Support for Qwen Diffsynth Controlnets canny and depth. (#9465)

These are not real controlnets but actually a patch on the model so they
will be treated as such.

Put them in the models/model_patches/ folder.

Use the new ModelPatchLoader and QwenImageDiffsynthControlnet nodes.
This commit is contained in:
comfyanonymous
2025-08-20 19:26:37 -07:00
committed by GitHub
parent e73a9dbe30
commit 0963493a9c
7 changed files with 184 additions and 1 deletions

View File

@@ -416,6 +416,7 @@ class QwenImageTransformer2DModel(nn.Module):
)
patches_replace = transformer_options.get("patches_replace", {})
patches = transformer_options.get("patches", {})
blocks_replace = patches_replace.get("dit", {})
for i, block in enumerate(self.transformer_blocks):
@@ -436,6 +437,12 @@ class QwenImageTransformer2DModel(nn.Module):
image_rotary_emb=image_rotary_emb,
)
if "double_block" in patches:
for p in patches["double_block"]:
out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i})
hidden_states = out["img"]
encoder_hidden_states = out["txt"]
hidden_states = self.norm_out(hidden_states, temb)
hidden_states = self.proj_out(hidden_states)

View File

@@ -593,7 +593,13 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
else:
minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())
models = set(models)
models_temp = set()
for m in models:
models_temp.add(m)
for mm in m.model_patches_models():
models_temp.add(mm)
models = models_temp
models_to_load = []

View File

@@ -430,6 +430,9 @@ class ModelPatcher:
def set_model_forward_timestep_embed_patch(self, patch):
self.set_model_patch(patch, "forward_timestep_embed_patch")
def set_model_double_block_patch(self, patch):
self.set_model_patch(patch, "double_block")
def add_object_patch(self, name, obj):
self.object_patches[name] = obj
@@ -486,6 +489,30 @@ class ModelPatcher:
if hasattr(wrap_func, "to"):
self.model_options["model_function_wrapper"] = wrap_func.to(device)
def model_patches_models(self):
to = self.model_options["transformer_options"]
models = []
if "patches" in to:
patches = to["patches"]
for name in patches:
patch_list = patches[name]
for i in range(len(patch_list)):
if hasattr(patch_list[i], "models"):
models += patch_list[i].models()
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], "models"):
models += patch_list[k].models()
if "model_function_wrapper" in self.model_options:
wrap_func = self.model_options["model_function_wrapper"]
if hasattr(wrap_func, "models"):
models += wrap_func.models()
return models
def model_dtype(self):
if hasattr(self.model, "get_dtype"):
return self.model.get_dtype()