From 4c4be1bba5ae714c6f455a49757bd7fc2e32c577 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Sat, 14 Mar 2026 07:53:00 -0700 Subject: [PATCH 01/65] comfy-aimdo 0.2.12 (#12941) comfy-aimdo 0.2.12 fixes support for non-ASCII filepaths in the new mmap helper. --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 52bc0fd12..c32a765a0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,7 @@ SQLAlchemy filelock av>=14.2.0 comfy-kitchen>=0.2.8 -comfy-aimdo>=0.2.11 +comfy-aimdo>=0.2.12 requests simpleeval>=1.0.0 blake3 From e0982a7174a9cacb0c3cd3fb6bd1f8e06d9aaf51 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sat, 14 Mar 2026 15:25:09 -0700 Subject: [PATCH 02/65] fix: use no-store cache headers to prevent stale frontend chunks (#12911) After a frontend update (e.g. nightly build), browsers could load outdated cached index.html and JS/CSS chunks, causing dynamically imported modules to fail with MIME type errors and vite:preloadError. Hard refresh (Ctrl+Shift+R) was insufficient to fix the issue because Cache-Control: no-cache still allows the browser to cache and revalidate via ETags. aiohttp's FileResponse auto-generates ETags based on file mtime+size, which may not change after pip reinstall, so the browser gets 304 Not Modified and serves stale content. Clearing ALL site data in DevTools did fix it, confirming the HTTP cache was the root cause. The fix changes: - index.html: no-cache -> no-store, must-revalidate - JS/CSS/JSON entry points: no-cache -> no-store no-store instructs browsers to never cache these responses, ensuring every page load fetches the current index.html with correct chunk references. This is a small tradeoff (~5KB re-download per page load) for guaranteed correctness after updates. --- middleware/cache_middleware.py | 2 +- server.py | 2 +- tests-unit/server_test/test_cache_control.py | 16 ++++++++-------- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/middleware/cache_middleware.py b/middleware/cache_middleware.py index f02135369..7a18821b0 100644 --- a/middleware/cache_middleware.py +++ b/middleware/cache_middleware.py @@ -32,7 +32,7 @@ async def cache_control( ) if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point: - response.headers.setdefault("Cache-Control", "no-cache") + response.headers.setdefault("Cache-Control", "no-store") return response # Early return for non-image files - no cache headers needed diff --git a/server.py b/server.py index 76904ebc9..85a8964be 100644 --- a/server.py +++ b/server.py @@ -310,7 +310,7 @@ class PromptServer(): @routes.get("/") async def get_root(request): response = web.FileResponse(os.path.join(self.web_root, "index.html")) - response.headers['Cache-Control'] = 'no-cache' + response.headers['Cache-Control'] = 'no-store, must-revalidate' response.headers["Pragma"] = "no-cache" response.headers["Expires"] = "0" return response diff --git a/tests-unit/server_test/test_cache_control.py b/tests-unit/server_test/test_cache_control.py index fa68d9408..1d0366387 100644 --- a/tests-unit/server_test/test_cache_control.py +++ b/tests-unit/server_test/test_cache_control.py @@ -28,31 +28,31 @@ CACHE_SCENARIOS = [ }, # JavaScript/CSS scenarios { - "name": "js_no_cache", + "name": "js_no_store", "path": "/script.js", "status": 200, - "expected_cache": "no-cache", + "expected_cache": "no-store", "should_have_header": True, }, { - "name": "css_no_cache", + "name": "css_no_store", "path": "/styles.css", "status": 200, - "expected_cache": "no-cache", + "expected_cache": "no-store", "should_have_header": True, }, { - "name": "index_json_no_cache", + "name": "index_json_no_store", "path": "/api/index.json", "status": 200, - "expected_cache": "no-cache", + "expected_cache": "no-store", "should_have_header": True, }, { - "name": "localized_index_json_no_cache", + "name": "localized_index_json_no_store", "path": "/templates/index.zh.json", "status": 200, - "expected_cache": "no-cache", + "expected_cache": "no-store", "should_have_header": True, }, # Non-matching files From 1c5db7397d59eace38acef078b618c2f04e4e7fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sun, 15 Mar 2026 00:36:29 +0200 Subject: [PATCH 03/65] feat: Support mxfp8 (#12907) --- comfy/float.py | 36 ++++++++++++++++++++++++++++++ comfy/model_management.py | 13 +++++++++++ comfy/ops.py | 19 ++++++++++++++++ comfy/quant_ops.py | 47 +++++++++++++++++++++++++++++++++++++++ 4 files changed, 115 insertions(+) diff --git a/comfy/float.py b/comfy/float.py index 88c47cd80..184b3d6d0 100644 --- a/comfy/float.py +++ b/comfy/float.py @@ -209,3 +209,39 @@ def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed= output_block[i:i + slice_size].copy_(block) return output_fp4, to_blocked(output_block, flatten=False) + + +def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0): + def roundup(x_val, multiple): + return ((x_val + multiple - 1) // multiple) * multiple + + if pad_32x: + rows, cols = x.shape + padded_rows = roundup(rows, 32) + padded_cols = roundup(cols, 32) + if padded_rows != rows or padded_cols != cols: + x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows)) + + F8_E4M3_MAX = 448.0 + E8M0_BIAS = 127 + BLOCK_SIZE = 32 + + rows, cols = x.shape + x_blocked = x.reshape(rows, -1, BLOCK_SIZE) + max_abs = torch.amax(torch.abs(x_blocked), dim=-1) + + # E8M0 block scales (power-of-2 exponents) + scale_needed = torch.clamp(max_abs.float() / F8_E4M3_MAX, min=2**(-127)) + exp_biased = torch.clamp(torch.ceil(torch.log2(scale_needed)).to(torch.int32) + E8M0_BIAS, 0, 254) + block_scales_e8m0 = exp_biased.to(torch.uint8) + + zero_mask = (max_abs == 0) + block_scales_f32 = (block_scales_e8m0.to(torch.int32) << 23).view(torch.float32) + block_scales_f32 = torch.where(zero_mask, torch.ones_like(block_scales_f32), block_scales_f32) + + # Scale per-block then stochastic round + data_scaled = (x_blocked.float() / block_scales_f32.unsqueeze(-1)).reshape(rows, cols) + output_fp8 = stochastic_rounding(data_scaled, torch.float8_e4m3fn, seed=seed) + + block_scales_e8m0 = torch.where(zero_mask, torch.zeros_like(block_scales_e8m0), block_scales_e8m0) + return output_fp8, to_blocked(block_scales_e8m0, flatten=False).view(torch.float8_e8m0fnu) diff --git a/comfy/model_management.py b/comfy/model_management.py index 4d5851bc0..bb77cff47 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1712,6 +1712,19 @@ def supports_nvfp4_compute(device=None): return True +def supports_mxfp8_compute(device=None): + if not is_nvidia(): + return False + + if torch_version_numeric < (2, 10): + return False + + props = torch.cuda.get_device_properties(device) + if props.major < 10: + return False + + return True + def extended_fp16_support(): # TODO: check why some models work with fp16 on newer torch versions but not on older if torch_version_numeric < (2, 7): diff --git a/comfy/ops.py b/comfy/ops.py index 3f2da4e63..59c0df87d 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -857,6 +857,22 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec orig_shape=(self.out_features, self.in_features), ) + elif self.quant_format == "mxfp8": + # MXFP8: E8M0 block scales stored as uint8 in safetensors + block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys, + dtype=torch.uint8) + + if block_scale is None: + raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}") + + block_scale = block_scale.view(torch.float8_e8m0fnu) + + params = layout_cls.Params( + scale=block_scale, + orig_dtype=MixedPrecisionOps._compute_dtype, + orig_shape=(self.out_features, self.in_features), + ) + elif self.quant_format == "nvfp4": # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale) tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys) @@ -1006,12 +1022,15 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None): fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device) + mxfp8_compute = comfy.model_management.supports_mxfp8_compute(load_device) if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config: logging.info("Using mixed precision operations") disabled = set() if not nvfp4_compute: disabled.add("nvfp4") + if not mxfp8_compute: + disabled.add("mxfp8") if not fp8_compute: disabled.add("float8_e4m3fn") disabled.add("float8_e5m2") diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py index 15a4f457b..42ee08fb2 100644 --- a/comfy/quant_ops.py +++ b/comfy/quant_ops.py @@ -43,6 +43,18 @@ except ImportError as e: def get_layout_class(name): return None +_CK_MXFP8_AVAILABLE = False +if _CK_AVAILABLE: + try: + from comfy_kitchen.tensor import TensorCoreMXFP8Layout as _CKMxfp8Layout + _CK_MXFP8_AVAILABLE = True + except ImportError: + logging.warning("comfy_kitchen does not support MXFP8, please update comfy_kitchen.") + +if not _CK_MXFP8_AVAILABLE: + class _CKMxfp8Layout: + pass + import comfy.float # ============================================================================== @@ -84,6 +96,31 @@ class _TensorCoreFP8LayoutBase(_CKFp8Layout): return qdata, params +class TensorCoreMXFP8Layout(_CKMxfp8Layout): + @classmethod + def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False): + if tensor.dim() != 2: + raise ValueError(f"MXFP8 requires 2D tensor, got {tensor.dim()}D") + + orig_dtype = tensor.dtype + orig_shape = tuple(tensor.shape) + + padded_shape = cls.get_padded_shape(orig_shape) + needs_padding = padded_shape != orig_shape + + if stochastic_rounding > 0: + qdata, block_scale = comfy.float.stochastic_round_quantize_mxfp8_by_block(tensor, pad_32x=needs_padding, seed=stochastic_rounding) + else: + qdata, block_scale = ck.quantize_mxfp8(tensor, pad_32x=needs_padding) + + params = cls.Params( + scale=block_scale, + orig_dtype=orig_dtype, + orig_shape=orig_shape, + ) + return qdata, params + + class TensorCoreNVFP4Layout(_CKNvfp4Layout): @classmethod def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False): @@ -137,6 +174,8 @@ register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout) register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout) register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout) register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout) +if _CK_MXFP8_AVAILABLE: + register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout) QUANT_ALGOS = { "float8_e4m3fn": { @@ -157,6 +196,14 @@ QUANT_ALGOS = { }, } +if _CK_MXFP8_AVAILABLE: + QUANT_ALGOS["mxfp8"] = { + "storage_t": torch.float8_e4m3fn, + "parameters": {"weight_scale", "input_scale"}, + "comfy_tensor_layout": "TensorCoreMXFP8Layout", + "group_size": 32, + } + # ============================================================================== # Re-exports for backward compatibility From c711b8f437923d9e732fa1d22ed101f81575683c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 14 Mar 2026 16:18:19 -0700 Subject: [PATCH 04/65] Add --fp16-intermediates to use fp16 for intermediate values between nodes (#12953) This is an experimental WIP option that might not work in your workflow but should lower memory usage if it does. Currently only the VAE and the load image node will output in fp16 when this option is turned on. --- comfy/cli_args.py | 2 ++ comfy/model_management.py | 6 ++++++ comfy/sd.py | 27 +++++++++++++++------------ nodes.py | 6 ++++-- 4 files changed, 27 insertions(+), 14 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index e9832acaf..0a0bf2f30 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -83,6 +83,8 @@ fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.") +parser.add_argument("--fp16-intermediates", action="store_true", help="Experimental: Use fp16 for intermediate tensors between nodes instead of fp32.") + parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") diff --git a/comfy/model_management.py b/comfy/model_management.py index bb77cff47..442d5a40a 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1050,6 +1050,12 @@ def intermediate_device(): else: return torch.device("cpu") +def intermediate_dtype(): + if args.fp16_intermediates: + return torch.float16 + else: + return torch.float32 + def vae_device(): if args.cpu_vae: return torch.device("cpu") diff --git a/comfy/sd.py b/comfy/sd.py index adcd67767..4d427bb9a 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -871,13 +871,16 @@ class VAE: pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value) return pixels + def vae_output_dtype(self): + return model_management.intermediate_dtype() + def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) output = self.process_output( (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + @@ -887,16 +890,16 @@ class VAE: def decode_tiled_1d(self, samples, tile_x=256, overlap=32): if samples.ndim == 3: - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) else: og_shape = samples.shape samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1)) - decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float() + decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device)) def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): @@ -905,7 +908,7 @@ class VAE: steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) @@ -914,7 +917,7 @@ class VAE: def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048): if self.latent_dim == 1: - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) out_channels = self.latent_channels upscale_amount = 1 / self.downscale_ratio else: @@ -923,7 +926,7 @@ class VAE: tile_x = tile_x // extra_channel_size overlap = overlap // extra_channel_size upscale_amount = 1 / self.downscale_ratio - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float() + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).to(dtype=self.vae_output_dtype()) out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device) if self.latent_dim == 1: @@ -932,7 +935,7 @@ class VAE: return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1) def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)): - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype()) return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device) def decode(self, samples_in, vae_options={}): @@ -950,9 +953,9 @@ class VAE: for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float()) + out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).to(dtype=self.vae_output_dtype())) if pixel_samples is None: - pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) + pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) pixel_samples[x:x+batch_number] = out except Exception as e: model_management.raise_non_oom(e) @@ -1025,9 +1028,9 @@ class VAE: samples = None for x in range(0, pixel_samples.shape[0], batch_number): pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device) - out = self.first_stage_model.encode(pixels_in).to(self.output_device).float() + out = self.first_stage_model.encode(pixels_in).to(self.output_device).to(dtype=self.vae_output_dtype()) if samples is None: - samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device) + samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) samples[x:x + batch_number] = out except Exception as e: diff --git a/nodes.py b/nodes.py index eb63f9d44..1e19a8223 100644 --- a/nodes.py +++ b/nodes.py @@ -1724,6 +1724,8 @@ class LoadImage: output_masks = [] w, h = None, None + dtype = comfy.model_management.intermediate_dtype() + for i in ImageSequence.Iterator(img): i = node_helpers.pillow(ImageOps.exif_transpose, i) @@ -1748,8 +1750,8 @@ class LoadImage: mask = 1. - torch.from_numpy(mask) else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") - output_images.append(image) - output_masks.append(mask.unsqueeze(0)) + output_images.append(image.to(dtype=dtype)) + output_masks.append(mask.unsqueeze(0).to(dtype=dtype)) if img.format == "MPO": break # ignore all frames except the first one for MPO format From 4941cd046eb1cd3021708ab7fe4e81e90a7b5dbe Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 14 Mar 2026 16:53:31 -0700 Subject: [PATCH 05/65] Update comfyui-frontend-package to version 1.41.20 (#12954) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c32a765a0..7e59ef206 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.41.19 +comfyui-frontend-package==1.41.20 comfyui-workflow-templates==0.9.21 comfyui-embedded-docs==0.4.3 torch From 0904cc3fe5a551e3716851f12a568e481badd301 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sun, 15 Mar 2026 03:09:09 +0200 Subject: [PATCH 06/65] LTXV: Accumulate VAE decode results on intermediate_device (#12955) --- comfy/ldm/lightricks/vae/causal_video_autoencoder.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index 5b57dfc5e..9f14f64a5 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -11,6 +11,7 @@ from .causal_conv3d import CausalConv3d from .pixel_norm import PixelNorm from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings import comfy.ops +import comfy.model_management from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed ops = comfy.ops.disable_weight_init @@ -536,7 +537,7 @@ class Decoder(nn.Module): mark_conv3d_ended(self.conv_out) sample = self.conv_out(sample, causal=self.causal) if sample is not None and sample.shape[2] > 0: - output.append(sample) + output.append(sample.to(comfy.model_management.intermediate_device())) return up_block = self.up_blocks[idx] From 192cb8eeb9f644cda8e52ae24171491228ac8bb1 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Mon, 16 Mar 2026 03:48:56 +0900 Subject: [PATCH 07/65] bump manager version to 4.1b5 (#12957) --- manager_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manager_requirements.txt b/manager_requirements.txt index 37a33bd4f..1c5e8f071 100644 --- a/manager_requirements.txt +++ b/manager_requirements.txt @@ -1 +1 @@ -comfyui_manager==4.1b4 \ No newline at end of file +comfyui_manager==4.1b5 \ No newline at end of file From e84a200a3c68044c2b5d6621ea80d27d1585703f Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Sun, 15 Mar 2026 11:49:49 -0700 Subject: [PATCH 08/65] ops: opt out of deferred weight init if subclassed (#12967) If a subclass BYO _load_from_state_dict and doesnt call the super() the needed default init of these weights is missed and can lead to problems for uninitialized weights. --- comfy/ops.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/comfy/ops.py b/comfy/ops.py index 59c0df87d..f47d4137a 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -336,7 +336,10 @@ class disable_weight_init: class Linear(torch.nn.Linear, CastWeightBiasOp): def __init__(self, in_features, out_features, bias=True, device=None, dtype=None): - if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled: + # don't trust subclasses that BYO state dict loader to call us. + if (not comfy.model_management.WINDOWS + or not comfy.memory_management.aimdo_enabled + or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict): super().__init__(in_features, out_features, bias, device, dtype) return @@ -357,7 +360,9 @@ class disable_weight_init: def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): - if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled: + if (not comfy.model_management.WINDOWS + or not comfy.memory_management.aimdo_enabled + or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict): return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) disable_weight_init._lazy_load_from_state_dict( @@ -564,7 +569,10 @@ class disable_weight_init: def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, _freeze=False, device=None, dtype=None): - if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled: + # don't trust subclasses that BYO state dict loader to call us. + if (not comfy.model_management.WINDOWS + or not comfy.memory_management.aimdo_enabled + or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict): super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight, _freeze, device, dtype) @@ -590,7 +598,9 @@ class disable_weight_init: def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): - if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled: + if (not comfy.model_management.WINDOWS + or not comfy.memory_management.aimdo_enabled + or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict): return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) disable_weight_init._lazy_load_from_state_dict( From d062becb336da8430052381111e952d6ab51d39c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 15 Mar 2026 12:37:27 -0700 Subject: [PATCH 09/65] Make EmptyLatentImage follow intermediate dtype. (#12974) --- nodes.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/nodes.py b/nodes.py index 1e19a8223..dd9298b18 100644 --- a/nodes.py +++ b/nodes.py @@ -1211,9 +1211,6 @@ class GLIGENTextBoxApply: return (c, ) class EmptyLatentImage: - def __init__(self): - self.device = comfy.model_management.intermediate_device() - @classmethod def INPUT_TYPES(s): return { @@ -1232,7 +1229,7 @@ class EmptyLatentImage: SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"] def generate(self, width, height, batch_size=1): - latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) + latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return ({"samples": latent, "downscale_ratio_spacial": 8}, ) From 3814bf4454ef3302fd7f91750d7a194dcf979630 Mon Sep 17 00:00:00 2001 From: lostdisc <194321775+lostdisc@users.noreply.github.com> Date: Sun, 15 Mar 2026 15:45:30 -0400 Subject: [PATCH 10/65] Enable Pytorch Attention for gfx1150 (#12973) --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 442d5a40a..a4af5ddb2 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -400,7 +400,7 @@ try: if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: if aotriton_supported(arch): # AMD efficient attention implementation depends on aotriton. if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much - if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches, TODO: gfx950 + if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]): # TODO: more arches, TODO: gfx950 ENABLE_PYTORCH_ATTENTION = True if rocm_version >= (7, 0): if any((a in arch) for a in ["gfx1200", "gfx1201"]): From 593be209a45a8a306c26de550e240a363de405a7 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 15 Mar 2026 16:18:04 -0700 Subject: [PATCH 11/65] feat: add essentials_category to nodes and blueprints for Essentials tab (#12573) * feat: add essentials_category to nodes and blueprints for Essentials tab Add ESSENTIALS_CATEGORY or essentials_category to 12 node classes and all 36 blueprint JSONs. Update SubgraphEntry TypedDict and subgraph_manager to extract and pass through the field. Fixes COM-15221 Amp-Thread-ID: https://ampcode.com/threads/T-019c83de-f7ab-7779-a451-0ba5940b56a9 * fix: import NotRequired from typing_extensions for Python 3.10 compat * refactor: keep only node class ESSENTIALS_CATEGORY, remove blueprint/subgraph changes Frontend will own blueprint categorization separately. * fix: remove essentials_category from CreateVideo (not in spec) --------- Co-authored-by: guill --- comfy_api_nodes/nodes_kling.py | 1 + comfy_api_nodes/nodes_recraft.py | 1 + comfy_extras/nodes_audio.py | 2 ++ comfy_extras/nodes_image_compare.py | 1 + comfy_extras/nodes_images.py | 1 + comfy_extras/nodes_post_processing.py | 1 + nodes.py | 3 +++ 7 files changed, 10 insertions(+) diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 8963c335d..9a37ccc53 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -1459,6 +1459,7 @@ class OmniProEditVideoNode(IO.ComfyNode): node_id="KlingOmniProEditVideoNode", display_name="Kling 3.0 Omni Edit Video", category="api node/video/Kling", + essentials_category="Video Generation", description="Edit an existing video with the latest model from Kling.", inputs=[ IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]), diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py index 4d1d508fa..c60cfbc4a 100644 --- a/comfy_api_nodes/nodes_recraft.py +++ b/comfy_api_nodes/nodes_recraft.py @@ -833,6 +833,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode): node_id="RecraftVectorizeImageNode", display_name="Recraft Vectorize Image", category="api node/image/Recraft", + essentials_category="Image Tools", description="Generates SVG synchronously from an input image.", inputs=[ IO.Image.Input("image"), diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 5d8d9bf6f..a395392d8 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -19,6 +19,7 @@ class EmptyLatentAudio(IO.ComfyNode): node_id="EmptyLatentAudio", display_name="Empty Latent Audio", category="latent/audio", + essentials_category="Audio", inputs=[ IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1), IO.Int.Input( @@ -185,6 +186,7 @@ class SaveAudioMP3(IO.ComfyNode): search_aliases=["export mp3"], display_name="Save Audio (MP3)", category="audio", + essentials_category="Audio", inputs=[ IO.Audio.Input("audio"), IO.String.Input("filename_prefix", default="audio/ComfyUI"), diff --git a/comfy_extras/nodes_image_compare.py b/comfy_extras/nodes_image_compare.py index 8e9f809e6..3d943be67 100644 --- a/comfy_extras/nodes_image_compare.py +++ b/comfy_extras/nodes_image_compare.py @@ -14,6 +14,7 @@ class ImageCompare(IO.ComfyNode): display_name="Image Compare", description="Compares two images side by side with a slider.", category="image", + essentials_category="Image Tools", is_experimental=True, is_output_node=True, inputs=[ diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index 4c57bb5cb..a8223cf8b 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -58,6 +58,7 @@ class ImageCropV2(IO.ComfyNode): search_aliases=["trim"], display_name="Image