Merge remote-tracking branch 'upstream/master' into addBatchIndex

comfy/k_diffusion/sampling.py

@@ -605,3 +605,47 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
         old_denoised = denoised
     return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+    """DPM-Solver++(2M) SDE."""
+
+    if solver_type not in {'heun', 'midpoint'}:
+        raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
+
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    old_denoised = None
+    h_last = None
+    h = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        if sigmas[i + 1] == 0:
+            # Denoising step
+            x = denoised
+        else:
+            # DPM-Solver++(2M) SDE
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            eta_h = eta * h
+
+            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
+
+            if old_denoised is not None:
+                r = h_last / h
+                if solver_type == 'heun':
+                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
+                elif solver_type == 'midpoint':
+                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
+
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
+
+        old_denoised = denoised
+        h_last = h
+    return x

comfy/ldm/modules/diffusionmodules/model.py

@@ -146,6 +146,41 @@ class ResnetBlock(nn.Module):
 
         return x+h
 
+def slice_attention(q, k, v):
+    r1 = torch.zeros_like(k, device=q.device)
+    scale = (int(q.shape[-1])**(-0.5))
+
+    mem_free_total = model_management.get_free_memory(q.device)
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+    while True:
+        try:
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            for i in range(0, q.shape[1], slice_size):
+                end = i + slice_size
+                s1 = torch.bmm(q[:, i:end], k) * scale
+
+                s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
+                del s1
+
+                r1[:, :, i:end] = torch.bmm(v, s2)
+                del s2
+            break
+        except model_management.OOM_EXCEPTION as e:
+            steps *= 2
+            if steps > 128:
+                raise e
+            print("out of memory error, increasing steps and trying again", steps)
+
+    return r1
 
 class AttnBlock(nn.Module):
     def __init__(self, in_channels):
@@ -183,48 +218,15 @@ class AttnBlock(nn.Module):
 
         # compute attention
         b,c,h,w = q.shape
-        scale = (int(c)**(-0.5))
 
         q = q.reshape(b,c,h*w)
         q = q.permute(0,2,1)   # b,hw,c
         k = k.reshape(b,c,h*w) # b,c,hw
         v = v.reshape(b,c,h*w)
 
-        r1 = torch.zeros_like(k, device=q.device)
-
-        mem_free_total = model_management.get_free_memory(q.device)
-
-        gb = 1024 ** 3
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
-        modifier = 3 if q.element_size() == 2 else 2.5
-        mem_required = tensor_size * modifier
-        steps = 1
-
-        if mem_required > mem_free_total:
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-
-        while True:
-            try:
-                slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-                for i in range(0, q.shape[1], slice_size):
-                    end = i + slice_size
-                    s1 = torch.bmm(q[:, i:end], k) * scale
-
-                    s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
-                    del s1
-
-                    r1[:, :, i:end] = torch.bmm(v, s2)
-                    del s2
-                break
-            except model_management.OOM_EXCEPTION as e:
-                steps *= 2
-                if steps > 128:
-                    raise e
-                print("out of memory error, increasing steps and trying again", steps)
-
+        r1 = slice_attention(q, k, v)
         h_ = r1.reshape(b,c,h,w)
         del r1
-
         h_ = self.proj_out(h_)
 
         return x+h_
@@ -331,25 +333,18 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
 
         # compute attention
         B, C, H, W = q.shape
-        q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
-
         q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .contiguous(),
+            lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
             (q, k, v),
         )
-        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
 
-        out = (
-            out.unsqueeze(0)
-            .reshape(B, 1, out.shape[1], C)
-            .permute(0, 2, 1, 3)
-            .reshape(B, out.shape[1], C)
-        )
-        out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
+        try:
+            out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+            out = out.transpose(2, 3).reshape(B, C, H, W)
+        except model_management.OOM_EXCEPTION as e:
+            print("scaled_dot_product_attention OOMed: switched to slice attention")
+            out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+
         out = self.proj_out(out)
         return x+out
 

comfy/ldm/modules/tomesd.py

@@ -36,7 +36,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor,
     """
     B, N, _ = metric.shape
 
-    if r <= 0:
+    if r <= 0 or w == 1 or h == 1:
         return do_nothing, do_nothing
 
     gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather

comfy/model_management.py

@@ -127,6 +127,32 @@ if args.cpu:
 
 print(f"Set vram state to: {vram_state.name}")
 
+def get_torch_device():
+    global xpu_available
+    global directml_enabled
+    if directml_enabled:
+        global directml_device
+        return directml_device
+    if vram_state == VRAMState.MPS:
+        return torch.device("mps")
+    if vram_state == VRAMState.CPU:
+        return torch.device("cpu")
+    else:
+        if xpu_available:
+            return torch.device("xpu")
+        else:
+            return torch.cuda.current_device()
+
+def get_torch_device_name(device):
+    if hasattr(device, 'type'):
+        return "{}".format(device.type)
+    return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
+
+try:
+    print("Using device:", get_torch_device_name(get_torch_device()))
+except:
+    print("Could not pick default device.")
+
 
 current_loaded_model = None
 current_gpu_controlnets = []
@@ -233,22 +259,6 @@ def unload_if_low_vram(model):
         return model.cpu()
     return model
 
-def get_torch_device():
-    global xpu_available
-    global directml_enabled
-    if directml_enabled:
-        global directml_device
-        return directml_device
-    if vram_state == VRAMState.MPS:
-        return torch.device("mps")
-    if vram_state == VRAMState.CPU:
-        return torch.device("cpu")
-    else:
-        if xpu_available:
-            return torch.device("xpu")
-        else:
-            return torch.cuda.current_device()
-
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
         return dev.type

comfy/sample.py

@@ -2,17 +2,26 @@ import torch
 import comfy.model_management
 import comfy.samplers
 import math
+import numpy as np
 
-def prepare_noise(latent_image, seed, skip=0):
+def prepare_noise(latent_image, seed, noise_inds=None):
     """
     creates random noise given a latent image and a seed.
     optional arg skip can be used to skip and discard x number of noise generations for a given seed
     """
     generator = torch.manual_seed(seed)
-    for _ in range(skip):
+    if noise_inds is None:
+        return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+    
+    unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
+    noises = []
+    for i in range(unique_inds[-1]+1):
         noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
-    noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
-    return noise
+        if i in unique_inds:
+            noises.append(noise)
+    noises = [noises[i] for i in inverse]
+    noises = torch.cat(noises, axis=0)
+    return noises
 
 def prepare_mask(noise_mask, shape, device):
     """ensures noise mask is of proper dimensions"""

comfy/samplers.py

@@ -6,6 +6,10 @@ import contextlib
 from comfy import model_management
 from .ldm.models.diffusion.ddim import DDIMSampler
 from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
+import math
+
+def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
+    return abs(a*b) // math.gcd(a, b)
 
 #The main sampling function shared by all the samplers
 #Returns predicted noise
@@ -90,8 +94,16 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
             if c1.keys() != c2.keys():
                 return False
             if 'c_crossattn' in c1:
-                if c1['c_crossattn'].shape != c2['c_crossattn'].shape:
-                    return False
+                s1 = c1['c_crossattn'].shape
+                s2 = c2['c_crossattn'].shape
+                if s1 != s2:
+                    if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
+                        return False
+
+                    mult_min = lcm(s1[1], s2[1])
+                    diff = mult_min // min(s1[1], s2[1])
+                    if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
+                        return False
             if 'c_concat' in c1:
                 if c1['c_concat'].shape != c2['c_concat'].shape:
                     return False
@@ -124,16 +136,28 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
             c_crossattn = []
             c_concat = []
             c_adm = []
+            crossattn_max_len = 0
             for x in c_list:
                 if 'c_crossattn' in x:
-                    c_crossattn.append(x['c_crossattn'])
+                    c = x['c_crossattn']
+                    if crossattn_max_len == 0:
+                        crossattn_max_len = c.shape[1]
+                    else:
+                        crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+                    c_crossattn.append(c)
                 if 'c_concat' in x:
                     c_concat.append(x['c_concat'])
                 if 'c_adm' in x:
                     c_adm.append(x['c_adm'])
             out = {}
-            if len(c_crossattn) > 0:
-                out['c_crossattn'] = [torch.cat(c_crossattn)]
+            c_crossattn_out = []
+            for c in c_crossattn:
+                if c.shape[1] < crossattn_max_len:
+                    c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
+                c_crossattn_out.append(c)
+
+            if len(c_crossattn_out) > 0:
+                out['c_crossattn'] = [torch.cat(c_crossattn_out)]
             if len(c_concat) > 0:
                 out['c_concat'] = [torch.cat(c_concat)]
             if len(c_adm) > 0:
@@ -471,10 +495,10 @@ def encode_adm(noise_augmentor, conds, batch_size, device):
 
 
 class KSampler:
-    SCHEDULERS = ["karras", "normal", "simple", "ddim_uniform"]
+    SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
                 "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde",
-                "dpmpp_2m", "ddim", "uni_pc", "uni_pc_bh2"]
+                "dpmpp_2m", "dpmpp_2m_sde", "ddim", "uni_pc", "uni_pc_bh2"]
 
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
         self.model = model
@@ -508,6 +532,8 @@ class KSampler:
 
         if self.scheduler == "karras":
             sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+        elif self.scheduler == "exponential":
+            sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
         elif self.scheduler == "normal":
             sigmas = self.model_wrap.get_sigmas(steps)
         elif self.scheduler == "simple":

comfy/sd.py

@@ -581,12 +581,9 @@ class VAE:
         samples = samples.cpu()
         return samples
 
-def resize_image_to(tensor, target_latent_tensor, batched_number):
-    tensor = utils.common_upscale(tensor, target_latent_tensor.shape[3] * 8, target_latent_tensor.shape[2] * 8, 'nearest-exact', "center")
-    target_batch_size = target_latent_tensor.shape[0]
-
+def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
-    print(current_batch_size, target_batch_size)
+    #print(current_batch_size, target_batch_size)
     if current_batch_size == 1:
         return tensor
 
