From e632e5de281b91dd7199636dd6d82126fbfb07d5 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 9 Nov 2025 15:06:39 -0800 Subject: [PATCH 01/47] Add logging for model unloading. (#10692) --- comfy/model_patcher.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 5a31a8734..17e06a869 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -909,6 +909,7 @@ class ModelPatcher: self.model.model_lowvram = True self.model.lowvram_patch_counter += patch_counter self.model.model_loaded_weight_memory -= memory_freed + logging.info("loaded partially: {:.2f} MB loaded, lowvram patches: {}".format(self.model.model_loaded_weight_memory / (1024 * 1024), self.model.lowvram_patch_counter)) return memory_freed def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): From dea899f22125d38a8b48147d6cce89a2b659fdeb Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 9 Nov 2025 15:51:33 -0800 Subject: [PATCH 02/47] Unload weights if vram usage goes up between runs. (#10690) --- comfy/model_management.py | 11 +++++++++-- comfy/model_patcher.py | 20 +++++++++++++------- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 7012df858..a4410f2ec 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -503,7 +503,11 @@ class LoadedModel: use_more_vram = lowvram_model_memory if use_more_vram == 0: use_more_vram = 1e32 - self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) + if use_more_vram > 0: + self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) + else: + self.model.partially_unload(self.model.offload_device, -use_more_vram, force_patch_weights=force_patch_weights) + real_model = self.model.model if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None: @@ -689,7 +693,10 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu current_free_mem = get_free_memory(torch_dev) + loaded_memory lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) - lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory) + lowvram_model_memory = lowvram_model_memory - loaded_memory + + if lowvram_model_memory == 0: + lowvram_model_memory = 0.1 if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 0.1 diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 17e06a869..68b0a9192 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -843,7 +843,7 @@ class ModelPatcher: self.object_patches_backup.clear() - def partially_unload(self, device_to, memory_to_free=0): + def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False): with self.use_ejected(): hooks_unpatched = False memory_freed = 0 @@ -887,13 +887,19 @@ class ModelPatcher: module_mem += move_weight_functions(m, device_to) if lowvram_possible: if weight_key in self.patches: - _, set_func, convert_func = get_key_weight(self.model, weight_key) - m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func)) - patch_counter += 1 + if force_patch_weights: + self.patch_weight_to_device(weight_key) + else: + _, set_func, convert_func = get_key_weight(self.model, weight_key) + m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func)) + patch_counter += 1 if bias_key in self.patches: - _, set_func, convert_func = get_key_weight(self.model, bias_key) - m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func)) - patch_counter += 1 + if force_patch_weights: + self.patch_weight_to_device(bias_key) + else: + _, set_func, convert_func = get_key_weight(self.model, bias_key) + m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func)) + patch_counter += 1 cast_weight = True if cast_weight: From c350009236e5d172a3050c04043ea70a301378ca Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Mon, 10 Nov 2025 13:52:11 +1000 Subject: [PATCH 03/47] ops: Put weight cast on the offload stream (#10697) This needs to be on the offload stream. This reproduced a black screen with low resolution images on a slow bus when using FP8. --- comfy/ops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/ops.py b/comfy/ops.py index 733bff99d..96dffa85d 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -110,9 +110,9 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of for f in s.bias_function: bias = f(bias) - weight = weight.to(dtype=dtype) - if weight_has_function: + if weight_has_function or weight.dtype != dtype: with wf_context: + weight = weight.to(dtype=dtype) for f in s.weight_function: weight = f(weight) From 5ebcab3c7d974963a89cecd37296a22fdb73bd2b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 10 Nov 2025 12:35:29 -0800 Subject: [PATCH 04/47] Update CI workflow to remove dead macOS runner. (#10704) * Update CI workflow to remove dead macOS runner. * revert * revert --- .github/workflows/test-ci.yml | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/.github/workflows/test-ci.yml b/.github/workflows/test-ci.yml index 418dca0ab..1660ec8e3 100644 --- a/.github/workflows/test-ci.yml +++ b/.github/workflows/test-ci.yml @@ -21,14 +21,15 @@ jobs: fail-fast: false matrix: # os: [macos, linux, windows] - os: [macos, linux] - python_version: ["3.9", "3.10", "3.11", "3.12"] + # os: [macos, linux] + os: [linux] + python_version: ["3.10", "3.11", "3.12"] cuda_version: ["12.1"] torch_version: ["stable"] include: - - os: macos - runner_label: [self-hosted, macOS] - flags: "--use-pytorch-cross-attention" + # - os: macos + # runner_label: [self-hosted, macOS] + # flags: "--use-pytorch-cross-attention" - os: linux runner_label: [self-hosted, Linux] flags: "" @@ -73,14 +74,15 @@ jobs: strategy: fail-fast: false matrix: - os: [macos, linux] + # os: [macos, linux] + os: [linux] python_version: ["3.11"] cuda_version: ["12.1"] torch_version: ["nightly"] include: - - os: macos - runner_label: [self-hosted, macOS] - flags: "--use-pytorch-cross-attention" + # - os: macos + # runner_label: [self-hosted, macOS] + # flags: "--use-pytorch-cross-attention" - os: linux runner_label: [self-hosted, Linux] flags: "" From 119941174704081a16a4c3f303d99f2fb1e95cde Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 11 Nov 2025 16:33:30 -0800 Subject: [PATCH 05/47] Don't pin tensor if not a torch.nn.parameter.Parameter (#10718) --- comfy/model_management.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index a4410f2ec..d8913082a 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1107,6 +1107,9 @@ def pin_memory(tensor): if MAX_PINNED_MEMORY <= 0: return False + if type(tensor) is not torch.nn.parameter.Parameter: + return False + if not is_device_cpu(tensor.device): return False @@ -1116,6 +1119,9 @@ def pin_memory(tensor): #on the GPU async. So dont trust the CUDA API and guard here return False + if not tensor.is_contiguous(): + return False + size = tensor.numel() * tensor.element_size() if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY: return False From e1d85e7577d8f6355bd4cb3449bcb0a7e5f80cb8 Mon Sep 17 00:00:00 2001 From: Qiacheng Li Date: Wed, 12 Nov 2025 12:21:05 -0800 Subject: [PATCH 06/47] Update README.md for Intel Arc GPU installation, remove IPEX (#10729) IPEX is no longer needed for Intel Arc GPUs. Removing instruction to setup ipex. --- README.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/README.md b/README.md index 8142f595b..9e28803a2 100644 --- a/README.md +++ b/README.md @@ -242,7 +242,7 @@ RDNA 4 (RX 9000 series): ### Intel GPUs (Windows and Linux) -(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html) +Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html) 1. To install PyTorch xpu, use the following command: @@ -252,10 +252,6 @@ This is the command to install the Pytorch xpu nightly which might have some per ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu``` -(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance. - -1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information. - ### NVIDIA Nvidia users should install stable pytorch using this command: From 18e7d6dba5f1012d4cf09e8f777dc85d56ff25c0 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 13 Nov 2025 07:19:53 +1000 Subject: [PATCH 07/47] mm/mp: always unload re-used but modified models (#10724) The partial unloader path in model re-use flow skips straight to the actual unload without any check of the patching UUID. This means that if you do an upscale flow with a model patch on an existing model, it will not apply your patchings. Fix by delaying the partial_unload until after the uuid checks. This is done by making partial_unload a model of partial_load where extra_mem is -ve. --- comfy/model_management.py | 5 +---- comfy/model_patcher.py | 3 +++ 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index d8913082a..a21df54b3 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -503,10 +503,7 @@ class LoadedModel: use_more_vram = lowvram_model_memory if use_more_vram == 0: use_more_vram = 1e32 - if use_more_vram > 0: - self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) - else: - self.model.partially_unload(self.model.offload_device, -use_more_vram, force_patch_weights=force_patch_weights) + self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) real_model = self.model.model diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 68b0a9192..cf1b0d441 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -928,6 +928,9 @@ class ModelPatcher: extra_memory += (used - self.model.model_loaded_weight_memory) self.patch_model(load_weights=False) + if extra_memory < 0 and not unpatch_weights: + self.partially_unload(self.offload_device, -extra_memory, force_patch_weights=force_patch_weights) + return 0 full_load = False if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0: self.apply_hooks(self.forced_hooks, force_apply=True) From 1c7eaeca1013e4315f36e0d4d274faa106001121 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 13 Nov 2025 07:20:53 +1000 Subject: [PATCH 08/47] qwen: reduce VRAM usage (#10725) Clean up a bunch of stacked and no-longer-needed tensors on the QWEN VRAM peak (currently FFN). With this I go from OOMing at B=37x1328x1328 to being able to succesfully run B=47 (RTX5090). --- comfy/ldm/qwen_image/model.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py index e5d0d17c1..427ea19c1 100644 --- a/comfy/ldm/qwen_image/model.py +++ b/comfy/ldm/qwen_image/model.py @@ -236,10 +236,10 @@ class QwenImageTransformerBlock(nn.Module): img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1) txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1) - img_normed = self.img_norm1(hidden_states) - img_modulated, img_gate1 = self._modulate(img_normed, img_mod1) - txt_normed = self.txt_norm1(encoder_hidden_states) - txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1) + img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1) + del img_mod1 + txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1) + del txt_mod1 img_attn_output, txt_attn_output = self.attn( hidden_states=img_modulated, @@ -248,16 +248,20 @@ class QwenImageTransformerBlock(nn.Module): image_rotary_emb=image_rotary_emb, transformer_options=transformer_options, ) + del img_modulated + del txt_modulated hidden_states = hidden_states + img_gate1 * img_attn_output encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output + del img_attn_output + del txt_attn_output + del img_gate1 + del txt_gate1 - img_normed2 = self.img_norm2(hidden_states) - img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2) + img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2) hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2)) - txt_normed2 = self.txt_norm2(encoder_hidden_states) - txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2) + txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2) encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2)) return encoder_hidden_states, hidden_states From 8b0b93df51d04f08eb779cb84dc331fa18b43ae8 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 12 Nov 2025 14:04:41 -0800 Subject: [PATCH 09/47] Update Python 3.14 compatibility notes in README (#10730) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9e28803a2..f51807ad5 100644 --- a/README.md +++ b/README.md @@ -200,7 +200,7 @@ comfy install ## Manual Install (Windows, Linux) -Python 3.14 will work if you comment out the `kornia` dependency in the requirements.txt file (breaks the canny node) but it is not recommended. +Python 3.14 works but you may encounter issues with the torch compile node. The free threaded variant is still missing some dependencies. Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12 From 3b3ef9a77ac03ed516a45063f9736f33085cecca Mon Sep 17 00:00:00 2001 From: contentis Date: Thu, 13 Nov 2025 00:26:52 +0100 Subject: [PATCH 10/47] Quantized Ops fixes (#10715) * offload support, bug fixes, remove mixins * add readme --- QUANTIZATION.md | 168 +++++++++++++++++++++++++++++++++++++++++++++ comfy/ops.py | 37 ++++------ comfy/quant_ops.py | 39 ++++++++++- 3 files changed, 219 insertions(+), 25 deletions(-) create mode 100644 QUANTIZATION.md diff --git a/QUANTIZATION.md b/QUANTIZATION.md new file mode 100644 index 000000000..1693e13f3 --- /dev/null +++ b/QUANTIZATION.md @@ -0,0 +1,168 @@ +# The Comfy guide to Quantization + + +## How does quantization work? + +Quantization aims to map a high-precision value x_f to a lower precision format with minimal loss in accuracy. These smaller formats then serve to reduce the models memory footprint and increase throughput by using specialized hardware. + +When simply converting a value from FP16 to FP8 using the round-nearest method we might hit two issues: +- The dynamic range of FP16 (-65,504, 65,504) far exceeds FP8 formats like E4M3 (-448, 448) or E5M2 (-57,344, 57,344), potentially resulting in clipped values +- The original values are concentrated in a small range (e.g. -1,1) leaving many FP8-bits "unused" + +By using a scaling factor, we aim to map these values into the quantized-dtype range, making use of the full spectrum. One of the easiest approaches, and common, is using per-tensor absolute-maximum scaling. + +``` +absmax = max(abs(tensor)) +scale = amax / max_dynamic_range_low_precision + +# Quantization +tensor_q = (tensor / scale).to(low_precision_dtype) + +# De-Quantization +tensor_dq = tensor_q.to(fp16) * scale + +tensor_dq ~ tensor +``` + +Given that additional information (scaling factor) is needed to "interpret" the quantized values, we describe those as derived datatypes. + + +## Quantization in Comfy + +``` +QuantizedTensor (torch.Tensor subclass) + ↓ __torch_dispatch__ +Two-Level Registry (generic + layout handlers) + ↓ +MixedPrecisionOps + Metadata Detection +``` + +### Representation + +To represent these derived datatypes, ComfyUI uses a subclass of torch.Tensor to implements these using the `QuantizedTensor` class found in `comfy/quant_ops.py` + +A `Layout` class defines how a specific quantization format behaves: +- Required parameters +- Quantize method +- De-Quantize method + +```python +from comfy.quant_ops import QuantizedLayout + +class MyLayout(QuantizedLayout): + @classmethod + def quantize(cls, tensor, **kwargs): + # Convert to quantized format + qdata = ... + params = {'scale': ..., 'orig_dtype': tensor.dtype} + return qdata, params + + @staticmethod + def dequantize(qdata, scale, orig_dtype, **kwargs): + return qdata.to(orig_dtype) * scale +``` + +To then run operations using these QuantizedTensors we use two registry systems to define supported operations. +The first is a **generic registry** that handles operations common to all quantized formats (e.g., `.to()`, `.clone()`, `.reshape()`). + +The second registry is layout-specific and allows to implement fast-paths like nn.Linear. +```python +from comfy.quant_ops import register_layout_op + +@register_layout_op(torch.ops.aten.linear.default, MyLayout) +def my_linear(func, args, kwargs): + # Extract tensors, call optimized kernel + ... +``` +When `torch.nn.functional.linear()` is called with QuantizedTensor arguments, `__torch_dispatch__` automatically routes to the registered implementation. +For any unsupported operation, QuantizedTensor will fallback to call `dequantize` and dispatch using the high-precision implementation. + + +### Mixed Precision + +The `MixedPrecisionOps` class (lines 542-648 in `comfy/ops.py`) enables per-layer quantization decisions, allowing different layers in a model to use different precisions. This is activated when a model config contains a `layer_quant_config` dictionary that specifies which layers should be quantized and how. + +**Architecture:** + +```python +class MixedPrecisionOps(disable_weight_init): + _layer_quant_config = {} # Maps layer names to quantization configs + _compute_dtype = torch.bfloat16 # Default compute / dequantize precision +``` + +**Key mechanism:** + +The custom `Linear._load_from_state_dict()` method inspects each layer during model loading: +- If the layer name is **not** in `_layer_quant_config`: load weight as regular tensor in `_compute_dtype` +- If the layer name **is** in `_layer_quant_config`: + - Load weight as `QuantizedTensor` with the specified layout (e.g., `TensorCoreFP8Layout`) + - Load associated quantization parameters (scales, block_size, etc.) + +**Why it's needed:** + +Not all layers tolerate quantization equally. Sensitive operations like final projections can be kept in higher precision, while compute-heavy matmuls are quantized. This provides most of the performance benefits while maintaining quality. + +The system is selected in `pick_operations()` when `model_config.layer_quant_config` is present, making it the highest-priority operation mode. + + +## Checkpoint Format + +Quantized checkpoints are stored as standard safetensors files with quantized weight tensors and associated scaling parameters, plus a `_quantization_metadata` JSON entry describing the quantization scheme. + +The quantized checkpoint will contain the same layers as the original checkpoint but: +- The weights are stored as quantized values, sometimes using a different storage datatype. E.g. uint8 container for fp8. +- For each quantized weight a number of additional scaling parameters are stored alongside depending on the recipe. +- We store a metadata.json in the metadata of the final safetensor containing the `_quantization_metadata` describing which layers are quantized and what layout has been used. + +### Scaling Parameters details +We define 4 possible scaling parameters that should cover most recipes in the near-future: +- **weight_scale**: quantization scalers for the weights +- **weight_scale_2**: global scalers in the context of double scaling +- **pre_quant_scale**: scalers used for smoothing salient weights +- **input_scale**: quantization scalers for the activations + +| Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale | +|--------|---------------|--------------|----------------|-----------------|-------------| +| float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) | + +You can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS). + +### Quantization Metadata + +The metadata stored alongside the checkpoint contains: +- **format_version**: String to define a version of the standard +- **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`. + +Example: +```json +{ + "_quantization_metadata": { + "format_version": "1.0", + "layers": { + "model.layers.0.mlp.up_proj": "float8_e4m3fn", + "model.layers.0.mlp.down_proj": "float8_e4m3fn", + "model.layers.1.mlp.up_proj": "float8_e4m3fn" + } + } +} +``` + + +## Creating Quantized Checkpoints + +To create compatible checkpoints, use any quantization tool provided the output follows the checkpoint format described above and uses a layout defined in `QUANT_ALGOS`. + +### Weight Quantization + +Weight quantization is straightforward - compute the scaling factor directly from the weight tensor using the absolute maximum method described earlier. Each layer's weights are quantized independently and stored with their corresponding `weight_scale` parameter. + +### Calibration (for Activation Quantization) + +Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_scale` parameters that cannot be determined from static weights alone. Since activation values depend on actual inputs, we use **post-training calibration (PTQ)**: + +1. **Collect statistics**: Run inference on N representative samples +2. **Track activations**: Record the absolute maximum (`amax`) of inputs to each quantized layer +3. **Compute scales**: Derive `input_scale` from collected statistics +4. **Store in checkpoint**: Save `input_scale` parameters alongside weights + +The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters. \ No newline at end of file diff --git a/comfy/ops.py b/comfy/ops.py index 96dffa85d..2a90a5ba2 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -77,7 +77,10 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of # will add async-offload support to your cast and improve performance. if input is not None: if dtype is None: - dtype = input.dtype + if isinstance(input, QuantizedTensor): + dtype = input._layout_params["orig_dtype"] + else: + dtype = input.dtype if bias_dtype is None: bias_dtype = dtype if device is None: @@ -534,18 +537,7 @@ if CUBLAS_IS_AVAILABLE: # ============================================================================== # Mixed Precision Operations # ============================================================================== -from .quant_ops import QuantizedTensor - -QUANT_FORMAT_MIXINS = { - "float8_e4m3fn": { - "dtype": torch.float8_e4m3fn, - "layout_type": "TensorCoreFP8Layout", - "parameters": { - "weight_scale": torch.nn.Parameter(torch.zeros((), dtype=torch.float32), requires_grad=False), - "input_scale": torch.nn.Parameter(torch.zeros((), dtype=torch.float32), requires_grad=False), - } - } -} +from .quant_ops import QuantizedTensor, QUANT_ALGOS class MixedPrecisionOps(disable_weight_init): _layer_quant_config = {} @@ -596,23 +588,24 @@ class MixedPrecisionOps(disable_weight_init): if quant_format is None: raise ValueError(f"Unknown quantization format for layer {layer_name}") - mixin = QUANT_FORMAT_MIXINS[quant_format] - self.layout_type = mixin["layout_type"] + qconfig = QUANT_ALGOS[quant_format] + self.layout_type = qconfig["comfy_tensor_layout"] - scale_key = f"{prefix}weight_scale" + weight_scale_key = f"{prefix}weight_scale" layout_params = { - 'scale': state_dict.pop(scale_key, None), - 'orig_dtype': MixedPrecisionOps._compute_dtype + 'scale': state_dict.pop(weight_scale_key, None), + 'orig_dtype': MixedPrecisionOps._compute_dtype, + 'block_size': qconfig.get("group_size", None), } if layout_params['scale'] is not None: - manually_loaded_keys.append(scale_key) + manually_loaded_keys.append(weight_scale_key) self.weight = torch.nn.Parameter( - QuantizedTensor(weight.to(device=device, dtype=mixin["dtype"]), self.layout_type, layout_params), + QuantizedTensor(weight.to(device=device), self.layout_type, layout_params), requires_grad=False ) - for param_name, param_value in mixin["parameters"].items(): + for param_name in qconfig["parameters"]: param_key = f"{prefix}{param_name}" _v = state_dict.pop(param_key, None) if _v is None: @@ -643,7 +636,7 @@ class MixedPrecisionOps(disable_weight_init): if (getattr(self, 'layout_type', None) is not None and getattr(self, 'input_scale', None) is not None and not isinstance(input, QuantizedTensor)): - input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, fp8_dtype=self.weight.dtype) + input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype) return self._forward(input, self.weight, self.bias) diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py index c56e32a73..1d058bece 100644 --- a/comfy/quant_ops.py +++ b/comfy/quant_ops.py @@ -74,6 +74,12 @@ def _copy_layout_params(params): new_params[k] = v return new_params +def _copy_layout_params_inplace(src, dst, non_blocking=False): + for k, v in src.items(): + if isinstance(v, torch.Tensor): + dst[k].copy_(v, non_blocking=non_blocking) + else: + dst[k] = v class QuantizedLayout: """ @@ -318,13 +324,13 @@ def generic_to_dtype_layout(func, args, kwargs): def generic_copy_(func, args, kwargs): qt_dest = args[0] src = args[1] - + non_blocking = args[2] if len(args) > 2 else False if isinstance(qt_dest, QuantizedTensor): if isinstance(src, QuantizedTensor): # Copy from another quantized tensor - qt_dest._qdata.copy_(src._qdata) + qt_dest._qdata.copy_(src._qdata, non_blocking=non_blocking) qt_dest._layout_type = src._layout_type - qt_dest._layout_params = _copy_layout_params(src._layout_params) + _copy_layout_params_inplace(src._layout_params, qt_dest._layout_params, non_blocking=non_blocking) else: # Copy from regular tensor - just copy raw data qt_dest._qdata.copy_(src) @@ -336,6 +342,26 @@ def generic_copy_(func, args, kwargs): def generic_has_compatible_shallow_copy_type(func, args, kwargs): return True + +@register_generic_util(torch.ops.aten.empty_like.default) +def generic_empty_like(func, args, kwargs): + """Empty_like operation - creates an empty tensor with the same quantized structure.""" + qt = args[0] + if isinstance(qt, QuantizedTensor): + # Create empty tensor with same shape and dtype as the quantized data + hp_dtype = kwargs.pop('dtype', qt._layout_params["orig_dtype"]) + new_qdata = torch.empty_like(qt._qdata, **kwargs) + + # Handle device transfer for layout params + target_device = kwargs.get('device', new_qdata.device) + new_params = _move_layout_params_to_device(qt._layout_params, target_device) + + # Update orig_dtype if dtype is specified + new_params['orig_dtype'] = hp_dtype + + return QuantizedTensor(new_qdata, qt._layout_type, new_params) + return func(*args, **kwargs) + # ============================================================================== # FP8 Layout + Operation Handlers # ============================================================================== @@ -378,6 +404,13 @@ class TensorCoreFP8Layout(QuantizedLayout): def get_plain_tensors(cls, qtensor): return qtensor._qdata, qtensor._layout_params['scale'] +QUANT_ALGOS = { + "float8_e4m3fn": { + "storage_t": torch.float8_e4m3fn, + "parameters": {"weight_scale", "input_scale"}, + "comfy_tensor_layout": "TensorCoreFP8Layout", + }, +} LAYOUTS = { "TensorCoreFP8Layout": TensorCoreFP8Layout, From f91078b1ffa484c424f78814f54de4d5846e4daa Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 13 Nov 2025 20:05:26 +0200 Subject: [PATCH 11/47] add PR template for API-Nodes (#10736) --- .github/PULL_REQUEST_TEMPLATE/api-node.md | 21 ++++++++ .github/workflows/api-node-template.yml | 58 +++++++++++++++++++++++ 2 files changed, 79 insertions(+) create mode 100644 .github/PULL_REQUEST_TEMPLATE/api-node.md create mode 100644 .github/workflows/api-node-template.yml diff --git a/.github/PULL_REQUEST_TEMPLATE/api-node.md b/.github/PULL_REQUEST_TEMPLATE/api-node.md new file mode 100644 index 000000000..f62744878 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/api-node.md @@ -0,0 +1,21 @@ + + +## API Node PR Checklist + +### Scope +- [ ] **Is API Node Change** + +### Pricing & Billing +- [ ] **Need pricing update** +- [ ] **No pricing update** + +If **Need pricing update**: +- [ ] Metronome rate cards updated +- [ ] Auto‑billing tests updated and passing + +### QA +- [ ] **QA done** +- [ ] **QA not required** + +### Comms +- [ ] Informed **@Kosinkadink** diff --git a/.github/workflows/api-node-template.yml b/.github/workflows/api-node-template.yml new file mode 100644 index 000000000..0775f9979 --- /dev/null +++ b/.github/workflows/api-node-template.yml @@ -0,0 +1,58 @@ +name: Append API Node PR template + +on: + pull_request_target: + types: [opened, reopened, synchronize, edited, ready_for_review] + paths: + - 'comfy_api_nodes/**' # only run if these files changed + +permissions: + contents: read + pull-requests: write + +jobs: + inject: + runs-on: ubuntu-latest + steps: + - name: Ensure template exists and append to PR body + uses: actions/github-script@v7 + with: + script: | + const { owner, repo } = context.repo; + const number = context.payload.pull_request.number; + const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md'; + const marker = ''; + + const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number }); + + let templateText; + try { + const res = await github.rest.repos.getContent({ + owner, + repo, + path: templatePath, + ref: pr.base.ref + }); + const buf = Buffer.from(res.data.content, res.data.encoding || 'base64'); + templateText = buf.toString('utf8'); + } catch (e) { + core.setFailed(`Required PR template not found at "${templatePath}" on ${pr.base.ref}. Please add it to the repo.`); + return; + } + + // Enforce the presence of the marker inside the template (for idempotence) + if (!templateText.includes(marker)) { + core.setFailed(`Template at "${templatePath}" does not contain the required marker:\n${marker}\nAdd it so we can detect duplicates safely.`); + return; + } + + // If the PR already contains the marker, do not append again. + const body = pr.body || ''; + if (body.includes(marker)) { + core.info('Template already present in PR body; nothing to inject.'); + return; + } + + const newBody = (body ? body + '\n\n' : '') + templateText + '\n'; + await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody }); + core.notice('API Node template appended to PR description.'); From 2fde9597f4b02c5f06c1a5ceb3ca2fa6d74966ec Mon Sep 17 00:00:00 2001 From: ric-yu Date: Thu, 13 Nov 2025 15:11:52 -0800 Subject: [PATCH 12/47] feat: add create_time dict to prompt field in /history and /queue (#10741) --- server.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/server.py b/server.py index 5d773b10a..d059d3dc9 100644 --- a/server.py +++ b/server.py @@ -2,6 +2,7 @@ import os import sys import asyncio import traceback +import time import nodes import folder_paths @@ -733,6 +734,7 @@ class PromptServer(): for sensitive_val in execution.SENSITIVE_EXTRA_DATA_KEYS: if sensitive_val in extra_data: sensitive[sensitive_val] = extra_data.pop(sensitive_val) + extra_data["create_time"] = int(time.time() * 1000) # timestamp in milliseconds self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive)) response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} return web.json_response(response) From 94c298f9625b0fd9af8ea07a73075fdefe0d9e57 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Fri, 14 Nov 2025 10:02:03 +1000 Subject: [PATCH 13/47] flux: reduce VRAM usage (#10737) Cleanup a bunch of stack tensors on Flux. This take me from B=19 to B=22 for 1600x1600 on RTX5090. --- comfy/ldm/flux/layers.py | 33 ++++++++++++++++++++++++++------- 1 file changed, 26 insertions(+), 7 deletions(-) diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py index a3eab0470..f4bf56e01 100644 --- a/comfy/ldm/flux/layers.py +++ b/comfy/ldm/flux/layers.py @@ -167,39 +167,55 @@ class DoubleStreamBlock(nn.Module): img_modulated = self.img_norm1(img) img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img) img_qkv = self.img_attn.qkv(img_modulated) + del img_modulated img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + del img_qkv img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) # prepare txt for attention txt_modulated = self.txt_norm1(txt) txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt) txt_qkv = self.txt_attn.qkv(txt_modulated) + del txt_modulated txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + del txt_qkv txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) if self.flipped_img_txt: + q = torch.cat((img_q, txt_q), dim=2) + del img_q, txt_q + k = torch.cat((img_k, txt_k), dim=2) + del img_k, txt_k + v = torch.cat((img_v, txt_v), dim=2) + del img_v, txt_v # run actual attention - attn = attention(torch.cat((img_q, txt_q), dim=2), - torch.cat((img_k, txt_k), dim=2), - torch.cat((img_v, txt_v), dim=2), + attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) + del q, k, v img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:] else: + q = torch.cat((txt_q, img_q), dim=2) + del txt_q, img_q + k = torch.cat((txt_k, img_k), dim=2) + del txt_k, img_k + v = torch.cat((txt_v, img_v), dim=2) + del txt_v, img_v # run actual attention - attn = attention(torch.cat((txt_q, img_q), dim=2), - torch.cat((txt_k, img_k), dim=2), - torch.cat((txt_v, img_v), dim=2), + attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) + del q, k, v txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] # calculate the img bloks img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img) + del img_attn img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img) # calculate the txt bloks txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt) + del txt_attn txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt) if txt.dtype == torch.float16: @@ -249,12 +265,15 @@ class SingleStreamBlock(nn.Module): qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + del qkv q, k = self.norm(q, k, v) # compute attention attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) + del q, k, v # compute activation in mlp stream, cat again and run second linear layer - output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + mlp = self.mlp_act(mlp) + output = self.linear2(torch.cat((attn, mlp), 2)) x += apply_mod(output, mod.gate, None, modulation_dims) if x.dtype == torch.float16: x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) From 1ef328c007a419c2c429df0f80532cc11579dc97 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 13 Nov 2025 18:32:39 -0800 Subject: [PATCH 14/47] Better instructions for the portable. (#10743) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f51807ad5..cd8273b0d 100644 --- a/README.md +++ b/README.md @@ -173,7 +173,7 @@ There is a portable standalone build for Windows that should work for running on ### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) -Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints +Simply download, extract with [7-Zip](https://7-zip.org) or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\models\ If you have trouble extracting it, right click the file -> properties -> unblock From f60923590c3f2fd05e166e2ec57968aaf7007dd0 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 13 Nov 2025 22:28:05 -0800 Subject: [PATCH 15/47] Use same code for chroma and flux blocks so that optimizations are shared. (#10746) --- comfy/ldm/chroma/layers.py | 121 ----------------------------- comfy/ldm/chroma/model.py | 7 +- comfy/ldm/chroma_radiance/model.py | 7 +- comfy/ldm/flux/layers.py | 31 ++++++-- 4 files changed, 31 insertions(+), 135 deletions(-) diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index fc7110cce..9f4ad5bd2 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -1,12 +1,9 @@ import torch from torch import Tensor, nn -from comfy.ldm.flux.math import attention from comfy.ldm.flux.layers import ( MLPEmbedder, RMSNorm, - QKNorm, - SelfAttention, ModulationOut, ) @@ -48,124 +45,6 @@ class Approximator(nn.Module): return x -class DoubleStreamBlock(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): - super().__init__() - - mlp_hidden_dim = int(hidden_size * mlp_ratio) - self.num_heads = num_heads - self.hidden_size = hidden_size - self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - - self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - - self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - self.flipped_img_txt = flipped_img_txt - - def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}): - (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec - - # prepare image for attention - img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img)) - img_qkv = self.img_attn.qkv(img_modulated) - img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) - - # prepare txt for attention - txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt)) - txt_qkv = self.txt_attn.qkv(txt_modulated) - txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) - - # run actual attention - attn = attention(torch.cat((txt_q, img_q), dim=2), - torch.cat((txt_k, img_k), dim=2), - torch.cat((txt_v, img_v), dim=2), - pe=pe, mask=attn_mask, transformer_options=transformer_options) - - txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] - - # calculate the img bloks - img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn)) - img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img)))) - - # calculate the txt bloks - txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn)) - txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt)))) - - if txt.dtype == torch.float16: - txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) - - return img, txt - - -class SingleStreamBlock(nn.Module): - """ - A DiT block with parallel linear layers as described in - https://arxiv.org/abs/2302.05442 and adapted modulation interface. - """ - - def __init__( - self, - hidden_size: int, - num_heads: int, - mlp_ratio: float = 4.0, - qk_scale: float = None, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.hidden_dim = hidden_size - self.num_heads = num_heads - head_dim = hidden_size // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.mlp_hidden_dim = int(hidden_size * mlp_ratio) - # qkv and mlp_in - self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) - # proj and mlp_out - self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) - - self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - - self.hidden_size = hidden_size - self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - self.mlp_act = nn.GELU(approximate="tanh") - - def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor: - mod = vec - x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x)) - qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) - - q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k = self.norm(q, k, v) - - # compute attention - attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) - # compute activation in mlp stream, cat again and run second linear layer - output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) - x.addcmul_(mod.gate, output) - if x.dtype == torch.float16: - x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) - return x - - class LastLayer(nn.Module): def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): super().__init__() diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py index ad1c523fe..67bf70eb1 100644 --- a/comfy/ldm/chroma/model.py +++ b/comfy/ldm/chroma/model.py @@ -11,12 +11,12 @@ import comfy.ldm.common_dit from comfy.ldm.flux.layers import ( EmbedND, timestep_embedding, + DoubleStreamBlock, + SingleStreamBlock, ) from .layers import ( - DoubleStreamBlock, LastLayer, - SingleStreamBlock, Approximator, ChromaModulationOut, ) @@ -90,6 +90,7 @@ class Chroma(nn.Module): self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, + modulation=False, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) @@ -98,7 +99,7 @@ class Chroma(nn.Module): self.single_blocks = nn.ModuleList( [ - SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations) for _ in range(params.depth_single_blocks) ] ) diff --git a/comfy/ldm/chroma_radiance/model.py b/comfy/ldm/chroma_radiance/model.py index 7d7be80f5..e643b4414 100644 --- a/comfy/ldm/chroma_radiance/model.py +++ b/comfy/ldm/chroma_radiance/model.py @@ -10,12 +10,10 @@ from torch import Tensor, nn from einops import repeat import comfy.ldm.common_dit -from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.flux.layers import EmbedND, DoubleStreamBlock, SingleStreamBlock from comfy.ldm.chroma.model import Chroma, ChromaParams from comfy.ldm.chroma.layers import ( - DoubleStreamBlock, - SingleStreamBlock, Approximator, ) from .layers import ( @@ -89,7 +87,6 @@ class ChromaRadiance(Chroma): dtype=dtype, device=device, operations=operations ) - self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( @@ -97,6 +94,7 @@ class ChromaRadiance(Chroma): self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, + modulation=False, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) @@ -109,6 +107,7 @@ class ChromaRadiance(Chroma): self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, + modulation=False, dtype=dtype, device=device, operations=operations, ) for _ in range(params.depth_single_blocks) diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py index f4bf56e01..23150a712 100644 --- a/comfy/ldm/flux/layers.py +++ b/comfy/ldm/flux/layers.py @@ -130,13 +130,17 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None): class DoubleStreamBlock(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, dtype=None, device=None, operations=None): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) self.num_heads = num_heads self.hidden_size = hidden_size - self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.modulation = modulation + + if self.modulation: + self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) @@ -147,7 +151,9 @@ class DoubleStreamBlock(nn.Module): operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) - self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + if self.modulation: + self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) @@ -160,8 +166,11 @@ class DoubleStreamBlock(nn.Module): self.flipped_img_txt = flipped_img_txt def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}): - img_mod1, img_mod2 = self.img_mod(vec) - txt_mod1, txt_mod2 = self.txt_mod(vec) + if self.modulation: + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + else: + (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec # prepare image for attention img_modulated = self.img_norm1(img) @@ -236,6 +245,7 @@ class SingleStreamBlock(nn.Module): num_heads: int, mlp_ratio: float = 4.0, qk_scale: float = None, + modulation=True, dtype=None, device=None, operations=None @@ -258,10 +268,17 @@ class SingleStreamBlock(nn.Module): self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.mlp_act = nn.GELU(approximate="tanh") - self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) + if modulation: + self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) + else: + self.modulation = None def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor: - mod, _ = self.modulation(vec) + if self.modulation: + mod, _ = self.modulation(vec) + else: + mod = vec + qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) From 443056c401c53953bb8eee6da71b9ad29afe2581 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 14 Nov 2025 00:26:05 -0800 Subject: [PATCH 16/47] Fix custom nodes import error. (#10747) This should fix the import errors but will break if the custom nodes actually try to use the class. --- comfy/ldm/chroma/layers.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index 9f4ad5bd2..2d5684348 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -7,6 +7,9 @@ from comfy.ldm.flux.layers import ( ModulationOut, ) +# TODO: remove this in a few months +SingleStreamBlock = None +DoubleStreamBlock = None class ChromaModulationOut(ModulationOut): From bd01d9f7fd241a45bd08b60dfedbe78577383cc4 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 15 Nov 2025 03:54:40 -0800 Subject: [PATCH 17/47] Add left padding support to tokenizers. (#10753) --- comfy/sd1_clip.