Crop", category="image/transform", + essentials_category="Image Tools", inputs=[ IO.Image.Input("image"), IO.BoundingBox.Input("crop_region", component="ImageCrop"), diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 4a0f7141a..06626f9dd 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -21,6 +21,7 @@ class Blend(io.ComfyNode): node_id="ImageBlend", display_name="Image Blend", category="image/postprocessing", + essentials_category="Image Tools", inputs=[ io.Image.Input("image1"), io.Image.Input("image2"), diff --git a/nodes.py b/nodes.py index dd9298b18..03dcc9d4a 100644 --- a/nodes.py +++ b/nodes.py @@ -81,6 +81,7 @@ class CLIPTextEncode(ComfyNodeABC): class ConditioningCombine: + ESSENTIALS_CATEGORY = "Image Generation" @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} @@ -1778,6 +1779,7 @@ class LoadImage: return True class LoadImageMask: + ESSENTIALS_CATEGORY = "Image Tools" SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"] _color_channels = ["alpha", "red", "green", "blue"] @@ -1886,6 +1888,7 @@ class ImageScale: return (s,) class ImageScaleBy: + ESSENTIALS_CATEGORY = "Image Tools" upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] @classmethod From 2bd4d82b4f19c30dc979a3a16ddae97068e1bdc8 Mon Sep 17 00:00:00 2001 From: Luke Mino-Altherr Date: Mon, 16 Mar 2026 15:34:04 -0400 Subject: [PATCH 12/65] feat(assets): align local API with cloud spec (#12863) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * feat(assets): align local API with cloud spec Unify response models, add missing fields, and align input schemas with the cloud OpenAPI spec at cloud.comfy.org/openapi. - Replace AssetSummary/AssetDetail/AssetUpdated with single Asset model - Add is_immutable, metadata (system_metadata), prompt_id fields - Support mime_type and preview_id in update endpoint - Make CreateFromHashBody.name optional, add mime_type, require >=1 tag - Add id/mime_type/preview_id to upload, relax tags to optional - Rename total_tags → tags in tag add/remove responses - Add GET /api/assets/tags/refine histogram endpoint - Add DB migration for system_metadata and prompt_id columns Co-Authored-By: Claude Opus 4.6 * Fix review issues: tags validation, size nullability, type annotation, hash mismatch check, and add tag histogram tests - Remove contradictory min_length=1 from CreateFromHashBody.tags default - Restore size field to int|None=None for proper null semantics - Add Union type annotation to _build_asset_response result param - Add hash mismatch validation on idempotent upload path (409 HASH_MISMATCH) - Add unit tests for list_tag_histogram service function Amp-Thread-ID: https://ampcode.com/threads/T-019cd993-f43c-704e-b3d7-6cfc3d4d4a80 Co-authored-by: Amp * Add preview_url to /assets API response using /api/view endpoint For input and output assets, generate a preview_url pointing to the existing /api/view endpoint using the asset's filename and tag-derived type (input/output). Handles subdirectories via subfolder param and URL-encodes filenames with spaces, unicode, and special characters. This aligns the OSS backend response with the frontend AssetCard expectation for thumbnail rendering. Amp-Thread-ID: https://ampcode.com/threads/T-019cda3f-5c2c-751a-a906-ac6c9153ac5c Co-authored-by: Amp * chore: remove unused imports from asset_reference queries Amp-Thread-ID: https://ampcode.com/threads/T-019cda7d-cb21-77b4-a51b-b965af60208c Co-authored-by: Amp * feat: resolve blake3 hashes in /view endpoint via asset database Amp-Thread-ID: https://ampcode.com/threads/T-019cda7d-cb21-77b4-a51b-b965af60208c Co-authored-by: Amp * Register uploaded images in asset database when --enable-assets is set Add register_file_in_place() service function to ingest module for registering already-saved files without moving them. Call it from the /upload/image endpoint to return asset metadata in the response. Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2 Co-authored-by: Amp * Exclude None fields from asset API JSON responses Add exclude_none=True to model_dump() calls across asset routes to keep response payloads clean by omitting unset optional fields. Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2 Co-authored-by: Amp * Add comment explaining why /view resolves blake3 hashes Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2 Co-authored-by: Amp * Move blake3 hash resolution to asset_management service Extract resolve_hash_to_path() into asset_management.py and remove _resolve_blake3_to_path from server.py. Also revert loopback origin check to original logic. Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2 Co-authored-by: Amp * Require at least one tag in UploadAssetSpec Enforce non-empty tags at the Pydantic validation layer so uploads with no tags are rejected with a 400 before reaching ingest. Adds test_upload_empty_tags_rejected to cover this case. Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9 Co-authored-by: Amp * Add owner_id check to resolve_hash_to_path Filter asset references by owner visibility so the /view endpoint only resolves hashes for assets the requesting user can access. Adds table-driven tests for owner visibility cases. Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9 Co-authored-by: Amp * Make ReferenceData.created_at and updated_at required Remove None defaults and type: ignore comments. Move fields before optional fields to satisfy dataclass ordering. Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9 Co-authored-by: Amp * Fix double commit in create_from_hash Move mime_type update into _register_existing_asset so it shares a single transaction with reference creation. Log a warning when the hash is not found instead of silently returning None. Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9 Co-authored-by: Amp * Add exclude_none=True to create/upload responses Align with get/update/list endpoints for consistent JSON output. Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9 Co-authored-by: Amp * Change preview_id to reference asset by reference ID, not content ID Clients receive preview_id in API responses but could not dereference it through public routes (which use reference IDs). Now preview_id is a self-referential FK to asset_references.id so the value is directly usable in the public API. Co-Authored-By: Claude Opus 4.6 * Filter soft-deleted and missing refs from visibility queries list_references_by_asset_id and list_tags_with_usage were not filtering out deleted_at/is_missing refs, allowing /view?filename=blake3:... to serve files through hidden references and inflating tag usage counts. Add list_all_file_paths_by_asset_id for orphan cleanup which intentionally needs unfiltered access. Co-Authored-By: Claude Opus 4.6 * Pass preview_id and mime_type through all asset creation fast paths The duplicate-content upload path and hash-based creation paths were silently dropping preview_id and mime_type. This wires both fields through _register_existing_asset, create_from_hash, and all route call sites so behavior is consistent regardless of whether the asset content already exists. Co-Authored-By: Claude Opus 4.6 * Remove unimplemented client-provided ID from upload API The `id` field on UploadAssetSpec was advertised for idempotent creation but never actually honored when creating new references. Remove it rather than implementing the feature. Co-Authored-By: Claude Opus 4.6 * Make asset mime_type immutable after first ingest Prevents cross-tenant metadata mutation when multiple references share the same content-addressed Asset row. mime_type can now only be set when NULL (first ingest); subsequent attempts to change it are silently ignored. Co-Authored-By: Claude Opus 4.6 * Use resolved content_type from asset lookup in /view endpoint The /view endpoint was discarding the content_type computed by resolve_hash_to_path() and re-guessing from the filename, which produced wrong results for extensionless files or mismatched extensions. Co-Authored-By: Claude Opus 4.6 * Merge system+user metadata into filter projection Extract rebuild_metadata_projection() to build AssetReferenceMeta rows from {**system_metadata, **user_metadata}, so system-generated metadata is queryable via metadata_filter and user keys override system keys. Co-Authored-By: Claude Opus 4.6 * Standardize tag ordering to alphabetical across all endpoints Co-Authored-By: Claude Opus 4.6 * Derive subfolder tags from path in register_file_in_place Co-Authored-By: Claude Opus 4.6 * Reject client-provided id, fix preview URLs, rename tags→total_tags - Reject 'id' field in multipart upload with 400 UNSUPPORTED_FIELD instead of silently ignoring it - Build preview URL from the preview asset's own metadata rather than the parent asset's - Rename 'tags' to 'total_tags' in TagsAdd/TagsRemove response schemas for clarity Co-Authored-By: Claude Opus 4.6 * fix: SQLite migration 0003 FK drop fails on file-backed DBs (MB-2) Add naming_convention to Base.metadata so Alembic batch-mode reflection can match unnamed FK constraints created by migration 0002. Pass naming_convention and render_as_batch=True through env.py online config. Add migration roundtrip tests (upgrade/downgrade/cycle from baseline). Amp-Thread-ID: https://ampcode.com/threads/T-019ce466-1683-7471-b6e1-bb078223cda0 Co-authored-by: Amp * Fix missing tag count for is_missing references and update test for total_tags field - Allow is_missing=True references to be counted in list_tags_with_usage when the tag is 'missing', so the missing tag count reflects all references that have been tagged as missing - Add update_is_missing_by_asset_id query helper for bulk updates by asset - Update test_add_and_remove_tags to use 'total_tags' matching the API schema Amp-Thread-ID: https://ampcode.com/threads/T-019ce482-05e7-7324-a1b0-a56a929cc7ef Co-authored-by: Amp * Remove unused imports in scanner.py Co-Authored-By: Claude Opus 4.6 * Rename prompt_id to job_id on asset_references Rename the column in the DB model, migration, and service schemas. The API response emits both job_id and prompt_id (deprecated alias) for backward compatibility with the cloud API. Amp-Thread-ID: https://ampcode.com/threads/T-019cef41-60b0-752a-aa3c-ed7f20fda2f7 Co-authored-by: Amp * Add index on asset_references.preview_id for FK cascade performance Amp-Thread-ID: https://ampcode.com/threads/T-019cef45-a4d2-7548-86d2-d46bcd3db419 Co-authored-by: Amp * Add clarifying comments for Asset/AssetReference naming and preview_id Amp-Thread-ID: https://ampcode.com/threads/T-019cef49-f94e-7348-bf23-9a19ebf65e0d Co-authored-by: Amp * Disallow all-null meta rows: add CHECK constraint, skip null values on write - convert_metadata_to_rows returns [] for None values instead of an all-null row - Remove dead None branch from _scalar_to_row - Simplify null filter in common.py to just check for row absence - Add CHECK constraint ck_asset_reference_meta_has_value to model and migration 0003 Amp-Thread-ID: https://ampcode.com/threads/T-019cef4e-5240-7749-bb25-1f17fcf9c09c Co-authored-by: Amp * Remove dead None guards on result.asset in upload handler register_file_in_place guarantees a non-None asset, so the 'if result.asset else None' checks were unreachable. Amp-Thread-ID: https://ampcode.com/threads/T-019cef5b-4cf8-723c-8a98-8fb8f333c133 Co-authored-by: Amp * Remove mime_type from asset update API Clients can no longer modify mime_type after asset creation via the PUT /api/assets/{id} endpoint. This reduces the risk of mime_type spoofing. The internal update_asset_hash_and_mime function remains available for server-side use (e.g., enrichment). Amp-Thread-ID: https://ampcode.com/threads/T-019cef5d-8d61-75cc-a1c6-2841ac395648 Co-authored-by: Amp * Fix migration constraint naming double-prefix and NULL in mixed metadata lists - Use fully-rendered constraint names in migration 0003 to avoid the naming convention doubling the ck_ prefix on batch operations. - Add table_args to downgrade so SQLite batch mode can find the CHECK constraint (not exposed by SQLite reflection). - Fix model CheckConstraint name to use bare 'has_value' (convention auto-prefixes). - Skip None items when converting metadata lists to rows, preventing all-NULL rows that violate the has_value check constraint. Amp-Thread-ID: https://ampcode.com/threads/T-019cef87-94f9-7172-a6af-c6282290ce4f Co-authored-by: Amp --------- Co-authored-by: Claude Opus 4.6 Co-authored-by: Amp --- alembic_db/env.py | 7 +- .../versions/0003_add_metadata_job_id.py | 98 +++++++ app/assets/api/routes.py | 172 +++++++----- app/assets/api/schemas_in.py | 64 ++++- app/assets/api/schemas_out.py | 63 ++--- app/assets/api/upload.py | 14 + app/assets/database/models.py | 25 +- app/assets/database/queries/__init__.py | 12 + app/assets/database/queries/asset.py | 4 +- .../database/queries/asset_reference.py | 247 +++++++++--------- app/assets/database/queries/common.py | 79 +++++- app/assets/database/queries/tags.py | 70 ++++- app/assets/scanner.py | 6 +- app/assets/services/asset_management.py | 72 ++++- app/assets/services/ingest.py | 126 +++++++-- app/assets/services/schemas.py | 6 +- app/assets/services/tagging.py | 23 ++ app/database/models.py | 11 +- server.py | 79 ++++-- tests-unit/app_test/test_migrations.py | 57 ++++ tests-unit/assets_test/queries/test_asset.py | 43 +++ .../assets_test/queries/test_asset_info.py | 21 +- .../assets_test/queries/test_metadata.py | 51 +++- .../services/test_asset_management.py | 54 +++- .../assets_test/services/test_ingest.py | 12 +- .../services/test_tag_histogram.py | 123 +++++++++ tests-unit/assets_test/test_uploads.py | 9 + 27 files changed, 1218 insertions(+), 330 deletions(-) create mode 100644 alembic_db/versions/0003_add_metadata_job_id.py create mode 100644 tests-unit/app_test/test_migrations.py create mode 100644 tests-unit/assets_test/services/test_tag_histogram.py diff --git a/alembic_db/env.py b/alembic_db/env.py index 4d7770679..4ce37c012 100644 --- a/alembic_db/env.py +++ b/alembic_db/env.py @@ -8,7 +8,7 @@ from alembic import context config = context.config -from app.database.models import Base +from app.database.models import Base, NAMING_CONVENTION target_metadata = Base.metadata # other values from the config, defined by the needs of env.py, @@ -51,7 +51,10 @@ def run_migrations_online() -> None: with connectable.connect() as connection: context.configure( - connection=connection, target_metadata=target_metadata + connection=connection, + target_metadata=target_metadata, + render_as_batch=True, + naming_convention=NAMING_CONVENTION, ) with context.begin_transaction(): diff --git a/alembic_db/versions/0003_add_metadata_job_id.py b/alembic_db/versions/0003_add_metadata_job_id.py new file mode 100644 index 000000000..2a14ee924 --- /dev/null +++ b/alembic_db/versions/0003_add_metadata_job_id.py @@ -0,0 +1,98 @@ +""" +Add system_metadata and job_id columns to asset_references. +Change preview_id FK from assets.id to asset_references.id. + +Revision ID: 0003_add_metadata_job_id +Revises: 0002_merge_to_asset_references +Create Date: 2026-03-09 +""" + +from alembic import op +import sqlalchemy as sa + +from app.database.models import NAMING_CONVENTION + +revision = "0003_add_metadata_job_id" +down_revision = "0002_merge_to_asset_references" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + with op.batch_alter_table("asset_references") as batch_op: + batch_op.add_column( + sa.Column("system_metadata", sa.JSON(), nullable=True) + ) + batch_op.add_column( + sa.Column("job_id", sa.String(length=36), nullable=True) + ) + + # Change preview_id FK from assets.id to asset_references.id (self-ref). + # Existing values are asset-content IDs that won't match reference IDs, + # so null them out first. + op.execute("UPDATE asset_references SET preview_id = NULL WHERE preview_id IS NOT NULL") + with op.batch_alter_table( + "asset_references", naming_convention=NAMING_CONVENTION + ) as batch_op: + batch_op.drop_constraint( + "fk_asset_references_preview_id_assets", type_="foreignkey" + ) + batch_op.create_foreign_key( + "fk_asset_references_preview_id_asset_references", + "asset_references", + ["preview_id"], + ["id"], + ondelete="SET NULL", + ) + batch_op.create_index( + "ix_asset_references_preview_id", ["preview_id"] + ) + + # Purge any all-null meta rows before adding the constraint + op.execute( + "DELETE FROM asset_reference_meta" + " WHERE val_str IS NULL AND val_num IS NULL AND val_bool IS NULL AND val_json IS NULL" + ) + with op.batch_alter_table("asset_reference_meta") as batch_op: + batch_op.create_check_constraint( + "ck_asset_reference_meta_has_value", + "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL", + ) + + +def downgrade() -> None: + # SQLite doesn't reflect CHECK constraints, so we must declare it + # explicitly via table_args for the batch recreate to find it. + # Use the fully-rendered constraint name to avoid the naming convention + # doubling the prefix. + with op.batch_alter_table( + "asset_reference_meta", + table_args=[ + sa.CheckConstraint( + "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL", + name="ck_asset_reference_meta_has_value", + ), + ], + ) as batch_op: + batch_op.drop_constraint( + "ck_asset_reference_meta_has_value", type_="check" + ) + + with op.batch_alter_table( + "asset_references", naming_convention=NAMING_CONVENTION + ) as batch_op: + batch_op.drop_index("ix_asset_references_preview_id") + batch_op.drop_constraint( + "fk_asset_references_preview_id_asset_references", type_="foreignkey" + ) + batch_op.create_foreign_key( + "fk_asset_references_preview_id_assets", + "assets", + ["preview_id"], + ["id"], + ondelete="SET NULL", + ) + + with op.batch_alter_table("asset_references") as batch_op: + batch_op.drop_column("job_id") + batch_op.drop_column("system_metadata") diff --git a/app/assets/api/routes.py b/app/assets/api/routes.py index 40dee9f46..68126b6a5 100644 --- a/app/assets/api/routes.py +++ b/app/assets/api/routes.py @@ -13,6 +13,7 @@ from pydantic import ValidationError import folder_paths from app import user_manager from app.assets.api import schemas_in, schemas_out +from app.assets.services import schemas from app.assets.api.schemas_in import ( AssetValidationError, UploadError, @@ -38,6 +39,7 @@ from app.assets.services import ( update_asset_metadata, upload_from_temp_path, ) +from app.assets.services.tagging import list_tag_histogram ROUTES = web.RouteTableDef() USER_MANAGER: user_manager.UserManager | None = None @@ -122,6 +124,61 @@ def _validate_sort_field(requested: str | None) -> str: return "created_at" +def _build_preview_url_from_view(tags: list[str], user_metadata: dict[str, Any] | None) -> str | None: + """Build a /api/view preview URL from asset tags and user_metadata filename.""" + if not user_metadata: + return None + filename = user_metadata.get("filename") + if not filename: + return None + + if "input" in tags: + view_type = "input" + elif "output" in tags: + view_type = "output" + else: + return None + + subfolder = "" + if "/" in filename: + subfolder, filename = filename.rsplit("/", 1) + + encoded_filename = urllib.parse.quote(filename, safe="") + url = f"/api/view?type={view_type}&filename={encoded_filename}" + if subfolder: + url += f"&subfolder={urllib.parse.quote(subfolder, safe='')}" + return url + + +def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResult) -> schemas_out.Asset: + """Build an Asset response from a service result.""" + if result.ref.preview_id: + preview_detail = get_asset_detail(result.ref.preview_id) + if preview_detail: + preview_url = _build_preview_url_from_view(preview_detail.tags, preview_detail.ref.user_metadata) + else: + preview_url = None + else: + preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata) + return schemas_out.Asset( + id=result.ref.id, + name=result.ref.name, + asset_hash=result.asset.hash if result.asset else None, + size=int(result.asset.size_bytes) if result.asset else None, + mime_type=result.asset.mime_type if result.asset else None, + tags=result.tags, + preview_url=preview_url, + preview_id=result.ref.preview_id, + user_metadata=result.ref.user_metadata or {}, + metadata=result.ref.system_metadata, + job_id=result.ref.job_id, + prompt_id=result.ref.job_id, # deprecated: mirrors job_id for cloud compat + created_at=result.ref.created_at, + updated_at=result.ref.updated_at, + last_access_time=result.ref.last_access_time, + ) + + @ROUTES.head("/api/assets/hash/{hash}") @_require_assets_feature_enabled async def head_asset_by_hash(request: web.Request) -> web.Response: @@ -164,20 +221,7 @@ async def list_assets_route(request: web.Request) -> web.Response: order=order, ) - summaries = [ - schemas_out.AssetSummary( - id=item.ref.id, - name=item.ref.name, - asset_hash=item.asset.hash if item.asset else None, - size=int(item.asset.size_bytes) if item.asset else None, - mime_type=item.asset.mime_type if item.asset else None, - tags=item.tags, - created_at=item.ref.created_at, - updated_at=item.ref.updated_at, - last_access_time=item.ref.last_access_time, - ) - for item in result.items - ] + summaries = [_build_asset_response(item) for item in result.items] payload = schemas_out.AssetsList( assets=summaries, @@ -207,18 +251,7 @@ async def get_asset_route(request: web.Request) -> web.Response: {"id": reference_id}, ) - payload = schemas_out.AssetDetail( - id=result.ref.id, - name=result.ref.name, - asset_hash=result.asset.hash if result.asset else None, - size=int(result.asset.size_bytes) if result.asset else None, - mime_type=result.asset.mime_type if result.asset else None, - tags=result.tags, - user_metadata=result.ref.user_metadata or {}, - preview_id=result.ref.preview_id, - created_at=result.ref.created_at, - last_access_time=result.ref.last_access_time, - ) + payload = _build_asset_response(result) except ValueError as e: return _build_error_response( 404, "ASSET_NOT_FOUND", str(e), {"id": reference_id} @@ -230,7 +263,7 @@ async def get_asset_route(request: web.Request) -> web.Response: USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") - return web.json_response(payload.model_dump(mode="json"), status=200) + return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content") @@ -312,32 +345,31 @@ async def create_asset_from_hash_route(request: web.Request) -> web.Response: 400, "INVALID_JSON", "Request body must be valid JSON." ) + # Derive name from hash if not provided + name = body.name + if name is None: + name = body.hash.split(":", 1)[1] if ":" in body.hash else body.hash + result = create_from_hash( hash_str=body.hash, - name=body.name, + name=name, tags=body.tags, user_metadata=body.user_metadata, owner_id=USER_MANAGER.get_request_user_id(request), + mime_type=body.mime_type, + preview_id=body.preview_id, ) if result is None: return _build_error_response( 404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist" ) + asset = _build_asset_response(result) payload_out = schemas_out.AssetCreated( - id=result.ref.id, - name=result.ref.name, - asset_hash=result.asset.hash, - size=int(result.asset.size_bytes), - mime_type=result.asset.mime_type, - tags=result.tags, - user_metadata=result.ref.user_metadata or {}, - preview_id=result.ref.preview_id, - created_at=result.ref.created_at, - last_access_time=result.ref.last_access_time, + **asset.model_dump(), created_new=result.created_new, ) - return web.json_response(payload_out.model_dump(mode="json"), status=201) + return web.json_response(payload_out.model_dump(mode="json", exclude_none=True), status=201) @ROUTES.post("/api/assets") @@ -358,6 +390,8 @@ async def upload_asset(request: web.Request) -> web.Response: "name": parsed.provided_name, "user_metadata": parsed.user_metadata_raw, "hash": parsed.provided_hash, + "mime_type": parsed.provided_mime_type, + "preview_id": parsed.provided_preview_id, } ) except ValidationError as ve: @@ -386,6 +420,8 @@ async def upload_asset(request: web.Request) -> web.Response: tags=spec.tags, user_metadata=spec.user_metadata or {}, owner_id=owner_id, + mime_type=spec.mime_type, + preview_id=spec.preview_id, ) if result is None: delete_temp_file_if_exists(parsed.tmp_path) @@ -410,6 +446,8 @@ async def upload_asset(request: web.Request) -> web.Response: client_filename=parsed.file_client_name, owner_id=owner_id, expected_hash=spec.hash, + mime_type=spec.mime_type, + preview_id=spec.preview_id, ) except AssetValidationError as e: delete_temp_file_if_exists(parsed.tmp_path) @@ -428,21 +466,13 @@ async def upload_asset(request: web.Request) -> web.Response: logging.exception("upload_asset failed for owner_id=%s", owner_id) return _build_error_response(500, "INTERNAL", "Unexpected server error.") - payload = schemas_out.AssetCreated( - id=result.ref.id, - name=result.ref.name, - asset_hash=result.asset.hash, - size=int(result.asset.size_bytes), - mime_type=result.asset.mime_type, - tags=result.tags, - user_metadata=result.ref.user_metadata or {}, - preview_id=result.ref.preview_id, - created_at=result.ref.created_at, - last_access_time=result.ref.last_access_time, + asset = _build_asset_response(result) + payload_out = schemas_out.AssetCreated( + **asset.model_dump(), created_new=result.created_new, ) status = 201 if result.created_new else 200 - return web.json_response(payload.model_dump(mode="json"), status=status) + return web.json_response(payload_out.model_dump(mode="json", exclude_none=True), status=status) @ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}") @@ -464,15 +494,9 @@ async def