Merge pull request #5 from xbol0/ext_i18n

update  & merge

.github/workflows/windows_release_cu118_dependencies_2.yml

@@ -2,6 +2,13 @@ name: "Windows Release cu118 dependencies 2"
 
 on:
   workflow_dispatch:
+    inputs:
+      xformers:
+        description: 'xformers version'
+        required: true
+        type: string
+        default: "xformers"
+
 #  push:
 #    branches:
 #      - master
@@ -17,7 +24,7 @@ jobs:
 
         - shell: bash
           run: |
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio xformers --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

CODEOWNERS

@@ -0,0 +1 @@
+*       @comfyanonymous

README.md

@@ -47,6 +47,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 | Ctrl + O                  | Load workflow                                                                                                      |
 | Ctrl + A                  | Select all nodes                                                                                                   |
 | Ctrl + M                  | Mute/unmute selected nodes                                                                                         |
+| Ctrl + B                  | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
 | Delete/Backspace          | Delete selected nodes                                                                                              |
 | Ctrl + Delete/Backspace   | Delete the current graph                                                                                           |
 | Space                     | Move the canvas around when held and moving the cursor                                                             |

comfy/cli_args.py

@@ -38,6 +38,7 @@ parser.add_argument("--port", type=int, default=8188, help="Set the listen port.
 parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
 parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
 parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
 parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")

comfy/clip_vision.py

@@ -24,8 +24,8 @@ class ClipVisionModel():
         return self.model.load_state_dict(sd, strict=False)
 
     def encode_image(self, image):
-        img = torch.clip((255. * image[0]), 0, 255).round().int()
-        inputs = self.processor(images=[img], return_tensors="pt")
+        img = torch.clip((255. * image), 0, 255).round().int()
+        inputs = self.processor(images=img, return_tensors="pt")
         outputs = self.model(**inputs)
         return outputs
 

comfy/k_diffusion/sampling.py

@@ -631,23 +631,78 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
                 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
+            if eta:
+                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
 
+@torch.no_grad()
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """DPM-Solver++(3M) SDE."""
+
+    seed = extra_args.get("seed", None)
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) 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]])
+
+    denoised_1, denoised_2 = None, None
+    h_1, h_2 = None, 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:
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            h_eta = h * (eta + 1)
+
+            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
+
+            if h_2 is not None:
+                r0 = h_1 / h
+                r1 = h_2 / h
+                d1_0 = (denoised - denoised_1) / r0
+                d1_1 = (denoised_1 - denoised_2) / r1
+                d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
+                d2 = (d1_0 - d1_1) / (r0 + r1)
+                phi_2 = h_eta.neg().expm1() / h_eta + 1
+                phi_3 = phi_2 / h_eta - 0.5
+                x = x + phi_2 * d1 - phi_3 * d2
+            elif h_1 is not None:
+                r = h_1 / h
+                d = (denoised - denoised_1) / r
+                phi_2 = h_eta.neg().expm1() / h_eta + 1
+                x = x + phi_2 * d
+
+            if eta:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+
+        denoised_1, denoised_2 = denoised, denoised_1
+        h_1, h_2 = h, h_1
+    return x
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+    return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
+
 @torch.no_grad()
 def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
 
-
 @torch.no_grad()
 def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
 
-

comfy/model_base.py

@@ -105,6 +105,29 @@ class BaseModel(torch.nn.Module):
 
         return {**unet_state_dict, **vae_state_dict, **clip_state_dict}
 
+def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0):
+    adm_inputs = []
+    weights = []
+    noise_aug = []
+    for unclip_cond in unclip_conditioning:
+        for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
+            weight = unclip_cond["strength"]
+            noise_augment = unclip_cond["noise_augmentation"]
+            noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
+            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
+            adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
+            weights.append(weight)
+            noise_aug.append(noise_augment)
+            adm_inputs.append(adm_out)
+
+    if len(noise_aug) > 1:
+        adm_out = torch.stack(adm_inputs).sum(0)
+        noise_augment = noise_augment_merge
+        noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
+        c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
+        adm_out = torch.cat((c_adm, noise_level_emb), 1)
+
+    return adm_out
 
