Merge remote-tracking branch 'origin/master' into group-nodes

comfy/controlnet.py

@@ -33,7 +33,7 @@ class ControlBase:
         self.cond_hint_original = None
         self.cond_hint = None
         self.strength = 1.0
-        self.timestep_percent_range = (1.0, 0.0)
+        self.timestep_percent_range = (0.0, 1.0)
         self.timestep_range = None
 
         if device is None:
@@ -42,7 +42,7 @@ class ControlBase:
         self.previous_controlnet = None
         self.global_average_pooling = False
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range

comfy/k_diffusion/sampling.py

@@ -750,3 +750,61 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
         if sigmas[i + 1] > 0:
             x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
     return x
+
+
+
+@torch.no_grad()
+def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    s_end = sigmas[-1]
+    for i in trange(len(sigmas) - 1, disable=disable):
+        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        eps = torch.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        if sigmas[i + 1] == s_end:
+            # Euler method
+            x = x + d * dt
+        elif sigmas[i + 2] == s_end:
+
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+
+            w = 2 * sigmas[0]
+            w2 = sigmas[i+1]/w
+            w1 = 1 - w2
+
+            d_prime = d * w1 + d_2 * w2
+
+
+            x = x + d_prime * dt
+
+        else:
+            # Heun++
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+            dt_2 = sigmas[i + 2] - sigmas[i + 1]
+
+            x_3 = x_2 + d_2 * dt_2
+            denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
+            d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
+
+            w = 3 * sigmas[0]
+            w2 = sigmas[i + 1] / w
+            w3 = sigmas[i + 2] / w
+            w1 = 1 - w2 - w3
+
+            d_prime = w1 * d + w2 * d_2 + w3 * d_3
+            x = x + d_prime * dt
+    return x

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -255,7 +255,10 @@ def apply_control(h, control, name):
     if control is not None and name in control and len(control[name]) > 0:
         ctrl = control[name].pop()
         if ctrl is not None:
-            h += ctrl
+            try:
+                h += ctrl
+            except:
+                print("warning control could not be applied", h.shape, ctrl.shape)
     return h
 
 class UNetModel(nn.Module):

comfy/model_sampling.py

@@ -76,5 +76,10 @@ class ModelSamplingDiscrete(torch.nn.Module):
         return log_sigma.exp()
 
     def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return torch.tensor(999999999.9)
+        if percent >= 1.0:
+            return torch.tensor(0.0)
+        percent = 1.0 - percent
         return self.sigma(torch.tensor(percent * 999.0))
 

comfy/samplers.py

@@ -220,6 +220,8 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
                         transformer_options["patches"] = patches
 
                 transformer_options["cond_or_uncond"] = cond_or_uncond[:]
+                transformer_options["sigmas"] = timestep
+
                 c['transformer_options'] = transformer_options
 
                 if 'model_function_wrapper' in model_options:
@@ -518,7 +520,7 @@ class UNIPCBH2(Sampler):
     def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
         return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
 
-KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
+KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
 

comfy/sd1_clip.py

@@ -173,9 +173,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         if getattr(self.transformer, self.inner_name).final_layer_norm.weight.dtype != torch.float32:
             precision_scope = torch.autocast
         else:
-            precision_scope = lambda a, b: contextlib.nullcontext(a)
+            precision_scope = lambda a, dtype: contextlib.nullcontext(a)
 
-        with precision_scope(model_management.get_autocast_device(device), torch.float32):
+        with precision_scope(model_management.get_autocast_device(device), dtype=torch.float32):
             attention_mask = None
             if self.enable_attention_masks:
                 attention_mask = torch.zeros_like(tokens)

comfy_extras/nodes_model_advanced.py

@@ -66,6 +66,11 @@ class ModelSamplingDiscreteLCM(torch.nn.Module):
         return log_sigma.exp()
 
     def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return torch.tensor(999999999.9)
+        if percent >= 1.0:
+            return torch.tensor(0.0)
+        percent = 1.0 - percent
         return self.sigma(torch.tensor(percent * 999.0))
 
