Merge branch 'Main' into refactor/execution

comfy/ldm/modules/attention.py

@@ -12,8 +12,6 @@ from .sub_quadratic_attention import efficient_dot_product_attention
 from comfy import model_management
 import comfy.ops
 
-from . import tomesd
-
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
@@ -519,23 +517,39 @@ class BasicTransformerBlock(nn.Module):
         self.norm2 = nn.LayerNorm(dim, dtype=dtype)
         self.norm3 = nn.LayerNorm(dim, dtype=dtype)
         self.checkpoint = checkpoint
+        self.n_heads = n_heads
+        self.d_head = d_head
 
     def forward(self, x, context=None, transformer_options={}):
         return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
 
     def _forward(self, x, context=None, transformer_options={}):
         extra_options = {}
+        block = None
+        block_index = 0
         if "current_index" in transformer_options:
             extra_options["transformer_index"] = transformer_options["current_index"]
         if "block_index" in transformer_options:
-            extra_options["block_index"] = transformer_options["block_index"]
+            block_index = transformer_options["block_index"]
+            extra_options["block_index"] = block_index
         if "original_shape" in transformer_options:
             extra_options["original_shape"] = transformer_options["original_shape"]
+        if "block" in transformer_options:
+            block = transformer_options["block"]
+            extra_options["block"] = block
         if "patches" in transformer_options:
             transformer_patches = transformer_options["patches"]
         else:
             transformer_patches = {}
 
+        extra_options["n_heads"] = self.n_heads
+        extra_options["dim_head"] = self.d_head
+
+        if "patches_replace" in transformer_options:
+            transformer_patches_replace = transformer_options["patches_replace"]
+        else:
+            transformer_patches_replace = {}
+
         n = self.norm1(x)
         if self.disable_self_attn:
             context_attn1 = context
@@ -551,12 +565,29 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
 
-        if "tomesd" in transformer_options:
+        transformer_block = (block[0], block[1], block_index)
-            m, u = tomesd.get_functions(x, transformer_options["tomesd"]["ratio"], transformer_options["original_shape"])
+        attn1_replace_patch = transformer_patches_replace.get("attn1", {})
-            n = u(self.attn1(m(n), context=context_attn1, value=value_attn1))
+        block_attn1 = transformer_block
+        if block_attn1 not in attn1_replace_patch:
+            block_attn1 = block
+
+        if block_attn1 in attn1_replace_patch:
+            if context_attn1 is None:
+                context_attn1 = n
+                value_attn1 = n
+            n = self.attn1.to_q(n)
+            context_attn1 = self.attn1.to_k(context_attn1)
+            value_attn1 = self.attn1.to_v(value_attn1)
+            n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
+            n = self.attn1.to_out(n)
         else:
             n = self.attn1(n, context=context_attn1, value=value_attn1)
 
+        if "attn1_output_patch" in transformer_patches:
+            patch = transformer_patches["attn1_output_patch"]
+            for p in patch:
+                n = p(n, extra_options)
+
         x += n
         if "middle_patch" in transformer_patches:
             patch = transformer_patches["middle_patch"]
@@ -573,6 +604,20 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
 
+        attn2_replace_patch = transformer_patches_replace.get("attn2", {})
+        block_attn2 = transformer_block
+        if block_attn2 not in attn2_replace_patch:
+            block_attn2 = block
+
+        if block_attn2 in attn2_replace_patch:
+            if value_attn2 is None:
+                value_attn2 = context_attn2
+            n = self.attn2.to_q(n)
+            context_attn2 = self.attn2.to_k(context_attn2)
+            value_attn2 = self.attn2.to_v(value_attn2)
+            n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
+            n = self.attn2.to_out(n)
+        else:
             n = self.attn2(n, context=context_attn2, value=value_attn2)
 
         if "attn2_output_patch" in transformer_patches:

comfy/ldm/modules/diffusionmodules/model.py

@@ -735,203 +735,3 @@ class Decoder(nn.Module):
         if self.tanh_out:
             h = torch.tanh(h)
         return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -830,17 +830,20 @@ class UNetModel(nn.Module):
 
         h = x.type(self.dtype)
         for id, module in enumerate(self.input_blocks):
+            transformer_options["block"] = ("input", id)
             h = forward_timestep_embed(module, h, emb, context, transformer_options)
             if control is not None and 'input' in control and len(control['input']) > 0:
                 ctrl = control['input'].pop()
                 if ctrl is not None:
                     h += ctrl
             hs.append(h)
+        transformer_options["block"] = ("middle", 0)
         h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
         if control is not None and 'middle' in control and len(control['middle']) > 0:
             h += control['middle'].pop()
 
