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageInvert_v3", + display_name="Invert Image", + category="image/filters", + description="Inverts the colors of an image", + inputs=[ + io.Image.Input("image", tooltip="Image to invert") + ], + outputs=[ + io.Image.Output("inverted", tooltip="Inverted image") + ] + ) + + @classmethod + def execute(cls, image): + # Invert: 1.0 - image + inverted = 1.0 - image + return io.NodeOutput(inverted, ui=ui.PreviewImage(inverted)) +``` + +### Math Operations + +```python +class MathOperationV3(io.ComfyNode): + """Performs math operations on two values.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="MathOperation_v3", + display_name="Math Operation", + category="utils/math", + inputs=[ + io.Float.Input("a", default=0.0), + io.Float.Input("b", default=0.0), + io.Combo.Input("operation", + options=["add", "subtract", "multiply", "divide", "power"], + default="add" + ) + ], + outputs=[ + io.Float.Output("result") + ] + ) + + @classmethod + def execute(cls, a, b, operation): + operations = { + "add": a + b, + "subtract": a - b, + "multiply": a * b, + "divide": a / b if b != 0 else 0, + "power": a ** b + } + result = operations[operation] + return io.NodeOutput(result) +``` + +## Async Examples + +### API Integration + +```python +import aiohttp + +class TextGeneratorV3(io.ComfyNode): + """Generates text using external API.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextGenerator_v3", + display_name="AI Text Generator", + category="text/generation", + inputs=[ + io.String.Input("prompt", multiline=True), + io.String.Input("api_url", default="http://localhost:11434/api/generate"), + io.String.Input("model", default="llama2"), + io.Float.Input("temperature", default=0.7, min=0.0, max=2.0) + ], + outputs=[ + io.String.Output("generated_text") + ] + ) + + @classmethod + async def execute(cls, prompt, api_url, model, temperature): + async with aiohttp.ClientSession() as session: + payload = { + "model": model, + "prompt": prompt, + "temperature": temperature, + "stream": False + } + + async with session.post(api_url, json=payload) as response: + if response.status == 200: + data = await response.json() + text = data.get("response", "") + return io.NodeOutput(text) + else: + raise RuntimeError(f"API error: {response.status}") +``` + +### Batch Processing with Progress + +```python +from comfy.utils import ProgressBar +import asyncio + +class BatchImageProcessorV3(io.ComfyNode): + """Processes images in batch with progress tracking.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="BatchImageProcessor_v3", + display_name="Batch Image Processor", + category="image/batch", + inputs=[ + io.Image.Input("images"), + io.Float.Input("process_time", default=0.1, min=0.01, max=1.0, + tooltip="Simulated processing time per image") + ], + outputs=[ + io.Image.Output("processed") + ], + hidden=[io.Hidden.unique_id] + ) + + @classmethod + async def execute(cls, images, process_time, **kwargs): + batch_size = images.shape[0] + pbar = ProgressBar(batch_size, node_id=cls.hidden.unique_id) + + processed = [] + for i in range(batch_size): + # Simulate async processing + await asyncio.sleep(process_time) + + # Example: Apply blur + import torch.nn.functional as F + blurred = F.gaussian_blur(images[i:i+1], kernel_size=5) + processed.append(blurred) + + pbar.update(1) + + result = torch.cat(processed, dim=0) + return io.NodeOutput(result, ui=ui.PreviewImage(result)) +``` + +## Advanced Examples + +### Model Loader with Resources + +```python +import folder_paths +import comfy.utils +import comfy.sd + +class CheckpointLoaderV3(io.ComfyNode): + """Loads checkpoint models with caching.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CheckpointLoader_v3", + display_name="Load Checkpoint", + category="loaders", + inputs=[ + io.Combo.Input("ckpt_name", + options=folder_paths.get_filename_list("checkpoints"), + tooltip="Select checkpoint to load" + ) + ], + outputs=[ + io.Model.Output("model"), + io.Clip.Output("clip"), + io.Vae.Output("vae") + ] + ) + + @classmethod + def execute(cls, ckpt_name): + # Use resource caching + ckpt = cls.resources.get( + resources.TorchDictFolderFilename("checkpoints", ckpt_name) + ) + + # Load components + model, clip, vae = comfy.sd.load_checkpoint_guess_config( + ckpt, + embedding_directory=folder_paths.get_folder_paths("embeddings") + ) + + return io.NodeOutput(model, clip, vae) +``` + +### State Management Example + +```python +class IterativeRefinerV3(io.ComfyNode): + """Refines images iteratively with state tracking.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="IterativeRefiner_v3", + display_name="Iterative Refiner", + category="image/processing", + inputs=[ + io.Image.Input("image"), + io.Int.Input("iterations", default=3, min=1, max=10), + io.Boolean.Input("reset", default=False, + tooltip="Reset refinement history") + ], + outputs=[ + io.Image.Output("refined"), + io.Int.Output("total_iterations") + ] + ) + + @classmethod + def execute(cls, image, iterations, reset): + # Initialize or reset state + if reset or cls.state.history is None: + cls.state.history = [] + cls.state.total_iterations = 0 + + # Get last refined image or use input + current = cls.state.history[-1] if cls.state.history else image + + # Iterative refinement + for i in range(iterations): + # Example: Progressive sharpening + import torch.nn.functional as F + kernel = torch.tensor([[-1,-1,-1], + [-1, 9,-1], + [-1,-1,-1]], dtype=torch.float32) + kernel = kernel.view(1, 1, 3, 3) + kernel = kernel.repeat(current.shape[-1], 1, 1, 1) + + current = current.permute(0, 3, 1, 2) + sharpened = F.conv2d(current, kernel, padding=1, groups=current.shape[1]) + current = sharpened.permute(0, 2, 3, 1) + current = torch.clamp(current, 0, 1) + + # Update state + cls.state.history.append(current) + cls.state.total_iterations += iterations + + # Keep history size manageable + if len(cls.state.history) > 10: + cls.state.history.pop(0) + + return io.NodeOutput( + current, + cls.state.total_iterations, + ui=ui.PreviewImage(current) + ) +``` + +### Dynamic Inputs Example + +```python +class ImageBlenderV3(io.ComfyNode): + """Blends multiple images with weights.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageBlender_v3", + display_name="Image Blender", + category="image/blend", + inputs=[ + io.AutoGrowDynamicInput("images", + template_input=io.Image.Input("image"), + min=2, + max=8 + ), + io.Combo.Input("mode", + options=["average", "weighted", "max", "min"], + default="average" + ) + ], + outputs=[ + io.Image.Output("blended") + ] + ) + + @classmethod + def execute(cls, mode, **kwargs): + # Collect all image inputs + images = [] + for key, value in sorted(kwargs.items()): + if key.startswith("image"): + images.append(value) + + if not images: + raise ValueError("No images provided") + + # Stack images + stacked = torch.stack(images, dim=0) + + # Blend based on mode + if mode == "average": + blended = torch.mean(stacked, dim=0) + elif mode == "weighted": + # Simple linear weighting + weights = torch.linspace(1, 0.1, len(images)) + weights = weights / weights.sum() + weights = weights.view(-1, 1, 1, 1, 1) + blended = (stacked * weights).sum(dim=0) + elif mode == "max": + blended = torch.max(stacked, dim=0)[0] + elif mode == "min": + blended = torch.min(stacked, dim=0)[0] + + return io.NodeOutput(blended, ui=ui.PreviewImage(blended)) +``` + +### Multi-Type Input Example + +```python +class UniversalInverterV3(io.ComfyNode): + """Inverts images, masks, or conditioning.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="UniversalInverter_v3", + display_name="Universal Inverter", + category="utils/invert", + inputs=[ + io.MultiType.Input("input", + types=[io.Image, io.Mask, io.Conditioning] + ), + io.Float.Input("strength", default=1.0, min=0.0, max=1.0) + ], + outputs=[ + io.MultiType.Output("inverted", + types=[io.Image, io.Mask, io.Conditioning] + ) + ] + ) + + @classmethod + def execute(cls, input, strength): + # Detect input type and process accordingly + if isinstance(input, torch.Tensor): + # Image or Mask + if input.dim() == 4: # Image [B,H,W,C] + inverted = 1.0 - input + inverted = input + (inverted - input) * strength + return io.NodeOutput(inverted, ui=ui.PreviewImage(inverted)) + else: # Mask [H,W] or [B,H,W] + inverted = 1.0 - input + inverted = input + (inverted - input) * strength + return io.NodeOutput(inverted, ui=ui.PreviewMask(inverted)) + + elif isinstance(input, list): # Conditioning + # Invert conditioning strength + inverted = [] + for cond, data in input: + new_data = data.copy() + if 'strength' in new_data: + new_data['strength'] = 1.0 - new_data['strength'] + inverted.append((cond, new_data)) + return io.NodeOutput(inverted) + + else: + raise ValueError(f"Unsupported input type: {type(input)}") +``` + +### Custom Type Example + +```python +class CustomDataProcessorV3(io.ComfyNode): + """Processes custom data types.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CustomDataProcessor_v3", + display_name="Custom Data Processor", + category="utils/custom", + inputs=[ + io.Custom(io_type="MY_CUSTOM_TYPE").Input("custom_data",, + tooltip="Custom data type input" + ), + io.Float.Input("scale", default=1.0, min=0.1, max=10.0) + ], + outputs=[ + io.Custom(io_type="MY_CUSTOM_TYPE").Output("processed_data", + tooltip="Processed custom data" + ) + ] + ) + + @classmethod + def execute(cls, custom_data, scale): + # Process custom data type + # Assuming custom_data is a dict with 'value' and 'metadata' + processed = { + 'value': custom_data.get('value', 0) * scale, + 'metadata': custom_data.get('metadata', {}), + 'processed': True + } + + return io.NodeOutput(processed) +``` + +## Process Isolation Example + +### Node with Specific Dependencies + +```python +# manifest.yaml +""" +name: scientific_processor +version: 1.0.0 +dependencies: + - numpy==1.24.0 # Specific older version needed + - scipy==1.10.0 + - scikit-image==0.20.0 +isolated: true +share_torch: true +""" + +# __init__.py +from comfy_api.latest import io, io.ComfyNode, io.Schema +import numpy as np +from skimage import filters + +class ScientificProcessorV3(io.ComfyNode): + """Image processing with scientific libraries.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ScientificProcessor_v3", + display_name="Scientific Processor", + category="image/scientific", + inputs=[ + io.Image.Input("image"), + io.Combo.Input("filter_type", + options=["gaussian", "sobel", "laplacian", "butterworth"], + default="gaussian" + ), + io.Float.Input("sigma", default=1.0, min=0.1, max=10.0) + ], + outputs=[ + io.Image.Output("filtered") + ] + ) + + @classmethod + def execute(cls, image, filter_type, sigma): + # Convert to numpy + img_np = image.cpu().numpy() + batch_size = img_np.shape[0] + + results = [] + for i in range(batch_size): + img = img_np[i] + + if filter_type == "gaussian": + filtered = filters.gaussian(img, sigma=sigma, channel_axis=-1) + elif filter_type == "sobel": + gray = np.mean(img, axis=-1) + filtered = filters.sobel(gray) + filtered = np.stack([filtered]*3, axis=-1) + elif filter_type == "laplacian": + gray = np.mean(img, axis=-1) + filtered = filters.laplace(gray) + filtered = np.stack([filtered]*3, axis=-1) + elif filter_type == "butterworth": + # Frequency domain filtering + for c in range(3): + channel = img[:,:,c] + fft = np.fft.fft2(channel) + fft_shift = np.fft.fftshift(fft) + # Apply Butterworth filter + H = 1 / (1 + (D/sigma)**4) # Simplified + filtered_fft = fft_shift * H + filtered[:,:,c] = np.real(np.fft.ifft2(np.fft.ifftshift(filtered_fft))) + + results.append(filtered) + + # Convert back to tensor + result = torch.from_numpy(np.stack(results)).float() + return io.NodeOutput(result, ui=ui.PreviewImage(result)) + +# Entry point for pyisolate +from pyisolate import ExtensionBase + +class ScientificExtension(ExtensionBase): + def on_module_loaded(self, module): + self.nodes = { + "ScientificProcessor_v3": ScientificProcessorV3 + } + +def create_extension(): + return ScientificExtension() +``` + +## Complete Workflow Example + +```python +class TextToImageWorkflowV3(io.ComfyNode): + """Complete text-to-image workflow in one node.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextToImageWorkflow_v3", + display_name="Text to Image Workflow", + category="workflows", + description="All-in-one text to image generation", + inputs=[ + io.String.Input("positive_prompt", multiline=True), + io.String.Input("negative_prompt", multiline=True, default=""), + io.Model.Input("model"), + io.Clip.Input("clip"), + io.Vae.Input("vae"), + io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff), + io.Int.Input("steps", default=20, min=1, max=150), + io.Float.Input("cfg", default=7.0, min=0.0, max=30.0), + io.Combo.Input("sampler_name", + options=comfy.samplers.KSampler.SAMPLERS, + default="euler" + ), + io.Combo.Input("scheduler", + options=comfy.samplers.KSampler.SCHEDULERS, + default="normal" + ), + io.Int.Input("width", default=1024, min=64, max=8192, step=8), + io.Int.Input("height", default=1024, min=64, max=8192, step=8), + io.Int.Input("batch_size", default=1, min=1, max=64) + ], + outputs=[ + io.Image.Output("images", is_output_list=True), + io.Latent.Output("latents") + ], + is_output_node=True + ) + + @classmethod + async def execute(cls, positive_prompt, negative_prompt, model, clip, vae, + seed, steps, cfg, sampler_name, scheduler, + width, height, batch_size): + import comfy.samplers + + # Encode prompts + positive_cond = clip.encode_from_text(positive_prompt) + negative_cond = clip.encode_from_text(negative_prompt) + + # Create empty latent + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + + # Set up sampler + sampler = comfy.samplers.KSampler( + model, steps, cfg, sampler_name, scheduler, + positive_cond, negative_cond, latent, + denoise=1.0, seed=seed + ) + + # Sample with progress callback + def callback(step, x0, x, total_steps): + # Could update progress here + pass + + samples = sampler.sample(latent, callback=callback) + + # Decode latents + images = vae.decode(samples["samples"]) + + return io.NodeOutput( + images, + samples, + ui=ui.PreviewImage(images) + ) +``` \ No newline at end of file diff --git a/.github/instructions/v3.migration.instructions.md b/.github/instructions/v3.migration.instructions.md new file mode 100644 index 0000000..5edc3a3 --- /dev/null +++ b/.github/instructions/v3.migration.instructions.md @@ -0,0 +1,530 @@ +--- +applyTo: "**/*.py" +description: "ComfyUI v3 Migration Guide" +--- + +# ComfyUI v3 Migration Guide + +This guide helps developers migrate existing v1 nodes to the new v3 schema and take advantage of async execution and process isolation. + +## Quick Start: The Core Changes + +1. **Inherit from `io.ComfyNode`**: Your node class now subclasses `io.ComfyNode`. +2. **Use `define_schema`**: All metadata (`INPUT_TYPES`, `CATEGORY`, etc.) moves into a single `@classmethod def define_schema(cls)` that returns an `io.Schema` object. +3. **Use `execute`**: The main logic function is now always a `@classmethod def execute(cls, ...)` method. +4. **Use Typed I/O**: Inputs and outputs are now strongly-typed objects from the `io` module (e.g., `io.Image.Input(...)