 

nodes.py

@@ -248,8 +248,8 @@ class ConditioningSetTimestepRange:
         c = []
         for t in conditioning:
             d = t[1].copy()
-            d['start_percent'] = 1.0 - start
+            d['start_percent'] = start
-            d['end_percent'] = 1.0 - end
+            d['end_percent'] = end
             n = [t[0], d]
             c.append(n)
         return (c, )
@@ -685,7 +685,7 @@ class ControlNetApplyAdvanced:
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net
 

web/extensions/core/colorPalette.js

@@ -174,6 +174,213 @@ const colorPalettes = {
 				"tr-odd-bg-color": "#073642",
 			}
 		},
+	},
+	"arc": {
+		"id": "arc",
+		"name": "Arc",
+		"colors": {
+			"node_slot": {
+			  "BOOLEAN": "",
+			  "CLIP": "#eacb8b",
+			  "CLIP_VISION": "#A8DADC",
+			  "CLIP_VISION_OUTPUT": "#ad7452",
+			  "CONDITIONING": "#cf876f",
+			  "CONTROL_NET": "#00d78d",
+			  "CONTROL_NET_WEIGHTS": "",
+			  "FLOAT": "",
+			  "GLIGEN": "",
+			  "IMAGE": "#80a1c0",
+			  "IMAGEUPLOAD": "",
+			  "INT": "",
+			  "LATENT": "#b38ead",
+			  "LATENT_KEYFRAME": "",
+			  "MASK": "#a3bd8d",
+			  "MODEL": "#8978a7",
+			  "SAMPLER": "",
+			  "SIGMAS": "",
+			  "STRING": "",
+			  "STYLE_MODEL": "#C2FFAE",
+			  "T2I_ADAPTER_WEIGHTS": "",
+			  "TAESD": "#DCC274",
+			  "TIMESTEP_KEYFRAME": "",
+			  "UPSCALE_MODEL": "",
+			  "VAE": "#be616b"
+			},
+			"litegraph_base": {
+			  "BACKGROUND_IMAGE": "data:image/png;base64,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",
+			  "CLEAR_BACKGROUND_COLOR": "#2b2f38",
+			  "NODE_TITLE_COLOR": "#b2b7bd",
+			  "NODE_SELECTED_TITLE_COLOR": "#FFF",
+			  "NODE_TEXT_SIZE": 14,
+			  "NODE_TEXT_COLOR": "#AAA",
+			  "NODE_SUBTEXT_SIZE": 12,
+			  "NODE_DEFAULT_COLOR": "#2b2f38",
+			  "NODE_DEFAULT_BGCOLOR": "#242730",
+			  "NODE_DEFAULT_BOXCOLOR": "#6e7581",
+			  "NODE_DEFAULT_SHAPE": "box",
+			  "NODE_BOX_OUTLINE_COLOR": "#FFF",
+			  "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0.5)",
+			  "DEFAULT_GROUP_FONT": 22,
+			  "WIDGET_BGCOLOR": "#2b2f38",
+			  "WIDGET_OUTLINE_COLOR": "#6e7581",
+			  "WIDGET_TEXT_COLOR": "#DDD",
+			  "WIDGET_SECONDARY_TEXT_COLOR": "#b2b7bd",
+			  "LINK_COLOR": "#9A9",
+			  "EVENT_LINK_COLOR": "#A86",
+			  "CONNECTING_LINK_COLOR": "#AFA"
+			},
+			"comfy_base": {
+			  "fg-color": "#fff",
+			  "bg-color": "#2b2f38",
+			  "comfy-menu-bg": "#242730",
+			  "comfy-input-bg": "#2b2f38",
+			  "input-text": "#ddd",
+			  "descrip-text": "#b2b7bd",