 class SD21UNCLIP(BaseModel):
     def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):
@@ -114,33 +137,11 @@ class SD21UNCLIP(BaseModel):
     def encode_adm(self, **kwargs):
         unclip_conditioning = kwargs.get("unclip_conditioning", None)
         device = kwargs["device"]
-
-        if unclip_conditioning is not None:
-            adm_inputs = []
-            weights = []
-            noise_aug = []
-            for unclip_cond in unclip_conditioning:
-                adm_cond = unclip_cond["clip_vision_output"].image_embeds
-                weight = unclip_cond["strength"]
-                noise_augment = unclip_cond["noise_augmentation"]
-                noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)
-                c_adm, noise_level_emb = self.noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
-                adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
-                weights.append(weight)
-                noise_aug.append(noise_augment)
-                adm_inputs.append(adm_out)
-
-            if len(noise_aug) > 1:
-                adm_out = torch.stack(adm_inputs).sum(0)
-                #TODO: add a way to control this
-                noise_augment = 0.05
-                noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)
-                c_adm, noise_level_emb = self.noise_augmentor(adm_out[:, :self.noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
-                adm_out = torch.cat((c_adm, noise_level_emb), 1)
+        if unclip_conditioning is None:
+            return torch.zeros((1, self.adm_channels))
         else:
-            adm_out = torch.zeros((1, self.adm_channels))
+            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05))
 
-        return adm_out
 
 class SDInpaint(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):

comfy/model_detection.py

@@ -113,6 +113,7 @@ def model_config_from_unet_config(unet_config):
         if model_config.matches(unet_config):
             return model_config(unet_config)
 
+    print("no match", unet_config)
     return None
 
 def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):

comfy/samplers.py

@@ -189,12 +189,13 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                     continue
 
                 to_run += [(p, COND)]
-            for x in uncond:
-                p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
-                if p is None:
-                    continue
+            if uncond is not None:
+                for x in uncond:
+                    p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
+                    if p is None:
+                        continue
 
-                to_run += [(p, UNCOND)]
+                    to_run += [(p, UNCOND)]
 
             while len(to_run) > 0:
                 first = to_run[0]
@@ -282,6 +283,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
 
 
         max_total_area = model_management.maximum_batch_area()
+        if math.isclose(cond_scale, 1.0):
+            uncond = None
+
         cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options)
         if "sampler_cfg_function" in model_options:
             args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
@@ -343,6 +347,17 @@ def ddim_scheduler(model, steps):
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
+def sgm_scheduler(model, steps):
+    sigs = []
+    timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int)
+    for x in range(len(timesteps)):
+        ts = timesteps[x]
+        if ts > 999:
+            ts = 999
+        sigs.append(model.t_to_sigma(torch.tensor(ts)))
+    sigs += [0.0]
+    return torch.FloatTensor(sigs)
+
 def blank_inpaint_image_like(latent_image):
     blank_image = torch.ones_like(latent_image)
     # these are the values for "zero" in pixel space translated to latent space
@@ -521,10 +536,10 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
 
 
 class KSampler:
-    SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
+    SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
                 "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
+                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
 
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
         self.model = model
@@ -566,6 +581,8 @@ class KSampler:
             sigmas = simple_scheduler(self.model_wrap, steps)
         elif self.scheduler == "ddim_uniform":
             sigmas = ddim_scheduler(self.model_wrap, steps)
+        elif self.scheduler == "sgm_uniform":
+            sigmas = sgm_scheduler(self.model_wrap, steps)
         else:
             print("error invalid scheduler", self.scheduler)
 

comfy/sd.py

@@ -72,6 +72,7 @@ def load_lora(lora, to_load):
 
         regular_lora = "{}.lora_up.weight".format(x)
         diffusers_lora = "{}_lora.up.weight".format(x)
+        transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
 
         if regular_lora in lora.keys():
@@ -82,6 +83,10 @@ def load_lora(lora, to_load):
             A_name = diffusers_lora
             B_name = "{}_lora.down.weight".format(x)
             mid_name = None
+        elif transformers_lora in lora.keys():
+            A_name = transformers_lora
+            B_name ="{}.lora_linear_layer.down.weight".format(x)
+            mid_name = None
 
         if A_name is not None:
             mid = None
@@ -181,20 +186,29 @@ def model_lora_keys_clip(model, key_map={}):
                 key_map[lora_key] = k
                 lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
                 key_map[lora_key] = k
+                lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+                key_map[lora_key] = k
 
             k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
             if k in sdk:
                 lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
                 key_map[lora_key] = k
                 clip_l_present = True
+                lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+                key_map[lora_key] = k
 
             k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
             if k in sdk:
                 if clip_l_present:
                     lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
+                    key_map[lora_key] = k
+                    lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+                    key_map[lora_key] = k
                 else:
                     lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
-                key_map[lora_key] = k
+                    key_map[lora_key] = k
+                    lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+                    key_map[lora_key] = k
 
     return key_map
 
@@ -209,13 +223,16 @@ def model_lora_keys_unet(model, key_map={}):
     diffusers_keys = utils.unet_to_diffusers(model.model_config.unet_config)
     for k in diffusers_keys:
         if k.endswith(".weight"):
+            unet_key = "diffusion_model.{}".format(diffusers_keys[k])
             key_lora = k[:-len(".weight")].replace(".", "_")
-            key_map["lora_unet_{}".format(key_lora)] = "diffusion_model.{}".format(diffusers_keys[k])
+            key_map["lora_unet_{}".format(key_lora)] = unet_key
 
-            diffusers_lora_key = "unet.{}".format(k[:-len(".weight")].replace(".to_", ".processor.to_"))
-            if diffusers_lora_key.endswith(".to_out.0"):
-                diffusers_lora_key = diffusers_lora_key[:-2]
-            key_map[diffusers_lora_key] = "diffusion_model.{}".format(diffusers_keys[k])
+            diffusers_lora_prefix = ["", "unet."]
+            for p in diffusers_lora_prefix:
+                diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
+                if diffusers_lora_key.endswith(".to_out.0"):
+                    diffusers_lora_key = diffusers_lora_key[:-2]
+                key_map[diffusers_lora_key] = unet_key
     return key_map
 
 def set_attr(obj, attr, value):

comfy_extras/nodes_mask.py

@@ -2,6 +2,37 @@ import torch
 
 from nodes import MAX_RESOLUTION
 
+def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
+    if resize_source:
+        source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
+
+    x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
+    y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
+
+    left, top = (x // multiplier, y // multiplier)
+    right, bottom = (left + source.shape[3], top + source.shape[2],)
+
+    if mask is None:
+        mask = torch.ones_like(source)
+    else:
+        mask = mask.clone()
+        mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
+        mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
+
+    # calculate the bounds of the source that will be overlapping the destination
+    # this prevents the source trying to overwrite latent pixels that are out of bounds
+    # of the destination
+    visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
+
+    mask = mask[:, :, :visible_height, :visible_width]
+    inverse_mask = torch.ones_like(mask) - mask
+
+    source_portion = mask * source[:, :, :visible_height, :visible_width]
+    destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]
+
+    destination[:, :, top:bottom, left:right] = source_portion + destination_portion
+    return destination
+
 class LatentCompositeMasked:
     @classmethod
     def INPUT_TYPES(s):
@@ -11,6 +42,7 @@ class LatentCompositeMasked:
                 "source": ("LATENT",),
                 "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                 "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "resize_source": ("BOOLEAN", {"default": False}),
             },
             "optional": {
                 "mask": ("MASK",),
@@ -21,40 +53,36 @@ class LatentCompositeMasked:
 
     CATEGORY = "latent"
 
-    def composite(self, destination, source, x, y, mask = None):
+    def composite(self, destination, source, x, y, resize_source, mask = None):
         output = destination.copy()
         destination = destination["samples"].clone()
         source = source["samples"]
+        output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
+        return (output,)
 
-        x = max(-source.shape[3] * 8, min(x, destination.shape[3] * 8))
-        y = max(-source.shape[2] * 8, min(y, destination.shape[2] * 8))
+class ImageCompositeMasked:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "destination": ("IMAGE",),
+                "source": ("IMAGE",),
+                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "resize_source": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "composite"
 
-        left, top = (x // 8, y // 8)
-        right, bottom = (left + source.shape[3], top + source.shape[2],)
-
-
-        if mask is None:
-            mask = torch.ones_like(source)
-        else:
-            mask = mask.clone()
-            mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
-            mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
-
-        # calculate the bounds of the source that will be overlapping the destination
-        # this prevents the source trying to overwrite latent pixels that are out of bounds
-        # of the destination
-        visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
-
-        mask = mask[:, :, :visible_height, :visible_width]
-        inverse_mask = torch.ones_like(mask) - mask
-
-        source_portion = mask * source[:, :, :visible_height, :visible_width]
-        destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]
-
-        destination[:, :, top:bottom, left:right] = source_portion + destination_portion
-
-        output["samples"] = destination
+    CATEGORY = "image"
 