`). +5. **Return `NodeOutput`**: The `execute` method must return an `io.NodeOutput` instance. +6. **Use `NODES_LIST`**: Node registration is done by adding the class to a `NODES_LIST` at the end of the file, replacing `NODE_CLASS_MAPPINGS` and `NODE_DISPLAY_NAME_MAPPINGS`. + +## Step-by-Step Migration + +### Step 1: Class Definition and Schema + +**V1:** +```python +class Canny: + CATEGORY = "image/preprocessors" + FUNCTION = "detect_edge" + RETURN_TYPES = ("IMAGE",) + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "low_threshold": ("FLOAT", {"default": 0.4}), + "high_threshold": ("FLOAT", {"default": 0.8}), + }} + + def detect_edge(self, image, low_threshold, high_threshold): + # ... logic ... + return (img_out,) + +NODE_CLASS_MAPPINGS = {"Canny": Canny} +``` + +**V3:** +```python +from comfy_api.latest import io + +class Canny(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="Canny_V3", + category="image/preprocessors", + inputs=[ + io.Image.Input("image"), + io.Float.Input("low_threshold", default=0.4), + io.Float.Input("high_threshold", default=0.8), + ], + outputs=[io.Image.Output()], + ) + + @classmethod + def execute(cls, image, low_threshold, high_threshold): + # ... logic ... + return io.NodeOutput(img_out) + +NODES_LIST = [Canny] +``` + +### Step 2: Naming and Registration (`node_id`, `display_name`, `NODES_LIST`) + +This is a critical step for ensuring your V3 node coexists with or replaces the V1 version correctly. + +1. **Remove Old Mappings**: Delete the `NODE_CLASS_MAPPINGS` and `NODE_DISPLAY_NAME_MAPPINGS` dictionaries. +2. **Create `NODES_LIST`**: Create a new list called `NODES_LIST` and add your V3 class to it. +3. **Set `node_id`**: The `node_id` in `Schema` **must** be the key from the old `NODE_CLASS_MAPPINGS`. +4. **Set `display_name` (Conditionally)**: + - Check if a key existed in the old `NODE_DISPLAY_NAME_MAPPINGS`. + - **If yes**: Set `display_name` to that value. + - **If no**: **Omit** the `display_name` parameter from `Schema` entirely. + +**Example:** + +**V1 Registration:** +```python +NODE_CLASS_MAPPINGS = { + "APG": APG, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "APG": "Adaptive Projected Guidance", +} +``` + +**V3 `define_schema`:** +```python +@classmethod +def define_schema(cls): + return io.Schema( + node_id="APG_V3", # From MAPPINGS key + "_V3" + display_name="Adaptive Projected Guidance _V3", # From DISPLAY MAPPINGS value + " _V3" + # ... other parameters + ) + +NODES_LIST = [APG] # ... at end of file +``` + +### Step 3: Converting I/O + +| V1 Type (`string`) | V3 Class (`io.`) | Common `Input()` Options (as keyword arguments) | +|:-----------------------|:------------------------|:--------------------------------------------------------------------------| +| `STRING` | `io.String` | `default`, `multiline`, `dynamic_prompts`, `placeholder` | +| `INT` | `io.Int` | `default`, `min`, `max`, `step`, `display_mode`, `control_after_generate` | +| `FLOAT` | `io.Float` | `default`, `min`, `max`, `step`, `round`, `display_mode` | +| `BOOLEAN` | `io.Boolean` | `default`, `label_on`, `label_off` | +| `COMBO` | `io.Combo` | `options`, `default`, `upload`, `image_folder`, `remote` | +| (custom) | `io.MultiCombo` | `options`, `default`, `placeholder`, `chip` | +| `IMAGE` | `io.Image` | | +| `MASK` | `io.Mask` | | +| `MESH` | `io.Mesh` | | +| `HOOKS` | `io.Hooks` | | +| `HOOK_KEYFRAMES` | `io.HookKeyframes` | | +| `LATENT` | `io.Latent` | | +| `LATENT_OPERATION` | `io.LatentOperation` | | +| `LOAD3D_CAMERA` | `io.Load3DCamera` | | +| `LOAD_3D` | `io.Load3D` | | +| `LOAD_3D_ANIMATION` | `io.Load3DAnimation` | | +| `LOSS_MAP` | `io.LossMap` | | +| `LORA_MODEL` | `io.LoraModel` | | +| `CONDITIONING` | `io.Conditioning` | | +| `CLIP` | `io.Clip` | | +| `CLIP_VISION_OUTPUT` | `io.ClipVisionOutput` | | +| `NOISE` | `io.Noise` | | +| `VAE` | `io.Vae` | | +| `MODEL` | `io.Model` | | +| `CONTROL_NET` | `io.ControlNet` | | +| `SAMPLER` | `io.Sampler` | | +| `SIGMAS` | `io.Sigmas` | | +| `GUIDER` | `io.Guider` | | +| `CLIP_VISION` | `io.ClipVision` | | +| `UPSCALE_MODEL` | `io.UpscaleModel` | | +| `AUDIO` | `io.Audio` | | +| `VIDEO` | `io.Video` | | +| `VOXEL` | `io.Voxel` | | +| `WAN_CAMERA_EMBEDDING` | `io.WanCameraEmbedding` | | +| `WEBCAM` | `io.Webcam` | `default`, `socketless` | +| `*` | `io.AnyType` | Used for inputs that can accept any type, like the PreviewAny node. | + +#### Advanced Input Types + +**MultiType Input (accepts multiple types):** +```python +io.MultiType.Input("input", types=[io.Mask, io.Float, io.Int], optional=True) +``` + +**Combo with Remote Options:** +```python +io.Combo.Input( + "lora_name", + options=folder_paths.get_filename_list("loras"), + tooltip="The name of the LoRA." +) +``` + +**Optional Parameters:** +```python +io.Boolean.Input( + "case_sensitive", + default=True, + optional=True, # Makes this input optional + tooltip="Whether to use case-sensitive matching" +) +``` + + +### Step 4: Migrating Logic + +- **Execution Method**: Rename your old `FUNCTION` to `execute` and make it a `@classmethod`. +- **Return Value**: Wrap your return tuple in `io.NodeOutput()`. For UI updates, use the `ui` keyword argument: `io.NodeOutput(ui=ui.PreviewImage(image))`. +- **State**: Replace `self.variable` with `cls.state.variable`. +- **Hidden Inputs**: Replace `prompt` and `unique_id` parameters with `cls.hidden.prompt` and `cls.hidden.unique_id`. Request them in the schema with `hidden=[io.Hidden.prompt, io.Hidden.unique_id]`. +- **Optional Methods**: `IS_CHANGED` becomes `fingerprint_inputs`, and `VALIDATE_INPUTS` becomes `validate_inputs`. Both should be `@classmethod`. + +## Common Migration Patterns + +### 1. Hidden Inputs + +**V1:** +```python +"hidden": { + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID" +} + +def execute(self, ..., prompt=None, unique_id=None): + ... # Use hidden inputs +``` + +**V3:** +```python +hidden=[ + io.Hidden.prompt, + io.Hidden.unique_id +] + +@classmethod +def execute(cls, ...): + # Access via **cls** + prompt = cls.hidden.prompt + unique_id = cls.hidden.unique_id +``` + +### 2. State Management + +**V1:** +```python +def __init__(self): + self.last_seed = None + self.cache = {} + +def execute(self, seed, ...): + if seed != self.last_seed: + self.cache.clear() + self.last_seed = seed +``` + +**V3:** +```python +@classmethod +def execute(cls, seed, ...): + if cls.state.last_seed != seed: + cls.state.cache = {} + cls.state.last_seed = seed +``` + +### 3. UI Output + +**V1:** +```python +def execute(self, image): + # Save preview manually + preview = save_temp_image(image) + return {"ui": {"images": preview}, "result": (image,)} +``` + +**V3:** +```python +@classmethod +def execute(cls, image): + return io.NodeOutput(image, ui=ui.PreviewImage(image)) +``` + +### 4. Dynamic Inputs + +**V1:** +```python +@classmethod +def INPUT_TYPES(s): + # Complex logic to generate dynamic inputs + inputs = {"required": {}} + for i in range(get_dynamic_count()): + inputs["required"][f"input_{i}"] = ("IMAGE",) + return inputs +``` + +**V3:** +```python +inputs=[ + io.AutoGrowDynamic.Input("images", + template_input=io.Image.Input("image"), + min=1, + max=10 + ) +] +``` + +### 5. Resource Loading + +**V1:** +```python +def execute(self, model_name): + # Direct file loading + model_path = folder_paths.get_full_path("checkpoints", model_name) + model = comfy.utils.load_torch_file(model_path) +``` + +**V3:** +```python +from comfy_api.latest import resources + +@classmethod +def execute(cls, model_name): + # Cached resource loading + model = cls.resources.get( + resources.TorchDictFolderFilename("checkpoints", model_name) + ) +``` + +## Making Nodes Async + +### Basic Async Node + +```python +class AsyncNodeV3(io.ComfyNode): + @classmethod + async def execute(cls, image, url): + # Network request without blocking + async with aiohttp.ClientSession() as session: + async with session.get(url) as response: + data = await response.json() + + # Process with the data + result = process_image_with_data(image, data) + return io.NodeOutput(result) +``` + +### Progress Tracking + +```python +@classmethod +async def execute(cls, images, unique_id): + from comfy.utils import ProgressBar + + batch_size = images.shape[0] + pbar = ProgressBar(batch_size, node_id=unique_id) + + results = [] + for i in range(batch_size): + # Async processing + result = await process_single(images[i]) + results.append(result) + pbar.update(1) + + return io.NodeOutput(torch.cat(results)) +``` + +## Enabling Process Isolation + +### 1. Create manifest.yaml + +```yaml +name: my_custom_nodes +version: 1.0.0 +description: My custom node collection +author: Your Name +dependencies: + - numpy==1.26.4 + - scikit-image>=0.22.0 + - opencv-python +isolated: true +share_torch: true +``` + +### 2. Update __init__.py + +```python +from pyisolate import ExtensionBase +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +class MyNodesExtension(ExtensionBase): + def on_module_loaded(self, module): + # Nodes are automatically registered + pass + + async def get_node_mappings(self): + return NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +# Extension entry point +def create_extension(): + return MyNodesExtension() +``` + +## Practical Migration Examples + +### Complete String Node Conversion + +This example shows a full conversion of the StringConcatenate node from v1 to v3: + +**V1 Implementation:** +```python +class StringConcatenate(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string_a": (IO.STRING, {"multiline": True}), + "string_b": (IO.STRING, {"multiline": True}), + "delimiter": (IO.STRING, {"multiline": False, "default": ""}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string_a, string_b, delimiter, **kwargs): + return delimiter.join((string_a, string_b)), +``` + +**V3 Implementation:** +```python +from comfy_api.latest import io, ui + +class StringConcatenate(io.ComfyNode): + """Concatenates two strings with an optional delimiter between them.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringConcatenate", + display_name="String Concatenate", + category="utils/string", + description="Concatenates two strings together with an optional delimiter between them.", + inputs=[ + io.String.Input( + "string_a", + display_name="String A", + multiline=True, + tooltip="The first string to concatenate" + ), + io.String.Input( + "string_b", + display_name="String B", + multiline=True, + tooltip="The second string to concatenate" + ), + io.String.Input( + "delimiter", + display_name="Delimiter", + default="", + multiline=False, + tooltip="The delimiter to insert between the two strings (empty by default)" + ), + ], + outputs=[ + io.String.Output( + "concatenated", + display_name="Concatenated String", + tooltip="The result of concatenating string_a and string_b with the delimiter" + ), + ], + ) + + @classmethod + def execute(cls, string_a: str, string_b: str, delimiter: str) -> io.NodeOutput: + """Concatenates two strings with an optional delimiter.""" + result = delimiter.join((string_a, string_b)) + return io.NodeOutput(result) +``` + +### Replacing V1 Nodes Strategy + +When replacing v1 nodes with v3 implementations: + +1. **Keep Original Node Names**: Don't add "V3" suffix to maintain compatibility +2. **Preserve All Parameters**: Keep same parameter names and defaults +3. **Maintain Return Structure**: v3 automatically generates v1-compatible returns +4. **Test Workflow Compatibility**: Ensure existing workflows continue to work + +Example migration workflow: +```bash +# 1. Create new branch +git checkout -b v3-node-migration + +# 2. Backup original +cp nodes_original.py nodes_original.py.bak + +# 3. Replace with v3 version +cp nodes_v3.py nodes_original.py + +# 4. Test with existing workflows +comfy-cli test-workflows ./test-workflows/ +``` + +## Testing Your Migration + +### 1. Backward Compatibility Test + +```python +# Your v3 node should work with v1 calls +def test_v1_compatibility(): + node = MyNodeV3() + inputs = node.INPUT_TYPES() + assert "required" in inputs + assert hasattr(node, "FUNCTION") + assert hasattr(node, "RETURN_TYPES") +``` + +### 2. Async Execution Test + +```python +import asyncio + +async def test_async_execution(): + result = await MyAsyncNode.execute(image=test_image) + assert result is not None +``` + +### 3. Isolation Test + +```bash +# Test with conflicting dependencies +comfy-cli test-node --isolated my_custom_nodes +``` + +## Best Practices + +1. **Keep nodes stateless** - Use `cls.state` for any mutable data +2. **Make I/O operations async** - Network, disk, database operations +3. **Use resource caching** - Via `cls.resources.get()` +4. **Declare all dependencies** - In manifest.yaml +5. **Test both sync and async** - Ensure compatibility +6. **Document type changes** - Help users update workflows + +## Common Issues + +### Issue: State not persisting +**Solution:** Use `cls.state` instead of instance variables + +### Issue: Hidden inputs not working +**Solution:** Access via `cls.hidden.unique_id` not function parameters + +### Issue: Async not executing +**Solution:** Ensure method is `async def` and use `await` for async calls + +### Issue: Import errors in isolation +**Solution:** Add all dependencies to manifest.yaml + +### Issue: Tensors not sharing +**Solution:** Enable `share_torch: true` in manifest.yaml \ No newline at end of file diff --git a/.github/instructions/v3.reference.instructions.md b/.github/instructions/v3.reference.instructions.md new file mode 100644 index 0000000..f945597 --- /dev/null +++ b/.github/instructions/v3.reference.instructions.md @@ -0,0 +1,635 @@ +--- +applyTo: "**/*.py" +description: "ComfyUI v3 API Reference" +--- + +# ComfyUI v3 API Reference + +Complete reference for the ComfyUI v3 node API, including all types, methods, and decorators. + +## Core Classes + +### ComfyNodeV3 + +Base class for all v3 nodes. + +```python +from comfy_api.latest import io + +class CustomNode(io.ComfyNode): + # Class properties set during execution + state: NodeState # Persistent state storage + resources: Resources # Resource loader with caching + hidden: HiddenHolder # Access to hidden inputs + + @classmethod + @abstractmethod + def define_schema(cls) -> io.ComfyNode: + """Define node schema. Must be overridden.""" + pass + + @classmethod + @abstractmethod + def execute(cls, **kwargs) -> io.NodeOutput: + """Execute node logic. Can be async.""" + pass + + @classmethod + def validate_inputs(cls, **kwargs) -> bool: + """Optional: Validate inputs before execution.""" + pass + + @classmethod + def fingerprint_inputs(cls, **kwargs) -> Any: + """Optional: Generate a fingerprint for caching.""" + pass + + @classmethod + def GET_SERIALIZERS(cls) -> list[Serializer]: + """Optional: Define custom serializers.""" + return [] +``` + +### io.ComfyNode + +Node definition schema. + +```python +@dataclass +class io.ComfyNode: + node_id: str # Globally unique ID + display_name: str = None # UI display name + category: str = "sd" # Node category + inputs: list[InputV3] = None # Input definitions + outputs: list[OutputV3] = None # Output definitions + hidden: list[Hidden] = None # Hidden inputs + description: str = "" # Tooltip description + is_input_list: bool = False # Handle list inputs + is_output_node: bool = False # Force execution + is_deprecated: bool = False # Mark as deprecated + is_experimental: bool = False # Mark as experimental + is_api_node: bool = False # API node flag + not_idempotent: bool = False # Disable caching +``` + +### NodeOutput + +Structured return value from `execute`. + +```python +class NodeOutput: + def __init__( + self, + *args: Any, # Output values + ui: UIOutput | dict = None, # UI elements + expand: dict = None, # Subgraph expansion + block_execution: str = None # Execution blocker + ): + pass +``` + +## Input Types + +### Basic Inputs + +```python +# Integer input +io.Int.Input( + id: str, + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + default: int = None, + min: int = None, + max: int = None, + step: int = None, + control_after_generate: bool = None, + display_mode: NumberDisplay = None, + socketless: bool = None, + force_input: bool = None +) + +# Float input +io.Float.Input( + id: str, + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + default: float = None, + min: float = None, + max: float = None, + step: float = None, + round: float = None, + display_mode: NumberDisplay = None, + socketless: bool = None, + force_input: bool = None +) + +# String input +io.String.Input( + id: str, + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + multiline: bool = False, + placeholder: str = None, + default: str = None, + dynamic_prompts: bool = None, + socketless: bool = None, + force_input: bool = None +) + +# Boolean input +io.Boolean.Input( + id: str, + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + default: bool = None, + label_on: str = None, + label_off: str = None, + socketless: bool = None, + force_input: bool = None +) + +# Combo (dropdown) input +io.Combo.Input( + id: str, + options: list[str] = None, + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + default: str = None, + control_after_generate: bool = None, + upload: UploadType = None, + image_folder: FolderType = None, + remote: RemoteOptions = None, + socketless: bool = None +) + +# Multi-select combo +io.MultiCombo.Input( + id: str, + options: list[str], + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + default: list[str] = None, + placeholder: str = None, + chip: bool = None, + control_after_generate: bool = None, + socketless: bool = None +) +# cusotm type +io.Custom(io_type="MY_TYPE").Input( + id: str, + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None, + placeholder: str = None, +) +``` + +### ComfyUI Types + +```python +# Core types +io.Image.Input(id, ...) # Type: torch.Tensor [B,H,W,C] +io.Mask.Input(id, ...) # Type: torch.Tensor [H,W] or [B,H,W] +io.Latent.Input(id, ...) # Type: dict with 'samples' tensor +io.Conditioning.Input(id, ...) # Type: list[tuple[tensor, dict]] +io.Model.Input(id, ...) # Type: ModelPatcher +io.Clip.Input(id, ...) # Type: CLIP +io.Vae.Input(id, ...) # Type: VAE +io.ControlNet.Input(id, ...) # Type: ControlNet + +# Sampling types +io.Sampler.Input(id, ...) # Type: Sampler +io.Sigmas.Input(id, ...) # Type: torch.Tensor +io.Noise.Input(id, ...) # Type: torch.Tensor +io.Guider.Input(id, ...) # Type: CFGGuider + +# Additional types +io.ClipVision.Input(id, ...) # Type: ClipVisionModel +io.ClipVisionOutput.Input(id, ...) # Type: ClipVisionOutput +io.StyleModel.Input(id, ...) # Type: StyleModel +io.Gligen.Input(id, ...) # Type: ModelPatcher +io.UpscaleModel.Input(id, ...) # Type: ImageModelDescriptor +io.Audio.Input(id, ...) # Type: dict with 'waveform' and 'sample_rate' +io.Video.Input(id, ...) # Type: VideoInput +io.Webcam.Input(id, ...) # Type: str (filepath) +io.WanCameraEmbedding.Input(id, ...) # Type: torch.Tensor +io.LoraModel.Input(id, ...) # Type: dict[str, Tensor] +io.Hooks.Input(id, ...) # Type: HookGroup +io.HookKeyframes.Input(id, ...) # Type: HookKeyframeGroup +io.SVG.Input(id, ...) # Type: SVG (custom class) +io.Voxel.Input(id, ...) # Type: Voxel data (custom class) +io.Mesh.Input(id, ...) # Type: Mesh data (custom class) +``` + +### Advanced Inputs + +```python +# Multi-type input (accepts multiple types) +io.MultiType.Input( + id: str | InputV3, # Can override from existing input + types: list[type[ComfyType]], + display_name: str = None, + optional: bool = False, + tooltip: str = None, + lazy: bool = None +) + +# Dynamic growing input +io.AutogrowDynamic.Input( + id: str, + template_input: InputV3, # Template for each new input + min: int = 1, # Minimum inputs + max: int = None # Maximum inputs +) + +# Custom type +@io.comfytype(io_type="MY_CUSTOM") +class MyCustom: + Type = MyDataClass + class Input(io.InputV3): + ... + class Output(io.OutputV3): + ... +``` + +## Output Types + +```python +# Basic output +io.Image.Output( + id: str = None, + display_name: str = None, + tooltip: str = None, + is_output_list: bool = False # Output is list +) + +# All ComfyUI types have corresponding outputs +io.Mask.Output(id, ...) +io.Latent.Output(id, ...) +io.Model.Output(id, ...) +io.Clip.Output(id, ...) +io.Vae.Output(id, ...) +io.Conditioning.Output(id, ...) +io.String.Output(id, ...) +io.Int.Output(id, ...) +io.Float.Output(id, ...) +io.Boolean.Output(id, ...) +# ... etc +``` + +## Hidden Inputs + +```python +from comfy_api.latest import Hidden + +# Available hidden inputs +Hidden.unique_id # Node's unique ID +Hidden.prompt # Complete prompt +Hidden.extra_pnginfo # PNG metadata dict +Hidden.dynprompt # Dynamic prompt object +Hidden.auth_token_comfy_org # ComfyOrg auth token +Hidden.api_key_comfy_org # ComfyOrg API key + +# Usage in schema +hidden=[ + Hidden.unique_id, + Hidden.prompt +] + +# Access in execute +unique_id = cls.hidden.unique_id +prompt = cls.hidden.prompt +``` + +## State Management + +```python +# NodeState interface +class NodeState: + def get_value(self, key: str) -> Any + def set_value(self, key: str, value: Any) + def pop(self, key: str) -> Any + def __contains__(self, key: str) -> bool + + # Attribute access + cls.state.my_value = 42 + value = cls.state.my_value + + # Dictionary access + cls.state["key"] = "value" + value = cls.state["key"] +``` + +## Practical Input/Output Examples + +### Enhanced Documentation with Tooltips and Display Names + +```python +# String input with full documentation +io.String.Input( + "prompt", + display_name="Text Prompt", + multiline=True, + default="A beautiful landscape", + tooltip="Enter the text description for image generation", + placeholder="Type your prompt here..." +) + +# Integer with constraints and UI hints +io.Int.Input( + "steps", + display_name="Sampling Steps", + default=20, + min=1, + max=150, + tooltip="Number of denoising steps. Higher values take longer but may produce better results", + display_mode=io.NumberDisplay.slider +) + +# Combo with dynamic options +io.Combo.Input( + "checkpoint", + options=folder_paths.get_filename_list("checkpoints"), + display_name="Model Checkpoint", + tooltip="Select the AI model to use for generation" +) + +# Output with documentation +io.Image.Output( + "generated_image", + display_name="Generated Image", + tooltip="The final generated image based on your prompt" +) + +# Combo with dynamic options and file upload +io.Combo.Input( + "audio_file", + options=sorted(folder_paths.filter_files_content_types(os.listdir(folder_paths.get_input_directory()), ["audio", "video"])), + display_name="Audio File", + tooltip="Select an audio file or upload a new one", + upload=io.UploadType.audio +) +``` + +### Return Pattern with NodeOutput + +```python +@classmethod +def execute(cls, text: str, count: int) -> io.NodeOutput: + # Single output + result = process_text(text, count) + return io.NodeOutput(result) + + # Multiple outputs + image, mask = generate_image_and_mask(text) + return io.NodeOutput(image, mask) + + # With UI preview + image = generate_image(text) + return io.NodeOutput(image, ui=ui.PreviewImage(image)) + + # With multiple UI elements + images = batch_generate(text, count) + previews = [ui.PreviewImage(img) for img in images] + return io.NodeOutput(images, ui={"images": previews}) +``` + +## Resource Management + +```python +# Load cached resources +from comfy_api.latest import resources + +# Load torch file +model = cls.resources.get( + resources.TorchDictFolderFilename( + folder_name="checkpoints", # Folder category + file_name="model.safetensors" + ) +) + +# With default value +model = cls.resources.get(key, default=None) + +# Custom resource types (future) +class MyResourceKey(ResourceKey): + Type = MyResourceType + def __init__(self, ...): + pass +``` + +## UI Output Classes + +```python +from comfy_api.latest import ui + +# Image preview +ui.PreviewImage( + image: torch.Tensor, + animated: bool = False +) + +# Mask preview +ui.PreviewMask( + mask: torch.Tensor, + animated: bool = False +) + +# Audio preview +ui.PreviewAudio( + values: list[SavedResult | dict] +) + +# Text output +ui.PreviewText( + value: str +) + +# 3D preview +ui.PreviewUI3D( + values: list[SavedResult | dict] +) +``` + +## Decorators and Helpers + +```python +# Create custom ComfyType +@io.comfytype(io_type="CUSTOM_TYPE") +class CustomType: + Type = CustomClass + class Input(io.InputV3): + ... + class Output(io.OutputV3): + ... + +# Custom serializer +class MySerializer(Serializer, io_type="MY_TYPE"): + @classmethod + def serialize(cls, obj: Any) -> str: + return json.dumps(obj) + + @classmethod + def deserialize(cls, s: str) -> Any: + return json.loads(s) +``` + +## Async Support + +```python +# Async execute +class AsyncNode(io.ComfyNode): + @classmethod + async def execute(cls, **kwargs): + result = await async_operation() + return io.NodeOutput(result) + +# Async validation +@classmethod +async def VALIDATE_INPUTS(cls, **kwargs): + is_valid = await check_validity() + return True if is_valid else "Error message" + +# Async lazy check +async def check_lazy_status(cls, **kwargs): + needed = await determine_needed_inputs() + return needed # List of input names +``` + +## Complete Example + +```python +from comfy_api.latest import io, ui, resources, io.ComfyNode, io.ComfyNode +import torch + +class AdvancedNodeV3(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.ComfyNode( + node_id="AdvancedNode", + display_name="Advanced Node", + category="examples/advanced", + description="Demonstrates v3 features", + inputs=[ + # Basic inputs + io.Image.Input("image", tooltip="Input image"), + io.Model.Input("model", tooltip="Model to use"), + + # Configured inputs + io.Float.Input("strength", + default=0.75, + min=0.0, + max=1.0, + step=0.05, + display_mode=io.NumberDisplay.slider + ), + + # Multi-type + io.MultiType.Input("flexible", + types=[io.Image, io.Mask, io.Latent], + optional=True + ), + + # Dynamic + io.AutoGrowDynamic.Input("extra_images", + template_input=io.Image.Input("img"), + min=0, + max=5 + ) + ], + outputs=[ + io.Image.Output("result", tooltip="Processed image"), + io.Latent.Output("latent", is_output_list=True) + ], + hidden=[ + io.Hidden.unique_id, + io.Hidden.prompt + ], + is_output_node=True, + is_experimental=True + ) + + @classmethod + async def execute(cls, image, model, strength, flexible=None, **kwargs): + # Access state + if cls.state.last_model != model: + cls.state.cache = {} + cls.state.last_model = model + + # Load resources + weights = cls.resources.get( + resources.TorchDictFolderFilename("loras", "style.safetensors"), + default=None + ) + + # Access hidden + node_id = cls.hidden.unique_id + + # Process async + result = await process_with_model(image, model, strength) + + # Handle dynamic inputs + extra_images = [v for k, v in kwargs.items() if k.startswith("extra_")] + + # Return with UI + return io.NodeOutput( + result, + [latent], + ui=ui.PreviewImage(result) + ) + + @classmethod + async def fingerprint_inputs(cls, strength, **kwargs): + if strength < 0.1: + return "Strength too low for good results" + return True +``` + +## Type Reference + +### Type Mappings + +| v3 Type | Python Type | Shape/Format | +|---------|------------|--------------| +| `io.Image.Type` | `torch.Tensor` | `[B,H,W,C]` float32 0-1 | +| `io.Mask.Type` | `torch.Tensor` | `[H,W]` or `[B,H,W]` float32 | +| `io.Latent.Type` | `dict` | `{"samples": tensor, ...}` | +| `io.Conditioning.Type` | `list` | `[(tensor, dict), ...]` | +| `io.Audio.Type` | `dict` | `{"waveform": tensor, "sample_rate": int}` | +| `io.Int.Type` | `int` | Python integer | +| `io.Float.Type` | `float` | Python float | +| `io.String.Type` | `str` | Python string | +| `io.Boolean.Type` | `bool` | Python boolean | + +### Enum Types + +```python +# Number display modes +io.NumberDisplay.number # Standard input +io.NumberDisplay.slider # Slider widget +io.NumberDisplay.color # Color picker widget + +# Folder types +io.FolderType.input # Input folder +io.FolderType.output # Output folder +io.FolderType.temp # Temp folder + +# Upload types +io.UploadType.image +io.UploadType.audio +io.UploadType.video +io.UploadType.model +``` \ No newline at end of file diff --git a/ComfyUI-Easy-Use-Frontend b/ComfyUI-Easy-Use-Frontend index fadf19f..e3ee9ef 160000 --- a/ComfyUI-Easy-Use-Frontend +++ b/ComfyUI-Easy-Use-Frontend @@ -1 +1 @@ -Subproject commit fadf19fe360fc84df3781c8501f8c208a4eb19c5 +Subproject commit e3ee9efe9b7bcd2308bef7a4755e984181b4b045 diff --git a/locales/zh/main.json b/locales/zh/main.json index f8bbe90..9adc24e 100644 --- a/locales/zh/main.json +++ b/locales/zh/main.json @@ -3,7 +3,8 @@ "Hotkeys": "快捷键", "Nodes": "节点相关", "NodesMap": "管理节点组", - "StylesSelector": "样式选择器" + "StylesSelector": "样式选择器", + "MultiAngle": "摄影机多角度提示词" }, "nodeCategories": { "Util": "工具", diff --git a/locales/zh/settings.json b/locales/zh/settings.json index f53db76..490b8ef 100644 --- a/locales/zh/settings.json +++ b/locales/zh/settings.json @@ -71,5 +71,12 @@ "Grid": "网格", "List": "列表" } + }, + "EasyUse_MultiAngle_HollowMode": { + "name": "启用多角度镂空展示模式", + "tooltip": "在多角度节点中启用镂空展示模式，可以更直观地查看相机角度" + }, + "EasyUse_MultiAngle_AddAnglePrompt": { + "name": "启用添加多角度提示词" } } \ No newline at end of file diff --git a/py/nodes/prompt.py b/py/nodes/prompt.py index 5cf4892..9469d51 100644 --- a/py/nodes/prompt.py +++ b/py/nodes/prompt.py @@ -1,15 +1,13 @@ import json import os -from urllib.request import urlopen - import folder_paths from .. import easyCache from ..config import FOOOCUS_STYLES_DIR, MAX_SEED_NUM, PROMPT_TEMPLATE, RESOURCES_DIR from ..libs.log import log_node_info -from ..libs.utils import AlwaysEqualProxy from ..libs.wildcards import WildcardProcessor, get_wildcard_list, process +from urllib.request import urlopen from comfy_api.latest import io @@ -666,7 +664,7 @@ class multiAngle(io.ComfyNode): @classmethod def execute(cls, multi_angle=None, **kwargs): - if multi_angle is None or not isinstance(multi_angle, list): + if multi_angle is None: return io.NodeOutput([""]) if isinstance(multi_angle, str): diff --git a/web_version/v2/assets/extensions-CPPCvYdJ.js b/web_version/v2/assets/extensions-CPPCvYdJ.js new file mode 100644 index 0000000..275f2f5 --- /dev/null +++ b/web_version/v2/assets/extensions-CPPCvYdJ.js @@ -0,0 +1 @@ +var e,t,i,s,a,n,o,l,r,d,u,A,c,p,h,m,f=Object.defineProperty,g=e=>{throw TypeError(e)},v=(e,t,i)=>((e,t,i)=>t in e?f(e,t,{enumerable:!0,configurable:!0,writable:!0,value:i}):e[t]=i)(e,"symbol"!