Merge branch 'comfyanonymous:master' into connect-primitives-to-reroutes

README.md

@@ -11,7 +11,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x and SD2.x
+- Fully supports SD1.x, SD2.x and SDXL
 - Asynchronous Queue system
 - 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)
@@ -154,11 +154,13 @@ And then you can use that terminal to run ComfyUI without installing any depende
 
 ```python main.py```
 
-### For AMD 6700, 6600 and maybe others
+### For AMD cards not officially supported by ROCm
 
 Try running it with this command if you have issues:
 
-```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
+For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
+
+For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
 
 # Notes
 
@@ -191,7 +193,7 @@ You can set this command line setting to disable the upcasting to fp32 in some c
 
 Use ```--preview-method auto``` to enable previews.
 
-The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_encoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth) and [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
 
 ## Support and dev channel
 

comfy/cli_args.py

@@ -53,7 +53,8 @@ class LatentPreviewMethod(enum.Enum):
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
 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-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
+attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . 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.")

comfy/clip_vision.py

@@ -52,7 +52,8 @@ def convert_to_transformers(sd, prefix):
         sd = transformers_convert(sd, prefix, "vision_model.", 32)
     return sd
 
-def load_clipvision_from_sd(sd, prefix):
+def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
+    if convert_keys:
         sd = convert_to_transformers(sd, prefix)
     if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")

comfy/diffusers_convert.py

@@ -202,11 +202,13 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 code2idx = {"q": 0, "k": 1, "v": 2}
 
 
-def convert_text_enc_state_dict_v20(text_enc_dict):
+def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
     capture_qkv_weight = {}
     capture_qkv_bias = {}
     for k, v in text_enc_dict.items():
+        if not k.startswith(prefix):
+            continue
         if (
                 k.endswith(".self_attn.q_proj.weight")
                 or k.endswith(".self_attn.k_proj.weight")

comfy/k_diffusion/sampling.py

@@ -77,7 +77,7 @@ class BatchedBrownianTree:
         except TypeError:
             seed = [seed]
             self.batched = False
-        self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+        self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
 
     @staticmethod
     def sort(a, b):
@@ -85,7 +85,7 @@ class BatchedBrownianTree:
 
     def __call__(self, t0, t1):
         t0, t1, sign = self.sort(t0, t1)
-        w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+        w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
         return w if self.batched else w[0]
 
 
@@ -543,7 +543,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     """DPM-Solver++ (stochastic)."""
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    seed = extra_args.get("seed", None)
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
@@ -613,8 +614,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     if solver_type not in {'heun', 'midpoint'}:
         raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
 
+    seed = extra_args.get("seed", None)
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
 

comfy/latent_formats.py

@@ -0,0 +1,31 @@
+
+class LatentFormat:
+    def process_in(self, latent):
+        return latent * self.scale_factor
+
+    def process_out(self, latent):
+        return latent / self.scale_factor
+
+class SD15(LatentFormat):
+    def __init__(self, scale_factor=0.18215):
+        self.scale_factor = scale_factor
+        self.latent_rgb_factors = [
+                    #   R        G        B
+                    [0.298, 0.207, 0.208],  # L1
+                    [0.187, 0.286, 0.173],  # L2
+                    [-0.158, 0.189, 0.264],  # L3
+                    [-0.184, -0.271, -0.473],  # L4
+                ]
+        self.taesd_decoder_name = "taesd_decoder.pth"
+
+class SDXL(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.13025
+        self.latent_rgb_factors = [ #TODO: these are the factors for SD1.5, need to estimate new ones for SDXL
+                    #   R        G        B
+                    [0.298, 0.207, 0.208],  # L1
+                    [0.187, 0.286, 0.173],  # L2
+                    [-0.158, 0.189, 0.264],  # L3
+                    [-0.184, -0.271, -0.473],  # L4
+                ]
+        self.taesd_decoder_name = "taesdxl_decoder.pth"

comfy/ldm/models/diffusion/ddim.py

@@ -180,6 +180,12 @@ class DDIMSampler(object):
                                                     )
         return samples, intermediates
 
+    def q_sample(self, x_start, t, noise=None):
+        if noise is None:
+            noise = torch.randn_like(x_start)
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
     @torch.no_grad()
     def ddim_sampling(self, cond, shape,
                       x_T=None, ddim_use_original_steps=False,
@@ -214,7 +220,7 @@ class DDIMSampler(object):
 
             if mask is not None:
                 assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img_orig = self.q_sample(x0, ts)  # TODO: deterministic forward pass?
