Merge branch 'comfyanonymous:master' into feature/blockweights

README.md

@@ -14,7 +14,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram)
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
-- Can load both ckpt and safetensors models/checkpoints. Standalone VAEs and CLIP models.
+- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - Loading full workflows (with seeds) from generated PNG files.

comfy/cli_args.py

@@ -0,0 +1,31 @@
+import argparse
+
+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("--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("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
+parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
+parser.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
+
+attn_group = parser.add_mutually_exclusive_group()
+attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization instead of the sub-quadratic one. Ignored when xformers is used.")
+attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
+
+parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
+
+vram_group = parser.add_mutually_exclusive_group()
+vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
+vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
+vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
+vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
+vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
+
+parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
+parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
+parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build.")
+
+args = parser.parse_args()

comfy/diffusers_convert.py

@@ -0,0 +1,362 @@
+import json
+import os
+import yaml
+
+import folder_paths
+from comfy.ldm.util import instantiate_from_config
+from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE
+import os.path as osp
+import re
+import torch
+from safetensors.torch import load_file, save_file
+
+# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("out.0.weight", "conv_norm_out.weight"),
+    ("out.0.bias", "conv_norm_out.bias"),
+    ("out.2.weight", "conv_out.weight"),
+    ("out.2.bias", "conv_out.bias"),
+]
+
+unet_conversion_map_resnet = [
+    # (stable-diffusion, HF Diffusers)
+    ("in_layers.0", "norm1"),
+    ("in_layers.2", "conv1"),
+    ("out_layers.0", "norm2"),
+    ("out_layers.3", "conv2"),
+    ("emb_layers.1", "time_emb_proj"),
+    ("skip_connection", "conv_shortcut"),
+]
+
+unet_conversion_map_layer = []
+# hardcoded number of downblocks and resnets/attentions...
+# would need smarter logic for other networks.
+for i in range(4):
+    # loop over downblocks/upblocks
+
+    for j in range(2):
+        # loop over resnets/attentions for downblocks
+        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+        sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
+        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+        if i < 3:
+            # no attention layers in down_blocks.3
+            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+            sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
+            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+    for j in range(3):
+        # loop over resnets/attentions for upblocks
+        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+        sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
+        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+        if i > 0:
+            # no attention layers in up_blocks.0
+            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
+            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+    if i < 3:
+        # no downsample in down_blocks.3
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+        sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
+        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        # no upsample in up_blocks.3
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
+        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+hf_mid_atn_prefix = "mid_block.attentions.0."
+sd_mid_atn_prefix = "middle_block.1."
+unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+for j in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{j}."
+    sd_mid_res_prefix = f"middle_block.{2 * j}."
+    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+    # buyer beware: this is a *brittle* function,
+    # and correct output requires that all of these pieces interact in
+    # the exact order in which I have arranged them.
+    mapping = {k: k for k in unet_state_dict.keys()}
+    for sd_name, hf_name in unet_conversion_map:
+        mapping[hf_name] = sd_name
+    for k, v in mapping.items():
+        if "resnets" in k:
+            for sd_part, hf_part in unet_conversion_map_resnet:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    for k, v in mapping.items():
+        for sd_part, hf_part in unet_conversion_map_layer:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+    return new_state_dict
+
+
+# ================#
+# VAE Conversion #
+# ================#
+
+vae_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("nin_shortcut", "conv_shortcut"),
+    ("norm_out", "conv_norm_out"),
+    ("mid.attn_1.", "mid_block.attentions.0."),
+]
+
+for i in range(4):
+    # down_blocks have two resnets
+    for j in range(2):
+        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
+        sd_down_prefix = f"encoder.down.{i}.block.{j}."
+        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
+
+    if i < 3:
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
+        sd_downsample_prefix = f"down.{i}.downsample."
+        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"up.{3 - i}.upsample."
+        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
+
+    # up_blocks have three resnets
+    # also, up blocks in hf are numbered in reverse from sd
+    for j in range(3):
+        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
+        sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
+        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
+
+# this part accounts for mid blocks in both the encoder and the decoder
+for i in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{i}."
+    sd_mid_res_prefix = f"mid.block_{i + 1}."
+    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+vae_conversion_map_attn = [
+    # (stable-diffusion, HF Diffusers)
+    ("norm.", "group_norm."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+def convert_vae_state_dict(vae_state_dict):
+    mapping = {k: k for k in vae_state_dict.keys()}
+    for k, v in mapping.items():
+        for sd_part, hf_part in vae_conversion_map:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    for k, v in mapping.items():
+        if "attentions" in k:
+            for sd_part, hf_part in vae_conversion_map_attn:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
+    weights_to_convert = ["q", "k", "v", "proj_out"]