=typeof t?t+"":t,i),y=(e,t,i)=>t.has(e)||g("Cannot "+i),b=(e,t,i)=>(y(e,t,"read from private field"),i?i.call(e):t.get(e)),w=(e,t,i)=>t.has(e)?g("Cannot add the same private member more than once"):t instanceof WeakSet?t.add(e):t.set(e,i),_=(e,t,i,s)=>(y(e,t,"write to private 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0:A.applyTextReplacements)||null,ye=(null==(p=null==(c=window.comfyAPI)?void 0:c.groupNode)?void 0:p.GroupNodeConfig)||null;const be=e=>{var t,i;return e?null==(i=null==(t=null==pe?void 0:pe.ui)?void 0:t.settings)?void 0:i.getSettingValue(e):null};function we(e,t=null){try{let i=e?be(e):null;return null==i&&(i=t?localStorage[t]:localStorage[e]||null),i}catch(i){return null}}function _e(e,t=e=>{}){var i;const s=null==(i=pe.ui.settings.settingsLookup)?void 0:i[e];s&&(s.onChange=e=>t(e))}async function Se(e,t,i=null){var s,a;if(!e)throw new Error("Invalid arguments");try{(null==(a=null==(s=null==pe?void 0:pe.ui)?void 0:s.settings)?void 0:a.setSettingValue)?pe.ui.settings.setSettingValue(e,t):await he.storeSetting(e,t),i&&(localStorage[i]="object"==typeof t?JSON.stringify(t):t)}catch(n){}}const xe={"Workflow created by":"工作流创建者","Watch more video content":"观看更多视频内容","Workflow Guide":"工作流指南","💎 View Checkpoint Info...":"💎 查看 Checkpoint 信息...","💎 View Lora Info...":"💎 查看 Lora 信息...","🔃 Reload Node":"🔃 刷新节点","Updated At:":"最近更新：","Created At:":"首次发布：","✏️ Edit":"✏️ 编辑","💾 Save":"💾 保存","No notes":"当前还没有备注内容","Saving Notes...":"正在保存备注...","Type your notes here":"在这里输入备注内容",ModelName:"模型名称","Models Required":"所需模型","Download Model":"下载模型","Source Url":"模型源地址",Notes:"备注",Type:"类型","Trained Words":"训练词",BaseModel:"基础算法",Details:"详情",Description:"描述",Download:"下载量",Source:"来源","Saving Preview...":"正在保存预览图...","Saving Succeed":"保存成功","Clean SuccessFully":"清理成功","Clean Failed":"清理失败","Saving Failed":"保存失败","No COMBO link":"沒有找到COMBO连接","Reboot ComfyUI":"重启ComfyUI","Are you sure you'd like to reboot the server?":"是否要重启ComfyUI？","NODES MAP":"管理节点与组",NodesMap:"节点与组","Nodes map sorting mode":"管理节点组排序模式","Search by Node ID/Name...":"通过节点ID/名称搜索...","No Nodes":"未找到节点","No nodes found in the map":"在工作流程中没有找到节点","No nodes found in the search":"未找到与搜索匹配的节点","Jump to this node":"跳转到该节点","Rename node":"重命名节点","Delete node":"删除节点","Rename group":"重命名组","Delete group":"删除组","Expand All":"展开所有组","Collapse All":"折叠所有组",Close:"关闭","Default automatic sorting, if set to manual, groups can be dragged and dropped and the sorting results saved.":"默认自动排序，如果设置为手动，组可以拖放并保存排序结果。","For drag and drop sorting, please find Nodes map sorting mode in Settings->EasyUse and change it to manual":"如需拖拽排序请在设置->EasyUse节点中找到管理节点组排序模式并修改成 manual",Queue:"队列","Cleanup Of VRAM Usage":"清理显存占用","Please stop all running tasks before cleaning GPU":"请在清理GPU之前停止所有运行中的任务",Always:"启用中",Bypass:"已忽略",Never:"已停用","Auto Sorting":"自动排序","Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never":"点击`启用中/已忽略`可设置组模式, 长按可停用该组节点","Enable Shift+Up/Down/Left/Right key and Shift+Ctrl+Alt+Left/Right to align selected nodes":"启用 Shift+上/下/左/右 和 Shift+Ctrl+Alt+左/右 键对齐选中的节点","Enable Shift+Ctrl+Left/Right key to normalize selected nodes":"启用 Shift+Ctrl+左/右 键规范化选中的节点","Enable Shift+g to add selected nodes to a group":"启用 Shift+g 键将选中的节点添加一个组","Enable Shift+r to unload models and node cache":"启用 Shift+r 键卸载模型和节点缓存","Enable Shift+m to toggle nodes map":"启用 Shift+m 键显隐管理节点组","Enable Up/Down/Left/Right key to jump nearest nodes":"启用 上/下/左/右 键跳转到最近的前后节点","Enable Alt+1~9 to paste nodes from nodes template":"启用 Alt+1~9 从节点模板粘贴到工作流中","Enable contextMenu auto nest subdirectories":"启用上下文菜单自动嵌套子目录","Enable right-click menu to add node A~Z sorting":"启用右键菜单中新建节点A~Z排序","Enable model thumbnails display":"启动模型预览图显示","Enable nodes runtime display":"启动节点运行时间显示","Enable chain get node and set node with parent nodes":"启用将获取点和设置点与父节点链在一起","Maximum number of model thumbnails displayed":"显示的模型缩略图的最大数量","Too many thumbnails will affect the first loading time, set the maximum value to not load the thumbnail function when there are too many models's thumbnail":"太多的缩略图会影响首次加载时间，当模型缩略图太多时，设置最大值以不加载缩略图功能","Too many thumbnails, have closed the display":"模型缩略图太多啦，为您关闭了显示","Shift+Up/Down/Left/Right can align selected nodes, Shift+Ctrl+Alt+Left/Right can distribute horizontal/vertical nodes":"Shift+上/下/左/右 可以对齐选中的节点, Shift+Ctrl+Alt+左/右 可以水平/垂直分布节点","Enable Shift+Ctrl+Left key to normalize width and Shift+Ctrl+Right key to normalize height":"启用 Shift+Ctrl+左 键规范化宽度和 Shift+Ctrl+右 键规范化高度","After v1.2.39, Ctrl+g can be used instead of it":"从v1.2.39开始，可以使用Ctrl+g代替","Styles Selector Display Type":"样式选择器显示类型","Styles Selector Display Type, if set to 'Grid', it will display as a Grid, if set to 'List', it will display as a list":"样式选择器显示类型，如果设置为“网格”，则显示为网格，如果设置为“列表”，则显示为列表","Use three shortcut buttons in the right-click menu":"在右键菜单中使用三个快捷按钮","Enable Nodes Map":"启用节点组管理","Display Groups Only":"仅显示组","Display Node ID":"显示节点ID","You need to refresh the page to update successfully":"您需要刷新页面以成功更新","Get styles list Failed":"获取样式列表失败","Get style image Failed":"获取样式图片失败","Empty All":"清空所有","Type here to search styles ...":"在此处输入以搜索样式 ...","Loading UserInfo...":"正在获取用户信息...","Please set the APIKEY first":"请先设置APIKEY","Setting APIKEY":"设置APIKEY","Save Account 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wallet":"包，钱包",scarf:"围巾",umbrella:"雨伞",hood:"帽兜",collar:"领子",lapel:"翻领",epaulette:"肩章",sleeve:"袖子",pocket:"口袋",neckline:"领口",buckle:"扣环",zipper:"拉链",applique:"贴花",bead:"珠子",bow:"蝴蝶结",flower:"花朵装饰",fringe:"流苏",ribbon:"丝带",rivet:"铆钉",ruffle:"褶边",sequin:"亮片",tassel:"流苏穗",background:"背景",skin:"皮肤",nose:"鼻子",eyeglasses:"眼镜",left_eye:"左眼",right_eye:"右眼",left_eyebrow:"左眉毛",right_eyebrow:"右眉毛",left_ear:"左耳",right_ear:"右耳",mouth:"嘴巴",upper_lip:"上唇",lower_lip:"下唇",hair:"头发",earring:"耳环",necklace:"项链",neck:"脖子",clothing:"服装",s:"秒","No Node Templates Found":"未找到节点模板预设","Get Node Templates File Failed":"获取节点模板文件失败","Node template with {key} not set":"未设置快捷键为{key}的节点预设","ComfyUI Basic":"ComfyUI 基础节点","Recommend Nodes":"推荐节点","Others A~Z":"其他节点 A~Z","The maximum number of inputs is 20":"输入数量最大20个","The maximum number of outputs is 20":"输出数量最大20个","Stop recording":"停止录音","Voice input":"语音输入",Continue:"继续",Stop:"停止",now:"当前",prev:"上一步","Add New Tab":"添加新标签","Add Angle Prompt":"添加角度提示词",Hollow:"镂空展示",Reset:"重置","Angle Prompt":"角度词",Image:"图像",F:"前",B:"后",U:"顶",D:"底",L:"左",R:"右",Rotate:"旋转",Vertical:"垂直",Zoom:"缩放"},Le=(e,t=!1)=>{switch(we("Comfy.Locale")){case"zh-CN":case"zh":return xe[e]||e;default:return e}};const ke="comfyui-easyuse-",Ee="dark-theme",Ne={PIPE_LINE:"#7737AA",PIPE_LINE_SDXL:"#7737AA",INT:"#29699C",X_Y:"#38291f",XYPLOT:"#74DA5D",LORA_STACK:"#94dccd",CONTROL_NET_STACK:"#94dccd",FLOW_CONTROL:"#373780"},Ce=0x4000000000000,Ie=["loaders","latent","image","mask","sampling","_for_testing","advanced","utils","api","api node","加载器","Latent","图像","遮罩","采样","_用于测试","高级","工具","API","api 节点","chargeurs","latent","image","masque","échantillonnage","_pour_test","avancé","outils","nœud api","загрузчики","латентный","изображение","маска","выборка","_для_тестирования","расширенный","инструменты","api","api узел","로더","잠재","이미지","마스크","샘플링","_테스트용","고급","도구","api","api 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Ge=JSON.parse(JSON.stringify(Pe));delete Ge.NODE_COLORS,Ge.ColorPalette.id="obsidian_dark",Ge.ColorPalette.name="Obsidian 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ze="#FFFFFF",Ve={ColorPalette:{id:"milk_white",name:"Milk 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t=this.value,a=!0;(s.current_submenu&&s.current_submenu.close(e),i.callback)&&(!0===i.callback.call(this,t,i,e,s,i.node)&&(a=!1));if(t){if(t.callback&&!i.ignore_item_callbacks&&!0!==t.disabled)!0===t.callback.call(this,t,i,e,s,i.extra)&&(a=!1);if(t.submenu){if(!t.submenu.options)throw"ContextMenu submenu needs options";new s.constructor(t.submenu.options,{callback:t.submenu.callback,event:e,parentMenu:s,ignore_item_callbacks:t.submenu.ignore_item_callbacks,title:t.submenu.title,extra:t.submenu.extra,autoopen:i.autoopen}),a=!1}}a&&!s.lock&&s.close()}return null===t?a.classList.add("separator"):t.is_category_title?(a.classList.remove("litemenu-entry"),a.classList.remove("submenu"),a.classList.add("litemenu-title"),a.innerHTML=t.title):(a.innerHTML=t&&t.title?t.title:e,a.value=t,t&&(t.disabled&&(n=!0,a.classList.add("disabled")),(t.submenu||t.has_submenu)&&a.classList.add("has_submenu")),"function"==typeof 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forefront"==e?t.unshift(o):t.push(o)}}return e.apply(this,[...arguments])}{const s=function(e,t){const i=we("EasyUse.ContextMenu.ModelsThumbnails",null),s=we("EasyUse.ContextMenu.SubDirectories",null);if(!i&&!s)return null;if(vi)return null;const a=["ckpt","pt","bin","pth","safetensors","gguf"];if((null==e?void 0:e.length)>0){const t=null==(n=e[e.length-1])?void 0:n.substring(n.lastIndexOf(".")+1);if(!a.includes(t))return null}var n;const o=t.callback,l=[...e];t.callback=null;const r=(e,t)=>{["None","无","無","なし"].includes(e.content)?o("None",t):o(l.find((t=>t.endsWith(e.content)),t))};if(i&&!s)return e.map((e=>{var t,i;let s=null==(t=gi[e])?void 0:t.folder,a=null==(i=gi[e])?void 0:i.pathIndex;const n=window.location.protocol,o=window.location.host;let l=s?`${`${n}//${o}`}/api/experiment/models/preview/${s}/${a}/${bi(e)}`:"";return{folder:s,content:e,fullName:e,title:me("div.comfyui-easyuse-contextmenu-model",{},[me("span",{},e)]).outerHTML,thumbnail:l,callback:r}}));const d=e,u={},A=[],c=[];for(const f of d){const e=f.indexOf("/")>-1?"/":"\\",t=f.split(e);if(t.length>1){const i=t.shift();u[i]=u[i]||[],u[i].push({value:t.join(e),fullValue:f})}else"CHOOSE"===f||f.startsWith("DISABLE ")?A.push({value:f,fullValue:f}):c.push({value:f,fullValue:f})}const p=Object.values(u).length,h=[],m=(e,t="",s)=>{var a,n;let o;o=me("div.comfyui-easyuse-contextmenu-model",{},[me("span",{},e)]);let l=null==(a=gi[s])?void 0:a.folder,d=null==(n=gi[s])?void 0:n.pathIndex;const u=window.location.protocol,A=window.location.host;let c=l?`${`${u}//${A}`}/api/experiment/models/preview/${l}/${d}/${bi(s)}`:"";return{folder:l,content:e,fullName:s,thumbnail:i?c:null,title:o.outerHTML,callback:r}};if(p>0){const e=(t,i)=>{let s=[],a=[];const n=t.map((({value:e,fullValue:t})=>{const n={},o=e.indexOf("/")>-1?"/":"\\",l=e.split(o);if(l.length>1){const e=l.shift();n[e]=n[e]||[],n[e].push({value:l.join(o),fullValue:t})}if(Object.values(u).length>0){let i=Object.keys(n)[0];i&&n[i]?s.push({key:i,value:n[i][0]}):a.push(m(e,i,t))}return m(e,i,t)}));if(s.length>0){let t={};return s.forEach((e=>{t[e.key]=t[e.key]||[],t[e.key].push(e.value)})),[...Object.entries(t).map((t=>({content:t[0],has_submenu:!0,callback:()=>{},submenu:{options:e(t[1],t[0])}}))),...a]}return n};for(const[t,i]of Object.entries(u))h.push({content:t,has_submenu:!0,callback:()=>{},submenu:{options:e(i,t)}})}h.push(...c.map((e=>m(e.value,"",e.fullValue)))),A.length>0&&h.push(...A.map((e=>m(e.value,"",e.fullValue))));return h}(t,i);return s?e.call(this,s,i):e.apply(this,[...arguments])}var s},LiteGraph.ContextMenu.prototype=e.prototype,we("EasyUse.ContextMenu.NodesSort")&&(LiteGraph.ContextMenu.prototype.addItem=wi),document.getElementById("graph-canvas").addEventListener("mouseenter",(e=>{setTimeout((e=>{const t=document.getElementById("easyuse-model-thumbnail");t&&0!=t.style.opacity&&(t.style.display="none",t.style.opacity=0,t.style.left="0px",t.style.top="0px")}),100)}))},async beforeRegisterNodeDef(e,t,i){const s=e.prototype.onNodeCreated;["CheckpointLoader|pysssss","LoraLoader|pysssss"].includes(t.name)&&(e.prototype.onNodeCreated=async function(){s&&s.apply(this,[]);let e=vt(this,"lora_name")||vt(this,"ckpt_name");if(e){let t=e.onClick;e.onClick=function(e){return vi=!0,setTimeout((e=>{vi=!1}),500),t.call(this,e)}}})}});const _i=async()=>{["checkpoints","loras","diffusion_models"].map((async e=>{const t=await he.getModels(e);(null==t?void 0:t.length)>0&&t.map((t=>{gi[t.name]={folder:e,pathIndex:t.pathIndex}}))}))},Si=(e,t,i)=>s=>{var a;const n=t=>{setTimeout((s=>{const a=i.getBoundingClientRect();if(!a)return;const n=document.body.getBoundingClientRect();if(!n)return;const{left:o,top:l}=((e,t,i)=>{const{x:s}=e.getBoundingClientRect();let{top:a,left:n}=t;const{width:o,height:l}=i;return s<=o&&(n+=t.width),t.top<=l||(a=l),{left:n,top:a}})(e,a,n),r=document.getElementById("easyuse-model-thumbnail");r.src=t,r.style.left=`${o}px`,r.style.top=`${l}px`,r.style.display="block",r.style.opacity=1,r.onerror=e=>{r.src=De}}),10)};if(null==(a=null==gi?void 0:gi[t.fullName])?void 0:a.img){let e=gi[t.fullName].img;n("no_preview_image"==e?De:e.src)}else{let e=new Image;e.src=t.thumbnail,e.onload=i=>{gi[t.fullName].img=e,n(t.thumbnail)},e.onerror=i=>{e=null,gi[t.fullName].img="no_preview_image",n(De)}}},xi=()=>e=>{const t=document.getElementById("easyuse-model-thumbnail");t&&0!=t.style.opacity&&(t.style.display="none",t.style.opacity=0,t.style.left="0px",t.style.top="0px")};class Li extends fe{constructor(){super(),this.element.classList.add("easyuse-model-metadata")}show(e){super.show(me("div",Object.keys(e).map((t=>me("div",[me("label",{textContent:t}),me("span",{textContent:e[t]})])))))}}class ki extends fe{constructor(e){super(),this.name=e,this.element.classList.add("easyuse-model-info")}get customNotes(){return this.metadata["easyuse.notes"]}set customNotes(e){this.metadata["easyuse.notes"]=e}get hash(){return this.metadata["easyuse.sha256"]}async show(e,t){this.type=e;const i=he.fetchApi("/easyuse/metadata/"+encodeURIComponent(`${e}/${t}`));this.info=me("div",{style:{flex:"auto"}}),this.imgCurrent=0,this.imgList=me("div.easyuse-preview-list",{style:{display:"none"}}),this.imgWrapper=me("div.easyuse-preview",[me("div.easyuse-preview-group",[this.imgList])]),this.main=me("main",{style:{display:"flex"}},[this.imgWrapper,this.info]),this.content=me("div.easyuse-model-content",[me("div.easyuse-model-header",[me("h2",{textContent:this.name})]),this.main]);const s=me("div",{textContent:"ℹ️ Loading...",parent:this.content});super.show(this.content),this.metadata=await(await i).json(),this.viewMetadata.style.cursor=this.viewMetadata.style.opacity="",this.viewMetadata.removeAttribute("disabled"),s.remove(),this.addInfo()}createButtons(){const e=super.createButtons();return this.viewMetadata=me("button",{type:"button",textContent:"View raw metadata",disabled:"disabled",style:{opacity:.5,cursor:"not-allowed"},onclick:e=>{this.metadata&&(new Li).show(this.metadata)}}),e.unshift(this.viewMetadata),e}parseNote(){if(!this.customNotes)return[];let e=[];const t=new RegExp("(\\bhttps?:\\/\\/[^\\s]+)","g");let i,s=0;do{let a;i=t.exec(this.customNotes);let n=0;i?(a=i.index,n=i.index+i[0].length):a=this.customNotes.length;let o=this.customNotes.substring(s,a);o&&(o=o.replaceAll("\n","