update_asset_route(request: web.Request) -> web.Response: name=body.name, user_metadata=body.user_metadata, owner_id=USER_MANAGER.get_request_user_id(request), + preview_id=body.preview_id, ) - payload = schemas_out.AssetUpdated( - id=result.ref.id, - name=result.ref.name, - asset_hash=result.asset.hash if result.asset else None, - tags=result.tags, - user_metadata=result.ref.user_metadata or {}, - updated_at=result.ref.updated_at, - ) + payload = _build_asset_response(result) except PermissionError as pe: return _build_error_response(403, "FORBIDDEN", str(pe), {"id": reference_id}) except ValueError as ve: @@ -486,7 +510,7 @@ async def update_asset_route(request: web.Request) -> web.Response: USER_MANAGER.get_request_user_id(request), ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") - return web.json_response(payload.model_dump(mode="json"), status=200) + return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}") @@ -555,7 +579,7 @@ async def get_tags(request: web.Request) -> web.Response: payload = schemas_out.TagsList( tags=tags, total=total, has_more=(query.offset + len(tags)) < total ) - return web.json_response(payload.model_dump(mode="json")) + return web.json_response(payload.model_dump(mode="json", exclude_none=True)) @ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags") @@ -603,7 +627,7 @@ async def add_asset_tags(request: web.Request) -> web.Response: ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") - return web.json_response(payload.model_dump(mode="json"), status=200) + return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags") @@ -650,7 +674,29 @@ async def delete_asset_tags(request: web.Request) -> web.Response: ) return _build_error_response(500, "INTERNAL", "Unexpected server error.") - return web.json_response(payload.model_dump(mode="json"), status=200) + return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) + + +@ROUTES.get("/api/assets/tags/refine") +@_require_assets_feature_enabled +async def get_tags_refine(request: web.Request) -> web.Response: + """GET request to get tag histogram for filtered assets.""" + query_dict = get_query_dict(request) + try: + q = schemas_in.TagsRefineQuery.model_validate(query_dict) + except ValidationError as ve: + return _build_validation_error_response("INVALID_QUERY", ve) + + tag_counts = list_tag_histogram( + owner_id=USER_MANAGER.get_request_user_id(request), + include_tags=q.include_tags, + exclude_tags=q.exclude_tags, + name_contains=q.name_contains, + metadata_filter=q.metadata_filter, + limit=q.limit, + ) + payload = schemas_out.TagHistogram(tag_counts=tag_counts) + return web.json_response(payload.model_dump(mode="json", exclude_none=True), status=200) @ROUTES.post("/api/assets/seed") diff --git a/app/assets/api/schemas_in.py b/app/assets/api/schemas_in.py index d255c938e..186a6ae1e 100644 --- a/app/assets/api/schemas_in.py +++ b/app/assets/api/schemas_in.py @@ -45,6 +45,8 @@ class ParsedUpload: user_metadata_raw: str | None provided_hash: str | None provided_hash_exists: bool | None + provided_mime_type: str | None = None + provided_preview_id: str | None = None class ListAssetsQuery(BaseModel): @@ -98,11 +100,17 @@ class ListAssetsQuery(BaseModel): class UpdateAssetBody(BaseModel): name: str | None = None user_metadata: dict[str, Any] | None = None + preview_id: str | None = None # references an asset_reference id, not an asset id @model_validator(mode="after") def _validate_at_least_one_field(self): - if self.name is None and self.user_metadata is None: - raise ValueError("Provide at least one of: name, user_metadata.") + if all( + v is None + for v in (self.name, self.user_metadata, self.preview_id) + ): + raise ValueError( + "Provide at least one of: name, user_metadata, preview_id." + ) return self @@ -110,9 +118,11 @@ class CreateFromHashBody(BaseModel): model_config = ConfigDict(extra="ignore", str_strip_whitespace=True) hash: str - name: str + name: str | None = None tags: list[str] = Field(default_factory=list) user_metadata: dict[str, Any] = Field(default_factory=dict) + mime_type: str | None = None + preview_id: str | None = None # references an asset_reference id, not an asset id @field_validator("hash") @classmethod @@ -138,6 +148,44 @@ class CreateFromHashBody(BaseModel): return [] +class TagsRefineQuery(BaseModel): + include_tags: list[str] = Field(default_factory=list) + exclude_tags: list[str] = Field(default_factory=list) + name_contains: str | None = None + metadata_filter: dict[str, Any] | None = None + limit: conint(ge=1, le=1000) = 100 + + @field_validator("include_tags", "exclude_tags", mode="before") + @classmethod + def _split_csv_tags(cls, v): + if v is None: + return [] + if isinstance(v, str): + return [t.strip() for t in v.split(",") if t.strip()] + if isinstance(v, list): + out: list[str] = [] + for item in v: + if isinstance(item, str): + out.extend([t.strip() for t in item.split(",") if t.strip()]) + return out + return v + + @field_validator("metadata_filter", mode="before") + @classmethod + def _parse_metadata_json(cls, v): + if v is None or isinstance(v, dict): + return v + if isinstance(v, str) and v.strip(): + try: + parsed = json.loads(v) + except Exception as e: + raise ValueError(f"metadata_filter must be JSON: {e}") from e + if not isinstance(parsed, dict): + raise ValueError("metadata_filter must be a JSON object") + return parsed + return None + + class TagsListQuery(BaseModel): model_config = ConfigDict(extra="ignore", str_strip_whitespace=True) @@ -186,21 +234,25 @@ class TagsRemove(TagsAdd): class UploadAssetSpec(BaseModel): """Upload Asset operation. - - tags: ordered; first is root ('models'|'input'|'output'); + - tags: optional list; if provided, first is root ('models'|'input'|'output'); if root == 'models', second must be a valid category - name: display name - user_metadata: arbitrary JSON object (optional) - hash: optional canonical 'blake3:' for validation / fast-path + - mime_type: optional MIME type override + - preview_id: optional asset_reference ID for preview Files are stored using the content hash as filename stem. """ model_config = ConfigDict(extra="ignore", str_strip_whitespace=True) - tags: list[str] = Field(..., min_length=1) + tags: list[str] = Field(default_factory=list) name: str | None = Field(default=None, max_length=512, description="Display Name") user_metadata: dict[str, Any] = Field(default_factory=dict) hash: str | None = Field(default=None) + mime_type: str | None = Field(default=None) + preview_id: str | None = Field(default=None) # references an asset_reference id @field_validator("hash", mode="before") @classmethod @@ -279,7 +331,7 @@ class UploadAssetSpec(BaseModel): @model_validator(mode="after") def _validate_order(self): if not self.tags: - raise ValueError("tags must be provided and non-empty") + raise ValueError("at least one tag is required for uploads") root = self.tags[0] if root not in {"models", "input", "output"}: raise ValueError("first tag must be one of: models, input, output") diff --git a/app/assets/api/schemas_out.py b/app/assets/api/schemas_out.py index f36447856..d99b1098d 100644 --- a/app/assets/api/schemas_out.py +++ b/app/assets/api/schemas_out.py @@ -4,7 +4,10 @@ from typing import Any from pydantic import BaseModel, ConfigDict, Field, field_serializer -class AssetSummary(BaseModel): +class Asset(BaseModel): + """API view of an asset. Maps to DB ``AssetReference`` joined with its ``Asset`` blob; + ``id`` here is the AssetReference id, not the content-addressed Asset id.""" + id: str name: str asset_hash: str | None = None @@ -12,8 +15,14 @@ class AssetSummary(BaseModel): mime_type: str | None = None tags: list[str] = Field(default_factory=list) preview_url: str | None = None - created_at: datetime | None = None - updated_at: datetime | None = None + preview_id: str | None = None # references an asset_reference id, not an asset id + user_metadata: dict[str, Any] = Field(default_factory=dict) + is_immutable: bool = False + metadata: dict[str, Any] | None = None + job_id: str | None = None + prompt_id: str | None = None # deprecated: use job_id + created_at: datetime + updated_at: datetime last_access_time: datetime | None = None model_config = ConfigDict(from_attributes=True) @@ -23,50 +32,16 @@ class AssetSummary(BaseModel): return v.isoformat() if v else None +class AssetCreated(Asset): + created_new: bool + + class AssetsList(BaseModel): - assets: list[AssetSummary] + assets: list[Asset] total: int has_more: bool -class AssetUpdated(BaseModel): - id: str - name: str - asset_hash: str | None = None - tags: list[str] = Field(default_factory=list) - user_metadata: dict[str, Any] = Field(default_factory=dict) - updated_at: datetime | None = None - - model_config = ConfigDict(from_attributes=True) - - @field_serializer("updated_at") - def _serialize_updated_at(self, v: datetime | None, _info): - return v.isoformat() if v else None - - -class AssetDetail(BaseModel): - id: str - name: str - asset_hash: str | None = None - size: int | None = None - mime_type: str | None = None - tags: list[str] = Field(default_factory=list) - user_metadata: dict[str, Any] = Field(default_factory=dict) - preview_id: str | None = None - created_at: datetime | None = None - last_access_time: datetime | None = None - - model_config = ConfigDict(from_attributes=True) - - @field_serializer("created_at", "last_access_time") - def _serialize_datetime(self, v: datetime | None, _info): - return v.isoformat() if v else None - - -class AssetCreated(AssetDetail): - created_new: bool - - class TagUsage(BaseModel): name: str count: int @@ -91,3 +66,7 @@ class TagsRemove(BaseModel): removed: list[str] = Field(default_factory=list) not_present: list[str] = Field(default_factory=list) total_tags: list[str] = Field(default_factory=list) + + +class TagHistogram(BaseModel): + tag_counts: dict[str, int] diff --git a/app/assets/api/upload.py b/app/assets/api/upload.py index 721c12f4d..13d3d372c 100644 --- a/app/assets/api/upload.py +++ b/app/assets/api/upload.py @@ -52,6 +52,8 @@ async def parse_multipart_upload( user_metadata_raw: str | None = None provided_hash: str | None = None provided_hash_exists: bool | None = None + provided_mime_type: str | None = None + provided_preview_id: str | None = None file_written = 0 tmp_path: str | None = None @@ -128,6 +130,16 @@ async def parse_multipart_upload( provided_name = (await field.text()) or None elif fname == "user_metadata": user_metadata_raw = (await field.text()) or None + elif fname == "id": + raise UploadError( + 400, + "UNSUPPORTED_FIELD", + "Client-provided 'id' is not supported. Asset IDs are assigned by the server.", + ) + elif fname == "mime_type": + provided_mime_type = ((await field.text()) or "").strip() or None + elif fname == "preview_id": + provided_preview_id = ((await field.text()) or "").strip() or None if not file_present and not (provided_hash and provided_hash_exists): raise UploadError( @@ -152,6 +164,8 @@ async def parse_multipart_upload( user_metadata_raw=user_metadata_raw, provided_hash=provided_hash, provided_hash_exists=provided_hash_exists, + provided_mime_type=provided_mime_type, + provided_preview_id=provided_preview_id, ) diff --git a/app/assets/database/models.py b/app/assets/database/models.py index 03c1c1707..a3af8a192 100644 --- a/app/assets/database/models.py +++ b/app/assets/database/models.py @@ -45,13 +45,7 @@ class Asset(Base): passive_deletes=True, ) - preview_of: Mapped[list[AssetReference]] = relationship( - "AssetReference", - back_populates="preview_asset", - primaryjoin=lambda: Asset.id == foreign(AssetReference.preview_id), - foreign_keys=lambda: [AssetReference.preview_id], - viewonly=True, - ) + # preview_id on AssetReference is a self-referential FK to asset_references.id __table_args__ = ( Index("uq_assets_hash", "hash", unique=True), @@ -91,11 +85,15 @@ class AssetReference(Base): owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="") name: Mapped[str] = mapped_column(String(512), nullable=False) preview_id: Mapped[str | None] = mapped_column( - String(36), ForeignKey("assets.id", ondelete="SET NULL") + String(36), ForeignKey("asset_references.id", ondelete="SET NULL") ) user_metadata: Mapped[dict[str, Any] | None] = mapped_column( JSON(none_as_null=True) ) + system_metadata: Mapped[dict[str, Any] | None] = mapped_column( + JSON(none_as_null=True), nullable=True, default=None + ) + job_id: Mapped[str | None] = mapped_column(String(36), nullable=True, default=None) created_at: Mapped[datetime] = mapped_column( DateTime(timezone=False), nullable=False, default=get_utc_now ) @@ -115,10 +113,10 @@ class AssetReference(Base): foreign_keys=[asset_id], lazy="selectin", ) - preview_asset: Mapped[Asset | None] = relationship( - "Asset", - back_populates="preview_of", + preview_ref: Mapped[AssetReference | None] = relationship( + "AssetReference", foreign_keys=[preview_id], + remote_side=lambda: [AssetReference.id], ) metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship( @@ -152,6 +150,7 @@ class AssetReference(Base): Index("ix_asset_references_created_at", "created_at"), Index("ix_asset_references_last_access_time", "last_access_time"), Index("ix_asset_references_deleted_at", "deleted_at"), + Index("ix_asset_references_preview_id", "preview_id"), Index("ix_asset_references_owner_name", "owner_id", "name"), CheckConstraint( "(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg" @@ -192,6 +191,10 @@ class AssetReferenceMeta(Base): Index("ix_asset_reference_meta_key_val_str", "key", "val_str"), Index("ix_asset_reference_meta_key_val_num", "key", "val_num"), Index("ix_asset_reference_meta_key_val_bool", "key", "val_bool"), + CheckConstraint( + "val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL", + name="has_value", + ), ) diff --git a/app/assets/database/queries/__init__.py b/app/assets/database/queries/__init__.py index 7888d0645..1632937b2 100644 --- a/app/assets/database/queries/__init__.py +++ b/app/assets/database/queries/__init__.py @@ -31,16 +31,21 @@ from app.assets.database.queries.asset_reference import ( get_unenriched_references, get_unreferenced_unhashed_asset_ids, insert_reference, + list_all_file_paths_by_asset_id, list_references_by_asset_id, list_references_page, mark_references_missing_outside_prefixes, + rebuild_metadata_projection, + reference_exists, reference_exists_for_asset_id, restore_references_by_paths, set_reference_metadata, set_reference_preview, + set_reference_system_metadata, soft_delete_reference_by_id, update_reference_access_time, update_reference_name, + update_is_missing_by_asset_id, update_reference_timestamps, update_reference_updated_at, upsert_reference, @@ -54,6 +59,7 @@ from app.assets.database.queries.tags import ( bulk_insert_tags_and_meta, ensure_tags_exist, get_reference_tags, + list_tag_counts_for_filtered_assets, list_tags_with_usage, remove_missing_tag_for_asset_id, remove_tags_from_reference, @@ -97,20 +103,26 @@ __all__ = [ "get_unenriched_references", "get_unreferenced_unhashed_asset_ids", "insert_reference", + "list_all_file_paths_by_asset_id", "list_references_by_asset_id", "list_references_page", + "list_tag_counts_for_filtered_assets", "list_tags_with_usage", "mark_references_missing_outside_prefixes", "reassign_asset_references", + "rebuild_metadata_projection", + "reference_exists", "reference_exists_for_asset_id", "remove_missing_tag_for_asset_id", "remove_tags_from_reference", "restore_references_by_paths", "set_reference_metadata", "set_reference_preview", + "set_reference_system_metadata", "soft_delete_reference_by_id", "set_reference_tags", "update_asset_hash_and_mime", + "update_is_missing_by_asset_id", "update_reference_access_time", "update_reference_name", "update_reference_timestamps", diff --git a/app/assets/database/queries/asset.py b/app/assets/database/queries/asset.py index a21f5b68f..594d1f1b2 100644 --- a/app/assets/database/queries/asset.py +++ b/app/assets/database/queries/asset.py @@ -69,7 +69,7 @@ def upsert_asset( if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0: asset.size_bytes = int(size_bytes) changed = True - if mime_type and asset.mime_type != mime_type: + if mime_type and not asset.mime_type: asset.mime_type = mime_type changed = True if changed: @@ -118,7 +118,7 @@ def update_asset_hash_and_mime( return False if asset_hash is not None: asset.hash = asset_hash - if mime_type is not None: + if mime_type is not None and not asset.mime_type: asset.mime_type = mime_type return True diff --git a/app/assets/database/queries/asset_reference.py b/app/assets/database/queries/asset_reference.py index 6524791cc..084a32512 100644 --- a/app/assets/database/queries/asset_reference.py +++ b/app/assets/database/queries/asset_reference.py @@ -10,7 +10,7 @@ from decimal import Decimal from typing import NamedTuple, Sequence import sqlalchemy as sa -from sqlalchemy import delete, exists, select +from sqlalchemy import delete, select from sqlalchemy.dialects import sqlite from sqlalchemy.exc import IntegrityError from sqlalchemy.orm import Session, noload @@ -24,12 +24,14 @@ from app.assets.database.models import ( ) from app.assets.database.queries.common import ( MAX_BIND_PARAMS, + apply_metadata_filter, + apply_tag_filters, build_prefix_like_conditions, build_visible_owner_clause, calculate_rows_per_statement, iter_chunks, ) -from app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags +from app.assets.helpers import escape_sql_like_string, get_utc_now def _check_is_scalar(v): @@ -44,15 +46,6 @@ def _check_is_scalar(v): def _scalar_to_row(key: str, ordinal: int, value) -> dict: """Convert a scalar value to a typed projection row.""" - if value is None: - return { - "key": key, - "ordinal": ordinal, - "val_str": None, - "val_num": None, - "val_bool": None, - "val_json": None, - } if isinstance(value, bool): return {"key": key, "ordinal": ordinal, "val_bool": bool(value)} if isinstance(value, (int, float, Decimal)): @@ -66,96 +59,19 @@ def _scalar_to_row(key: str, ordinal: int, value) -> dict: def convert_metadata_to_rows(key: str, value) -> list[dict]: """Turn a metadata key/value into typed projection rows.""" if value is None: - return [_scalar_to_row(key, 0, None)] + return [] if _check_is_scalar(value): return [_scalar_to_row(key, 0, value)] if isinstance(value, list): if all(_check_is_scalar(x) for x in value): - return [_scalar_to_row(key, i, x) for i, x in enumerate(value)] - return [{"key": key, "ordinal": i, "val_json": x} for i, x in enumerate(value)] + return [_scalar_to_row(key, i, x) for i, x in enumerate(value) if x is not None] + return [{"key": key, "ordinal": i, "val_json": x} for i, x in enumerate(value) if x is not None] return [{"key": key, "ordinal": 0, "val_json": value}] -def _apply_tag_filters( - stmt: sa.sql.Select, - include_tags: Sequence[str] | None = None, - exclude_tags: Sequence[str] | None = None, -) -> sa.sql.Select: - """include_tags: every tag must be present; exclude_tags: none may be present.""" - include_tags = normalize_tags(include_tags) - exclude_tags = normalize_tags(exclude_tags) - - if include_tags: - for tag_name in include_tags: - stmt = stmt.where( - exists().where( - (AssetReferenceTag.asset_reference_id == AssetReference.id) - & (AssetReferenceTag.tag_name == tag_name) - ) - ) - - if exclude_tags: - stmt = stmt.where( - ~exists().where( - (AssetReferenceTag.asset_reference_id == AssetReference.id) - & (AssetReferenceTag.tag_name.in_(exclude_tags)) - ) - ) - return stmt - - -def _apply_metadata_filter( - stmt: sa.sql.Select, - metadata_filter: dict | None = None, -) -> sa.sql.Select: - """Apply filters using asset_reference_meta projection table.""" - if not metadata_filter: - return stmt - - def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement: - return sa.exists().where( - AssetReferenceMeta.asset_reference_id == AssetReference.id, - AssetReferenceMeta.key == key, - *preds, - ) - - def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement: - if value is None: - no_row_for_key = sa.not_( - sa.exists().where( - AssetReferenceMeta.asset_reference_id == AssetReference.id, - AssetReferenceMeta.key == key, - ) - ) - null_row = _exists_for_pred( - key, - AssetReferenceMeta.val_json.is_(None), - AssetReferenceMeta.val_str.is_(None), - AssetReferenceMeta.val_num.is_(None), - AssetReferenceMeta.val_bool.is_(None), - ) - return sa.or_(no_row_for_key, null_row) - - if isinstance(value, bool): - return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value)) - if isinstance(value, (int, float, Decimal)): - num = value if isinstance(value, Decimal) else Decimal(str(value)) - return _exists_for_pred(key, AssetReferenceMeta.val_num == num) - if isinstance(value, str): - return _exists_for_pred(key, AssetReferenceMeta.val_str == value) - return _exists_for_pred(key, AssetReferenceMeta.val_json == value) - - for k, v in metadata_filter.items(): - if isinstance(v, list): - ors = [_exists_clause_for_value(k, elem) for elem in v] - if ors: - stmt = stmt.where(sa.or_(*ors)) - else: - stmt = stmt.where(_exists_clause_for_value(k, v)) - return stmt def get_reference_by_id( @@ -212,6 +128,21 @@ def reference_exists_for_asset_id( return session.execute(q).first() is not None +def reference_exists( + session: Session, + reference_id: str, +) -> bool: + """Return True if a reference with the given ID exists (not soft-deleted).""" + q = ( + select(sa.literal(True)) + .select_from(AssetReference) + .where(AssetReference.id == reference_id) + .where(AssetReference.deleted_at.is_(None)) + .limit(1) + ) + return session.execute(q).first() is not None + + def insert_reference( session: Session, asset_id: str, @@ -336,8 +267,8 @@ def list_references_page( escaped, esc = escape_sql_like_string(name_contains) base = base.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc)) - base = _apply_tag_filters(base, include_tags, exclude_tags) - base = _apply_metadata_filter(base, metadata_filter) + base = apply_tag_filters(base, include_tags, exclude_tags) + base = apply_metadata_filter(base, metadata_filter) sort = (sort or "created_at").lower() order = (order or "desc").lower() @@ -366,8 +297,8 @@ def list_references_page( count_stmt = count_stmt.where( AssetReference.name.ilike(f"%{escaped}%", escape=esc) ) - count_stmt = _apply_tag_filters(count_stmt, include_tags, exclude_tags) - count_stmt = _apply_metadata_filter(count_stmt, metadata_filter) + count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags) + count_stmt = apply_metadata_filter(count_stmt, metadata_filter) total = int(session.execute(count_stmt).scalar_one() or 0) refs = session.execute(base).unique().scalars().all() @@ -379,7 +310,7 @@ def list_references_page( select(AssetReferenceTag.asset_reference_id, Tag.name) .join(Tag, Tag.name == AssetReferenceTag.tag_name) .where(AssetReferenceTag.asset_reference_id.in_(id_list)) - .order_by(AssetReferenceTag.added_at) + .order_by(AssetReferenceTag.tag_name.asc()) ) for ref_id, tag_name in rows.all(): tag_map[ref_id].append(tag_name) @@ -492,6 +423,42 @@ def update_reference_updated_at( ) +def rebuild_metadata_projection(session: Session, ref: AssetReference) -> None: + """Delete and rebuild AssetReferenceMeta rows from merged system+user metadata. + + The merged dict is ``{**system_metadata, **user_metadata}`` so user keys + override system keys of the same name. + """ + session.execute( + delete(AssetReferenceMeta).where( + AssetReferenceMeta.asset_reference_id == ref.id + ) + ) + session.flush() + + merged = {**(ref.system_metadata or {}), **(ref.user_metadata or {})} + if not merged: + return + + rows: list[AssetReferenceMeta] = [] + for k, v in merged.items(): + for r in convert_metadata_to_rows(k, v): + rows.append( + AssetReferenceMeta( + asset_reference_id=ref.id, + key=r["key"], + ordinal=int(r["ordinal"]), + val_str=r.get("val_str"), + val_num=r.get("val_num"), + val_bool=r.get("val_bool"), + val_json=r.get("val_json"), + ) + ) + if rows: + session.add_all(rows) + session.flush() + + def set_reference_metadata( session: Session, reference_id: str, @@ -505,33 +472,24 @@ def set_reference_metadata( ref.updated_at = get_utc_now() session.flush() - session.execute( - delete(AssetReferenceMeta).where( - AssetReferenceMeta.asset_reference_id == reference_id - ) - ) + rebuild_metadata_projection(session, ref) + + +def set_reference_system_metadata( + session: Session, + reference_id: str, + system_metadata: dict | None = None, +) -> None: + """Set system_metadata on a reference and rebuild the merged projection.""" + ref = session.get(AssetReference, reference_id) + if not ref: + raise ValueError(f"AssetReference {reference_id} not found") + + ref.system_metadata = system_metadata or {} + ref.updated_at = get_utc_now() session.flush() - if not user_metadata: - return - - rows: list[AssetReferenceMeta] = [] - for k, v in user_metadata.items(): - for r in convert_metadata_to_rows(k, v): - rows.append( - AssetReferenceMeta( - asset_reference_id=reference_id, - key=r["key"], - ordinal=int(r["ordinal"]), - val_str=r.get("val_str"), - val_num=r.get("val_num"), - val_bool=r.get("val_bool"), - val_json=r.get("val_json"), - ) - ) - if rows: - session.add_all(rows) - session.flush() + rebuild_metadata_projection(session, ref) def delete_reference_by_id( @@ -571,19 +529,19 @@ def soft_delete_reference_by_id( def set_reference_preview( session: Session, reference_id: str, - preview_asset_id: str | None = None, + preview_reference_id: str | None = None, ) -> None: """Set or clear preview_id and bump updated_at. Raises on unknown IDs.""" ref = session.get(AssetReference, reference_id) if not ref: raise ValueError(f"AssetReference {reference_id} not found") - if preview_asset_id is None: + if preview_reference_id is None: ref.preview_id = None else: - if not session.get(Asset, preview_asset_id): - raise ValueError(f"Preview Asset {preview_asset_id} not found") - ref.preview_id = preview_asset_id + if not session.get(AssetReference, preview_reference_id): + raise ValueError(f"Preview AssetReference {preview_reference_id} not found") + ref.preview_id = preview_reference_id ref.updated_at = get_utc_now() session.flush() @@ -609,6 +567,8 @@ def list_references_by_asset_id( session.execute( select(AssetReference) .where(AssetReference.asset_id == asset_id) + .where(AssetReference.is_missing == False) # noqa: E712 + .where(AssetReference.deleted_at.is_(None)) .order_by(AssetReference.id.asc()) ) .scalars() @@ -616,6 +576,25 @@ def list_references_by_asset_id( ) +def list_all_file_paths_by_asset_id( + session: Session, + asset_id: str, +) -> list[str]: + """Return every file_path for an asset, including soft-deleted/missing refs. + + Used for orphan cleanup where all on-disk files must be removed. + """ + return list( + session.execute( + select(AssetReference.file_path) + .where(AssetReference.asset_id == asset_id) + .where(AssetReference.file_path.isnot(None)) + ) + .scalars() + .all() + ) + + def upsert_reference( session: Session, asset_id: str, @@ -855,6 +834,22 @@ def bulk_update_is_missing( return total +def update_is_missing_by_asset_id( + session: Session, asset_id: str, value: bool +) -> int: + """Set is_missing flag for ALL references belonging to an asset. + + Returns: Number of rows updated + """ + result = session.execute( + sa.update(AssetReference) + .where(AssetReference.asset_id == asset_id) + .where(AssetReference.deleted_at.is_(None)) + .values(is_missing=value) + ) + return result.rowcount + + def delete_references_by_ids(session: Session, reference_ids: list[str]) -> int: """Delete references by their IDs. diff --git a/app/assets/database/queries/common.py b/app/assets/database/queries/common.py index 194c39a1e..89bb49327 100644 --- a/app/assets/database/queries/common.py +++ b/app/assets/database/queries/common.py @@ -1,12 +1,14 @@ """Shared utilities for database query modules.""" import os -from typing import Iterable +from decimal import Decimal +from typing import Iterable, Sequence import sqlalchemy as sa +from sqlalchemy import exists -from app.assets.database.models import AssetReference -from app.assets.helpers import escape_sql_like_string +from app.assets.database.models import AssetReference, AssetReferenceMeta, AssetReferenceTag +from app.assets.helpers import escape_sql_like_string, normalize_tags MAX_BIND_PARAMS = 800 @@ -52,3 +54,74 @@ def build_prefix_like_conditions( escaped, esc = escape_sql_like_string(base) conds.append(AssetReference.file_path.like(escaped + "%", escape=esc)) return conds + + +def apply_tag_filters( + stmt: sa.sql.Select, + include_tags: Sequence[str] | None = None, + exclude_tags: Sequence[str] | None = None, +) -> sa.sql.Select: + """include_tags: every tag must be present; exclude_tags: none may be present.""" + include_tags = normalize_tags(include_tags) + exclude_tags = normalize_tags(exclude_tags) + + if include_tags: + for tag_name in include_tags: + stmt = stmt.where( + exists().where( + (AssetReferenceTag.asset_reference_id == AssetReference.id) + & (AssetReferenceTag.tag_name == tag_name) + ) + ) + + if exclude_tags: + stmt = stmt.where( + ~exists().where( + (AssetReferenceTag.asset_reference_id == AssetReference.id) + & (AssetReferenceTag.tag_name.in_(exclude_tags)) + ) + ) + return stmt + + +def apply_metadata_filter( + stmt: sa.sql.Select, + metadata_filter: dict | None = None, +) -> sa.sql.Select: + """Apply filters using asset_reference_meta projection table.""" + if not metadata_filter: + return stmt + + def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement: + return sa.exists().where( + AssetReferenceMeta.asset_reference_id == AssetReference.id, + AssetReferenceMeta.key == key, + *preds, + ) + + def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement: + if value is None: + return sa.not_( + sa.exists().where( + AssetReferenceMeta.asset_reference_id == AssetReference.id, + AssetReferenceMeta.key == key, + ) + ) + + if isinstance(value, bool): + return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value)) + if isinstance(value, (int, float, Decimal)): + num = value if isinstance(value, Decimal) else Decimal(str(value)) + return _exists_for_pred(key, AssetReferenceMeta.val_num == num) + if isinstance(value, str): + return _exists_for_pred(key, AssetReferenceMeta.val_str == value) + return _exists_for_pred(key, AssetReferenceMeta.val_json == value) + + for k, v in metadata_filter.items(): + if isinstance(v, list): + ors = [_exists_clause_for_value(k, elem) for elem in v] + if ors: + stmt = stmt.where(sa.or_(*ors)) + else: + stmt = stmt.where(_exists_clause_for_value(k, v)) + return stmt diff --git a/app/assets/database/queries/tags.py b/app/assets/database/queries/tags.py index 8b25fee67..f4126dba8 100644 --- a/app/assets/database/queries/tags.py +++ b/app/assets/database/queries/tags.py @@ -8,12 +8,15 @@ from sqlalchemy.exc import IntegrityError from sqlalchemy.orm import Session from app.assets.database.models import ( + Asset, AssetReference, AssetReferenceMeta, AssetReferenceTag, Tag, ) from app.assets.database.queries.common import ( + apply_metadata_filter, + apply_tag_filters, build_visible_owner_clause, iter_row_chunks, ) @@ -72,9 +75,9 @@ def get_reference_tags(session: Session, reference_id: str) -> list[str]: tag_name for (tag_name,) in ( session.execute( - select(AssetReferenceTag.tag_name).where( - AssetReferenceTag.asset_reference_id == reference_id - ) + select(AssetReferenceTag.tag_name) + .where(AssetReferenceTag.asset_reference_id == reference_id) + .order_by(AssetReferenceTag.tag_name.asc()) ) ).all() ] @@ -117,7 +120,7 @@ def set_reference_tags( ) session.flush() - return SetTagsResult(added=to_add, removed=to_remove, total=desired) + return SetTagsResult(added=sorted(to_add), removed=sorted(to_remove), total=sorted(desired)) def add_tags_to_reference( @@ -272,6 +275,12 @@ def list_tags_with_usage( .select_from(AssetReferenceTag) .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id) .where(build_visible_owner_clause(owner_id)) + .where( + sa.or_( + AssetReference.is_missing == False, # noqa: E712 + AssetReferenceTag.tag_name == "missing", + ) + ) .where(AssetReference.deleted_at.is_(None)) .group_by(AssetReferenceTag.tag_name) .subquery() @@ -308,6 +317,12 @@ def list_tags_with_usage( select(AssetReferenceTag.tag_name) .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id) .where(build_visible_owner_clause(owner_id)) + .where( + sa.or_( + AssetReference.is_missing == False, # noqa: E712 + AssetReferenceTag.tag_name == "missing", + ) + ) .where(AssetReference.deleted_at.is_(None)) .group_by(AssetReferenceTag.tag_name) ) @@ -320,6 +335,53 @@ def list_tags_with_usage( return rows_norm, int(total or 0) +def list_tag_counts_for_filtered_assets( + session: Session, + owner_id: str = "", + include_tags: Sequence[str] | None = None, + exclude_tags: Sequence[str] | None = None, + name_contains: str | None = None, + metadata_filter: dict | None = None, + limit: int = 100, +) -> dict[str, int]: + """Return tag counts for assets matching the given filters. + + Uses the same filtering logic as list_references_page but returns + {tag_name: count} instead of paginated references. + """ + # Build a subquery of matching reference IDs + ref_sq = ( + select(AssetReference.id) + .join(Asset, Asset.id == AssetReference.asset_id) + .where(build_visible_owner_clause(owner_id)) + .where(AssetReference.is_missing == False) # noqa: E712 + .where(AssetReference.deleted_at.is_(None)) + ) + + if name_contains: + escaped, esc = escape_sql_like_string(name_contains) + ref_sq = ref_sq.where(AssetReference.name.ilike(f"%{escaped}%", escape=esc)) + + ref_sq = apply_tag_filters(ref_sq, include_tags, exclude_tags) + ref_sq = apply_metadata_filter(ref_sq, metadata_filter) + ref_sq = ref_sq.subquery() + + # Count tags across those references + q = ( + select( + AssetReferenceTag.tag_name, + func.count(AssetReferenceTag.asset_reference_id).label("cnt"), + ) + .where(AssetReferenceTag.asset_reference_id.in_(select(ref_sq.c.id))) + .group_by(AssetReferenceTag.tag_name) + .order_by(func.count(AssetReferenceTag.asset_reference_id).desc(), AssetReferenceTag.tag_name.asc()) + .limit(limit) + ) + + rows = session.execute(q).all() + return {tag_name: int(cnt) for tag_name, cnt in rows} + + def bulk_insert_tags_and_meta( session: Session, tag_rows: list[dict], diff --git a/app/assets/scanner.py b/app/assets/scanner.py index e27ea5123..4e05a97b5 100644 --- a/app/assets/scanner.py +++ b/app/assets/scanner.py @@ -18,7 +18,7 @@ from app.assets.database.queries import ( mark_references_missing_outside_prefixes, reassign_asset_references, remove_missing_tag_for_asset_id, - set_reference_metadata, + set_reference_system_metadata, update_asset_hash_and_mime, ) from app.assets.services.bulk_ingest import ( @@ -490,8 +490,8 @@ def enrich_asset( logging.warning("Failed to hash %s: %s", file_path, e) if extract_metadata and metadata: - user_metadata = metadata.to_user_metadata() - set_reference_metadata(session, reference_id, user_metadata) + system_metadata = metadata.to_user_metadata() + set_reference_system_metadata(session, reference_id, system_metadata) if full_hash: existing = get_asset_by_hash(session, full_hash) diff --git a/app/assets/services/asset_management.py b/app/assets/services/asset_management.py index 3fe7115c8..5aefd9956 100644 --- a/app/assets/services/asset_management.py +++ b/app/assets/services/asset_management.py @@ -16,10 +16,12 @@ from app.assets.database.queries import ( get_reference_by_id, get_reference_with_owner_check, list_references_page, + list_all_file_paths_by_asset_id, list_references_by_asset_id, set_reference_metadata, set_reference_preview, set_reference_tags, + update_asset_hash_and_mime, update_reference_access_time, update_reference_name, update_reference_updated_at, @@ -67,6 +69,8 @@ def update_asset_metadata( user_metadata: UserMetadata = None, tag_origin: str = "manual", owner_id: str = "", + mime_type: str | None = None, + preview_id: str | None = None, ) -> AssetDetailResult: with create_session() as session: ref = get_reference_with_owner_check(session, reference_id, owner_id) @@ -103,6 +107,21 @@ def update_asset_metadata( ) touched = True + if mime_type is not None: + updated = update_asset_hash_and_mime( + session, asset_id=ref.asset_id, mime_type=mime_type + ) + if updated: + touched = True + + if preview_id is not None: + set_reference_preview( + session, + reference_id=reference_id, + preview_reference_id=preview_id, + ) + touched = True + if touched and user_metadata is None: update_reference_updated_at(session, reference_id=reference_id) @@ -159,11 +178,9 @@ def delete_asset_reference( session.commit() return True - # Orphaned asset - delete it and its files - refs = list_references_by_asset_id(session, asset_id=asset_id) - file_paths = [ - r.file_path for r in (refs or []) if getattr(r, "file_path", None) - ] + # Orphaned asset - gather ALL file paths (including + # soft-deleted / missing refs) so their on-disk files get cleaned up. + file_paths = list_all_file_paths_by_asset_id(session, asset_id=asset_id) # Also include the just-deleted file path if file_path: file_paths.append(file_path) @@ -185,7 +202,7 @@ def delete_asset_reference( def set_asset_preview( reference_id: str, - preview_asset_id: str | None = None, + preview_reference_id: str | None = None, owner_id: str = "", ) -> AssetDetailResult: with create_session() as session: @@ -194,7 +211,7 @@ def set_asset_preview( set_reference_preview( session, reference_id=reference_id, - preview_asset_id=preview_asset_id, + preview_reference_id=preview_reference_id, ) result = fetch_reference_asset_and_tags( @@ -263,6 +280,47 @@ def list_assets_page( return ListAssetsResult(items=items, total=total) +def resolve_hash_to_path( + asset_hash: str, + owner_id: str = "", +) -> DownloadResolutionResult | None: + """Resolve a blake3 hash to an on-disk file path. + + Only references visible to *owner_id* are considered (owner-less + references are always visible). + + Returns a DownloadResolutionResult with abs_path, content_type, and + download_name, or None if no asset or live path is found. + """ + with create_session() as session: + asset = queries_get_asset_by_hash(session, asset_hash) + if not asset: + return None + refs = list_references_by_asset_id(session, asset_id=asset.id) + visible = [ + r for r in refs + if r.owner_id == "" or r.owner_id == owner_id + ] + abs_path = select_best_live_path(visible) + if not abs_path: + return None + display_name = os.path.basename(abs_path) + for ref in visible: + if ref.file_path == abs_path and ref.name: + display_name = ref.name + break + ctype = ( + asset.mime_type + or mimetypes.guess_type(display_name)[0] + or "application/octet-stream" + ) + return DownloadResolutionResult( + abs_path=abs_path, + content_type=ctype, + download_name=display_name, + ) + + def resolve_asset_for_download( reference_id: str, owner_id: str = "", diff --git a/app/assets/services/ingest.py b/app/assets/services/ingest.py index 44d7aef36..90c51994f 100644 --- a/app/assets/services/ingest.py +++ b/app/assets/services/ingest.py @@ -11,13 +11,14 @@ from app.assets.database.queries import ( add_tags_to_reference, fetch_reference_and_asset, get_asset_by_hash, - get_existing_asset_ids, get_reference_by_file_path, get_reference_tags, get_or_create_reference, + reference_exists, remove_missing_tag_for_asset_id, set_reference_metadata, set_reference_tags, + update_asset_hash_and_mime, upsert_asset, upsert_reference, validate_tags_exist, @@ -26,6 +27,7 @@ from app.assets.helpers import normalize_tags from app.assets.services.file_utils import get_size_and_mtime_ns from app.assets.services.path_utils import ( compute_relative_filename, + get_name_and_tags_from_asset_path, resolve_destination_from_tags, validate_path_within_base, ) @@ -65,7 +67,7 @@ def _ingest_file_from_path( with create_session() as session: if preview_id: - if preview_id not in get_existing_asset_ids(session, [preview_id]): + if not reference_exists(session, preview_id): preview_id = None asset, asset_created, asset_updated = upsert_asset( @@ -135,6 +137,8 @@ def _register_existing_asset( tags: list[str] | None = None, tag_origin: str = "manual", owner_id: str = "", + mime_type: str | None = None, + preview_id: str | None = None, ) -> RegisterAssetResult: user_metadata = user_metadata or {} @@ -143,14 +147,25 @@ def _register_existing_asset( if not asset: raise ValueError(f"No asset with hash {asset_hash}") + if mime_type and not asset.mime_type: + update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=mime_type) + + if preview_id: + if not reference_exists(session, preview_id): + preview_id = None + ref, ref_created = get_or_create_reference( session, asset_id=asset.id, owner_id=owner_id, name=name, + preview_id=preview_id, ) if not ref_created: + if preview_id and ref.preview_id != preview_id: + ref.preview_id = preview_id + tag_names = get_reference_tags(session, reference_id=ref.id) result = RegisterAssetResult( ref=extract_reference_data(ref), @@ -242,6 +257,8 @@ def upload_from_temp_path( client_filename: str | None = None, owner_id: str = "", expected_hash: str | None = None, + mime_type: str | None = None, + preview_id: str | None = None, ) -> UploadResult: try: digest, _ = hashing.compute_blake3_hash(temp_path) @@ -270,6 +287,8 @@ def upload_from_temp_path( tags=tags or [], tag_origin="manual", owner_id=owner_id, + mime_type=mime_type, + preview_id=preview_id, ) return UploadResult( ref=result.ref, @@ -291,7 +310,7 @@ def upload_from_temp_path( dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename)) validate_path_within_base(dest_abs, base_dir) - content_type = ( + content_type = mime_type or ( mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0] or mimetypes.guess_type(hashed_basename, strict=False)[0] or "application/octet-stream" @@ -315,7 +334,7 @@ def upload_from_temp_path( mime_type=content_type, info_name=_sanitize_filename(name or client_filename, fallback=digest), owner_id=owner_id, - preview_id=None, + preview_id=preview_id, user_metadata=user_metadata or {}, tags=tags, tag_origin="manual", @@ -342,30 +361,99 @@ def upload_from_temp_path( ) +def register_file_in_place( + abs_path: str, + name: str, + tags: list[str], + owner_id: str = "", + mime_type: str | None = None, +) -> UploadResult: + """Register an already-saved file in the asset database without moving it. + + Tags are derived from the filesystem path (root category + subfolder names), + merged with any caller-provided tags, matching the behavior of the scanner. + If the path is not under a known root, only the caller-provided tags are used. + """ + try: + _, path_tags = get_name_and_tags_from_asset_path(abs_path) + except ValueError: + path_tags = [] + merged_tags = normalize_tags([*path_tags, *tags]) + + try: + digest, _ = hashing.compute_blake3_hash(abs_path) + except ImportError as e: + raise DependencyMissingError(str(e)) + except Exception as e: + raise RuntimeError(f"failed to hash file: {e}") + asset_hash = "blake3:" + digest + + size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path) + content_type = mime_type or ( + mimetypes.guess_type(abs_path, strict=False)[0] + or "application/octet-stream" + ) + + ingest_result = _ingest_file_from_path( + abs_path=abs_path, + asset_hash=asset_hash, + size_bytes=size_bytes, + mtime_ns=mtime_ns, + mime_type=content_type, + info_name=_sanitize_filename(name, fallback=digest), + owner_id=owner_id, + tags=merged_tags, + tag_origin="upload", + require_existing_tags=False, + ) + reference_id = ingest_result.reference_id + if not reference_id: + raise RuntimeError("failed to create asset reference") + + with create_session() as session: + pair = fetch_reference_and_asset( + session, reference_id=reference_id, owner_id=owner_id + ) + if not pair: + raise RuntimeError("inconsistent DB state after ingest") + ref, asset = pair + tag_names = get_reference_tags(session, reference_id=ref.id) + + return UploadResult( + ref=extract_reference_data(ref), + asset=extract_asset_data(asset), + tags=tag_names, + created_new=ingest_result.asset_created, + ) + + def create_from_hash( hash_str: str, name: str, tags: list[str] | None = None, user_metadata: dict | None = None, owner_id: str = "", + mime_type: str | None = None, + preview_id: str | None = None, ) -> UploadResult | None: canonical = hash_str.strip().lower() - with create_session() as session: - asset = get_asset_by_hash(session, asset_hash=canonical) - if not asset: - return None - - result = _register_existing_asset( - asset_hash=canonical, - name=_sanitize_filename( - name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical - ), - user_metadata=user_metadata or {}, - tags=tags or [], - tag_origin="manual", - owner_id=owner_id, - ) + try: + result = _register_existing_asset( + asset_hash=canonical, + name=_sanitize_filename( + name, fallback=canonical.split(":", 1)[1] if ":" in canonical else canonical + ), + user_metadata=user_metadata or {}, + tags=tags or [], + tag_origin="manual", + owner_id=owner_id, + mime_type=mime_type, + preview_id=preview_id, + ) + except ValueError: + logging.warning("create_from_hash: no asset found for hash %s", canonical) + return None return UploadResult( ref=result.ref, diff --git a/app/assets/services/schemas.py b/app/assets/services/schemas.py index 8b1f1f4dc..0eb128f58 100644 --- a/app/assets/services/schemas.py +++ b/app/assets/services/schemas.py @@ -25,7 +25,9 @@ class ReferenceData: preview_id: str | None created_at: datetime updated_at: datetime - last_access_time: datetime | None + system_metadata: dict[str, Any] | None = None + job_id: str | None = None + last_access_time: datetime | None = None @dataclass(frozen=True) @@ -93,6 +95,8 @@ def extract_reference_data(ref: AssetReference) -> ReferenceData: file_path=ref.file_path, user_metadata=ref.user_metadata, preview_id=ref.preview_id, + system_metadata=ref.system_metadata, + job_id=ref.job_id, created_at=ref.created_at, updated_at=ref.updated_at, last_access_time=ref.last_access_time, diff --git a/app/assets/services/tagging.py b/app/assets/services/tagging.py index 28900464d..37b612753 100644 --- a/app/assets/services/tagging.py +++ b/app/assets/services/tagging.py @@ -1,3 +1,5 @@ +from typing import Sequence + from app.assets.database.queries import ( AddTagsResult, RemoveTagsResult, @@ -6,6 +8,7 @@ from app.assets.database.queries import ( list_tags_with_usage, remove_tags_from_reference, ) +from app.assets.database.queries.tags import list_tag_counts_for_filtered_assets from app.assets.services.schemas import TagUsage from app.database.db import create_session @@ -73,3 +76,23 @@ def list_tags( ) return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total + + +def list_tag_histogram( + owner_id: str = "", + include_tags: Sequence[str] | None = None, + exclude_tags: Sequence[str] | None = None, + name_contains: str | None = None, + metadata_filter: dict | None = None, + limit: int = 100, +) -> dict[str, int]: + with create_session() as session: + return list_tag_counts_for_filtered_assets( + session, + owner_id=owner_id, + include_tags=include_tags, + exclude_tags=exclude_tags, + name_contains=name_contains, + metadata_filter=metadata_filter, + limit=limit, + ) diff --git a/app/database/models.py b/app/database/models.py index e7572677a..b02856f6e 100644 --- a/app/database/models.py +++ b/app/database/models.py @@ -1,9 +1,18 @@ from typing import Any from datetime import datetime +from sqlalchemy import MetaData from sqlalchemy.orm import DeclarativeBase +NAMING_CONVENTION = { + "ix": "ix_%(table_name)s_%(column_0_N_name)s", + "uq": "uq_%(table_name)s_%(column_0_N_name)s", + "ck": "ck_%(table_name)s_%(constraint_name)s", + "fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s", + "pk": "pk_%(table_name)s", +} + class Base(DeclarativeBase): - pass + metadata = MetaData(naming_convention=NAMING_CONVENTION) def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]: fields = obj.__table__.columns.keys() diff --git a/server.py b/server.py index 85a8964be..173a28376 100644 --- a/server.py +++ b/server.py @@ -35,6 +35,8 @@ from app.frontend_management import FrontendManager, parse_version from comfy_api.internal import _ComfyNodeInternal from app.assets.seeder import asset_seeder from app.assets.api.routes import register_assets_routes +from app.assets.services.ingest import register_file_in_place +from app.assets.services.asset_management import resolve_hash_to_path from app.user_manager import UserManager from app.model_manager import ModelFileManager @@ -419,7 +421,24 @@ class PromptServer(): with open(filepath, "wb") as f: f.write(image.file.read()) - return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) + resp = {"name" : filename, "subfolder": subfolder, "type": image_upload_type} + + if args.enable_assets: + try: + tag = image_upload_type if image_upload_type in ("input", "output") else "input" + result = register_file_in_place(abs_path=filepath, name=filename, tags=[tag]) + resp["asset"] = { + "id": result.ref.id, + "name": result.ref.name, + "asset_hash": result.asset.hash, + "size": result.asset.size_bytes, + "mime_type": result.asset.mime_type, + "tags": result.tags, + } + except Exception: + logging.warning("Failed to register uploaded image as asset", exc_info=True) + + return web.json_response(resp) else: return web.Response(status=400) @@ -479,30 +498,43 @@ class PromptServer(): async def view_image(request): if "filename" in request.rel_url.query: filename = request.rel_url.query["filename"] - filename, output_dir = folder_paths.annotated_filepath(filename) - if not filename: - return web.Response(status=400) + # The frontend's LoadImage combo widget uses asset_hash values + # (e.g. "blake3:...") as widget values. When litegraph renders the + # node preview, it constructs /view?filename=, so this + # endpoint must resolve blake3 hashes to their on-disk file paths. + if filename.startswith("blake3:"): + owner_id = self.user_manager.get_request_user_id(request) + result = resolve_hash_to_path(filename, owner_id=owner_id) + if result is None: + return web.Response(status=404) + file, filename, resolved_content_type = result.abs_path, result.download_name, result.content_type + else: + resolved_content_type = None + filename, output_dir = folder_paths.annotated_filepath(filename) - # validation for security: prevent accessing arbitrary path - if filename[0] == '/' or '..' in filename: - return web.Response(status=400) + if not filename: + return web.Response(status=400) - if output_dir is None: - type = request.rel_url.query.get("type", "output") - output_dir = folder_paths.get_directory_by_type(type) + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) - if output_dir is None: - return web.Response(status=400) + if output_dir is None: + type = request.rel_url.query.