+    for k, v in new_state_dict.items():
+        for weight_name in weights_to_convert:
+            if f"mid.attn_1.{weight_name}.weight" in k:
+                print(f"Reshaping {k} for SD format")
+                new_state_dict[k] = reshape_weight_for_sd(v)
+    return new_state_dict
+
+
+# =========================#
+# Text Encoder Conversion #
+# =========================#
+
+
+textenc_conversion_lst = [
+    # (stable-diffusion, HF Diffusers)
+    ("resblocks.", "text_model.encoder.layers."),
+    ("ln_1", "layer_norm1"),
+    ("ln_2", "layer_norm2"),
+    (".c_fc.", ".fc1."),
+    (".c_proj.", ".fc2."),
+    (".attn", ".self_attn"),
+    ("ln_final.", "transformer.text_model.final_layer_norm."),
+    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
+]
+protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
+textenc_pattern = re.compile("|".join(protected.keys()))
+
+# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
+code2idx = {"q": 0, "k": 1, "v": 2}
+
+
+def convert_text_enc_state_dict_v20(text_enc_dict):
+    new_state_dict = {}
+    capture_qkv_weight = {}
+    capture_qkv_bias = {}
+    for k, v in text_enc_dict.items():
+        if (
+                k.endswith(".self_attn.q_proj.weight")
+                or k.endswith(".self_attn.k_proj.weight")
+                or k.endswith(".self_attn.v_proj.weight")
+        ):
+            k_pre = k[: -len(".q_proj.weight")]
+            k_code = k[-len("q_proj.weight")]
+            if k_pre not in capture_qkv_weight:
+                capture_qkv_weight[k_pre] = [None, None, None]
+            capture_qkv_weight[k_pre][code2idx[k_code]] = v
+            continue
+
+        if (
+                k.endswith(".self_attn.q_proj.bias")
+                or k.endswith(".self_attn.k_proj.bias")
+                or k.endswith(".self_attn.v_proj.bias")
+        ):
+            k_pre = k[: -len(".q_proj.bias")]
+            k_code = k[-len("q_proj.bias")]
+            if k_pre not in capture_qkv_bias:
+                capture_qkv_bias[k_pre] = [None, None, None]
+            capture_qkv_bias[k_pre][code2idx[k_code]] = v
+            continue
+
+        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
+        new_state_dict[relabelled_key] = v
+
+    for k_pre, tensors in capture_qkv_weight.items():
+        if None in tensors:
+            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
+        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
+        new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
+
+    for k_pre, tensors in capture_qkv_bias.items():
+        if None in tensors:
+            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
+        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
+        new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
+
+    return new_state_dict
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+    return text_enc_dict
+
+
+def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None):
+    diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json")))
+    diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json")))
+
+    # magic
+    v2 = diffusers_unet_conf["sample_size"] == 96
+    if 'prediction_type' in diffusers_scheduler_conf:
+        v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction'
+
+    if v2:
+        if v_pred:
+            config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml')
+        else:
+            config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml')
+    else:
+        config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml')
+
+    with open(config_path, 'r') as stream:
+        config = yaml.safe_load(stream)
+
+    model_config_params = config['model']['params']
+    clip_config = model_config_params['cond_stage_config']
+    scale_factor = model_config_params['scale_factor']
+    vae_config = model_config_params['first_stage_config']
+    vae_config['scale_factor'] = scale_factor
+    model_config_params["unet_config"]["params"]["use_fp16"] = fp16
+
+    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
+    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
+    text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
+
+    # Load models from safetensors if it exists, if it doesn't pytorch
+    if osp.exists(unet_path):
+        unet_state_dict = load_file(unet_path, device="cpu")
+    else:
+        unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
+        unet_state_dict = torch.load(unet_path, map_location="cpu")
+
+    if osp.exists(vae_path):
+        vae_state_dict = load_file(vae_path, device="cpu")
+    else:
+        vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
+        vae_state_dict = torch.load(vae_path, map_location="cpu")
+
+    if osp.exists(text_enc_path):
+        text_enc_dict = load_file(text_enc_path, device="cpu")
+    else:
+        text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
+        text_enc_dict = torch.load(text_enc_path, map_location="cpu")
+
+    # Convert the UNet model
+    unet_state_dict = convert_unet_state_dict(unet_state_dict)
+    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
+
+    # Convert the VAE model
+    vae_state_dict = convert_vae_state_dict(vae_state_dict)
+    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
+
+    # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
+    is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
+
+    if is_v20_model:
+        # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
+        text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
+        text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
+        text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
+    else:
+        text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
+        text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
+
+    # Put together new checkpoint
+    sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
+
+    clip = None
+    vae = None
+
+    class WeightsLoader(torch.nn.Module):
+        pass
+
+    w = WeightsLoader()
+    load_state_dict_to = []
+    if output_vae:
+        vae = VAE(scale_factor=scale_factor, config=vae_config)
+        w.first_stage_model = vae.first_stage_model
+        load_state_dict_to = [w]
+
+    if output_clip:
+        clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
+        w.cond_stage_model = clip.cond_stage_model
+        load_state_dict_to = [w]
+
+    model = instantiate_from_config(config["model"])
+    model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
+
+    if fp16:
+        model = model.half()
+
+    return ModelPatcher(model), clip, vae

comfy/ldm/modules/attention.py

@@ -21,6 +21,8 @@ if model_management.xformers_enabled():
 import os
 _ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
 
+from cli_args import args
+
 def exists(val):
     return val is not None
 
@@ -474,7 +476,6 @@ class CrossAttentionPytorch(nn.Module):
 
         return self.to_out(out)
 