"),e.push(me("span",{innerHTML:o}))),i&&e.push(me("a",{href:i[0],textContent:i[0],target:"_blank"})),s=n}while(i);return e}addInfoEntry(e,t){return me("p",{parent:this.info},["string"==typeof e?me("label",{textContent:e+": "}):e,"string"==typeof t?me("span",{textContent:t}):t])}async getCivitaiDetails(){const e=await fetch("https://civitai.com/api/v1/model-versions/by-hash/"+this.hash);if(200===e.status)return await e.json();throw 404===e.status?new Error("Model not found"):new Error(`Error loading info (${e.status}) ${e.statusText}`)}addCivitaiInfo(){const e=this.getCivitaiDetails(),t=me("span",{textContent:"ℹ️ Loading..."});return this.addInfoEntry(me("label",[me("img",{style:{width:"18px",position:"relative",top:"3px",margin:"0 5px 0 0"},src:"https://civitai.com/favicon.ico"}),me("span",{textContent:"Civitai: "})]),t),e.then((e=>{var t,i;this.imgWrapper.style.display="block";let s=this.element.querySelector(".easyuse-model-header");s&&s.replaceChildren(me("h2",{textContent:this.name}),me("div.easyuse-model-header-remark",[me("h5",{textContent:Le("Updated At:")+Ue(new Date(e.updatedAt),"yyyy/MM/dd")}),me("h5",{textContent:Le("Created At:")+Ue(new Date(e.updatedAt),"yyyy/MM/dd")})]));let a=null,n=this.parseNote.call(this),o=Le("✏️ Edit"),l=me("div.easyuse-model-detail-textarea",[me("p",(null==n?void 0:n.length)>0?n:{textContent:Le("No notes")})]);if(n&&0!=n.length?l.classList.remove("empty"):l.classList.add("empty"),this.info.replaceChildren(me("div.easyuse-model-detail",[me("div.easyuse-model-detail-head.flex-b",[me("span",Le("Notes")),me("a",{textContent:o,href:"#",style:{fontSize:"12px",float:"right",color:"var(--warning-color)",textDecoration:"none"},onclick:async e=>{if(e.preventDefault(),a){if(a.value!=this.customNotes){Ze.showLoading(Le("Saving Notes...")),this.customNotes=a.value;const e=await he.fetchApi("/easyuse/metadata/notes/"+encodeURIComponent(`${this.type}/${this.name}`),{method:"POST",body:this.customNotes});if(Ze.hideLoading(),200!==e.status)return Ze.error(Le("Saving Failed")),void alert(`Error saving notes (${e.status}) ${e.statusText}`);Ze.success(Le("Saving Succeed")),n=this.parseNote.call(this),l.replaceChildren(me("p",(null==n?void 0:n.length)>0?n:{textContent:Le("No notes")})),a.value?l.classList.remove("empty"):l.classList.add("empty")}else l.replaceChildren(me("p",{textContent:Le("No notes")})),l.classList.add("empty");e.target.textContent=o,a.remove(),a=null}else e.target.textContent="💾 Save",a=me("textarea",{placeholder:Le("Type your notes here"),style:{width:"100%",minWidth:"200px",minHeight:"50px",height:"100px"},textContent:this.customNotes}),l.replaceChildren(a),a.focus()}})]),l]),me("div.easyuse-model-detail",[me("div.easyuse-model-detail-head",{textContent:Le("Details")}),me("div.easyuse-model-detail-body",[me("div.easyuse-model-detail-item",[me("div.easyuse-model-detail-item-label",{textContent:Le("Type")}),me("div.easyuse-model-detail-item-value",{textContent:e.model.type})]),me("div.easyuse-model-detail-item",[me("div.easyuse-model-detail-item-label",{textContent:Le("BaseModel")}),me("div.easyuse-model-detail-item-value",{textContent:e.baseModel})]),me("div.easyuse-model-detail-item",[me("div.easyuse-model-detail-item-label",{textContent:Le("Download")}),me("div.easyuse-model-detail-item-value",{textContent:(null==(t=e.stats)?void 0:t.downloadCount)||0})]),me("div.easyuse-model-detail-item",[me("div.easyuse-model-detail-item-label",{textContent:Le("Trained Words")}),me("div.easyuse-model-detail-item-value",{textContent:(null==e?void 0:e.trainedWords.join(","))||"-"})]),me("div.easyuse-model-detail-item",[me("div.easyuse-model-detail-item-label",{textContent:Le("Source")}),me("div.easyuse-model-detail-item-value",[me("label",[me("img",{style:{width:"14px",position:"relative",top:"3px",margin:"0 5px 0 0"},src:"https://civitai.com/favicon.ico"}),me("a",{href:"https://civitai.com/models/"+e.modelId,textContent:"View "+e.model.name,target:"_blank"})])])])])])),null==(i=e.images)?void 0:i.length){this.imgCurrent=0,this.isSaving=!1,e.images.map((e=>e.url&&this.imgList.appendChild(me("div.easyuse-preview-slide",[me("div.easyuse-preview-slide-content",[me("img",{src:e.url}),me("div.save",{textContent:"Save as preview",onclick:async()=>{if(this.isSaving)return;this.isSaving=!0,Ze.showLoading(Le("Saving Preview..."));const t=await(await fetch(e.url)).blob(),i="temp_preview."+new URL(e.url).pathname.split(".")[1],s=new FormData;s.append("image",new File([t],i)),s.append("overwrite","true"),s.append("type","temp");if(200!==(await he.fetchApi("/upload/image",{method:"POST",body:s})).status)return this.isSaving=!1,Ze.error(Le("Saving Failed")),Ze.hideLoading(),void alert(`Error saving preview (${req.status}) ${req.statusText}`);await he.fetchApi("/easyuse/save/"+encodeURIComponent(`${this.type}/${this.name}`),{method:"POST",body:JSON.stringify({filename:i,type:"temp"}),headers:{"content-type":"application/json"}}).then((e=>{Ze.success(Le("Saving Succeed")),Ze.hideLoading()})),this.isSaving=!1,app.refreshComboInNodes()}})])]))));let t=this;this.imgDistance=(-660*this.imgCurrent).toString(),this.imgList.style.display="",this.imgList.style.transform="translate3d("+this.imgDistance+"px, 0px, 0px)",this.slides=this.imgList.querySelectorAll(".easyuse-preview-slide"),this.slideLeftButton=me("button.left",{parent:this.imgWrapper,style:{display:e.images.length<=2?"none":"block"},innerHTML:'',onclick:()=>{e.images.length<=2||(t.imgList.classList.remove("no-transition"),0==t.imgCurrent?(t.imgCurrent=e.images.length/2-1,this.slides[this.slides.length-1].style.transform="translate3d("+(-660*(this.imgCurrent+1)).toString()+"px, 0px, 0px)",this.slides[this.slides.length-2].style.transform="translate3d("+(-660*(this.imgCurrent+1)).toString()+"px, 0px, 0px)",t.imgList.style.transform="translate3d(660px, 0px, 0px)",setTimeout((e=>{this.slides[this.slides.length-1].style.transform="translate3d(0px, 0px, 0px)",this.slides[this.slides.length-2].style.transform="translate3d(0px, 0px, 0px)",t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)",t.imgList.classList.add("no-transition")}),500)):(t.imgCurrent=t.imgCurrent-1,t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)"))}}),this.slideRightButton=me("button.right",{parent:this.imgWrapper,style:{display:e.images.length<=2?"none":"block"},innerHTML:'',onclick:()=>{if(!(e.images.length<=2))if(t.imgList.classList.remove("no-transition"),t.imgCurrent>=e.images.length/2-1){t.imgCurrent=0;const i=e.images.length/2;this.slides[0].style.transform="translate3d("+(660*i).toString()+"px, 0px, 0px)",this.slides[1].style.transform="translate3d("+(660*i).toString()+"px, 0px, 0px)",t.imgList.style.transform="translate3d("+(-660*i).toString()+"px, 0px, 0px)",setTimeout((e=>{this.slides[0].style.transform="translate3d(0px, 0px, 0px)",this.slides[1].style.transform="translate3d(0px, 0px, 0px)",t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)",t.imgList.classList.add("no-transition")}),500)}else t.imgCurrent=t.imgCurrent+1,t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)"}})}return e.description&&me("div",{parent:this.content,innerHTML:e.description,style:{marginTop:"10px"}}),e})).catch((e=>{this.imgWrapper.style.display="none",t.textContent="⚠️ "+e.message})).finally((e=>{}))}}class Ei extends ki{async addInfo(){await this.addCivitaiInfo()}}class Ni extends ki{getTagFrequency(){if(!this.metadata.ss_tag_frequency)return[];const e=JSON.parse(this.metadata.ss_tag_frequency),t={};for(const i in e){const s=e[i];for(const e in s)e in t?t[e]+=s[e]:t[e]=s[e]}return Object.entries(t).sort(((e,t)=>t[1]-e[1]))}getResolutions(){let e=[];if(this.metadata.ss_bucket_info){const t=JSON.parse(this.metadata.ss_bucket_info);if(null==t?void 0:t.buckets)for(const{resolution:i,count:s}of Object.values(t.buckets))e.push([s,`${i.join("x")} * ${s}`])}e=e.sort(((e,t)=>t[0]-e[0])).map((e=>e[1]));let t=this.metadata.ss_resolution;if(t){const i=t.split(","),s=i[0].replace("(",""),a=i[1].replace(")","");e.push(`${s.trim()}x${a.trim()} (Base res)`)}else(t=this.metadata["modelspec.resolution"])&&e.push(t+" (Base res");return e.length||e.push("⚠️ Unknown"),e}getTagList(e){return e.map((e=>me("li.easyuse-model-tag",{dataset:{tag:e[0]},$:e=>{e.onclick=()=>{e.classList.toggle("easyuse-model-tag--selected")}}},[me("p",{textContent:e[0]}),me("span",{textContent:e[1]})])))}addTags(){let e,t=this.getTagFrequency();if(null==t?void 0:t.length){const i=t.length;let s;i>500&&(t=t.slice(0,500),e=me("p",[me("span",{textContent:"⚠️ Only showing first 500 tags "}),me("a",{href:"#",textContent:`Show all ${i}`,onclick:()=>{s.replaceChildren(...this.getTagList(this.getTagFrequency())),e.remove()}})])),s=me("ol.easyuse-model-tags-list",this.getTagList(t)),this.tags=me("div",[s])}else this.tags=me("p",{textContent:"⚠️ No tag frequency metadata found"});this.content.append(this.tags),e&&this.content.append(e)}async addInfo(){const e=this.addCivitaiInfo();this.addTags();const t=await e;t&&me("div",{parent:this.content,innerHTML:t.description,style:{maxHeight:"250px",overflow:"auto"}})}createButtons(){const e=super.createButtons();function t(e,t){const i=me("textarea",{parent:document.body,style:{position:"fixed"},textContent:t.map((e=>e.dataset.tag)).join(", ")});i.select();try{document.execCommand("copy"),e.target.dataset.text||(e.target.dataset.text=e.target.textContent),e.target.textContent="Copied "+t.length+" tags",setTimeout((()=>{e.target.textContent=e.target.dataset.text}),1e3)}catch(s){prompt("Copy to clipboard: Ctrl+C, Enter",text)}finally{document.body.removeChild(i)}}return e.unshift(me("button",{type:"button",textContent:"Copy Selected",onclick:e=>{t(e,[...this.tags.querySelectorAll(".easyuse-model-tag--selected")])}}),me("button",{type:"button",textContent:"Copy 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kSamplerInpainting","easy kSamplerDownscaleUnet","easy kSamplerSDTurbo"],Hi=["easy controlnetLoader","easy controlnetLoaderADV"],qi=["easy instantIDApply","easy instantIDApplyADV"],Yi=["easy ipadapterApply","easy ipadapterApplyADV","easy ipadapterApplyFaceIDKolors","easy ipadapterStyleComposition"],Ki=["easy pipeIn","easy pipeOut","easy pipeEdit","easy pipeEditPrompt"],Ji=["easy XYPlot","easy XYPlotAdvanced"],Bi=["easy setNode"],Qi=["RescaleCFG","LoraLoaderModelOnly","LoraLoader","FreeU","FreeU_v2",...Yi,...Bi],$i={"easy seed":{from:{INT:[...Xi,"easy fullkSampler"]}},"easy positive":{from:{STRING:[...Fi]}},"easy negative":{from:{STRING:[...Fi]}},"easy wildcards":{from:{STRING:["easy showAnything",...Fi]}},"easy stylesSelector":{from:{STRING:["easy showAnything",...Fi]}},"easy promptConcat":{from:{STRING:["easy showAnything",...Fi]}},"easy promptReplace":{from:{STRING:["easy showAnything",...Fi]}},"easy fullLoader":{from:{PIPE_LINE:[...Xi,"easy 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sv3dLoader":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced","easy preSamplingDynamicCFG",...Ki,...Bi],MODEL:Qi},to:{STRING:[...Fi]}},"easy preSampling":{from:{PIPE_LINE:[...Zi,...Ki,...Hi,...Ji,...Bi]}},"easy preSamplingAdvanced":{from:{PIPE_LINE:[...Zi,...Ki,...Hi,...Ji,...Bi]}},"easy preSamplingDynamicCFG":{from:{PIPE_LINE:[...Zi,...Ki,...Hi,...Ji,...Bi]}},"easy preSamplingCustom":{from:{PIPE_LINE:[...Zi,...Ki,...Hi,...Ji,...Bi]}},"easy preSamplingLayerDiffusion":{from:{PIPE_LINE:["easy kSamplerLayerDiffusion",...Zi,...Ki,...Hi,...Ji,...Bi]}},"easy preSamplingNoiseIn":{from:{PIPE_LINE:[...Zi,...Ki,...Hi,...Ji,...Bi]}},"easy fullkSampler":{from:{PIPE_LINE:[...Ki.reverse(),"easy preDetailerFix","easy preMaskDetailerFix",...Xi,...Bi]}},"easy kSampler":{from:{PIPE_LINE:[...Ki.reverse(),"easy preDetailerFix","easy preMaskDetailerFix","easy hiresFix",...Xi,...Bi]}},"easy controlnetLoader":{from:{PIPE_LINE:[...Xi,...Hi,...qi,...Ki,...Bi]}},"easy controlnetLoaderADV":{from:{PIPE_LINE:[...Xi,...Hi,...qi,...Ki,...Bi]}},"easy instantIDApply":{from:{PIPE_LINE:[...Xi,...Hi,...qi,...Ki,...Bi],MODEL:Qi},to:{COMBO:["easy promptLine"]}},"easy instantIDApplyADV":{from:{PIPE_LINE:[...Xi,...Hi,...qi,...Ki,...Bi],MODEL:Qi},to:{COMBO:["easy promptLine"]}},"easy ipadapterApply":{to:{COMBO:["easy promptLine"]}},"easy ipadapterApplyADV":{to:{STRING:["easy sliderControl",...Fi],COMBO:["easy promptLine"]}},"easy ipadapterStyleComposition":{to:{COMBO:["easy promptLine"]}},"easy preDetailerFix":{from:{PIPE_LINE:["easy detailerFix",...Ki,...Bi]},to:{PIPE_LINE:["easy ultralyticsDetectorPipe","easy samLoaderPipe","easy kSampler","easy fullkSampler"]}},"easy preMaskDetailerFix":{from:{PIPE_LINE:["easy detailerFix",...Ki,...Bi]}},"easy samLoaderPipe":{from:{PIPE_LINE:["easy preDetailerFix",...Ki,...Bi]}},"easy ultralyticsDetectorPipe":{from:{PIPE_LINE:["easy preDetailerFix",...Ki,...Bi]}},"easy cascadeLoader":{from:{PIPE_LINE:["easy fullCascadeKSampler","easy preSamplingCascade",...Hi,...Ki,...Bi],MODEL:Qi.filter((e=>!Yi.includes(e)))}},"easy fullCascadeKSampler":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced",...Ki,...Bi]}},"easy preSamplingCascade":{from:{PIPE_LINE:["easy cascadeKSampler",...Ki,...Bi]}},"easy cascadeKSampler":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced",...Ki,...Bi]}},"easy pipeEdit":{from:{PIPE_LINE:[...Xi,...Hi,...qi,...Bi]},to:{PIPE_LINE:[...Xi,...Hi,...qi,...Ki,...Bi]}},"easy pipeEditPrompt":{from:{PIPE_LINE:[...Xi,...Hi,...qi,...Bi]},to:{PIPE_LINE:[...Xi,...Hi,...qi,...Ki,...Bi]}}};class es extends Error{constructor(e="Attempted to access LGraph reference that was null or undefined.",t){super(e,{cause:t}),this.name="NullGraphError"}}pe.registerExtension({name:"Comfy.EasyUse.Suggestions",async setup(e){const t=LGraphCanvas.prototype.createDefaultNodeForSlot;LGraphCanvas.prototype.createDefaultNodeForSlot=function(e){var i;const s=Object.assign({nodeFrom:null,slotFrom:null,nodeTo:null,slotTo:null,position:[],nodeType:null,posAdd:[0,0],posSizeFix:[0,0]},e||{}),a=this,n=s.nodeFrom&&null!==s.slotFrom,o=!n&&s.nodeTo&&null!==s.slotTo,l=n?s.nodeFrom:s.nodeTo;if(!l||!Object.keys($i).includes(l.type))return t.call(this,e);if(!n&&!o)return!1;if(!s.nodeType)return!1;const r=n?s.nodeFrom:s.nodeTo;if(!r)throw new TypeError("nodeX was null when creating default node for slot.");const d=r.type;let u=n?s.slotFrom:s.slotTo,A=!1;switch(typeof u){case"string":A=n?r.findOutputSlot(u,!1):r.findInputSlot(u,!1),u=n?r.outputs[u]:r.inputs[u];break;case"object":if(null===u)return!1;A=n?r.findOutputSlot(u.name):r.findInputSlot(u.name);break;case"number":A=u,u=n?r.outputs[u]:r.inputs[u];break;default:return!1}var c=u.type==LiteGraph.EVENT?"_event_":u.type,p=n?LiteGraph.slot_types_default_out:LiteGraph.slot_types_default_in;if(null==p?void 0:p[c]){let e=!1;const t=n?"from":"to";if($i[d]&&$i[d][t]&&(null==(i=$i[d][t][c])?void 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A){case"string":c=l?u.findOutputSlot(A,!1):u.findInputSlot(A,!1),A=l?u.outputs[A]:u.inputs[A];break;case"object":if(null===A)return;c=l?u.findOutputSlot(A.name):u.findInputSlot(A.name);break;case"number":c=A,A=l?u.outputs[A]:u.inputs[A];break;default:return!1}const p=["Add Node","Add Reroute",null];s.allow_searchbox&&p.push("Search",null);var h=A.type==LiteGraph.EVENT?"_event_":A.type,m=l?LiteGraph.slot_types_default_out:LiteGraph.slot_types_default_in,f=u.type;if(m&&m[h]){const e=l?"from":"to";if($i[f]&&$i[f][e]&&(null==(t=$i[f][e][h])?void 0:t.length)>0)for(var g in $i[f][e][h])p.push($i[f][e][h][g]);else if("object"==typeof m[h]||"array"==typeof m[h])for(var g in m[h])p.push(m[h][g]);else p.push(m[h])}var v=new LiteGraph.ContextMenu(p,{event:s.e,extra:A,title:(A&&""!=A.name?A.name+(h?" | ":""):"")+(A&&h?h:""),callback:function(e,t,i){var r,d;switch(e){case"Add Node":LGraphCanvas.onMenuAdd(null,null,i,v,(function(e){var t,i;e&&(l?null==(t=s.nodeFrom)||t.connectByType(c,e,h,{afterRerouteId:o}):null==(i=s.nodeTo)||i.connectByTypeOutput(c,e,h,{afterRerouteId:o}))}));break;case"Add Reroute":const u=l?s.nodeFrom:s.nodeTo,p=t.extra;if(!n)throw new es;if(!u)throw new TypeError("Cannot add reroute: node was null");if(!p)throw new TypeError("Cannot add reroute: slot was null");if(!s.e)throw new TypeError("Cannot add reroute: CanvasPointerEvent was null");if(!u.connectFloatingReroute([s.e.canvasX,s.e.canvasY],p,o))throw new Error("Failed to create reroute");a.dirty_canvas=!0,a.dirty_bgcanvas=!0;break;case"Search":l?s.showSearchBox(i,{node_from:s.nodeFrom,slot_from:A,type_filter_in:h}):s.showSearchBox(i,{node_to:s.nodeTo,slot_from:A,type_filter_out:h});break;default:const m={position:[(null==(r=s.e)?void 0:r.canvasX)??0,(null==(d=s.e)?void 0:d.canvasY)??0],nodeType:e,afterRerouteId:o};a.createDefaultNodeForSlot(Object.assign(s,m))}}});return v}}}),pe.registerExtension({name:"Comfy.EasyUse.TimeTaken",setup(){let e=new Map,t=0;he.addEventListener("execution_start",(e=>{graph&&graph._nodes.forEach((e=>{e.executionDuration&&delete e.executionDuration}))})),he.addEventListener("executing",(i=>{if(!we("EasyUse.Nodes.Runtime",null))return;const s=(null==i?void 0:i.node)||(null==i?void 0:i.detail)||null,a=e.get(t);if(e.delete(t),t&&a){const e=Date.now()-a,i=_t(t);i&&(i.executionDuration||(i.executionDuration=0),i.executionDuration=i.executionDuration+e/1e3)}t=s,e.set(s,Date.now())}))},beforeRegisterNodeDef(e,t){const i=e.prototype.onDrawForeground;e.prototype.onDrawForeground=function(...e){const[t]=e;return function(e,t){if(!t)return;t=parseFloat(t).toFixed(3)+Le("s"),e.save(),e.fillStyle=LiteGraph.NODE_DEFAULT_BGCOLOR,function(e,t,i,s,a,n){e.beginPath(),e.moveTo(t+n,i),e.lineTo(t+s-n,i),e.arcTo(t+s,i,t+s,i+n,n),e.lineTo(t+s,i+a-n),e.arcTo(t+s,i+a,t+s-n,i+a,n),e.lineTo(t+n,i+a),e.arcTo(t,i+a,t,i+a-n,n),e.lineTo(t,i+n),e.arcTo(t,i,t+n,i,n),e.closePath()}(e,0,-LiteGraph.NODE_TITLE_HEIGHT-20,e.measureText(t).width+10,LiteGraph.NODE_TITLE_HEIGHT-10,4),e.fill(),function(e,t,i,s,a="#000",n=12,o="Inter"){e.font=`${n}px ${o}`,e.fillStyle=a,e.fillText(t,i,s)}(e,t,8,-LiteGraph.NODE_TITLE_HEIGHT-6,LiteGraph.NODE_TITLE_COLOR),e.restore()}(t,this.executionDuration),null==i?void 0:i.apply(this,e)}}});let ts=null;pe.registerExtension({name:"Comfy.EasyUse.HotKeys",setup(){if(void 0!