                 img = img_orig * mask + (1. - mask) * img
 
             if ucg_schedule is not None:

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 +565,32 @@ 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:
+        if block is not None:
-            m, u = tomesd.get_functions(x, transformer_options["tomesd"]["ratio"], transformer_options["original_shape"])
+            transformer_block = (block[0], block[1], block_index)
-            n = u(self.attn1(m(n), context=context_attn1, value=value_attn1))
+        else:
+            transformer_block = None
+        attn1_replace_patch = transformer_patches_replace.get("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 +607,20 @@ 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/model_base.py

@@ -4,11 +4,15 @@ from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugme
 from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep
 import numpy as np
+from . import utils
 
 class BaseModel(torch.nn.Module):
-    def __init__(self, unet_config, v_prediction=False):
+    def __init__(self, model_config, v_prediction=False):
         super().__init__()
 
+        unet_config = model_config.unet_config
+        self.latent_format = model_config.latent_format
+        self.model_config = model_config
         self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
         self.diffusion_model = UNetModel(**unet_config)
         self.v_prediction = v_prediction
@@ -75,9 +79,26 @@ class BaseModel(torch.nn.Module):
         del to_load
         return self
 
+    def process_latent_in(self, latent):
+        return self.latent_format.process_in(latent)
+
+    def process_latent_out(self, latent):
+        return self.latent_format.process_out(latent)
+
+    def state_dict_for_saving(self, clip_state_dict, vae_state_dict):
+        clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict)
+        unet_state_dict = self.diffusion_model.state_dict()
+        unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
+        vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict)
+        if self.get_dtype() == torch.float16:
+            clip_state_dict = utils.convert_sd_to(clip_state_dict, torch.float16)
+            vae_state_dict = utils.convert_sd_to(vae_state_dict, torch.float16)
+        return {**unet_state_dict, **vae_state_dict, **clip_state_dict}
+
+
 class SD21UNCLIP(BaseModel):
-    def __init__(self, unet_config, noise_aug_config, v_prediction=True):
+    def __init__(self, model_config, noise_aug_config, v_prediction=True):
-        super().__init__(unet_config, v_prediction)
+        super().__init__(model_config, v_prediction)
         self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)
 
     def encode_adm(self, **kwargs):
@@ -112,13 +133,13 @@ class SD21UNCLIP(BaseModel):
         return adm_out
 
 class SDInpaint(BaseModel):
-    def __init__(self, unet_config, v_prediction=False):
+    def __init__(self, model_config, v_prediction=False):
-        super().__init__(unet_config, v_prediction)
+        super().__init__(model_config, v_prediction)
         self.concat_keys = ("mask", "masked_image")
 
 class SDXLRefiner(BaseModel):
-    def __init__(self, unet_config, v_prediction=False):
+    def __init__(self, model_config, v_prediction=False):
-        super().__init__(unet_config, v_prediction)
+        super().__init__(model_config, v_prediction)
         self.embedder = Timestep(256)
 
     def encode_adm(self, **kwargs):
@@ -144,8 +165,8 @@ class SDXLRefiner(BaseModel):
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
 
 class SDXL(BaseModel):
-    def __init__(self, unet_config, v_prediction=False):
+    def __init__(self, model_config, v_prediction=False):
-        super().__init__(unet_config, v_prediction)
+        super().__init__(model_config, v_prediction)
         self.embedder = Timestep(256)
 
     def encode_adm(self, **kwargs):

comfy/model_management.py

@@ -139,7 +139,23 @@ else:
     except:
         XFORMERS_IS_AVAILABLE = False
 
+def is_nvidia():
+    global cpu_state
+    if cpu_state == CPUState.GPU:
+        if torch.version.cuda:
+            return True
+
 ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention
+
+if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+    try:
+        if is_nvidia():
+            torch_version = torch.version.__version__
+            if int(torch_version[0]) >= 2:
+                ENABLE_PYTORCH_ATTENTION = True
+    except:
+        pass
+
 if ENABLE_PYTORCH_ATTENTION:
     torch.backends.cuda.enable_math_sdp(True)
     torch.backends.cuda.enable_flash_sdp(True)
@@ -347,7 +363,7 @@ def pytorch_attention_flash_attention():
     global ENABLE_PYTORCH_ATTENTION
     if ENABLE_PYTORCH_ATTENTION:
         #TODO: more reliable way of checking for flash attention?