+			  "drag-text": "#ccc",
+			  "error-text": "#ff4444",
+			  "border-color": "#6e7581",
+			  "tr-even-bg-color": "#2b2f38",
+			  "tr-odd-bg-color": "#242730"
+			}
+		},
+	},
+	"nord": {
+		"id": "nord",
+		"name": "Nord",
+		"colors": {
+			"node_slot": {
+			  "BOOLEAN": "",
+			  "CLIP": "#eacb8b",
+			  "CLIP_VISION": "#A8DADC",
+			  "CLIP_VISION_OUTPUT": "#ad7452",
+			  "CONDITIONING": "#cf876f",
+			  "CONTROL_NET": "#00d78d",
+			  "CONTROL_NET_WEIGHTS": "",
+			  "FLOAT": "",
+			  "GLIGEN": "",
+			  "IMAGE": "#80a1c0",
+			  "IMAGEUPLOAD": "",
+			  "INT": "",
+			  "LATENT": "#b38ead",
+			  "LATENT_KEYFRAME": "",
+			  "MASK": "#a3bd8d",
+			  "MODEL": "#8978a7",
+			  "SAMPLER": "",
+			  "SIGMAS": "",
+			  "STRING": "",
+			  "STYLE_MODEL": "#C2FFAE",
+			  "T2I_ADAPTER_WEIGHTS": "",
+			  "TAESD": "#DCC274",
+			  "TIMESTEP_KEYFRAME": "",
+			  "UPSCALE_MODEL": "",
+			  "VAE": "#be616b"
+			},
+			"litegraph_base": {
+			  "BACKGROUND_IMAGE": "data:image/png;base64,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",
+			  "CLEAR_BACKGROUND_COLOR": "#212732",
+			  "NODE_TITLE_COLOR": "#999",
+			  "NODE_SELECTED_TITLE_COLOR": "#e5eaf0",
+			  "NODE_TEXT_SIZE": 14,
+			  "NODE_TEXT_COLOR": "#bcc2c8",
+			  "NODE_SUBTEXT_SIZE": 12,
+			  "NODE_DEFAULT_COLOR": "#2e3440",
+			  "NODE_DEFAULT_BGCOLOR": "#161b22",
+			  "NODE_DEFAULT_BOXCOLOR": "#545d70",
+			  "NODE_DEFAULT_SHAPE": "box",
+			  "NODE_BOX_OUTLINE_COLOR": "#e5eaf0",
+			  "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0.5)",
+			  "DEFAULT_GROUP_FONT": 24,
+			  "WIDGET_BGCOLOR": "#2e3440",
+			  "WIDGET_OUTLINE_COLOR": "#545d70",
+			  "WIDGET_TEXT_COLOR": "#bcc2c8",
+			  "WIDGET_SECONDARY_TEXT_COLOR": "#999",
+			  "LINK_COLOR": "#9A9",
+			  "EVENT_LINK_COLOR": "#A86",
+			  "CONNECTING_LINK_COLOR": "#AFA"
+			},
+			"comfy_base": {
+			  "fg-color": "#e5eaf0",
+			  "bg-color": "#2e3440",
+			  "comfy-menu-bg": "#161b22",
+			  "comfy-input-bg": "#2e3440",
+			  "input-text": "#bcc2c8",
+			  "descrip-text": "#999",
+			  "drag-text": "#ccc",
+			  "error-text": "#ff4444",
+			  "border-color": "#545d70",
+			  "tr-even-bg-color": "#2e3440",
+			  "tr-odd-bg-color": "#161b22"
+			}
+		},
+	},
+	"github": {
+		"id": "github",
+		"name": "Github",
+		"colors": {
+			"node_slot": {
+			  "BOOLEAN": "",
+			  "CLIP": "#eacb8b",
+			  "CLIP_VISION": "#A8DADC",
+			  "CLIP_VISION_OUTPUT": "#ad7452",
+			  "CONDITIONING": "#cf876f",
+			  "CONTROL_NET": "#00d78d",