==x){x("up,down,left,right",(function(e,t){var i,s,a,n,o,l,r,d,u,A,c,p,h,m,f;e.preventDefault();if(!we("EasyUse.Hotkeys.JumpNearestNodes",null))return;const g=St();if(0===g.length)return;const v=g[0];switch(t.key){case"up":case"left":let e=null;if(Ct(v)){const e=null==(i=v.widgets_values)?void 0:i[0],t=null==(s=v.graph)?void 0:s._nodes,a=null==t?void 0:t.find((t=>{var i;if(It(t)){if((null==(i=t.widgets_values)?void 0:i[0])===e)return t}return null}));a&&Lt(a)}else if((null==(a=v.inputs)?void 0:a.length)>0){for(let t=0;t{var i;if(Ct(t)){if((null==(i=t.widgets_values)?void 0:i[0])===e)return t}return null}));i&&Lt(i)}else if((null==(A=v.outputs)?void 0:A.length)>0){for(let e=0;e0&&v.outputs[e].links[0]){t=v.outputs[e].links[0];break}if(t){const e=null==(p=v.graph)?void 0:p.links;if(e[t]){const i=null==(h=e[t])?void 0:h.target_id,s=null==(f=null==(m=v.graph)?void 0:m._nodes_by_id)?void 0:f[i];s&&Lt(s)}}}}})),x("shift+up,shift+down,shift+left,shift+right,shift+alt+⌘+left,shift+alt+⌘+right,shift+alt+ctrl+left,shift+alt+ctrl+right",(function(e,t){e.preventDefault();if(!we("EasyUse.Hotkeys.AlignSelectedNodes",null))return;const i=St();if(i.length<=1)return;const s=i;switch(t.key){case"shift+up":LGraphCanvas.alignNodes(s,"top",s[0]);break;case"shift+down":LGraphCanvas.alignNodes(s,"bottom",s[0]);break;case"shift+left":LGraphCanvas.alignNodes(s,"left",s[0]);break;case"shift+right":LGraphCanvas.alignNodes(s,"right",s[0]);break;case"shift+alt+ctrl+left":case"shift+alt+⌘+left":Wt(s,"horizontal");break;case"shift+alt+ctrl+right":case"shift+alt+⌘+right":Wt(s,"vertical")}ts||(ts=He()),ts&&ts.update()})),x("shift+⌘+left,shift+⌘+right,shift+ctrl+left,shift+ctrl+right",(function(e,t){e.preventDefault();if(!we("EasyUse.Hotkeys.NormalizeSelectedNodes",null))return;const i=St();if(i.length<=1)return;const s=i;switch(t.key){case"shift+ctrl+left":case"shift+⌘+left":jt(s,"width");break;case"shift+ctrl+right":case"shift+⌘+right":jt(s,"height")}ts||(ts=He()),ts&&ts.update()})),x("shift+g",(function(e,t){e.preventDefault();we("EasyUse.Hotkeys.AddGroup",null)&&(ss(),ts||(ts=He()),ts&&ts.update())})),x("shift+r",(function(e,t){e.preventDefault();we("EasyUse.Hotkeys.cleanVRAMused",null)&&fi()})),x("shift+m",(function(e,t){var i,s,a;if(!we("EasyUse.Hotkeys.toggleNodesMap",null))return;let n=(null==(i=pe.extensionManager)?void 0:i.sidebarTab)||pe.extensionManager,o=(null==(s=pe.extensionManager.sidebarTab)?void 0:s.activeSidebarTabId)||(null==(a=pe.extensionManager)?void 0:a.activeSidebarTab);n.activeSidebarTabId=o==Me?null:Me}));const e=[];Array.from(Array(10).keys()).forEach((t=>e.push(`alt+${t}`))),x(e.join(","),(async function(e,t){e.preventDefault();if(!we("EasyUse.Hotkeys.NodesTemplate",null))return;const i=t.key;let s=parseInt(i.split("+")[1]);const a=await he.getUserData("comfy.templates.json");let n=null;if(200==a.status)try{n=await a.json()}catch(l){Ze.error(Le("Get Node Templates File Failed"))}else localStorage["Comfy.NodeTemplates"]?n=JSON.parse(localStorage["Comfy.NodeTemplates"]):Ze.warn(Le("No Node Templates Found"));if(!n)return void Ze.warn(Le("No Node Templates Found"));s=0===s?9:s-1;const o=n[s];if(o)try{const e=(null==o?void 0:o.name)||"Group",t=(null==o?void 0:o.data)?JSON.parse(o.data):[];is((async()=>{await ye.registerFromWorkflow(t.groupNodes,{}),localStorage.litegrapheditor_clipboard=o.data,pe.canvas.pasteFromClipboard(),t.groupNodes||ss(e)}))}catch(l){Ze.error(l)}else Ze.warn(Le("Node template with {key} not set").replace("{key}",i))}));const t=async function(e){if(("b"===e.key||"m"==e.key)&&(e.metaKey||e.ctrlKey)){if(0===St().length)return;ts||(ts=He()),ts&&ts.update()}};window.addEventListener("keydown",t,!0)}}});const is=async e=>{const t=localStorage.litegrapheditor_clipboard;await e(),localStorage.litegrapheditor_clipboard=t},ss=e=>{const t=St();if(0===t.length)return;const i=t;let s=new LiteGraph.LGraphGroup;s.title=e||"Group",((e,t=[],i=20)=>{var s,a,n,o,l,r,d,u,A,c;for(var p of(a=n=o=l=-1,r=d=u=A=-1,[e._nodes,t]))for(var h in p)r=(c=p[h]).pos[0],d=c.pos[1],u=c.pos[0]+c.size[0],A=c.pos[1]+c.size[1],"Reroute"!=c.type&&(d-=LiteGraph.NODE_TITLE_HEIGHT),(null==(s=c.flags)?void 0:s.collapsed)&&(A=d+LiteGraph.NODE_TITLE_HEIGHT,(null==c?void 0:c._collapsed_width)&&(u=r+Math.round(c._collapsed_width))),(-1==a||ro)&&(o=u),(-1==l||A>l)&&(l=A);n-=Math.round(1.4*e.font_size),e.pos=[a-i,n-i],e.size=[o-a+2*i,l-n+2*i]})(s,i),pe.canvas.graph.add(s)},as="easy imageChooser",ns=e=>(null==e?void 0:e.comfyClass)===as;pe.registerExtension({name:"Comfy.EasyUse.imageChooser",setup(){const e=he.interrupt;he.interrupt=function(){var t;(null==(t=pe.graph)?void 0:t._nodes_by_id)&&(()=>{var e;Object.values(null==(e=pe.graph)?void 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t=this.selected_images.size,i=(null==(e=this.imgs)?void 0:e.length)||0;if(this.isCanceling)this.confirm_button_widget.name=Le("Canceling"),this.cancel_button_widget.name="",this.confirm_button_widget.disabled=!0,this.cancel_button_widget.disabled=!0;else if(this.isWaitingSelection)t>0?(this.confirm_button_widget.name=t>1?`${Le("Progress selected")} (${t}/${i})`:`${Le("Progress selected image")}`,this.confirm_button_widget.disabled=!1):(this.confirm_button_widget.name=Le("Please select images to continue"),this.confirm_button_widget.disabled=!0),this.cancel_button_widget.name=Le("Cancel current run"),this.cancel_button_widget.disabled=!1;else{const e={"Always Pause":Le("Waiting for selection..."),"Keep Last Selection":Le("Using last selection")};this.confirm_button_widget.name=(t>0&&"Always Pause"==this.currentMode?Le("Please run first"):null==e?void 0:e[this.currentMode])||Le("Unknown 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Reload Node":"🔃 刷新节点","Updated At:":"最近更新：","Created At:":"首次发布：","✏️ Edit":"✏️ 编辑","💾 Save":"💾 保存","No notes":"当前还没有备注内容","Saving Notes...":"正在保存备注...","Type your notes here":"在这里输入备注内容",ModelName:"模型名称","Models Required":"所需模型","Download Model":"下载模型","Source Url":"模型源地址",Notes:"备注",Type:"类型","Trained Words":"训练词",BaseModel:"基础算法",Details:"详情",Description:"描述",Download:"下载量",Source:"来源","Saving Preview...":"正在保存预览图...","Saving Succeed":"保存成功","Clean SuccessFully":"清理成功","Clean Failed":"清理失败","Saving Failed":"保存失败","No COMBO link":"沒有找到COMBO连接","Reboot ComfyUI":"重启ComfyUI","Are you sure you'd like to reboot the server?":"是否要重启ComfyUI？","NODES MAP":"管理节点与组",NodesMap:"节点与组","Nodes map sorting mode":"管理节点组排序模式","Search by Node ID/Name...":"通过节点ID/名称搜索...","No Nodes":"未找到节点","No nodes found in the map":"在工作流程中没有找到节点","No nodes found in the search":"未找到与搜索匹配的节点","Jump to this node":"跳转到该节点","Rename node":"重命名节点","Delete node":"删除节点","Rename group":"重命名组","Delete group":"删除组","Expand All":"展开所有组","Collapse All":"折叠所有组",Close:"关闭","Default automatic sorting, if set to manual, groups can be dragged and dropped and the sorting results saved.":"默认自动排序，如果设置为手动，组可以拖放并保存排序结果。","For drag and drop sorting, please find Nodes map sorting mode in Settings->EasyUse and change it to manual":"如需拖拽排序请在设置->EasyUse节点中找到管理节点组排序模式并修改成 manual",Queue:"队列","Cleanup Of VRAM Usage":"清理显存占用","Please stop all running tasks before cleaning GPU":"请在清理GPU之前停止所有运行中的任务",Always:"启用中",Bypass:"已忽略",Never:"已停用","Auto Sorting":"自动排序","Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never":"点击`启用中/已忽略`可设置组模式, 长按可停用该组节点","Enable Shift+Up/Down/Left/Right key and Shift+Ctrl+Alt+Left/Right to align selected nodes":"启用 Shift+上/下/左/右 和 Shift+Ctrl+Alt+左/右 键对齐选中的节点","Enable Shift+Ctrl+Left/Right key to normalize selected nodes":"启用 Shift+Ctrl+左/右 键规范化选中的节点","Enable Shift+g to add selected nodes to a group":"启用 Shift+g 键将选中的节点添加一个组","Enable Shift+r to unload models and node cache":"启用 Shift+r 键卸载模型和节点缓存","Enable Shift+m to toggle nodes map":"启用 Shift+m 键显隐管理节点组","Enable Up/Down/Left/Right key to jump nearest nodes":"启用 上/下/左/右 键跳转到最近的前后节点","Enable Alt+1~9 to paste nodes from nodes template":"启用 Alt+1~9 从节点模板粘贴到工作流中","Enable contextMenu auto nest subdirectories":"启用上下文菜单自动嵌套子目录","Enable right-click menu to add node A~Z sorting":"启用右键菜单中新建节点A~Z排序","Enable model thumbnails display":"启动模型预览图显示","Enable nodes runtime display":"启动节点运行时间显示","Enable chain get node and set node with parent nodes":"启用将获取点和设置点与父节点链在一起","Maximum number of model thumbnails displayed":"显示的模型缩略图的最大数量","Too many thumbnails will affect the first loading time, set the maximum value to not load the thumbnail function when there are too many models's thumbnail":"太多的缩略图会影响首次加载时间，当模型缩略图太多时，设置最大值以不加载缩略图功能","Too many thumbnails, have closed the display":"模型缩略图太多啦，为您关闭了显示","Shift+Up/Down/Left/Right can align selected nodes, Shift+Ctrl+Alt+Left/Right can distribute horizontal/vertical nodes":"Shift+上/下/左/右 可以对齐选中的节点, Shift+Ctrl+Alt+左/右 可以水平/垂直分布节点","Enable Shift+Ctrl+Left key to normalize width and Shift+Ctrl+Right key to normalize height":"启用 Shift+Ctrl+左 键规范化宽度和 Shift+Ctrl+右 键规范化高度","After v1.2.39, Ctrl+g can be used instead of it":"从v1.2.39开始，可以使用Ctrl+g代替","Styles Selector Display Type":"样式选择器显示类型","Styles Selector Display Type, if set to 'Grid', it will display as a Grid, if set to 'List', it will display as a list":"样式选择器显示类型，如果设置为“网格”，则显示为网格，如果设置为“列表”，则显示为列表","Use three shortcut buttons in the right-click menu":"在右键菜单中使用三个快捷按钮","Enable Nodes Map":"启用节点组管理","Display Groups Only":"仅显示组","Display Node ID":"显示节点ID","You need to refresh the page to update successfully":"您需要刷新页面以成功更新","Get styles list Failed":"获取样式列表失败","Get style image Failed":"获取样式图片失败","Empty All":"清空所有","Type here to search styles ...":"在此处输入以搜索样式 ...","Loading UserInfo...":"正在获取用户信息...","Please set the APIKEY first":"请先设置APIKEY","Setting APIKEY":"设置APIKEY","Save Account Info":"保存账号信息",Choose:"选择",Delete:"删除",Edit:"编辑","At least one account is required":"删除失败: 至少需要一个账户","APIKEY is not Empty":"APIKEY 不能为空","Add Account":"添加账号","Getting Your APIKEY":"获取您的APIKEY",Canceling:"正在取消","Cancel current run":"取消当前运行","Waiting for selection...":"等待选择中...","Using last selection":"使用上次选择","Pass Through":"跳过选择","Unknown Mode":"未知模式","Choose Selected Images":"选择选中的图片","Choose images to continue":"选择图片以继续","Please run first":"请先运行队列","Progress selected":"处理选中的图片","Progress selected image":"处理选中的图片","Please select images to continue":"请选择图片以继续",Background:"背景",Hat:"帽子",Hair:"头发",Body:"身体",Face:"脸部",Clothes:"衣服",Others:"其他",Glove:"手套",Glasses:"眼镜",Sunglasses:"太阳镜","Upper-clothes":"上衣","Top-clothes":"上衣","Bottom-clothes":"下身装","Torso-skin":"皮肤",Dress:"连衣裙",Belt:"腰带",Coat:"外套",Socks:"袜子",Pants:"裤子",Jumpsuits:"连体衣",Bag:"包",Scarf:"围巾",Skirt:"裙子","Left-arm":"左臂","Right-arm":"右臂","Left-leg":"左腿","Right-leg":"右腿","Left-foot":"左脚","Right-foot":"右脚","Left-shoe":"左鞋","Right-shoe":"右鞋","select mask components":"选择分割部位","select items":"选择项",Unlabelled:"未标记","shirt, blouse":"衬衫，女式衬衫","top, t-shirt, sweatshirt":"上衣，T恤，运动衫",sweater:"毛衣",cardigan:"开襟毛衣",jacket:"夹克",vest:"背心",pants:"长裤",shorts:"短裤",skirt:"裙子",coat:"外套",dress:"连衣裙",jumpsuit:"连体衣",cape:"斗篷",glasses:"眼镜",hat:"帽子","headband, head covering, hair accessory":"头带，头饰，发饰",tie:"领带",glove:"手套",watch:"手表",belt:"腰带","leg warmer":"护腿","tights, stockings":"紧身袜，丝袜",sock:"袜子",shoe:"鞋子","bag, wallet":"包，钱包",scarf:"围巾",umbrella:"雨伞",hood:"帽兜",collar:"领子",lapel:"翻领",epaulette:"肩章",sleeve:"袖子",pocket:"口袋",neckline:"领口",buckle:"扣环",zipper:"拉链",applique:"贴花",bead:"珠子",bow:"蝴蝶结",flower:"花朵装饰",fringe:"流苏",ribbon:"丝带",rivet:"铆钉",ruffle:"褶边",sequin:"亮片",tassel:"流苏穗",background:"背景",skin:"皮肤",nose:"鼻子",eyeglasses:"眼镜",left_eye:"左眼",right_eye:"右眼",left_eyebrow:"左眉毛",right_eyebrow:"右眉毛",left_ear:"左耳",right_ear:"右耳",mouth:"嘴巴",upper_lip:"上唇",lower_lip:"下唇",hair:"头发",earring:"耳环",necklace:"项链",neck:"脖子",clothing:"服装",s:"秒","No Node Templates Found":"未找到节点模板预设","Get Node Templates File Failed":"获取节点模板文件失败","Node template with {key} not set":"未设置快捷键为{key}的节点预设","ComfyUI Basic":"ComfyUI 基础节点","Recommend Nodes":"推荐节点","Others A~Z":"其他节点 A~Z","The maximum number of inputs is 20":"输入数量最大20个","The maximum number of outputs is 20":"输出数量最大20个","Stop recording":"停止录音","Voice input":"语音输入",Continue:"继续",Stop:"停止",now:"当前",prev:"上一步","Add New Tab":"添加新标签","Add Angle Prompt":"添加角度提示词",Reset:"重置","Angle Prompt":"角度词",Image:"图像",Back:"后面",Top:"顶部",Bottom:"底部",Left:"左侧",Right:"右侧",Rotate:"旋转",Vertical:"垂直",Zoom:"缩放"},ke=(e,t=!1)=>{switch(_e("Comfy.Locale")){case"zh-CN":case"zh":return xe[e]||e;default:return e}};const Ee="comfyui-easyuse-",Ne="dark-theme",Ce={PIPE_LINE:"#7737AA",PIPE_LINE_SDXL:"#7737AA",INT:"#29699C",X_Y:"#38291f",XYPLOT:"#74DA5D",LORA_STACK:"#94dccd",CONTROL_NET_STACK:"#94dccd",FLOW_CONTROL:"#373780"},Ie=0x4000000000000,Te=["loaders","latent","image","mask","sampling","_for_testing","advanced","utils","api","api node","加载器","Latent","图像","遮罩","采样","_用于测试","高级","工具","API","api 节点","chargeurs","latent","image","masque","échantillonnage","_pour_test","avancé","outils","nœud api","загрузчики","латентный","изображение","маска","выборка","_для_тестирования","расширенный","инструменты","api","api узел","로더","잠재","이미지","마스크","샘플링","_테스트용","고급","도구","api","api 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ze=JSON.parse(JSON.stringify(Ge));delete ze.NODE_COLORS,ze.ColorPalette.id="obsidian_dark",ze.ColorPalette.name="Obsidian 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Ve="#FFFFFF",je={ColorPalette:{id:"milk_white",name:"Milk 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d=e,u={},A=[],c=[];for(const f of d){const e=f.indexOf("/")>-1?"/":"\\",t=f.split(e);if(t.length>1){const i=t.shift();u[i]=u[i]||[],u[i].push({value:t.join(e),fullValue:f})}else"CHOOSE"===f||f.startsWith("DISABLE ")?A.push({value:f,fullValue:f}):c.push({value:f,fullValue:f})}const p=Object.values(u).length,h=[],m=(e,t="",s)=>{var n,a;let o;o=fe("div.comfyui-easyuse-contextmenu-model",{},[fe("span",{},e)]);let l=null==(n=vi[s])?void 0:n.folder,d=null==(a=vi[s])?void 0:a.pathIndex;const u=window.location.protocol,A=window.location.host;let c=l?`${`${u}//${A}`}/api/experiment/models/preview/${l}/${d}/${wi(s)}`:"";return{folder:l,content:e,fullName:s,thumbnail:i?c:null,title:o.outerHTML,callback:r}};if(p>0){const e=(t,i)=>{let s=[],n=[];const a=t.map((({value:e,fullValue:t})=>{const a={},o=e.indexOf("/")>-1?"/":"\\",l=e.split(o);if(l.length>1){const e=l.shift();a[e]=a[e]||[],a[e].push({value:l.join(o),fullValue:t})}if(Object.values(u).length>0){let 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t=document.getElementById("easyuse-model-thumbnail");t&&0!=t.style.opacity&&(t.style.display="none",t.style.opacity=0,t.style.left="0px",t.style.top="0px")}),100)}))},async beforeRegisterNodeDef(e,t,i){const s=e.prototype.onNodeCreated;["CheckpointLoader|pysssss","LoraLoader|pysssss"].includes(t.name)&&(e.prototype.onNodeCreated=async function(){s&&s.apply(this,[]);let e=yt(this,"lora_name")||yt(this,"ckpt_name");if(e){let t=e.onClick;e.onClick=function(e){return yi=!0,setTimeout((e=>{yi=!1}),500),t.call(this,e)}}})}});const Si=async()=>{["checkpoints","loras","diffusion_models"].map((async e=>{const t=await me.getModels(e);(null==t?void 0:t.length)>0&&t.map((t=>{vi[t.name]={folder:e,pathIndex:t.pathIndex}}))}))},Li=(e,t,i)=>s=>{var n;const a=t=>{setTimeout((s=>{const n=i.getBoundingClientRect();if(!n)return;const a=document.body.getBoundingClientRect();if(!a)return;const{left:o,top:l}=((e,t,i)=>{const{x:s}=e.getBoundingClientRect();let{top:n,left:a}=t;const{width:o,height:l}=i;return s<=o&&(a+=t.width),t.top<=l||(n=l),{left:a,top:n}})(e,n,a),r=document.getElementById("easyuse-model-thumbnail");r.src=t,r.style.left=`${o}px`,r.style.top=`${l}px`,r.style.display="block",r.style.opacity=1,r.onerror=e=>{r.src=Re}}),10)};if(null==(n=null==vi?void 0:vi[t.fullName])?void 0:n.img){let e=vi[t.fullName].img;a("no_preview_image"==e?Re:e.src)}else{let e=new Image;e.src=t.thumbnail,e.onload=i=>{vi[t.fullName].img=e,a(t.thumbnail)},e.onerror=i=>{e=null,vi[t.fullName].img="no_preview_image",a(Re)}}},xi=()=>e=>{const t=document.getElementById("easyuse-model-thumbnail");t&&0!