-        if torch.version.cuda: #pytorch flash attention only works on Nvidia
+        if is_nvidia(): #pytorch flash attention only works on Nvidia
             return True
     return False
 
@@ -438,7 +454,7 @@ def soft_empty_cache():
     elif xpu_available:
         torch.xpu.empty_cache()
     elif torch.cuda.is_available():
-        if torch.version.cuda: #This seems to make things worse on ROCm so I only do it for cuda
+        if is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
 

comfy/sample.py

@@ -65,7 +65,7 @@ def cleanup_additional_models(models):
     for m in models:
         m.cleanup()
 
-def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False):
+def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
     device = comfy.model_management.get_torch_device()
 
     if noise_mask is not None:
@@ -85,7 +85,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
 
     sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 
-    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar)
+    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
     samples = samples.cpu()
 
     cleanup_additional_models(models)

comfy/samplers.py

@@ -13,7 +13,7 @@ def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
 
 #The main sampling function shared by all the samplers
 #Returns predicted noise
-def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
+def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None):
         def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
             area = (x_in.shape[2], x_in.shape[3], 0, 0)
             strength = 1.0
@@ -292,8 +292,8 @@ class CFGNoisePredictor(torch.nn.Module):
         super().__init__()
         self.inner_model = model
         self.alphas_cumprod = model.alphas_cumprod
-    def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}):
+    def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None):
-        out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options)
+        out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options, seed=seed)
         return out
 
 
@@ -301,11 +301,11 @@ class KSamplerX0Inpaint(torch.nn.Module):
     def __init__(self, model):
         super().__init__()
         self.inner_model = model
-    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}):
+    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}, seed=None):
         if denoise_mask is not None:
             latent_mask = 1. - denoise_mask
             x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
-        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options)
+        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options, seed=seed)
         if denoise_mask is not None:
             out *= denoise_mask
 
@@ -542,7 +542,7 @@ class KSampler:
             sigmas = self.calculate_sigmas(new_steps).to(self.device)
             self.sigmas = sigmas[-(steps + 1):]
 
-    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False):
+    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
         if sigmas is None:
             sigmas = self.sigmas
         sigma_min = self.sigma_min
@@ -586,7 +586,10 @@ class KSampler:
             positive = encode_adm(self.model, positive, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "positive")
             negative = encode_adm(self.model, negative, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "negative")
 
-        extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options}
+        if latent_image is not None:
+            latent_image = self.model.process_latent_in(latent_image)
+
+        extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options, "seed":seed}
 
         cond_concat = None
         if hasattr(self.model, 'concat_keys'): #inpaint
@@ -672,4 +675,4 @@ class KSampler:
                 else:
                     samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
 
-        return samples.to(torch.float32)
+        return self.model.process_latent_out(samples.to(torch.float32))

comfy/sd.py

@@ -19,6 +19,7 @@ from . import model_detection
 
 from . import sd1_clip
 from . import sd2_clip
+from . import sdxl_clip
 
 def load_model_weights(model, sd):
     m, u = model.load_state_dict(sd, strict=False)
@@ -284,6 +285,11 @@ def model_lora_keys(model, key_map={}):
         if key_in:
             counter += 1
 
+    for k in sdk:
+        if k.startswith("diffusion_model.") and k.endswith(".weight"):
+            key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
+            key_map["lora_unet_{}".format(key_lora)] = k
+
     return key_map
 
 
@@ -315,9 +321,6 @@ 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 +333,29 @@ 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 +368,13 @@ 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()
@@ -390,7 +417,11 @@ class ModelPatcher:
                     weight *= strength_model
 
                 if len(v) == 1:
-                    weight += alpha * (v[0]).type(weight.dtype).to(weight.device)
+                    w1 = v[0]
+                    if w1.shape != weight.shape:
+                        print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
+                    else:
+                        weight += alpha * w1.type(weight.dtype).to(weight.device)
                 elif len(v) == 4: #lora/locon
                     mat1 = v[0]
                     mat2 = v[1]
@@ -499,7 +530,7 @@ class CLIP:
         return n
 
     def load_from_state_dict(self, sd):
-        self.cond_stage_model.transformer.load_state_dict(sd, strict=False)
+        self.cond_stage_model.load_sd(sd)
 
     def add_patches(self, patches, strength=1.0):