-import sys
 if model_management.xformers_enabled():
     print("Using xformers cross attention")
     CrossAttention = MemoryEfficientCrossAttention
@@ -482,7 +483,7 @@ elif model_management.pytorch_attention_enabled():
     print("Using pytorch cross attention")
     CrossAttention = CrossAttentionPytorch
 else:
-    if "--use-split-cross-attention" in sys.argv:
+    if args.use_split_cross_attention:
         print("Using split optimization for cross attention")
         CrossAttention = CrossAttentionDoggettx
     else:

comfy/model_management.py

@@ -1,36 +1,42 @@
+import psutil
+from enum import Enum
+from cli_args import args
 
-CPU = 0
+class VRAMState(Enum):
-NO_VRAM = 1
+    CPU = 0
-LOW_VRAM = 2
+    NO_VRAM = 1
-NORMAL_VRAM = 3
+    LOW_VRAM = 2
-HIGH_VRAM = 4
+    NORMAL_VRAM = 3
-MPS = 5
+    HIGH_VRAM = 4
+    MPS = 5
 
-accelerate_enabled = False
+# Determine VRAM State
-vram_state = NORMAL_VRAM
+vram_state = VRAMState.NORMAL_VRAM
+set_vram_to = VRAMState.NORMAL_VRAM
 
 total_vram = 0
 total_vram_available_mb = -1
 
-import sys
+accelerate_enabled = False
-import psutil
+xpu_available = False
-
-forced_cpu = "--cpu" in sys.argv
-
-set_vram_to = NORMAL_VRAM
 
 try:
     import torch
-    total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
+    try:
+        import intel_extension_for_pytorch as ipex
+        if torch.xpu.is_available():
+            xpu_available = True
+            total_vram = torch.xpu.get_device_properties(torch.xpu.current_device()).total_memory / (1024 * 1024)
+    except:
+        total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
     total_ram = psutil.virtual_memory().total / (1024 * 1024)
-    forced_normal_vram = "--normalvram" in sys.argv
+    if not args.normalvram and not args.cpu:
-    if not forced_normal_vram and not forced_cpu:
         if total_vram <= 4096:
             print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
-            set_vram_to = LOW_VRAM
+            set_vram_to = VRAMState.LOW_VRAM
         elif total_vram > total_ram * 1.1 and total_vram > 14336:
             print("Enabling highvram mode because your GPU has more vram than your computer has ram. If you don't want this use: --normalvram")
-            vram_state = HIGH_VRAM
+            vram_state = VRAMState.HIGH_VRAM
 except:
     pass
 
@@ -39,34 +45,37 @@ try:
 except:
     OOM_EXCEPTION = Exception
 
-if "--disable-xformers" in sys.argv:
+if args.disable_xformers:
-    XFORMERS_IS_AVAILBLE = False
+    XFORMERS_IS_AVAILABLE = False
 else:
     try:
         import xformers
         import xformers.ops
-        XFORMERS_IS_AVAILBLE = True
+        XFORMERS_IS_AVAILABLE = True
     except:
-        XFORMERS_IS_AVAILBLE = False
+        XFORMERS_IS_AVAILABLE = False
 
-ENABLE_PYTORCH_ATTENTION = False
+ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention
-if "--use-pytorch-cross-attention" in sys.argv:
+if ENABLE_PYTORCH_ATTENTION:
     torch.backends.cuda.enable_math_sdp(True)
     torch.backends.cuda.enable_flash_sdp(True)
     torch.backends.cuda.enable_mem_efficient_sdp(True)
-    ENABLE_PYTORCH_ATTENTION = True
+    XFORMERS_IS_AVAILABLE = False
-    XFORMERS_IS_AVAILBLE = False
+
+if args.lowvram:
+    set_vram_to = VRAMState.LOW_VRAM
+elif args.novram:
+    set_vram_to = VRAMState.NO_VRAM
+elif args.highvram:
+    vram_state = VRAMState.HIGH_VRAM
+
+FORCE_FP32 = False
+if args.force_fp32:
+    print("Forcing FP32, if this improves things please report it.")
+    FORCE_FP32 = True
 
 
-if "--lowvram" in sys.argv:
+if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
-    set_vram_to = LOW_VRAM
-if "--novram" in sys.argv:
-    set_vram_to = NO_VRAM
-if "--highvram" in sys.argv:
-    vram_state = HIGH_VRAM
-
-
-if set_vram_to == LOW_VRAM or set_vram_to == NO_VRAM:
     try:
         import accelerate
         accelerate_enabled = True
@@ -81,14 +90,14 @@ if set_vram_to == LOW_VRAM or set_vram_to == NO_VRAM:
 
 try:
     if torch.backends.mps.is_available():
-        vram_state = MPS
+        vram_state = VRAMState.MPS
 except:
     pass
 
-if forced_cpu:
+if args.cpu:
-    vram_state = CPU
+    vram_state = VRAMState.CPU
 
-print("Set vram state to:", ["CPU", "NO VRAM", "LOW VRAM", "NORMAL VRAM", "HIGH VRAM", "MPS"][vram_state])
+print(f"Set vram state to: {vram_state.name}")
 
 
 current_loaded_model = None
@@ -109,12 +118,12 @@ def unload_model():
             model_accelerated = False
 
         #never unload models from GPU on high vram
-        if vram_state != HIGH_VRAM:
+        if vram_state != VRAMState.HIGH_VRAM:
             current_loaded_model.model.cpu()
         current_loaded_model.unpatch_model()
         current_loaded_model = None
 