@@ -623,7 +620,9 @@ class ControlNet:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).to(self.control_model.dtype).to(self.device)
+            self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
         if self.control_model.dtype == torch.float16:
             precision_scope = torch.autocast
@@ -794,10 +793,14 @@ class T2IAdapter:
         if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
+            self.control_input = None
             self.cond_hint = None
-            self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).float().to(self.device)
+            self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").float().to(self.device)
             if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
                 self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+        if self.control_input is None:
             self.t2i_model.to(self.device)
             self.control_input = self.t2i_model(self.cond_hint)
             self.t2i_model.cpu()

comfy_extras/nodes_mask.py

@@ -72,7 +72,7 @@ class MaskToImage:
     FUNCTION = "mask_to_image"
 
     def mask_to_image(self, mask):
-        result = mask[None, :, :, None].expand(-1, -1, -1, 3)
+        result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
         return (result,)
 
 class ImageToMask:

comfy_extras/nodes_post_processing.py

@@ -59,6 +59,12 @@ class Blend:
     def g(self, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
+def gaussian_kernel(kernel_size: int, sigma: float):
+    x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size), indexing="ij")
+    d = torch.sqrt(x * x + y * y)
+    g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
+    return g / g.sum()
+
 class Blur:
     def __init__(self):
         pass
@@ -88,12 +94,6 @@ class Blur:
 
     CATEGORY = "image/postprocessing"
 
-    def gaussian_kernel(self, kernel_size: int, sigma: float):
-        x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size), indexing="ij")
-        d = torch.sqrt(x * x + y * y)
-        g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
-        return g / g.sum()
-
     def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
         if blur_radius == 0:
             return (image,)
@@ -101,10 +101,11 @@ class Blur:
         batch_size, height, width, channels = image.shape
 
         kernel_size = blur_radius * 2 + 1
-        kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
+        kernel = gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
 
         image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
-        blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
+        padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')
+        blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
         blurred = blurred.permute(0, 2, 3, 1)
 
         return (blurred,)
@@ -167,9 +168,15 @@ class Sharpen:
                     "max": 31,
                     "step": 1
                 }),
-                "alpha": ("FLOAT", {
+                "sigma": ("FLOAT", {
                     "default": 1.0,
                     "min": 0.1,
+                    "max": 10.0,
+                    "step": 0.1
+                }),
+                "alpha": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
                     "max": 5.0,
                     "step": 0.1
                 }),
@@ -181,21 +188,21 @@ class Sharpen:
 
     CATEGORY = "image/postprocessing"
 
-    def sharpen(self, image: torch.Tensor, sharpen_radius: int, alpha: float):
+    def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
         if sharpen_radius == 0:
             return (image,)
 
         batch_size, height, width, channels = image.shape
 
         kernel_size = sharpen_radius * 2 + 1
-        kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
+        kernel = gaussian_kernel(kernel_size, sigma) * -(alpha*10)
         center = kernel_size // 2
-        kernel[center, center] = kernel_size**2
-        kernel *= alpha
+        kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
         kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
 
         tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
-        sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
+        tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
+        sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
         sharpened = sharpened.permute(0, 2, 3, 1)
 
         result = torch.clamp(sharpened, 0, 1)

comfy_extras/nodes_rebatch.py

@@ -0,0 +1,108 @@
+import torch
+
+class LatentRebatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "latents": ("LATENT",),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True, )
+
+    FUNCTION = "rebatch"
+
+    CATEGORY = "latent/batch"
+
+    @staticmethod
+    def get_batch(latents, list_ind, offset):
+        '''prepare a batch out of the list of latents'''
+        samples = latents[list_ind]['samples']
+        shape = samples.shape
+        mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu')
+        if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]:
+            torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear")
+        if mask.shape[0] < samples.shape[0]:
+            mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]
+        if 'batch_index' in latents[list_ind]:
+            batch_inds = latents[list_ind]['batch_index']
+        else:
+            batch_inds = [x+offset for x in range(shape[0])]
+        return samples, mask, batch_inds
+
+    @staticmethod
+    def get_slices(indexable, num, batch_size):
+        '''divides an indexable object into num slices of length batch_size, and a remainder'''
+        slices = []
+        for i in range(num):
+            slices.append(indexable[i*batch_size:(i+1)*batch_size])
+        if num * batch_size < len(indexable):
+            return slices, indexable[num * batch_size:]
+        else:
+            return slices, None
+    
+    @staticmethod
+    def slice_batch(batch, num, batch_size):
+        result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch]
+        return list(zip(*result))
+
+    @staticmethod
+    def cat_batch(batch1, batch2):
+        if batch1[0] is None:
+            return batch2
+        result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
+        return result
+
+    def rebatch(self, latents, batch_size):
+        batch_size = batch_size[0]
+
+        output_list = []
+        current_batch = (None, None, None)
+        processed = 0
+
+        for i in range(len(latents)):
+            # fetch new entry of list
+            #samples, masks, indices = self.get_batch(latents, i)
+            next_batch = self.get_batch(latents, i, processed)
+            processed += len(next_batch[2])
+            # set to current if current is None
+            if current_batch[0] is None:
+                current_batch = next_batch
+            # add previous to list if dimensions do not match
+            elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
+                sliced, _ = self.slice_batch(current_batch, 1, batch_size)
+                output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
+                current_batch = next_batch
+            # cat if everything checks out
+            else:
+                current_batch = self.cat_batch(current_batch, next_batch)
+
+            # add to list if dimensions gone above target batch size
+            if current_batch[0].shape[0] > batch_size:
+                num = current_batch[0].shape[0] // batch_size
+                sliced, remainder = self.slice_batch(current_batch, num, batch_size)
+                
+                for i in range(num):
+                    output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
+
+                current_batch = remainder
+
+        #add remainder
+        if current_batch[0] is not None:
+            sliced, _ = self.slice_batch(current_batch, 1, batch_size)
+            output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
+
+        #get rid of empty masks
+        for s in output_list:
+            if s['noise_mask'].mean() == 1.0:
+                del s['noise_mask']
+
+        return (output_list,)
+
+NODE_CLASS_MAPPINGS = {
+    "RebatchLatents": LatentRebatch,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RebatchLatents": "Rebatch Latents",
+}

comfy_extras/nodes_upscale_model.py

@@ -17,7 +17,7 @@ class UpscaleModelLoader:
 
     def load_model(self, model_name):
         model_path = folder_paths.get_full_path("upscale_models", model_name)
-        sd = comfy.utils.load_torch_file(model_path)
+        sd = comfy.utils.load_torch_file(model_path, safe_load=True)
         out = model_loading.load_state_dict(sd).eval()
         return (out, )
 

execution.py

@@ -6,6 +6,7 @@ import threading
 import heapq
 import traceback
 import gc
+import time
 
 import torch
 import nodes
@@ -26,21 +27,82 @@ def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_da
             input_data_all[x] = obj
         else:
             if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
-                input_data_all[x] = input_data
+                input_data_all[x] = [input_data]
 
     if "hidden" in valid_inputs:
         h = valid_inputs["hidden"]
         for x in h:
             if h[x] == "PROMPT":
-                input_data_all[x] = prompt
+                input_data_all[x] = [prompt]
             if h[x] == "EXTRA_PNGINFO":
                 if "extra_pnginfo" in extra_data:
-                    input_data_all[x] = extra_data['extra_pnginfo']
+                    input_data_all[x] = [extra_data['extra_pnginfo']]
             if h[x] == "UNIQUE_ID":
-                input_data_all[x] = unique_id
+                input_data_all[x] = [unique_id]
     return input_data_all
 
-def recursive_execute(server, prompt, outputs, current_item, extra_data, executed):
+def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
+    # check if node wants the lists
+    intput_is_list = False
+    if hasattr(obj, "INPUT_IS_LIST"):
+        intput_is_list = obj.INPUT_IS_LIST
+
+    max_len_input = max([len(x) for x in input_data_all.values()])
+     
+    # get a slice of inputs, repeat last input when list isn't long enough
+    def slice_dict(d, i):
+        d_new = dict()
+        for k,v in d.items():
+            d_new[k] = v[i if len(v) > i else -1]
+        return d_new
+    
+    results = []
+    if intput_is_list:
+        if allow_interrupt:
+            nodes.before_node_execution()
+        results.append(getattr(obj, func)(**input_data_all))
+    else: 
+        for i in range(max_len_input):
+            if allow_interrupt:
+                nodes.before_node_execution()
+            results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
+    return results
+
+def get_output_data(obj, input_data_all):
+    
+    results = []
+    uis = []
+    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
+
+    for r in return_values:
+        if isinstance(r, dict):
+            if 'ui' in r:
+                uis.append(r['ui'])
+            if 'result' in r:
+                results.append(r['result'])
+        else:
+            results.append(r)
+    
+    output = []
+    if len(results) > 0:
+        # check which outputs need concatenating
+        output_is_list = [False] * len(results[0])
+        if hasattr(obj, "OUTPUT_IS_LIST"):
+            output_is_list = obj.OUTPUT_IS_LIST
+
+        # merge node execution results
+        for i, is_list in zip(range(len(results[0])), output_is_list):
+            if is_list:
+                output.append([x for o in results for x in o[i]])
+            else:
+                output.append([o[i] for o in results])
+
+    ui = dict()    
+    if len(uis) > 0:
+        ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
+    return output, ui
+
+def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui):
     unique_id = current_item
     inputs = prompt[unique_id]['inputs']
     class_type = prompt[unique_id]['class_type']
@@ -55,21 +117,20 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
             input_unique_id = input_data[0]
             output_index = input_data[1]
             if input_unique_id not in outputs:
-                recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed)
+                recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui)
 
     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)
+        server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
     obj = class_def()
 