         return self.patcher.add_patches(patches, strength)
@@ -514,11 +545,11 @@ class CLIP:
         if self.layer_idx is not None:
             self.cond_stage_model.clip_layer(self.layer_idx)
         try:
-            self.patcher.patch_model()
+            self.patch_model()
             cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
-            self.patcher.unpatch_model()
+            self.unpatch_model()
         except Exception as e:
-            self.patcher.unpatch_model()
+            self.unpatch_model()
             raise e
 
         cond_out = cond
@@ -530,9 +561,20 @@ class CLIP:
         tokens = self.tokenize(text)
         return self.encode_from_tokens(tokens)
 
+    def load_sd(self, sd):
+        return self.cond_stage_model.load_sd(sd)
+
+    def get_sd(self):
+        return self.cond_stage_model.state_dict()
+
+    def patch_model(self):
+        self.patcher.patch_model()
+
+    def unpatch_model(self):
+        self.patcher.unpatch_model()
 
 class VAE:
-    def __init__(self, ckpt_path=None, scale_factor=0.18215, device=None, config=None):
+    def __init__(self, ckpt_path=None, device=None, config=None):
         if config is None:
             #default SD1.x/SD2.x VAE parameters
             ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
@@ -546,7 +588,6 @@ class VAE:
                 sd = diffusers_convert.convert_vae_state_dict(sd)
             self.first_stage_model.load_state_dict(sd, strict=False)
 
-        self.scale_factor = scale_factor
         if device is None:
             device = model_management.get_torch_device()
         self.device = device
@@ -557,7 +598,7 @@ class VAE:
         steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = utils.ProgressBar(steps)
 
-        decode_fn = lambda a: (self.first_stage_model.decode(1. / self.scale_factor * a.to(self.device)) + 1.0)
+        decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.device)) + 1.0)
         output = torch.clamp((
             (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
             utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
@@ -571,7 +612,7 @@ class VAE:
         steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = utils.ProgressBar(steps)
 
-        encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.device) - 1.).sample() * self.scale_factor
+        encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.device) - 1.).sample()
         samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
@@ -589,7 +630,7 @@ class VAE:
             pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.device)
-                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(1. / self.scale_factor * samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
+                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             pixel_samples = self.decode_tiled_(samples_in)
@@ -616,7 +657,7 @@ class VAE:
             samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu() * self.scale_factor
+                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu()
 
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
@@ -633,6 +674,10 @@ class VAE:
         self.first_stage_model = self.first_stage_model.cpu()
         return samples
 
+    def get_sd(self):
+        return self.first_stage_model.state_dict()
+
+
 def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
     #print(current_batch_size, target_batch_size)
@@ -935,15 +980,42 @@ def load_style_model(ckpt_path):
     return StyleModel(model)
 
 
-def load_clip(ckpt_path, embedding_directory=None):
+def load_clip(ckpt_paths, embedding_directory=None):
-    clip_data = utils.load_torch_file(ckpt_path, safe_load=True)
+    clip_data = []
-    config = {}
+    for p in ckpt_paths:
-    if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
+        clip_data.append(utils.load_torch_file(p, safe_load=True))
-        config['target'] = 'comfy.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
+
+    class EmptyClass:
+        pass
+
+    for i in range(len(clip_data)):
+        if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
+            clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32)
+
+    clip_target = EmptyClass()
+    clip_target.params = {}
+    if len(clip_data) == 1:
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
+            clip_target.clip = sdxl_clip.SDXLRefinerClipModel
+            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+        elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
+            clip_target.clip = sd2_clip.SD2ClipModel
+            clip_target.tokenizer = sd2_clip.SD2Tokenizer
         else:
-        config['target'] = 'comfy.ldm.modules.encoders.modules.FrozenCLIPEmbedder'
+            clip_target.clip = sd1_clip.SD1ClipModel
-    clip = CLIP(config=config, embedding_directory=embedding_directory)
+            clip_target.tokenizer = sd1_clip.SD1Tokenizer
-    clip.load_from_state_dict(clip_data)
+    else:
+        clip_target.clip = sdxl_clip.SDXLClipModel
+        clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+
+    clip = CLIP(clip_target, embedding_directory=embedding_directory)
+    for c in clip_data:
+        m, u = clip.load_sd(c)
+        if len(m) > 0:
+            print("clip missing:", m)
+
+        if len(u) > 0:
+            print("clip unexpected:", u)
     return clip
 
 def load_gligen(ckpt_path):
@@ -954,6 +1026,7 @@ def load_gligen(ckpt_path):
     return model
 
 def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
+    #TODO: this function is a mess and should be removed eventually
     if config is None:
         with open(config_path, 'r') as stream:
             config = yaml.safe_load(stream)
@@ -988,12 +1061,20 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
     if state_dict is None:
         state_dict = utils.load_torch_file(ckpt_path)
 
+    class EmptyClass:
+        pass
+
+    model_config = EmptyClass()
+    model_config.unet_config = unet_config
+    from . import latent_formats
+    model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor)
+
     if config['model']["target"].endswith("LatentInpaintDiffusion"):
-        model = model_base.SDInpaint(unet_config, v_prediction=v_prediction)
+        model = model_base.SDInpaint(model_config, v_prediction=v_prediction)
     elif config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"):
-        model = model_base.SD21UNCLIP(unet_config, noise_aug_config["params"], v_prediction=v_prediction)
+        model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], v_prediction=v_prediction)
     else:
-        model = model_base.BaseModel(unet_config, v_prediction=v_prediction)
+        model = model_base.BaseModel(model_config, v_prediction=v_prediction)
 
     if fp16:
         model = model.half()
@@ -1002,16 +1083,14 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     if output_vae:
         w = WeightsLoader()
-        vae = VAE(scale_factor=scale_factor, config=vae_config)
+        vae = VAE(config=vae_config)
         w.first_stage_model = vae.first_stage_model
         load_model_weights(w, state_dict)
 
     if output_clip:
         w = WeightsLoader()
-        class EmptyClass:
-            pass
         clip_target = EmptyClass()
-        clip_target.params = clip_config["params"]
+        clip_target.params = clip_config.get("params", {})
         if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"):
             clip_target.clip = sd2_clip.SD2ClipModel
             clip_target.tokenizer = sd2_clip.SD2Tokenizer
@@ -1045,13 +1124,13 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
 
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
-            clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix)
+            clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
 
     model = model_config.get_model(sd)
     model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
-        vae = VAE(scale_factor=model_config.vae_scale_factor)
+        vae = VAE()
         w = WeightsLoader()
         w.first_stage_model = vae.first_stage_model
         load_model_weights(w, sd)
@@ -1069,3 +1148,16 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         print("left over keys:", left_over)
 
     return (ModelPatcher(model), clip, vae, clipvision)
+
+def save_checkpoint(output_path, model, clip, vae, metadata=None):
+    try:
+        model.patch_model()
+        clip.patch_model()
+        sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
+        utils.save_torch_file(sd, output_path, metadata=metadata)
+        model.unpatch_model()
+        clip.unpatch_model()
+    except Exception as e:
+        model.unpatch_model()
+        clip.unpatch_model()
+        raise e

comfy/sd1_clip.py

@@ -128,6 +128,9 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def encode(self, tokens):
         return self(tokens)
 
+    def load_sd(self, sd):
+        return self.transformer.load_state_dict(sd, strict=False)
+
 def parse_parentheses(string):
     result = []
     current_item = ""

comfy/sdxl_clip.py

@@ -31,6 +31,11 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
             self.layer = "hidden"
             self.layer_idx = layer_idx
 
+    def load_sd(self, sd):
+        if "text_projection" in sd:
+            self.text_projection[:] = sd.pop("text_projection")
+        return super().load_sd(sd)
+
 class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None):
         super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280)
@@ -68,6 +73,12 @@ class SDXLClipModel(torch.nn.Module):
         l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
         return torch.cat([l_out, g_out], dim=-1), g_pooled
 
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+            return self.clip_g.load_sd(sd)
+        else:
+            return self.clip_l.load_sd(sd)
+
 class SDXLRefinerClipModel(torch.nn.Module):
     def __init__(self, device="cpu"):
         super().__init__()
@@ -81,3 +92,5 @@ class SDXLRefinerClipModel(torch.nn.Module):
         g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
         return g_out, g_pooled
 
+    def load_sd(self, sd):
+        return self.clip_g.load_sd(sd)

comfy/supported_models.py

@@ -7,6 +7,9 @@ from . import sd2_clip
 from . import sdxl_clip
 
 from . import supported_models_base
+from . import latent_formats
+
+from . import diffusers_convert
 
 class SD15(supported_models_base.BASE):
     unet_config = {
@@ -21,7 +24,7 @@ class SD15(supported_models_base.BASE):
         "num_head_channels": -1,
     }
 
-    vae_scale_factor = 0.18215
+    latent_format = latent_formats.SD15
 
     def process_clip_state_dict(self, state_dict):
         k = list(state_dict.keys())
@@ -48,7 +51,7 @@ class SD20(supported_models_base.BASE):
         "adm_in_channels": None,
     }
 
-    vae_scale_factor = 0.18215
+    latent_format = latent_formats.SD15
 
     def v_prediction(self, state_dict):
         if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
@@ -62,6 +65,13 @@ class SD20(supported_models_base.BASE):
         state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
         return state_dict
 
+    def process_clip_state_dict_for_saving(self, state_dict):
+        replace_prefix = {}
+        replace_prefix[""] = "cond_stage_model.model."