-    if vram_state != HIGH_VRAM:
+    if vram_state != VRAMState.HIGH_VRAM:
         if len(current_gpu_controlnets) > 0:
             for n in current_gpu_controlnets:
                 n.cpu()
@@ -135,32 +144,32 @@ def load_model_gpu(model):
         model.unpatch_model()
         raise e
     current_loaded_model = model
-    if vram_state == CPU:
+    if vram_state == VRAMState.CPU:
         pass
-    elif vram_state == MPS:
+    elif vram_state == VRAMState.MPS:
         mps_device = torch.device("mps")
         real_model.to(mps_device)
         pass
-    elif vram_state == NORMAL_VRAM or vram_state == HIGH_VRAM:
+    elif vram_state == VRAMState.NORMAL_VRAM or vram_state == VRAMState.HIGH_VRAM:
         model_accelerated = False
-        real_model.cuda()
+        real_model.to(get_torch_device())
     else:
-        if vram_state == NO_VRAM:
+        if vram_state == VRAMState.NO_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
-        elif vram_state == LOW_VRAM:
+        elif vram_state == VRAMState.LOW_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"})
 
-        accelerate.dispatch_model(real_model, device_map=device_map, main_device="cuda")
+        accelerate.dispatch_model(real_model, device_map=device_map, main_device=get_torch_device())
         model_accelerated = True
     return current_loaded_model
 
 def load_controlnet_gpu(models):
     global current_gpu_controlnets
     global vram_state
-    if vram_state == CPU:
+    if vram_state == VRAMState.CPU:
         return
 
-    if vram_state == LOW_VRAM or vram_state == NO_VRAM:
+    if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
         #don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after
         return
 
@@ -176,23 +185,27 @@ def load_controlnet_gpu(models):
 
 def load_if_low_vram(model):
     global vram_state
-    if vram_state == LOW_VRAM or vram_state == NO_VRAM:
+    if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
-        return model.cuda()
+        return model.to(get_torch_device())
     return model
 
 def unload_if_low_vram(model):
     global vram_state
-    if vram_state == LOW_VRAM or vram_state == NO_VRAM:
+    if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
         return model.cpu()
     return model
 
 def get_torch_device():
-    if vram_state == MPS:
+    global xpu_available
+    if vram_state == VRAMState.MPS:
         return torch.device("mps")
-    if vram_state == CPU:
+    if vram_state == VRAMState.CPU:
         return torch.device("cpu")
     else:
-        return torch.cuda.current_device()
+        if xpu_available:
+            return torch.device("xpu")
+        else:
+            return torch.cuda.current_device()
 
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
@@ -201,9 +214,9 @@ def get_autocast_device(dev):
 
 
 def xformers_enabled():
-    if vram_state == CPU:
+    if vram_state == VRAMState.CPU:
         return False
-    return XFORMERS_IS_AVAILBLE
+    return XFORMERS_IS_AVAILABLE
 
 
 def xformers_enabled_vae():
@@ -222,6 +235,7 @@ def pytorch_attention_enabled():
     return ENABLE_PYTORCH_ATTENTION
 
 def get_free_memory(dev=None, torch_free_too=False):
+    global xpu_available
     if dev is None:
         dev = get_torch_device()
 
@@ -229,12 +243,16 @@ def get_free_memory(dev=None, torch_free_too=False):
         mem_free_total = psutil.virtual_memory().available
         mem_free_torch = mem_free_total
     else:
-        stats = torch.cuda.memory_stats(dev)
+        if xpu_available:
-        mem_active = stats['active_bytes.all.current']
+            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
-        mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_torch = mem_free_total
-        mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
+        else:
-        mem_free_torch = mem_reserved - mem_active
+            stats = torch.cuda.memory_stats(dev)
-        mem_free_total = mem_free_cuda + mem_free_torch
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_cuda + mem_free_torch
 
     if torch_free_too:
         return (mem_free_total, mem_free_torch)
@@ -243,7 +261,7 @@ def get_free_memory(dev=None, torch_free_too=False):
 
 def maximum_batch_area():
     global vram_state
-    if vram_state == NO_VRAM:
+    if vram_state == VRAMState.NO_VRAM:
         return 0
 
     memory_free = get_free_memory() / (1024 * 1024)
@@ -252,14 +270,18 @@ def maximum_batch_area():
 
 def cpu_mode():
     global vram_state
-    return vram_state == CPU
+    return vram_state == VRAMState.CPU
 
 def mps_mode():
     global vram_state
-    return vram_state == MPS
+    return vram_state == VRAMState.MPS
 
 def should_use_fp16():
-    if cpu_mode() or mps_mode():
+    global xpu_available
+    if FORCE_FP32:
+        return False
+
+    if cpu_mode() or mps_mode() or xpu_available:
         return False #TODO ?
 
     if torch.cuda.is_bf16_supported():

folder_paths.py

@@ -23,10 +23,45 @@ folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_
 folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
 folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
+folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
 
 folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
 folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
 