-    nodes.before_node_execution()
-    outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all)
-    if "ui" in outputs[unique_id]:
+    output_data, output_ui = get_output_data(obj, input_data_all)
+    outputs[unique_id] = output_data
+    if len(output_ui) > 0:
+        outputs_ui[unique_id] = output_ui
         if server.client_id is not None:
-            server.send_sync("executed", { "node": unique_id, "output": outputs[unique_id]["ui"] }, server.client_id)
-        if "result" in outputs[unique_id]:
-            outputs[unique_id] = outputs[unique_id]["result"]
+            server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
     executed.add(unique_id)
 
 def recursive_will_execute(prompt, outputs, current_item):
@@ -105,7 +166,8 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
             input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
             if input_data_all is not None:
                 try:
-                    is_changed = class_def.IS_CHANGED(**input_data_all)
+                    #is_changed = class_def.IS_CHANGED(**input_data_all)
+                    is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
                     prompt[unique_id]['is_changed'] = is_changed
                 except:
                     to_delete = True
@@ -144,10 +206,11 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
 class PromptExecutor:
     def __init__(self, server):
         self.outputs = {}
+        self.outputs_ui = {}
         self.old_prompt = {}
         self.server = server
 
-    def execute(self, prompt, extra_data={}):
+    def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
         nodes.interrupt_processing(False)
 
         if "client_id" in extra_data:
@@ -155,6 +218,10 @@ class PromptExecutor:
         else:
             self.server.client_id = None
 
+        execution_start_time = time.perf_counter()
+        if self.server.client_id is not None:
+            self.server.send_sync("execution_start", { "prompt_id": prompt_id}, self.server.client_id)
+
         with torch.inference_mode():
             #delete cached outputs if nodes don't exist for them
             to_delete = []
@@ -169,32 +236,34 @@ class PromptExecutor:
                 recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
 
             current_outputs = set(self.outputs.keys())
+            for x in list(self.outputs_ui.keys()):
+                if x not in current_outputs:
+                    d = self.outputs_ui.pop(x)
+                    del d
+
+            if self.server.client_id is not None:
+                self.server.send_sync("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, self.server.client_id)
             executed = set()
             try:
                 to_execute = []
-                for x in prompt:
-                    class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
-                    if hasattr(class_, 'OUTPUT_NODE'):
-                        to_execute += [(0, x)]
+                for x in list(execute_outputs):
+                    to_execute += [(0, x)]
 
                 while len(to_execute) > 0:
                     #always execute the output that depends on the least amount of unexecuted nodes first
                     to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
                     x = to_execute.pop(0)[-1]
 
-                    class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
-                    if hasattr(class_, 'OUTPUT_NODE'):
-                        if class_.OUTPUT_NODE == True:
-                            valid = False
-                            try:
-                                m = validate_inputs(prompt, x)
-                                valid = m[0]
-                            except:
-                                valid = False
-                            if valid:
-                                recursive_execute(self.server, prompt, self.outputs, x, extra_data, executed)
+                    recursive_execute(self.server, prompt, self.outputs, x, extra_data, executed, prompt_id, self.outputs_ui)
             except Exception as e:
-                print(traceback.format_exc())
+                if isinstance(e, comfy.model_management.InterruptProcessingException):
+                    print("Processing interrupted")
+                else:
+                    message = str(traceback.format_exc())
+                    print(message)
+                    if self.server.client_id is not None:
+                        self.server.send_sync("execution_error", { "message": message, "prompt_id": prompt_id }, self.server.client_id)
+
                 to_delete = []
                 for o in self.outputs:
                     if (o not in current_outputs) and (o not in executed):
@@ -210,14 +279,18 @@ class PromptExecutor:
                     self.old_prompt[x] = copy.deepcopy(prompt[x])
                 self.server.last_node_id = None
                 if self.server.client_id is not None:
-                    self.server.send_sync("executing", { "node": None }, self.server.client_id)
+                    self.server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, self.server.client_id)
 
+        print("Prompt executed in {:.2f} seconds".format(time.perf_counter() - execution_start_time))
         gc.collect()
         comfy.model_management.soft_empty_cache()
 
 
-def validate_inputs(prompt, item):
+def validate_inputs(prompt, item, validated):
     unique_id = item
+    if unique_id in validated:
+        return validated[unique_id]
+
     inputs = prompt[unique_id]['inputs']
     class_type = prompt[unique_id]['class_type']
     obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
@@ -238,8 +311,9 @@ def validate_inputs(prompt, item):
             r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
             if r[val[1]] != type_input:
                 return (False, "Return type mismatch. {}, {}, {} != {}".format(class_type, x, r[val[1]], type_input))
-            r = validate_inputs(prompt, o_id)
+            r = validate_inputs(prompt, o_id, validated)
             if r[0] == False:
+                validated[o_id] = r
                 return r
         else:
             if type_input == "INT":
@@ -254,20 +328,25 @@ def validate_inputs(prompt, item):
 
             if len(info) > 1:
                 if "min" in info[1] and val < info[1]["min"]:
-                    return (False, "Value smaller than min. {}, {}".format(class_type, x))
+                    return (False, "Value {} smaller than min of {}. {}, {}".format(val, info[1]["min"], class_type, x))
                 if "max" in info[1] and val > info[1]["max"]:
-                    return (False, "Value bigger than max. {}, {}".format(class_type, x))
+                    return (False, "Value {} bigger than max of {}. {}, {}".format(val, info[1]["max"], class_type, x))
 
             if hasattr(obj_class, "VALIDATE_INPUTS"):
                 input_data_all = get_input_data(inputs, obj_class, unique_id)
-                ret = obj_class.VALIDATE_INPUTS(**input_data_all)
-                if ret != True:
-                    return (False, "{}, {}".format(class_type, ret))
+                #ret = obj_class.VALIDATE_INPUTS(**input_data_all)
+                ret = map_node_over_list(obj_class, input_data_all, "VALIDATE_INPUTS")
+                for r in ret:
+                    if r != True:
+                        return (False, "{}, {}".format(class_type, r))
             else:
                 if isinstance(type_input, list):
                     if val not in type_input:
                         return (False, "Value not in list. {}, {}: {} not in {}".format(class_type, x, val, type_input))
-    return (True, "")
+
+    ret = (True, "")
+    validated[unique_id] = ret
+    return ret
 
 def validate_prompt(prompt):
     outputs = set()
@@ -281,11 +360,12 @@ def validate_prompt(prompt):
 
     good_outputs = set()
     errors = []
+    validated = {}
     for o in outputs:
         valid = False
         reason = ""
         try:
-            m = validate_inputs(prompt, o)
+            m = validate_inputs(prompt, o, validated)
             valid = m[0]
             reason = m[1]
         except Exception as e:
@@ -294,7 +374,7 @@ def validate_prompt(prompt):
             reason = "Parsing error"
 
         if valid == True:
-            good_outputs.add(x)
+            good_outputs.add(o)
         else:
             print("Failed to validate prompt for output {} {}".format(o, reason))
             print("output will be ignored")
@@ -304,7 +384,7 @@ def validate_prompt(prompt):
         errors_list = "\n".join(set(map(lambda a: "{}".format(a[1]), errors)))
         return (False, "Prompt has no properly connected outputs\n {}".format(errors_list))
 
-    return (True, "")
+    return (True, "", list(good_outputs))
 
 
 class PromptQueue:
@@ -340,8 +420,7 @@ class PromptQueue:
             prompt = self.currently_running.pop(item_id)
             self.history[prompt[1]] = { "prompt": prompt, "outputs": {} }
             for o in outputs:
-                if "ui" in outputs[o]:
-                    self.history[prompt[1]]["outputs"][o] = outputs[o]["ui"]
+                self.history[prompt[1]]["outputs"][o] = outputs[o]
             self.server.queue_updated()
 
     def get_current_queue(self):

folder_paths.py

@@ -147,4 +147,37 @@ def get_filename_list(folder_name):
         output_list.update(filter_files_extensions(recursive_search(x), folders[1]))
     return sorted(list(output_list))
 
+def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0):
+    def map_filename(filename):
+        prefix_len = len(os.path.basename(filename_prefix))
+        prefix = filename[:prefix_len + 1]
+        try:
+            digits = int(filename[prefix_len + 1:].split('_')[0])
+        except:
+            digits = 0
+        return (digits, prefix)
 
+    def compute_vars(input, image_width, image_height):
+        input = input.replace("%width%", str(image_width))
+        input = input.replace("%height%", str(image_height))
+        return input
+
+    filename_prefix = compute_vars(filename_prefix, image_width, image_height)
+
+    subfolder = os.path.dirname(os.path.normpath(filename_prefix))
+    filename = os.path.basename(os.path.normpath(filename_prefix))
+
+    full_output_folder = os.path.join(output_dir, subfolder)
+
+    if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir:
+        print("Saving image outside the output folder is not allowed.")
+        return {}
+
+    try:
+        counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
+    except ValueError:
+        counter = 1
+    except FileNotFoundError:
+        os.makedirs(full_output_folder, exist_ok=True)
+        counter = 1
+    return full_output_folder, filename, counter, subfolder, filename_prefix

main.py

@@ -33,8 +33,8 @@ def prompt_worker(q, server):
     e = execution.PromptExecutor(server)
     while True:
         item, item_id = q.get()
-        e.execute(item[-2], item[-1])
-        q.task_done(item_id, e.outputs)
+        e.execute(item[2], item[1], item[3], item[4])
+        q.task_done(item_id, e.outputs_ui)
 
 async def run(server, address='', port=8188, verbose=True, call_on_start=None):
     await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())

nodes.py

@@ -6,10 +6,12 @@ import json
 import hashlib
 import traceback
 import math
+import time
 
-from PIL import Image
+from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
+import safetensors.torch
 
 
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
@@ -28,6 +30,7 @@ import importlib
 
 import folder_paths
 
+
 def before_node_execution():
     comfy.model_management.throw_exception_if_processing_interrupted()
 