=t.style.opacity&&(t.style.display="none",t.style.opacity=0,t.style.left="0px",t.style.top="0px")};class ki extends ge{constructor(){super(),this.element.classList.add("easyuse-model-metadata")}show(e){super.show(fe("div",Object.keys(e).map((t=>fe("div",[fe("label",{textContent:t}),fe("span",{textContent:e[t]})])))))}}class Ei extends ge{constructor(e){super(),this.name=e,this.element.classList.add("easyuse-model-info")}get customNotes(){return this.metadata["easyuse.notes"]}set customNotes(e){this.metadata["easyuse.notes"]=e}get hash(){return this.metadata["easyuse.sha256"]}async show(e,t){this.type=e;const i=me.fetchApi("/easyuse/metadata/"+encodeURIComponent(`${e}/${t}`));this.info=fe("div",{style:{flex:"auto"}}),this.imgCurrent=0,this.imgList=fe("div.easyuse-preview-list",{style:{display:"none"}}),this.imgWrapper=fe("div.easyuse-preview",[fe("div.easyuse-preview-group",[this.imgList])]),this.main=fe("main",{style:{display:"flex"}},[this.imgWrapper,this.info]),this.content=fe("div.easyuse-model-content",[fe("div.easyuse-model-header",[fe("h2",{textContent:this.name})]),this.main]);const s=fe("div",{textContent:"ℹ️ Loading...",parent:this.content});super.show(this.content),this.metadata=await(await i).json(),this.viewMetadata.style.cursor=this.viewMetadata.style.opacity="",this.viewMetadata.removeAttribute("disabled"),s.remove(),this.addInfo()}createButtons(){const e=super.createButtons();return this.viewMetadata=fe("button",{type:"button",textContent:"View raw metadata",disabled:"disabled",style:{opacity:.5,cursor:"not-allowed"},onclick:e=>{this.metadata&&(new ki).show(this.metadata)}}),e.unshift(this.viewMetadata),e}parseNote(){if(!this.customNotes)return[];let e=[];const t=new RegExp("(\\bhttps?:\\/\\/[^\\s]+)","g");let i,s=0;do{let n;i=t.exec(this.customNotes);let a=0;i?(n=i.index,a=i.index+i[0].length):n=this.customNotes.length;let o=this.customNotes.substring(s,n);o&&(o=o.replaceAll("\n","
"),e.push(fe("span",{innerHTML:o}))),i&&e.push(fe("a",{href:i[0],textContent:i[0],target:"_blank"})),s=a}while(i);return e}addInfoEntry(e,t){return fe("p",{parent:this.info},["string"==typeof e?fe("label",{textContent:e+": "}):e,"string"==typeof t?fe("span",{textContent:t}):t])}async getCivitaiDetails(){const e=await fetch("https://civitai.com/api/v1/model-versions/by-hash/"+this.hash);if(200===e.status)return await e.json();throw 404===e.status?new Error("Model not found"):new Error(`Error loading info (${e.status}) ${e.statusText}`)}addCivitaiInfo(){const e=this.getCivitaiDetails(),t=fe("span",{textContent:"ℹ️ Loading..."});return this.addInfoEntry(fe("label",[fe("img",{style:{width:"18px",position:"relative",top:"3px",margin:"0 5px 0 0"},src:"https://civitai.com/favicon.ico"}),fe("span",{textContent:"Civitai: "})]),t),e.then((e=>{var t,i;this.imgWrapper.style.display="block";let s=this.element.querySelector(".easyuse-model-header");s&&s.replaceChildren(fe("h2",{textContent:this.name}),fe("div.easyuse-model-header-remark",[fe("h5",{textContent:ke("Updated At:")+Fe(new Date(e.updatedAt),"yyyy/MM/dd")}),fe("h5",{textContent:ke("Created At:")+Fe(new Date(e.updatedAt),"yyyy/MM/dd")})]));let n=null,a=this.parseNote.call(this),o=ke("✏️ Edit"),l=fe("div.easyuse-model-detail-textarea",[fe("p",(null==a?void 0:a.length)>0?a:{textContent:ke("No notes")})]);if(a&&0!=a.length?l.classList.remove("empty"):l.classList.add("empty"),this.info.replaceChildren(fe("div.easyuse-model-detail",[fe("div.easyuse-model-detail-head.flex-b",[fe("span",ke("Notes")),fe("a",{textContent:o,href:"#",style:{fontSize:"12px",float:"right",color:"var(--warning-color)",textDecoration:"none"},onclick:async e=>{if(e.preventDefault(),n){if(n.value!=this.customNotes){qe.showLoading(ke("Saving Notes...")),this.customNotes=n.value;const e=await me.fetchApi("/easyuse/metadata/notes/"+encodeURIComponent(`${this.type}/${this.name}`),{method:"POST",body:this.customNotes});if(qe.hideLoading(),200!==e.status)return qe.error(ke("Saving Failed")),void alert(`Error saving notes (${e.status}) ${e.statusText}`);qe.success(ke("Saving Succeed")),a=this.parseNote.call(this),l.replaceChildren(fe("p",(null==a?void 0:a.length)>0?a:{textContent:ke("No notes")})),n.value?l.classList.remove("empty"):l.classList.add("empty")}else l.replaceChildren(fe("p",{textContent:ke("No notes")})),l.classList.add("empty");e.target.textContent=o,n.remove(),n=null}else e.target.textContent="💾 Save",n=fe("textarea",{placeholder:ke("Type your notes here"),style:{width:"100%",minWidth:"200px",minHeight:"50px",height:"100px"},textContent:this.customNotes}),l.replaceChildren(n),n.focus()}})]),l]),fe("div.easyuse-model-detail",[fe("div.easyuse-model-detail-head",{textContent:ke("Details")}),fe("div.easyuse-model-detail-body",[fe("div.easyuse-model-detail-item",[fe("div.easyuse-model-detail-item-label",{textContent:ke("Type")}),fe("div.easyuse-model-detail-item-value",{textContent:e.model.type})]),fe("div.easyuse-model-detail-item",[fe("div.easyuse-model-detail-item-label",{textContent:ke("BaseModel")}),fe("div.easyuse-model-detail-item-value",{textContent:e.baseModel})]),fe("div.easyuse-model-detail-item",[fe("div.easyuse-model-detail-item-label",{textContent:ke("Download")}),fe("div.easyuse-model-detail-item-value",{textContent:(null==(t=e.stats)?void 0:t.downloadCount)||0})]),fe("div.easyuse-model-detail-item",[fe("div.easyuse-model-detail-item-label",{textContent:ke("Trained Words")}),fe("div.easyuse-model-detail-item-value",{textContent:(null==e?void 0:e.trainedWords.join(","))||"-"})]),fe("div.easyuse-model-detail-item",[fe("div.easyuse-model-detail-item-label",{textContent:ke("Source")}),fe("div.easyuse-model-detail-item-value",[fe("label",[fe("img",{style:{width:"14px",position:"relative",top:"3px",margin:"0 5px 0 0"},src:"https://civitai.com/favicon.ico"}),fe("a",{href:"https://civitai.com/models/"+e.modelId,textContent:"View "+e.model.name,target:"_blank"})])])])])])),null==(i=e.images)?void 0:i.length){this.imgCurrent=0,this.isSaving=!1,e.images.map((e=>e.url&&this.imgList.appendChild(fe("div.easyuse-preview-slide",[fe("div.easyuse-preview-slide-content",[fe("img",{src:e.url}),fe("div.save",{textContent:"Save as preview",onclick:async()=>{if(this.isSaving)return;this.isSaving=!0,qe.showLoading(ke("Saving Preview..."));const t=await(await fetch(e.url)).blob(),i="temp_preview."+new URL(e.url).pathname.split(".")[1],s=new FormData;s.append("image",new File([t],i)),s.append("overwrite","true"),s.append("type","temp");if(200!==(await me.fetchApi("/upload/image",{method:"POST",body:s})).status)return this.isSaving=!1,qe.error(ke("Saving Failed")),qe.hideLoading(),void alert(`Error saving preview (${req.status}) ${req.statusText}`);await me.fetchApi("/easyuse/save/"+encodeURIComponent(`${this.type}/${this.name}`),{method:"POST",body:JSON.stringify({filename:i,type:"temp"}),headers:{"content-type":"application/json"}}).then((e=>{qe.success(ke("Saving Succeed")),qe.hideLoading()})),this.isSaving=!1,app.refreshComboInNodes()}})])]))));let t=this;this.imgDistance=(-660*this.imgCurrent).toString(),this.imgList.style.display="",this.imgList.style.transform="translate3d("+this.imgDistance+"px, 0px, 0px)",this.slides=this.imgList.querySelectorAll(".easyuse-preview-slide"),this.slideLeftButton=fe("button.left",{parent:this.imgWrapper,style:{display:e.images.length<=2?"none":"block"},innerHTML:'',onclick:()=>{e.images.length<=2||(t.imgList.classList.remove("no-transition"),0==t.imgCurrent?(t.imgCurrent=e.images.length/2-1,this.slides[this.slides.length-1].style.transform="translate3d("+(-660*(this.imgCurrent+1)).toString()+"px, 0px, 0px)",this.slides[this.slides.length-2].style.transform="translate3d("+(-660*(this.imgCurrent+1)).toString()+"px, 0px, 0px)",t.imgList.style.transform="translate3d(660px, 0px, 0px)",setTimeout((e=>{this.slides[this.slides.length-1].style.transform="translate3d(0px, 0px, 0px)",this.slides[this.slides.length-2].style.transform="translate3d(0px, 0px, 0px)",t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)",t.imgList.classList.add("no-transition")}),500)):(t.imgCurrent=t.imgCurrent-1,t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)"))}}),this.slideRightButton=fe("button.right",{parent:this.imgWrapper,style:{display:e.images.length<=2?"none":"block"},innerHTML:'',onclick:()=>{if(!(e.images.length<=2))if(t.imgList.classList.remove("no-transition"),t.imgCurrent>=e.images.length/2-1){t.imgCurrent=0;const i=e.images.length/2;this.slides[0].style.transform="translate3d("+(660*i).toString()+"px, 0px, 0px)",this.slides[1].style.transform="translate3d("+(660*i).toString()+"px, 0px, 0px)",t.imgList.style.transform="translate3d("+(-660*i).toString()+"px, 0px, 0px)",setTimeout((e=>{this.slides[0].style.transform="translate3d(0px, 0px, 0px)",this.slides[1].style.transform="translate3d(0px, 0px, 0px)",t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)",t.imgList.classList.add("no-transition")}),500)}else t.imgCurrent=t.imgCurrent+1,t.imgDistance=(-660*this.imgCurrent).toString(),t.imgList.style.transform="translate3d("+t.imgDistance+"px, 0px, 0px)"}})}return e.description&&fe("div",{parent:this.content,innerHTML:e.description,style:{marginTop:"10px"}}),e})).catch((e=>{this.imgWrapper.style.display="none",t.textContent="⚠️ "+e.message})).finally((e=>{}))}}class Ni extends Ei{async addInfo(){await this.addCivitaiInfo()}}class Ci extends Ei{getTagFrequency(){if(!this.metadata.ss_tag_frequency)return[];const e=JSON.parse(this.metadata.ss_tag_frequency),t={};for(const i in e){const s=e[i];for(const e in s)e in t?t[e]+=s[e]:t[e]=s[e]}return Object.entries(t).sort(((e,t)=>t[1]-e[1]))}getResolutions(){let e=[];if(this.metadata.ss_bucket_info){const t=JSON.parse(this.metadata.ss_bucket_info);if(null==t?void 0:t.buckets)for(const{resolution:i,count:s}of Object.values(t.buckets))e.push([s,`${i.join("x")} * ${s}`])}e=e.sort(((e,t)=>t[0]-e[0])).map((e=>e[1]));let t=this.metadata.ss_resolution;if(t){const i=t.split(","),s=i[0].replace("(",""),n=i[1].replace(")","");e.push(`${s.trim()}x${n.trim()} (Base res)`)}else(t=this.metadata["modelspec.resolution"])&&e.push(t+" (Base res");return e.length||e.push("⚠️ Unknown"),e}getTagList(e){return e.map((e=>fe("li.easyuse-model-tag",{dataset:{tag:e[0]},$:e=>{e.onclick=()=>{e.classList.toggle("easyuse-model-tag--selected")}}},[fe("p",{textContent:e[0]}),fe("span",{textContent:e[1]})])))}addTags(){let e,t=this.getTagFrequency();if(null==t?void 0:t.length){const i=t.length;let s;i>500&&(t=t.slice(0,500),e=fe("p",[fe("span",{textContent:"⚠️ Only showing first 500 tags "}),fe("a",{href:"#",textContent:`Show all ${i}`,onclick:()=>{s.replaceChildren(...this.getTagList(this.getTagFrequency())),e.remove()}})])),s=fe("ol.easyuse-model-tags-list",this.getTagList(t)),this.tags=fe("div",[s])}else this.tags=fe("p",{textContent:"⚠️ No tag frequency metadata found"});this.content.append(this.tags),e&&this.content.append(e)}async addInfo(){const e=this.addCivitaiInfo();this.addTags();const t=await e;t&&fe("div",{parent:this.content,innerHTML:t.description,style:{maxHeight:"250px",overflow:"auto"}})}createButtons(){const e=super.createButtons();function t(e,t){const i=fe("textarea",{parent:document.body,style:{position:"fixed"},textContent:t.map((e=>e.dataset.tag)).join(", ")});i.select();try{document.execCommand("copy"),e.target.dataset.text||(e.target.dataset.text=e.target.textContent),e.target.textContent="Copied "+t.length+" tags",setTimeout((()=>{e.target.textContent=e.target.dataset.text}),1e3)}catch(s){prompt("Copy to clipboard: Ctrl+C, Enter",text)}finally{document.body.removeChild(i)}}return e.unshift(fe("button",{type:"button",textContent:"Copy Selected",onclick:e=>{t(e,[...this.tags.querySelectorAll(".easyuse-model-tag--selected")])}}),fe("button",{type:"button",textContent:"Copy 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kSamplerInpainting","easy kSamplerDownscaleUnet","easy kSamplerSDTurbo"],Hi=["easy controlnetLoader","easy controlnetLoaderADV"],Yi=["easy instantIDApply","easy instantIDApplyADV"],Ki=["easy ipadapterApply","easy ipadapterApplyADV","easy ipadapterApplyFaceIDKolors","easy ipadapterStyleComposition"],Ji=["easy pipeIn","easy pipeOut","easy pipeEdit","easy pipeEditPrompt"],Bi=["easy XYPlot","easy XYPlotAdvanced"],Qi=["easy setNode"],$i=["RescaleCFG","LoraLoaderModelOnly","LoraLoader","FreeU","FreeU_v2",...Ki,...Qi],es={"easy seed":{from:{INT:[...Zi,"easy fullkSampler"]}},"easy positive":{from:{STRING:[...Xi]}},"easy negative":{from:{STRING:[...Xi]}},"easy wildcards":{from:{STRING:["easy showAnything",...Xi]}},"easy stylesSelector":{from:{STRING:["easy showAnything",...Xi]}},"easy promptConcat":{from:{STRING:["easy showAnything",...Xi]}},"easy promptReplace":{from:{STRING:["easy showAnything",...Xi]}},"easy fullLoader":{from:{PIPE_LINE:[...Zi,"easy 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sv3dLoader":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced","easy preSamplingDynamicCFG",...Ji,...Qi],MODEL:$i},to:{STRING:[...Xi]}},"easy preSampling":{from:{PIPE_LINE:[...qi,...Ji,...Hi,...Bi,...Qi]}},"easy preSamplingAdvanced":{from:{PIPE_LINE:[...qi,...Ji,...Hi,...Bi,...Qi]}},"easy preSamplingDynamicCFG":{from:{PIPE_LINE:[...qi,...Ji,...Hi,...Bi,...Qi]}},"easy preSamplingCustom":{from:{PIPE_LINE:[...qi,...Ji,...Hi,...Bi,...Qi]}},"easy preSamplingLayerDiffusion":{from:{PIPE_LINE:["easy kSamplerLayerDiffusion",...qi,...Ji,...Hi,...Bi,...Qi]}},"easy preSamplingNoiseIn":{from:{PIPE_LINE:[...qi,...Ji,...Hi,...Bi,...Qi]}},"easy fullkSampler":{from:{PIPE_LINE:[...Ji.reverse(),"easy preDetailerFix","easy preMaskDetailerFix",...Zi,...Qi]}},"easy kSampler":{from:{PIPE_LINE:[...Ji.reverse(),"easy preDetailerFix","easy preMaskDetailerFix","easy hiresFix",...Zi,...Qi]}},"easy controlnetLoader":{from:{PIPE_LINE:[...Zi,...Hi,...Yi,...Ji,...Qi]}},"easy controlnetLoaderADV":{from:{PIPE_LINE:[...Zi,...Hi,...Yi,...Ji,...Qi]}},"easy instantIDApply":{from:{PIPE_LINE:[...Zi,...Hi,...Yi,...Ji,...Qi],MODEL:$i},to:{COMBO:["easy promptLine"]}},"easy instantIDApplyADV":{from:{PIPE_LINE:[...Zi,...Hi,...Yi,...Ji,...Qi],MODEL:$i},to:{COMBO:["easy promptLine"]}},"easy ipadapterApply":{to:{COMBO:["easy promptLine"]}},"easy ipadapterApplyADV":{to:{STRING:["easy sliderControl",...Xi],COMBO:["easy promptLine"]}},"easy ipadapterStyleComposition":{to:{COMBO:["easy promptLine"]}},"easy preDetailerFix":{from:{PIPE_LINE:["easy detailerFix",...Ji,...Qi]},to:{PIPE_LINE:["easy ultralyticsDetectorPipe","easy samLoaderPipe","easy kSampler","easy fullkSampler"]}},"easy preMaskDetailerFix":{from:{PIPE_LINE:["easy detailerFix",...Ji,...Qi]}},"easy samLoaderPipe":{from:{PIPE_LINE:["easy preDetailerFix",...Ji,...Qi]}},"easy ultralyticsDetectorPipe":{from:{PIPE_LINE:["easy preDetailerFix",...Ji,...Qi]}},"easy cascadeLoader":{from:{PIPE_LINE:["easy fullCascadeKSampler","easy preSamplingCascade",...Hi,...Ji,...Qi],MODEL:$i.filter((e=>!Ki.includes(e)))}},"easy fullCascadeKSampler":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced",...Ji,...Qi]}},"easy preSamplingCascade":{from:{PIPE_LINE:["easy cascadeKSampler",...Ji,...Qi]}},"easy cascadeKSampler":{from:{PIPE_LINE:["easy preSampling","easy preSamplingAdvanced",...Ji,...Qi]}},"easy pipeEdit":{from:{PIPE_LINE:[...Zi,...Hi,...Yi,...Qi]},to:{PIPE_LINE:[...Zi,...Hi,...Yi,...Ji,...Qi]}},"easy pipeEditPrompt":{from:{PIPE_LINE:[...Zi,...Hi,...Yi,...Qi]},to:{PIPE_LINE:[...Zi,...Hi,...Yi,...Ji,...Qi]}}};class ts extends Error{constructor(e="Attempted to access LGraph reference that was null or undefined.",t){super(e,{cause:t}),this.name="NullGraphError"}}he.registerExtension({name:"Comfy.EasyUse.Suggestions",async setup(e){const t=LGraphCanvas.prototype.createDefaultNodeForSlot;LGraphCanvas.prototype.createDefaultNodeForSlot=function(e){var i;const s=Object.assign({nodeFrom:null,slotFrom:null,nodeTo:null,slotTo:null,position:[],nodeType:null,posAdd:[0,0],posSizeFix:[0,0]},e||{}),n=this,a=s.nodeFrom&&null!