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) - if "subfolder" in request.rel_url.query: - full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) - if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: - return web.Response(status=403) - output_dir = full_output_dir + if output_dir is None: + return web.Response(status=400) - filename = os.path.basename(filename) - file = os.path.join(output_dir, filename) + if "subfolder" in request.rel_url.query: + full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + filename = os.path.basename(filename) + file = os.path.join(output_dir, filename) if os.path.isfile(file): if 'preview' in request.rel_url.query: @@ -562,8 +594,13 @@ class PromptServer(): return web.Response(body=alpha_buffer.read(), content_type='image/png', headers={"Content-Disposition": f"filename=\"{filename}\""}) else: - # Get content type from mimetype, defaulting to 'application/octet-stream' - content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream' + # Use the content type from asset resolution if available, + # otherwise guess from the filename. + content_type = ( + resolved_content_type + or mimetypes.guess_type(filename)[0] + or 'application/octet-stream' + ) # For security, force certain mimetypes to download instead of display if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}: diff --git a/tests-unit/app_test/test_migrations.py b/tests-unit/app_test/test_migrations.py new file mode 100644 index 000000000..fa10c1727 --- /dev/null +++ b/tests-unit/app_test/test_migrations.py @@ -0,0 +1,57 @@ +"""Test that Alembic migrations run cleanly on a file-backed SQLite DB. + +This catches problems like unnamed FK constraints that prevent batch-mode +drop_constraint from working on real SQLite files (see MB-2). + +Migrations 0001 and 0002 are already shipped, so we only exercise +upgrade/downgrade for 0003+. +""" + +import os + +import pytest +from alembic import command +from alembic.config import Config + + +# Oldest shipped revision — we upgrade to here as a baseline and never +# downgrade past it. +_BASELINE = "0002_merge_to_asset_references" + + +def _make_config(db_path: str) -> Config: + root = os.path.join(os.path.dirname(__file__), "../..") + config_path = os.path.abspath(os.path.join(root, "alembic.ini")) + scripts_path = os.path.abspath(os.path.join(root, "alembic_db")) + + cfg = Config(config_path) + cfg.set_main_option("script_location", scripts_path) + cfg.set_main_option("sqlalchemy.url", f"sqlite:///{db_path}") + return cfg + + +@pytest.fixture +def migration_db(tmp_path): + """Yield an alembic Config pre-upgraded to the baseline revision.""" + db_path = str(tmp_path / "test_migration.db") + cfg = _make_config(db_path) + command.upgrade(cfg, _BASELINE) + yield cfg + + +def test_upgrade_to_head(migration_db): + """Upgrade from baseline to head must succeed on a file-backed DB.""" + command.upgrade(migration_db, "head") + + +def test_downgrade_to_baseline(migration_db): + """Upgrade to head then downgrade back to baseline.""" + command.upgrade(migration_db, "head") + command.downgrade(migration_db, _BASELINE) + + +def test_upgrade_downgrade_cycle(migration_db): + """Full cycle: upgrade → downgrade → upgrade again.""" + command.upgrade(migration_db, "head") + command.downgrade(migration_db, _BASELINE) + command.upgrade(migration_db, "head") diff --git a/tests-unit/assets_test/queries/test_asset.py b/tests-unit/assets_test/queries/test_asset.py index 08f84cd11..9b7eb4bac 100644 --- a/tests-unit/assets_test/queries/test_asset.py +++ b/tests-unit/assets_test/queries/test_asset.py @@ -10,6 +10,7 @@ from app.assets.database.queries import ( get_asset_by_hash, upsert_asset, bulk_insert_assets, + update_asset_hash_and_mime, ) @@ -142,3 +143,45 @@ class TestBulkInsertAssets: session.commit() assert session.query(Asset).count() == 200 + + +class TestMimeTypeImmutability: + """mime_type on Asset is write-once: set on first ingest, never overwritten.""" + + @pytest.mark.parametrize( + "initial_mime,second_mime,expected_mime", + [ + ("image/png", "image/jpeg", "image/png"), + (None, "image/png", "image/png"), + ], + ids=["preserves_existing", "fills_null"], + ) + def test_upsert_mime_immutability(self, session: Session, initial_mime, second_mime, expected_mime): + h = f"blake3:upsert_{initial_mime}_{second_mime}" + upsert_asset(session, asset_hash=h, size_bytes=100, mime_type=initial_mime) + session.commit() + + asset, created, _ = upsert_asset(session, asset_hash=h, size_bytes=100, mime_type=second_mime) + assert created is False + assert asset.mime_type == expected_mime + + @pytest.mark.parametrize( + "initial_mime,update_mime,update_hash,expected_mime,expected_hash", + [ + (None, "image/png", None, "image/png", "blake3:upd0"), + ("image/png", "image/jpeg", None, "image/png", "blake3:upd1"), + ("image/png", "image/jpeg", "blake3:upd2_new", "image/png", "blake3:upd2_new"), + ], + ids=["fills_null", "preserves_existing", "hash_updates_mime_locked"], + ) + def test_update_asset_hash_and_mime_immutability( + self, session: Session, initial_mime, update_mime, update_hash, expected_mime, expected_hash, + ): + h = expected_hash.removesuffix("_new") + asset = Asset(hash=h, size_bytes=100, mime_type=initial_mime) + session.add(asset) + session.flush() + + update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=update_mime, asset_hash=update_hash) + assert asset.mime_type == expected_mime + assert asset.hash == expected_hash diff --git a/tests-unit/assets_test/queries/test_asset_info.py b/tests-unit/assets_test/queries/test_asset_info.py index 8f6c7fcdb..fe510e342 100644 --- a/tests-unit/assets_test/queries/test_asset_info.py +++ b/tests-unit/assets_test/queries/test_asset_info.py @@ -242,22 +242,24 @@ class TestSetReferencePreview: asset = _make_asset(session, "hash1") preview_asset = _make_asset(session, "preview_hash") ref = _make_reference(session, asset) + preview_ref = _make_reference(session, preview_asset, name="preview.png") session.commit() - set_reference_preview(session, reference_id=ref.id, preview_asset_id=preview_asset.id) + set_reference_preview(session, reference_id=ref.id, preview_reference_id=preview_ref.id) session.commit() session.refresh(ref) - assert ref.preview_id == preview_asset.id + assert ref.preview_id == preview_ref.id def test_clears_preview(self, session: Session): asset = _make_asset(session, "hash1") preview_asset = _make_asset(session, "preview_hash") ref = _make_reference(session, asset) - ref.preview_id = preview_asset.id + preview_ref = _make_reference(session, preview_asset, name="preview.png") + ref.preview_id = preview_ref.id session.commit() - set_reference_preview(session, reference_id=ref.id, preview_asset_id=None) + set_reference_preview(session, reference_id=ref.id, preview_reference_id=None) session.commit() session.refresh(ref) @@ -265,15 +267,15 @@ class TestSetReferencePreview: def test_raises_for_nonexistent_reference(self, session: Session): with pytest.raises(ValueError, match="not found"): - set_reference_preview(session, reference_id="nonexistent", preview_asset_id=None) + set_reference_preview(session, reference_id="nonexistent", preview_reference_id=None) def test_raises_for_nonexistent_preview(self, session: Session): asset = _make_asset(session, "hash1") ref = _make_reference(session, asset) session.commit() - with pytest.raises(ValueError, match="Preview Asset"): - set_reference_preview(session, reference_id=ref.id, preview_asset_id="nonexistent") + with pytest.raises(ValueError, match="Preview AssetReference"): + set_reference_preview(session, reference_id=ref.id, preview_reference_id="nonexistent") class TestInsertReference: @@ -351,13 +353,14 @@ class TestUpdateReferenceTimestamps: asset = _make_asset(session, "hash1") preview_asset = _make_asset(session, "preview_hash") ref = _make_reference(session, asset) + preview_ref = _make_reference(session, preview_asset, name="preview.png") session.commit() - update_reference_timestamps(session, ref, preview_id=preview_asset.id) + update_reference_timestamps(session, ref, preview_id=preview_ref.id) session.commit() session.refresh(ref) - assert ref.preview_id == preview_asset.id + assert ref.preview_id == preview_ref.id class TestSetReferenceMetadata: diff --git a/tests-unit/assets_test/queries/test_metadata.py b/tests-unit/assets_test/queries/test_metadata.py index 6a545e819..d7a747789 100644 --- a/tests-unit/assets_test/queries/test_metadata.py +++ b/tests-unit/assets_test/queries/test_metadata.py @@ -20,6 +20,7 @@ def _make_reference( asset: Asset, name: str, metadata: dict | None = None, + system_metadata: dict | None = None, ) -> AssetReference: now = get_utc_now() ref = AssetReference( @@ -27,6 +28,7 @@ def _make_reference( name=name, asset_id=asset.id, user_metadata=metadata, + system_metadata=system_metadata, created_at=now, updated_at=now, last_access_time=now, @@ -34,8 +36,10 @@ def _make_reference( session.add(ref) session.flush() - if metadata: - for key, val in metadata.items(): + # Build merged projection: {**system_metadata, **user_metadata} + merged = {**(system_metadata or {}), **(metadata or {})} + if merged: + for key, val in merged.items(): for row in convert_metadata_to_rows(key, val): meta_row = AssetReferenceMeta( asset_reference_id=ref.id, @@ -182,3 +186,46 @@ class TestMetadataFilterEmptyDict: refs, _, total = list_references_page(session, metadata_filter={}) assert total == 2 + + +class TestSystemMetadataProjection: + """Tests for system_metadata merging into the filter projection.""" + + def test_system_metadata_keys_are_filterable(self, session: Session): + """system_metadata keys should appear in the merged projection.""" + asset = _make_asset(session, "hash1") + _make_reference( + session, asset, "with_sys", + system_metadata={"source": "scanner"}, + ) + _make_reference(session, asset, "without_sys") + session.commit() + + refs, _, total = list_references_page( + session, metadata_filter={"source": "scanner"} + ) + assert total == 1 + assert refs[0].name == "with_sys" + + def test_user_metadata_overrides_system_metadata(self, session: Session): + """user_metadata should win when both have the same key.""" + asset = _make_asset(session, "hash1") + _make_reference( + session, asset, "overridden", + metadata={"origin": "user_upload"}, + system_metadata={"origin": "auto_scan"}, + ) + session.commit() + + # Should match the user value, not the system value + refs, _, total = list_references_page( + session, metadata_filter={"origin": "user_upload"} + ) + assert total == 1 + assert refs[0].name == "overridden" + + # Should NOT match the system value (it was overridden) + refs, _, total = list_references_page( + session, metadata_filter={"origin": "auto_scan"} + ) + assert total == 0 diff --git a/tests-unit/assets_test/services/test_asset_management.py b/tests-unit/assets_test/services/test_asset_management.py index 101ef7292..e8ff989e9 100644 --- a/tests-unit/assets_test/services/test_asset_management.py +++ b/tests-unit/assets_test/services/test_asset_management.py @@ -11,6 +11,7 @@ from app.assets.services import ( delete_asset_reference, set_asset_preview, ) +from app.assets.services.asset_management import resolve_hash_to_path def _make_asset(session: Session, hash_val: str = "blake3:test", size: int = 1024) -> Asset: @@ -219,31 +220,33 @@ class TestSetAssetPreview: asset = _make_asset(session, hash_val="blake3:main") preview_asset = _make_asset(session, hash_val="blake3:preview") ref = _make_reference(session, asset) + preview_ref = _make_reference(session, preview_asset, name="preview.png") ref_id = ref.id - preview_id = preview_asset.id + preview_ref_id = preview_ref.id session.commit() set_asset_preview( reference_id=ref_id, - preview_asset_id=preview_id, + preview_reference_id=preview_ref_id, ) # Verify by re-fetching from DB session.expire_all() updated_ref = session.get(AssetReference, ref_id) - assert updated_ref.preview_id == preview_id + assert updated_ref.preview_id == preview_ref_id def test_clears_preview(self, mock_create_session, session: Session): asset = _make_asset(session) preview_asset = _make_asset(session, hash_val="blake3:preview") ref = _make_reference(session, asset) - ref.preview_id = preview_asset.id + preview_ref = _make_reference(session, preview_asset, name="preview.png") + ref.preview_id = preview_ref.id ref_id = ref.id session.commit() set_asset_preview( reference_id=ref_id, - preview_asset_id=None, + preview_reference_id=None, ) # Verify by re-fetching from DB @@ -263,6 +266,45 @@ class TestSetAssetPreview: with pytest.raises(PermissionError, match="not owner"): set_asset_preview( reference_id=ref.id, - preview_asset_id=None, + preview_reference_id=None, owner_id="user2", ) + + +class TestResolveHashToPath: + def test_returns_none_for_unknown_hash(self, mock_create_session): + result = resolve_hash_to_path("blake3:" + "a" * 64) + assert result is None + + @pytest.mark.parametrize( + "ref_owner, query_owner, expect_found", + [ + ("user1", "user1", True), + ("user1", "user2", False), + ("", "anyone", True), + ("", "", True), + ], + ids=[ + "owner_sees_own_ref", + "other_owner_blocked", + "ownerless_visible_to_anyone", + "ownerless_visible_to_empty", + ], + ) + def test_owner_visibility( + self, ref_owner, query_owner, expect_found, + mock_create_session, session: Session, temp_dir, + ): + f = temp_dir / "file.bin" + f.write_bytes(b"data") + asset = _make_asset(session, hash_val="blake3:" + "b" * 64) + ref = _make_reference(session, asset, name="file.bin", owner_id=ref_owner) + ref.file_path = str(f) + session.commit() + + result = resolve_hash_to_path(asset.hash, owner_id=query_owner) + if expect_found: + assert result is not None + assert result.abs_path == str(f) + else: + assert result is None diff --git a/tests-unit/assets_test/services/test_ingest.py b/tests-unit/assets_test/services/test_ingest.py index 367bc7721..dbb8441c2 100644 --- a/tests-unit/assets_test/services/test_ingest.py +++ b/tests-unit/assets_test/services/test_ingest.py @@ -113,11 +113,19 @@ class TestIngestFileFromPath: file_path = temp_dir / "with_preview.bin" file_path.write_bytes(b"data") - # Create a preview asset first + # Create a preview asset and reference preview_asset = Asset(hash="blake3:preview", size_bytes=100) session.add(preview_asset) + session.flush() + from app.assets.helpers import get_utc_now + now = get_utc_now() + preview_ref = AssetReference( + asset_id=preview_asset.id, name="preview.png", owner_id="", + created_at=now, updated_at=now, last_access_time=now, + ) + session.add(preview_ref) session.commit() - preview_id = preview_asset.id + preview_id = preview_ref.id result = _ingest_file_from_path( abs_path=str(file_path), diff --git a/tests-unit/assets_test/services/test_tag_histogram.py b/tests-unit/assets_test/services/test_tag_histogram.py new file mode 100644 index 000000000..7bcd518ec --- /dev/null +++ b/tests-unit/assets_test/services/test_tag_histogram.py @@ -0,0 +1,123 @@ +"""Tests for list_tag_histogram service function.""" +from sqlalchemy.orm import Session + +from app.assets.database.models import Asset, AssetReference +from app.assets.database.queries import ensure_tags_exist, add_tags_to_reference +from app.assets.helpers import get_utc_now +from app.assets.services.tagging import list_tag_histogram + + +def _make_asset(session: Session, hash_val: str = "blake3:test") -> Asset: + asset = Asset(hash=hash_val, size_bytes=1024) + session.add(asset) + session.flush() + return asset + + +def _make_reference( + session: Session, + asset: Asset, + name: str = "test", + owner_id: str = "", +) -> AssetReference: + now = get_utc_now() + ref = AssetReference( + owner_id=owner_id, + name=name, + asset_id=asset.id, + created_at=now, + updated_at=now, + last_access_time=now, + ) + session.add(ref) + session.flush() + return ref + + +class TestListTagHistogram: + def test_returns_counts_for_all_tags(self, mock_create_session, session: Session): + ensure_tags_exist(session, ["alpha", "beta"]) + a1 = _make_asset(session, "blake3:aaa") + r1 = _make_reference(session, a1, name="r1") + add_tags_to_reference(session, reference_id=r1.id, tags=["alpha", "beta"]) + + a2 = _make_asset(session, "blake3:bbb") + r2 = _make_reference(session, a2, name="r2") + add_tags_to_reference(session, reference_id=r2.id, tags=["alpha"]) + session.commit() + + result = list_tag_histogram() + + assert result["alpha"] == 2 + assert result["beta"] == 1 + + def test_empty_when_no_assets(self, mock_create_session, session: Session): + ensure_tags_exist(session, ["unused"]) + session.commit() + + result = list_tag_histogram() + + assert result == {} + + def test_include_tags_filter(self, mock_create_session, session: Session): + ensure_tags_exist(session, ["models", "loras", "input"]) + a1 = _make_asset(session, "blake3:aaa") + r1 = _make_reference(session, a1, name="r1") + add_tags_to_reference(session, reference_id=r1.id, tags=["models", "loras"]) + + a2 = _make_asset(session, "blake3:bbb") + r2 = _make_reference(session, a2, name="r2") + add_tags_to_reference(session, reference_id=r2.id, tags=["input"]) + session.commit() + + result = list_tag_histogram(include_tags=["models"]) + + # Only r1 has "models", so only its tags appear + assert "models" in result + assert "loras" in result + assert "input" not in result + + def test_exclude_tags_filter(self, mock_create_session, session: Session): + ensure_tags_exist(session, ["models", "loras", "input"]) + a1 = _make_asset(session, "blake3:aaa") + r1 = _make_reference(session, a1, name="r1") + add_tags_to_reference(session, reference_id=r1.id, tags=["models", "loras"]) + + a2 = _make_asset(session, "blake3:bbb") + r2 = _make_reference(session, a2, name="r2") + add_tags_to_reference(session, reference_id=r2.id, tags=["input"]) + session.commit() + + result = list_tag_histogram(exclude_tags=["models"]) + + # r1 excluded, only r2's tags remain + assert "input" in result + assert "loras" not in result + + def test_name_contains_filter(self, mock_create_session, session: Session): + ensure_tags_exist(session, ["alpha", "beta"]) + a1 = _make_asset(session, "blake3:aaa") + r1 = _make_reference(session, a1, name="my_model.safetensors") + add_tags_to_reference(session, reference_id=r1.id, tags=["alpha"]) + + a2 = _make_asset(session, "blake3:bbb") + r2 = _make_reference(session, a2, name="picture.png") + add_tags_to_reference(session, reference_id=r2.id, tags=["beta"]) + session.commit() + + result = list_tag_histogram(name_contains="model") + + assert "alpha" in result + assert "beta" not in result + + def test_limit_caps_results(self, mock_create_session, session: Session): + tags = [f"tag{i}" for i in range(10)] + ensure_tags_exist(session, tags) + a = _make_asset(session, "blake3:aaa") + r = _make_reference(session, a, name="r1") + add_tags_to_reference(session, reference_id=r.id, tags=tags) + session.commit() + + result = list_tag_histogram(limit=3) + + assert len(result) == 3 diff --git a/tests-unit/assets_test/test_uploads.py b/tests-unit/assets_test/test_uploads.py index d68e5b5d7..0f2b124a3 100644 --- a/tests-unit/assets_test/test_uploads.py +++ b/tests-unit/assets_test/test_uploads.py @@ -243,6 +243,15 @@ def test_upload_tags_traversal_guard(http: requests.Session, api_base: str): assert body["error"]["code"] in ("BAD_REQUEST", "INVALID_BODY") +def test_upload_empty_tags_rejected(http: requests.Session, api_base: str): + files = {"file": ("notags.bin", b"A" * 64, "application/octet-stream")} + form = {"tags": json.dumps([]), "name": "notags.bin", "user_metadata": json.dumps({})} + r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120) + body = r.json() + assert r.status_code == 400 + assert body["error"]["code"] == "INVALID_BODY" + + @pytest.mark.parametrize("root", ["input", "output"]) def test_duplicate_upload_same_display_name_does_not_clobber( root: str, From 7d5f5252c3dfdc8a6227e6f6ffb7aab5b3ec827c Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Mon, 16 Mar 2026 12:53:13 -0700 Subject: [PATCH 13/65] ci: add check to block AI agent Co-authored-by trailers in PRs (#12799) Add a GitHub Actions workflow and shell script that scan all commits in a pull request for Co-authored-by trailers from known AI coding agents (Claude, Cursor, Copilot, Codex, Aider, Devin, Gemini, Jules, Windsurf, Cline, Amazon Q, Continue, OpenCode, etc.). The check fails with clear instructions on how to remove the trailers via interactive rebase. --- .github/scripts/check-ai-co-authors.sh | 103 ++++++++++++++++++++++ .github/workflows/check-ai-co-authors.yml | 19 ++++ 2 files changed, 122 insertions(+) create mode 100755 .github/scripts/check-ai-co-authors.sh create mode 100644 .github/workflows/check-ai-co-authors.yml diff --git a/.github/scripts/check-ai-co-authors.sh b/.github/scripts/check-ai-co-authors.sh new file mode 100755 index 000000000..842b1f2d8 --- /dev/null +++ b/.github/scripts/check-ai-co-authors.sh @@ -0,0 +1,103 @@ +#!/usr/bin/env bash +# Checks pull request commits for AI agent Co-authored-by trailers. +# Exits non-zero when any are found and prints fix instructions. +set -euo pipefail + +base_sha="${1:?usage: check-ai-co-authors.sh }" +head_sha="${2:?usage: check-ai-co-authors.sh }" + +# Known AI coding-agent trailer patterns (case-insensitive). +# Each entry is an extended-regex fragment matched against Co-authored-by lines. +AGENT_PATTERNS=( + # Anthropic — Claude Code / Amp + 'noreply@anthropic\.com' + # Cursor + 'cursoragent@cursor\.com' + # GitHub Copilot + 'copilot-swe-agent\[bot\]' + 'copilot@github\.com' + # OpenAI Codex + 'noreply@openai\.com' + 'codex@openai\.com' + # Aider + 'aider@aider\.chat' + # Google — Gemini / Jules + 'gemini@google\.com' + 'jules@google\.com' + # Windsurf / Codeium + '@codeium\.com' + # Devin + 'devin-ai-integration\[bot\]' + 'devin@cognition\.ai' + 'devin@cognition-labs\.com' + # Amazon Q Developer + 'amazon-q-developer' + '@amazon\.com.*[Qq].[Dd]eveloper' + # Cline + 'cline-bot' + 'cline@cline\.ai' + # Continue + 'continue-agent' + 'continue@continue\.dev' + # Sourcegraph + 'noreply@sourcegraph\.com' + # Generic catch-alls for common agent name patterns + 'Co-authored-by:.*\b[Cc]laude\b' + 'Co-authored-by:.*\b[Cc]opilot\b' + 'Co-authored-by:.*\b[Cc]ursor\b' + 'Co-authored-by:.*\b[Cc]odex\b' + 'Co-authored-by:.*\b[Gg]emini\b' + 'Co-authored-by:.*\b[Aa]ider\b' + 'Co-authored-by:.*\b[Dd]evin\b' + 'Co-authored-by:.*\b[Ww]indsurf\b' + 'Co-authored-by:.*\b[Cc]line\b' + 'Co-authored-by:.*\b[Aa]mazon Q\b' + 'Co-authored-by:.*\b[Jj]ules\b' + 'Co-authored-by:.*\bOpenCode\b' +) + +# Build a single alternation regex from all patterns. +regex="" +for pattern in "${AGENT_PATTERNS[@]}"; do + if [[ -n "$regex" ]]; then + regex="${regex}|${pattern}" + else + regex="$pattern" + fi +done + +# Collect Co-authored-by lines from every commit in the PR range. +violations="" +while IFS= read -r sha; do + message="$(git log -1 --format='%B' "$sha")" + matched_lines="$(echo "$message" | grep -iE "^Co-authored-by:" || true)" + if [[ -z "$matched_lines" ]]; then + continue + fi + + while IFS= read -r line; do + if echo "$line" | grep -iqE "$regex"; then + short="$(git log -1 --format='%h' "$sha")" + violations="${violations} ${short}: ${line}"$'\n' + fi + done <<< "$matched_lines" +done < <(git rev-list "${base_sha}..${head_sha}") + +if [[ -n "$violations" ]]; then + echo "::error::AI agent Co-authored-by trailers detected in PR commits." + echo "" + echo "The following commits contain Co-authored-by trailers from AI coding agents:" + echo "" + echo "$violations" + echo "These trailers should be removed before merging." + echo "" + echo "To fix, rewrite the commit messages with:" + echo " git rebase -i ${base_sha}" + echo "" + echo "and remove the Co-authored-by lines, then force-push your branch." + echo "" + echo "If you believe this is a false positive, please open an issue." + exit 1 +fi + +echo "No AI agent Co-authored-by trailers found." diff --git a/.github/workflows/check-ai-co-authors.yml b/.github/workflows/check-ai-co-authors.yml new file mode 100644 index 000000000..2ad9ac972 --- /dev/null +++ b/.github/workflows/check-ai-co-authors.yml @@ -0,0 +1,19 @@ +name: Check AI Co-Authors + +on: + pull_request: + branches: ['*'] + +jobs: + check-ai-co-authors: + name: Check for AI agent co-author trailers + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Check commits for AI co-author trailers + run: bash .github/scripts/check-ai-co-authors.sh "${{ github.event.pull_request.base.sha }}" "${{ github.event.pull_request.head.sha }}" From b202f842af10824b62a3158f0887ee371e16beb6 Mon Sep 17 00:00:00 2001 From: blepping <157360029+blepping@users.noreply.github.com> Date: Mon, 16 Mar 2026 14:00:42 -0600 Subject: [PATCH 14/65] Skip running model finalizers at exit (#12994) --- comfy/model_management.