feat: KSamplerAdvancedProgress

improve: KSamplerProgress - add ommit_start_latent parameter

__init__.py

@@ -7,7 +7,7 @@
 
 import importlib
 
-print(f"### Loading: ComfyUI-Inspire-Pack (V0.27.2)")
+print(f"### Loading: ComfyUI-Inspire-Pack (V0.28)")
 
 node_list = [
     "lora_block_weight",

sampler_nodes.py

@@ -18,6 +18,7 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
                      "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}),
+                     "ommit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
                      }
                 }
 
@@ -26,12 +27,16 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
     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):
+    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, interval, ommit_start_latent):
         adv_steps = int(steps / denoise)
 
         sampler = a1111_compat.KSamplerAdvanced_inspire()
 
-        result = []
+        if ommit_start_latent:
+            result = []
+        else:
+            result = [latent_image['samples']]
+
         for i in range(0, adv_steps+1):
             add_noise = i == 0
             return_with_leftover_noise = i != adv_steps
@@ -48,10 +53,71 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
         return (latent_image, result)
 
 
+class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
+                     "noise_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, "step":0.5, "round": 0.01}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                     "positive": ("CONDITIONING", ),
+                     "negative": ("CONDITIONING", ),
+                     "latent_image": ("LATENT", ),
+                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
+                     "noise_mode": (["GPU(=A1111)", "CPU"],),
+                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
+                     "interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
+                     "ommit_start_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
+                     },
+                "optional": {"prev_progress_latent_opt": ("LATENT",), }
+                }
+
+    FUNCTION = "sample"
+
+    CATEGORY = "InspirePack/analysis"
+
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("latent", "progress_latent")
+
+    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise, interval, ommit_start_latent, prev_progress_latent_opt=None):
+        sampler = a1111_compat.KSamplerAdvanced_inspire()
+
+        if ommit_start_latent:
+            result = []
+        else:
+            result = [latent_image['samples']]
+
+        for i in range(start_at_step, end_at_step+1):
+            cur_add_noise = i == start_at_step and add_noise
+            cur_return_with_leftover_noise = i != steps or return_with_leftover_noise
+            latent_image = sampler.sample(model, cur_add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, i, i+1, noise_mode, cur_return_with_leftover_noise)[0]
+            print(f"{i}, {i+1}")
+            if i % interval == 0 or i == steps:
+                result.append(latent_image['samples'])
+
+        if len(result) > 0:
+            result = torch.cat(result)
+            result = {'samples': result}
+        else:
+            result = latent_image
+
+        if prev_progress_latent_opt is not None:
+            result['samples'] = torch.cat((prev_progress_latent_opt['samples'], result['samples']), dim=0)
+
+        return (latent_image, result)
+
+
 NODE_CLASS_MAPPINGS = {
     "KSamplerProgress //Inspire": KSampler_progress,
+    "KSamplerAdvancedProgress //Inspire": KSamplerAdvanced_progress,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "KSamplerProgress //Inspire": "KSampler Progress (Inspire)",
+    "KSamplerAdvancedProgress //Inspire": "KSampler Advanced Progress (Inspire)",
 }