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index f8a7c2a1b..3066de2d7 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -460,7 +460,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No return embed_out class SDTokenizer: - def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}): + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) @@ -468,6 +468,7 @@ class SDTokenizer: self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length) self.end_token = None self.min_padding = min_padding + self.pad_left = pad_left empty = self.tokenizer('')["input_ids"] self.tokenizer_adds_end_token = has_end_token @@ -522,6 +523,12 @@ class SDTokenizer: return (embed, "{} {}".format(embedding_name[len(stripped):], leftover)) return (embed, leftover) + def pad_tokens(self, tokens, amount): + if self.pad_left: + for i in range(amount): + tokens.insert(0, (self.pad_token, 1.0, 0)) + else: + tokens.extend([(self.pad_token, 1.0, 0)] * amount) def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs): ''' @@ -600,7 +607,7 @@ class SDTokenizer: if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: - batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) + self.pad_tokens(batch, remaining_length) #start new batch batch = [] if self.start_token is not None: @@ -614,11 +621,11 @@ class SDTokenizer: if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) if min_padding is not None: - batch.extend([(self.pad_token, 1.0, 0)] * min_padding) + self.pad_tokens(batch, min_padding) if self.pad_to_max_length and len(batch) < self.max_length: - batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) + self.pad_tokens(batch, self.max_length - len(batch)) if min_length is not None and len(batch) < min_length: - batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch))) + self.pad_tokens(batch, min_length - len(batch)) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] From 9a0238256873711bd38ce0e0b1d15a617a1ee454 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Sat, 15 Nov 2025 21:18:49 +0200 Subject: [PATCH 18/47] chore(api-nodes): mark OpenAIDalle2 and OpenAIDalle3 nodes as deprecated (#10757) --- comfy_api_nodes/nodes_openai.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index acf35d276..e08bec08c 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -169,6 +169,7 @@ class OpenAIDalle2(IO.ComfyNode): IO.Hidden.unique_id, ], is_api_node=True, + is_deprecated=True, ) @classmethod @@ -299,6 +300,7 @@ class OpenAIDalle3(IO.ComfyNode): IO.Hidden.unique_id, ], is_api_node=True, + is_deprecated=True, ) @classmethod From 2d4a08b717c492fa45e98bd70beb48d4e77cb464 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Sat, 15 Nov 2025 22:37:34 +0200 Subject: [PATCH 19/47] Revert "chore(api-nodes): mark OpenAIDalle2 and OpenAIDalle3 nodes as deprecated (#10757)" (#10759) This reverts commit 9a0238256873711bd38ce0e0b1d15a617a1ee454. --- comfy_api_nodes/nodes_openai.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index e08bec08c..acf35d276 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -169,7 +169,6 @@ class OpenAIDalle2(IO.ComfyNode): IO.Hidden.unique_id, ], is_api_node=True, - is_deprecated=True, ) @classmethod @@ -300,7 +299,6 @@ class OpenAIDalle3(IO.ComfyNode): IO.Hidden.unique_id, ], is_api_node=True, - is_deprecated=True, ) @classmethod From 7d6103325e1c97aa54f963253e3e7f1d6da6947f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 16 Nov 2025 00:01:14 -0800 Subject: [PATCH 20/47] Change ROCm nightly install command to 7.1 (#10764) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cd8273b0d..c0384099d 100644 --- a/README.md +++ b/README.md @@ -221,7 +221,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins This is the command to install the nightly with ROCm 7.0 which might have some performance improvements: -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1``` ### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only. From 3d0003c24c1aec9f0c021dbc70ffb7cd8cf0685c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 17 Nov 2025 17:17:24 -0500 Subject: [PATCH 21/47] ComfyUI version 0.3.69 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 25d1a4157..1e554eb9f 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.68" +__version__ = "0.3.69" diff --git a/pyproject.toml b/pyproject.toml index 79ff3f74a..63778286f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.68" +version = "0.3.69" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 27cbac865ec226cfd9c1563327b0d62cf5dbd484 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 17 Nov 2025 16:04:04 -0800 Subject: [PATCH 22/47] Add release workflow for NVIDIA cu126 (#10777) --- .github/workflows/release-stable-all.yml | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/.github/workflows/release-stable-all.yml b/.github/workflows/release-stable-all.yml index 7dca7277b..f7de3a7c3 100644 --- a/.github/workflows/release-stable-all.yml +++ b/.github/workflows/release-stable-all.yml @@ -43,6 +43,23 @@ jobs: test_release: true secrets: inherit + release_nvidia_cu126: + permissions: + contents: "write" + packages: "write" + pull-requests: "read" + name: "Release NVIDIA cu126" + uses: ./.github/workflows/stable-release.yml + with: + git_tag: ${{ inputs.git_tag }} + cache_tag: "cu126" + python_minor: "12" + python_patch: "10" + rel_name: "nvidia" + rel_extra_name: "_cu126" + test_release: true + secrets: inherit + release_amd_rocm: permissions: contents: "write" From f41e5f398d5d4059a3c87cf157bd932afcce3c0d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 17 Nov 2025 16:59:19 -0800 Subject: [PATCH 23/47] Update README with new portable download link (#10778) --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c0384099d..323dfc587 100644 --- a/README.md +++ b/README.md @@ -183,7 +183,9 @@ Update your Nvidia drivers if it doesn't start. [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z) -[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs). +[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z). + +[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs). #### How do I share models between another UI and ComfyUI? From fdf49a28617f742d746ad209e57ed7420b3535dc Mon Sep 17 00:00:00 2001 From: ComfyUI Wiki Date: Tue, 18 Nov 2025 11:04:06 +0800 Subject: [PATCH 24/47] Fix the portable download link for CUDA 12.6 (#10780) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 323dfc587..28beec427 100644 --- a/README.md +++ b/README.md @@ -185,7 +185,7 @@ Update your Nvidia drivers if it doesn't start. [Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z). -[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs). +[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs). #### How do I share models between another UI and ComfyUI? From 47bfd5a33fa984a1102fc2bd7b25c91a69ace288 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 17 Nov 2025 21:26:44 -0800 Subject: [PATCH 25/47] Native block swap custom nodes considered harmful. (#10783) --- comfy_extras/nodes_nop.py | 39 +++++++++++++++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 40 insertions(+) create mode 100644 comfy_extras/nodes_nop.py diff --git a/comfy_extras/nodes_nop.py b/comfy_extras/nodes_nop.py new file mode 100644 index 000000000..953061bcb --- /dev/null +++ b/comfy_extras/nodes_nop.py @@ -0,0 +1,39 @@ +from comfy_api.latest import ComfyExtension, io +from typing_extensions import override +# If you write a node that is so useless that it breaks ComfyUI it will be featured in this exclusive list + +# "native" block swap nodes are placebo at best and break the ComfyUI memory management system. +# They are also considered harmful because instead of users reporting issues with the built in +# memory management they install these stupid nodes and complain even harder. Now it completely +# breaks with some of the new ComfyUI memory optimizations so I have made the decision to NOP it +# out of all workflows. +class wanBlockSwap(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="wanBlockSwap", + category="", + description="NOP", + inputs=[ + io.Model.Input("model"), + ], + outputs=[ + io.Model.Output(), + ], + is_deprecated=True, + ) + + @classmethod + def execute(cls, model) -> io.NodeOutput: + return io.NodeOutput(model) + + +class NopExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + wanBlockSwap + ] + +async def comfy_entrypoint() -> NopExtension: + return NopExtension() diff --git a/nodes.py b/nodes.py index 5689f6fe1..f6aeedc78 100644 --- a/nodes.py +++ b/nodes.py @@ -2330,6 +2330,7 @@ async def init_builtin_extra_nodes(): "nodes_easycache.py", "nodes_audio_encoder.py", "nodes_rope.py", + "nodes_nop.py", ] import_failed = [] From 048f49adbd19ac2d9c7c87682c832b7827a4b29d Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Tue, 18 Nov 2025 13:59:27 +0200 Subject: [PATCH 26/47] chore(api-nodes): adjusted PR template; set min python version for pylint to 3.10 (#10787) --- .github/PULL_REQUEST_TEMPLATE/api-node.md | 2 +- .github/workflows/api-node-template.yml | 2 +- pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE/api-node.md b/.github/PULL_REQUEST_TEMPLATE/api-node.md index f62744878..c1f1bafb1 100644 --- a/.github/PULL_REQUEST_TEMPLATE/api-node.md +++ b/.github/PULL_REQUEST_TEMPLATE/api-node.md @@ -18,4 +18,4 @@ If **Need pricing update**: - [ ] **QA not required** ### Comms -- [ ] Informed **@Kosinkadink** +- [ ] Informed **Kosinkadink** diff --git a/.github/workflows/api-node-template.yml b/.github/workflows/api-node-template.yml index 0775f9979..fdb81c0c5 100644 --- a/.github/workflows/api-node-template.yml +++ b/.github/workflows/api-node-template.yml @@ -2,7 +2,7 @@ name: Append API Node PR template on: pull_request_target: - types: [opened, reopened, synchronize, edited, ready_for_review] + types: [opened, reopened, synchronize, ready_for_review] paths: - 'comfy_api_nodes/**' # only run if these files changed diff --git a/pyproject.toml b/pyproject.toml index 63778286f..a14b383b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ lint.select = [ exclude = ["*.ipynb", "**/generated/*.pyi"] [tool.pylint] -master.py-version = "3.9" +master.py-version = "3.10" master.extension-pkg-allow-list = [ "pydantic", ] From e1ab6bb394b82fa654d5bc84043f97479d12f84c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Tue, 18 Nov 2025 17:00:21 +0200 Subject: [PATCH 27/47] EasyCache: Fix for mismatch in input/output channels with some models (#10788) Slices model input with output channels so the caching tracks only the noise channels, resolves channel mismatch with models like WanVideo I2V Also fix for slicing deprecation in pytorch 2.9 --- comfy_extras/nodes_easycache.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py index 1359e2f99..11b23ffdb 100644 --- a/comfy_extras/nodes_easycache.py +++ b/comfy_extras/nodes_easycache.py @@ -11,13 +11,13 @@ if TYPE_CHECKING: def easycache_forward_wrapper(executor, *args, **kwargs): # get values from args - x: torch.Tensor = args[0] transformer_options: dict[str] = args[-1] if not isinstance(transformer_options, dict): transformer_options = kwargs.get("transformer_options") if not transformer_options: transformer_options = args[-2] easycache: EasyCacheHolder = transformer_options["easycache"] + x: torch.Tensor = args[0][:, :easycache.output_channels] sigmas = transformer_options["sigmas"] uuids = transformer_options["uuids"] if sigmas is not None and easycache.is_past_end_timestep(sigmas): @@ -82,13 +82,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs): def lazycache_predict_noise_wrapper(executor, *args, **kwargs): # get values from args - x: torch.Tensor = args[0] timestep: float = args[1] model_options: dict[str] = args[2] easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"] if easycache.is_past_end_timestep(timestep): return executor(*args, **kwargs) # prepare next x_prev + x: torch.Tensor = args[0][:, :easycache.output_channels] next_x_prev = x input_change = None do_easycache = easycache.should_do_easycache(timestep) @@ -173,7 +173,7 @@ def easycache_sample_wrapper(executor, *args, **kwargs): class EasyCacheHolder: - def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): + def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None): self.name = "EasyCache" self.reuse_threshold = reuse_threshold self.start_percent = start_percent @@ -202,6 +202,7 @@ class EasyCacheHolder: self.allow_mismatch = True self.cut_from_start = True self.state_metadata = None + self.output_channels = output_channels def is_past_end_timestep(self, timestep: float) -> bool: return not (timestep[0] > self.end_t).item() @@ -264,7 +265,7 @@ class EasyCacheHolder: else: slicing.append(slice(None)) batch_slice = batch_slice + slicing - x[batch_slice] += self.uuid_cache_diffs[uuid].to(x.device) + x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device) return x def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]): @@ -283,7 +284,7 @@ class EasyCacheHolder: else: slicing.append(slice(None)) skip_dim = False - x = x[slicing] + x = x[tuple(slicing)] diff = output - x batch_offset = diff.shape[0] // len(uuids) for i, uuid in enumerate(uuids): @@ -323,7 +324,7 @@ class EasyCacheHolder: return self def clone(self): - return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) + return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels) class EasyCacheNode(io.ComfyNode): @@ -350,7 +351,7 @@ class EasyCacheNode(io.ComfyNode): @classmethod def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: model = model.clone() - model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) + model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper) @@ -358,7 +359,7 @@ class EasyCacheNode(io.ComfyNode): class LazyCacheHolder: - def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): + def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None): self.name = "LazyCache" self.reuse_threshold = reuse_threshold self.start_percent = start_percent @@ -382,6 +383,7 @@ class LazyCacheHolder: self.approx_output_change_rates = [] self.total_steps_skipped = 0 self.state_metadata = None + self.output_channels = output_channels def has_cache_diff(self) -> bool: return self.cache_diff is not None @@ -456,7 +458,7 @@ class LazyCacheHolder: return self def clone(self): - return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) + return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels) class LazyCacheNode(io.ComfyNode): @classmethod @@ -482,7 +484,7 @@ class LazyCacheNode(io.ComfyNode): @classmethod def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: model = model.clone() - model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) + model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper) return io.NodeOutput(model) From d52697457608a045cafc3b6d6cb89f0a49ba0709 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 18 Nov 2025 13:46:19 -0800 Subject: [PATCH 28/47] Fix hunyuan 3d 2.0 (#10792) --- comfy/ldm/flux/math.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py index 158420290..6a22df8bc 100644 --- a/comfy/ldm/flux/math.py +++ b/comfy/ldm/flux/math.py @@ -7,7 +7,8 @@ import comfy.model_management def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor: - q, k = apply_rope(q, k, pe) + if pe is not None: + q, k = apply_rope(q, k, pe) heads = q.shape[1] x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options) return x From 24fdb92edf2e96fe757c480aa7f12be5bdfa3a15 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Wed, 19 Nov 2025 00:26:44 +0200 Subject: [PATCH 29/47] feat(api-nodes): add new Gemini model (#10789) --- comfy_api_nodes/apis/gemini_api.py | 231 +++++++++++++++++++++++++++-- comfy_api_nodes/nodes_gemini.py | 47 +++--- 2 files changed, 246 insertions(+), 32 deletions(-) diff --git a/comfy_api_nodes/apis/gemini_api.py b/comfy_api_nodes/apis/gemini_api.py index 2bf28bf93..f63e02693 100644 --- a/comfy_api_nodes/apis/gemini_api.py +++ b/comfy_api_nodes/apis/gemini_api.py @@ -1,22 +1,229 @@ -from typing import Optional +from datetime import date +from enum import Enum +from typing import Any -from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata -from pydantic import BaseModel +from pydantic import BaseModel, Field + + +class GeminiSafetyCategory(str, Enum): + HARM_CATEGORY_SEXUALLY_EXPLICIT = "HARM_CATEGORY_SEXUALLY_EXPLICIT" + HARM_CATEGORY_HATE_SPEECH = "HARM_CATEGORY_HATE_SPEECH" + HARM_CATEGORY_HARASSMENT = "HARM_CATEGORY_HARASSMENT" + HARM_CATEGORY_DANGEROUS_CONTENT = "HARM_CATEGORY_DANGEROUS_CONTENT" + + +class GeminiSafetyThreshold(str, Enum): + OFF = "OFF" + BLOCK_NONE = "BLOCK_NONE" + BLOCK_LOW_AND_ABOVE = "BLOCK_LOW_AND_ABOVE" + BLOCK_MEDIUM_AND_ABOVE = "BLOCK_MEDIUM_AND_ABOVE" + BLOCK_ONLY_HIGH = "BLOCK_ONLY_HIGH" + + +class GeminiSafetySetting(BaseModel): + category: GeminiSafetyCategory + threshold: GeminiSafetyThreshold + + +class GeminiRole(str, Enum): + user = "user" + model = "model" + + +class GeminiMimeType(str, Enum): + application_pdf = "application/pdf" + audio_mpeg = "audio/mpeg" + audio_mp3 = "audio/mp3" + audio_wav = "audio/wav" + image_png = "image/png" + image_jpeg = "image/jpeg" + image_webp = "image/webp" + text_plain = "text/plain" + video_mov = "video/mov" + video_mpeg = "video/mpeg" + video_mp4 = "video/mp4" + video_mpg = "video/mpg" + video_avi = "video/avi" + video_wmv = "video/wmv" + video_mpegps = "video/mpegps" + video_flv = "video/flv" + + +class GeminiInlineData(BaseModel): + data: str | None = Field( + None, + description="The base64 encoding of the image, PDF, or video to include inline in the prompt. " + "When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB", + ) + mimeType: GeminiMimeType | None = Field(None) + + +class GeminiPart(BaseModel): + inlineData: GeminiInlineData | None = Field(None) + text: str | None = Field(None) + + +class GeminiTextPart(BaseModel): + text: str | None = Field(None) + + +class GeminiContent(BaseModel): + parts: list[GeminiPart] = Field(...) + role: GeminiRole = Field(..., examples=["user"]) + + +class GeminiSystemInstructionContent(BaseModel): + parts: list[GeminiTextPart] = Field( + ..., + description="A list of ordered parts that make up a single message. " + "Different parts may have different IANA MIME types.", + ) + role: GeminiRole = Field( + ..., + description="The identity of the entity that creates the message. " + "The following values are supported: " + "user: This indicates that the message is sent by a real person, typically a user-generated message. " + "model: This indicates that the message is generated by the model. " + "The model value is used to insert messages from model into the conversation during multi-turn conversations. " + "For non-multi-turn conversations, this field can be left blank or unset.", + ) + + +class GeminiFunctionDeclaration(BaseModel): + description: str | None = Field(None) + name: str = Field(...) + parameters: dict[str, Any] = Field(..., description="JSON schema for the function parameters") + + +class GeminiTool(BaseModel): + functionDeclarations: list[GeminiFunctionDeclaration] | None = Field(None) + + +class GeminiOffset(BaseModel): + nanos: int | None = Field(None, ge=0, le=999999999) + seconds: int | None = Field(None, ge=-315576000000, le=315576000000) + + +class GeminiVideoMetadata(BaseModel): + endOffset: GeminiOffset | None = Field(None) + startOffset: GeminiOffset | None = Field(None) + + +class GeminiGenerationConfig(BaseModel): + maxOutputTokens: int | None = Field(None, ge=16, le=8192) + seed: int | None = Field(None) + stopSequences: list[str] | None = Field(None) + temperature: float | None = Field(1, ge=0.0, le=2.0) + topK: int | None = Field(40, ge=1) + topP: float | None = Field(0.95, ge=0.0, le=1.0) class GeminiImageConfig(BaseModel): - aspectRatio: Optional[str] = None + aspectRatio: str | None = Field(None) + resolution: str | None = Field(None) class GeminiImageGenerationConfig(GeminiGenerationConfig): - responseModalities: Optional[list[str]] = None - imageConfig: Optional[GeminiImageConfig] = None + responseModalities: list[str] | None = Field(None) + imageConfig: GeminiImageConfig | None = Field(None) class GeminiImageGenerateContentRequest(BaseModel): - contents: list[GeminiContent] - generationConfig: Optional[GeminiImageGenerationConfig] = None - safetySettings: Optional[list[GeminiSafetySetting]] = None - systemInstruction: Optional[GeminiSystemInstructionContent] = None - tools: Optional[list[GeminiTool]] = None - videoMetadata: Optional[GeminiVideoMetadata] = None + contents: list[GeminiContent] = Field(...) + generationConfig: GeminiImageGenerationConfig | None = Field(None) + safetySettings: list[GeminiSafetySetting] | None = Field(None) + systemInstruction: GeminiSystemInstructionContent | None = Field(None) + tools: list[GeminiTool] | None = Field(None) + videoMetadata: GeminiVideoMetadata | None = Field(None) + + +class GeminiGenerateContentRequest(BaseModel): + contents: list[GeminiContent] = Field(...) + generationConfig: GeminiGenerationConfig | None = Field(None) + safetySettings: list[GeminiSafetySetting] | None = Field(None) + systemInstruction: GeminiSystemInstructionContent | None = Field(None) + tools: list[GeminiTool] | None = Field(None) + videoMetadata: GeminiVideoMetadata | None = Field(None) + + +class Modality(str, Enum): + MODALITY_UNSPECIFIED = "MODALITY_UNSPECIFIED" + TEXT = "TEXT" + IMAGE = "IMAGE" + VIDEO = "VIDEO" + AUDIO = "AUDIO" + DOCUMENT = "DOCUMENT" + + +class ModalityTokenCount(BaseModel): + modality: Modality | None = None + tokenCount: int | None = Field(None, description="Number of tokens for the given modality.") + + +class Probability(str, Enum): + NEGLIGIBLE = "NEGLIGIBLE" + LOW = "LOW" + MEDIUM = "MEDIUM" + HIGH = "HIGH" + UNKNOWN = "UNKNOWN" + + +class GeminiSafetyRating(BaseModel): + category: GeminiSafetyCategory | None = None + probability: Probability | None = Field( + None, + description="The probability that the content violates the specified safety category", + ) + + +class GeminiCitation(BaseModel): + authors: list[str] | None = None + endIndex: int | None = None + license: str | None = None + publicationDate: date | None = None + startIndex: int | None = None + title: str | None = None + uri: str | None = None + + +class GeminiCitationMetadata(BaseModel): + citations: list[GeminiCitation] | None = None + + +class GeminiCandidate(BaseModel): + citationMetadata: GeminiCitationMetadata | None = None + content: GeminiContent | None = None + finishReason: str | None = None + safetyRatings: list[GeminiSafetyRating] | None = None + + +class GeminiPromptFeedback(BaseModel): + blockReason: str | None = None + blockReasonMessage: str | None = None + safetyRatings: list[GeminiSafetyRating] | None = None + + +class GeminiUsageMetadata(BaseModel): + cachedContentTokenCount: int | None = Field( + None, + description="Output only. Number of tokens in the cached part in the input (the cached content).", + ) + candidatesTokenCount: int | None = Field(None, description="Number of tokens in the response(s).") + candidatesTokensDetails: list[ModalityTokenCount] | None = Field( + None, description="Breakdown of candidate tokens by modality." + ) + promptTokenCount: int | None = Field( + None, + description="Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.", + ) + promptTokensDetails: list[ModalityTokenCount] | None = Field( + None, description="Breakdown of prompt tokens by modality." + ) + thoughtsTokenCount: int | None = Field(None, description="Number of tokens present in thoughts output.") + toolUsePromptTokenCount: int | None = Field(None, description="Number of tokens present in tool-use prompt(s).") + + +class GeminiGenerateContentResponse(BaseModel): + candidates: list[GeminiCandidate] | None = Field(None) + promptFeedback: GeminiPromptFeedback | None = Field(None) + usageMetadata: GeminiUsageMetadata | None = Field(None) diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py index 67f2469ad..6e746eebd 100644 --- a/comfy_api_nodes/nodes_gemini.py +++ b/comfy_api_nodes/nodes_gemini.py @@ -3,8 +3,6 @@ API Nodes for Gemini Multimodal LLM Usage via Remote API See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference """ -from __future__ import annotations - import base64 import json import os @@ -12,7 +10,7 @@ import time import uuid from enum import Enum from io import BytesIO -from typing import Literal, Optional +from typing import Literal import torch from typing_extensions import override @@ -20,18 +18,17 @@ from typing_extensions import override import folder_paths from comfy_api.latest import IO, ComfyExtension, Input from comfy_api.util import VideoCodec, VideoContainer -from comfy_api_nodes.apis import ( +from comfy_api_nodes.apis.gemini_api import ( GeminiContent, GeminiGenerateContentRequest, GeminiGenerateContentResponse, - GeminiInlineData, - GeminiMimeType, - GeminiPart, -) -from comfy_api_nodes.apis.gemini_api import ( GeminiImageConfig, GeminiImageGenerateContentRequest, GeminiImageGenerationConfig, + GeminiInlineData, + GeminiMimeType, + GeminiPart, + GeminiRole, ) from comfy_api_nodes.util import ( ApiEndpoint, @@ -57,6 +54,7 @@ class GeminiModel(str, Enum): gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17" gemini_2_5_pro = "gemini-2.5-pro" gemini_2_5_flash = "gemini-2.5-flash" + gemini_3_0_pro = "gemini-3-pro-preview" class GeminiImageModel(str, Enum): @@ -103,6 +101,16 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera Returns: List of response parts matching the requested type. """ + if response.candidates is None: + if response.promptFeedback.blockReason: + feedback = response.promptFeedback + raise ValueError( + f"Gemini API blocked the request. Reason: {feedback.blockReason} ({feedback.blockReasonMessage})" + ) + raise NotImplementedError( + "Gemini returned no response candidates. " + "Please report to ComfyUI repository with the example of workflow to reproduce this." + ) parts = [] for part in response.candidates[0].content.parts: if part_type == "text" and hasattr(part, "text") and part.text: @@ -272,10 +280,10 @@ class GeminiNode(IO.ComfyNode): prompt: str, model: str, seed: int, - images: Optional[torch.Tensor] = None, - audio: Optional[Input.Audio] = None, - video: Optional[Input.Video] = None, - files: Optional[list[GeminiPart]] = None, + images: torch.Tensor | None = None, + audio: Input.Audio | None = None, + video: Input.Video | None = None, + files: list[GeminiPart] | None = None, ) -> IO.NodeOutput: validate_string(prompt, strip_whitespace=False) @@ -300,7 +308,7 @@ class GeminiNode(IO.ComfyNode): data=GeminiGenerateContentRequest( contents=[ GeminiContent( - role="user", + role=GeminiRole.user, parts=parts, ) ] @@ -308,7 +316,6 @@ class GeminiNode(IO.ComfyNode): response_model=GeminiGenerateContentResponse, ) - # Get result output output_text = get_text_from_response(response) if output_text: # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. @@ -406,7 +413,7 @@ class GeminiInputFiles(IO.ComfyNode): ) @classmethod - def execute(cls, file: str, GEMINI_INPUT_FILES: Optional[list[GeminiPart]] = None) -> IO.NodeOutput: + def execute(cls, file: str, GEMINI_INPUT_FILES: list[GeminiPart] | None = None) -> IO.NodeOutput: """Loads and formats input files for Gemini API.""" if GEMINI_INPUT_FILES is None: GEMINI_INPUT_FILES = [] @@ -421,7 +428,7 @@ class GeminiImage(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="GeminiImageNode", - display_name="Google Gemini Image", + display_name="Nano Banana (Google Gemini Image)", category="api node/image/Gemini", description="Edit images synchronously via Google API.", inputs=[ @@ -488,8 +495,8 @@ class GeminiImage(IO.ComfyNode): prompt: str, model: str, seed: int, - images: Optional[torch.Tensor] = None, - files: Optional[list[GeminiPart]] = None, + images: torch.Tensor | None = None, + files: list[GeminiPart] | None = None, aspect_ratio: str = "auto", ) -> IO.NodeOutput: validate_string(prompt, strip_whitespace=True, min_length=1) @@ -510,7 +517,7 @@ class GeminiImage(IO.ComfyNode): endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"), data=GeminiImageGenerateContentRequest( contents=[ - GeminiContent(role="user", parts=parts), + GeminiContent(role=GeminiRole.user, parts=parts), ], generationConfig=GeminiImageGenerationConfig( responseModalities=["TEXT", "IMAGE"], From b5c8be8b1db44ded07cb1b437b9f33ebff5848c1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 18 Nov 2025 19:37:20 -0500 Subject: [PATCH 30/47] ComfyUI 0.3.70 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 1e554eb9f..9b77aabe9 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.69" +__version__ = "0.3.70" diff --git a/pyproject.toml b/pyproject.toml index a14b383b3..289b7145b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.69" +version = "0.3.70" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 17027f2a6a20a31e2c6f3be2b1a06f39ad3a68d9 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 18 Nov 2025 19:36:03 -0800 Subject: [PATCH 31/47] Add a way to disable the final norm in the llama based TE models. (#10794) --- comfy/text_encoders/llama.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index c050759fe..feb44bbb0 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -32,6 +32,7 @@ class Llama2Config: q_norm = None k_norm = None rope_scale = None + final_norm: bool = True @dataclass class Qwen25_3BConfig: @@ -53,6 +54,7 @@ class Qwen25_3BConfig: q_norm = None k_norm = None rope_scale = None + final_norm: bool = True @dataclass class Qwen25_7BVLI_Config: @@ -74,6 +76,7 @@ class Qwen25_7BVLI_Config: q_norm = None k_norm = None rope_scale = None + final_norm: bool = True @dataclass class Gemma2_2B_Config: @@ -96,6 +99,7 @@ class Gemma2_2B_Config: k_norm = None sliding_attention = None rope_scale = None + final_norm: bool = True @dataclass class Gemma3_4B_Config: @@ -118,6 +122,7 @@ class Gemma3_4B_Config: k_norm = "gemma3" sliding_attention = [False, False, False, False, False, 1024] rope_scale = [1.0, 8.0] + final_norm: bool = True class RMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None): @@ -366,7 +371,12 @@ class Llama2_(nn.Module): transformer(config, index=i, device=device, dtype=dtype, ops=ops) for i in range(config.num_hidden_layers) ]) - self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + + if config.final_norm: + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + else: + self.norm = None + # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]): @@ -421,14 +431,16 @@ class Llama2_(nn.Module): if i == intermediate_output: intermediate = x.clone() - x = self.norm(x) + if self.norm is not None: + x = self.norm(x) + if all_intermediate is not None: all_intermediate.append(x.unsqueeze(1).clone()) if all_intermediate is not None: intermediate = torch.cat(all_intermediate, dim=1) - if intermediate is not None and final_layer_norm_intermediate: + if intermediate is not None and final_layer_norm_intermediate and self.norm is not None: intermediate = self.norm(intermediate) return x, intermediate From 65ee24c9789b93660ebe978a3186486f105298c2 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Wed, 19 Nov 2025 11:25:28 +0200 Subject: [PATCH 32/47] change display name of PreviewAny node to "Preview as Text" (#10796) --- comfy_extras/nodes_preview_any.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py index e749fa6ae..139b07c93 100644 --- a/comfy_extras/nodes_preview_any.py +++ b/comfy_extras/nodes_preview_any.py @@ -39,5 +39,5 @@ NODE_CLASS_MAPPINGS = { } NODE_DISPLAY_NAME_MAPPINGS = { - "PreviewAny": "Preview Any", + "PreviewAny": "Preview as Text", } From 6a1d3a1ae131f3fff7f45a7e835eb10e9d1338ee Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 20 Nov 2025 00:49:01 +0200 Subject: [PATCH 33/47] convert hunyuan3d.py to V3 schema (#10664) --- comfy_api/latest/__init__.py | 4 +- comfy_api/latest/_io.py | 5 +- comfy_api/latest/_util/__init__.py | 3 + comfy_api/latest/_util/geometry_types.py | 12 + comfy_extras/nodes_hunyuan3d.py | 274 +++++++++++++---------- 5 files changed, 178 insertions(+), 120 deletions(-) create mode 100644 comfy_api/latest/_util/geometry_types.py diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py index b7a3fa9c1..176ae36e0 100644 --- a/comfy_api/latest/__init__.py +++ b/comfy_api/latest/__init__.py @@ -7,7 +7,7 @@ from comfy_api.internal.singleton import ProxiedSingleton from comfy_api.internal.async_to_sync import create_sync_class from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents -from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents +from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL from . import _io as io from . import _ui as ui # from comfy_api.latest._resources import _RESOURCES as resources #noqa: F401 @@ -104,6 +104,8 @@ class Types: VideoCodec = VideoCodec VideoContainer = VideoContainer VideoComponents = VideoComponents + MESH = MESH + VOXEL = VOXEL ComfyAPI = ComfyAPI_latest diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py index 0b701260f..863254ce7 100644 --- a/comfy_api/latest/_io.py +++ b/comfy_api/latest/_io.py @@ -27,6 +27,7 @@ from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classpr prune_dict, shallow_clone_class) from comfy_api.latest._resources import Resources, ResourcesLocal from comfy_execution.graph_utils import ExecutionBlocker +from ._util import MESH, VOXEL # from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference @@ -656,11 +657,11 @@ class LossMap(ComfyTypeIO): @comfytype(io_type="VOXEL") class Voxel(ComfyTypeIO): - Type = Any # TODO: VOXEL class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3 + Type = VOXEL @comfytype(io_type="MESH") class Mesh(ComfyTypeIO): - Type = Any # TODO: MESH class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3 + Type = MESH @comfytype(io_type="HOOKS") class Hooks(ComfyTypeIO): diff --git a/comfy_api/latest/_util/__init__.py b/comfy_api/latest/_util/__init__.py index 9019c46db..fc5431dda 100644 --- a/comfy_api/latest/_util/__init__.py +++ b/comfy_api/latest/_util/__init__.py @@ -1,8 +1,11 @@ from .video_types import VideoContainer, VideoCodec, VideoComponents +from .geometry_types import VOXEL, MESH __all__ = [ # Utility Types "VideoContainer", "VideoCodec", "VideoComponents", + "VOXEL", + "MESH", ] diff --git a/comfy_api/latest/_util/geometry_types.py b/comfy_api/latest/_util/geometry_types.py new file mode 100644 index 000000000..385122778 --- /dev/null +++ b/comfy_api/latest/_util/geometry_types.py @@ -0,0 +1,12 @@ +import torch + + +class VOXEL: + def __init__(self, data: torch.Tensor): + self.data = data + + +class MESH: + def __init__(self, vertices: torch.Tensor, faces: torch.Tensor): + self.vertices = vertices + self.faces = faces diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py index f6e71e0a8..adca14f62 100644 --- a/comfy_extras/nodes_hunyuan3d.py +++ b/comfy_extras/nodes_hunyuan3d.py @@ -7,63 +7,79 @@ from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_fro import folder_paths import comfy.model_management from comfy.cli_args import args +from typing_extensions import override +from comfy_api.latest import ComfyExtension, IO, Types +from comfy_api.latest._util import MESH, VOXEL # only for backward compatibility if someone import it from this file (will be removed later) # noqa -class EmptyLatentHunyuan3Dv2: + +class EmptyLatentHunyuan3Dv2(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), - } - } + def define_schema(cls): + return IO.Schema( + node_id="EmptyLatentHunyuan3Dv2", + category="latent/3d", + inputs=[ + IO.Int.Input("resolution", default=3072, min=1, max=8192), + IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."), + ], + outputs=[ + IO.Latent.Output(), + ] + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/3d" - - def generate(self, resolution, batch_size): + @classmethod + def execute(cls, resolution, batch_size) -> IO.NodeOutput: latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device()) - return ({"samples": latent, "type": "hunyuan3dv2"}, ) + return IO.NodeOutput({"samples": latent, "type": "hunyuan3dv2"}) -class Hunyuan3Dv2Conditioning: + generate = execute # TODO: remove + + +class Hunyuan3Dv2Conditioning(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"clip_vision_output": ("CLIP_VISION_OUTPUT",), - }} + def define_schema(cls): + return IO.Schema( + node_id="Hunyuan3Dv2Conditioning", + category="conditioning/video_models", + inputs=[ + IO.ClipVisionOutput.Input("clip_vision_output"), + ], + outputs=[ + IO.Conditioning.Output(display_name="positive"), + IO.Conditioning.Output(display_name="negative"), + ] + ) - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - - FUNCTION = "encode" - - CATEGORY = "conditioning/video_models" - - def encode(self, clip_vision_output): + @classmethod + def execute(cls, clip_vision_output) -> IO.NodeOutput: embeds = clip_vision_output.last_hidden_state positive = [[embeds, {}]] negative = [[torch.zeros_like(embeds), {}]] - return (positive, negative) + return IO.NodeOutput(positive, negative) + + encode = execute # TODO: remove -class Hunyuan3Dv2ConditioningMultiView: +class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {}, - "optional": {"front": ("CLIP_VISION_OUTPUT",), - "left": ("CLIP_VISION_OUTPUT",), - "back": ("CLIP_VISION_OUTPUT",), - "right": ("CLIP_VISION_OUTPUT",), }} + def define_schema(cls): + return IO.Schema( + node_id="Hunyuan3Dv2ConditioningMultiView", + category="conditioning/video_models", + inputs=[ + IO.ClipVisionOutput.Input("front", optional=True), + IO.ClipVisionOutput.Input("left", optional=True), + IO.ClipVisionOutput.Input("back", optional=True), + IO.ClipVisionOutput.Input("right", optional=True), + ], + outputs=[ + IO.Conditioning.Output(display_name="positive"), + IO.Conditioning.Output(display_name="negative"), + ] + ) - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - - FUNCTION = "encode" - - CATEGORY = "conditioning/video_models" - - def encode(self, front=None, left=None, back=None, right=None): + @classmethod + def execute(cls, front=None, left=None, back=None, right=None) -> IO.NodeOutput: all_embeds = [front, left, back, right] out = [] pos_embeds = None @@ -76,29 +92,35 @@ class Hunyuan3Dv2ConditioningMultiView: embeds = torch.cat(out, dim=1) positive = [[embeds, {}]] negative = [[torch.zeros_like(embeds), {}]] - return (positive, negative) + return IO.NodeOutput(positive, negative) + + encode = execute # TODO: remove -class VOXEL: - def __init__(self, data): - self.data = data - -class VAEDecodeHunyuan3D: +class VAEDecodeHunyuan3D(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"samples": ("LATENT", ), - "vae": ("VAE", ), - "num_chunks": ("INT", {"default": 8000, "min": 1000, "max": 500000}), - "octree_resolution": ("INT", {"default": 256, "min": 16, "max": 512}), - }} - RETURN_TYPES = ("VOXEL",) - FUNCTION = "decode" + def define_schema(cls): + return IO.Schema( + node_id="VAEDecodeHunyuan3D", + category="latent/3d", + inputs=[ + IO.Latent.Input("samples"), + IO.Vae.Input("vae"), + IO.Int.Input("num_chunks", default=8000, min=1000, max=500000), + IO.Int.Input("octree_resolution", default=256, min=16, max=512), + ], + outputs=[ + IO.Voxel.Output(), + ] + ) - CATEGORY = "latent/3d" + @classmethod + def execute(cls, vae, samples, num_chunks, octree_resolution) -> IO.NodeOutput: + voxels = Types.VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution})) + return IO.NodeOutput(voxels) + + decode = execute # TODO: remove - def decode(self, vae, samples, num_chunks, octree_resolution): - voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution})) - return (voxels, ) def voxel_to_mesh(voxels, threshold=0.5, device=None): if device is None: @@ -396,24 +418,24 @@ def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None): return final_vertices, faces -class MESH: - def __init__(self, vertices, faces): - self.vertices = vertices - self.faces = faces - -class VoxelToMeshBasic: +class VoxelToMeshBasic(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"voxel": ("VOXEL", ), - "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MESH",) - FUNCTION = "decode" + def define_schema(cls): + return IO.Schema( + node_id="VoxelToMeshBasic", + category="3d", + inputs=[ + IO.Voxel.Input("voxel"), + IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01), + ], + outputs=[ + IO.Mesh.Output(), + ] + ) - CATEGORY = "3d" - - def decode(self, voxel, threshold): + @classmethod + def execute(cls, voxel, threshold) -> IO.NodeOutput: vertices = [] faces = [] for x in voxel.data: @@ -421,21 +443,29 @@ class VoxelToMeshBasic: vertices.append(v) faces.append(f) - return (MESH(torch.stack(vertices), torch.stack(faces)), ) + return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces))) -class VoxelToMesh: + decode = execute # TODO: remove + + +class VoxelToMesh(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"voxel": ("VOXEL", ), - "algorithm": (["surface net", "basic"], ), - "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MESH",) - FUNCTION = "decode" + def define_schema(cls): + return IO.Schema( + node_id="VoxelToMesh", + category="3d", + inputs=[ + IO.Voxel.Input("voxel"), + IO.Combo.Input("algorithm", options=["surface net", "basic"]), + IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01), + ], + outputs=[ + IO.Mesh.Output(), + ] + ) - CATEGORY = "3d" - - def decode(self, voxel, algorithm, threshold): + @classmethod + def execute(cls, voxel, algorithm, threshold) -> IO.NodeOutput: vertices = [] faces = [] @@ -449,7 +479,9 @@ class VoxelToMesh: vertices.append(v) faces.append(f) - return (MESH(torch.stack(vertices), torch.stack(faces)), ) + return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces))) + + decode = execute # TODO: remove def save_glb(vertices, faces, filepath, metadata=None): @@ -581,31 +613,32 @@ def save_glb(vertices, faces, filepath, metadata=None): return filepath -class SaveGLB: +class SaveGLB(IO.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"mesh": ("MESH", ), - "filename_prefix": ("STRING", {"default": "mesh/ComfyUI"}), }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } + def define_schema(cls): + return IO.Schema( + node_id="SaveGLB", + category="3d", + is_output_node=True, + inputs=[ + IO.Mesh.Input("mesh"), + IO.String.Input("filename_prefix", default="mesh/ComfyUI"), + ], + hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo] + ) - RETURN_TYPES = () - FUNCTION = "save" - - OUTPUT_NODE = True - - CATEGORY = "3d" - - def save(self, mesh, filename_prefix, prompt=None, extra_pnginfo=None): + @classmethod + def execute(cls, mesh, filename_prefix) -> IO.NodeOutput: full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory()) results = [] metadata = {} if not args.disable_metadata: - if prompt is not None: - metadata["prompt"] = json.dumps(prompt) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) + if cls.hidden.prompt is not None: + metadata["prompt"] = json.dumps(cls.hidden.prompt) + if cls.hidden.extra_pnginfo is not None: + for x in cls.hidden.extra_pnginfo: + metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x]) for i in range(mesh.vertices.shape[0]): f = f"{filename}_{counter:05}_.glb" @@ -616,15 +649,22 @@ class SaveGLB: "type": "output" }) counter += 1 - return {"ui": {"3d": results}} + return IO.NodeOutput(ui={"3d": results}) -NODE_CLASS_MAPPINGS = { - "EmptyLatentHunyuan3Dv2": EmptyLatentHunyuan3Dv2, - "Hunyuan3Dv2Conditioning": Hunyuan3Dv2Conditioning, - "Hunyuan3Dv2ConditioningMultiView": Hunyuan3Dv2ConditioningMultiView, - "VAEDecodeHunyuan3D": VAEDecodeHunyuan3D, - "VoxelToMeshBasic": VoxelToMeshBasic, - "VoxelToMesh": VoxelToMesh, - "SaveGLB": SaveGLB, -} +class Hunyuan3dExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + EmptyLatentHunyuan3Dv2, + Hunyuan3Dv2Conditioning, + Hunyuan3Dv2ConditioningMultiView, + VAEDecodeHunyuan3D, + VoxelToMeshBasic, + VoxelToMesh, + SaveGLB, + ] + + +async def comfy_entrypoint() -> Hunyuan3dExtension: + return Hunyuan3dExtension() From 7601e89255cde24667d3b4e6022f1385d901748b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 19 Nov 2025 17:17:15 -0800 Subject: [PATCH 34/47] Fix workflow name. (#10806) --- .github/workflows/release-stable-all.