@@ -145,9 +148,6 @@ class ConditioningSetMask:
         return (c, )
 
 class VAEDecode:
-    def __init__(self, device="cpu"):
-        self.device = device
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
@@ -160,9 +160,6 @@ class VAEDecode:
         return (vae.decode(samples["samples"]), )
 
 class VAEDecodeTiled:
-    def __init__(self, device="cpu"):
-        self.device = device
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
@@ -175,9 +172,6 @@ class VAEDecodeTiled:
         return (vae.decode_tiled(samples["samples"]), )
 
 class VAEEncode:
-    def __init__(self, device="cpu"):
-        self.device = device
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
@@ -202,9 +196,6 @@ class VAEEncode:
         return ({"samples":t}, )
 
 class VAEEncodeTiled:
-    def __init__(self, device="cpu"):
-        self.device = device
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
@@ -219,9 +210,6 @@ class VAEEncodeTiled:
         return ({"samples":t}, )
 
 class VAEEncodeForInpaint:
-    def __init__(self, device="cpu"):
-        self.device = device
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
@@ -260,6 +248,81 @@ class VAEEncodeForInpaint:
 
         return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
 
+
+class SaveLatent:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT", ),
+                              "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing"
+
+    def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        # support save metadata for latent sharing
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = {"prompt": prompt_info}
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata[x] = json.dumps(extra_pnginfo[x])
+
+        file = f"{filename}_{counter:05}_.latent"
+        file = os.path.join(full_output_folder, file)
+
+        output = {}
+        output["latent_tensor"] = samples["samples"]
+
+        safetensors.torch.save_file(output, file, metadata=metadata)
+
+        return {}
+
+
+class LoadLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
+        return {"required": {"latent": [sorted(files), ]}, }
+
+    CATEGORY = "_for_testing"
+
+    RETURN_TYPES = ("LATENT", )
+    FUNCTION = "load"
+
+    def load(self, latent):
+        latent_path = folder_paths.get_annotated_filepath(latent)
+        latent = safetensors.torch.load_file(latent_path, device="cpu")
+        samples = {"samples": latent["latent_tensor"].float()}
+        return (samples, )
+
+    @classmethod
+    def IS_CHANGED(s, latent):
+        image_path = folder_paths.get_annotated_filepath(latent)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, latent):
+        if not folder_paths.exists_annotated_filepath(latent):
+            return "Invalid latent file: {}".format(latent)
+        return True
+
+
 class CheckpointLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -296,7 +359,10 @@ class DiffusersLoader:
         paths = []
         for search_path in folder_paths.get_folder_paths("diffusers"):
             if os.path.exists(search_path):
-                paths += next(os.walk(search_path))[1]
+                for root, subdir, files in os.walk(search_path, followlinks=True):
+                    if "model_index.json" in files:
+                        paths.append(os.path.relpath(root, start=search_path))
+
         return {"required": {"model_path": (paths,), }}
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
@@ -306,9 +372,9 @@ class DiffusersLoader:
     def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
         for search_path in folder_paths.get_folder_paths("diffusers"):
             if os.path.exists(search_path):
-                paths = next(os.walk(search_path))[1]
-                if model_path in paths:
-                    model_path = os.path.join(search_path, model_path)
+                path = os.path.join(search_path, model_path)
+                if os.path.exists(path):
+                    model_path = path
                     break
 
         return comfy.diffusers_convert.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
@@ -629,18 +695,57 @@ class LatentFromBatch:
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
                               "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
+                              "length": ("INT", {"default": 1, "min": 1, "max": 64}),
                               }}
     RETURN_TYPES = ("LATENT",)
-    FUNCTION = "rotate"
+    FUNCTION = "frombatch"
 
-    CATEGORY = "latent"
+    CATEGORY = "latent/batch"
 
-    def rotate(self, samples, batch_index):
+    def frombatch(self, samples, batch_index, length):
         s = samples.copy()
         s_in = samples["samples"]
         batch_index = min(s_in.shape[0] - 1, batch_index)
-        s["samples"] = s_in[batch_index:batch_index + 1].clone()
-        s["batch_index"] = batch_index
+        length = min(s_in.shape[0] - batch_index, length)
+        s["samples"] = s_in[batch_index:batch_index + length].clone()
+        if "noise_mask" in samples:
+            masks = samples["noise_mask"]
+            if masks.shape[0] == 1:
+                s["noise_mask"] = masks.clone()
+            else:
+                if masks.shape[0] < s_in.shape[0]:
+                    masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
+                s["noise_mask"] = masks[batch_index:batch_index + length].clone()
+        if "batch_index" not in s:
+            s["batch_index"] = [x for x in range(batch_index, batch_index+length)]
+        else:
+            s["batch_index"] = samples["batch_index"][batch_index:batch_index + length]
+        return (s,)
+    
+class RepeatLatentBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "latent/batch"
+
+    def repeat(self, samples, amount):
+        s = samples.copy()
+        s_in = samples["samples"]
+        
+        s["samples"] = s_in.repeat((amount, 1,1,1))
+        if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1:
+            masks = samples["noise_mask"]
+            if masks.shape[0] < s_in.shape[0]:
+                masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
+            s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1))
+        if "batch_index" in s:
+            offset = max(s["batch_index"]) - min(s["batch_index"]) + 1
+            s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]]
         return (s,)
 
 class LatentUpscale:
@@ -795,7 +900,7 @@ class SetLatentNoiseMask:
 
     def set_mask(self, samples, mask):
         s = samples.copy()
-        s["noise_mask"] = mask
+        s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
         return (s,)
 
 def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
@@ -805,8 +910,8 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     if disable_noise:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
     else:
-        skip = latent["batch_index"] if "batch_index" in latent else 0
-        noise = comfy.sample.prepare_noise(latent_image, seed, skip)
+        batch_inds = latent["batch_index"] if "batch_index" in latent else None
+        noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
 
     noise_mask = None
     if "noise_mask" in latent:
@@ -901,39 +1006,7 @@ class SaveImage:
     CATEGORY = "image"
 
     def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        def map_filename(filename):
-            prefix_len = len(os.path.basename(filename_prefix))
-            prefix = filename[:prefix_len + 1]
-            try:
-                digits = int(filename[prefix_len + 1:].split('_')[0])
-            except:
-                digits = 0
-            return (digits, prefix)
-
-        def compute_vars(input):
-            input = input.replace("%width%", str(images[0].shape[1]))
-            input = input.replace("%height%", str(images[0].shape[0]))
-            return input
-
-        filename_prefix = compute_vars(filename_prefix)
-
-        subfolder = os.path.dirname(os.path.normpath(filename_prefix))
-        filename = os.path.basename(os.path.normpath(filename_prefix))
-
-        full_output_folder = os.path.join(self.output_dir, subfolder)
-
-        if os.path.commonpath((self.output_dir, os.path.abspath(full_output_folder))) != self.output_dir:
-            print("Saving image outside the output folder is not allowed.")
-            return {}
-
-        try:
-            counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
-        except ValueError:
-            counter = 1
-        except FileNotFoundError:
-            os.makedirs(full_output_folder, exist_ok=True)
-            counter = 1
-
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         results = list()
         for image in images:
             i = 255. * image.cpu().numpy()
@@ -984,6 +1057,7 @@ class LoadImage:
     def load_image(self, image):
         image_path = folder_paths.get_annotated_filepath(image)
         i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
         image = i.convert("RGB")
         image = np.array(image).astype(np.float32) / 255.0
         image = torch.from_numpy(image)[None,]
@@ -1027,6 +1101,7 @@ class LoadImageMask:
     def load_image(self, image, channel):
         image_path = folder_paths.get_annotated_filepath(image)
         i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
         if i.getbands() != ("R", "G", "B", "A"):
             i = i.convert("RGBA")
         mask = None
@@ -1170,6 +1245,7 @@ NODE_CLASS_MAPPINGS = {
     "EmptyLatentImage": EmptyLatentImage,
     "LatentUpscale": LatentUpscale,
     "LatentFromBatch": LatentFromBatch,
+    "RepeatLatentBatch": RepeatLatentBatch,
     "SaveImage": SaveImage,
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
@@ -1206,6 +1282,9 @@ NODE_CLASS_MAPPINGS = {
 
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
+
+    "LoadLatent": LoadLatent,
+    "SaveLatent": SaveLatent
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -1244,6 +1323,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "EmptyLatentImage": "Empty Latent Image",
     "LatentUpscale": "Upscale Latent",
     "LatentComposite": "Latent Composite",
+    "LatentFromBatch" : "Latent From Batch",
+    "RepeatLatentBatch": "Repeat Latent Batch",
     # Image
     "SaveImage": "Save Image",
     "PreviewImage": "Preview Image",
@@ -1275,14 +1356,18 @@ def load_custom_node(module_path):
             NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
             if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
+            return True
         else:
             print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
+            return False
     except Exception as e:
         print(traceback.format_exc())
         print(f"Cannot import {module_path} module for custom nodes:", e)
+        return False
 
 def load_custom_nodes():
     node_paths = folder_paths.get_folder_paths("custom_nodes")
+    node_import_times = []
     for custom_node_path in node_paths:
         possible_modules = os.listdir(custom_node_path)
         if "__pycache__" in possible_modules:
@@ -1291,11 +1376,25 @@ def load_custom_nodes():
         for possible_module in possible_modules:
             module_path = os.path.join(custom_node_path, possible_module)
             if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
-            load_custom_node(module_path)
+            if module_path.endswith(".disabled"): continue
+            time_before = time.perf_counter()
+            success = load_custom_node(module_path)
+            node_import_times.append((time.perf_counter() - time_before, module_path, success))
+
+    if len(node_import_times) > 0:
+        print("\nImport times for custom nodes:")
+        for n in sorted(node_import_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (IMPORT FAILED)"
+            print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
+        print()
 
 def init_custom_nodes():
-    load_custom_nodes()
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py"))
+    load_custom_nodes()

notebooks/comfyui_colab.ipynb

@@ -175,6 +175,8 @@
         "import threading\n",
         "import time\n",
         "import socket\n",
+        "import urllib.request\n",
+        "\n",
         "def iframe_thread(port):\n",
         "  while True:\n",
         "      time.sleep(0.5)\n",
@@ -183,7 +185,9 @@
         "      if result == 0:\n",
         "        break\n",
         "      sock.close()\n",
-        "  print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\")\n",
+        "  print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n",
+        "\n",
+        "  print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n",
         "  p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n",
         "  for line in p.stdout:\n",
         "    print(line.decode(), end='')\n",

server.py

@@ -81,7 +81,7 @@ class PromptServer():
                 # Reusing existing session, remove old
                 self.sockets.pop(sid, None)
             else:
-                sid = uuid.uuid4().hex      
+                sid = uuid.uuid4().hex
 
             self.sockets[sid] = ws
 
@@ -115,21 +115,23 @@ class PromptServer():
 
         def get_dir_by_type(dir_type):
             if dir_type is None:
-                type_dir = folder_paths.get_input_directory()
-            elif dir_type == "input":
+                dir_type = "input"
+
+            if dir_type == "input":
                 type_dir = folder_paths.get_input_directory()
             elif dir_type == "temp":
                 type_dir = folder_paths.get_temp_directory()
             elif dir_type == "output":
                 type_dir = folder_paths.get_output_directory()
 