+			  "CONTROL_NET_WEIGHTS": "",
+			  "FLOAT": "",
+			  "GLIGEN": "",
+			  "IMAGE": "#80a1c0",
+			  "IMAGEUPLOAD": "",
+			  "INT": "",
+			  "LATENT": "#b38ead",
+			  "LATENT_KEYFRAME": "",
+			  "MASK": "#a3bd8d",
+			  "MODEL": "#8978a7",
+			  "SAMPLER": "",
+			  "SIGMAS": "",
+			  "STRING": "",
+			  "STYLE_MODEL": "#C2FFAE",
+			  "T2I_ADAPTER_WEIGHTS": "",
+			  "TAESD": "#DCC274",
+			  "TIMESTEP_KEYFRAME": "",
+			  "UPSCALE_MODEL": "",
+			  "VAE": "#be616b"
+			},
+			"litegraph_base": {
+			  "BACKGROUND_IMAGE": "data:image/png;base64,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",
+			  "CLEAR_BACKGROUND_COLOR": "#040506",
+			  "NODE_TITLE_COLOR": "#999",
+			  "NODE_SELECTED_TITLE_COLOR": "#e5eaf0",
+			  "NODE_TEXT_SIZE": 14,
+			  "NODE_TEXT_COLOR": "#bcc2c8",
+			  "NODE_SUBTEXT_SIZE": 12,
+			  "NODE_DEFAULT_COLOR": "#161b22",
+			  "NODE_DEFAULT_BGCOLOR": "#13171d",
+			  "NODE_DEFAULT_BOXCOLOR": "#30363d",
+			  "NODE_DEFAULT_SHAPE": "box",
+			  "NODE_BOX_OUTLINE_COLOR": "#e5eaf0",
+			  "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0.5)",
+			  "DEFAULT_GROUP_FONT": 24,
+			  "WIDGET_BGCOLOR": "#161b22",
+			  "WIDGET_OUTLINE_COLOR": "#30363d",
+			  "WIDGET_TEXT_COLOR": "#bcc2c8",
+			  "WIDGET_SECONDARY_TEXT_COLOR": "#999",
+			  "LINK_COLOR": "#9A9",
+			  "EVENT_LINK_COLOR": "#A86",
+			  "CONNECTING_LINK_COLOR": "#AFA"
+			},
+			"comfy_base": {
+			  "fg-color": "#e5eaf0",
+			  "bg-color": "#161b22",
+			  "comfy-menu-bg": "#13171d",
+			  "comfy-input-bg": "#161b22",
+			  "input-text": "#bcc2c8",
+			  "descrip-text": "#999",
+			  "drag-text": "#ccc",
+			  "error-text": "#ff4444",
+			  "border-color": "#30363d",
+			  "tr-even-bg-color": "#161b22",
+			  "tr-odd-bg-color": "#13171d"
+			}
+		},
 	}
 };
 

web/scripts/app.js

@@ -1508,16 +1508,18 @@ export class ComfyApp {
 
 		let reset_invalid_values = false;
 		if (!graphData) {
-			if (typeof structuredClone === "undefined")
+			graphData = defaultGraph;
-			{
-				graphData = JSON.parse(JSON.stringify(defaultGraph));
-			}else
-			{
-				graphData = structuredClone(defaultGraph);
-			}
 			reset_invalid_values = true;
 		}
 
+		if (typeof structuredClone === "undefined")
+		{
+			graphData = JSON.parse(JSON.stringify(graphData));
+		}else
+		{
+			graphData = structuredClone(graphData);
+		}
+
 		const missingNodeTypes = [];
 		await this.#invokeExtensionsAsync("beforeConfigureGraph", graphData, missingNodeTypes);
 		for (let n of graphData.nodes) {