==s.slotFrom,o=!a&&s.nodeTo&&null!==s.slotTo,l=a?s.nodeFrom:s.nodeTo;if(!l||!Object.keys(es).includes(l.type))return t.call(this,e);if(!a&&!o)return!1;if(!s.nodeType)return!1;const r=a?s.nodeFrom:s.nodeTo;if(!r)throw new TypeError("nodeX was null when creating default node for slot.");const d=r.type;let u=a?s.slotFrom:s.slotTo,A=!1;switch(typeof u){case"string":A=a?r.findOutputSlot(u,!1):r.findInputSlot(u,!1),u=a?r.outputs[u]:r.inputs[u];break;case"object":if(null===u)return!1;A=a?r.findOutputSlot(u.name):r.findInputSlot(u.name);break;case"number":A=u,u=a?r.outputs[u]:r.inputs[u];break;default:return!1}var c=u.type==LiteGraph.EVENT?"_event_":u.type,p=a?LiteGraph.slot_types_default_out:LiteGraph.slot_types_default_in;if(null==p?void 0:p[c]){let e=!1;const t=a?"from":"to";if(es[d]&&es[d][t]&&(null==(i=es[d][t][c])?void 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CustomEvent("connect-new-default-node",{detail:e,cancelable:!0})))return!0;if(a){if(!s.nodeFrom)throw new TypeError("createDefaultNodeForSlot - nodeFrom was null");s.nodeFrom.connectByType(A,f,c)}else{if(!s.nodeTo)throw new TypeError("createDefaultNodeForSlot - nodeTo was null");s.nodeTo.connectByTypeOutput(A,f,c)}return!0}}}return!1};let i=LGraphCanvas.prototype.showConnectionMenu;LGraphCanvas.prototype.showConnectionMenu=function(e){var t;const s=Object.assign({nodeFrom:null,slotFrom:null,nodeTo:null,slotTo:null,e:void 0,allow_searchbox:this.allow_searchbox,showSearchBox:this.showSearchBox},e||{}),n=this,{graph:a}=this,{afterRerouteId:o}=s,l=s.nodeFrom&&s.slotFrom,r=!l&&s.nodeTo&&s.slotTo,d=l?s.nodeFrom:s.nodeTo;if(!d||!Object.keys(es).includes(d.type))return i.call(this,e);if(!l&&!r)return!1;const u=l?s.nodeFrom:s.nodeTo;if(!u)throw new TypeError("nodeX was null when creating default node for slot.");let A=l?s.slotFrom:s.slotTo,c=!1;switch(typeof A){case"string":c=l?u.findOutputSlot(A,!1):u.findInputSlot(A,!1),A=l?u.outputs[A]:u.inputs[A];break;case"object":if(null===A)return;c=l?u.findOutputSlot(A.name):u.findInputSlot(A.name);break;case"number":c=A,A=l?u.outputs[A]:u.inputs[A];break;default:return!1}const p=["Add Node","Add Reroute",null];s.allow_searchbox&&p.push("Search",null);var h=A.type==LiteGraph.EVENT?"_event_":A.type,m=l?LiteGraph.slot_types_default_out:LiteGraph.slot_types_default_in,f=u.type;if(m&&m[h]){const e=l?"from":"to";if(es[f]&&es[f][e]&&(null==(t=es[f][e][h])?void 0:t.length)>0)for(var g in es[f][e][h])p.push(es[f][e][h][g]);else if("object"==typeof m[h]||"array"==typeof m[h])for(var g in m[h])p.push(m[h][g]);else p.push(m[h])}var v=new LiteGraph.ContextMenu(p,{event:s.e,extra:A,title:(A&&""!=A.name?A.name+(h?" | ":""):"")+(A&&h?h:""),callback:function(e,t,i){var r,d;switch(e){case"Add Node":LGraphCanvas.onMenuAdd(null,null,i,v,(function(e){var t,i;e&&(l?null==(t=s.nodeFrom)||t.connectByType(c,e,h,{afterRerouteId:o}):null==(i=s.nodeTo)||i.connectByTypeOutput(c,e,h,{afterRerouteId:o}))}));break;case"Add Reroute":const u=l?s.nodeFrom:s.nodeTo,p=t.extra;if(!a)throw new ts;if(!u)throw new TypeError("Cannot add reroute: node was null");if(!p)throw new TypeError("Cannot add reroute: slot was null");if(!s.e)throw new TypeError("Cannot add reroute: CanvasPointerEvent was null");if(!u.connectFloatingReroute([s.e.canvasX,s.e.canvasY],p,o))throw new Error("Failed to create reroute");n.dirty_canvas=!0,n.dirty_bgcanvas=!0;break;case"Search":l?s.showSearchBox(i,{node_from:s.nodeFrom,slot_from:A,type_filter_in:h}):s.showSearchBox(i,{node_to:s.nodeTo,slot_from:A,type_filter_out:h});break;default:const m={position:[(null==(r=s.e)?void 0:r.canvasX)??0,(null==(d=s.e)?void 0:d.canvasY)??0],nodeType:e,afterRerouteId:o};n.createDefaultNodeForSlot(Object.assign(s,m))}}});return v}}}),he.registerExtension({name:"Comfy.EasyUse.TimeTaken",setup(){let e=new Map,t=0;me.addEventListener("execution_start",(e=>{graph&&graph._nodes.forEach((e=>{e.executionDuration&&delete e.executionDuration}))})),me.addEventListener("executing",(i=>{if(!_e("EasyUse.Nodes.Runtime",null))return;const s=(null==i?void 0:i.node)||(null==i?void 0:i.detail)||null,n=e.get(t);if(e.delete(t),t&&n){const e=Date.now()-n,i=St(t);i&&(i.executionDuration||(i.executionDuration=0),i.executionDuration=i.executionDuration+e/1e3)}t=s,e.set(s,Date.now())}))},beforeRegisterNodeDef(e,t){const i=e.prototype.onDrawForeground;e.prototype.onDrawForeground=function(...e){const[t]=e;return function(e,t){if(!t)return;t=parseFloat(t).toFixed(3)+ke("s"),e.save(),e.fillStyle=LiteGraph.NODE_DEFAULT_BGCOLOR,function(e,t,i,s,n,a){e.beginPath(),e.moveTo(t+a,i),e.lineTo(t+s-a,i),e.arcTo(t+s,i,t+s,i+a,a),e.lineTo(t+s,i+n-a),e.arcTo(t+s,i+n,t+s-a,i+n,a),e.lineTo(t+a,i+n),e.arcTo(t,i+n,t,i+n-a,a),e.lineTo(t,i+a),e.arcTo(t,i,t+a,i,a),e.closePath()}(e,0,-LiteGraph.NODE_TITLE_HEIGHT-20,e.measureText(t).width+10,LiteGraph.NODE_TITLE_HEIGHT-10,4),e.fill(),function(e,t,i,s,n="#000",a=12,o="Inter"){e.font=`${a}px ${o}`,e.fillStyle=n,e.fillText(t,i,s)}(e,t,8,-LiteGraph.NODE_TITLE_HEIGHT-6,LiteGraph.NODE_TITLE_COLOR),e.restore()}(t,this.executionDuration),null==i?void 0:i.apply(this,e)}}});let is=null;he.registerExtension({name:"Comfy.EasyUse.HotKeys",setup(){if(void 0!==L){L("up,down,left,right",(function(e,t){var i,s,n,a,o,l,r,d,u,A,c,p,h,m,f;e.preventDefault();if(!_e("EasyUse.Hotkeys.JumpNearestNodes",null))return;const g=Lt();if(0===g.length)return;const v=g[0];switch(t.key){case"up":case"left":let e=null;if(It(v)){const e=null==(i=v.widgets_values)?void 0:i[0],t=null==(s=v.graph)?void 0:s._nodes,n=null==t?void 0:t.find((t=>{var i;if(Tt(t)){if((null==(i=t.widgets_values)?void 0:i[0])===e)return t}return null}));n&&kt(n)}else if((null==(n=v.inputs)?void 0:n.length)>0){for(let t=0;t{var i;if(It(t)){if((null==(i=t.widgets_values)?void 0:i[0])===e)return t}return null}));i&&kt(i)}else if((null==(A=v.outputs)?void 0:A.length)>0){for(let e=0;e0&&v.outputs[e].links[0]){t=v.outputs[e].links[0];break}if(t){const e=null==(p=v.graph)?void 0:p.links;if(e[t]){const i=null==(h=e[t])?void 0:h.target_id,s=null==(f=null==(m=v.graph)?void 0:m._nodes_by_id)?void 0:f[i];s&&kt(s)}}}}})),L("shift+up,shift+down,shift+left,shift+right,shift+alt+⌘+left,shift+alt+⌘+right,shift+alt+ctrl+left,shift+alt+ctrl+right",(function(e,t){e.preventDefault();if(!_e("EasyUse.Hotkeys.AlignSelectedNodes",null))return;const i=Lt();if(i.length<=1)return;const s=i;switch(t.key){case"shift+up":LGraphCanvas.alignNodes(s,"top",s[0]);break;case"shift+down":LGraphCanvas.alignNodes(s,"bottom",s[0]);break;case"shift+left":LGraphCanvas.alignNodes(s,"left",s[0]);break;case"shift+right":LGraphCanvas.alignNodes(s,"right",s[0]);break;case"shift+alt+ctrl+left":case"shift+alt+⌘+left":Ut(s,"horizontal");break;case"shift+alt+ctrl+right":case"shift+alt+⌘+right":Ut(s,"vertical")}is||(is=He()),is&&is.update()})),L("shift+⌘+left,shift+⌘+right,shift+ctrl+left,shift+ctrl+right",(function(e,t){e.preventDefault();if(!_e("EasyUse.Hotkeys.NormalizeSelectedNodes",null))return;const i=Lt();if(i.length<=1)return;const s=i;switch(t.key){case"shift+ctrl+left":case"shift+⌘+left":Wt(s,"width");break;case"shift+ctrl+right":case"shift+⌘+right":Wt(s,"height")}is||(is=He()),is&&is.update()})),L("shift+g",(function(e,t){e.preventDefault();_e("EasyUse.Hotkeys.AddGroup",null)&&(ns(),is||(is=He()),is&&is.update())})),L("shift+r",(function(e,t){e.preventDefault();_e("EasyUse.Hotkeys.cleanVRAMused",null)&&gi()})),L("shift+m",(function(e,t){var i,s,n;if(!_e("EasyUse.Hotkeys.toggleNodesMap",null))return;let a=(null==(i=he.extensionManager)?void 0:i.sidebarTab)||he.extensionManager,o=(null==(s=he.extensionManager.sidebarTab)?void 0:s.activeSidebarTabId)||(null==(n=he.extensionManager)?void 0:n.activeSidebarTab);a.activeSidebarTabId=o==De?null:De}));const e=[];Array.from(Array(10).keys()).forEach((t=>e.push(`alt+${t}`))),L(e.join(","),(async function(e,t){e.preventDefault();if(!_e("EasyUse.Hotkeys.NodesTemplate",null))return;const i=t.key;let s=parseInt(i.split("+")[1]);const n=await me.getUserData("comfy.templates.json");let a=null;if(200==n.status)try{a=await n.json()}catch(l){qe.error(ke("Get Node Templates File Failed"))}else localStorage["Comfy.NodeTemplates"]?a=JSON.parse(localStorage["Comfy.NodeTemplates"]):qe.warn(ke("No Node Templates Found"));if(!a)return void qe.warn(ke("No Node Templates Found"));s=0===s?9:s-1;const o=a[s];if(o)try{const e=(null==o?void 0:o.name)||"Group",t=(null==o?void 0:o.data)?JSON.parse(o.data):[];ss((async()=>{await be.registerFromWorkflow(t.groupNodes,{}),localStorage.litegrapheditor_clipboard=o.data,he.canvas.pasteFromClipboard(),t.groupNodes||ns(e)}))}catch(l){qe.error(l)}else qe.warn(ke("Node template with {key} not set").replace("{key}",i))}));const t=async function(e){if(("b"===e.key||"m"==e.key)&&(e.metaKey||e.ctrlKey)){if(0===Lt().length)return;is||(is=He()),is&&is.update()}};window.addEventListener("keydown",t,!0)}}});const ss=async e=>{const t=localStorage.litegrapheditor_clipboard;await e(),localStorage.litegrapheditor_clipboard=t},ns=e=>{const t=Lt();if(0===t.length)return;const i=t;let s=new LiteGraph.LGraphGroup;s.title=e||"Group",((e,t=[],i=20)=>{var s,n,a,o,l,r,d,u,A,c;for(var p of(n=a=o=l=-1,r=d=u=A=-1,[e._nodes,t]))for(var h in p)r=(c=p[h]).pos[0],d=c.pos[1],u=c.pos[0]+c.size[0],A=c.pos[1]+c.size[1],"Reroute"!=c.type&&(d-=LiteGraph.NODE_TITLE_HEIGHT),(null==(s=c.flags)?void 0:s.collapsed)&&(A=d+LiteGraph.NODE_TITLE_HEIGHT,(null==c?void 0:c._collapsed_width)&&(u=r+Math.round(c._collapsed_width))),(-1==n||ro)&&(o=u),(-1==l||A>l)&&(l=A);a-=Math.round(1.4*e.font_size),e.pos=[n-i,a-i],e.size=[o-n+2*i,l-a+2*i]})(s,i),he.canvas.graph.add(s)},as="easy imageChooser",os=e=>(null==e?void 0:e.comfyClass)===as;he.registerExtension({name:"Comfy.EasyUse.imageChooser",setup(){const e=me.interrupt;me.interrupt=function(){var t;(null==(t=he.graph)?void 0:t._nodes_by_id)&&(()=>{var e;Object.values(null==(e=he.graph)?void 0:e._nodes_by_id).forEach((e=>{os(e)&&e.cancelSelection("interrupt")}))})(),e.apply(this,arguments)},me.addEventListener("easyuse-image-choose",(e=>{var t;const i=e.detail,s=he.graph._nodes_by_id[i.id];if(!s||!os(s))return;s.setExecutingState(!1),s.selected_images.clear(),s.anti_selected.clear(),s.isWaitingSelection=["Always Pause","Keep Last Selection"].includes(s.currentMode),s.isCanceling=!1,s.overIndex=null;const n=(null==(t=i.urls)?void 0:t.map(((e,t)=>({index:t,filename:e.filename,subfolder:e.subfolder,type:e.type}))))||[];s.imageData=n,s.imgs=[],n.forEach((e=>{const t=new Image;t.onload=()=>{he.graph.setDirtyCanvas(!0)},t.src=`/view?filename=${encodeURIComponent(e.filename)}${(e=>`&type=${e.type}&subfolder=${e.subfolder}&rand=${e.random}`)(e)}`,s.imgs.push(t)})),s.update(),"Always Pause"===s.currentMode&&s.setExecutingState(!0)})),me.addEventListener("easyuse-image-keep-selection",(e=>{const t=e.detail,i=he.graph._nodes_by_id[t.id];i&&os(i)&&(i.isWaitingSelection=!1,i.isCancelling=!1,t.selected&&Array.isArray(t.selected)&&(i.selected_images.clear(),t.selected.forEach((e=>{i.selected_images.add(e)}))),i.update())}))},beforeRegisterNodeDef(e,t){if(t.name==as){e.prototype.confirmSelection=function(){me.fetchApi("/easyuse/image_chooser_message",{method:"POST",body:JSON.stringify({node_id:this.id.toString(),action:"select",selected:Array.from(this.selected_images)})}).catch((e=>{})).finally((e=>{this.isWaitingSelection=!1,this.update(),this.setExecutingState(!1)}))},e.prototype.cancelSelection=function(e){me.fetchApi("/easyuse/image_chooser_message",{method:"POST",body:JSON.stringify({node_id:this.id.toString(),action:"cancel",selected:[]})}).catch((e=>{})).finally((e=>{this.isCanceling=!1,this.isWaitingSelection=!1,this.update(),this.setExecutingState(!1)}))},e.prototype.updateWidgets=function(){var e;if(!this.confirm_button_widget||!this.cancel_button_widget)return;const t=this.selected_images.size,i=(null==(e=this.imgs)?void 0:e.length)||0;if(this.isCanceling)this.confirm_button_widget.name=ke("Canceling"),this.cancel_button_widget.name="",this.confirm_button_widget.disabled=!0,this.cancel_button_widget.disabled=!0;else if(this.isWaitingSelection)t>0?(this.confirm_button_widget.name=t>1?`${ke("Progress selected")} (${t}/${i})`:`${ke("Progress selected image")}`,this.confirm_button_widget.disabled=!1):(this.confirm_button_widget.name=ke("Please select images to continue"),this.confirm_button_widget.disabled=!0),this.cancel_button_widget.name=ke("Cancel current run"),this.cancel_button_widget.disabled=!1;else{const e={"Always Pause":ke("Waiting for selection..."),"Keep Last Selection":ke("Using last selection")};this.confirm_button_widget.name=(t>0&&"Always Pause"==this.currentMode?ke("Please run first"):null==e?void 0:e[this.currentMode])||ke("Unknown 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i=e.prototype.onNodeCreated;e.prototype.onNodeCreated=function(){null==i||i.call(this,arguments),this.selected_images=new Set,this.anti_selected=new Set,this.currentMode="Always Pause",this.isWaitingSelection=!1,this.isCanceling=!1,this.imageData=[],this.imgs,this.confirm_button_widget=this.addWidget("button","","",(()=>{this.confirmSelection()})),this.cancel_button_widget=this.addWidget("button","","",(()=>{this.cancelSelection()})),this.confirm_button_widget.serialize=!1,this.cancel_button_widget.serialize=!1,setTimeout((e=>{const t=yt(this,"mode");t.callback=e=>{requestAnimationFrame((t=>{this.currentMode=e,this.updateWidgets(),this.setDirtyCanvas(!0,!0)}))},this.currentMode=t.value||"Always Pause",this.updateWidgets()}),1),Object.defineProperty(this.confirm_button_widget,"clicked",{get:function(){return this._clicked},set:function(e){this._clicked=e&&""!=this.name}}),Object.defineProperty(this.cancel_button_widget,"clicked",{get:function(){return 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t=Array.prototype.indexOf.call(this.dialog_div.querySelectorAll(".easyuse-account-dialog-item"),e.target.parentNode);As.length<=1?qe.error(ke("At least one account is required")):(As.splice(t,1),this.dialog_div.removeChild(e.target.parentNode))}})])}show(e){As.forEach(((t,i)=>{this.lists.push(this.addItem(i,e))})),this.dialog_div=fe("div.easyuse-account-dialog",this.lists),super.show(fe("div.easyuse-account-dialog-main",[fe("div",[fe("a",{href:"https://platform.stability.ai/account/keys",target:"_blank",textContent:ke("Getting Your APIKEY")})]),this.dialog_div]))}createButtons(){const e=super.createButtons();return e.unshift(fe("button",{type:"button",textContent:ke("Save Account Info"),onclick:e=>{let t=!0;for(let i=0;i{200==e.status?qe.success(ke("Save Succeed")):qe.error(ke("Save Failed"))}))}else qe.error(ke("APIKEY is not Empty"))}})),e.unshift(fe("button",{type:"button",textContent:ke("Add Account"),onclick:e=>{const t="Account 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