Merge branch 'master' into aitemplate

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)
+        r1 = slice_attention(q, k, v)
-
-        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)
-
         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)
+            lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .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 = (
+        try:
-            out.unsqueeze(0)
+            out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
-            .reshape(B, 1, out.shape[1], C)
+            out = out.transpose(2, 3).reshape(B, C, H, W)
-            .permute(0, 2, 1, 3)
+        except model_management.OOM_EXCEPTION as e:
-            .reshape(B, out.shape[1], C)
+            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 = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
+
         out = self.proj_out(out)
         return x+out
 

comfy/samplers.py

@@ -555,10 +555,10 @@ class AITemplateModelWrapper:
         return noise_pred
 
 class KSampler:
-    SCHEDULERS = ["normal", "karras", "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={}, aitemplate=None, cfg=None):
         self.model = model
@@ -596,6 +596,8 @@ 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_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[center, center] = kernel[center, center] - kernel.sum() + 1.0
-        kernel *= alpha
         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)

execution.py

@@ -328,9 +328,9 @@ def validate_inputs(prompt, item, validated):
 
             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)

folder_paths.py

@@ -148,4 +148,37 @@ 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

nodes.py

@@ -7,16 +7,15 @@ import hashlib
 import traceback
 import math
 import time
-from comfy.aitemplate.model import Model
+from PIL import Image, ImageOps
-from diffusers import LMSDiscreteScheduler
-from PIL import Image
 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"))
 
-
+from comfy.aitemplate.model import Model
 import comfy.diffusers_convert
 import comfy.samplers
 import comfy.sample
@@ -30,6 +29,7 @@ import importlib
 
 import folder_paths
 
+
 def before_node_execution():
     comfy.model_management.throw_exception_if_processing_interrupted()
 
@@ -247,6 +247,81 @@ 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):
@@ -1258,39 +1333,7 @@ class SaveImage:
     CATEGORY = "image"
 
     def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        def map_filename(filename):
+        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])
-            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
-
         results = list()
         for image in images:
             i = 255. * image.cpu().numpy()
@@ -1341,6 +1384,7 @@ 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,]
@@ -1384,6 +1428,7 @@ 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
@@ -1566,6 +1611,9 @@ NODE_CLASS_MAPPINGS = {
 
     "CheckpointLoader": CheckpointLoader,
     "DiffusersLoader": DiffusersLoader,
+
+    "LoadLatent": LoadLatent,
+    "SaveLatent": SaveLatent
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

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

@@ -261,23 +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]
+                out[x] = node_info(x)
-                info = {}
+            return web.json_response(out)
-                info['input'] = obj_class.INPUT_TYPES()
+
-                info['output'] = obj_class.RETURN_TYPES
+        @routes.get("/object_info/{node_class}")
-                info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
+        async def get_object_info_node(request):
-                info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']
+            node_class = request.match_info.get("node_class", None)
-                info['name'] = x
+            out = {}
-                info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[x] if x in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else x
+            if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS):
-                info['description'] = ''
+                out[node_class] = node_info(node_class)
-                info['category'] = 'sd'
-                if hasattr(obj_class, 'CATEGORY'):
-                    info['category'] = obj_class.CATEGORY
-                out[x] = info
             return web.json_response(out)
 
         @routes.get("/history")
@@ -320,7 +331,8 @@ class PromptServer():
                     extra_data["client_id"] = json_data["client_id"]
                 if valid[0]:
                     prompt_id = str(uuid.uuid4())
-                    self.prompt_queue.put((number, prompt_id, prompt, extra_data, valid[2]))
+                    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:
                     print("invalid prompt:", valid[1])

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

@@ -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) {
+                        const label = w.label || w.name;    
-                            ctx.fillText(w.name, margin * 2, y + H * 0.7);
+                        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) {
+                        const label = w.label || w.name;	
-                            ctx.fillText(w.name, margin * 2, y + H * 0.7);
+                        if (label != null) {
+                            ctx.fillText(label, margin * 2, y + H * 0.7);
                         }
                         ctx.fillStyle = text_color;
                         ctx.textAlign = "right";

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
@@ -902,7 +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);
 
@@ -1306,6 +1308,11 @@ 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;
+		if (targetWidget.type == "combo" && v !== "fixed") {
-		let max = targetWidget.options.max;
+			let current_index = targetWidget.options.values.indexOf(targetWidget.value);
-		// limit to something that javascript can handle
+			let current_length = targetWidget.options.values.length;
-		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) {
-		switch (v) {
+				case "increment":
-			case "fixed":
+					current_index += 1;
-				break;
+					break;
-			case "increment":
+				case "decrement":
-				targetWidget.value += targetWidget.options.step / 10;
+					current_index -= 1;
-				break;
+					break;
-			case "decrement":
+				case "randomize":
-				targetWidget.value -= targetWidget.options.step / 10;
+					current_index = Math.floor(Math.random() * current_length);
-				break;
+				default:
-			case "randomize":
+					break;
-				targetWidget.value = Math.floor(Math.random() * range) * (targetWidget.options.step / 10) + min;
+			}
-			default:
+			current_index = Math.max(0, current_index);
-				break;
+			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: `${t.a * margin + t.e}px`,
+				transformOrigin: "0 0",
-				top: `${t.d * (y + widgetHeight - margin - 3) + t.f}px`,
+				transform: transform,
-				width: `${(widgetWidth - margin * 2 - 3) * t.a}px`,
+				left: "0px",
-				background: (!node.color)?'':node.color,
+				top: "0px",
-				height: `${(this.parent.inputHeight - margin * 2 - 4) * t.d}px`,
+				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 {