feat: add KSampler_progress

README.md

@@ -55,6 +55,8 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
 * Image Util
   * `Load Images From Dir (Inspire)`: This is almost same as `LoadImagesFromDirectory` of [ComfyUI-Advanced-Controlnet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet). This is just a modified version. Just note that this node forcibly normalizes the size of the loaded image to match the size of the first image, even if they are not the same size, to create a batch image.  
 
+* KSampler Progress - In KSampler, the sampling process generates latent batches. By using `Video Combine` node from [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite), you can create a video from the progress.
+
 ## Credits
 
 ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.

__init__.py

@@ -7,7 +7,7 @@
 
 import importlib
 
-print(f"### Loading: ComfyUI-Inspire-Pack (V0.23.1)")
+print(f"### Loading: ComfyUI-Inspire-Pack (V0.24)")
 
 node_list = [
     "lora_block_weight",
@@ -17,6 +17,7 @@ node_list = [
     "inspire_server",
     "image_util",
     "regional_nodes",
+    "sampler_nodes",
 ]
 
 NODE_CLASS_MAPPINGS = {}

sampler_nodes.py

@@ -0,0 +1,57 @@
+import torch
+from . import a1111_compat
+import comfy
+
+class KSampler_progress(a1111_compat.KSampler_inspire):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                     "positive": ("CONDITIONING", ),
+                     "negative": ("CONDITIONING", ),
+                     "latent_image": ("LATENT", ),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                     "noise_mode": (["GPU(=A1111)", "CPU"],),
+                     "interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
+                     }
+                }
+
+    CATEGORY = "InspirePack/analysis"
+
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("latent", "progress_latent")
+
+    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, interval):
+        adv_steps = int(steps / denoise)
+
+        sampler = a1111_compat.KSamplerAdvanced_inspire()
+
+        result = []
+        for i in range(0, adv_steps+1):
+            add_noise = i == 0
+            return_with_leftover_noise = i != adv_steps
+            latent_image = sampler.sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, i, i+1, noise_mode, return_with_leftover_noise)[0]
+            if i % interval == 0 or i == adv_steps:
+                result.append(latent_image['samples'])
+
+        if len(result) > 0:
+            result = torch.cat(result)
+            result = {'samples': result}
+        else:
+            result = latent_image
+
+        return (latent_image, result)
+
+
+NODE_CLASS_MAPPINGS = {
+    "KSamplerProgress //Inspire": KSampler_progress,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "KSamplerProgress //Inspire": "KSampler Progress (Inspire)",
+}