+output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
+temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
+input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
+
+if not os.path.exists(input_directory):
+    os.makedirs(input_directory)
+
+def set_output_directory(output_dir):
+    global output_directory
+    output_directory = output_dir
+
+def get_output_directory():
+    global output_directory
+    return output_directory
+
+def get_temp_directory():
+    global temp_directory
+    return temp_directory
+
+def get_input_directory():
+    global input_directory
+    return input_directory
+
+
+#NOTE: used in http server so don't put folders that should not be accessed remotely
+def get_directory_by_type(type_name):
+    if type_name == "output":
+        return get_output_directory()
+    if type_name == "temp":
+        return get_temp_directory()
+    if type_name == "input":
+        return get_input_directory()
+    return None
+
 
 def add_model_folder_path(folder_name, full_folder_path):
     global folder_names_and_paths

main.py

@@ -1,56 +1,33 @@
-import os
-import sys
-import shutil
-
-import threading
 import asyncio
+import itertools
+import os
+import shutil
+import threading
+
+from comfy.cli_args import args
 
 if os.name == "nt":
     import logging
     logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
 if __name__ == "__main__":
-    if '--help' in sys.argv:
+    if args.dont_upcast_attention:
-        print()
-        print("Valid Command line Arguments:")
-        print("\t--listen [ip]\t\t\tListen on ip or 0.0.0.0 if none given so the UI can be accessed from other computers.")
-        print("\t--port 8188\t\t\tSet the listen port.")
-        print()
-        print("\t--extra-model-paths-config file.yaml\tload an extra_model_paths.yaml file.")
-        print()
-        print()
-        print("\t--dont-upcast-attention\t\tDisable upcasting of attention \n\t\t\t\t\tcan boost speed but increase the chances of black images.\n")
-        print("\t--use-split-cross-attention\tUse the split cross attention optimization instead of the sub-quadratic one.\n\t\t\t\t\tIgnored when xformers is used.")
-        print("\t--use-pytorch-cross-attention\tUse the new pytorch 2.0 cross attention function.")
-        print("\t--disable-xformers\t\tdisables xformers")
-        print("\t--cuda-device 1\t\tSet the id of the cuda device this instance will use.")
-        print()
-        print("\t--highvram\t\t\tBy default models will be unloaded to CPU memory after being used.\n\t\t\t\t\tThis option keeps them in GPU memory.\n")
-        print("\t--normalvram\t\t\tUsed to force normal vram use if lowvram gets automatically enabled.")
-        print("\t--lowvram\t\t\tSplit the unet in parts to use less vram.")
-        print("\t--novram\t\t\tWhen lowvram isn't enough.")
-        print()
-        print("\t--cpu\t\t\tTo use the CPU for everything (slow).")
-        exit()
-
-    if '--dont-upcast-attention' in sys.argv:
         print("disabling upcasting of attention")
         os.environ['ATTN_PRECISION'] = "fp16"
 
-    try:
+    if args.cuda_device is not None:
-        index = sys.argv.index('--cuda-device')
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
-        device = sys.argv[index + 1]
+        print("Set cuda device to:", args.cuda_device)
-        os.environ['CUDA_VISIBLE_DEVICES'] = device
+
-        print("Set cuda device to:", device)
-    except:
-        pass
 
-from nodes import init_custom_nodes
-import execution
-import server
-import folder_paths
 import yaml
 
+import execution
+import folder_paths
+import server
+from nodes import init_custom_nodes
+
+
 def prompt_worker(q, server):
     e = execution.PromptExecutor(server)
     while True:
@@ -109,43 +86,31 @@ if __name__ == "__main__":
     hijack_progress(server)
 
     threading.Thread(target=prompt_worker, daemon=True, args=(q,server,)).start()
-    try:
-        address = '0.0.0.0'
-        p_index = sys.argv.index('--listen')
-        try:
-            ip = sys.argv[p_index + 1]
-            if ip[:2] != '--':
-                address = ip
-        except:
-            pass
-    except:
-        address = '127.0.0.1'
 
-    dont_print = False
+    address = args.listen
-    if '--dont-print-server' in sys.argv:
+
-        dont_print = True
+    dont_print = args.dont_print_server
 
     extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
     if os.path.isfile(extra_model_paths_config_path):
         load_extra_path_config(extra_model_paths_config_path)
 
-    if '--extra-model-paths-config' in sys.argv:
+    if args.extra_model_paths_config:
-        indices = [(i + 1) for i in range(len(sys.argv) - 1) if sys.argv[i] == '--extra-model-paths-config']
+        for config_path in itertools.chain(*args.extra_model_paths_config):
-        for i in indices:
+            load_extra_path_config(config_path)
-            load_extra_path_config(sys.argv[i])
 
-    port = 8188
+    if args.output_directory:
-    try:
+        output_dir = os.path.abspath(args.output_directory)
-        p_index = sys.argv.index('--port')
+        print(f"Setting output directory to: {output_dir}")
-        port = int(sys.argv[p_index + 1])
+        folder_paths.set_output_directory(output_dir)
-    except:
-        pass
 
-    if '--quick-test-for-ci' in sys.argv:
+    port = args.port
+
+    if args.quick_test_for_ci:
         exit(0)
 
     call_on_start = None
-    if "--windows-standalone-build" in sys.argv:
+    if args.windows_standalone_build:
         def startup_server(address, port):
             import webbrowser
             webbrowser.open("http://{}:{}".format(address, port))

nodes.py

@@ -4,16 +4,17 @@ import os
 import sys
 import json
 import hashlib
-import copy
 import traceback
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 
+
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
 