-        for module in self.output_blocks:
+        for id, module in enumerate(self.output_blocks):
+            transformer_options["block"] = ("output", id)
             hsp = hs.pop()
             if control is not None and 'output' in control and len(control['output']) > 0:
                 ctrl = control['output'].pop()

comfy/sd.py

@@ -315,9 +315,6 @@ class ModelPatcher:
         n.model_keys = self.model_keys
         return n
 
-    def set_model_tomesd(self, ratio):
-        self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio}
-
     def set_model_sampler_cfg_function(self, sampler_cfg_function):
         if len(inspect.signature(sampler_cfg_function).parameters) == 3:
             self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
@@ -330,12 +327,29 @@ class ModelPatcher:
             to["patches"] = {}
         to["patches"][name] = to["patches"].get(name, []) + [patch]
 
+    def set_model_patch_replace(self, patch, name, block_name, number):
+        to = self.model_options["transformer_options"]
+        if "patches_replace" not in to:
+            to["patches_replace"] = {}
+        if name not in to["patches_replace"]:
+            to["patches_replace"][name] = {}
+        to["patches_replace"][name][(block_name, number)] = patch
+
     def set_model_attn1_patch(self, patch):
         self.set_model_patch(patch, "attn1_patch")
 
     def set_model_attn2_patch(self, patch):
         self.set_model_patch(patch, "attn2_patch")
 
+    def set_model_attn1_replace(self, patch, block_name, number):
+        self.set_model_patch_replace(patch, "attn1", block_name, number)
+
+    def set_model_attn2_replace(self, patch, block_name, number):
+        self.set_model_patch_replace(patch, "attn2", block_name, number)
+
+    def set_model_attn1_output_patch(self, patch):
+        self.set_model_patch(patch, "attn1_output_patch")
+
     def set_model_attn2_output_patch(self, patch):
         self.set_model_patch(patch, "attn2_output_patch")
 
@@ -348,6 +362,13 @@ class ModelPatcher:
                 for i in range(len(patch_list)):
                     if hasattr(patch_list[i], "to"):
                         patch_list[i] = patch_list[i].to(device)
+        if "patches_replace" in to:
+            patches = to["patches_replace"]
+            for name in patches:
+                patch_list = patches[name]
+                for k in patch_list:
+                    if hasattr(patch_list[k], "to"):
+                        patch_list[k] = patch_list[k].to(device)
 
     def model_dtype(self):
         return self.model.get_dtype()

comfy_extras/nodes_tomesd.py

@@ -142,3 +142,36 @@ def get_functions(x, ratio, original_shape):
 
     nothing = lambda y: y
     return nothing, nothing
+
+
+
+class TomePatchModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, ratio):
+        self.u = None
+        def tomesd_m(q, k, v, extra_options):
+            #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q
+            #however from my basic testing it seems that using q instead gives better results
+            m, self.u = get_functions(q, ratio, extra_options["original_shape"])
+            return m(q), k, v
+        def tomesd_u(n, extra_options):
+            return self.u(n)
+
+        m = model.clone()
+        m.set_model_attn1_patch(tomesd_m)
+        m.set_model_attn1_output_patch(tomesd_u)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "TomePatchModel": TomePatchModel,
+}

nodes.py

@@ -437,22 +437,6 @@ class LoraLoader:
         model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
         return (model_lora, clip_lora)
 
-class TomePatchModel:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "_for_testing"
-
-    def patch(self, model, ratio):
-        m = model.clone()
-        m.set_model_tomesd(ratio)
-        return (m, )
-
 class VAELoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -1341,7 +1325,6 @@ NODE_CLASS_MAPPINGS = {
     "CLIPVisionLoader": CLIPVisionLoader,
     "VAEDecodeTiled": VAEDecodeTiled,
     "VAEEncodeTiled": VAEEncodeTiled,
-    "TomePatchModel": TomePatchModel,
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "GLIGENLoader": GLIGENLoader,
     "GLIGENTextBoxApply": GLIGENTextBoxApply,
@@ -1466,5 +1449,6 @@ def init_custom_nodes():
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_execution_control.py"))
     load_custom_nodes()

server.py

@@ -64,7 +64,7 @@ class PromptServer():
     def __init__(self, loop):
         PromptServer.instance = self
 
-        mimetypes.init(); 
+        mimetypes.init()
         mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
         self.prompt_queue = None
         self.loop = loop
@@ -186,12 +186,37 @@ class PromptServer():
             post = await request.post()
             return image_upload(post)
 
