Fix issues with #4302 and support loading diffusers format flux.

comfy/ldm/hydit/attn_layers.py

@@ -47,7 +47,7 @@ def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x
 
 
 def rotate_half(x):
-    x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+    x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
     return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
 
 
@@ -78,10 +78,9 @@ def apply_rotary_emb(
     xk_out = None
     if isinstance(freqs_cis, tuple):
         cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first)    # [S, D]
-        cos, sin = cos.to(xq.device), sin.to(xq.device)
+        xq_out = (xq * cos + rotate_half(xq) * sin)
-        xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
         if xk is not None:
-            xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
+            xk_out = (xk * cos + rotate_half(xk) * sin)
     else:
         xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))  # [B, S, H, D//2]
         freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device)   # [S, D//2] --> [1, S, 1, D//2]

comfy/ldm/hydit/models.py

@@ -21,6 +21,7 @@ def calc_rope(x, patch_size, head_size):
     sub_args = [start, stop, (th, tw)]
     # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
     rope = get_2d_rotary_pos_embed(head_size, *sub_args)
+    rope = (rope[0].to(x), rope[1].to(x))
     return rope
 
 

comfy/model_detection.py

@@ -495,7 +495,12 @@ def model_config_from_diffusers_unet(state_dict):
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
     out_sd = {}
 
-    if 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3
+    if 'transformer_blocks.0.attn.norm_added_k.weight' in state_dict: #Flux
+        depth = count_blocks(state_dict, 'transformer_blocks.{}.')
+        depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+        hidden_size = state_dict["x_embedder.bias"].shape[0]
+        sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix)
+    elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
         sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
@@ -521,7 +526,12 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
                     old_weight = out_sd.get(t[0], None)
                     if old_weight is None:
                         old_weight = torch.empty_like(weight)
-                        old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
+                    if old_weight.shape[offset[0]] < offset[1] + offset[2]:
+                        exp = list(weight.shape)
+                        exp[offset[0]] = offset[1] + offset[2]
+                        new = torch.empty(exp, device=weight.device, dtype=weight.dtype)
+                        new[:old_weight.shape[0]] = old_weight
+                        old_weight = new
 
                     w = old_weight.narrow(offset[0], offset[1], offset[2])
                 else:

comfy/model_management.py

@@ -296,7 +296,7 @@ class LoadedModel:
 
     def model_memory_required(self, device):
         if device == self.model.current_loaded_device():
-            return 0
+            return self.model_offloaded_memory()
         else:
             return self.model_memory()
 
@@ -308,15 +308,21 @@ class LoadedModel:
 
         load_weights = not self.weights_loaded
 
-        try:
+        if self.model.loaded_size() > 0:
-            if lowvram_model_memory > 0 and load_weights:
+            use_more_vram = lowvram_model_memory
-                self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
+            if use_more_vram == 0:
-            else:
+                use_more_vram = 1e32
-                self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
+            self.model_use_more_vram(use_more_vram)
-        except Exception as e:
+        else:
-            self.model.unpatch_model(self.model.offload_device)
+            try:
-            self.model_unload()
+                if lowvram_model_memory > 0 and load_weights:
-            raise e
+                    self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
+                else:
+                    self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
+            except Exception as e:
+                self.model.unpatch_model(self.model.offload_device)
+                self.model_unload()
+                raise e
 
         if is_intel_xpu() and not args.disable_ipex_optimize:
             self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
@@ -484,18 +490,21 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
 
     total_memory_required = {}
     for loaded_model in models_to_load:
-        if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True:#unload clones where the weights are different
+        unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different
-            total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
 
-    for device in total_memory_required:
+    for loaded_model in models_already_loaded:
-        if device != torch.device("cpu"):
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
-            free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
 
     for loaded_model in models_to_load:
         weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded
         if weights_unloaded is not None:
             loaded_model.weights_loaded = not weights_unloaded
 
+    for device in total_memory_required:
+        if device != torch.device("cpu"):
+            free_memory(total_memory_required[device] * 1.1 + extra_mem, device, models_already_loaded)
+
     for loaded_model in models_to_load:
         model = loaded_model.model
         torch_dev = model.load_device

comfy/model_patcher.py

@@ -102,7 +102,7 @@ class ModelPatcher:
         self.size = size
         self.model = model
         if not hasattr(self.model, 'device'):
-            logging.info("Model doesn't have a device attribute.")
+            logging.debug("Model doesn't have a device attribute.")
             self.model.device = offload_device
         elif self.model.device is None:
             self.model.device = offload_device

comfy/utils.py

@@ -457,8 +457,27 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
             key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
             key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
 
-        block_map = {"attn.to_out.0.weight": "img_attn.proj.weight",
+        block_map = {
-                     "attn.to_out.0.bias": "img_attn.proj.bias",
+                        "attn.to_out.0.weight": "img_attn.proj.weight",
+                        "attn.to_out.0.bias": "img_attn.proj.bias",
+                        "norm1.linear.weight": "img_mod.lin.weight",
+                        "norm1.linear.bias": "img_mod.lin.bias",
+                        "norm1_context.linear.weight": "txt_mod.lin.weight",
+                        "norm1_context.linear.bias": "txt_mod.lin.bias",
+                        "attn.to_add_out.weight": "txt_attn.proj.weight",
+                        "attn.to_add_out.bias": "txt_attn.proj.bias",
+                        "ff.net.0.proj.weight": "img_mlp.0.weight",
+                        "ff.net.0.proj.bias": "img_mlp.0.bias",
+                        "ff.net.2.weight": "img_mlp.2.weight",
+                        "ff.net.2.bias": "img_mlp.2.bias",
+                        "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
+                        "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
+                        "ff_context.net.2.weight": "txt_mlp.2.weight",
+                        "ff_context.net.2.bias": "txt_mlp.2.bias",
+                        "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
+                        "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
+                        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
+                        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
                     }
 
         for k in block_map:
@@ -474,15 +493,41 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
             key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
             key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
             key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
-            key_map["{}proj_mlp.{}".format(k, end)] = (qkv, (0, hidden_size * 3, hidden_size))
+            key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4))
 
-        block_map = {#TODO
+        block_map = {
+                        "norm.linear.weight": "modulation.lin.weight",
+                        "norm.linear.bias": "modulation.lin.bias",
+                        "proj_out.weight": "linear2.weight",
+                        "proj_out.bias": "linear2.bias",
+                        "attn.norm_q.weight": "norm.query_norm.scale",
+                        "attn.norm_k.weight": "norm.key_norm.scale",
                     }
 
         for k in block_map:
             key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
 
-    MAP_BASIC = { #TODO
+    MAP_BASIC = {
+        ("final_layer.linear.bias", "proj_out.bias"),
+        ("final_layer.linear.weight", "proj_out.weight"),
+        ("img_in.bias", "x_embedder.bias"),
+        ("img_in.weight", "x_embedder.weight"),
+        ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+        ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+        ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+        ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+        ("txt_in.bias", "context_embedder.bias"),
+        ("txt_in.weight", "context_embedder.weight"),
+        ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"),
+        ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"),
+        ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"),
+        ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"),
+        ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"),
+        ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"),
+        ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"),
+        ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"),
+        ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+        ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
     }
 
     for k in MAP_BASIC: