Merge branch 'master' into async-file-list

.github/workflows/test-unit.yml

@@ -8,10 +8,15 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
     steps:
     - uses: actions/checkout@v4
-    - uses: actions/setup-python@v4
+    - name: Set up Python      
+      uses: actions/setup-python@v4
       with:
         python-version: '3.10'
     - name: Install requirements

api_server/routes/internal/internal_routes.py

@@ -1,6 +1,6 @@
 from aiohttp import web
 from typing import Optional
-from folder_paths import models_dir, user_directory, output_directory
+from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
 from api_server.services.file_service import FileService
 import app.logger
 
@@ -36,6 +36,13 @@ async def list_files(request):
         async def get_logs(request):
             return web.json_response(app.logger.get_logs())
 
+        @self.routes.get('/folder_paths')
+        async def get_folder_paths(request):
+            response = {}
+            for key in folder_names_and_paths:
+                response[key] = folder_names_and_paths[key][0]
+            return web.json_response(response)
+
     def get_app(self):
         if self._app is None:
             self._app = web.Application()

app/user_manager.py

@@ -118,6 +118,29 @@ async def post_users(request):
 
         @routes.get("/userdata")
         async def listuserdata(request):
+            """
+            List user data files in a specified directory.
+
+            This endpoint allows listing files in a user's data directory, with options for recursion,
+            full file information, and path splitting.
+
+            Query Parameters:
+            - dir (required): The directory to list files from.
+            - recurse (optional): If "true", recursively list files in subdirectories.
+            - full_info (optional): If "true", return detailed file information (path, size, modified time).
+            - split (optional): If "true", split file paths into components (only applies when full_info is false).
+
+            Returns:
+            - 400: If 'dir' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 404: If the requested directory does not exist.
+            - 200: JSON response with the list of files or file information.
+
+            The response format depends on the query parameters:
+            - Default: List of relative file paths.
+            - full_info=true: List of dictionaries with file details.
+            - split=true (and full_info=false): List of lists, each containing path components.
+            """
             directory = request.rel_url.query.get('dir', '')
             if not directory:
                 return web.Response(status=400, text="Directory not provided")

comfy/cli_args.py

@@ -36,7 +36,7 @@ def __call__(self, parser, namespace, values, option_string=None):
 
 parser = argparse.ArgumentParser()
 
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
+parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
 parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
 parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
 parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")

comfy/controlnet.py

@@ -88,6 +88,8 @@ def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
         if self.latent_format is not None:
+            if vae is None:
+                logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.")
             self.vae = vae
         self.extra_concat_orig = extra_concat.copy()
         if self.concat_mask and len(self.extra_concat_orig) == 0:
@@ -222,6 +224,9 @@ def get_control(self, x_noisy, t, cond, batched_number):
             compression_ratio = self.compression_ratio
             if self.vae is not None:
                 compression_ratio *= self.vae.downscale_ratio
+            else:
+                if self.latent_format is not None:
+                    raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
             self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
@@ -335,7 +340,7 @@ def forward(self, input):
 
 
 class ControlLora(ControlNet):
-    def __init__(self, control_weights, global_average_pooling=False, device=None):
+    def __init__(self, control_weights, global_average_pooling=False, device=None, model_options={}): #TODO? model_options
         ControlBase.__init__(self, device)
         self.control_weights = control_weights
         self.global_average_pooling = global_average_pooling
@@ -392,19 +397,25 @@ def get_models(self):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
-def controlnet_config(sd):
+def controlnet_config(sd, model_options={}):
     model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
 
-    supported_inference_dtypes = model_config.supported_inference_dtypes
+    unet_dtype = model_options.get("dtype", None)
+    if unet_dtype is None:
+        weight_dtype = comfy.utils.weight_dtype(sd)
+
+        supported_inference_dtypes = list(model_config.supported_inference_dtypes)
+        if weight_dtype is not None:
+            supported_inference_dtypes.append(weight_dtype)
+
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
 
-    controlnet_config = model_config.unet_config
-    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
     load_device = comfy.model_management.get_torch_device()
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
-        operations = comfy.ops.manual_cast
-    else:
-        operations = comfy.ops.disable_weight_init
+
+    operations = model_options.get("custom_operations", None)
+    if operations is None:
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
 
     offload_device = comfy.model_management.unet_offload_device()
     return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device
@@ -419,9 +430,9 @@ def controlnet_load_state_dict(control_model, sd):
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
     return control_model
 
-def load_controlnet_mmdit(sd):
+def load_controlnet_mmdit(sd, model_options={}):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
     num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
     for k in sd:
         new_sd[k] = sd[k]
@@ -440,8 +451,8 @@ def load_controlnet_mmdit(sd):
     return control
 
 
-def load_controlnet_hunyuandit(controlnet_data):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data)
+def load_controlnet_hunyuandit(controlnet_data, model_options={}):
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)
 
     control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)
     control_model = controlnet_load_state_dict(control_model, controlnet_data)
@@ -451,17 +462,17 @@ def load_controlnet_hunyuandit(controlnet_data):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
     return control
 
-def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd)
+def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
     control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, sd)
     extra_conds = ['y', 'guidance']
     control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
-def load_controlnet_flux_instantx(sd):
+def load_controlnet_flux_instantx(sd, model_options={}):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
     for k in sd:
         new_sd[k] = sd[k]
 
@@ -487,13 +498,13 @@ def convert_mistoline(sd):
     return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
 
 
-def load_controlnet(ckpt_path, model=None):
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+def load_controlnet_state_dict(state_dict, model=None, model_options={}):
+    controlnet_data = state_dict
     if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
-        return load_controlnet_hunyuandit(controlnet_data)
+        return load_controlnet_hunyuandit(controlnet_data, model_options=model_options)
 
     if "lora_controlnet" in controlnet_data:
-        return ControlLora(controlnet_data)
+        return ControlLora(controlnet_data, model_options=model_options)
 
     controlnet_config = None
     supported_inference_dtypes = None
@@ -550,13 +561,13 @@ def load_controlnet(ckpt_path, model=None):
         controlnet_data = new_sd
     elif "controlnet_blocks.0.weight" in controlnet_data:
         if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
-            return load_controlnet_flux_xlabs_mistoline(controlnet_data)
+            return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)
         elif "pos_embed_input.proj.weight" in controlnet_data:
-            return load_controlnet_mmdit(controlnet_data) #SD3 diffusers controlnet
+            return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
         elif "controlnet_x_embedder.weight" in controlnet_data:
-            return load_controlnet_flux_instantx(controlnet_data)
+            return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
     elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
-        return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True)
+        return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False
@@ -568,25 +579,36 @@ def load_controlnet(ckpt_path, model=None):
     elif key in controlnet_data:
         prefix = ""
     else:
-        net = load_t2i_adapter(controlnet_data)
+        net = load_t2i_adapter(controlnet_data, model_options=model_options)
         if net is None:
-            logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+            logging.error("error could not detect control model type.")
         return net
 
     if controlnet_config is None:
         model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
-        supported_inference_dtypes = model_config.supported_inference_dtypes
+        supported_inference_dtypes = list(model_config.supported_inference_dtypes)
         controlnet_config = model_config.unet_config
 
+    unet_dtype = model_options.get("dtype", None)
+    if unet_dtype is None:
+        weight_dtype = comfy.utils.weight_dtype(controlnet_data)
+
+        if supported_inference_dtypes is None:
+            supported_inference_dtypes = [comfy.model_management.unet_dtype()]
+
+        if weight_dtype is not None:
+            supported_inference_dtypes.append(weight_dtype)
+
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+
     load_device = comfy.model_management.get_torch_device()
-    if supported_inference_dtypes is None:
-        unet_dtype = comfy.model_management.unet_dtype()
-    else:
-        unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
 
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
-        controlnet_config["operations"] = comfy.ops.manual_cast
+    operations = model_options.get("custom_operations", None)
+    if operations is None:
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype)
+
+    controlnet_config["operations"] = operations
     controlnet_config["dtype"] = unet_dtype
     controlnet_config["device"] = comfy.model_management.unet_offload_device()
     controlnet_config.pop("out_channels")
@@ -622,14 +644,21 @@ class WeightsLoader(torch.nn.Module):
     if len(unexpected) > 0:
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
-    global_average_pooling = False
-    filename = os.path.splitext(ckpt_path)[0]
-    if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
-        global_average_pooling = True
-
+    global_average_pooling = model_options.get("global_average_pooling", False)
     control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
+def load_controlnet(ckpt_path, model=None, model_options={}):
+    if "global_average_pooling" not in model_options:
+        filename = os.path.splitext(ckpt_path)[0]
+        if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
+            model_options["global_average_pooling"] = True
+
+    cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options)
+    if cnet is None:
+        logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+    return cnet
+
 class T2IAdapter(ControlBase):
     def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
         super().__init__(device)
@@ -685,7 +714,7 @@ def copy(self):
         self.copy_to(c)
         return c
 
-def load_t2i_adapter(t2i_data):
+def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
     compression_ratio = 8
     upscale_algorithm = 'nearest-exact'
 

comfy/k_diffusion/sampling.py

@@ -44,6 +44,17 @@ def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
     return append_zero(sigmas)
 
 
+def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
+    """Constructs the noise schedule proposed by Tiankai et al. (2024). """
+    epsilon = 1e-5 # avoid log(0)
+    x = torch.linspace(0, 1, n, device=device)
+    clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
+    lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
+    sigmas = clamp(torch.exp(lmb))
+    return sigmas
+
+
+
 def to_d(x, sigma, denoised):
     """Converts a denoiser output to a Karras ODE derivative."""
     return (x - denoised) / utils.append_dims(sigma, x.ndim)
@@ -1143,3 +1154,36 @@ def post_cfg_function(args):
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    t_fn = lambda sigma: sigma.log().neg()
+
+    old_uncond_denoised = None
+    uncond_denoised = None
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    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})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_uncond_denoised is None or sigmas[i + 1] == 0:
+            denoised_mix = -torch.exp(-h) * uncond_denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
+        x = denoised + denoised_mix + torch.exp(-h) * x
+        old_uncond_denoised = uncond_denoised
+    return x

comfy/latent_formats.py

@@ -4,6 +4,7 @@ class LatentFormat:
     scale_factor = 1.0
     latent_channels = 4
     latent_rgb_factors = None
+    latent_rgb_factors_bias = None
     taesd_decoder_name = None
 
     def process_in(self, latent):