 
+import comfy.diffusers_convert
 import comfy.samplers
 import comfy.sd
 import comfy.utils
@@ -219,6 +220,30 @@ class CheckpointLoaderSimple:
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out
 
+class DiffusersLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        paths = []
+        for search_path in folder_paths.get_folder_paths("diffusers"):
+            if os.path.exists(search_path):
+                paths += next(os.walk(search_path))[1]
+        return {"required": {"model_path": (paths,), }}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "advanced/loaders"
+
+    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)
+                    break
+
+        return comfy.diffusers_convert.load_diffusers(model_path, fp16=model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+
+
 class unCLIPCheckpointLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -853,7 +878,7 @@ class KSamplerAdvanced:
 
 class SaveImage:
     def __init__(self):
-        self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
+        self.output_dir = folder_paths.get_output_directory()
         self.type = "output"
 
     @classmethod
@@ -905,9 +930,6 @@ class SaveImage:
             os.makedirs(full_output_folder, exist_ok=True)
             counter = 1
 
-        if not os.path.exists(self.output_dir):
-            os.makedirs(self.output_dir)
-
         results = list()
         for image in images:
             i = 255. * image.cpu().numpy()
@@ -932,7 +954,7 @@ class SaveImage:
 
 class PreviewImage(SaveImage):
     def __init__(self):
-        self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
+        self.output_dir = folder_paths.get_temp_directory()
         self.type = "temp"
 
     @classmethod
@@ -943,13 +965,11 @@ class PreviewImage(SaveImage):
                 }
 
 class LoadImage:
-    input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
     @classmethod
     def INPUT_TYPES(s):
-        if not os.path.exists(s.input_dir):
+        input_dir = folder_paths.get_input_directory()
-            os.makedirs(s.input_dir)
         return {"required":
-                    {"image": (sorted(os.listdir(s.input_dir)), )},
+                    {"image": (sorted(os.listdir(input_dir)), )},
                 }
 
     CATEGORY = "image"
@@ -957,7 +977,8 @@ class LoadImage:
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "load_image"
     def load_image(self, image):
-        image_path = os.path.join(self.input_dir, image)
+        input_dir = folder_paths.get_input_directory()
+        image_path = os.path.join(input_dir, image)
         i = Image.open(image_path)
         image = i.convert("RGB")
         image = np.array(image).astype(np.float32) / 255.0
@@ -971,18 +992,19 @@ class LoadImage:
 
     @classmethod
     def IS_CHANGED(s, image):
-        image_path = os.path.join(s.input_dir, image)
+        input_dir = folder_paths.get_input_directory()
+        image_path = os.path.join(input_dir, image)
         m = hashlib.sha256()
         with open(image_path, 'rb') as f:
             m.update(f.read())
         return m.digest().hex()
 
 class LoadImageMask:
-    input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
     @classmethod
     def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
         return {"required":
-                    {"image": (sorted(os.listdir(s.input_dir)), ),
+                    {"image": (sorted(os.listdir(input_dir)), ),
                     "channel": (["alpha", "red", "green", "blue"], ),}
                 }
 
@@ -991,7 +1013,8 @@ class LoadImageMask:
     RETURN_TYPES = ("MASK",)
     FUNCTION = "load_image"
     def load_image(self, image, channel):
-        image_path = os.path.join(self.input_dir, image)
+        input_dir = folder_paths.get_input_directory()
+        image_path = os.path.join(input_dir, image)
         i = Image.open(image_path)
         mask = None
         c = channel[0].upper()
@@ -1006,7 +1029,8 @@ class LoadImageMask:
 
     @classmethod
     def IS_CHANGED(s, image, channel):
-        image_path = os.path.join(s.input_dir, image)
+        input_dir = folder_paths.get_input_directory()
+        image_path = os.path.join(input_dir, image)
         m = hashlib.sha256()
         with open(image_path, 'rb') as f:
             m.update(f.read())
@@ -1154,6 +1178,7 @@ NODE_CLASS_MAPPINGS = {
     "TomePatchModel": TomePatchModel,
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "CheckpointLoader": CheckpointLoader,
+    "DiffusersLoader": DiffusersLoader,
 }
 
 def load_custom_node(module_path):

requirements.txt

@@ -4,7 +4,7 @@ torchsde
 einops
 open-clip-torch
 transformers>=4.25.1
-safetensors
+safetensors>=0.3.0
 pytorch_lightning
 aiohttp
 accelerate

server.py

@@ -18,6 +18,7 @@ except ImportError:
     sys.exit()
 
 import mimetypes
+from comfy.cli_args import args
 
 
 @web.middleware
@@ -27,6 +28,23 @@ async def cache_control(request: web.Request, handler):
         response.headers.setdefault('Cache-Control', 'no-cache')
     return response
 