+
         @routes.post("/upload/mask")
         async def upload_mask(request):
             post = await request.post()
 
             def image_save_function(image, post, filepath):
-                original_pil = Image.open(post.get("original_image").file).convert('RGBA')
+                original_ref = json.loads(post.get("original_ref"))
+                filename, output_dir = folder_paths.annotated_filepath(original_ref['filename'])
+
+                # validation for security: prevent accessing arbitrary path
+                if filename[0] == '/' or '..' in filename:
+                    return web.Response(status=400)
+
+                if output_dir is None:
+                    type = original_ref.get("type", "output")
+                    output_dir = folder_paths.get_directory_by_type(type)
+
+                if output_dir is None:
+                    return web.Response(status=400)
+
+                if original_ref.get("subfolder", "") != "":
+                    full_output_dir = os.path.join(output_dir, original_ref["subfolder"])
+                    if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir:
+                        return web.Response(status=403)
+                    output_dir = full_output_dir
+
+                file = os.path.join(output_dir, filename)
+
+                if os.path.isfile(file):
+                    with Image.open(file) as original_pil:
+                        original_pil = original_pil.convert('RGBA')
                         mask_pil = Image.open(image.file).convert('RGBA')
 
                         # alpha copy
@@ -231,9 +256,8 @@ class PromptServer():
                     if 'preview' in request.rel_url.query:
                         with Image.open(file) as img:
                             preview_info = request.rel_url.query['preview'].split(';')
-
                             image_format = preview_info[0]
-                            if image_format not in ['webp', 'jpeg']:
+                            if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''):
                                 image_format = 'webp'
 
                             quality = 90
@@ -241,7 +265,7 @@ class PromptServer():
                                 quality = int(preview_info[-1])
 
                             buffer = BytesIO()
-                            if image_format in ['jpeg']:
+                            if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb':
                                 img = img.convert("RGB")
                             img.save(buffer, format=image_format, quality=quality)
                             buffer.seek(0)

web/extensions/core/maskeditor.js

@@ -346,7 +346,6 @@ class MaskEditorDialog extends ComfyDialog {
 
 		const rgb_url = new URL(ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src);
 		rgb_url.searchParams.delete('channel');
-		rgb_url.searchParams.delete('preview');
 		rgb_url.searchParams.set('channel', 'rgb');
 		orig_image.src = rgb_url;
 		this.image = orig_image;
@@ -618,10 +617,20 @@ class MaskEditorDialog extends ComfyDialog {
 		const dataURL = this.backupCanvas.toDataURL();
 		const blob = dataURLToBlob(dataURL);
 
-		const original_blob = loadedImageToBlob(this.image);
+		let original_url = new URL(this.image.src);
+
+		const original_ref = { filename: original_url.searchParams.get('filename') };
+
+		let original_subfolder = original_url.searchParams.get("subfolder");
+		if(original_subfolder)
+			original_ref.subfolder = original_subfolder;
+
+		let original_type = original_url.searchParams.get("type");
+		if(original_type)
+			original_ref.type = original_type;
 
 		formData.append('image', blob, filename);
-		formData.append('original_image', original_blob);
+		formData.append('original_ref', JSON.stringify(original_ref));
 		formData.append('type', "input");
 		formData.append('subfolder', "clipspace");
 

web/scripts/app.js

@@ -159,14 +159,19 @@ export class ComfyApp {
 					const clip_image = ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']];
 					const index = node.widgets.findIndex(obj => obj.name === 'image');
 					if(index >= 0) {
+						if(node.widgets[index].type != 'image' && typeof node.widgets[index].value == "string" && clip_image.filename) {
+							node.widgets[index].value = (clip_image.subfolder?clip_image.subfolder+'/':'') + clip_image.filename + (clip_image.type?` [${clip_image.type}]`:'');
+						}
+						else {
 							node.widgets[index].value = clip_image;
 						}
 					}
+				}
 				if(ComfyApp.clipspace.widgets) {
 					ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => {
 						const prop = Object.values(node.widgets).find(obj => obj.type === type && obj.name === name);
-						if (prop && prop.type != 'image') {
+						if (prop && prop.type != 'button') {
-							if(typeof prop.value == "string" && value.filename) {
+							if(prop.type != 'image' && typeof prop.value == "string" && value.filename) {
 								prop.value = (value.subfolder?value.subfolder+'/':'') + value.filename + (value.type?` [${value.type}]`:'');
 							}
 							else {
@@ -174,10 +179,6 @@ export class ComfyApp {
 								prop.callback(value);
 							}
 						}
-						else if (prop && prop.type != 'button') {
-							prop.value = value;
-							prop.callback(value);
-						}
 					});
 				}
 			}