+        state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
+        state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
+        return state_dict
+
     def clip_target(self):
         return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
 
@@ -97,10 +107,10 @@ class SDXLRefiner(supported_models_base.BASE):
         "transformer_depth": [0, 4, 4, 0],
     }
 
-    vae_scale_factor = 0.13025
+    latent_format = latent_formats.SDXL
 
     def get_model(self, state_dict):
-        return model_base.SDXLRefiner(self.unet_config)
+        return model_base.SDXLRefiner(self)
 
     def process_clip_state_dict(self, state_dict):
         keys_to_replace = {}
@@ -112,6 +122,13 @@ class SDXLRefiner(supported_models_base.BASE):
         state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
         return state_dict
 
+    def process_clip_state_dict_for_saving(self, state_dict):
+        replace_prefix = {}
+        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
+        replace_prefix["clip_g"] = "conditioner.embedders.0.model"
+        state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix)
+        return state_dict_g
+
     def clip_target(self):
         return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
 
@@ -124,10 +141,10 @@ class SDXL(supported_models_base.BASE):
         "adm_in_channels": 2816
     }
 
-    vae_scale_factor = 0.13025
+    latent_format = latent_formats.SDXL
 
     def get_model(self, state_dict):
-        return model_base.SDXL(self.unet_config)
+        return model_base.SDXL(self)
 
     def process_clip_state_dict(self, state_dict):
         keys_to_replace = {}
@@ -141,6 +158,19 @@ class SDXL(supported_models_base.BASE):
         state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
         return state_dict
 
+    def process_clip_state_dict_for_saving(self, state_dict):
+        replace_prefix = {}
+        keys_to_replace = {}
+        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
+        for k in state_dict:
+            if k.startswith("clip_l"):
+                state_dict_g[k] = state_dict[k]
+
+        replace_prefix["clip_g"] = "conditioner.embedders.1.model"
+        replace_prefix["clip_l"] = "conditioner.embedders.0"
+        state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix)
+        return state_dict_g
+
     def clip_target(self):
         return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
 

comfy/supported_models_base.py

@@ -49,17 +49,30 @@ class BASE:
 
     def __init__(self, unet_config):
         self.unet_config = unet_config
+        self.latent_format = self.latent_format()
         for x in self.unet_extra_config:
             self.unet_config[x] = self.unet_extra_config[x]
 
     def get_model(self, state_dict):
         if self.inpaint_model():
-            return model_base.SDInpaint(self.unet_config, v_prediction=self.v_prediction(state_dict))
+            return model_base.SDInpaint(self, v_prediction=self.v_prediction(state_dict))
         elif self.noise_aug_config is not None:
-            return model_base.SD21UNCLIP(self.unet_config, self.noise_aug_config, v_prediction=self.v_prediction(state_dict))
+            return model_base.SD21UNCLIP(self, self.noise_aug_config, v_prediction=self.v_prediction(state_dict))
         else:
-            return model_base.BaseModel(self.unet_config, v_prediction=self.v_prediction(state_dict))
+            return model_base.BaseModel(self, v_prediction=self.v_prediction(state_dict))
 
     def process_clip_state_dict(self, state_dict):
         return state_dict
 
+    def process_clip_state_dict_for_saving(self, state_dict):
+        replace_prefix = {"": "cond_stage_model."}
+        return state_dict_prefix_replace(state_dict, replace_prefix)
+
+    def process_unet_state_dict_for_saving(self, state_dict):
+        replace_prefix = {"": "model.diffusion_model."}
+        return state_dict_prefix_replace(state_dict, replace_prefix)
+
+    def process_vae_state_dict_for_saving(self, state_dict):
+        replace_prefix = {"": "first_stage_model."}
+        return state_dict_prefix_replace(state_dict, replace_prefix)
+

comfy/utils.py

@@ -2,10 +2,10 @@ import torch
 import math
 import struct
 import comfy.checkpoint_pickle
+import safetensors.torch
 
 def load_torch_file(ckpt, safe_load=False):
     if ckpt.lower().endswith(".safetensors"):
-        import safetensors.torch
         sd = safetensors.torch.load_file(ckpt, device="cpu")
     else:
         if safe_load:
@@ -24,6 +24,12 @@ def load_torch_file(ckpt, safe_load=False):
             sd = pl_sd
     return sd
 
+def save_torch_file(sd, ckpt, metadata=None):
+    if metadata is not None:
+        safetensors.torch.save_file(sd, ckpt, metadata=metadata)
+    else:
+        safetensors.torch.save_file(sd, ckpt)
+
 def transformers_convert(sd, prefix_from, prefix_to, number):
     keys_to_replace = {
         "{}positional_embedding": "{}embeddings.position_embedding.weight",
@@ -64,6 +70,12 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
                     sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
     return sd
 
+def convert_sd_to(state_dict, dtype):
+    keys = list(state_dict.keys())
+    for k in keys:
+        state_dict[k] = state_dict[k].to(dtype)
+    return state_dict
+
 def safetensors_header(safetensors_path, max_size=100*1024*1024):
     with open(safetensors_path, "rb") as f:
         header = f.read(8)

comfy_extras/nodes_model_merging.py

@@ -1,4 +1,8 @@
-
+import comfy.sd
+import comfy.utils
+import folder_paths
+import json
+import os
 
 class ModelMergeSimple:
     @classmethod
@@ -49,7 +53,43 @@ class ModelMergeBlocks:
             m.add_patches({k: (sd[k], )}, 1.0 - ratio, ratio)
         return (m, )
 
+class CheckpointSave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "clip": ("CLIP",),
+                              "vae": ("VAE",),
+                              "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing/model_merging"
+
+    def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = {"prompt": prompt_info}
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata[x] = json.dumps(extra_pnginfo[x])
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
+        return {}
+
+
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSimple": ModelMergeSimple,
-    "ModelMergeBlocks": ModelMergeBlocks
+    "ModelMergeBlocks": ModelMergeBlocks,
+    "CheckpointSave": CheckpointSave,
 }

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,
+}

extra_model_paths.yaml.example

@@ -8,7 +8,9 @@ a111:
     checkpoints: models/Stable-diffusion
     configs: models/Stable-diffusion
     vae: models/VAE
-    loras: models/Lora
+    loras: |
+         models/Lora
+         models/LyCORIS
     upscale_models: |
                   models/ESRGAN
                   models/SwinIR
@@ -21,5 +23,3 @@ a111:
 #    checkpoints: models/checkpoints
 #    gligen: models/gligen
 #    custom_nodes: path/custom_nodes
-
-

latent_preview.py

@@ -49,14 +49,8 @@ class TAESDPreviewerImpl(LatentPreviewer):
 
 
 class Latent2RGBPreviewer(LatentPreviewer):
-    def __init__(self):
+    def __init__(self, latent_rgb_factors):
-        self.latent_rgb_factors = torch.tensor([
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
-                    #   R        G        B
-                    [0.298, 0.207, 0.208],  # L1
-                    [0.187, 0.286, 0.173],  # L2
-                    [-0.158, 0.189, 0.264],  # L3
-                    [-0.184, -0.271, -0.473],  # L4
-                ], device="cpu")
 
     def decode_latent_to_preview(self, x0):
         latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
@@ -69,12 +63,12 @@ class Latent2RGBPreviewer(LatentPreviewer):
         return Image.fromarray(latents_ubyte.numpy())
 
 
-def get_previewer(device):
+def get_previewer(device, latent_format):
     previewer = None
     method = args.preview_method
     if method != LatentPreviewMethod.NoPreviews:
         # TODO previewer methods
-        taesd_decoder_path = folder_paths.get_full_path("vae_approx", "taesd_decoder.pth")
+        taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
 
         if method == LatentPreviewMethod.Auto:
             method = LatentPreviewMethod.Latent2RGB
@@ -86,10 +80,10 @@ def get_previewer(device):
                 taesd = TAESD(None, taesd_decoder_path).to(device)
                 previewer = TAESDPreviewerImpl(taesd)
             else:
-                print("Warning: TAESD previews enabled, but could not find models/vae_approx/taesd_decoder.pth")
+                print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
 
         if previewer is None:
-            previewer = Latent2RGBPreviewer()
+            previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
     return previewer
 
 

nodes.py

@@ -284,9 +284,9 @@ class SaveLatent:
 
         output = {}
         output["latent_tensor"] = samples["samples"]
+        output["latent_format_version_0"] = torch.tensor([])
 