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index a4af5ddb2..2c250dacc 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -541,6 +541,7 @@ class LoadedModel: if model.parent is not None: self._parent_model = weakref.ref(model.parent) self._patcher_finalizer = weakref.finalize(model, self._switch_parent) + self._patcher_finalizer.atexit = False def _switch_parent(self): model = self._parent_model() @@ -587,6 +588,7 @@ class LoadedModel: self.real_model = weakref.ref(real_model) self.model_finalizer = weakref.finalize(real_model, cleanup_models) + self.model_finalizer.atexit = False return real_model def should_reload_model(self, force_patch_weights=False): From 7a16e8aa4e4672733280887a38758be530ba13ea Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 16 Mar 2026 13:50:13 -0700 Subject: [PATCH 15/65] Add --enable-dynamic-vram options to force enable it. (#13002) --- comfy/cli_args.py | 3 +++ main.py | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 0a0bf2f30..13612175e 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -149,6 +149,7 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.") parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.") parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.") +parser.add_argument("--enable-dynamic-vram", action="store_true", help="Enable dynamic VRAM on systems where it's not enabled by default.") parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.") @@ -262,4 +263,6 @@ else: args.fast = set(args.fast) def enables_dynamic_vram(): + if args.enable_dynamic_vram: + return True return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu diff --git a/main.py b/main.py index 8905fd09a..f99aee38e 100644 --- a/main.py +++ b/main.py @@ -206,8 +206,8 @@ import hook_breaker_ac10a0 import comfy.memory_management import comfy.model_patcher -if enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl(): - if comfy.model_management.torch_version_numeric < (2, 8): +if args.enable_dynamic_vram or (enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl()): + if (not args.enable_dynamic_vram) and (comfy.model_management.torch_version_numeric < (2, 8)): logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") elif comfy_aimdo.control.init_device(comfy.model_management.get_torch_device().index): if args.verbose == 'DEBUG': From 20561aa91926508c6ad6db185193c9604cfdf3c9 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Tue, 17 Mar 2026 09:31:50 +0800 Subject: [PATCH 16/65] [Trainer] FP4, 8, 16 training by native dtype support and quant linear autograd function (#12681) --- comfy/ops.py | 101 ++++++++++++++++++++++++++++++++++-- comfy/utils.py | 4 ++ comfy_extras/nodes_train.py | 68 +++++++++++++++++------- 3 files changed, 150 insertions(+), 23 deletions(-) diff --git a/comfy/ops.py b/comfy/ops.py index f47d4137a..1518ec9de 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -776,6 +776,71 @@ from .quant_ops import ( ) +class QuantLinearFunc(torch.autograd.Function): + """Custom autograd function for quantized linear: quantized forward, compute_dtype backward. + Handles any input rank by flattening to 2D for matmul and restoring shape after. + """ + + @staticmethod + def forward(ctx, input_float, weight, bias, layout_type, input_scale, compute_dtype): + input_shape = input_float.shape + inp = input_float.detach().flatten(0, -2) # zero-cost view to 2D + + # Quantize input (same as inference path) + if layout_type is not None: + q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale) + else: + q_input = inp + + w = weight.detach() if weight.requires_grad else weight + b = bias.detach() if bias is not None and bias.requires_grad else bias + + output = torch.nn.functional.linear(q_input, w, b) + + # Restore original input shape + if len(input_shape) > 2: + output = output.unflatten(0, input_shape[:-1]) + + ctx.save_for_backward(input_float, weight) + ctx.input_shape = input_shape + ctx.has_bias = bias is not None + ctx.compute_dtype = compute_dtype + ctx.weight_requires_grad = weight.requires_grad + + return output + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output): + input_float, weight = ctx.saved_tensors + compute_dtype = ctx.compute_dtype + grad_2d = grad_output.flatten(0, -2).to(compute_dtype) + + # Dequantize weight to compute dtype for backward matmul + if isinstance(weight, QuantizedTensor): + weight_f = weight.dequantize().to(compute_dtype) + else: + weight_f = weight.to(compute_dtype) + + # grad_input = grad_output @ weight + grad_input = torch.mm(grad_2d, weight_f) + if len(ctx.input_shape) > 2: + grad_input = grad_input.unflatten(0, ctx.input_shape[:-1]) + + # grad_weight (only if weight requires grad, typically frozen for quantized training) + grad_weight = None + if ctx.weight_requires_grad: + input_f = input_float.flatten(0, -2).to(compute_dtype) + grad_weight = torch.mm(grad_2d.t(), input_f) + + # grad_bias + grad_bias = None + if ctx.has_bias: + grad_bias = grad_2d.sum(dim=0) + + return grad_input, grad_weight, grad_bias, None, None, None + + def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]): class MixedPrecisionOps(manual_cast): _quant_config = quant_config @@ -970,10 +1035,37 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec #If cast needs to apply lora, it should be done in the compute dtype compute_dtype = input.dtype - if (getattr(self, 'layout_type', None) is not None and + _use_quantized = ( + getattr(self, 'layout_type', None) is not None and not isinstance(input, QuantizedTensor) and not self._full_precision_mm and not getattr(self, 'comfy_force_cast_weights', False) and - len(self.weight_function) == 0 and len(self.bias_function) == 0): + len(self.weight_function) == 0 and len(self.bias_function) == 0 + ) + + # Training path: quantized forward with compute_dtype backward via autograd function + if (input.requires_grad and _use_quantized): + + weight, bias, offload_stream = cast_bias_weight( + self, + input, + offloadable=True, + compute_dtype=compute_dtype, + want_requant=True + ) + + scale = getattr(self, 'input_scale', None) + if scale is not None: + scale = comfy.model_management.cast_to_device(scale, input.device, None) + + output = QuantLinearFunc.apply( + input, weight, bias, self.layout_type, scale, compute_dtype + ) + + uncast_bias_weight(self, weight, bias, offload_stream) + return output + + # Inference path (unchanged) + if _use_quantized: # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others) input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input @@ -1021,7 +1113,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec for key, param in self._parameters.items(): if param is None: continue - self.register_parameter(key, torch.nn.Parameter(fn(param), requires_grad=False)) + p = fn(param) + if p.is_inference(): + p = p.clone() + self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False)) for key, buf in self._buffers.items(): if buf is not None: self._buffers[key] = fn(buf) diff --git a/comfy/utils.py b/comfy/utils.py index 9931fe3b4..e331b618b 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -897,6 +897,10 @@ def set_attr(obj, attr, value): return prev def set_attr_param(obj, attr, value): + # Clone inference tensors (created under torch.inference_mode) since + # their version counter is frozen and nn.Parameter() cannot wrap them. + if value.is_inference(): + value = value.clone() return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) def set_attr_buffer(obj, attr, value): diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py index aa2d88673..0ad0acee6 100644 --- a/comfy_extras/nodes_train.py +++ b/comfy_extras/nodes_train.py @@ -15,6 +15,7 @@ import comfy.sampler_helpers import comfy.sd import comfy.utils import comfy.model_management +from comfy.cli_args import args, PerformanceFeature import comfy_extras.nodes_custom_sampler import folder_paths import node_helpers @@ -138,6 +139,7 @@ class TrainSampler(comfy.samplers.Sampler): training_dtype=torch.bfloat16, real_dataset=None, bucket_latents=None, + use_grad_scaler=False, ): self.loss_fn = loss_fn self.optimizer = optimizer @@ -152,6 +154,8 @@ class TrainSampler(comfy.samplers.Sampler): self.bucket_latents: list[torch.Tensor] | None = ( bucket_latents # list of (Bi, C, Hi, Wi) ) + # GradScaler for fp16 training + self.grad_scaler = torch.amp.GradScaler() if use_grad_scaler else None # Precompute bucket offsets and weights for sampling if bucket_latents is not None: self._init_bucket_data(bucket_latents) @@ -204,10 +208,13 @@ class TrainSampler(comfy.samplers.Sampler): batch_sigmas.requires_grad_(True), **batch_extra_args, ) - loss = self.loss_fn(x0_pred, x0) + loss = self.loss_fn(x0_pred.float(), x0.float()) if bwd: bwd_loss = loss / self.grad_acc - bwd_loss.backward() + if self.grad_scaler is not None: + self.grad_scaler.scale(bwd_loss).backward() + else: + bwd_loss.backward() return loss def _generate_batch_sigmas(self, model_wrap, batch_size, device): @@ -307,7 +314,10 @@ class TrainSampler(comfy.samplers.Sampler): ) total_loss += loss total_loss = total_loss / self.grad_acc / len(indicies) - total_loss.backward() + if self.grad_scaler is not None: + self.grad_scaler.scale(total_loss).backward() + else: + total_loss.backward() if self.loss_callback: self.loss_callback(total_loss.item()) pbar.set_postfix({"loss": f"{total_loss.item():.4f}"}) @@ -348,12 +358,18 @@ class TrainSampler(comfy.samplers.Sampler): self._train_step_multires_mode(model_wrap, cond, extra_args, noisegen, latent_image, dataset_size, pbar) if (i + 1) % self.grad_acc == 0: + if self.grad_scaler is not None: + self.grad_scaler.unscale_(self.optimizer) for param_groups in self.optimizer.param_groups: for param in param_groups["params"]: if param.grad is None: continue param.grad.data = param.grad.data.to(param.data.dtype) - self.optimizer.step() + if self.grad_scaler is not None: + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + else: + self.optimizer.step() self.optimizer.zero_grad() ui_pbar.update(1) torch.cuda.empty_cache() @@ -1004,9 +1020,9 @@ class TrainLoraNode(io.ComfyNode): ), io.Combo.Input( "training_dtype", - options=["bf16", "fp32"], + options=["bf16", "fp32", "none"], default="bf16", - tooltip="The dtype to use for training.", + tooltip="The dtype to use for training. 'none' preserves the model's native compute dtype instead of overriding it. For fp16 models, GradScaler is automatically enabled.", ), io.Combo.Input( "lora_dtype", @@ -1035,7 +1051,7 @@ class TrainLoraNode(io.ComfyNode): io.Boolean.Input( "offloading", default=False, - tooltip="Offload the Model to RAM. Requires Bypass Mode.", + tooltip="Offload model weights to CPU during training to save GPU memory.", ), io.Combo.Input( "existing_lora", @@ -1120,22 +1136,32 @@ class TrainLoraNode(io.ComfyNode): # Setup model and dtype mp = model.clone() - dtype = node_helpers.string_to_torch_dtype(training_dtype) + use_grad_scaler = False + if training_dtype != "none": + dtype = node_helpers.string_to_torch_dtype(training_dtype) + mp.set_model_compute_dtype(dtype) + else: + # Detect model's native dtype for autocast + model_dtype = mp.model.get_dtype() + if model_dtype == torch.float16: + dtype = torch.float16 + use_grad_scaler = True + # Warn about fp16 accumulation instability during training + if PerformanceFeature.Fp16Accumulation in args.fast: + logging.warning( + "WARNING: FP16 model detected with fp16_accumulation enabled. " + "This combination can be numerically unstable during training and may cause NaN values. " + "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)." + ) + else: + # For fp8, bf16, or other dtypes, use bf16 autocast + dtype = torch.bfloat16 lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) - mp.set_model_compute_dtype(dtype) - - if mp.is_dynamic(): - if not bypass_mode: - logging.info("Training MP is Dynamic - forcing bypass mode. Start comfy with --highvram to force weight diff mode") - bypass_mode = True - offloading = True - elif offloading: - if not bypass_mode: - logging.info("Training Offload selected - forcing bypass mode. Set bypass = True to remove this message") # Prepare latents and compute counts + latents_dtype = dtype if dtype not in (None,) else torch.bfloat16 latents, num_images, multi_res = _prepare_latents_and_count( - latents, dtype, bucket_mode + latents, latents_dtype, bucket_mode ) # Validate and expand conditioning @@ -1201,6 +1227,7 @@ class TrainLoraNode(io.ComfyNode): seed=seed, training_dtype=dtype, bucket_latents=latents, + use_grad_scaler=use_grad_scaler, ) else: train_sampler = TrainSampler( @@ -1213,6 +1240,7 @@ class TrainLoraNode(io.ComfyNode): seed=seed, training_dtype=dtype, real_dataset=latents if multi_res else None, + use_grad_scaler=use_grad_scaler, ) # Setup guider @@ -1337,7 +1365,7 @@ class SaveLoRA(io.ComfyNode): io.Int.Input( "steps", optional=True, - tooltip="Optional: The number of steps to LoRA has been trained for, used to name the saved file.", + tooltip="Optional: The number of steps the LoRA has been trained for, used to name the saved file.", ), ], outputs=[], From ca17fc835593593f04b0aec04e266afc32a2ccfb Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 16 Mar 2026 18:38:40 -0700 Subject: [PATCH 17/65] Fix potential issue. (#13009) --- comfy/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/utils.py b/comfy/utils.py index e331b618b..13b7ca6c8 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -899,7 +899,7 @@ def set_attr(obj, attr, value): def set_attr_param(obj, attr, value): # Clone inference tensors (created under torch.inference_mode) since # their version counter is frozen and nn.Parameter() cannot wrap them. - if value.is_inference(): + if (not torch.is_inference_mode_enabled()) and value.is_inference(): value = value.clone() return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) From 9a870b5102fa831d805f53b255123623d063f660 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Mon, 16 Mar 2026 18:56:35 -0700 Subject: [PATCH 18/65] fix: atomic writes for userdata to prevent data loss on crash (#12987) Write to a temp file in the same directory then os.replace() onto the target path. If the process crashes mid-write, the original file is left intact instead of being truncated to zero bytes. Fixes #11298 --- app/user_manager.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/app/user_manager.py b/app/user_manager.py index e2c00dab2..e18afb71b 100644 --- a/app/user_manager.py +++ b/app/user_manager.py @@ -6,6 +6,7 @@ import uuid import glob import shutil import logging +import tempfile from aiohttp import web from urllib import parse from comfy.cli_args import args @@ -377,8 +378,15 @@ class UserManager(): try: body = await request.read() - with open(path, "wb") as f: - f.write(body) + dir_name = os.path.dirname(path) + fd, tmp_path = tempfile.mkstemp(dir=dir_name) + try: + with os.fdopen(fd, "wb") as f: + f.write(body) + os.replace(tmp_path, path) + except: + os.unlink(tmp_path) + raise except OSError as e: logging.warning(f"Error saving file '{path}': {e}") return web.Response( From 8cc746a86411bd7a08d42829dc805f39f8bced65 Mon Sep 17 00:00:00 2001 From: Paulo Muggler Moreira Date: Tue, 17 Mar 2026 03:27:27 +0100 Subject: [PATCH 19/65] fix: disable SageAttention for Hunyuan3D v2.1 DiT (#12772) --- comfy/ldm/hunyuan3dv2_1/hunyuandit.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy/ldm/hunyuan3dv2_1/hunyuandit.py b/comfy/ldm/hunyuan3dv2_1/hunyuandit.py index d48d9d642..f67ba84e9 100644 --- a/comfy/ldm/hunyuan3dv2_1/hunyuandit.py +++ b/comfy/ldm/hunyuan3dv2_1/hunyuandit.py @@ -343,6 +343,7 @@ class CrossAttention(nn.Module): k.reshape(b, s2, self.num_heads * self.head_dim), v, heads=self.num_heads, + low_precision_attention=False, ) out = self.out_proj(x) @@ -412,6 +413,7 @@ class Attention(nn.Module): key.reshape(B, N, self.num_heads * self.head_dim), value, heads=self.num_heads, + low_precision_attention=False, ) x = self.out_proj(x) From 379fbd1a827cd2ce97984a7e8ea8b7159780cd1c Mon Sep 17 00:00:00 2001 From: ComfyUI Wiki Date: Tue, 17 Mar 2026 12:53:18 +0800 Subject: [PATCH 20/65] chore: update workflow templates to v0.9.26 (#13012) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7e59ef206..0ce163f71 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.41.20 -comfyui-workflow-templates==0.9.21 +comfyui-workflow-templates==0.9.26 comfyui-embedded-docs==0.4.3 torch torchsde From ed7c2c65790c36871b90fff2bdd3de25a17a5431 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Tue, 17 Mar 2026 07:24:00 -0700 Subject: [PATCH 21/65] Mark weight_dtype as advanced input in Load Diffusion Model node (#12769) Mark the weight_dtype parameter in UNETLoader (Load Diffusion Model) as an advanced input to reduce UI complexity for new users. The parameter is now hidden behind an expandable Advanced section, matching the pattern used for other advanced inputs like device, tile_size, and overlap. Amp-Thread-ID: https://ampcode.com/threads/T-019cbaf1-d3c0-718e-a325-318baba86dec --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 03dcc9d4a..e93fa9767 100644 --- a/nodes.py +++ b/nodes.py @@ -952,7 +952,7 @@ class UNETLoader: @classmethod def INPUT_TYPES(s): return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ), - "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"], {"advanced": True}) }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_unet" From 1a157e1f97d32c27b3b8bd842bfc5e448c240fe7 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Tue, 17 Mar 2026 14:32:43 -0700 Subject: [PATCH 22/65] Reduce LTX VAE VRAM usage and save use cases from OOMs/Tiler (#13013) * ltx: vae: scale the chunk size with the users VRAM Scale this linearly down for users with low VRAM. * ltx: vae: free non-chunking recursive intermediates * ltx: vae: cleanup some intermediates The conv layer can be the VRAM peak and it does a torch.cat. So cleanup the pieces of the cat. Also clear our the cache ASAP as each layer detect its end as this VAE surges in VRAM at the end due to the ended padding increasing the size of the final frame convolutions off-the-books to the chunker. So if all the earlier layers free up their cache it can offset that surge. Its a fragmentation nightmare, and the chance of it having to recache the pyt allocator is very high, but you wont OOM. --- comfy/ldm/lightricks/vae/causal_conv3d.py | 4 ++ .../vae/causal_video_autoencoder.py | 41 +++++++++++++++---- 2 files changed, 38 insertions(+), 7 deletions(-) diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py index b8341edbc..356394239 100644 --- a/comfy/ldm/lightricks/vae/causal_conv3d.py +++ b/comfy/ldm/lightricks/vae/causal_conv3d.py @@ -65,9 +65,13 @@ class CausalConv3d(nn.Module): self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False) x = torch.cat(pieces, dim=2) + del pieces + del cached if needs_caching: self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False) + elif is_end: + self.temporal_cache_state[tid] = (None, True) return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :] diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index 9f14f64a5..0504140ef 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -297,7 +297,23 @@ class Encoder(nn.Module): module.temporal_cache_state.pop(tid, None) -MAX_CHUNK_SIZE=(128 * 1024 ** 2) +MIN_VRAM_FOR_CHUNK_SCALING = 6 * 1024 ** 3 +MAX_VRAM_FOR_CHUNK_SCALING = 24 * 1024 ** 3 +MIN_CHUNK_SIZE = 32 * 1024 ** 2 +MAX_CHUNK_SIZE = 128 * 1024 ** 2 + +def get_max_chunk_size(device: torch.device) -> int: + total_memory = comfy.model_management.get_total_memory(dev=device) + + if total_memory <= MIN_VRAM_FOR_CHUNK_SCALING: + return MIN_CHUNK_SIZE + if total_memory >= MAX_VRAM_FOR_CHUNK_SCALING: + return MAX_CHUNK_SIZE + + interp = (total_memory - MIN_VRAM_FOR_CHUNK_SCALING) / ( + MAX_VRAM_FOR_CHUNK_SCALING - MIN_VRAM_FOR_CHUNK_SCALING + ) + return int(MIN_CHUNK_SIZE + interp * (MAX_CHUNK_SIZE - MIN_CHUNK_SIZE)) class Decoder(nn.Module): r""" @@ -525,8 +541,11 @@ class Decoder(nn.Module): timestep_shift_scale = ada_values.unbind(dim=1) output = [] + max_chunk_size = get_max_chunk_size(sample.device) - def run_up(idx, sample, ended): + def run_up(idx, sample_ref, ended): + sample = sample_ref[0] + sample_ref[0] = None if idx >= len(self.up_blocks): sample = self.conv_norm_out(sample) if timestep_shift_scale is not None: @@ -554,13 +573,21 @@ class Decoder(nn.Module): return total_bytes = sample.numel() * sample.element_size() - num_chunks = (total_bytes + MAX_CHUNK_SIZE - 1) // MAX_CHUNK_SIZE - samples = torch.chunk(sample, chunks=num_chunks, dim=2) + num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size - for chunk_idx, sample1 in enumerate(samples): - run_up(idx + 1, sample1, ended and chunk_idx == len(samples) - 1) + if num_chunks == 1: + # when we are not chunking, detach our x so the callee can free it as soon as they are done + next_sample_ref = [sample] + del sample + run_up(idx + 1, next_sample_ref, ended) + return + else: + samples = torch.chunk(sample, chunks=num_chunks, dim=2) - run_up(0, sample, True) + for chunk_idx, sample1 in enumerate(samples): + run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1) + + run_up(0, [sample], True) sample = torch.cat(output, dim=2) sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) From 035414ede49c1b043ea6de054ca512bcbf0f6b35 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Tue, 17 Mar 2026 14:34:39 -0700 Subject: [PATCH 23/65] Reduce WAN VAE VRAM, Save use cases for OOM/Tiler (#13014) * wan: vae: encoder: Add feature cache layer that corks singles If a downsample only gives you a single frame, save it to the feature cache and return nothing to the top level. This increases the efficiency of cacheability, but also prepares support for going two by two rather than four by four on the frames. * wan: remove all concatentation with the feature cache The loopers are now responsible for ensuring that non-final frames are processes at least two-by-two, elimiating the need for this cat case. * wan: vae: recurse and chunk for 2+2 frames on decode Avoid having to clone off slices of 4 frame chunks and reduce the size of the big 6 frame convolutions down to 4. Save the VRAMs. * wan: encode frames 2x2. Reduce VRAM usage greatly by encoding frames 2 at a time rather than 4. * wan: vae: remove cloning The loopers now control the chunking such there is noever more than 2 frames, so just cache these slices directly and avoid the clone allocations completely. * wan: vae: free consumer caller tensors on recursion * wan: vae: restyle a little to match LTX --- comfy/ldm/wan/vae.py | 180 +++++++++++++++++++------------------------ 1 file changed, 81 insertions(+), 99 deletions(-) diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py index 71f73c64e..a96b83c6c 100644 --- a/comfy/ldm/wan/vae.py +++ b/comfy/ldm/wan/vae.py @@ -99,7 +99,7 @@ class Resample(nn.Module): else: self.resample = nn.Identity() - def forward(self, x, feat_cache=None, feat_idx=[0]): + def forward(self, x, feat_cache=None, feat_idx=[0], final=False): b, c, t, h, w = x.size() if self.mode == 'upsample3d': if feat_cache is not None: @@ -109,22 +109,7 @@ class Resample(nn.Module): feat_idx[0] += 1 else: - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[ - idx] is not None and feat_cache[idx] != 'Rep': - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - if cache_x.shape[2] < 2 and feat_cache[ - idx] is not None and feat_cache[idx] == 'Rep': - cache_x = torch.cat([ - torch.zeros_like(cache_x).to(cache_x.device), - cache_x - ], - dim=2) + cache_x = x[:, :, -CACHE_T:, :, :] if feat_cache[idx] == 'Rep': x = self.time_conv(x) else: @@ -145,19 +130,24 @@ class Resample(nn.Module): if feat_cache is not None: idx = feat_idx[0] if feat_cache[idx] is None: - feat_cache[idx] = x.clone() - feat_idx[0] += 1 + feat_cache[idx] = x else: - cache_x = x[:, :, -1:, :, :].clone() - # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': - # # cache last frame of last two chunk - # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) - + cache_x = x[:, :, -1:, :, :] x = self.time_conv( torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) feat_cache[idx] = cache_x - feat_idx[0] += 1 + + deferred_x = feat_cache[idx + 1] + if deferred_x is not None: + x = torch.cat([deferred_x, x], 2) + feat_cache[idx + 1] = None + + if x.shape[2] == 1 and not final: + feat_cache[idx + 1] = x + x = None + + feat_idx[0] += 2 return x @@ -177,19 +167,12 @@ class ResidualBlock(nn.Module): self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ if in_dim != out_dim else nn.Identity() - def forward(self, x, feat_cache=None, feat_idx=[0]): + def forward(self, x, feat_cache=None, feat_idx=[0], final=False): old_x = x for layer in self.residual: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) + cache_x = x[:, :, -CACHE_T:, :, :] x = layer(x, cache_list=feat_cache, cache_idx=idx) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -213,7 +196,7 @@ class AttentionBlock(nn.Module): self.proj = ops.Conv2d(dim, dim, 1) self.optimized_attention = vae_attention() - def forward(self, x): + def forward(self, x, feat_cache=None, feat_idx=[0], final=False): identity = x b, c, t, h, w = x.size() x = rearrange(x, 'b c t h w -> (b t) c h w') @@ -283,17 +266,10 @@ class Encoder3d(nn.Module): RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, z_dim, 3, padding=1)) - def forward(self, x, feat_cache=None, feat_idx=[0]): + def forward(self, x, feat_cache=None, feat_idx=[0], final=False): if feat_cache is not None: idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) + cache_x = x[:, :, -CACHE_T:, :, :] x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -303,14 +279,16 @@ class Encoder3d(nn.Module): ## downsamples for layer in self.downsamples: if feat_cache is not None: - x = layer(x, feat_cache, feat_idx) + x = layer(x, feat_cache, feat_idx, final=final) + if x is None: + return None else: x = layer(x) ## middle for layer in self.middle: - if isinstance(layer, ResidualBlock) and feat_cache is not None: - x = layer(x, feat_cache, feat_idx) + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx, final=final) else: x = layer(x) @@ -318,14 +296,7 @@ class Encoder3d(nn.Module): for layer in self.head: if isinstance(layer, CausalConv3d) and feat_cache is not None: idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) + cache_x = x[:, :, -CACHE_T:, :, :] x = layer(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -393,14 +364,7 @@ class Decoder3d(nn.Module): ## conv1 if feat_cache is not None: idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) + cache_x = x[:, :, -CACHE_T:, :, :] x = self.conv1(x, feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -409,42 +373,56 @@ class Decoder3d(nn.Module): ## middle for layer in self.middle: - if isinstance(layer, ResidualBlock) and feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## upsamples - for layer in self.upsamples: if feat_cache is not None: x = layer(x, feat_cache, feat_idx) else: x = layer(x) - ## head - for layer in self.head: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - x = layer(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 + out_chunks = [] + + def run_up(layer_idx, x_ref, feat_idx): + x = x_ref[0] + x_ref[0] = None + if layer_idx >= len(self.upsamples): + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + cache_x = x[:, :, -CACHE_T:, :, :] + x = layer(x, feat_cache[feat_idx[0]]) + feat_cache[feat_idx[0]] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + out_chunks.append(x) + return + + layer = self.upsamples[layer_idx] + if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1: + for frame_idx in range(x.shape[2]): + run_up( + layer_idx, + [x[:, :, frame_idx:frame_idx + 1, :, :]], + feat_idx.copy(), + ) + del x + return + + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) else: x = layer(x) - return x + + next_x_ref = [x] + del x + run_up(layer_idx + 1, next_x_ref, feat_idx) + + run_up(0, [x], feat_idx) + return out_chunks -def count_conv3d(model): +def count_cache_layers(model): count = 0 for m in model.modules(): - if isinstance(m, CausalConv3d): + if isinstance(m, CausalConv3d) or (isinstance(m, Resample) and m.mode == 'downsample3d'): count += 1 return count @@ -482,11 +460,12 @@ class WanVAE(nn.Module): conv_idx = [0] ## cache t = x.shape[2] - iter_ = 1 + (t - 1) // 4 + t = 1 + ((t - 1) // 4) * 4 + iter_ = 1 + (t - 1) // 2 feat_map = None if iter_ > 1: - feat_map = [None] * count_conv3d(self.encoder) - ## 对encode输入的x，按时间拆分为1、4、4、4.... + feat_map = [None] * count_cache_layers(self.encoder) + ## 对encode输入的x，按时间拆分为1、2、2、2....