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release-stable-all.yml b/.github/workflows/release-stable-all.yml index f7de3a7c3..9274b4170 100644 --- a/.github/workflows/release-stable-all.yml +++ b/.github/workflows/release-stable-all.yml @@ -14,7 +14,7 @@ jobs: contents: "write" packages: "write" pull-requests: "read" - name: "Release NVIDIA Default (cu129)" + name: "Release NVIDIA Default (cu130)" uses: ./.github/workflows/stable-release.yml with: git_tag: ${{ inputs.git_tag }} From 394348f5caaa062eac11a57e2997aacccd4246eb Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 20 Nov 2025 03:44:04 +0200 Subject: [PATCH 35/47] feat(api-nodes): add Topaz API nodes (#10755) --- comfy_api_nodes/apis/topaz_api.py | 133 ++++++++++ comfy_api_nodes/nodes_topaz.py | 421 ++++++++++++++++++++++++++++++ comfy_api_nodes/util/client.py | 9 +- nodes.py | 1 + 4 files changed, 560 insertions(+), 4 deletions(-) create mode 100644 comfy_api_nodes/apis/topaz_api.py create mode 100644 comfy_api_nodes/nodes_topaz.py diff --git a/comfy_api_nodes/apis/topaz_api.py b/comfy_api_nodes/apis/topaz_api.py new file mode 100644 index 000000000..4d9e62e72 --- /dev/null +++ b/comfy_api_nodes/apis/topaz_api.py @@ -0,0 +1,133 @@ +from typing import Optional, Union + +from pydantic import BaseModel, Field + + +class ImageEnhanceRequest(BaseModel): + model: str = Field("Reimagine") + output_format: str = Field("jpeg") + subject_detection: str = Field("All") + face_enhancement: bool = Field(True) + face_enhancement_creativity: float = Field(0, description="Is ignored if face_enhancement is false") + face_enhancement_strength: float = Field(0.8, description="Is ignored if face_enhancement is false") + source_url: str = Field(...) + output_width: Optional[int] = Field(None) + output_height: Optional[int] = Field(None) + crop_to_fill: bool = Field(False) + prompt: Optional[str] = Field(None, description="Text prompt for creative upscaling guidance") + creativity: int = Field(3, description="Creativity settings range from 1 to 9") + face_preservation: str = Field("true", description="To preserve the identity of characters") + color_preservation: str = Field("true", description="To preserve the original color") + + +class ImageAsyncTaskResponse(BaseModel): + process_id: str = Field(...) + + +class ImageStatusResponse(BaseModel): + process_id: str = Field(...) + status: str = Field(...) + progress: Optional[int] = Field(None) + credits: int = Field(...) + + +class ImageDownloadResponse(BaseModel): + download_url: str = Field(...) + expiry: int = Field(...) + + +class Resolution(BaseModel): + width: int = Field(...) + height: int = Field(...) + + +class CreateCreateVideoRequestSource(BaseModel): + container: str = Field(...) + size: int = Field(..., description="Size of the video file in bytes") + duration: int = Field(..., description="Duration of the video file in seconds") + frameCount: int = Field(..., description="Total number of frames in the video") + frameRate: int = Field(...) + resolution: Resolution = Field(...) + + +class VideoFrameInterpolationFilter(BaseModel): + model: str = Field(...) + slowmo: Optional[int] = Field(None) + fps: int = Field(...) + duplicate: bool = Field(...) + duplicate_threshold: float = Field(...) + + +class VideoEnhancementFilter(BaseModel): + model: str = Field(...) + auto: Optional[str] = Field(None, description="Auto, Manual, Relative") + focusFixLevel: Optional[str] = Field(None, description="Downscales video input for correction of blurred subjects") + compression: Optional[float] = Field(None, description="Strength of compression recovery") + details: Optional[float] = Field(None, description="Amount of detail reconstruction") + prenoise: Optional[float] = Field(None, description="Amount of noise to add to input to reduce over-smoothing") + noise: Optional[float] = Field(None, description="Amount of noise reduction") + halo: Optional[float] = Field(None, description="Amount of halo reduction") + preblur: Optional[float] = Field(None, description="Anti-aliasing and deblurring strength") + blur: Optional[float] = Field(None, description="Amount of sharpness applied") + grain: Optional[float] = Field(None, description="Grain after AI model processing") + grainSize: Optional[float] = Field(None, description="Size of generated grain") + recoverOriginalDetailValue: Optional[float] = Field(None, description="Source details into the output video") + creativity: Optional[str] = Field(None, description="Creativity level(high, low) for slc-1 only") + isOptimizedMode: Optional[bool] = Field(None, description="Set to true for Starlight Creative (slc-1) only") + + +class OutputInformationVideo(BaseModel): + resolution: Resolution = Field(...) + frameRate: int = Field(...) + audioCodec: Optional[str] = Field(..., description="Required if audioTransfer is Copy or Convert") + audioTransfer: str = Field(..., description="Copy, Convert, None") + dynamicCompressionLevel: str = Field(..., description="Low, Mid, High") + + +class Overrides(BaseModel): + isPaidDiffusion: bool = Field(True) + + +class CreateVideoRequest(BaseModel): + source: CreateCreateVideoRequestSource = Field(...) + filters: list[Union[VideoFrameInterpolationFilter, VideoEnhancementFilter]] = Field(...) + output: OutputInformationVideo = Field(...) + overrides: Overrides = Field(Overrides(isPaidDiffusion=True)) + + +class CreateVideoResponse(BaseModel): + requestId: str = Field(...) + + +class VideoAcceptResponse(BaseModel): + uploadId: str = Field(...) + urls: list[str] = Field(...) + + +class VideoCompleteUploadRequestPart(BaseModel): + partNum: int = Field(...) + eTag: str = Field(...) + + +class VideoCompleteUploadRequest(BaseModel): + uploadResults: list[VideoCompleteUploadRequestPart] = Field(...) + + +class VideoCompleteUploadResponse(BaseModel): + message: str = Field(..., description="Confirmation message") + + +class VideoStatusResponseEstimates(BaseModel): + cost: list[int] = Field(...) + + +class VideoStatusResponseDownloadUrl(BaseModel): + url: str = Field(...) + + +class VideoStatusResponse(BaseModel): + status: str = Field(...) + estimates: Optional[VideoStatusResponseEstimates] = Field(None) + progress: Optional[float] = Field(None) + message: Optional[str] = Field("") + download: Optional[VideoStatusResponseDownloadUrl] = Field(None) diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py new file mode 100644 index 000000000..79c7bf43d --- /dev/null +++ b/comfy_api_nodes/nodes_topaz.py @@ -0,0 +1,421 @@ +import builtins +from io import BytesIO + +import aiohttp +import torch +from typing_extensions import override + +from comfy_api.input.video_types import VideoInput +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.apis import topaz_api +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_image_tensor, + download_url_to_video_output, + get_fs_object_size, + get_number_of_images, + poll_op, + sync_op, + upload_images_to_comfyapi, + validate_container_format_is_mp4, +) + +UPSCALER_MODELS_MAP = { + "Starlight (Astra) Fast": "slf-1", + "Starlight (Astra) Creative": "slc-1", +} +UPSCALER_VALUES_MAP = { + "FullHD (1080p)": 1920, + "4K (2160p)": 3840, +} + + +class TopazImageEnhance(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TopazImageEnhance", + display_name="Topaz Image Enhance", + category="api node/image/Topaz", + description="Industry-standard upscaling and image enhancement.", + inputs=[ + IO.Combo.Input("model", options=["Reimagine"]), + IO.Image.Input("image"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Optional text prompt for creative upscaling guidance.", + optional=True, + ), + IO.Combo.Input( + "subject_detection", + options=["All", "Foreground", "Background"], + optional=True, + ), + IO.Boolean.Input( + "face_enhancement", + default=True, + optional=True, + tooltip="Enhance faces (if present) during processing.", + ), + IO.Float.Input( + "face_enhancement_creativity", + default=0.0, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + optional=True, + tooltip="Set the creativity level for face enhancement.", + ), + IO.Float.Input( + "face_enhancement_strength", + default=1.0, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + optional=True, + tooltip="Controls how sharp enhanced faces are relative to the background.", + ), + IO.Boolean.Input( + "crop_to_fill", + default=False, + optional=True, + tooltip="By default, the image is letterboxed when the output aspect ratio differs. " + "Enable to crop the image to fill the output dimensions.", + ), + IO.Int.Input( + "output_width", + default=0, + min=0, + max=32000, + step=1, + display_mode=IO.NumberDisplay.number, + optional=True, + tooltip="Zero value means to calculate automatically (usually it will be original size or output_height if specified).", + ), + IO.Int.Input( + "output_height", + default=0, + min=0, + max=32000, + step=1, + display_mode=IO.NumberDisplay.number, + optional=True, + tooltip="Zero value means to output in the same height as original or output width.", + ), + IO.Int.Input( + "creativity", + default=3, + min=1, + max=9, + step=1, + display_mode=IO.NumberDisplay.slider, + optional=True, + ), + IO.Boolean.Input( + "face_preservation", + default=True, + optional=True, + tooltip="Preserve subjects' facial identity.", + ), + IO.Boolean.Input( + "color_preservation", + default=True, + optional=True, + tooltip="Preserve the original colors.", + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + image: torch.Tensor, + prompt: str = "", + subject_detection: str = "All", + face_enhancement: bool = True, + face_enhancement_creativity: float = 1.0, + face_enhancement_strength: float = 0.8, + crop_to_fill: bool = False, + output_width: int = 0, + output_height: int = 0, + creativity: int = 3, + face_preservation: bool = True, + color_preservation: bool = True, + ) -> IO.NodeOutput: + if get_number_of_images(image) != 1: + raise ValueError("Only one input image is supported.") + download_url = await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png") + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/topaz/image/v1/enhance-gen/async", method="POST"), + response_model=topaz_api.ImageAsyncTaskResponse, + data=topaz_api.ImageEnhanceRequest( + model=model, + prompt=prompt, + subject_detection=subject_detection, + face_enhancement=face_enhancement, + face_enhancement_creativity=face_enhancement_creativity, + face_enhancement_strength=face_enhancement_strength, + crop_to_fill=crop_to_fill, + output_width=output_width if output_width else None, + output_height=output_height if output_height else None, + creativity=creativity, + face_preservation=str(face_preservation).lower(), + color_preservation=str(color_preservation).lower(), + source_url=download_url[0], + output_format="png", + ), + content_type="multipart/form-data", + ) + + await poll_op( + cls, + poll_endpoint=ApiEndpoint(path=f"/proxy/topaz/image/v1/status/{initial_response.process_id}"), + response_model=topaz_api.ImageStatusResponse, + status_extractor=lambda x: x.status, + progress_extractor=lambda x: getattr(x, "progress", 0), + price_extractor=lambda x: x.credits * 0.08, + poll_interval=8.0, + max_poll_attempts=160, + estimated_duration=60, + ) + + results = await sync_op( + cls, + ApiEndpoint(path=f"/proxy/topaz/image/v1/download/{initial_response.process_id}"), + response_model=topaz_api.ImageDownloadResponse, + monitor_progress=False, + ) + return IO.NodeOutput(await download_url_to_image_tensor(results.download_url)) + + +class TopazVideoEnhance(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TopazVideoEnhance", + display_name="Topaz Video Enhance", + category="api node/video/Topaz", + description="Breathe new life into video with powerful upscaling and recovery technology.", + inputs=[ + IO.Video.Input("video"), + IO.Boolean.Input("upscaler_enabled", default=True), + IO.Combo.Input("upscaler_model", options=list(UPSCALER_MODELS_MAP.keys())), + IO.Combo.Input("upscaler_resolution", options=list(UPSCALER_VALUES_MAP.keys())), + IO.Combo.Input( + "upscaler_creativity", + options=["low", "middle", "high"], + default="low", + tooltip="Creativity level (applies only to Starlight (Astra) Creative).", + optional=True, + ), + IO.Boolean.Input("interpolation_enabled", default=False, optional=True), + IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True), + IO.Int.Input( + "interpolation_slowmo", + default=1, + min=1, + max=16, + display_mode=IO.NumberDisplay.number, + tooltip="Slow-motion factor applied to the input video. " + "For example, 2 makes the output twice as slow and doubles the duration.", + optional=True, + ), + IO.Int.Input( + "interpolation_frame_rate", + default=60, + min=15, + max=240, + display_mode=IO.NumberDisplay.number, + tooltip="Output frame rate.", + optional=True, + ), + IO.Boolean.Input( + "interpolation_duplicate", + default=False, + tooltip="Analyze the input for duplicate frames and remove them.", + optional=True, + ), + IO.Float.Input( + "interpolation_duplicate_threshold", + default=0.01, + min=0.001, + max=0.1, + step=0.001, + display_mode=IO.NumberDisplay.number, + tooltip="Detection sensitivity for duplicate frames.", + optional=True, + ), + IO.Combo.Input( + "dynamic_compression_level", + options=["Low", "Mid", "High"], + default="Low", + tooltip="CQP level.", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + video: VideoInput, + upscaler_enabled: bool, + upscaler_model: str, + upscaler_resolution: str, + upscaler_creativity: str = "low", + interpolation_enabled: bool = False, + interpolation_model: str = "apo-8", + interpolation_slowmo: int = 1, + interpolation_frame_rate: int = 60, + interpolation_duplicate: bool = False, + interpolation_duplicate_threshold: float = 0.01, + dynamic_compression_level: str = "Low", + ) -> IO.NodeOutput: + if upscaler_enabled is False and interpolation_enabled is False: + raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.") + src_width, src_height = video.get_dimensions() + video_components = video.get_components() + src_frame_rate = int(video_components.frame_rate) + duration_sec = video.get_duration() + estimated_frames = int(duration_sec * src_frame_rate) + validate_container_format_is_mp4(video) + src_video_stream = video.get_stream_source() + target_width = src_width + target_height = src_height + target_frame_rate = src_frame_rate + filters = [] + if upscaler_enabled: + target_width = UPSCALER_VALUES_MAP[upscaler_resolution] + target_height = UPSCALER_VALUES_MAP[upscaler_resolution] + filters.append( + topaz_api.VideoEnhancementFilter( + model=UPSCALER_MODELS_MAP[upscaler_model], + creativity=(upscaler_creativity if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None), + isOptimizedMode=(True if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None), + ), + ) + if interpolation_enabled: + target_frame_rate = interpolation_frame_rate + filters.append( + topaz_api.VideoFrameInterpolationFilter( + model=interpolation_model, + slowmo=interpolation_slowmo, + fps=interpolation_frame_rate, + duplicate=interpolation_duplicate, + duplicate_threshold=interpolation_duplicate_threshold, + ), + ) + initial_res = await sync_op( + cls, + ApiEndpoint(path="/proxy/topaz/video/", method="POST"), + response_model=topaz_api.CreateVideoResponse, + data=topaz_api.CreateVideoRequest( + source=topaz_api.CreateCreateVideoRequestSource( + container="mp4", + size=get_fs_object_size(src_video_stream), + duration=int(duration_sec), + frameCount=estimated_frames, + frameRate=src_frame_rate, + resolution=topaz_api.Resolution(width=src_width, height=src_height), + ), + filters=filters, + output=topaz_api.OutputInformationVideo( + resolution=topaz_api.Resolution(width=target_width, height=target_height), + frameRate=target_frame_rate, + audioCodec="AAC", + audioTransfer="Copy", + dynamicCompressionLevel=dynamic_compression_level, + ), + ), + wait_label="Creating task", + final_label_on_success="Task created", + ) + upload_res = await sync_op( + cls, + ApiEndpoint( + path=f"/proxy/topaz/video/{initial_res.requestId}/accept", + method="PATCH", + ), + response_model=topaz_api.VideoAcceptResponse, + wait_label="Preparing upload", + final_label_on_success="Upload started", + ) + if len(upload_res.urls) > 1: + raise NotImplementedError( + "Large files are not currently supported. Please open an issue in the ComfyUI repository." + ) + async with aiohttp.ClientSession(headers={"Content-Type": "video/mp4"}) as session: + if isinstance(src_video_stream, BytesIO): + src_video_stream.seek(0) + async with session.put(upload_res.urls[0], data=src_video_stream, raise_for_status=True) as res: + upload_etag = res.headers["Etag"] + else: + with builtins.open(src_video_stream, "rb") as video_file: + async with session.put(upload_res.urls[0], data=video_file, raise_for_status=True) as res: + upload_etag = res.headers["Etag"] + await sync_op( + cls, + ApiEndpoint( + path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload", + method="PATCH", + ), + response_model=topaz_api.VideoCompleteUploadResponse, + data=topaz_api.VideoCompleteUploadRequest( + uploadResults=[ + topaz_api.VideoCompleteUploadRequestPart( + partNum=1, + eTag=upload_etag, + ), + ], + ), + wait_label="Finalizing upload", + final_label_on_success="Upload completed", + ) + final_response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"), + response_model=topaz_api.VideoStatusResponse, + status_extractor=lambda x: x.status, + progress_extractor=lambda x: getattr(x, "progress", 0), + price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None), + poll_interval=10.0, + max_poll_attempts=320, + ) + return IO.NodeOutput(await download_url_to_video_output(final_response.download.url)) + + +class TopazExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + TopazImageEnhance, + TopazVideoEnhance, + ] + + +async def comfy_entrypoint() -> TopazExtension: + return TopazExtension() diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py index 2d5dcd648..ad6e3c0d0 100644 --- a/comfy_api_nodes/util/client.py +++ b/comfy_api_nodes/util/client.py @@ -77,9 +77,9 @@ class _PollUIState: _RETRY_STATUS = {408, 429, 500, 502, 503, 504} -COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done"] -FAILED_STATUSES = ["cancelled", "canceled", "fail", "failed", "error"] -QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"] +COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"] +FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"] +QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing"] async def sync_op( @@ -424,7 +424,8 @@ def _display_text( if status: display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}") if price is not None: - display_lines.append(f"Price: ${float(price):,.4f}") + p = f"{float(price):,.4f}".rstrip("0").rstrip(".") + display_lines.append(f"Price: ${p}") if text is not None: display_lines.append(text) if display_lines: diff --git a/nodes.py b/nodes.py index f6aeedc78..ac14e39a7 100644 --- a/nodes.py +++ b/nodes.py @@ -2359,6 +2359,7 @@ async def init_builtin_api_nodes(): "nodes_pika.py", "nodes_runway.py", "nodes_sora.py", + "nodes_topaz.py", "nodes_tripo.py", "nodes_moonvalley.py", "nodes_rodin.py", From cb96d4d18c78ee09d5fd70954ffcb4ad2c7f0d7a Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 19 Nov 2025 20:56:23 -0800 Subject: [PATCH 36/47] Disable workaround on newer cudnn. (#10807) --- comfy/ops.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/ops.py b/comfy/ops.py index 2a90a5ba2..640622fd1 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -58,7 +58,8 @@ except (ModuleNotFoundError, TypeError): NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False try: if comfy.model_management.is_nvidia(): - if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10): + cudnn_version = torch.backends.cudnn.version() + if (cudnn_version >= 91002 and cudnn_version < 91500) and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10): #TODO: change upper bound version once it's fixed' NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True logging.info("working around nvidia conv3d memory bug.") From 87b0359392219841c2214e1eb06678840cae470e Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Wed, 19 Nov 2025 22:36:56 -0800 Subject: [PATCH 37/47] Update server templates handler to use new multi-package distribution (comfyui-workflow-templates versions >=0.3) (#10791) * update templates for monorepo * refactor --- app/frontend_management.py | 67 ++++++++++++++++++++++++++++++++++++-- requirements.txt | 2 +- server.py | 32 ++++++++++++++---- 3 files changed, 92 insertions(+), 9 deletions(-) diff --git a/app/frontend_management.py b/app/frontend_management.py index cce0c117d..bdaa85812 100644 --- a/app/frontend_management.py +++ b/app/frontend_management.py @@ -10,7 +10,8 @@ import importlib from dataclasses import dataclass from functools import cached_property from pathlib import Path -from typing import TypedDict, Optional +from typing import Dict, TypedDict, Optional +from aiohttp import web from importlib.metadata import version import requests @@ -257,7 +258,54 @@ comfyui-frontend-package is not installed. sys.exit(-1) @classmethod - def templates_path(cls) -> str: + def template_asset_map(cls) -> Optional[Dict[str, str]]: + """Return a mapping of template asset names to their absolute paths.""" + try: + from comfyui_workflow_templates import ( + get_asset_path, + iter_templates, + ) + except ImportError: + logging.error( + f""" +********** ERROR *********** + +comfyui-workflow-templates is not installed. + +{frontend_install_warning_message()} + +********** ERROR *********** +""".strip() + ) + return None + + try: + template_entries = list(iter_templates()) + except Exception as exc: + logging.error(f"Failed to enumerate workflow templates: {exc}") + return None + + asset_map: Dict[str, str] = {} + try: + for entry in template_entries: + for asset in entry.assets: + asset_map[asset.filename] = get_asset_path( + entry.template_id, asset.filename + ) + except Exception as exc: + logging.error(f"Failed to resolve template asset paths: {exc}") + return None + + if not asset_map: + logging.error("No workflow template assets found. Did the packages install correctly?") + return None + + return asset_map + + + @classmethod + def legacy_templates_path(cls) -> Optional[str]: + """Return the legacy templates directory shipped inside the meta package.""" try: import comfyui_workflow_templates @@ -276,6 +324,7 @@ comfyui-workflow-templates is not installed. ********** ERROR *********** """.strip() ) + return None @classmethod def embedded_docs_path(cls) -> str: @@ -392,3 +441,17 @@ comfyui-workflow-templates is not installed. logging.info("Falling back to the default frontend.") check_frontend_version() return cls.default_frontend_path() + @classmethod + def template_asset_handler(cls): + assets = cls.template_asset_map() + if not assets: + return None + + async def serve_template(request: web.Request) -> web.StreamResponse: + rel_path = request.match_info.get("path", "") + target = assets.get(rel_path) + if target is None: + raise web.HTTPNotFound() + return web.FileResponse(target) + + return serve_template diff --git a/requirements.txt b/requirements.txt index 249c36dee..36c39f338 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.28.8 -comfyui-workflow-templates==0.2.11 +comfyui-workflow-templates==0.3.1 comfyui-embedded-docs==0.3.1 torch torchsde diff --git a/server.py b/server.py index d059d3dc9..d9d5c491f 100644 --- a/server.py +++ b/server.py @@ -30,7 +30,7 @@ import comfy.model_management from comfy_api import feature_flags import node_helpers from comfyui_version import __version__ -from app.frontend_management import FrontendManager +from app.frontend_management import FrontendManager, parse_version from comfy_api.internal import _ComfyNodeInternal from app.user_manager import UserManager @@ -849,11 +849,31 @@ class PromptServer(): for name, dir in nodes.EXTENSION_WEB_DIRS.items(): self.app.add_routes([web.static('/extensions/' + name, dir)]) - workflow_templates_path = FrontendManager.templates_path() - if workflow_templates_path: - self.app.add_routes([ - web.static('/templates', workflow_templates_path) - ]) + installed_templates_version = FrontendManager.get_installed_templates_version() + use_legacy_templates = True + if installed_templates_version: + try: + use_legacy_templates = ( + parse_version(installed_templates_version) + < parse_version("0.3.0") + ) + except Exception as exc: + logging.warning( + "Unable to parse templates version '%s': %s", + installed_templates_version, + exc, + ) + + if use_legacy_templates: + workflow_templates_path = FrontendManager.legacy_templates_path() + if workflow_templates_path: + self.app.add_routes([ + web.static('/templates', workflow_templates_path) + ]) + else: + handler = FrontendManager.template_asset_handler() + if handler: + self.app.router.add_get("/templates/{path:.*}", handler) # Serve embedded documentation from the package embedded_docs_path = FrontendManager.embedded_docs_path() From f5e66d5e47271253edad5c4eddd817b0d6a23340 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 20 Nov 2025 12:08:03 -0800 Subject: [PATCH 38/47] Fix ImageBatch with different channel count. (#10815) --- nodes.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/nodes.py b/nodes.py index ac14e39a7..75e820e66 100644 --- a/nodes.py +++ b/nodes.py @@ -1852,6 +1852,10 @@ class ImageBatch: CATEGORY = "image" def batch(self, image1, image2): + if image1.shape[-1] != image2.shape[-1]: + channels = min(image1.shape[-1], image2.shape[-1]) + image1 = image1[..., :channels] + image2 = image2[..., :channels] if image1.shape[1:] != image2.shape[1:]: image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) s = torch.cat((image1, image2), dim=0) From 9e00ce5b76ec04be37375310512a443605b95077 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Thu, 20 Nov 2025 14:42:46 -0800 Subject: [PATCH 39/47] Make Batch Images node add alpha channel when one of the inputs has it (#10816) * When one Batch Image input has alpha and one does not, add empty alpha channel * Use torch.nn.functional.pad --- nodes.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/nodes.py b/nodes.py index 75e820e66..030371633 100644 --- a/nodes.py +++ b/nodes.py @@ -1853,9 +1853,10 @@ class ImageBatch: def batch(self, image1, image2): if image1.shape[-1] != image2.shape[-1]: - channels = min(image1.shape[-1], image2.shape[-1]) - image1 = image1[..., :channels] - image2 = image2[..., :channels] + if image1.shape[-1] > image2.shape[-1]: + image2 = torch.nn.functional.pad(image2, (0,1), mode='constant', value=1.0) + else: + image1 = torch.nn.functional.pad(image1, (0,1), mode='constant', value=1.0) if image1.shape[1:] != image2.shape[1:]: image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) s = torch.cat((image1, image2), dim=0) From 7b8389578e88dcd13b1cf6aea5404047298c9183 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 21 Nov 2025 02:17:47 +0200 Subject: [PATCH 40/47] feat(api-nodes): add Nano Banana Pro (#10814) * feat(api-nodes): add Nano Banana Pro * frontend bump to 1.28.9 --- comfy_api_nodes/apis/gemini_api.py | 5 +- comfy_api_nodes/nodes_gemini.py | 205 ++++++++++++++++++++++++++++- comfy_api_nodes/util/client.py | 13 +- requirements.txt | 2 +- 4 files changed, 215 insertions(+), 10 deletions(-) diff --git a/comfy_api_nodes/apis/gemini_api.py b/comfy_api_nodes/apis/gemini_api.py index f63e02693..710f173f1 100644 --- a/comfy_api_nodes/apis/gemini_api.py +++ b/comfy_api_nodes/apis/gemini_api.py @@ -68,7 +68,7 @@ class GeminiTextPart(BaseModel): class GeminiContent(BaseModel): - parts: list[GeminiPart] = Field(...) + parts: list[GeminiPart] = Field([]) role: GeminiRole = Field(..., examples=["user"]) @@ -120,7 +120,7 @@ class GeminiGenerationConfig(BaseModel): class GeminiImageConfig(BaseModel): aspectRatio: str | None = Field(None) - resolution: str | None = Field(None) + imageSize: str | None = Field(None) class GeminiImageGenerationConfig(GeminiGenerationConfig): @@ -227,3 +227,4 @@ class GeminiGenerateContentResponse(BaseModel): candidates: list[GeminiCandidate] | None = Field(None) promptFeedback: GeminiPromptFeedback | None = Field(None) usageMetadata: GeminiUsageMetadata | None = Field(None) + modelVersion: str | None = Field(None) diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py index 6e746eebd..be752c885 100644 --- a/comfy_api_nodes/nodes_gemini.py +++ b/comfy_api_nodes/nodes_gemini.py @@ -29,11 +29,13 @@ from comfy_api_nodes.apis.gemini_api import ( GeminiMimeType, GeminiPart, GeminiRole, + Modality, ) from comfy_api_nodes.util import ( ApiEndpoint, audio_to_base64_string, bytesio_to_image_tensor, + get_number_of_images, sync_op, tensor_to_base64_string, validate_string, @@ -147,6 +149,49 @@ def get_image_from_response(response: GeminiGenerateContentResponse) -> torch.Te return torch.cat(image_tensors, dim=0) +def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | None: + if not response.modelVersion: + return None + # Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing + if response.modelVersion in ("gemini-2.5-pro-preview-05-06", "gemini-2.5-pro"): + input_tokens_price = 1.25 + output_text_tokens_price = 10.0 + output_image_tokens_price = 0.0 + elif response.modelVersion in ( + "gemini-2.5-flash-preview-04-17", + "gemini-2.5-flash", + ): + input_tokens_price = 0.30 + output_text_tokens_price = 2.50 + output_image_tokens_price = 0.0 + elif response.modelVersion in ( + "gemini-2.5-flash-image-preview", + "gemini-2.5-flash-image", + ): + input_tokens_price = 0.30 + output_text_tokens_price = 2.50 + output_image_tokens_price = 30.0 + elif response.modelVersion == "gemini-3-pro-preview": + input_tokens_price = 2 + output_text_tokens_price = 12.0 + output_image_tokens_price = 0.0 + elif response.modelVersion == "gemini-3-pro-image-preview": + input_tokens_price = 2 + output_text_tokens_price = 12.0 + output_image_tokens_price = 120.0 + else: + return None + final_price = response.usageMetadata.promptTokenCount * input_tokens_price + for i in response.usageMetadata.candidatesTokensDetails: + if i.modality == Modality.IMAGE: + final_price += output_image_tokens_price * i.tokenCount # for Nano Banana models + else: + final_price += output_text_tokens_price * i.tokenCount + if response.usageMetadata.thoughtsTokenCount: + final_price += output_text_tokens_price * response.usageMetadata.thoughtsTokenCount + return final_price / 1_000_000.0 + + class GeminiNode(IO.ComfyNode): """ Node to generate text responses from a Gemini model. @@ -314,6 +359,7 @@ class GeminiNode(IO.ComfyNode): ] ), response_model=GeminiGenerateContentResponse, + price_extractor=calculate_tokens_price, ) output_text = get_text_from_response(response) @@ -476,6 +522,13 @@ class GeminiImage(IO.ComfyNode): "or otherwise generates 1:1 squares.", optional=True, ), + IO.Combo.Input( + "response_modalities", + options=["IMAGE+TEXT", "IMAGE"], + tooltip="Choose 'IMAGE' for image-only output, or " + "'IMAGE+TEXT' to return both the generated image and a text response.", + optional=True, + ), ], outputs=[ IO.Image.Output(), @@ -498,6 +551,7 @@ class GeminiImage(IO.ComfyNode): images: torch.Tensor | None = None, files: list[GeminiPart] | None = None, aspect_ratio: str = "auto", + response_modalities: str = "IMAGE+TEXT", ) -> IO.NodeOutput: validate_string(prompt, strip_whitespace=True, min_length=1) parts: list[GeminiPart] = [GeminiPart(text=prompt)] @@ -520,17 +574,16 @@ class GeminiImage(IO.ComfyNode): GeminiContent(role=GeminiRole.user, parts=parts), ], generationConfig=GeminiImageGenerationConfig( - responseModalities=["TEXT", "IMAGE"], + responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]), imageConfig=None if aspect_ratio == "auto" else image_config, ), ), response_model=GeminiGenerateContentResponse, + price_extractor=calculate_tokens_price, ) - output_image = get_image_from_response(response) output_text = get_text_from_response(response) if output_text: - # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. render_spec = { "node_id": cls.hidden.unique_id, "component": "ChatHistoryWidget", @@ -551,9 +604,150 @@ class GeminiImage(IO.ComfyNode): "display_component", render_spec, ) + return IO.NodeOutput(get_image_from_response(response), output_text) - output_text = output_text or "Empty response from Gemini model..." - return IO.NodeOutput(output_image, output_text) + +class GeminiImage2(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GeminiImage2Node", + display_name="Nano Banana Pro (Google Gemini Image)", + category="api node/image/Gemini", + description="Generate or edit images synchronously via Google Vertex API.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + tooltip="Text prompt describing the image to generate or the edits to apply. " + "Include any constraints, styles, or details the model should follow.", + default="", + ), + IO.Combo.Input( + "model", + options=["gemini-3-pro-image-preview"], + ), + IO.Int.Input( + "seed", + default=42, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide " + "the same response for repeated requests. Deterministic output isn't guaranteed. " + "Also, changing the model or parameter settings, such as the temperature, " + "can cause variations in the response even when you use the same seed value. " + "By default, a random seed value is used.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"], + default="auto", + tooltip="If set to 'auto', matches your input image's aspect ratio; " + "if no image is provided, generates a 1:1 square.", + ), + IO.Combo.Input( + "resolution", + options=["1K", "2K", "4K"], + tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.", + ), + IO.Combo.Input( + "response_modalities", + options=["IMAGE+TEXT", "IMAGE"], + tooltip="Choose 'IMAGE' for image-only output, or " + "'IMAGE+TEXT' to return both the generated image and a text response.", + ), + IO.Image.Input( + "images", + optional=True, + tooltip="Optional reference image(s). " + "To include multiple images, use the Batch Images node (up to 14).", + ), + IO.Custom("GEMINI_INPUT_FILES").Input( + "files", + optional=True, + tooltip="Optional file(s) to use as context for the model. " + "Accepts inputs from the Gemini Generate Content Input Files node.", + ), + ], + outputs=[ + IO.Image.Output(), + IO.String.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + seed: int, + aspect_ratio: str, + resolution: str, + response_modalities: str, + images: torch.Tensor | None = None, + files: list[GeminiPart] | None = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + + parts: list[GeminiPart] = [GeminiPart(text=prompt)] + if images is not None: + if get_number_of_images(images) > 14: + raise ValueError("The current maximum number of supported images is 14.") + parts.extend(create_image_parts(images)) + if files is not None: + parts.extend(files) + + image_config = GeminiImageConfig(imageSize=resolution) + if aspect_ratio != "auto": + image_config.aspectRatio = aspect_ratio + + response = await sync_op( + cls, + ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"), + data=GeminiImageGenerateContentRequest( + contents=[ + GeminiContent(role=GeminiRole.user, parts=parts), + ], + generationConfig=GeminiImageGenerationConfig( + responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]), + imageConfig=image_config, + ), + ), + response_model=GeminiGenerateContentResponse, + price_extractor=calculate_tokens_price, + ) + + output_text = get_text_from_response(response) + if output_text: + render_spec = { + "node_id": cls.hidden.unique_id, + "component": "ChatHistoryWidget", + "props": { + "history": json.dumps( + [ + { + "prompt": prompt, + "response": output_text, + "response_id": str(uuid.uuid4()), + "timestamp": time.time(), + } + ] + ), + }, + } + PromptServer.instance.send_sync( + "display_component", + render_spec, + ) + return IO.NodeOutput(get_image_from_response(response), output_text) class GeminiExtension(ComfyExtension): @@ -562,6 +756,7 @@ class GeminiExtension(ComfyExtension): return [ GeminiNode, GeminiImage, + GeminiImage2, GeminiInputFiles, ] diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py index ad6e3c0d0..bf01d7d36 100644 --- a/comfy_api_nodes/util/client.py +++ b/comfy_api_nodes/util/client.py @@ -63,6 +63,7 @@ class _RequestConfig: estimated_total: Optional[int] = None final_label_on_success: Optional[str] = "Completed" progress_origin_ts: Optional[float] = None + price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None @dataclass @@ -87,6 +88,7 @@ async def sync_op( endpoint: ApiEndpoint, *, response_model: Type[M], + price_extractor: Optional[Callable[[M], Optional[float]]] = None, data: Optional[BaseModel] = None, files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None, content_type: str = "application/json", @@ -104,6 +106,7 @@ async def sync_op( raw = await sync_op_raw( cls, endpoint, + price_extractor=_wrap_model_extractor(response_model, price_extractor), data=data, files=files, content_type=content_type, @@ -175,6 +178,7 @@ async def sync_op_raw( cls: type[IO.ComfyNode], endpoint: ApiEndpoint, *, + price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None, data: Optional[Union[dict[str, Any], BaseModel]] = None, files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None, content_type: str = "application/json", @@ -216,6 +220,7 @@ async def sync_op_raw( estimated_total=estimated_duration, final_label_on_success=final_label_on_success, progress_origin_ts=progress_origin_ts, + price_extractor=price_extractor, ) return await _request_base(cfg, expect_binary=as_binary) @@ -425,7 +430,8 @@ def _display_text( display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}") if price is not None: p = f"{float(price):,.4f}".rstrip("0").rstrip(".") - display_lines.append(f"Price: ${p}") + if p != "0": + display_lines.append(f"Price: ${p}") if text is not None: display_lines.append(text) if display_lines: @@ -581,6 +587,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool): delay = cfg.retry_delay operation_succeeded: bool = False final_elapsed_seconds: Optional[int] = None + extracted_price: Optional[float] = None while True: attempt += 1 stop_event = asyncio.Event() @@ -768,6 +775,8 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool): except json.JSONDecodeError: payload = {"_raw": text} response_content_to_log = payload if isinstance(payload, dict) else text + with contextlib.suppress(Exception): + extracted_price = cfg.price_extractor(payload) if cfg.price_extractor else None operation_succeeded = True final_elapsed_seconds = int(time.monotonic() - start_time) try: @@ -872,7 +881,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool): else int(time.monotonic() - start_time) ), estimated_total=cfg.estimated_total, - price=None, + price=extracted_price, is_queued=False, processing_elapsed_seconds=final_elapsed_seconds, ) diff --git a/requirements.txt b/requirements.txt index 36c39f338..8c1946f3d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.28.8 +comfyui-frontend-package==1.28.9 comfyui-workflow-templates==0.3.1 comfyui-embedded-docs==0.3.1 torch From b75d349f25ccb702895c6f1b8af7aded63a7f7e2 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 21 Nov 2025 02:33:54 +0200 Subject: [PATCH 41/47] fix(KlingLipSyncAudioToVideoNode): convert audio to mp3 format (#10811) --- comfy_api_nodes/nodes_kling.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 7b23e9cf9..36852038b 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -518,7 +518,9 @@ async def execute_lipsync( # Upload the audio file to Comfy API and get download URL if audio: - audio_url = await upload_audio_to_comfyapi(cls, audio) + audio_url = await upload_audio_to_comfyapi( + cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg", filename="output.mp3" + ) logging.info("Uploaded audio to Comfy API. URL: %s", audio_url) else: audio_url = None From 10e90a5757906ecdb71b84d41173813d7f62c140 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Thu, 20 Nov 2025 18:20:52 -0800 Subject: [PATCH 42/47] bump comfyui-workflow-templates for nano banana 2 (#10818) * bump templates * bump templates --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8c1946f3d..624aa7362 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.28.9 -comfyui-workflow-templates==0.3.1 +comfyui-workflow-templates==0.6.0 comfyui-embedded-docs==0.3.1 torch torchsde From 943b3b615d40542ea19bc8ff8ad2950c0a094605 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 20 Nov 2025 19:44:43 -0800 Subject: [PATCH 43/47] HunyuanVideo 1.5 (#10819) * init * update * Update model.py * Update model.py * remove print * Fix text encoding * Prevent empty negative prompt Really doesn't work otherwise * fp16 works * I2V * Update model_base.py * Update nodes_hunyuan.py * Better latent rgb factors * Use the correct sigclip output... * Support HunyuanVideo1.5 SR model * whitespaces... * Proper latent channel count * SR model fixes This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already * vae_refiner: roll the convolution through temporal Work in progress. Roll the convolution through time using 2-latent-frame chunks and a FIFO queue for the convolution seams. * Support HunyuanVideo15 latent resampler * fix * Some cleanup Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> * Proper hyvid15 I2V channels Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> * Fix TokenRefiner for fp16 Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary. * Bugfix for the HunyuanVideo15 SR model * vae_refiner: roll the convolution through temporal II Roll the convolution through time using 2-latent-frame chunks and a FIFO queue for the convolution seams. Added support for encoder, lowered to 1 latent frame to save more VRAM, made work for Hunyuan Image 3.0 (as code shared). Fixed names, cleaned up code. * Allow any number of input frames in VAE. * Better VAE encode mem estimation. * Lowvram fix. * Fix hunyuan image 2.1 refiner. * Fix mistake. * Name changes. * Rename. * Whitespace. * Fix. * Fix. --------- Co-authored-by: kijai <40791699+kijai@users.noreply.github.com> Co-authored-by: Rattus --- comfy/latent_formats.py | 60 ++++ comfy/ldm/hunyuan_video/model.py | 54 +++- comfy/ldm/hunyuan_video/upsampler.py | 120 ++++++++ comfy/ldm/hunyuan_video/vae_refiner.py | 284 +++++++++++------- comfy/model_base.py | 91 ++++++ comfy/model_detection.py | 10 + comfy/sd.py | 12 +- comfy/supported_models.py | 50 ++- comfy/text_encoders/hunyuan_video.py | 9 + comfy/text_encoders/qwen_image.py | 4 +- comfy_api/latest/_io.py | 4 + comfy_extras/nodes_hunyuan.py | 201 ++++++++++++- folder_paths.py | 2 + .../put_latent_upscale_models_here | 0 nodes.py | 2 +- 15 files changed, 777 insertions(+), 126 deletions(-) create mode 100644 comfy/ldm/hunyuan_video/upsampler.py create mode 100644 models/latent_upscale_models/put_latent_upscale_models_here diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 77e642a94..204fc048d 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -611,6 +611,66 @@ class HunyuanImage21Refiner(LatentFormat): latent_dimensions = 3 scale_factor = 1.03682 + def process_in(self, latent): + out = latent * self.scale_factor + out = torch.cat((out[:, :, :1], out), dim=2) + out = out.permute(0, 2, 1, 3, 4) + b, f_times_2, c, h, w = out.shape + out = out.reshape(b, f_times_2 // 2, 2 * c, h, w) + out = out.permute(0, 2, 1, 3, 4).contiguous() + return out + + def process_out(self, latent): + z = latent / self.scale_factor + z = z.permute(0, 2, 1, 3, 4) + b, f, c, h, w = z.shape + z = z.reshape(b, f, 2, c // 2, h, w) + z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w) + z = z.permute(0, 2, 1, 3, 4) + z = z[:, :, 1:] + return z + +class HunyuanVideo15(LatentFormat): + latent_rgb_factors = [ + [ 0.0568, -0.0521, -0.0131], + [ 0.0014, 0.0735, 0.0326], + [ 0.0186, 0.0531, -0.0138], + [-0.0031, 0.0051, 0.0288], + [ 0.0110, 0.0556, 0.0432], + [-0.0041, -0.0023, -0.0485], + [ 0.0530, 0.0413, 0.0253], + [ 0.0283, 0.0251, 0.0339], + [ 0.0277, -0.0372, -0.0093], + [ 0.0393, 0.0944, 0.1131], + [ 0.0020, 0.0251, 0.0037], + [-0.0017, 0.0012, 0.0234], + [ 0.0468, 0.0436, 0.0203], + [ 0.0354, 0.0439, -0.0233], + [ 0.0090, 0.0123, 0.0346], + [ 0.0382, 0.0029, 0.0217], + [ 0.0261, -0.0300, 0.0030], + [-0.0088, -0.0220, -0.0283], + [-0.0272, -0.0121, -0.0363], + [-0.0664, -0.0622, 0.0144], + [ 0.0414, 0.0479, 0.0529], + [ 0.0355, 0.0612, -0.0247], + [ 0.0147, 0.0264, 0.0174], + [ 0.0438, 0.0038, 0.0542], + [ 0.0431, -0.0573, -0.0033], + [-0.0162, -0.0211, -0.0406], + [-0.0487, -0.0295, -0.0393], + [ 0.0005, -0.0109, 0.0253], + [ 0.0296, 0.0591, 0.0353], + [ 0.0119, 0.0181, -0.0306], + [-0.0085, -0.0362, 0.0229], + [ 0.0005, -0.0106, 0.0242] + ] + + latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644] + latent_channels = 32 + latent_dimensions = 3 + scale_factor = 1.03682 + class Hunyuan3Dv2(LatentFormat): latent_channels = 64 latent_dimensions = 1 diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py index 5132e6c07..f75c6e0e1 100644 --- a/comfy/ldm/hunyuan_video/model.py +++ b/comfy/ldm/hunyuan_video/model.py @@ -6,7 +6,6 @@ import comfy.ldm.flux.layers import comfy.ldm.modules.diffusionmodules.mmdit from comfy.ldm.modules.attention import optimized_attention - from dataclasses import dataclass from einops import repeat @@ -42,6 +41,8 @@ class HunyuanVideoParams: guidance_embed: bool byt5: bool meanflow: bool + use_cond_type_embedding: bool + vision_in_dim: int class SelfAttentionRef(nn.Module): @@ -157,7 +158,10 @@ class TokenRefiner(nn.Module): t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype)) # m = mask.float().unsqueeze(-1) # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise - c = x.sum(dim=1) / x.shape[1] + if x.dtype == torch.float16: + c = x.float().sum(dim=1) / x.shape[1] + else: + c = x.sum(dim=1) / x.shape[1] c = t + self.c_embedder(c.to(x.dtype)) x = self.input_embedder(x) @@ -196,11 +200,15 @@ class HunyuanVideo(nn.Module): def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + params = HunyuanVideoParams(**kwargs) self.params = params self.patch_size = params.patch_size self.in_channels = params.in_channels self.out_channels = params.out_channels + self.use_cond_type_embedding = params.use_cond_type_embedding + self.vision_in_dim = params.vision_in_dim if params.hidden_size % params.num_heads != 0: raise ValueError( f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" @@ -266,6 +274,18 @@ class HunyuanVideo(nn.Module): if final_layer: self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations) + # HunyuanVideo 1.5 specific modules + if self.vision_in_dim is not None: + from comfy.ldm.wan.model import MLPProj + self.vision_in = MLPProj(in_dim=self.vision_in_dim, out_dim=self.hidden_size, operation_settings=operation_settings) + else: + self.vision_in = None + if self.use_cond_type_embedding: + # 0: text_encoder feature 1: byt5 feature 2: vision_encoder feature + self.cond_type_embedding = nn.Embedding(3, self.hidden_size) + else: + self.cond_type_embedding = None + def forward_orig( self, img: Tensor, @@ -276,6 +296,7 @@ class HunyuanVideo(nn.Module): timesteps: Tensor, y: Tensor = None, txt_byt5=None, + clip_fea=None, guidance: Tensor = None, guiding_frame_index=None, ref_latent=None, @@ -331,12 +352,31 @@ class HunyuanVideo(nn.Module): txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options) + if self.cond_type_embedding is not None: + self.cond_type_embedding.to(txt.device) + cond_emb = self.cond_type_embedding(torch.zeros_like(txt[:, :, 0], device=txt.device, dtype=torch.long)) + txt = txt + cond_emb.to(txt.dtype) + if self.byt5_in is not None and txt_byt5 is not None: txt_byt5 = self.byt5_in(txt_byt5) + if self.cond_type_embedding is not None: + cond_emb = self.cond_type_embedding(torch.ones_like(txt_byt5[:, :, 0], device=txt_byt5.device, dtype=torch.long)) + txt_byt5 = txt_byt5 + cond_emb.to(txt_byt5.dtype) + txt = torch.cat((txt_byt5, txt), dim=1) # byt5 first for HunyuanVideo1.5 + else: + txt = torch.cat((txt, txt_byt5), dim=1) txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype) - txt = torch.cat((txt, txt_byt5), dim=1) txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1) + if clip_fea is not None: + txt_vision_states = self.vision_in(clip_fea) + if self.cond_type_embedding is not None: + cond_emb = self.cond_type_embedding(2 * torch.ones_like(txt_vision_states[:, :, 0], dtype=torch.long, device=txt_vision_states.device)) + txt_vision_states = txt_vision_states + cond_emb + txt = torch.cat((txt_vision_states.to(txt.dtype), txt), dim=1) + extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype) + txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1) + ids = torch.cat((img_ids, txt_ids), dim=1) pe = self.pe_embedder(ids) @@ -430,14 +470,14 @@ class HunyuanVideo(nn.Module): img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) return repeat(img_ids, "h w c -> b (h w) c", b=bs) - def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): + def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs) + ).execute(x, timestep, context, y, txt_byt5, clip_fea, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs) - def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): + def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): bs = x.shape[0] if len(self.patch_size) == 3: img_ids = self.img_ids(x) @@ -445,5 +485,5 @@ class HunyuanVideo(nn.Module): else: img_ids = self.img_ids_2d(x) txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype) - out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options) + out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, clip_fea, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options) return out diff --git a/comfy/ldm/hunyuan_video/upsampler.py b/comfy/ldm/hunyuan_video/upsampler.py new file mode 100644 index 000000000..9f5e91a59 --- /dev/null +++ b/comfy/ldm/hunyuan_video/upsampler.py @@ -0,0 +1,120 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm, ResnetBlock, VideoConv3d +import model_management, model_patcher + +class SRResidualCausalBlock3D(nn.Module): + def __init__(self, channels: int): + super().__init__() + self.block = nn.Sequential( + VideoConv3d(channels, channels, kernel_size=3), + nn.SiLU(inplace=True), + VideoConv3d(channels, channels, kernel_size=3), + nn.SiLU(inplace=True), + VideoConv3d(channels, channels, kernel_size=3), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x + self.block(x) + +class SRModel3DV2(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + hidden_channels: int = 64, + num_blocks: int = 6, + global_residual: bool = False, + ): + super().__init__() + self.in_conv = VideoConv3d(in_channels, hidden_channels, kernel_size=3) + self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)]) + self.out_conv = VideoConv3d(hidden_channels, out_channels, kernel_size=3) + self.global_residual = bool(global_residual) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + residual = x + y = self.in_conv(x) + for blk in self.blocks: + y = blk(y) + y = self.out_conv(y) + if self.global_residual and (y.shape == residual.shape): + y = y + residual + return y + + +class Upsampler(nn.Module): + def __init__( + self, + z_channels: int, + out_channels: int, + block_out_channels: tuple[int, ...], + num_res_blocks: int = 2, + ): + super().__init__() + self.num_res_blocks = num_res_blocks + self.block_out_channels = block_out_channels + self.z_channels = z_channels + + ch = block_out_channels[0] + self.conv_in = VideoConv3d(z_channels, ch, kernel_size=3) + + self.up = nn.ModuleList() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_shortcut=False, + conv_op=VideoConv3d, norm_op=RMS_norm) + for j in range(num_res_blocks + 1)]) + ch = tgt + self.up.append(stage) + + self.norm_out = RMS_norm(ch) + self.conv_out = VideoConv3d(ch, out_channels, kernel_size=3) + + def forward(self, z): + """ + Args: + z: (B, C, T, H, W) + target_shape: (H, W) + """ + # z to block_in + repeats = self.block_out_channels[0] // (self.z_channels) + x = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1) + + # upsampling + for stage in self.up: + for blk in stage.block: + x = blk(x) + + out = self.conv_out(F.silu(self.norm_out(x))) + return out + +UPSAMPLERS = { + "720p": SRModel3DV2, + "1080p": Upsampler, +} + +class HunyuanVideo15SRModel(): + def __init__(self, model_type, config): + self.load_device = model_management.vae_device() + offload_device = model_management.vae_offload_device() + self.dtype = model_management.vae_dtype(self.load_device) + self.model_class = UPSAMPLERS.get(model_type) + self.model = self.model_class(**config).eval() + + self.patcher = model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + + def load_sd(self, sd): + return self.model.load_state_dict(sd, strict=True) + + def get_sd(self): + return self.model.state_dict() + + def resample_latent(self, latent): + model_management.load_model_gpu(self.patcher) + return self.model(latent.to(self.load_device)) diff --git a/comfy/ldm/hunyuan_video/vae_refiner.py b/comfy/ldm/hunyuan_video/vae_refiner.py index c2a0b507d..9f750dcc4 100644 --- a/comfy/ldm/hunyuan_video/vae_refiner.py +++ b/comfy/ldm/hunyuan_video/vae_refiner.py @@ -4,8 +4,40 @@ import torch.nn.functional as F from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize import comfy.ops import comfy.ldm.models.autoencoder +import comfy.model_management ops = comfy.ops.disable_weight_init +class NoPadConv3d(nn.Module): + def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs): + super().__init__() + self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs) + + def forward(self, x): + return self.conv(x) + + +def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None): + + x = xl[0] + xl.clear() + + if conv_carry_out is not None: + to_push = x[:, :, -2:, :, :].clone() + conv_carry_out.append(to_push) + + if isinstance(op, NoPadConv3d): + if conv_carry_in is None: + x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate') + else: + carry_len = conv_carry_in[0].shape[2] + x = torch.cat([conv_carry_in.pop(0), x], dim=2) + x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate') + + out = op(x) + + return out + + class RMS_norm(nn.Module): def __init__(self, dim): super().__init__() @@ -14,7 +46,7 @@ class RMS_norm(nn.Module): self.gamma = nn.Parameter(torch.empty(shape)) def forward(self, x): - return F.normalize(x, dim=1) * self.scale * self.gamma + return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) class DnSmpl(nn.Module): def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d): @@ -27,11 +59,12 @@ class DnSmpl(nn.Module): self.tds = tds self.gs = fct * ic // oc - def forward(self, x): + def forward(self, x, conv_carry_in=None, conv_carry_out=None): r1 = 2 if self.tds else 1 - h = self.conv(x) + h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out) + + if self.tds and self.refiner_vae and conv_carry_in is None: - if self.tds and self.refiner_vae: hf = h[:, :, :1, :, :] b, c, f, ht, wd = hf.shape hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2) @@ -39,14 +72,7 @@ class DnSmpl(nn.Module): hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2) hf = torch.cat([hf, hf], dim=1) - hn = h[:, :, 1:, :, :] - b, c, frms, ht, wd = hn.shape - nf = frms // r1 - hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) - hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6) - hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) - - h = torch.cat([hf, hn], dim=2) + h = h[:, :, 1:, :, :] xf = x[:, :, :1, :, :] b, ci, f, ht, wd = xf.shape @@ -54,34 +80,32 @@ class DnSmpl(nn.Module): xf = xf.permute(0, 4, 6, 1, 2, 3, 5) xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2) B, C, T, H, W = xf.shape - xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2) + xf = xf.view(B, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2) - xn = x[:, :, 1:, :, :] - b, ci, frms, ht, wd = xn.shape - nf = frms // r1 - xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) - xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6) - xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) - B, C, T, H, W = xn.shape - xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) - sc = torch.cat([xf, xn], dim=2) - else: - b, c, frms, ht, wd = h.shape + x = x[:, :, 1:, :, :] - nf = frms // r1 - h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) - h = h.permute(0, 3, 5, 7, 1, 2, 4, 6) - h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) + if h.shape[2] == 0: + return hf + xf - b, ci, frms, ht, wd = x.shape - nf = frms // r1 - sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) - sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6) - sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) - B, C, T, H, W = sc.shape - sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) + b, c, frms, ht, wd = h.shape + nf = frms // r1 + h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) + h = h.permute(0, 3, 5, 7, 1, 2, 4, 6) + h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) - return h + sc + b, ci, frms, ht, wd = x.shape + nf = frms // r1 + x = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) + x = x.permute(0, 3, 5, 7, 1, 2, 4, 6) + x = x.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) + B, C, T, H, W = x.shape + x = x.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) + + if self.tds and self.refiner_vae and conv_carry_in is None: + h = torch.cat([hf, h], dim=2) + x = torch.cat([xf, x], dim=2) + + return h + x class UpSmpl(nn.Module): @@ -94,11 +118,11 @@ class UpSmpl(nn.Module): self.tus = tus self.rp = fct * oc // ic - def forward(self, x): + def forward(self, x, conv_carry_in=None, conv_carry_out=None): r1 = 2 if self.tus else 1 - h = self.conv(x) + h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out) - if self.tus and self.refiner_vae: + if self.tus and self.refiner_vae and conv_carry_in is None: hf = h[:, :, :1, :, :] b, c, f, ht, wd = hf.shape nc = c // (2 * 2) @@ -107,14 +131,7 @@ class UpSmpl(nn.Module): hf = hf.reshape(b, nc, f, ht * 2, wd * 2) hf = hf[:, : hf.shape[1] // 2] - hn = h[:, :, 1:, :, :] - b, c, frms, ht, wd = hn.shape - nc = c // (r1 * 2 * 2) - hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd) - hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3) - hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2) - - h = torch.cat([hf, hn], dim=2) + h = h[:, :, 1:, :, :] xf = x[:, :, :1, :, :] b, ci, f, ht, wd = xf.shape @@ -125,29 +142,43 @@ class UpSmpl(nn.Module): xf = xf.permute(0, 3, 4, 5, 1, 6, 2) xf = xf.reshape(b, nc, f, ht * 2, wd * 2) - xn = x[:, :, 1:, :, :] - xn = xn.repeat_interleave(repeats=self.rp, dim=1) - b, c, frms, ht, wd = xn.shape - nc = c // (r1 * 2 * 2) - xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd) - xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3) - xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2) - sc = torch.cat([xf, xn], dim=2) - else: - b, c, frms, ht, wd = h.shape - nc = c // (r1 * 2 * 2) - h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd) - h = h.permute(0, 4, 5, 1, 6, 2, 7, 3) - h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2) + x = x[:, :, 1:, :, :] - sc = x.repeat_interleave(repeats=self.rp, dim=1) - b, c, frms, ht, wd = sc.shape - nc = c // (r1 * 2 * 2) - sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd) - sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3) - sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2) + b, c, frms, ht, wd = h.shape + nc = c // (r1 * 2 * 2) + h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd) + h = h.permute(0, 4, 5, 1, 6, 2, 7, 3) + h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2) - return h + sc + x = x.repeat_interleave(repeats=self.rp, dim=1) + b, c, frms, ht, wd = x.shape + nc = c // (r1 * 2 * 2) + x = x.reshape(b, r1, 2, 2, nc, frms, ht, wd) + x = x.permute(0, 4, 5, 1, 6, 2, 7, 3) + x = x.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + if self.tus and self.refiner_vae and conv_carry_in is None: + h = torch.cat([hf, h], dim=2) + x = torch.cat([xf, x], dim=2) + + return h + x + +class HunyuanRefinerResnetBlock(ResnetBlock): + def __init__(self, in_channels, out_channels, conv_op=NoPadConv3d, norm_op=RMS_norm): + super().__init__(in_channels=in_channels, out_channels=out_channels, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + + def forward(self, x, conv_carry_in=None, conv_carry_out=None): + h = x + h = [ self.swish(self.norm1(x)) ] + h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out) + + h = [ self.dropout(self.swish(self.norm2(h))) ] + h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x+h class Encoder(nn.Module): def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks, @@ -160,7 +191,7 @@ class Encoder(nn.Module): self.refiner_vae = refiner_vae if self.refiner_vae: - conv_op = VideoConv3d + conv_op = NoPadConv3d norm_op = RMS_norm else: conv_op = ops.Conv3d @@ -175,10 +206,9 @@ class Encoder(nn.Module): for i, tgt in enumerate(block_out_channels): stage = nn.Module() - stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, - out_channels=tgt, - temb_channels=0, - conv_op=conv_op, norm_op=norm_op) + stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + conv_op=conv_op, norm_op=norm_op) for j in range(num_res_blocks)]) ch = tgt if i < depth: @@ -188,9 +218,9 @@ class Encoder(nn.Module): self.down.append(stage) self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op) - self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.norm_out = norm_op(ch) self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1) @@ -201,31 +231,50 @@ class Encoder(nn.Module): if not self.refiner_vae and x.shape[2] == 1: x = x.expand(-1, -1, self.ffactor_temporal, -1, -1) - x = self.conv_in(x) + if self.refiner_vae: + xl = [x[:, :, :1, :, :]] + if x.shape[2] > self.ffactor_temporal: + xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // self.ffactor_temporal) * self.ffactor_temporal, :, :], self.ffactor_temporal * 2, dim=2) + x = xl + else: + x = [x] + out = [] - for stage in self.down: - for blk in stage.block: - x = blk(x) - if hasattr(stage, 'downsample'): - x = stage.downsample(x) + conv_carry_in = None - x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + for i, x1 in enumerate(x): + conv_carry_out = [] + if i == len(x) - 1: + conv_carry_out = None + x1 = [ x1 ] + x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out) + + for stage in self.down: + for blk in stage.block: + x1 = blk(x1, conv_carry_in, conv_carry_out) + if hasattr(stage, 'downsample'): + x1 = stage.downsample(x1, conv_carry_in, conv_carry_out) + + out.append(x1) + conv_carry_in = conv_carry_out + + if len(out) > 1: + out = torch.cat(out, dim=2) + else: + out = out[0] + + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out))) + del out b, c, t, h, w = x.shape grp = c // (self.z_channels << 1) skip = x.view(b, c // grp, grp, t, h, w).mean(2) - out = self.conv_out(F.silu(self.norm_out(x))) + skip + out = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip if self.refiner_vae: out = self.regul(out)[0] - out = torch.cat((out[:, :, :1], out), dim=2) - out = out.permute(0, 2, 1, 3, 4) - b, f_times_2, c, h, w = out.shape - out = out.reshape(b, f_times_2 // 2, 2 * c, h, w) - out = out.permute(0, 2, 1, 3, 4).contiguous() - return out class Decoder(nn.Module): @@ -239,7 +288,7 @@ class Decoder(nn.Module): self.refiner_vae = refiner_vae if self.refiner_vae: - conv_op = VideoConv3d + conv_op = NoPadConv3d norm_op = RMS_norm else: conv_op = ops.Conv3d @@ -249,9 +298,9 @@ class Decoder(nn.Module): self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1) self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op) - self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op) self.up = nn.ModuleList() depth = (ffactor_spatial >> 1).bit_length() @@ -259,10 +308,9 @@ class Decoder(nn.Module): for i, tgt in enumerate(block_out_channels): stage = nn.Module() - stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, - out_channels=tgt, - temb_channels=0, - conv_op=conv_op, norm_op=norm_op) + stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + conv_op=conv_op, norm_op=norm_op) for j in range(num_res_blocks + 1)]) ch = tgt if i < depth: @@ -275,27 +323,41 @@ class Decoder(nn.Module): self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1) def forward(self, z): - if self.refiner_vae: - z = z.permute(0, 2, 1, 3, 4) - b, f, c, h, w = z.shape - z = z.reshape(b, f, 2, c // 2, h, w) - z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w) - z = z.permute(0, 2, 1, 3, 4) - z = z[:, :, 1:] - - x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) + x = conv_carry_causal_3d([z], self.conv_in) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) - for stage in self.up: - for blk in stage.block: - x = blk(x) - if hasattr(stage, 'upsample'): - x = stage.upsample(x) + if self.refiner_vae: + x = torch.split(x, 2, dim=2) + else: + x = [ x ] + out = [] - out = self.conv_out(F.silu(self.norm_out(x))) + conv_carry_in = None + + for i, x1 in enumerate(x): + conv_carry_out = [] + if i == len(x) - 1: + conv_carry_out = None + for stage in self.up: + for blk in stage.block: + x1 = blk(x1, conv_carry_in, conv_carry_out) + if hasattr(stage, 'upsample'): + x1 = stage.upsample(x1, conv_carry_in, conv_carry_out) + + x1 = [ F.silu(self.norm_out(x1)) ] + x1 = conv_carry_causal_3d(x1, self.conv_out, conv_carry_in, conv_carry_out) + out.append(x1) + conv_carry_in = conv_carry_out + del x + + if len(out) > 1: + out = torch.cat(out, dim=2) + else: + out = out[0] if not self.refiner_vae: if z.shape[-3] == 1: out = out[:, :, -1:] return out + diff --git a/comfy/model_base.py b/comfy/model_base.py index 7c788d085..e14b552c5 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1536,3 +1536,94 @@ class HunyuanImage21Refiner(HunyuanImage21): out = super().extra_conds(**kwargs) out['disable_time_r'] = comfy.conds.CONDConstant(True) return out + +class HunyuanVideo15(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + extra_channels = self.diffusion_model.img_in.proj.weight.shape[1] - noise.shape[1] - 1 #noise 32 img cond 32 + mask 1 + if extra_channels == 0: + return None + + image = kwargs.get("concat_latent_image", None) + device = kwargs["device"] + + if image is None: + shape_image = list(noise.shape) + shape_image[1] = extra_channels + image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) + else: + latent_dim = self.latent_format.latent_channels + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + for i in range(0, image.shape[1], latent_dim): + image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim]) + image = utils.resize_to_batch_size(image, noise.shape[0]) + + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + mask = torch.zeros_like(noise)[:, :1] + else: + mask = 1.0 - mask + mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + if mask.shape[-3] < noise.shape[-3]: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + + return torch.cat((image, mask), dim=1) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + conditioning_byt5small = kwargs.get("conditioning_byt5small", None) + if conditioning_byt5small is not None: + out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small) + + guidance = kwargs.get("guidance", 6.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + + clip_vision_output = kwargs.get("clip_vision_output", None) + if clip_vision_output is not None: + out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.last_hidden_state) + + return out + +class HunyuanVideo15_SR_Distilled(HunyuanVideo15): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + image = kwargs.get("concat_latent_image", None) + noise_augmentation = kwargs.get("noise_augmentation", 0.0) + device = kwargs["device"] + + if image is None: + image = torch.zeros([noise.shape[0], noise.shape[1] * 2 + 2, noise.shape[-3], noise.shape[-2], noise.shape[-1]], device=comfy.model_management.intermediate_device()) + else: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + #image = self.process_latent_in(image) # scaling wasn't applied in reference code + image = utils.resize_to_batch_size(image, noise.shape[0]) + lq_image_slice = slice(noise.shape[1] + 1, 2 * noise.shape[1] + 1) + if noise_augmentation > 0: + generator = torch.Generator(device="cpu") + generator.manual_seed(kwargs.get("seed", 0) - 10) + noise = torch.randn(image[:, lq_image_slice].shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device) + image[:, lq_image_slice] = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image[:, lq_image_slice] + else: + image[:, lq_image_slice] = 0.75 * image[:, lq_image_slice] + return image + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + out['disable_time_r'] = comfy.conds.CONDConstant(False) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 3142a7fc3..0131ca25a 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -186,6 +186,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys)) dit_config["guidance_embed"] = len(guidance_keys) > 0 + + # HunyuanVideo 1.5 + if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys: + dit_config["use_cond_type_embedding"] = True + else: + dit_config["use_cond_type_embedding"] = False + if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys: + dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0] + else: + dit_config["vision_in_dim"] = None return dit_config if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight) diff --git a/comfy/sd.py b/comfy/sd.py index 9e5ebbf15..dc0905ada 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -441,20 +441,20 @@ class VAE: elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32: ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True} ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] - self.latent_channels = 64 + self.latent_channels = 32 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) self.upscale_index_formula = (4, 16, 16) self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) self.downscale_index_formula = (4, 16, 16) self.latent_dim = 3 - self.not_video = True + self.not_video = False self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"}, encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig}) - self.memory_used_encode = lambda shape, dtype: (1400 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) - self.memory_used_decode = lambda shape, dtype: (1400 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (2800 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) elif "decoder.conv_in.conv.weight" in sd: 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} ddconfig["conv3d"] = True @@ -911,6 +911,7 @@ class CLIPType(Enum): OMNIGEN2 = 17 QWEN_IMAGE = 18 HUNYUAN_IMAGE = 19 + HUNYUAN_VIDEO_15 = 20 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): @@ -1126,6 +1127,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif clip_type == CLIPType.HUNYUAN_IMAGE: clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer + elif clip_type == CLIPType.HUNYUAN_VIDEO_15: + clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer else: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 4064bdae1..2e64b85e8 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1374,6 +1374,54 @@ class HunyuanImage21Refiner(HunyuanVideo): out = model_base.HunyuanImage21Refiner(self, device=device) return out -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage] +class HunyuanVideo15(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "vision_in_dim": 1152, + } + + sampling_settings = { + "shift": 7.0, + } + memory_usage_factor = 4.0 #TODO + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + latent_format = latent_formats.HunyuanVideo15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideo15(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect)) + + +class HunyuanVideo15_SR_Distilled(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "vision_in_dim": 1152, + "in_channels": 98, + } + + sampling_settings = { + "shift": 2.0, + } + memory_usage_factor = 4.0 #TODO + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + latent_format = latent_formats.HunyuanVideo15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideo15_SR_Distilled(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect)) + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage] models += [SVD_img2vid] diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py index b02148b33..557094f49 100644 --- a/comfy/text_encoders/hunyuan_video.py +++ b/comfy/text_encoders/hunyuan_video.py @@ -1,6 +1,7 @@ from comfy import sd1_clip import comfy.model_management import comfy.text_encoders.llama +from .hunyuan_image import HunyuanImageTokenizer from transformers import LlamaTokenizerFast import torch import os @@ -73,6 +74,14 @@ class HunyuanVideoTokenizer: return {} +class HunyuanVideo15Tokenizer(HunyuanImageTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.llama_template = "<|im_start|>system\nYou are a helpful assistant. Describe the video by detailing the following aspects:\n1. The main content and theme of the video.\n2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.\n3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.\n4. background environment, light, style and atmosphere.\n5. camera angles, movements, and transitions used in the video.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + return super().tokenize_with_weights(text, return_word_ids, prevent_empty_text=True, **kwargs) + class HunyuanVideoClipModel(torch.nn.Module): def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}): super().__init__() diff --git a/comfy/text_encoders/qwen_image.py b/comfy/text_encoders/qwen_image.py index 40fa67937..c0d32a6ef 100644 --- a/comfy/text_encoders/qwen_image.py +++ b/comfy/text_encoders/qwen_image.py @@ -17,12 +17,14 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer): self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" - def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs): + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, **kwargs): skip_template = False if text.startswith('<|im_start|>'): skip_template = True if text.startswith('<|start_header_id|>'): skip_template = True + if prevent_empty_text and text == '': + text = ' ' if skip_template: llama_text = text diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py index 863254ce7..79c0722a9 100644 --- a/comfy_api/latest/_io.py +++ b/comfy_api/latest/_io.py @@ -629,6 +629,10 @@ class UpscaleModel(ComfyTypeIO): if TYPE_CHECKING: Type = ImageModelDescriptor +@comfytype(io_type="LATENT_UPSCALE_MODEL") +class LatentUpscaleModel(ComfyTypeIO): + Type = Any + @comfytype(io_type="AUDIO") class Audio(ComfyTypeIO): class AudioDict(TypedDict): diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index f7c34d059..5a2e8cc61 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -4,7 +4,8 @@ import torch import comfy.model_management from typing_extensions import override from comfy_api.latest import ComfyExtension, io - +from comfy.ldm.hunyuan_video.upsampler import HunyuanVideo15SRModel +import folder_paths class CLIPTextEncodeHunyuanDiT(io.ComfyNode): @classmethod @@ -57,6 +58,199 @@ class EmptyHunyuanLatentVideo(io.ComfyNode): generate = execute # TODO: remove +class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo): + @classmethod + def define_schema(cls): + schema = super().define_schema() + schema.node_id = "EmptyHunyuanVideo15Latent" + return schema + + @classmethod + def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput: + # Using scale factor of 16 instead of 8 + latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples": latent}) + + generate = execute # TODO: remove + + +class HunyuanVideo15ImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="HunyuanVideo15ImageToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=33, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("start_image", optional=True), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput: + latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device()) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + encoded = vae.encode(start_image[:, :, :, :3]) + concat_latent_image = torch.zeros((latent.shape[0], 32, latent.shape[2], latent.shape[3], latent.shape[4]), device=comfy.model_management.intermediate_device()) + concat_latent_image[:, :, :encoded.shape[2], :, :] = encoded + + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + + +class HunyuanVideo15SuperResolution(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="HunyuanVideo15SuperResolution", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae", optional=True), + io.Image.Input("start_image", optional=True), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + io.Latent.Input("latent"), + io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01), + + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, latent, noise_augmentation, vae=None, start_image=None, clip_vision_output=None) -> io.NodeOutput: + in_latent = latent["samples"] + in_channels = in_latent.shape[1] + cond_latent = torch.zeros([in_latent.shape[0], in_channels * 2 + 2, in_latent.shape[-3], in_latent.shape[-2], in_latent.shape[-1]], device=comfy.model_management.intermediate_device()) + cond_latent[:, in_channels + 1 : 2 * in_channels + 1] = in_latent + cond_latent[:, 2 * in_channels + 1] = 1 + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image.movedim(-1, 1), in_latent.shape[-1] * 16, in_latent.shape[-2] * 16, "bilinear", "center").movedim(1, -1) + encoded = vae.encode(start_image[:, :, :, :3]) + cond_latent[:, :in_channels, :encoded.shape[2], :, :] = encoded + cond_latent[:, in_channels + 1, 0] = 1 + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation}) + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + return io.NodeOutput(positive, negative, latent) + + +class LatentUpscaleModelLoader(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LatentUpscaleModelLoader", + display_name="Load Latent Upscale Model", + category="loaders", + inputs=[ + io.Combo.Input("model_name", options=folder_paths.get_filename_list("latent_upscale_models")), + ], + outputs=[ + io.LatentUpscaleModel.Output(), + ], + ) + + @classmethod + def execute(cls, model_name) -> io.NodeOutput: + model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name) + sd = comfy.utils.load_torch_file(model_path, safe_load=True) + + if "blocks.0.block.0.conv.weight" in sd: + config = { + "in_channels": sd["in_conv.conv.weight"].shape[1], + "out_channels": sd["out_conv.conv.weight"].shape[0], + "hidden_channels": sd["in_conv.conv.weight"].shape[0], + "num_blocks": len([k for k in sd.keys() if k.startswith("blocks.") and k.endswith(".block.0.conv.weight")]), + "global_residual": False, + } + model_type = "720p" + elif "up.0.block.0.conv1.conv.weight" in sd: + sd = {key.replace("nin_shortcut", "nin_shortcut.conv", 1): value for key, value in sd.items()} + config = { + "z_channels": sd["conv_in.conv.weight"].shape[1], + "out_channels": sd["conv_out.conv.weight"].shape[0], + "block_out_channels": tuple(sd[f"up.{i}.block.0.conv1.conv.weight"].shape[0] for i in range(len([k for k in sd.keys() if k.startswith("up.") and k.endswith(".block.0.conv1.conv.weight")]))), + } + model_type = "1080p" + + model = HunyuanVideo15SRModel(model_type, config) + model.load_sd(sd) + + return io.NodeOutput(model) + + +class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="HunyuanVideo15LatentUpscaleWithModel", + display_name="Hunyuan Video 15 Latent Upscale With Model", + category="latent", + inputs=[ + io.LatentUpscaleModel.Input("model"), + io.Latent.Input("samples"), + io.Combo.Input("upscale_method", options=["nearest-exact", "bilinear", "area", "bicubic", "bislerp"], default="bilinear"), + io.Int.Input("width", default=1280, min=0, max=16384, step=8), + io.Int.Input("height", default=720, min=0, max=16384, step=8), + io.Combo.Input("crop", options=["disabled", "center"]), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, model, samples, upscale_method, width, height, crop) -> io.NodeOutput: + if width == 0 and height == 0: + return io.NodeOutput(samples) + else: + if width == 0: + height = max(64, height) + width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2])) + elif height == 0: + width = max(64, width) + height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1])) + else: + width = max(64, width) + height = max(64, height) + s = comfy.utils.common_upscale(samples["samples"], width // 16, height // 16, upscale_method, crop) + s = model.resample_latent(s) + return io.NodeOutput({"samples": s.cpu().float()}) + + PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " "1. The main content and theme of the video." @@ -210,6 +404,11 @@ class HunyuanExtension(ComfyExtension): CLIPTextEncodeHunyuanDiT, TextEncodeHunyuanVideo_ImageToVideo, EmptyHunyuanLatentVideo, + EmptyHunyuanVideo15Latent, + HunyuanVideo15ImageToVideo, + HunyuanVideo15SuperResolution, + HunyuanVideo15LatentUpscaleWithModel, + LatentUpscaleModelLoader, HunyuanImageToVideo, EmptyHunyuanImageLatent, HunyuanRefinerLatent, diff --git a/folder_paths.py b/folder_paths.py index f110d832b..ffdc4d020 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -38,6 +38,8 @@ folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], suppor folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) +folder_names_and_paths["latent_upscale_models"] = ([os.path.join(models_dir, "latent_upscale_models")], supported_pt_extensions) + folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set()) folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) diff --git a/models/latent_upscale_models/put_latent_upscale_models_here b/models/latent_upscale_models/put_latent_upscale_models_here new file mode 100644 index 000000000..e69de29bb diff --git a/nodes.py b/nodes.py index 030371633..f023ae3b6 100644 --- a/nodes.py +++ b/nodes.py @@ -957,7 +957,7 @@ class DualCLIPLoader: def INPUT_TYPES(s): return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image"], ), + "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image", "hunyuan_video_15"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), From 33981237527a3d84d4e9c3b113f75d6dd37af6a4 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 20 Nov 2025 20:39:37 -0800 Subject: [PATCH 44/47] Fix wrong path. (#10821) --- comfy_extras/nodes_hunyuan.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index 5a2e8cc61..aa36a471f 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -185,7 +185,7 @@ class LatentUpscaleModelLoader(io.ComfyNode): @classmethod def execute(cls, model_name) -> io.NodeOutput: - model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name) + model_path = folder_paths.get_full_path_or_raise("latent_upscale_models", model_name) sd = comfy.utils.load_torch_file(model_path, safe_load=True) if "blocks.0.block.0.conv.weight" in sd: From c55fd7481626d8bee8044ea7512ea996d13a1b90 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 21 Nov 2025 00:49:13 -0500 Subject: [PATCH 45/47] ComfyUI 0.3.71 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 9b77aabe9..b4655d553 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.70" +__version__ = "0.3.71" diff --git a/pyproject.toml b/pyproject.toml index 289b7145b..280dbaf53 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.70" +version = "0.3.71" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From ecb683b057a19f1a05d18d6d0b0ee9a6c6c8f4a0 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Fri, 21 Nov 2025 13:34:47 -0800 Subject: [PATCH 46/47] update frontend to 1.30 (#10793) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 624aa7362..f83d561c9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.28.9 +comfyui-frontend-package==1.30.6 comfyui-workflow-templates==0.6.0 comfyui-embedded-docs==0.3.1 torch From 532938b16b544e4492ba0ffbe18b201b1a7bc55f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 21 Nov 2025 14:51:55 -0800 Subject: [PATCH 47/47] --disable-api-nodes now sets CSP header to force frontend offline. (#10829) --- comfy/cli_args.py | 2 +- server.py | 19 +++++++++++++++++++ 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 2f30b72d2..d2b60e347 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -160,7 +160,7 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.") -parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.") +parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes. Also prevents the frontend from communicating with the internet.") parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") diff --git a/server.py b/server.py index d9d5c491f..0fd2e49e3 100644 --- a/server.py +++ b/server.py @@ -164,6 +164,22 @@ def create_origin_only_middleware(): return origin_only_middleware + +def create_block_external_middleware(): + @web.middleware + async def block_external_middleware(request: web.Request, handler): + if request.method == "OPTIONS": + # Pre-flight request. Reply successfully: + response = web.Response() + else: + response = await handler(request) + + response.headers['Content-Security-Policy'] = "default-src 'self'; script-src 'self' 'unsafe-inline' blob:; style-src 'self' 'unsafe-inline'; img-src 'self' data: blob:; font-src 'self'; connect-src 'self'; frame-src 'self'; object-src 'self';" + return response + + return block_external_middleware + + class PromptServer(): def __init__(self, loop): PromptServer.instance = self @@ -193,6 +209,9 @@ class PromptServer(): else: middlewares.append(create_origin_only_middleware()) + if args.disable_api_nodes: + middlewares.append(create_block_external_middleware()) + max_upload_size = round(args.max_upload_size * 1024 * 1024) self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) self.sockets = dict()