Merge remote-tracking branch 'origin/master' into menu-save-and-anchor

comfy/samplers.py

@@ -242,7 +242,10 @@ def ddim_scheduler(model, steps):
     sigs = []
     ddim_timesteps = make_ddim_timesteps(ddim_discr_method="uniform", num_ddim_timesteps=steps, num_ddpm_timesteps=model.inner_model.inner_model.num_timesteps, verbose=False)
     for x in range(len(ddim_timesteps) - 1, -1, -1):
-        sigs.append(model.t_to_sigma(torch.tensor(ddim_timesteps[x])))
+        ts = ddim_timesteps[x]
+        if ts > 999:
+            ts = 999
+        sigs.append(model.t_to_sigma(torch.tensor(ts)))
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
@@ -373,7 +376,7 @@ class KSampler:
 
     def set_steps(self, steps, denoise=None):
         self.steps = steps
-        if denoise is None:
+        if denoise is None or denoise > 0.9999:
             self.sigmas = self._calculate_sigmas(steps)
         else:
             new_steps = int(steps/denoise)

comfy/sd.py

@@ -439,9 +439,14 @@ class VAE:
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         try:
-            samples = samples_in.to(self.device)
+            free_memory = model_management.get_free_memory(self.device)
-            pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples)
+            batch_number = int((free_memory * 0.7) / (2562 * samples_in.shape[2] * samples_in.shape[3] * 64))
-            pixel_samples = torch.clamp((pixel_samples + 1.0) / 2.0, min=0.0, max=1.0)
+            batch_number = max(1, batch_number)
+
+            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
+            for x in range(0, samples_in.shape[0], batch_number):
+                samples = samples_in[x:x+batch_number].to(self.device)
+                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(1. / self.scale_factor * samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             pixel_samples = self.decode_tiled_(samples_in)

execution.py

@@ -10,7 +10,7 @@ import gc
 import torch
 import nodes
 
-def get_input_data(inputs, class_def, outputs={}, prompt={}, extra_data={}):
+def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
     valid_inputs = class_def.INPUT_TYPES()
     input_data_all = {}
     for x in inputs:
@@ -34,6 +34,8 @@ def get_input_data(inputs, class_def, outputs={}, prompt={}, extra_data={}):
             if h[x] == "EXTRA_PNGINFO":
                 if "extra_pnginfo" in extra_data:
                     input_data_all[x] = extra_data['extra_pnginfo']
+            if h[x] == "UNIQUE_ID":
+                input_data_all[x] = unique_id
     return input_data_all
 
 def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
@@ -55,7 +57,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
             if input_unique_id not in outputs:
                 executed += recursive_execute(server, prompt, outputs, input_unique_id, extra_data)
 
-    input_data_all = get_input_data(inputs, class_def, outputs, prompt, extra_data)
+    input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
     if server.client_id is not None:
         server.last_node_id = unique_id
         server.send_sync("executing", { "node": unique_id }, server.client_id)
@@ -96,7 +98,7 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
         if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
             is_changed_old = old_prompt[unique_id]['is_changed']
         if 'is_changed' not in prompt[unique_id]:
-            input_data_all = get_input_data(inputs, class_def, outputs)
+            input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
             if input_data_all is not None:
                 is_changed = class_def.IS_CHANGED(**input_data_all)
                 prompt[unique_id]['is_changed'] = is_changed

notebooks/comfyui_colab.ipynb

@@ -47,7 +47,7 @@
         "  !git pull\n",
         "\n",
         "!echo -= Install dependencies =-\n",
-        "!pip -q install xformers -r requirements.txt"
+        "!pip -q install xformers==0.0.16 -r requirements.txt"
       ]
     },
     {

web/scripts/app.js

@@ -721,6 +721,8 @@ class ComfyApp {
 	 * @param {*} graphData A serialized graph object
 	 */
 	loadGraphData(graphData) {
+		this.clean();
+
 		if (!graphData) {
 			graphData = defaultGraph;
 		}
@@ -903,6 +905,13 @@ class ComfyApp {
 			}
 		}
 	}
+
+	/**
+	 * Clean current state
+	 */
+	clean() {
+		this.nodeOutputs = {};
+	}
 }
 
 export const app = new ComfyApp();

web/scripts/ui.js

@@ -485,7 +485,10 @@ export class ComfyUI {
 			}),
 			$el("button", { textContent: "Load", onclick: () => fileInput.click() }),
 			$el("button", { textContent: "Refresh", onclick: () => app.refreshComboInNodes() }),
-			$el("button", { textContent: "Clear", onclick: () => app.graph.clear() }),
+			$el("button", { textContent: "Clear", onclick: () => {
+				app.clean();
+				app.graph.clear();
+			}}),
 			$el("button", { textContent: "Load Default", onclick: () => app.loadGraphData() }),
 		]);