-            return type_dir
+            return type_dir, dir_type
 
         def image_upload(post, image_save_function=None):
             image = post.get("image")
+            overwrite = post.get("overwrite")
 
             image_upload_type = post.get("type")
-            upload_dir = get_dir_by_type(image_upload_type)
+            upload_dir, image_upload_type = get_dir_by_type(image_upload_type)
 
             if image and image.file:
                 filename = image.filename
@@ -148,10 +150,14 @@ class PromptServer():
                 split = os.path.splitext(filename)
                 filepath = os.path.join(full_output_folder, filename)
 
-                i = 1
-                while os.path.exists(filepath):
-                    filename = f"{split[0]} ({i}){split[1]}"
-                    i += 1
+                if overwrite is not None and (overwrite == "true" or overwrite == "1"):
+                    pass
+                else:
+                    i = 1
+                    while os.path.exists(filepath):
+                        filename = f"{split[0]} ({i}){split[1]}"
+                        filepath = os.path.join(full_output_folder, filename)
+                        i += 1
 
                 if image_save_function is not None:
                     image_save_function(image, post, filepath)
@@ -255,22 +261,34 @@ class PromptServer():
         async def get_prompt(request):
             return web.json_response(self.get_queue_info())
 
+        def node_info(node_class):
+            obj_class = nodes.NODE_CLASS_MAPPINGS[node_class]
+            info = {}
+            info['input'] = obj_class.INPUT_TYPES()
+            info['output'] = obj_class.RETURN_TYPES
+            info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
+            info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']
+            info['name'] = node_class
+            info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class
+            info['description'] = ''
+            info['category'] = 'sd'
+            if hasattr(obj_class, 'CATEGORY'):
+                info['category'] = obj_class.CATEGORY
+            return info
+
         @routes.get("/object_info")
         async def get_object_info(request):
             out = {}
             for x in nodes.NODE_CLASS_MAPPINGS:
-                obj_class = nodes.NODE_CLASS_MAPPINGS[x]
-                info = {}
-                info['input'] = obj_class.INPUT_TYPES()
-                info['output'] = obj_class.RETURN_TYPES
-                info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']
-                info['name'] = x
-                info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[x] if x in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else x
-                info['description'] = ''
-                info['category'] = 'sd'
-                if hasattr(obj_class, 'CATEGORY'):
-                    info['category'] = obj_class.CATEGORY
-                out[x] = info
+                out[x] = node_info(x)
+            return web.json_response(out)
+
+        @routes.get("/object_info/{node_class}")
+        async def get_object_info_node(request):
+            node_class = request.match_info.get("node_class", None)
+            out = {}
+            if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS):
+                out[node_class] = node_info(node_class)
             return web.json_response(out)
 
         @routes.get("/history")
@@ -312,14 +330,16 @@ class PromptServer():
                 if "client_id" in json_data:
                     extra_data["client_id"] = json_data["client_id"]
                 if valid[0]:
-                    self.prompt_queue.put((number, id(prompt), prompt, extra_data))
+                    prompt_id = str(uuid.uuid4())
+                    outputs_to_execute = valid[2]
+                    self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+                    return web.json_response({"prompt_id": prompt_id})
                 else:
-                    resp_code = 400
-                    out_string = valid[1]
                     print("invalid prompt:", valid[1])
+                    return web.json_response({"error": valid[1]}, status=400)
+            else:
+                return web.json_response({"error": "no prompt"}, status=400)
 
-            return web.Response(body=out_string, status=resp_code)
-        
         @routes.post("/queue")
         async def post_queue(request):
             json_data =  await request.json()
@@ -329,9 +349,9 @@ class PromptServer():
             if "delete" in json_data:
                 to_delete = json_data['delete']
                 for id_to_delete in to_delete:
-                    delete_func = lambda a: a[1] == int(id_to_delete)
+                    delete_func = lambda a: a[1] == id_to_delete
                     self.prompt_queue.delete_queue_item(delete_func)
-                    
+
             return web.Response(status=200)
 
         @routes.post("/interrupt")
@@ -355,7 +375,7 @@ class PromptServer():
     def add_routes(self):
         self.app.add_routes(self.routes)
         self.app.add_routes([
-            web.static('/', self.web_root),
+            web.static('/', self.web_root, follow_symlinks=True),
         ])
 
     def get_queue_info(self):

web/extensions/core/maskeditor.js

@@ -72,40 +72,50 @@ function prepareRGB(image, backupCanvas, backupCtx) {
 
 class MaskEditorDialog extends ComfyDialog {
 	static instance = null;
+
+	static getInstance() {
+		if(!MaskEditorDialog.instance) {
+			MaskEditorDialog.instance = new MaskEditorDialog(app);
+		}
+
+		return MaskEditorDialog.instance;
+	}
+
+	is_layout_created =  false;
+
 	constructor() {
 		super();
 		this.element = $el("div.comfy-modal", { parent: document.body }, 
 			[ $el("div.comfy-modal-content", 
 				[...this.createButtons()]),
 			]);
-		MaskEditorDialog.instance = this;
 	}
 
 	createButtons() {
 		return [];
 	}
 
-	clearMask(self) {
-	}
-
 	createButton(name, callback) {
 		var button = document.createElement("button");
 		button.innerText = name;
 		button.addEventListener("click", callback);
 		return button;
 	}
+
 	createLeftButton(name, callback) {
 		var button = this.createButton(name, callback);
 		button.style.cssFloat = "left";
 		button.style.marginRight = "4px";
 		return button;
 	}
+
 	createRightButton(name, callback) {
 		var button = this.createButton(name, callback);
 		button.style.cssFloat = "right";
 		button.style.marginLeft = "4px";
 		return button;
 	}
+
 	createLeftSlider(self, name, callback) {
 		const divElement = document.createElement('div');
 		divElement.id = "maskeditor-slider";
@@ -164,7 +174,7 @@ class MaskEditorDialog extends ComfyDialog {
 		brush.style.MozBorderRadius = "50%";
 		brush.style.WebkitBorderRadius = "50%";
 		brush.style.position = "absolute";
-		brush.style.zIndex = 100;
+		brush.style.zIndex = 8889;
 		brush.style.pointerEvents = "none";
 		this.brush = brush;
 		this.element.appendChild(imgCanvas);
@@ -187,7 +197,8 @@ class MaskEditorDialog extends ComfyDialog {
 			document.removeEventListener("keydown", MaskEditorDialog.handleKeyDown);
 			self.close();
 		});
-		var saveButton = this.createRightButton("Save", () => {
+
+		this.saveButton = this.createRightButton("Save", () => {
 			document.removeEventListener("mouseup", MaskEditorDialog.handleMouseUp);
 			document.removeEventListener("keydown", MaskEditorDialog.handleKeyDown);
 				self.save();
@@ -199,11 +210,10 @@ class MaskEditorDialog extends ComfyDialog {
 		this.element.appendChild(bottom_panel);
 
 		bottom_panel.appendChild(clearButton);
-		bottom_panel.appendChild(saveButton);
+		bottom_panel.appendChild(this.saveButton);
 		bottom_panel.appendChild(cancelButton);
 		bottom_panel.appendChild(brush_size_slider);
 
-		this.element.style.display = "block";
 		imgCanvas.style.position = "relative";
 		imgCanvas.style.top = "200";
 		imgCanvas.style.left = "0";
@@ -212,25 +222,63 @@ class MaskEditorDialog extends ComfyDialog {
 	}
 
 	show() {
-		// layout
-		const imgCanvas = document.createElement('canvas');
-		const maskCanvas = document.createElement('canvas');
-		const backupCanvas = document.createElement('canvas');
+		if(!this.is_layout_created) {
+			// layout
+			const imgCanvas = document.createElement('canvas');
+			const maskCanvas = document.createElement('canvas');
+			const backupCanvas = document.createElement('canvas');
 
-		imgCanvas.id = "imageCanvas";
-		maskCanvas.id = "maskCanvas";
-		backupCanvas.id = "backupCanvas";
+			imgCanvas.id = "imageCanvas";
+			maskCanvas.id = "maskCanvas";
+			backupCanvas.id = "backupCanvas";
 
-		this.setlayout(imgCanvas, maskCanvas);
+			this.setlayout(imgCanvas, maskCanvas);
 
-		// prepare content
-		this.maskCanvas = maskCanvas;
-		this.backupCanvas = backupCanvas;
-		this.maskCtx = maskCanvas.getContext('2d');
-		this.backupCtx = backupCanvas.getContext('2d');
+			// prepare content
+			this.imgCanvas = imgCanvas;
+			this.maskCanvas = maskCanvas;
+			this.backupCanvas = backupCanvas;
+			this.maskCtx = maskCanvas.getContext('2d');
+			this.backupCtx = backupCanvas.getContext('2d');
 