(总帧数先按4N+1向下取整) for i in range(iter_): conv_idx = [0] if i == 0: @@ -496,20 +475,23 @@ class WanVAE(nn.Module): feat_idx=conv_idx) else: out_ = self.encoder( - x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :], feat_cache=feat_map, - feat_idx=conv_idx) + feat_idx=conv_idx, + final=(i == (iter_ - 1))) + if out_ is None: + continue out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) return mu def decode(self, z): - conv_idx = [0] # z: [b,c,t,h,w] - iter_ = z.shape[2] + iter_ = 1 + z.shape[2] // 2 feat_map = None if iter_ > 1: - feat_map = [None] * count_conv3d(self.decoder) + feat_map = [None] * count_cache_layers(self.decoder) x = self.conv2(z) for i in range(iter_): conv_idx = [0] @@ -520,8 +502,8 @@ class WanVAE(nn.Module): feat_idx=conv_idx) else: out_ = self.decoder( - x[:, :, i:i + 1, :, :], + x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :], feat_cache=feat_map, feat_idx=conv_idx) - out = torch.cat([out, out_], 2) - return out + out += out_ + return torch.cat(out, 2) From 8b9d039f26f5230ab3d3d6d9dd5d55590681b970 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Wed, 18 Mar 2026 07:17:03 +0900 Subject: [PATCH 24/65] bump manager version to 4.1b6 (#13022) --- manager_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manager_requirements.txt b/manager_requirements.txt index 1c5e8f071..5b06b56f6 100644 --- a/manager_requirements.txt +++ b/manager_requirements.txt @@ -1 +1 @@ -comfyui_manager==4.1b5 \ No newline at end of file +comfyui_manager==4.1b6 \ No newline at end of file From 735a0465e5daf1f77909b553b02a9d16d1671be9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Wed, 18 Mar 2026 02:20:49 +0200 Subject: [PATCH 25/65] Inplace VAE output processing to reduce peak RAM consumption. (#13028) --- comfy/sd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index 4d427bb9a..652e76d3e 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -455,7 +455,7 @@ class VAE: self.output_channels = 3 self.pad_channel_value = None self.process_input = lambda image: image * 2.0 - 1.0 - self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) + self.process_output = lambda image: image.add_(1.0).div_(2.0).clamp_(0.0, 1.0) self.working_dtypes = [torch.bfloat16, torch.float32] self.disable_offload = False self.not_video = False From 68d542cc0602132d3d2fe624ee7077e44b0fb0ab Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 17 Mar 2026 17:46:22 -0700 Subject: [PATCH 26/65] Fix case where pixel space VAE could cause issues. (#13030) --- comfy/sd.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 652e76d3e..df0c4d1d1 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -952,8 +952,8 @@ class VAE: batch_number = max(1, batch_number) for x in range(0, samples_in.shape[0], batch_number): - samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).to(dtype=self.vae_output_dtype())) + samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype) + out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True)) if pixel_samples is None: pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) pixel_samples[x:x+batch_number] = out From cad24ce26278a72095d33a2b4391572573201542 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Tue, 17 Mar 2026 17:59:10 -0700 Subject: [PATCH 27/65] cascade: remove dead weight init code (#13026) This weight init process is fully shadowed be the weight load and doesnt work in dynamic_vram were the weight allocation is deferred. --- comfy/ldm/cascade/stage_a.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py index 145e6e69a..e4e30cacd 100644 --- a/comfy/ldm/cascade/stage_a.py +++ b/comfy/ldm/cascade/stage_a.py @@ -136,16 +136,7 @@ class ResBlock(nn.Module): ops.Linear(c_hidden, c), ) - self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) - - # Init weights - def _basic_init(module): - if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - - self.apply(_basic_init) + self.gammas = nn.Parameter(torch.zeros(6), requires_grad=False) def _norm(self, x, norm): return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) From b941913f1d2d11dc69c098a375309b13c13bca23 Mon Sep 17 00:00:00 2001 From: Anton Bukov Date: Wed, 18 Mar 2026 05:21:32 +0400 Subject: [PATCH 28/65] fix: run text encoders on MPS GPU instead of CPU for Apple Silicon (#12809) On Apple Silicon, `vram_state` is set to `VRAMState.SHARED` because CPU and GPU share unified memory. However, `text_encoder_device()` only checked for `HIGH_VRAM` and `NORMAL_VRAM`, causing all text encoders to fall back to CPU on MPS devices. Adding `VRAMState.SHARED` to the condition allows non-quantized text encoders (e.g. bf16 Gemma 3 12B) to run on the MPS GPU, providing significant speedup for text encoding and prompt generation. Note: quantized models (fp4/fp8) that use float8_e4m3fn internally will still fall back to CPU via the `supports_cast()` check in `CLIP.__init__()`, since MPS does not support fp8 dtypes. --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 2c250dacc..5f2e6ef67 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1003,7 +1003,7 @@ def text_encoder_offload_device(): def text_encoder_device(): if args.gpu_only: return get_torch_device() - elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled: + elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM, VRAMState.SHARED) or comfy.memory_management.aimdo_enabled: if should_use_fp16(prioritize_performance=False): return get_torch_device() else: From 06957022d4cc6f91e101cf5afdd421e462f820c0 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Wed, 18 Mar 2026 19:21:58 +0200 Subject: [PATCH 29/65] fix(api-nodes): add support for "thought_image" in Nano Banana 2 and corrected price badges (#13038) --- comfy_api_nodes/apis/gemini.py | 1 + comfy_api_nodes/nodes_gemini.py | 17 ++++++++++++++--- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/comfy_api_nodes/apis/gemini.py b/comfy_api_nodes/apis/gemini.py index 639035fef..22879fe18 100644 --- a/comfy_api_nodes/apis/gemini.py +++ b/comfy_api_nodes/apis/gemini.py @@ -67,6 +67,7 @@ class GeminiPart(BaseModel): inlineData: GeminiInlineData | None = Field(None) fileData: GeminiFileData | None = Field(None) text: str | None = Field(None) + thought: bool | None = Field(None) class GeminiTextPart(BaseModel): diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py index 8225ea67e..25d747e76 100644 --- a/comfy_api_nodes/nodes_gemini.py +++ b/comfy_api_nodes/nodes_gemini.py @@ -63,7 +63,7 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge( $m := widgets.model; $r := widgets.resolution; $isFlash := $contains($m, "nano banana 2"); - $flashPrices := {"1k": 0.0696, "2k": 0.0696, "4k": 0.123}; + $flashPrices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154}; $proPrices := {"1k": 0.134, "2k": 0.134, "4k": 0.24}; $prices := $isFlash ? $flashPrices : $proPrices; {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}} @@ -188,10 +188,12 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str: return "\n".join([part.text for part in parts]) -async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image: +async def get_image_from_response(response: GeminiGenerateContentResponse, thought: bool = False) -> Input.Image: image_tensors: list[Input.Image] = [] parts = get_parts_by_type(response, "image/*") for part in parts: + if (part.thought is True) != thought: + continue if part.inlineData: image_data = base64.b64decode(part.inlineData.data) returned_image = bytesio_to_image_tensor(BytesIO(image_data)) @@ -931,6 +933,11 @@ class GeminiNanoBanana2(IO.ComfyNode): outputs=[ IO.Image.Output(), IO.String.Output(), + IO.Image.Output( + display_name="thought_image", + tooltip="First image from the model's thinking process. " + "Only available with thinking_level HIGH and IMAGE+TEXT modality.", + ), ], hidden=[ IO.Hidden.auth_token_comfy_org, @@ -992,7 +999,11 @@ class GeminiNanoBanana2(IO.ComfyNode): response_model=GeminiGenerateContentResponse, price_extractor=calculate_tokens_price, ) - return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response)) + return IO.NodeOutput( + await get_image_from_response(response), + get_text_from_response(response), + await get_image_from_response(response, thought=True), + ) class GeminiExtension(ComfyExtension): From b67ed2a45fad8322629289b3347ea15f8926cd45 Mon Sep 17 00:00:00 2001 From: Alexander Brown Date: Wed, 18 Mar 2026 13:36:39 -0700 Subject: [PATCH 30/65] Update comfyui-frontend-package version to 1.41.21 (#13035) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 0ce163f71..ad0344ed4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.41.20 +comfyui-frontend-package==1.41.21 comfyui-workflow-templates==0.9.26 comfyui-embedded-docs==0.4.3 torch From dcd659590faac35a1ac36393077f4ab8aac3fea8 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 18 Mar 2026 15:14:18 -0700 Subject: [PATCH 31/65] Make more intermediate values follow the intermediate dtype. (#13051) --- comfy/sample.py | 4 ++-- comfy/sd1_clip.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/comfy/sample.py b/comfy/sample.py index a2a39b527..e9c2259ab 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -64,10 +64,10 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) - samples = samples.to(comfy.model_management.intermediate_device()) + samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) - samples = samples.to(comfy.model_management.intermediate_device()) + samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return samples diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index d89550840..f970510ad 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -46,7 +46,7 @@ class ClipTokenWeightEncoder: out, pooled = o[:2] if pooled is not None: - first_pooled = pooled[0:1].to(model_management.intermediate_device()) + first_pooled = pooled[0:1].to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()) else: first_pooled = pooled @@ -63,16 +63,16 @@ class ClipTokenWeightEncoder: output.append(z) if (len(output) == 0): - r = (out[-1:].to(model_management.intermediate_device()), first_pooled) + r = (out[-1:].to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()), first_pooled) else: - r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled) + r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()), first_pooled) if len(o) > 2: extra = {} for k in o[2]: v = o[2][k] if k == "attention_mask": - v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()) + v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()) extra[k] = v r = r + (extra,) From 9fff091f354815378b913c6e0ee3a39c0ed79a70 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Thu, 19 Mar 2026 00:32:26 +0200 Subject: [PATCH 32/65] Further Reduce LTX VAE decode peak RAM usage (#13052) --- .../vae/causal_video_autoencoder.py | 42 +++++++++++++++---- comfy/sd.py | 19 +++++++-- 2 files changed, 48 insertions(+), 13 deletions(-) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index 0504140ef..f7aae26da 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -473,6 +473,17 @@ class Decoder(nn.Module): self.gradient_checkpointing = False + # Precompute output scale factors: (channels, (t_scale, h_scale, w_scale), t_offset) + ts, hs, ws, to = 1, 1, 1, 0 + for block in self.up_blocks: + if isinstance(block, DepthToSpaceUpsample): + ts *= block.stride[0] + hs *= block.stride[1] + ws *= block.stride[2] + if block.stride[0] > 1: + to = to * block.stride[0] + 1 + self._output_scale = (out_channels // (patch_size ** 2), (ts, hs * patch_size, ws * patch_size), to) + self.timestep_conditioning = timestep_conditioning if timestep_conditioning: @@ -494,11 +505,15 @@ class Decoder(nn.Module): ) - # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor: + def decode_output_shape(self, input_shape): + c, (ts, hs, ws), to = self._output_scale + return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws) + def forward_orig( self, sample: torch.FloatTensor, timestep: Optional[torch.Tensor] = None, + output_buffer: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: r"""The forward method of the `Decoder` class.""" batch_size = sample.shape[0] @@ -540,7 +555,13 @@ class Decoder(nn.Module): ) timestep_shift_scale = ada_values.unbind(dim=1) - output = [] + if output_buffer is None: + output_buffer = torch.empty( + self.decode_output_shape(sample.shape), + dtype=sample.dtype, device=comfy.model_management.intermediate_device(), + ) + output_offset = [0] + max_chunk_size = get_max_chunk_size(sample.device) def run_up(idx, sample_ref, ended): @@ -556,7 +577,10 @@ class Decoder(nn.Module): mark_conv3d_ended(self.conv_out) sample = self.conv_out(sample, causal=self.causal) if sample is not None and sample.shape[2] > 0: - output.append(sample.to(comfy.model_management.intermediate_device())) + sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) + t = sample.shape[2] + output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample) + output_offset[0] += t return up_block = self.up_blocks[idx] @@ -588,11 +612,8 @@ class Decoder(nn.Module): run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1) run_up(0, [sample], True) - sample = torch.cat(output, dim=2) - sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) - - return sample + return output_buffer def forward(self, *args, **kwargs): try: @@ -1226,7 +1247,10 @@ class VideoVAE(nn.Module): means, logvar = torch.chunk(self.encoder(x), 2, dim=1) return self.per_channel_statistics.normalize(means) - def decode(self, x): + def decode_output_shape(self, input_shape): + return self.decoder.decode_output_shape(input_shape) + + def decode(self, x, output_buffer=None): if self.timestep_conditioning: #TODO: seed x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x - return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep) + return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep, output_buffer=output_buffer) diff --git a/comfy/sd.py b/comfy/sd.py index df0c4d1d1..1f9510959 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -951,12 +951,23 @@ class VAE: batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) + # Pre-allocate output for VAEs that support direct buffer writes + preallocated = False + if hasattr(self.first_stage_model, 'decode_output_shape'): + pixel_samples = torch.empty(self.first_stage_model.decode_output_shape(samples_in.shape), device=self.output_device, dtype=self.vae_output_dtype()) + preallocated = True + for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype) - out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True)) - if pixel_samples is None: - pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) - pixel_samples[x:x+batch_number] = out + if preallocated: + self.first_stage_model.decode(samples, output_buffer=pixel_samples[x:x+batch_number], **vae_options) + else: + out = self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True) + if pixel_samples is None: + pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) + pixel_samples[x:x+batch_number].copy_(out) + del out + self.process_output(pixel_samples[x:x+batch_number]) except Exception as e: model_management.raise_non_oom(e) logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") From 56ff88f9511c4e25cd8ac08b2bfcd21c8ad83121 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 18 Mar 2026 15:35:25 -0700 Subject: [PATCH 33/65] Fix regression. (#13053) --- comfy/sd1_clip.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index f970510ad..a85170b26 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -72,7 +72,7 @@ class ClipTokenWeightEncoder: for k in o[2]: v = o[2][k] if k == "attention_mask": - v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()) + v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device()) extra[k] = v r = r + (extra,) From f6b869d7d35f7160bf2fdeabaed378d737834540 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 18 Mar 2026 16:42:28 -0700 Subject: [PATCH 34/65] fp16 intermediates doen't work for some text enc models. (#13056) --- comfy/sd1_clip.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index a85170b26..0eb30df27 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -46,7 +46,7 @@ class ClipTokenWeightEncoder: out, pooled = o[:2] if pooled is not None: - first_pooled = pooled[0:1].to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()) + first_pooled = pooled[0:1].to(device=model_management.intermediate_device()) else: first_pooled = pooled @@ -63,9 +63,9 @@ class ClipTokenWeightEncoder: output.append(z) if (len(output) == 0): - r = (out[-1:].to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()), first_pooled) + r = (out[-1:].to(device=model_management.intermediate_device()), first_pooled) else: - r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device(), dtype=model_management.intermediate_dtype()), first_pooled) + r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device()), first_pooled) if len(o) > 2: extra = {} From fabed694a2198b1662d521b1c47e11e625601ebe Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 19 Mar 2026 09:58:47 -0700 Subject: [PATCH 35/65] ltx: vae: implement chunked encoder + CPU IO chunking (Big VRAM reductions) (#13062) * ltx: vae: add cache state to downsample block * ltx: vae: Add time stride awareness to causal_conv_3d * ltx: vae: Automate truncation for encoder Other VAEs just truncate without error. Do the same. * sd/ltx: Make chunked_io a flag in its own right Taking this bi-direcitonal, so make it a for-purpose named flag. * ltx: vae: implement chunked encoder + CPU IO chunking People are doing things with big frame counts in LTX including V2V flows. Implement the time-chunked encoder to keep the VRAM down, with the converse of the new CPU pre-allocation technique, where the chunks are brought from the CPU JIT. * ltx: vae-encode: round chunk sizes more strictly Only powers of 2 and multiple of 8 are valid due to cache slicing. --- comfy/ldm/lightricks/vae/causal_conv3d.py | 16 +++- .../vae/causal_video_autoencoder.py | 91 +++++++++++++++---- comfy/sd.py | 11 ++- 3 files changed, 92 insertions(+), 26 deletions(-) diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py index 356394239..7515f0d4e 100644 --- a/comfy/ldm/lightricks/vae/causal_conv3d.py +++ b/comfy/ldm/lightricks/vae/causal_conv3d.py @@ -23,6 +23,11 @@ class CausalConv3d(nn.Module): self.in_channels = in_channels self.out_channels = out_channels + if isinstance(stride, int): + self.time_stride = stride + else: + self.time_stride = stride[0] + kernel_size = (kernel_size, kernel_size, kernel_size) self.time_kernel_size = kernel_size[0] @@ -58,18 +63,23 @@ class CausalConv3d(nn.Module): pieces = [ cached, x ] if is_end and not causal: pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1))) + input_length = sum([piece.shape[2] for piece in pieces]) + cache_length = (self.time_kernel_size - self.time_stride) + ((input_length - self.time_kernel_size) % self.time_stride) needs_caching = not is_end - if needs_caching and x.shape[2] >= self.time_kernel_size - 1: + if needs_caching and cache_length == 0: + self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False) needs_caching = False - self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False) + if needs_caching and x.shape[2] >= cache_length: + needs_caching = False + self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False) x = torch.cat(pieces, dim=2) del pieces del cached if needs_caching: - self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False) + self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False) elif is_end: self.temporal_cache_state[tid] = (None, True) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index f7aae26da..1a15cafd0 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -233,10 +233,7 @@ class Encoder(nn.Module): self.gradient_checkpointing = False - def forward_orig(self, sample: torch.FloatTensor) -> torch.FloatTensor: - r"""The forward method of the `Encoder` class.""" - - sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) + def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch.FloatTensor]: sample = self.conv_in(sample) checkpoint_fn = ( @@ -247,10 +244,14 @@ class Encoder(nn.Module): for down_block in self.down_blocks: sample = checkpoint_fn(down_block)(sample) + if sample is None or sample.shape[2] == 0: + return None sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) + if sample is None or sample.shape[2] == 0: + return None if self.latent_log_var == "uniform": last_channel = sample[:, -1:, ...] @@ -282,9 +283,35 @@ class Encoder(nn.Module): return sample + def forward_orig(self, sample: torch.FloatTensor, device=None) -> torch.FloatTensor: + r"""The forward method of the `Encoder` class.""" + + max_chunk_size = get_max_chunk_size(sample.device if device is None else device) * 2 # encoder is more memory-efficient than decoder + frame_size = sample[:, :, :1, :, :].numel() * sample.element_size() + frame_size = int(frame_size * (self.conv_in.out_channels / self.conv_in.in_channels)) + + outputs = [] + samples = [sample[:, :, :1, :, :]] + if sample.shape[2] > 1: + chunk_t = max(2, max_chunk_size // frame_size) + if chunk_t < 4: + chunk_t = 2 + elif chunk_t < 8: + chunk_t = 4 + else: + chunk_t = (chunk_t // 8) * 8 + samples += list(torch.split(sample[:, :, 1:, :, :], chunk_t, dim=2)) + for chunk_idx, chunk in enumerate(samples): + if chunk_idx == len(samples) - 1: + mark_conv3d_ended(self) + chunk = patchify(chunk, patch_size_hw=self.patch_size, patch_size_t=1).to(device=device) + output = self._forward_chunk(chunk) + if output is not None: + outputs.append(output) + + return torch_cat_if_needed(outputs, dim=2) + def forward(self, *args, **kwargs): - #No encoder support so just flag the end so it doesnt use the cache. - mark_conv3d_ended(self) try: return self.forward_orig(*args, **kwargs) finally: @@ -737,12 +764,25 @@ class SpaceToDepthDownsample(nn.Module): causal=True, spatial_padding_mode=spatial_padding_mode, ) + self.temporal_cache_state = {} def forward(self, x, causal: bool = True): - if self.stride[0] == 2: + tid = threading.get_ident() + cached, pad_first, cached_x, cached_input = self.temporal_cache_state.get(tid, (None, True, None, None)) + if cached_input is not None: + x = torch_cat_if_needed([cached_input, x], dim=2) + cached_input = None + + if self.stride[0] == 2 and pad_first: x = torch.cat( [x[:, :, :1, :, :], x], dim=2 ) # duplicate first frames for padding + pad_first = False + + if x.shape[2] < self.stride[0]: + cached_input = x + self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input) + return None # skip connection x_in = rearrange( @@ -757,15 +797,26 @@ class SpaceToDepthDownsample(nn.Module): # conv x = self.conv(x, causal=causal) - x = rearrange( - x, - "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", - p1=self.stride[0], - p2=self.stride[1], - p3=self.stride[2], - ) + if self.stride[0] == 2 and x.shape[2] == 1: + if cached_x is not None: + x = torch_cat_if_needed([cached_x, x], dim=2) + cached_x = None + else: + cached_x = x + x = None - x = x + x_in + if x is not None: + x = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + + cached = add_exchange_cache(x, cached, x_in, dim=2) + + self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input) return x @@ -1098,6 +1149,8 @@ class processor(nn.Module): return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x) class VideoVAE(nn.Module): + comfy_has_chunked_io = True + def __init__(self, version=0, config=None): super().