+    def composite(self, destination, source, x, y, resize_source, mask = None):
+        destination = destination.clone().movedim(-1, 1)
+        output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
         return (output,)
 
 class MaskToImage:
@@ -253,6 +281,7 @@ class FeatherMask:
 
 NODE_CLASS_MAPPINGS = {
     "LatentCompositeMasked": LatentCompositeMasked,
+    "ImageCompositeMasked": ImageCompositeMasked,
     "MaskToImage": MaskToImage,
     "ImageToMask": ImageToMask,
     "SolidMask": SolidMask,

comfy_extras/nodes_post_processing.py

@@ -59,8 +59,8 @@ 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")
+def gaussian_kernel(kernel_size: int, sigma: float, device=None):
+    x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij")
     d = torch.sqrt(x * x + y * y)
     g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
     return g / g.sum()
@@ -101,7 +101,7 @@ class Blur:
         batch_size, height, width, channels = image.shape
 
         kernel_size = blur_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device).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)
         padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')

cuda_malloc.py

@@ -36,13 +36,15 @@ def get_gpu_names():
     else:
         return set()
 
-def cuda_malloc_supported():
-    blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
-                 "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
-                 "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
-                 "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
-                 "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M"}
+blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
+                "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
+                "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
+                "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
+                "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M",
+                "GeForce GTX 1650", "GeForce GTX 1630"
+                }
 
+def cuda_malloc_supported():
     try:
         names = get_gpu_names()
     except:

folder_paths.py

@@ -43,6 +43,10 @@ def set_output_directory(output_dir):
     global output_directory
     output_directory = output_dir
 
+def set_temp_directory(temp_dir):
+    global temp_directory
+    temp_directory = temp_dir
+
 def get_output_directory():
     global output_directory
     return output_directory
@@ -111,6 +115,8 @@ def add_model_folder_path(folder_name, full_folder_path):
     global folder_names_and_paths
     if folder_name in folder_names_and_paths:
         folder_names_and_paths[folder_name][0].append(full_folder_path)
+    else:
+        folder_names_and_paths[folder_name] = ([full_folder_path], set())
 
 def get_folder_paths(folder_name):
     return folder_names_and_paths[folder_name][0][:]

main.py

@@ -72,6 +72,17 @@ from server import BinaryEventTypes
 from nodes import init_custom_nodes
 import comfy.model_management
 
+def cuda_malloc_warning():
+    device = comfy.model_management.get_torch_device()
+    device_name = comfy.model_management.get_torch_device_name(device)
+    cuda_malloc_warning = False
+    if "cudaMallocAsync" in device_name:
+        for b in cuda_malloc.blacklist:
+            if b in device_name:
+                cuda_malloc_warning = True
+        if cuda_malloc_warning:
+            print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
+
 def prompt_worker(q, server):
     e = execution.PromptExecutor(server)
     while True:
@@ -100,7 +111,7 @@ def hijack_progress(server):
 
 
 def cleanup_temp():
-    temp_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
+    temp_dir = folder_paths.get_temp_directory()
     if os.path.exists(temp_dir):
         shutil.rmtree(temp_dir, ignore_errors=True)
 
@@ -127,6 +138,10 @@ def load_extra_path_config(yaml_path):
 
 
 if __name__ == "__main__":
+    if args.temp_directory:
+        temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
+        print(f"Setting temp directory to: {temp_dir}")
+        folder_paths.set_temp_directory(temp_dir)
     cleanup_temp()
 
     loop = asyncio.new_event_loop()
@@ -143,6 +158,9 @@ if __name__ == "__main__":
             load_extra_path_config(config_path)
 
     init_custom_nodes()
+
+    cuda_malloc_warning()
+
     server.add_routes()
     hijack_progress(server)
 

nodes.py

@@ -771,7 +771,7 @@ class StyleModelApply:
     CATEGORY = "conditioning/style_model"
 
     def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
-        cond = style_model.get_cond(clip_vision_output)
+        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
         c = []
         for t in conditioning:
             n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
@@ -1448,6 +1448,44 @@ class ImageInvert:
         s = 1.0 - image
         return (s,)
 