-		this.setImages(imgCanvas, backupCanvas);
-		this.setEventHandler(maskCanvas);
+			this.setEventHandler(maskCanvas);
+
+			this.is_layout_created = true;
+
+			// replacement of onClose hook since close is not real close
+			const self = this;
+			const observer = new MutationObserver(function(mutations) {
+			mutations.forEach(function(mutation) {
+					if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
+						if(self.last_display_style && self.last_display_style != 'none' && self.element.style.display == 'none') {
+							ComfyApp.onClipspaceEditorClosed();
+						}
+
+						self.last_display_style = self.element.style.display;
+					}
+				});
+			});
+
+			const config = { attributes: true };
+			observer.observe(this.element, config);
+		}
+
+		this.setImages(this.imgCanvas, this.backupCanvas);
+
+		if(ComfyApp.clipspace_return_node) {
+			this.saveButton.innerText = "Save to node";
+		}
+		else {
+			this.saveButton.innerText = "Save";
+		}
+		this.saveButton.disabled = false;
+
+		this.element.style.display = "block";
+		this.element.style.zIndex = 8888; // NOTE: alert dialog must be high priority.
+	}
+
+	isOpened() {
+		return this.element.style.display == "block";
 	}
 
 	setImages(imgCanvas, backupCanvas) {
@@ -239,6 +287,10 @@ class MaskEditorDialog extends ComfyDialog {
 		const maskCtx = this.maskCtx;
 		const maskCanvas = this.maskCanvas;
 
+		backupCtx.clearRect(0,0,this.backupCanvas.width,this.backupCanvas.height);
+		imgCtx.clearRect(0,0,this.imgCanvas.width,this.imgCanvas.height);
+		maskCtx.clearRect(0,0,this.maskCanvas.width,this.maskCanvas.height);
+
 		// image load
 		const orig_image = new Image();
 		window.addEventListener("resize", () => {
@@ -296,8 +348,7 @@ class MaskEditorDialog extends ComfyDialog {
 		rgb_url.searchParams.set('channel', 'rgb');
 		orig_image.src = rgb_url;
 		this.image = orig_image;
-	}g
-
+	}
 
 	setEventHandler(maskCanvas) {
 		maskCanvas.addEventListener("contextmenu", (event) => {
@@ -327,6 +378,8 @@ class MaskEditorDialog extends ComfyDialog {
 			self.brush_size = Math.min(self.brush_size+2, 100);
 		} else if (event.key === '[') {
 			self.brush_size = Math.max(self.brush_size-2, 1);
+		} else if(event.key === 'Enter') {
+			self.save();
 		}
 
 		self.updateBrushPreview(self);
@@ -514,7 +567,7 @@ class MaskEditorDialog extends ComfyDialog {
 		}
 	}
 
-	save() {
+	async save() {
 		const backupCtx = this.backupCanvas.getContext('2d', {willReadFrequently:true});
 
 		backupCtx.clearRect(0,0,this.backupCanvas.width,this.backupCanvas.height);
@@ -570,7 +623,10 @@ class MaskEditorDialog extends ComfyDialog {
 		formData.append('type', "input");
 		formData.append('subfolder', "clipspace");
 
-		uploadMask(item, formData);
+		this.saveButton.innerText = "Saving...";
+		this.saveButton.disabled = true;
+		await uploadMask(item, formData);
+		ComfyApp.onClipspaceEditorSave();
 		this.close();
 	}
 }
@@ -578,13 +634,15 @@ class MaskEditorDialog extends ComfyDialog {
 app.registerExtension({
 	name: "Comfy.MaskEditor",
 	init(app) {
-		const callback =
+		ComfyApp.open_maskeditor =
 			function () {
-				let dlg = new MaskEditorDialog(app);
-				dlg.show();
+				const dlg = MaskEditorDialog.getInstance();
+				if(!dlg.isOpened()) {
+					dlg.show();
+				}
 			};
 
 		const context_predicate = () => ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0
-		ClipspaceDialog.registerButton("MaskEditor", context_predicate, callback);
+		ClipspaceDialog.registerButton("MaskEditor", context_predicate, ComfyApp.open_maskeditor);
 	}
 });

web/extensions/core/widgetInputs.js

@@ -300,7 +300,7 @@ app.registerExtension({
 					}
 				}
 
-				if (widget.type === "number") {
+				if (widget.type === "number" || widget.type === "combo") {
 					addValueControlWidget(this, widget, "fixed");
 				}
 

web/lib/litegraph.core.js

@@ -5880,13 +5880,13 @@ LGraphNode.prototype.executeAction = function(action)
 
             //when clicked on top of a node
             //and it is not interactive
-            if (node && this.allow_interaction && !skip_action && !this.read_only) {
+            if (node && (this.allow_interaction || node.flags.allow_interaction) && !skip_action && !this.read_only) {
                 if (!this.live_mode && !node.flags.pinned) {
                     this.bringToFront(node);
                 } //if it wasn't selected?
 
                 //not dragging mouse to connect two slots
-                if ( !this.connecting_node && !node.flags.collapsed && !this.live_mode ) {
+                if ( this.allow_interaction && !this.connecting_node && !node.flags.collapsed && !this.live_mode ) {
                     //Search for corner for resize
                     if ( !skip_action &&
                         node.resizable !== false && node.inResizeCorner(e.canvasX, e.canvasY)
@@ -6033,7 +6033,7 @@ LGraphNode.prototype.executeAction = function(action)
                     }
 
                     //double clicking
-                    if (is_double_click && this.selected_nodes[node.id]) {
+                    if (this.allow_interaction && is_double_click && this.selected_nodes[node.id]) {
                         //double click node
                         if (node.onDblClick) {
                             node.onDblClick( e, pos, this );
@@ -6307,6 +6307,9 @@ LGraphNode.prototype.executeAction = function(action)
             this.dirty_canvas = true;
         }
 
+        //get node over
+        var node = this.graph.getNodeOnPos(e.canvasX,e.canvasY,this.visible_nodes);
+
         if (this.dragging_rectangle)
 		{
             this.dragging_rectangle[2] = e.canvasX - this.dragging_rectangle[0];
@@ -6336,14 +6339,11 @@ LGraphNode.prototype.executeAction = function(action)
             this.ds.offset[1] += delta[1] / this.ds.scale;
             this.dirty_canvas = true;
             this.dirty_bgcanvas = true;
-        } else if (this.allow_interaction && !this.read_only) {
+        } else if ((this.allow_interaction || (node && node.flags.allow_interaction)) && !this.read_only) {
             if (this.connecting_node) {
                 this.dirty_canvas = true;
             }
 
-            //get node over
-            var node = this.graph.getNodeOnPos(e.canvasX,e.canvasY,this.visible_nodes);
-
             //remove mouseover flag
             for (var i = 0, l = this.graph._nodes.length; i < l; ++i) {
                 if (this.graph._nodes[i].mouseOver && node != this.graph._nodes[i] ) {
@@ -9734,7 +9734,7 @@ LGraphNode.prototype.executeAction = function(action)
                     if (show_text) {
                         ctx.textAlign = "center";
                         ctx.fillStyle = text_color;
-                        ctx.fillText(w.name, widget_width * 0.5, y + H * 0.7);
+                        ctx.fillText(w.label || w.name, widget_width * 0.5, y + H * 0.7);
                     }
                     break;
                 case "toggle":
@@ -9755,8 +9755,9 @@ LGraphNode.prototype.executeAction = function(action)
                     ctx.fill();
                     if (show_text) {
                         ctx.fillStyle = secondary_text_color;
-                        if (w.name != null) {
-                            ctx.fillText(w.name, margin * 2, y + H * 0.7);
+                        const label = w.label || w.name;    
+                        if (label != null) {
+                            ctx.fillText(label, margin * 2, y + H * 0.7);
                         }
                         ctx.fillStyle = w.value ? text_color : secondary_text_color;
                         ctx.textAlign = "right";
@@ -9791,7 +9792,7 @@ LGraphNode.prototype.executeAction = function(action)
                         ctx.textAlign = "center";
                         ctx.fillStyle = text_color;
                         ctx.fillText(
-                            w.name + "  " + Number(w.value).toFixed(3),
+                            w.label || w.name + "  " + Number(w.value).toFixed(3),
                             widget_width * 0.5,
                             y + H * 0.7
                         );
@@ -9826,7 +9827,7 @@ LGraphNode.prototype.executeAction = function(action)
 							ctx.fill();
 						}
                         ctx.fillStyle = secondary_text_color;
-                        ctx.fillText(w.name, margin * 2 + 5, y + H * 0.7);
+                        ctx.fillText(w.label || w.name, margin * 2 + 5, y + H * 0.7);
                         ctx.fillStyle = text_color;
                         ctx.textAlign = "right";
                         if (w.type == "number") {
@@ -9878,8 +9879,9 @@ LGraphNode.prototype.executeAction = function(action)
 
 	                    //ctx.stroke();
                         ctx.fillStyle = secondary_text_color;
-                        if (w.name != null) {
-                            ctx.fillText(w.name, margin * 2, y + H * 0.7);
+                        const label = w.label || w.name;	
+                        if (label != null) {
+                            ctx.fillText(label, margin * 2, y + H * 0.7);
                         }
                         ctx.fillStyle = text_color;
                         ctx.textAlign = "right";
@@ -9911,7 +9913,7 @@ LGraphNode.prototype.executeAction = function(action)
         event,
         active_widget
     ) {
-        if (!node.widgets || !node.widgets.length) {
+        if (!node.widgets || !node.widgets.length || (!this.allow_interaction && !node.flags.allow_interaction)) {
             return null;
         }
 
@@ -10300,6 +10302,119 @@ LGraphNode.prototype.executeAction = function(action)
         canvas.graph.add(group);
     };
 