-        safetensors.torch.save_file(output, file, metadata=metadata)
+        comfy.utils.save_torch_file(output, file, metadata=metadata)
-
         return {}
 
 
@@ -305,7 +305,10 @@ class LoadLatent:
     def load(self, latent):
         latent_path = folder_paths.get_annotated_filepath(latent)
         latent = safetensors.torch.load_file(latent_path, device="cpu")
-        samples = {"samples": latent["latent_tensor"].float()}
+        multiplier = 1.0
+        if "latent_format_version_0" not in latent:
+            multiplier = 1.0 / 0.18215
+        samples = {"samples": latent["latent_tensor"].float() * multiplier}
         return (samples, )
 
     @classmethod
@@ -433,22 +436,6 @@ 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):
@@ -532,11 +519,27 @@ class CLIPLoader:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
-    CATEGORY = "loaders"
+    CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name):
         clip_path = folder_paths.get_full_path("clip", clip_name)
-        clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return (clip,)
+
+class DualCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_clip(self, clip_name1, clip_name2):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
 class CLIPVisionLoader:
@@ -950,7 +953,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     if preview_format not in ["JPEG", "PNG"]:
         preview_format = "JPEG"
 
-    previewer = latent_preview.get_previewer(device)
+    previewer = latent_preview.get_previewer(device, model.model.latent_format)
 
     pbar = comfy.utils.ProgressBar(steps)
     def callback(step, x0, x, total_steps):
@@ -961,7 +964,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
 
     samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                   denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
-                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback)
+                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, seed=seed)
     out = latent.copy()
     out["samples"] = samples
     return (out, )
@@ -1327,6 +1330,7 @@ NODE_CLASS_MAPPINGS = {
     "LatentCrop": LatentCrop,
     "LoraLoader": LoraLoader,
     "CLIPLoader": CLIPLoader,
+    "DualCLIPLoader": DualCLIPLoader,
     "CLIPVisionEncode": CLIPVisionEncode,
     "StyleModelApply": StyleModelApply,
     "unCLIPConditioning": unCLIPConditioning,
@@ -1337,7 +1341,6 @@ NODE_CLASS_MAPPINGS = {
     "CLIPVisionLoader": CLIPVisionLoader,
     "VAEDecodeTiled": VAEDecodeTiled,
     "VAEEncodeTiled": VAEEncodeTiled,
-    "TomePatchModel": TomePatchModel,
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "GLIGENLoader": GLIGENLoader,
     "GLIGENTextBoxApply": GLIGENTextBoxApply,
@@ -1462,4 +1465,5 @@ 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_nodes()

notebooks/comfyui_colab.ipynb

@@ -144,6 +144,7 @@
         "\n",
         "\n",
         "# ESRGAN upscale model\n",
+        "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n",
         "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n",
         "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n",
         "\n",

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);
-						}
 					});
 				}
 			}
@@ -1467,7 +1468,7 @@ export class ComfyApp {
 				this.loadGraphData(JSON.parse(reader.result));
 			};
 			reader.readAsText(file);
-		} else if (file.name?.endsWith(".latent")) {
+		} else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) {
 			const info = await getLatentMetadata(file);
 			if (info.workflow) {
 				this.loadGraphData(JSON.parse(info.workflow));

web/scripts/pnginfo.js

@@ -55,11 +55,12 @@ export function getLatentMetadata(file) {
 			const dataView = new DataView(safetensorsData.buffer);
 			let header_size = dataView.getUint32(0, true);
 			let offset = 8;
-			let header = JSON.parse(String.fromCharCode(...safetensorsData.slice(offset, offset + header_size)));
+			let header = JSON.parse(new TextDecoder().decode(safetensorsData.slice(offset, offset + header_size)));
 			r(header.__metadata__);
 		};
 
-		reader.readAsArrayBuffer(file);
+		var slice = file.slice(0, 1024 * 1024 * 4);
+		reader.readAsArrayBuffer(slice);
 	});
 }
 

web/scripts/ui.js

@@ -545,7 +545,7 @@ export class ComfyUI {
 		const fileInput = $el("input", {
 			id: "comfy-file-input",
 			type: "file",
-			accept: ".json,image/png,.latent",
+			accept: ".json,image/png,.latent,.safetensors",
 			style: {display: "none"},
 			parent: document.body,
 			onchange: () => {