+class ImageBatch:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "batch"
+
+    CATEGORY = "image"
+
+    def batch(self, image1, image2):
+        if image1.shape[1:] != image2.shape[1:]:
+            image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
+        s = torch.cat((image1, image2), dim=0)
+        return (s,)
+
+class EmptyImage:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate"
+
+    CATEGORY = "image"
+
+    def generate(self, width, height, batch_size=1, color=0):
+        r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF)
+        g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF)
+        b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF)
+        return (torch.cat((r, g, b), dim=-1), )
 
 class ImagePadForOutpaint:
 
@@ -1533,7 +1571,9 @@ NODE_CLASS_MAPPINGS = {
     "ImageScale": ImageScale,
     "ImageScaleBy": ImageScaleBy,
     "ImageInvert": ImageInvert,
+    "ImageBatch": ImageBatch,
     "ImagePadForOutpaint": ImagePadForOutpaint,
+    "EmptyImage": EmptyImage,
     "ConditioningAverage": ConditioningAverage ,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningConcat": ConditioningConcat,
@@ -1627,6 +1667,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageUpscaleWithModel": "Upscale Image (using Model)",
     "ImageInvert": "Invert Image",
     "ImagePadForOutpaint": "Pad Image for Outpainting",
+    "ImageBatch": "Batch Images",
     # _for_testing
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",

requirements.txt

@@ -9,3 +9,4 @@ pyyaml
 Pillow
 scipy
 tqdm
+psutil

web/i18n/zh_CN.js

@@ -117,8 +117,7 @@ export default {
         "node.title.LoadLatent": "加载隐空间",
         "node.title.SaveLatent": "保存隐空间",
         "node.title.ConditioningZeroOut": "调节ZeroOut",
-        "node.title.ConditioningSetTimestepRange":
-            "ConditioningSetTimestepRange",
+        "node.title.ConditioningSetTimestepRange": "ConditioningSetTimestepRange",
 
         "node.input.text": "文本",
         "node.input.clip": "clip",
@@ -261,8 +260,7 @@ export default {
 
         "settings.Comfy.ConfirmClear": "清空工作流需要确认",
         "settings.Comfy.PromptFilename": "把工作流保存成文件",
-        "settings.Comfy.PreviewFormat":
-            "预览图格式和压缩尺寸, e.g. webp, jpeg, webp;50, etc.",
+        "settings.Comfy.PreviewFormat": "预览图格式和压缩尺寸, e.g. webp, jpeg, webp;50, etc.",
         "settings.Comfy.DisableSliders": "禁用滑动条",
         "settings.Comfy.DevMode": "启用开发模式",
         "settings.Comfy.ColorPalette": "主题",
@@ -278,4 +276,4 @@ export default {
         "settings.Comfy.ColorPalette.template": "模版",
         "settings.Comfy.ColorPalette.delete": "删除",
     },
-};
+};

web/lib/litegraph.core.js

@@ -9766,6 +9766,7 @@ LGraphNode.prototype.executeAction = function(action)
 
             switch (w.type) {
                 case "button":
+                    ctx.fillStyle = background_color;
                     if (w.clicked) {
                         ctx.fillStyle = "#AAA";
                         w.clicked = false;

web/scripts/app.js

@@ -299,6 +299,11 @@ export class ComfyApp {
 				}
 			}
 
+			options.push({
+					content: "Bypass",
+					callback: (obj) => { if (this.mode === 4) this.mode = 0; else this.mode = 4; this.graph.change(); }
+				});
+
 			// prevent conflict of clipspace content
 			if (!ComfyApp.clipspace_return_node) {
 				options.push({

web/scripts/widgets.js

@@ -433,7 +433,7 @@ export const ComfyWidgets = {
 		// Add handler to check if an image is being dragged over our node
 		node.onDragOver = function (e) {
 			if (e.dataTransfer && e.dataTransfer.items) {
-				const image = [...e.dataTransfer.items].find((f) => f.kind === "file" && f.type.startsWith("image/"));
+				const image = [...e.dataTransfer.items].find((f) => f.kind === "file");
 				return !!image;
 			}