+    /**
+     * Determines the furthest nodes in each direction
+     * @param nodes {LGraphNode[]} the nodes to from which boundary nodes will be extracted
+     * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}}
+     */
+    LGraphCanvas.getBoundaryNodes = function(nodes) {
+        let top = null;
+        let right = null;
+        let bottom = null;
+        let left = null;
+        for (const nID in nodes) {
+            const node = nodes[nID];
+            const [x, y] = node.pos;
+            const [width, height] = node.size;
+
+            if (top === null || y < top.pos[1]) {
+                top = node;
+            }
+            if (right === null || x + width > right.pos[0] + right.size[0]) {
+                right = node;
+            }
+            if (bottom === null || y + height > bottom.pos[1] + bottom.size[1]) {
+                bottom = node;
+            }
+            if (left === null || x < left.pos[0]) {
+                left = node;
+            }
+        }
+
+        return {
+            "top": top,
+            "right": right,
+            "bottom": bottom,
+            "left": left
+        };
+    }
+    /**
+     * Determines the furthest nodes in each direction for the currently selected nodes
+     * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}}
+     */
+    LGraphCanvas.prototype.boundaryNodesForSelection = function() {
+        return LGraphCanvas.getBoundaryNodes(Object.values(this.selected_nodes));
+    }
+
+    /**
+     *
+     * @param {LGraphNode[]} nodes a list of nodes
+     * @param {"top"|"bottom"|"left"|"right"} direction Direction to align the nodes
+     * @param {LGraphNode?} align_to Node to align to (if null, align to the furthest node in the given direction)
+     */
+    LGraphCanvas.alignNodes = function (nodes, direction, align_to) {
+        if (!nodes) {
+            return;
+        }
+
+        const canvas = LGraphCanvas.active_canvas;
+        let boundaryNodes = []
+        if (align_to === undefined) {
+            boundaryNodes = LGraphCanvas.getBoundaryNodes(nodes)
+        } else {
+            boundaryNodes = {
+                "top": align_to,
+                "right": align_to,
+                "bottom": align_to,
+                "left": align_to
+            }
+        }
+
+        for (const [_, node] of Object.entries(canvas.selected_nodes)) {
+            switch (direction) {
+                case "right":
+                    node.pos[0] = boundaryNodes["right"].pos[0] + boundaryNodes["right"].size[0] - node.size[0];
+                    break;
+                case "left":
+                    node.pos[0] = boundaryNodes["left"].pos[0];
+                    break;
+                case "top":
+                    node.pos[1] = boundaryNodes["top"].pos[1];
+                    break;
+                case "bottom":
+                    node.pos[1] = boundaryNodes["bottom"].pos[1] + boundaryNodes["bottom"].size[1] - node.size[1];
+                    break;
+            }
+        }
+
+        canvas.dirty_canvas = true;
+        canvas.dirty_bgcanvas = true;
+    };
+
+    LGraphCanvas.onNodeAlign = function(value, options, event, prev_menu, node) {
+        new LiteGraph.ContextMenu(["Top", "Bottom", "Left", "Right"], {
+            event: event,
+            callback: inner_clicked,
+            parentMenu: prev_menu,
+        });
+
+        function inner_clicked(value) {
+            LGraphCanvas.alignNodes(LGraphCanvas.active_canvas.selected_nodes, value.toLowerCase(), node);
+        }
+    }
+
+    LGraphCanvas.onGroupAlign = function(value, options, event, prev_menu) {
+        new LiteGraph.ContextMenu(["Top", "Bottom", "Left", "Right"], {
+            event: event,
+            callback: inner_clicked,
+            parentMenu: prev_menu,
+        });
+
+        function inner_clicked(value) {
+            LGraphCanvas.alignNodes(LGraphCanvas.active_canvas.selected_nodes, value.toLowerCase());
+        }
+    }
+
     LGraphCanvas.onMenuAdd = function (node, options, e, prev_menu, callback) {
 
         var canvas = LGraphCanvas.active_canvas;
@@ -12900,6 +13015,14 @@ LGraphNode.prototype.executeAction = function(action)
                 options.push({ content: "Options", callback: that.showShowGraphOptionsPanel });
             }*/
 
+            if (Object.keys(this.selected_nodes).length > 1) {
+                options.push({
+                    content: "Align",
+                    has_submenu: true,
+                    callback: LGraphCanvas.onGroupAlign,
+                })
+            }
+
             if (this._graph_stack && this._graph_stack.length > 0) {
                 options.push(null, {
                     content: "Close subgraph",
@@ -13014,6 +13137,14 @@ LGraphNode.prototype.executeAction = function(action)
 			callback: LGraphCanvas.onMenuNodeToSubgraph
 		});
 
+        if (Object.keys(this.selected_nodes).length > 1) {
+            options.push({
+                content: "Align Selected To",
+                has_submenu: true,
+                callback: LGraphCanvas.onNodeAlign,
+            })
+        }
+
 		options.push(null, {
 			content: "Remove",
 			disabled: !(node.removable !== false && !node.block_delete ),

web/scripts/api.js

@@ -163,7 +163,7 @@ class ComfyApi extends EventTarget {
 
 		if (res.status !== 200) {
 			throw {
-				response: await res.text(),
+				response: await res.json(),
 			};
 		}
 	}

web/scripts/app.js

@@ -2,7 +2,7 @@ import { ComfyWidgets } from "./widgets.js";
 import { ComfyUI, $el } from "./ui.js";
 import { api } from "./api.js";
 import { defaultGraph } from "./defaultGraph.js";
-import { getPngMetadata, importA1111 } from "./pnginfo.js";
+import { getPngMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
 
 /** 
  * @typedef {import("types/comfy").ComfyExtension} ComfyExtension
@@ -26,6 +26,8 @@ export class ComfyApp {
 	 */
 	static clipspace = null;
 	static clipspace_invalidate_handler = null;
+	static open_maskeditor = null;
+	static clipspace_return_node = null;
 
 	constructor() {
 		this.ui = new ComfyUI(this);
@@ -49,6 +51,114 @@ export class ComfyApp {
 		this.shiftDown = false;
 	}
 
+	static isImageNode(node) {
+		return node.imgs || (node && node.widgets && node.widgets.findIndex(obj => obj.name === 'image') >= 0);
+	}
+
+	static onClipspaceEditorSave() {
+		if(ComfyApp.clipspace_return_node) {
+			ComfyApp.pasteFromClipspace(ComfyApp.clipspace_return_node);
+		}
+	}
+
+	static onClipspaceEditorClosed() {
+		ComfyApp.clipspace_return_node = null;
+	}
+
+	static copyToClipspace(node) {
+		var widgets = null;
+		if(node.widgets) {
+			widgets = node.widgets.map(({ type, name, value }) => ({ type, name, value }));
+		}
+
+		var imgs = undefined;
+		var orig_imgs = undefined;
+		if(node.imgs != undefined) {
+			imgs = [];
+			orig_imgs = [];
+
+			for (let i = 0; i < node.imgs.length; i++) {
+				imgs[i] = new Image();
+				imgs[i].src = node.imgs[i].src;
+				orig_imgs[i] = imgs[i];
+			}
+		}
+
+		var selectedIndex = 0;
+		if(node.imageIndex) {
+			selectedIndex = node.imageIndex;
+		}
+
+		ComfyApp.clipspace = {
+			'widgets': widgets,
+			'imgs': imgs,
+			'original_imgs': orig_imgs,
+			'images': node.images,
+			'selectedIndex': selectedIndex,
+			'img_paste_mode': 'selected' // reset to default im_paste_mode state on copy action
+		};
+
+		ComfyApp.clipspace_return_node = null;
+
+		if(ComfyApp.clipspace_invalidate_handler) {
+			ComfyApp.clipspace_invalidate_handler();
+		}
+	}
+
+	static pasteFromClipspace(node) {
+		if(ComfyApp.clipspace) {
+			// image paste
+			if(ComfyApp.clipspace.imgs && node.imgs) {
+				if(node.images && ComfyApp.clipspace.images) {
+					if(ComfyApp.clipspace['img_paste_mode'] == 'selected') {
+						app.nodeOutputs[node.id + ""].images = node.images = [ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']]];
+					}
+					else
+						app.nodeOutputs[node.id + ""].images = node.images = ComfyApp.clipspace.images;
+				}
+
+				if(ComfyApp.clipspace.imgs) {
+					// deep-copy to cut link with clipspace
+					if(ComfyApp.clipspace['img_paste_mode'] == 'selected') {
+						const img = new Image();
+						img.src = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src;
+						node.imgs = [img];
+						node.imageIndex = 0;
+					}
+					else {
+						const imgs = [];
+						for(let i=0; i<ComfyApp.clipspace.imgs.length; i++) {
+							imgs[i] = new Image();
+							imgs[i].src = ComfyApp.clipspace.imgs[i].src;
+							node.imgs = imgs;
+						}
+					}
+				}
+			}
+
+			if(node.widgets) {
+				if(ComfyApp.clipspace.images) {
+					const clip_image = ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']];
+					const index = node.widgets.findIndex(obj => obj.name === 'image');
+					if(index >= 0) {
+						node.widgets[index].value = clip_image;
+					}
+				}
+				if(ComfyApp.clipspace.widgets) {
+					ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => {
+						const prop = Object.values(node.widgets).find(obj => obj.type === type && obj.name === name);
+						if (prop && prop.type != 'button') {
+							prop.value = value;
+							prop.callback(value);
+						}
+					});
+				}
+			}
+
+			app.graph.setDirtyCanvas(true);
+		}
+	}
+
 	/**
 	 * Invoke an extension callback
 	 * @param {keyof ComfyExtension} method The extension callback to execute
@@ -138,102 +248,30 @@ export class ComfyApp {
 				}
 			}
 
-			options.push(
-				{
-					content: "Copy (Clipspace)",
-					callback: (obj) => {
-						var widgets = null;
-						if(this.widgets) {
-							widgets = this.widgets.map(({ type, name, value }) => ({ type, name, value }));
-						}
-						
-						var imgs = undefined;
-						var orig_imgs = undefined;
-						if(this.imgs != undefined) {
-							imgs = [];
-							orig_imgs = [];
+			// prevent conflict of clipspace content
+			if(!ComfyApp.clipspace_return_node) {
+				options.push({
+						content: "Copy (Clipspace)",
+						callback: (obj) => { ComfyApp.copyToClipspace(this); }
+					});
 