+def create_cors_middleware(allowed_origin: str):
+    @web.middleware
+    async def cors_middleware(request: web.Request, handler):
+        if request.method == "OPTIONS":
+            # Pre-flight request. Reply successfully:
+            response = web.Response()
+        else:
+            response = await handler(request)
+
+        response.headers['Access-Control-Allow-Origin'] = allowed_origin
+        response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS'
+        response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization'
+        response.headers['Access-Control-Allow-Credentials'] = 'true'
+        return response
+
+    return cors_middleware
+
 class PromptServer():
     def __init__(self, loop):
         PromptServer.instance = self
@@ -37,7 +55,12 @@ class PromptServer():
         self.loop = loop
         self.messages = asyncio.Queue()
         self.number = 0
-        self.app = web.Application(client_max_size=20971520, middlewares=[cache_control])
+
+        middlewares = [cache_control]
+        if args.enable_cors_header:
+            middlewares.append(create_cors_middleware(args.enable_cors_header))
+
+        self.app = web.Application(client_max_size=20971520, middlewares=middlewares)
         self.sockets = dict()
         self.web_root = os.path.join(os.path.dirname(
             os.path.realpath(__file__)), "web")
@@ -89,7 +112,7 @@ class PromptServer():
 
         @routes.post("/upload/image")
         async def upload_image(request):
-            upload_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
+            upload_dir = folder_paths.get_input_directory()
 
             if not os.path.exists(upload_dir):
                 os.makedirs(upload_dir)
@@ -122,10 +145,10 @@ class PromptServer():
         async def view_image(request):
             if "filename" in request.rel_url.query:
                 type = request.rel_url.query.get("type", "output")
-                if type not in ["output", "input", "temp"]:
+                output_dir = folder_paths.get_directory_by_type(type)
+                if output_dir is None:
                     return web.Response(status=400)
 
-                output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), type)
                 if "subfolder" in request.rel_url.query:
                     full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"])
                     if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir:

web/scripts/app.js

@@ -112,6 +112,46 @@ class ComfyApp {
 		};
 	}
 
+	#addNodeKeyHandler(node) {
+		const app = this;
+		const origNodeOnKeyDown = node.prototype.onKeyDown;
+
+		node.prototype.onKeyDown = function(e) {
+			if (origNodeOnKeyDown && origNodeOnKeyDown.apply(this, e) === false) {
+				return false;
+			}
+
+			if (this.flags.collapsed || !this.imgs || this.imageIndex === null) {
+				return;
+			}
+
+			let handled = false;
+
+			if (e.key === "ArrowLeft" || e.key === "ArrowRight") {
+				if (e.key === "ArrowLeft") {
+					this.imageIndex -= 1;
+				} else if (e.key === "ArrowRight") {
+					this.imageIndex += 1;
+				}
+				this.imageIndex %= this.imgs.length;
+
+				if (this.imageIndex < 0) {
+					this.imageIndex = this.imgs.length + this.imageIndex;
+				}
+				handled = true;
+			} else if (e.key === "Escape") {
+				this.imageIndex = null;
+				handled = true;
+			}
+
+			if (handled === true) {
+				e.preventDefault();
+				e.stopImmediatePropagation();
+				return false;
+			}
+		}
+	}
+
 	/**
 	 * Adds Custom drawing logic for nodes
 	 * e.g. Draws images and handles thumbnail navigation on nodes that output images
@@ -803,6 +843,7 @@ class ComfyApp {
 
 			this.#addNodeContextMenuHandler(node);
 			this.#addDrawBackgroundHandler(node, app);
+			this.#addNodeKeyHandler(node);
 
 			await this.#invokeExtensionsAsync("beforeRegisterNodeDef", node, nodeData);
 			LiteGraph.registerNodeType(nodeId, node);

web/scripts/ui.js

@@ -115,14 +115,6 @@ function dragElement(dragEl, settings) {
 			savePos = value;
 		},
 	});
-	
-	settings.addSetting({
-		id: "Comfy.ConfirmClear",
-		name: "Require confirmation when clearing workflow",
-		type: "boolean",
-		defaultValue: true,
-	});
-
 	function dragMouseDown(e) {
 		e = e || window.event;
 		e.preventDefault();
@@ -170,7 +162,7 @@ class ComfyDialog {
 				$el("p", { $: (p) => (this.textElement = p) }),
 				$el("button", {
 					type: "button",
-					textContent: "CLOSE",
+					textContent: "Close",
 					onclick: () => this.close(),
 				}),
 			]),
@@ -233,6 +225,7 @@ class ComfySettingsDialog extends ComfyDialog {
 				};
 
 				let element;
+				value = this.getSettingValue(id, defaultValue);
 
 				if (typeof type === "function") {
 					element = type(name, setter, value, attrs);
@@ -289,6 +282,16 @@ class ComfySettingsDialog extends ComfyDialog {
 				return element;
 			},
 		});
+
+		const self = this;
+		return {
+			get value() {
+				return self.getSettingValue(id, defaultValue);
+			},
+			set value(v) {
+				self.setSettingValue(id, v);
+			},
+		};
 	}
 
 	show() {
@@ -410,6 +413,13 @@ export class ComfyUI {
 			this.history.update();
 		});
 