-							for (let i = 0; i < this.imgs.length; i++) {
-								imgs[i] = new Image();
-								imgs[i].src = this.imgs[i].src;
-								orig_imgs[i] = imgs[i];
+				if(ComfyApp.clipspace != null) {
+					options.push({
+							content: "Paste (Clipspace)",
+							callback: () => { ComfyApp.pasteFromClipspace(this); }
+						});
+				}
+
+				if(ComfyApp.isImageNode(this)) {
+					options.push({
+							content: "Open in MaskEditor",
+							callback: (obj) => {
+								ComfyApp.copyToClipspace(this);
+								ComfyApp.clipspace_return_node = this;
+								ComfyApp.open_maskeditor();
 							}
-						}
-
-						ComfyApp.clipspace = {
-							'widgets': widgets,
-							'imgs': imgs,
-							'original_imgs': orig_imgs,
-							'images': this.images,
-							'selectedIndex': 0,
-							'img_paste_mode': 'selected' // reset to default im_paste_mode state on copy action
-							};
-
-						if(ComfyApp.clipspace_invalidate_handler) {
-							ComfyApp.clipspace_invalidate_handler();
-						}
-					}
-				});
-
-			if(ComfyApp.clipspace != null) {
-				options.push(
-					{
-						content: "Paste (Clipspace)",
-						callback: () => {
-							if(ComfyApp.clipspace) {
-								// image paste
-								if(ComfyApp.clipspace.imgs && this.imgs) {
-									if(this.images && ComfyApp.clipspace.images) {
-										if(ComfyApp.clipspace['img_paste_mode'] == 'selected') {
-											app.nodeOutputs[this.id + ""].images = this.images = [ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']]];
-
-										}
-										else
-											app.nodeOutputs[this.id + ""].images = this.images = ComfyApp.clipspace.images;
-									}
-
-									if(ComfyApp.clipspace.imgs) {
-										// deep-copy to cut link with clipspace
-										if(ComfyApp.clipspace['img_paste_mode'] == 'selected') {
-												const img = new Image();
-												img.src = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src;
-												this.imgs = [img];
-										}
-										else {
-											const imgs = [];
-											for(let i=0; i<ComfyApp.clipspace.imgs.length; i++) {
-												imgs[i] = new Image();
-												imgs[i].src = ComfyApp.clipspace.imgs[i].src;
-												this.imgs = imgs;
-											}
-										}
-									}
-								}
-
-								if(this.widgets) {
-									if(ComfyApp.clipspace.images) {
-										const clip_image = ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']];
-										const index = this.widgets.findIndex(obj => obj.name === 'image');
-										if(index >= 0) {
-											this.widgets[index].value = clip_image;
-										}
-									}
-									if(ComfyApp.clipspace.widgets) {
-										ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => {
-											const prop = Object.values(this.widgets).find(obj => obj.type === type && obj.name === name);
-												if (prop && prop.type != 'button') {
-													prop.value = value;
-													prop.callback(value);
-												}
-										});
-									}
-								}
-							}
-
-							app.graph.setDirtyCanvas(true);
-						}
-					}
-				);
+						});
+				}
 			}
 		};
 	}
@@ -864,7 +902,9 @@ export class ComfyApp {
 		await this.#loadExtensions();
 
 		// Create and mount the LiteGraph in the DOM
-		const canvasEl = (this.canvasEl = Object.assign(document.createElement("canvas"), { id: "graph-canvas" }));
+		const mainCanvas = document.createElement("canvas")
+		mainCanvas.style.touchAction = "none"
+		const canvasEl = (this.canvasEl = Object.assign(mainCanvas, { id: "graph-canvas" }));
 		canvasEl.tabIndex = "1";
 		document.body.prepend(canvasEl);
 
@@ -976,7 +1016,8 @@ export class ComfyApp {
 					for (const o in nodeData["output"]) {
 						const output = nodeData["output"][o];
 						const outputName = nodeData["output_name"][o] || output;
-						this.addOutput(outputName, output);
+						const outputShape = nodeData["output_is_list"][o] ? LiteGraph.GRID_SHAPE : LiteGraph.CIRCLE_SHAPE ;
+						this.addOutput(outputName, output, { shape: outputShape });
 					}
 
 					const s = this.computeSize();
@@ -1237,7 +1278,7 @@ export class ComfyApp {
 					try {
 						await api.queuePrompt(number, p);
 					} catch (error) {
-						this.ui.dialog.show(error.response || error.toString());
+						this.ui.dialog.show(error.response.error || error.toString());
 						break;
 					}
 
@@ -1283,6 +1324,11 @@ export class ComfyApp {
 				this.loadGraphData(JSON.parse(reader.result));
 			};
 			reader.readAsText(file);
+		} else if (file.name?.endsWith(".latent")) {
+			const info = await getLatentMetadata(file);
+			if (info.workflow) {
+				this.loadGraphData(JSON.parse(info.workflow));
+			}
 		}
 	}
 

web/scripts/pnginfo.js

@@ -47,6 +47,22 @@ export function getPngMetadata(file) {
 	});
 }
 
+export function getLatentMetadata(file) {
+	return new Promise((r) => {
+		const reader = new FileReader();
+		reader.onload = (event) => {
+			const safetensorsData = new Uint8Array(event.target.result);
+			const dataView = new DataView(safetensorsData.buffer);
+			let header_size = dataView.getUint32(0, true);
+			let offset = 8;
+			let header = JSON.parse(String.fromCharCode(...safetensorsData.slice(offset, offset + header_size)));
+			r(header.__metadata__);
+		};
+
+		reader.readAsArrayBuffer(file);
+	});
+}
+
 export async function importA1111(graph, parameters) {
 	const p = parameters.lastIndexOf("\nSteps:");
 	if (p > -1) {

web/scripts/ui.js

@@ -465,7 +465,7 @@ export class ComfyUI {
 		const fileInput = $el("input", {
 			id: "comfy-file-input",
 			type: "file",
-			accept: ".json,image/png",
+			accept: ".json,image/png,.latent",
 			style: { display: "none" },
 			parent: document.body,
 			onchange: () => {

web/scripts/widgets.js

@@ -19,35 +19,60 @@ export function addValueControlWidget(node, targetWidget, defaultValue = "random
 
 		var v = valueControl.value;
 
-		let min = targetWidget.options.min;
-		let max = targetWidget.options.max;
-		// limit to something that javascript can handle
-		max = Math.min(1125899906842624, max);
-		min = Math.max(-1125899906842624, min);
-		let range = (max - min) / (targetWidget.options.step / 10);
+		if (targetWidget.type == "combo" && v !== "fixed") {
+			let current_index = targetWidget.options.values.indexOf(targetWidget.value);
+			let current_length = targetWidget.options.values.length;
 
-		//adjust values based on valueControl Behaviour
-		switch (v) {
-			case "fixed":
-				break;
-			case "increment":
-				targetWidget.value += targetWidget.options.step / 10;
-				break;
-			case "decrement":
-				targetWidget.value -= targetWidget.options.step / 10;
-				break;
-			case "randomize":
-				targetWidget.value = Math.floor(Math.random() * range) * (targetWidget.options.step / 10) + min;
-			default:
-				break;
+			switch (v) {
+				case "increment":
+					current_index += 1;
+					break;
+				case "decrement":
+					current_index -= 1;
+					break;
+				case "randomize":
+					current_index = Math.floor(Math.random() * current_length);
+				default:
+					break;
+			}
+			current_index = Math.max(0, current_index);
+			current_index = Math.min(current_length - 1, current_index);
+			if (current_index >= 0) {
+				let value = targetWidget.options.values[current_index];
+				targetWidget.value = value;
+				targetWidget.callback(value);
+			}
+		} else { //number
+			let min = targetWidget.options.min;
+			let max = targetWidget.options.max;
+			// limit to something that javascript can handle
+			max = Math.min(1125899906842624, max);
+			min = Math.max(-1125899906842624, min);
+			let range = (max - min) / (targetWidget.options.step / 10);
+
+			//adjust values based on valueControl Behaviour
+			switch (v) {
+				case "fixed":
+					break;
+				case "increment":
+					targetWidget.value += targetWidget.options.step / 10;
+					break;
+				case "decrement":
+					targetWidget.value -= targetWidget.options.step / 10;
+					break;
+				case "randomize":
+					targetWidget.value = Math.floor(Math.random() * range) * (targetWidget.options.step / 10) + min;
+				default:
+					break;
+			}
+		/*check if values are over or under their respective
+		* ranges and set them to min or max.*/
+			if (targetWidget.value < min)
+				targetWidget.value = min;
+
+			if (targetWidget.value > max)
+				targetWidget.value = max;
 		}
-	/*check if values are over or under their respective
-	 * ranges and set them to min or max.*/
-		if (targetWidget.value < min)
-			targetWidget.value = min;
-
-		if (targetWidget.value > max)
-			targetWidget.value = max;
 	}
 	return valueControl;	
 };
@@ -130,18 +155,24 @@ function addMultilineWidget(node, name, opts, app) {
 				computeSize(node.size);
 			}
 			const visible = app.canvas.ds.scale > 0.5 && this.type === "customtext";
-			const t = ctx.getTransform();
 			const margin = 10;
+			const elRect = ctx.canvas.getBoundingClientRect();
+			const transform = new DOMMatrix()
+				.scaleSelf(elRect.width / ctx.canvas.width, elRect.height / ctx.canvas.height)
+				.multiplySelf(ctx.getTransform())
+				.translateSelf(margin, margin + y);
+
 			Object.assign(this.inputEl.style, {
-				left: `${t.a * margin + t.e}px`,
-				top: `${t.d * (y + widgetHeight - margin - 3) + t.f}px`,
-				width: `${(widgetWidth - margin * 2 - 3) * t.a}px`,
-				background: (!node.color)?'':node.color,
-				height: `${(this.parent.inputHeight - margin * 2 - 4) * t.d}px`,
+				transformOrigin: "0 0",
+				transform: transform,
+				left: "0px",
+				top: "0px",
+				width: `${widgetWidth - (margin * 2)}px`,
+				height: `${this.parent.inputHeight - (margin * 2)}px`,
 				position: "absolute",
+				background: (!node.color)?'':node.color,
 				color: (!node.color)?'':'white',
 				zIndex: app.graph._nodes.indexOf(node),
-				fontSize: `${t.d * 10.0}px`,
 			});
 			this.inputEl.hidden = !visible;
 		},

web/style.css

@@ -39,6 +39,8 @@ body {
 	padding: 2px;
 	resize: none;
 	border: none;
+	box-sizing: border-box;
+	font-size: 10px;
 }
 
 .comfy-modal {