+		const confirmClear = this.settings.addSetting({
+			id: "Comfy.ConfirmClear",
+			name: "Require confirmation when clearing workflow",
+			type: "boolean",
+			defaultValue: true,
+		});
+		
 		const fileInput = $el("input", {
 			type: "file",
 			accept: ".json,image/png",
@@ -421,7 +431,7 @@ export class ComfyUI {
 		});
 
 		this.menuContainer = $el("div.comfy-menu", { parent: document.body }, [
-			$el("div", { style: { overflow: "hidden", position: "relative", width: "100%" } }, [
+			$el("div.drag-handle", { style: { overflow: "hidden", position: "relative", width: "100%", cursor: "default" } }, [
 				$el("span.drag-handle"),
 				$el("span", { $: (q) => (this.queueSize = q) }),
 				$el("button.comfy-settings-btn", { textContent: "⚙️", onclick: () => this.settings.show() }),
@@ -517,13 +527,13 @@ export class ComfyUI {
 			$el("button", { textContent: "Load", onclick: () => fileInput.click() }),
 			$el("button", { textContent: "Refresh", onclick: () => app.refreshComboInNodes() }),
 			$el("button", { textContent: "Clear", onclick: () => {
-				if (localStorage.getItem("Comfy.Settings.Comfy.ConfirmClear") == "false" || confirm("Clear workflow?")) {
+				if (!confirmClear.value || confirm("Clear workflow?")) {
 					app.clean();
 					app.graph.clear();
 				}
 			}}),
 			$el("button", { textContent: "Load Default", onclick: () => {
-				if (localStorage.getItem("Comfy.Settings.Comfy.ConfirmClear") == "false" || confirm("Load default workflow?")) {
+				if (!confirmClear.value || confirm("Load default workflow?")) {
 					app.loadGraphData()
 				}
 			}}),

web/scripts/widgets.js

@@ -306,7 +306,7 @@ export const ComfyWidgets = {
 		const fileInput = document.createElement("input");
 		Object.assign(fileInput, {
 			type: "file",
-			accept: "image/jpeg,image/png",
+			accept: "image/jpeg,image/png,image/webp",
 			style: "display: none",
 			onchange: async () => {
 				if (fileInput.files.length) {

web/style.css

@@ -39,18 +39,19 @@ body {
 	position: fixed; /* Stay in place */
 	z-index: 100; /* Sit on top */
 	padding: 30px 30px 10px 30px;
-	background-color: #ff0000; /* Modal background */
+	background-color: #353535; /* Modal background */
+	color: #ff4444;
 	box-shadow: 0px 0px 20px #888888;
 	border-radius: 10px;
-	text-align: center;
 	top: 50%;
 	left: 50%;
 	max-width: 80vw;
 	max-height: 80vh;
 	transform: translate(-50%, -50%);
 	overflow: hidden;
-	min-width: 60%;
 	justify-content: center;
+	font-family: monospace;
+	font-size: 15px;
 }
 
 .comfy-modal-content {
@@ -70,31 +71,11 @@ body {
 	margin: 3px 3px 3px 4px;
 }
 
-.comfy-modal button {
-	cursor: pointer;
-	color: #aaaaaa;
-	border: none;
-	background-color: transparent;
-	font-size: 24px;
-	font-weight: bold;
-	width: 100%;
-}
-
-.comfy-modal button:hover,
-.comfy-modal button:focus {
-	color: #000;
-	text-decoration: none;
-	cursor: pointer;
-}
-
 .comfy-menu {
-	width: 200px;
 	font-size: 15px;
 	position: absolute;
 	top: 50%;
 	right: 0%;
-	background-color: white;
-	color: #000;
 	text-align: center;
 	z-index: 100;
 	width: 170px;
@@ -109,7 +90,8 @@ body {
 	box-shadow: 3px 3px 8px rgba(0, 0, 0, 0.4);
 }
 
-.comfy-menu button {
+.comfy-menu button,
+.comfy-modal button {
 	font-size: 20px;
 }
 
@@ -130,7 +112,8 @@ body {
 
 .comfy-menu > button,
 .comfy-menu-btns button,
-.comfy-menu .comfy-list button {
+.comfy-menu .comfy-list button,
+.comfy-modal button{
 	color: #ddd;
 	background-color: #222;
 	border-radius: 8px;
@@ -220,11 +203,22 @@ button.comfy-queue-btn {
 }
 
 .comfy-modal.comfy-settings {
-	background-color: var(--bg-color);
+	text-align: center;
-	color: var(--fg-color);
+	font-family: sans-serif;
+	color: #999;
 	z-index: 99;
 }
 
+.comfy-modal input,
+.comfy-modal select {
+	color: #ddd;
+	background-color: #222;
+	border-radius: 8px;
+	border-color: #4e4e4e;
+	border-style: solid;
+	font-size: inherit;
+}
+
 @media only screen and (max-height: 850px) {
 	.comfy-menu {
 		top: 0 !important;
@@ -239,26 +233,26 @@ button.comfy-queue-btn {
 }
 
 .graphdialog {
-    min-height: 1em;
+	min-height: 1em;
 }
 
 .graphdialog .name {
-    font-size: 14px;
+	font-size: 14px;
-    font-family: sans-serif;
+	font-family: sans-serif;
-    color: #999999;
+	color: #999999;
 }
 
 .graphdialog button {
-    margin-top: unset;
+	margin-top: unset;
-    vertical-align: unset;
+	vertical-align: unset;
-    height: 1.6em;
+	height: 1.6em;
-    padding-right: 8px;
+	padding-right: 8px;
 }
 
 .graphdialog input, .graphdialog textarea, .graphdialog select {
-    background-color: #222;
+	background-color: #222;
-    border: 2px solid;
+	border: 2px solid;
-    border-color: #444444;
+	border-color: #444444;
-    color: #ddd;
+	color: #ddd;
-    border-radius: 12px 0 0 12px;
+	border-radius: 12px 0 0 12px;
 }