__init__() @@ -1240,11 +1293,9 @@ class VideoVAE(nn.Module): } return config - def encode(self, x): - frames_count = x.shape[2] - if ((frames_count - 1) % 8) != 0: - raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.") - means, logvar = torch.chunk(self.encoder(x), 2, dim=1) + def encode(self, x, device=None): + x = x[:, :, :max(1, 1 + ((x.shape[2] - 1) // 8) * 8), :, :] + means, logvar = torch.chunk(self.encoder(x, device=device), 2, dim=1) return self.per_channel_statistics.normalize(means) def decode_output_shape(self, input_shape): diff --git a/comfy/sd.py b/comfy/sd.py index 1f9510959..b5e7c93a9 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -953,7 +953,7 @@ class VAE: # Pre-allocate output for VAEs that support direct buffer writes preallocated = False - if hasattr(self.first_stage_model, 'decode_output_shape'): + if getattr(self.first_stage_model, 'comfy_has_chunked_io', False): pixel_samples = torch.empty(self.first_stage_model.decode_output_shape(samples_in.shape), device=self.output_device, dtype=self.vae_output_dtype()) preallocated = True @@ -1038,8 +1038,13 @@ class VAE: batch_number = max(1, batch_number) samples = None for x in range(0, pixel_samples.shape[0], batch_number): - pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device) - out = self.first_stage_model.encode(pixels_in).to(self.output_device).to(dtype=self.vae_output_dtype()) + pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype) + if getattr(self.first_stage_model, 'comfy_has_chunked_io', False): + out = self.first_stage_model.encode(pixels_in, device=self.device) + else: + pixels_in = pixels_in.to(self.device) + out = self.first_stage_model.encode(pixels_in) + out = out.to(self.output_device).to(dtype=self.vae_output_dtype()) if samples is None: samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype()) samples[x:x + batch_number] = out From 6589562ae3e35dd7694f430629a805306157f530 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 19 Mar 2026 10:01:12 -0700 Subject: [PATCH 36/65] ltx: vae: implement chunked encoder + CPU IO chunking (Big VRAM reductions) (#13062) * ltx: vae: add cache state to downsample block * ltx: vae: Add time stride awareness to causal_conv_3d * ltx: vae: Automate truncation for encoder Other VAEs just truncate without error. Do the same. * sd/ltx: Make chunked_io a flag in its own right Taking this bi-direcitonal, so make it a for-purpose named flag. * ltx: vae: implement chunked encoder + CPU IO chunking People are doing things with big frame counts in LTX including V2V flows. Implement the time-chunked encoder to keep the VRAM down, with the converse of the new CPU pre-allocation technique, where the chunks are brought from the CPU JIT. * ltx: vae-encode: round chunk sizes more strictly Only powers of 2 and multiple of 8 are valid due to cache slicing. From ab14541ef7965dc61956c447d3066dd3d5c9f33b Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 19 Mar 2026 10:03:20 -0700 Subject: [PATCH 37/65] memory: Add more exclusion criteria to pinned read (#13067) --- comfy/memory_management.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/comfy/memory_management.py b/comfy/memory_management.py index 563224098..f9078fe7c 100644 --- a/comfy/memory_management.py +++ b/comfy/memory_management.py @@ -39,7 +39,10 @@ def read_tensor_file_slice_into(tensor, destination): if (destination.device.type != "cpu" or file_obj is None or threading.get_ident() != info.thread_id - or destination.numel() * destination.element_size() < info.size): + or destination.numel() * destination.element_size() < info.size + or tensor.numel() * tensor.element_size() != info.size + or tensor.storage_offset() != 0 + or not tensor.is_contiguous()): return False if info.size == 0: From fd0261d2bc0c32fa6c21d20994702f44fd927d4c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Thu, 19 Mar 2026 19:29:34 +0200 Subject: [PATCH 38/65] Reduce tiled decode peak memory (#13050) --- comfy/utils.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/comfy/utils.py b/comfy/utils.py index 13b7ca6c8..78c491b98 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -1135,8 +1135,8 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am pbar.update(1) continue - out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) - out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) + out = output[b:b+1].zero_() + out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device) positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)] @@ -1151,7 +1151,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am upscaled.append(round(get_pos(d, pos))) ps = function(s_in).to(output_device) - mask = torch.ones_like(ps) + mask = torch.ones([1, 1] + list(ps.shape[2:]), device=output_device) for d in range(2, dims + 2): feather = round(get_scale(d - 2, overlap[d - 2])) @@ -1174,7 +1174,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am if pbar is not None: pbar.update(1) - output[b:b+1] = out/out_div + out.div_(out_div) return output def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): From 8458ae2686a8d62ee206d3903123868425a4e6a7 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 19 Mar 2026 12:27:55 -0700 Subject: [PATCH 39/65] =?UTF-8?q?Revert=20"fix:=20run=20text=20encoders=20?= =?UTF-8?q?on=20MPS=20GPU=20instead=20of=20CPU=20for=20Apple=20Silicon=20(?= =?UTF-8?q?#=E2=80=A6"=20(#13070)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit b941913f1d2d11dc69c098a375309b13c13bca23. --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 5f2e6ef67..2c250dacc 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1003,7 +1003,7 @@ def text_encoder_offload_device(): def text_encoder_device(): if args.gpu_only: return get_torch_device() - elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM, VRAMState.SHARED) or comfy.memory_management.aimdo_enabled: + elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled: if should_use_fp16(prioritize_performance=False): return get_torch_device() else: From 82b868a45a753c875677091d0a91bb5bbaf04cbe Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 19 Mar 2026 19:30:27 -0700 Subject: [PATCH 40/65] Fix VRAM leak in tiler fallback in video VAEs (#13073) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * sd: soft_empty_cache on tiler fallback This doesnt cost a lot and creates the expected VRAM reduction in resource monitors when you fallback to tiler. * wan: vae: Don't recursion in local fns (move run_up) Moved Decoder3d’s recursive run_up out of forward into a class method to avoid nested closure self-reference cycles. This avoids cyclic garbage that delays garbage of tensors which in turn delays VRAM release before tiled fallback. * ltx: vae: Don't recursion in local fns (move run_up) Mov the recursive run_up out of forward into a class method to avoid nested closure self-reference cycles. This avoids cyclic garbage that delays garbage of tensors which in turn delays VRAM release before tiled fallback. --- .../vae/causal_video_autoencoder.py | 96 +++++++++---------- comfy/ldm/wan/vae.py | 74 +++++++------- comfy/sd.py | 2 + 3 files changed, 88 insertions(+), 84 deletions(-) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index 1a15cafd0..dd1dfeba0 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -536,6 +536,53 @@ class Decoder(nn.Module): c, (ts, hs, ws), to = self._output_scale return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws) + def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size): + sample = sample_ref[0] + sample_ref[0] = None + if idx >= len(self.up_blocks): + sample = self.conv_norm_out(sample) + if timestep_shift_scale is not None: + shift, scale = timestep_shift_scale + sample = sample * (1 + scale) + shift + sample = self.conv_act(sample) + if ended: + mark_conv3d_ended(self.conv_out) + sample = self.conv_out(sample, causal=self.causal) + if sample is not None and sample.shape[2] > 0: + sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) + t = sample.shape[2] + output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample) + output_offset[0] += t + return + + up_block = self.up_blocks[idx] + if ended: + mark_conv3d_ended(up_block) + if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D): + sample = checkpoint_fn(up_block)( + sample, causal=self.causal, timestep=scaled_timestep + ) + else: + sample = checkpoint_fn(up_block)(sample, causal=self.causal) + + if sample is None or sample.shape[2] == 0: + return + + total_bytes = sample.numel() * sample.element_size() + num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size + + if num_chunks == 1: + # when we are not chunking, detach our x so the callee can free it as soon as they are done + next_sample_ref = [sample] + del sample + self.run_up(idx + 1, next_sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size) + return + else: + samples = torch.chunk(sample, chunks=num_chunks, dim=2) + + for chunk_idx, sample1 in enumerate(samples): + self.run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size) + def forward_orig( self, sample: torch.FloatTensor, @@ -591,54 +638,7 @@ class Decoder(nn.Module): max_chunk_size = get_max_chunk_size(sample.device) - def run_up(idx, sample_ref, ended): - sample = sample_ref[0] - sample_ref[0] = None - if idx >= len(self.up_blocks): - sample = self.conv_norm_out(sample) - if timestep_shift_scale is not None: - shift, scale = timestep_shift_scale - sample = sample * (1 + scale) + shift - sample = self.conv_act(sample) - if ended: - mark_conv3d_ended(self.conv_out) - sample = self.conv_out(sample, causal=self.causal) - if sample is not None and sample.shape[2] > 0: - sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) - t = sample.shape[2] - output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample) - output_offset[0] += t - return - - up_block = self.up_blocks[idx] - if (ended): - mark_conv3d_ended(up_block) - if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D): - sample = checkpoint_fn(up_block)( - sample, causal=self.causal, timestep=scaled_timestep - ) - else: - sample = checkpoint_fn(up_block)(sample, causal=self.causal) - - if sample is None or sample.shape[2] == 0: - return - - total_bytes = sample.numel() * sample.element_size() - num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size - - if num_chunks == 1: - # when we are not chunking, detach our x so the callee can free it as soon as they are done - next_sample_ref = [sample] - del sample - run_up(idx + 1, next_sample_ref, ended) - return - else: - samples = torch.chunk(sample, chunks=num_chunks, dim=2) - - for chunk_idx, sample1 in enumerate(samples): - run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1) - - run_up(0, [sample], True) + self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size) return output_buffer diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py index a96b83c6c..deeb8695b 100644 --- a/comfy/ldm/wan/vae.py +++ b/comfy/ldm/wan/vae.py @@ -360,6 +360,43 @@ class Decoder3d(nn.Module): RMS_norm(out_dim, images=False), nn.SiLU(), CausalConv3d(out_dim, output_channels, 3, padding=1)) + def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks): + x = x_ref[0] + x_ref[0] = None + if layer_idx >= len(self.upsamples): + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + cache_x = x[:, :, -CACHE_T:, :, :] + x = layer(x, feat_cache[feat_idx[0]]) + feat_cache[feat_idx[0]] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + out_chunks.append(x) + return + + layer = self.upsamples[layer_idx] + if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1: + for frame_idx in range(x.shape[2]): + self.run_up( + layer_idx, + [x[:, :, frame_idx:frame_idx + 1, :, :]], + feat_cache, + feat_idx.copy(), + out_chunks, + ) + del x + return + + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + next_x_ref = [x] + del x + self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks) + def forward(self, x, feat_cache=None, feat_idx=[0]): ## conv1 if feat_cache is not None: @@ -380,42 +417,7 @@ class Decoder3d(nn.Module): out_chunks = [] - def run_up(layer_idx, x_ref, feat_idx): - x = x_ref[0] - x_ref[0] = None - if layer_idx >= len(self.upsamples): - for layer in self.head: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - cache_x = x[:, :, -CACHE_T:, :, :] - x = layer(x, feat_cache[feat_idx[0]]) - feat_cache[feat_idx[0]] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - out_chunks.append(x) - return - - layer = self.upsamples[layer_idx] - if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1: - for frame_idx in range(x.shape[2]): - run_up( - layer_idx, - [x[:, :, frame_idx:frame_idx + 1, :, :]], - feat_idx.copy(), - ) - del x - return - - if feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - next_x_ref = [x] - del x - run_up(layer_idx + 1, next_x_ref, feat_idx) - - run_up(0, [x], feat_idx) + self.run_up(0, [x], feat_cache, feat_idx, out_chunks) return out_chunks diff --git a/comfy/sd.py b/comfy/sd.py index b5e7c93a9..e207bb0fd 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -978,6 +978,7 @@ class VAE: do_tile = True if do_tile: + comfy.model_management.soft_empty_cache() dims = samples_in.ndim - 2 if dims == 1 or self.extra_1d_channel is not None: pixel_samples = self.decode_tiled_1d(samples_in) @@ -1059,6 +1060,7 @@ class VAE: do_tile = True if do_tile: + comfy.model_management.soft_empty_cache() if self.latent_dim == 3: tile = 256 overlap = tile // 4 From f49856af57888f60d09f470a6509456f5ee23c99 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 19 Mar 2026 19:34:58 -0700 Subject: [PATCH 41/65] ltx: vae: Fix missing init variable (#13074) Forgot to push this ammendment. Previous test results apply to this. --- comfy/ldm/lightricks/vae/causal_video_autoencoder.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index dd1dfeba0..998122c85 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -602,6 +602,7 @@ class Decoder(nn.Module): ) timestep_shift_scale = None + scaled_timestep = None if self.timestep_conditioning: assert ( timestep is not None From e4455fd43acd3f975905455ace7497136962968a Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 20 Mar 2026 05:05:01 +0200 Subject: [PATCH 42/65] [API Nodes] mark seedream-3-0-t2i and seedance-1-0-lite models as deprecated (#13060) * chore(api-nodes): mark seedream-3-0-t2i and seedance-1-0-lite models as deprecated * fix(api-nodes): fixed old regression in the ByteDanceImageReference node --------- Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/nodes_bytedance.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index 6dbd5984e..de0c22e70 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -47,6 +47,10 @@ SEEDREAM_MODELS = { BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id} +DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"} + +logger = logging.getLogger(__name__) + def get_image_url_from_response(response: ImageTaskCreationResponse) -> str: if response.error: @@ -135,6 +139,7 @@ class ByteDanceImageNode(IO.ComfyNode): price_badge=IO.PriceBadge( expr="""{"type":"usd","usd":0.03}""", ), + is_deprecated=True, ) @classmethod @@ -942,7 +947,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode): ] return await process_video_task( cls, - payload=Image2VideoTaskCreationRequest(model=model, content=x), + payload=Image2VideoTaskCreationRequest(model=model, content=x, generate_audio=None), estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))), ) @@ -952,6 +957,12 @@ async def process_video_task( payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest, estimated_duration: int | None, ) -> IO.NodeOutput: + if payload.model in DEPRECATED_MODELS: + logger.warning( + "Model '%s' is deprecated and will be deactivated on May 13, 2026. " + "Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.", + payload.model, + ) initial_response = await sync_op( cls, ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), From 589228e671e84518bf77919ee4e574749ab772c8 Mon Sep 17 00:00:00 2001 From: drozbay <17261091+drozbay@users.noreply.github.com> Date: Thu, 19 Mar 2026 21:42:42 -0600 Subject: [PATCH 43/65] Add slice_cond and per-model context window cond resizing (#12645) * Add slice_cond and per-model context window cond resizing * Fix cond_value.size() call in context window cond resizing * Expose additional advanced inputs for ContextWindowsManualNode Necessary for WanAnimate context windows workflow, which needs cond_retain_index_list = 0 to work properly with its reference input. --------- --- comfy/context_windows.py | 54 ++++++++++++++++++++++++++- comfy/model_base.py | 32 ++++++++++++++++ comfy_extras/nodes_context_windows.py | 4 +- 3 files changed, 87 insertions(+), 3 deletions(-) diff --git a/comfy/context_windows.py b/comfy/context_windows.py index b54f7f39a..cb44ee6e8 100644 --- a/comfy/context_windows.py +++ b/comfy/context_windows.py @@ -93,6 +93,50 @@ class IndexListCallbacks: return {} +def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, device, temporal_dim: int, temporal_scale: int=1, temporal_offset: int=0, retain_index_list: list[int]=[]): + if not (hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor)): + return None + cond_tensor = cond_value.cond + if temporal_dim >= cond_tensor.ndim: + return None + + cond_size = cond_tensor.size(temporal_dim) + + if temporal_scale == 1: + expected_size = x_in.size(window.dim) - temporal_offset + if cond_size != expected_size: + return None + + if temporal_offset == 0 and temporal_scale == 1: + sliced = window.get_tensor(cond_tensor, device, dim=temporal_dim, retain_index_list=retain_index_list) + return cond_value._copy_with(sliced) + + # skip leading latent positions that have no corresponding conditioning (e.g. reference frames) + if temporal_offset > 0: + indices = [i - temporal_offset for i in window.index_list[temporal_offset:]] + indices = [i for i in indices if 0 <= i] + else: + indices = list(window.index_list) + + if not indices: + return None + + if temporal_scale > 1: + scaled = [] + for i in indices: + for k in range(temporal_scale): + si = i * temporal_scale + k + if si < cond_size: + scaled.append(si) + indices = scaled + if not indices: + return None + + idx = tuple([slice(None)] * temporal_dim + [indices]) + sliced = cond_tensor[idx].to(device) + return cond_value._copy_with(sliced) + + @dataclass class ContextSchedule: name: str @@ -177,10 +221,17 @@ class IndexListContextHandler(ContextHandlerABC): new_cond_item[cond_key] = result handled = True break + if not handled and self._model is not None: + result = self._model.resize_cond_for_context_window( + cond_key, cond_value, window, x_in, device, + retain_index_list=self.cond_retain_index_list) + if result is not None: + new_cond_item[cond_key] = result + handled = True if handled: continue if isinstance(cond_value, torch.Tensor): - if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \ + if (self.dim < cond_value.ndim and cond_value.size(self.dim) == x_in.size(self.dim)) or \ (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)): new_cond_item[cond_key] = window.get_tensor(cond_value, device) # Handle audio_embed (temporal dim is 1) @@ -224,6 +275,7 @@ class IndexListContextHandler(ContextHandlerABC): return context_windows def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): + self._model = model self.set_step(timestep, model_options) context_windows = self.get_context_windows(model, x_in, model_options) enumerated_context_windows = list(enumerate(context_windows)) diff --git a/comfy/model_base.py b/comfy/model_base.py index d9d5a9293..88905e191 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -285,6 +285,12 @@ class BaseModel(torch.nn.Module): return data return None + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + """Override in subclasses to handle model-specific cond slicing for context windows. + Return a sliced cond object, or None to fall through to default handling. + Use comfy.context_windows.slice_cond() for common cases.""" + return None + def extra_conds(self, **kwargs): out = {} concat_cond = self.concat_cond(**kwargs) @@ -1375,6 +1381,12 @@ class WAN21_Vace(WAN21): out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) return out + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + if cond_key == "vace_context": + import comfy.context_windows + return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=3, retain_index_list=retain_index_list) + return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) + class WAN21_Camera(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel) @@ -1427,6 +1439,12 @@ class WAN21_HuMo(WAN21): return out + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + if cond_key == "audio_embed": + import comfy.context_windows + return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1) + return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) + class WAN22_Animate(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel) @@ -1444,6 +1462,14 @@ class WAN22_Animate(WAN21): out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents)) return out + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + import comfy.context_windows + if cond_key == "face_pixel_values": + return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_scale=4, temporal_offset=1) + if cond_key == "pose_latents": + return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1) + return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) + class WAN22_S2V(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V) @@ -1480,6 +1506,12 @@ class WAN22_S2V(WAN21): out['reference_motion'] = reference_motion.shape return out + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + if cond_key == "audio_embed": + import comfy.context_windows + return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1) + return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) + class WAN22(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py index 93a5204e1..0e43f2e44 100644 --- a/comfy_extras/nodes_context_windows.py +++ b/comfy_extras/nodes_context_windows.py @@ -27,8 +27,8 @@ class ContextWindowsManualNode(io.ComfyNode): io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."), io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."), - #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."), - #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."), + io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."), + io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."), ], outputs=[ io.Model.Output(tooltip="The model with context windows applied during sampling."), From c646d211be359df56617ffabcdd43cb53e191e97 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 20 Mar 2026 21:23:16 +0200 Subject: [PATCH 44/65] feat(api-nodes): add Quiver SVG nodes (#13047) --- comfy_api_nodes/apis/quiver.py | 43 +++++ comfy_api_nodes/nodes_quiver.py | 291 ++++++++++++++++++++++++++++++++ 2 files changed, 334 insertions(+) create mode 100644 comfy_api_nodes/apis/quiver.py create mode 100644 comfy_api_nodes/nodes_quiver.py diff --git a/comfy_api_nodes/apis/quiver.py b/comfy_api_nodes/apis/quiver.py new file mode 100644 index 000000000..bc8708754 --- /dev/null +++ b/comfy_api_nodes/apis/quiver.py @@ -0,0 +1,43 @@ +from pydantic import BaseModel, Field + + +class QuiverImageObject(BaseModel): + url: str = Field(...) + + +class QuiverTextToSVGRequest(BaseModel): + model: str = Field(default="arrow-preview") + prompt: str = Field(...) + instructions: str | None = Field(default=None) + references: list[QuiverImageObject] | None = Field(default=None, max_length=4) + temperature: float | None = Field(default=None, ge=0, le=2) + top_p: float | None = Field(default=None, ge=0, le=1) + presence_penalty: float | None = Field(default=None, ge=-2, le=2) + + +class QuiverImageToSVGRequest(BaseModel): + model: str = Field(default="arrow-preview") + image: QuiverImageObject = Field(...) + auto_crop: bool | None = Field(default=None) + target_size: int | None = Field(default=None, ge=128, le=4096) + temperature: float | None = Field(default=None, ge=0, le=2) + top_p: float | None = Field(default=None, ge=0, le=1) + presence_penalty: float | None = Field(default=None, ge=-2, le=2) + + +class QuiverSVGResponseItem(BaseModel): + svg: str = Field(...) + mime_type: str | None = Field(default="image/svg+xml") + + +class QuiverSVGUsage(BaseModel): + total_tokens: int | None = Field(default=None) + input_tokens: int | None = Field(default=None) + output_tokens: int | None = Field(default=None) + + +class QuiverSVGResponse(BaseModel): + id: str | None = Field(default=None) + created: int | None = Field(default=None) + data: list[QuiverSVGResponseItem] = Field(...) + usage: QuiverSVGUsage | None = Field(default=None) diff --git a/comfy_api_nodes/nodes_quiver.py b/comfy_api_nodes/nodes_quiver.py new file mode 100644 index 000000000..61533263f --- /dev/null +++ b/comfy_api_nodes/nodes_quiver.py @@ -0,0 +1,291 @@ +from io import BytesIO + +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.apis.quiver import ( + QuiverImageObject, + QuiverImageToSVGRequest, + QuiverSVGResponse, + QuiverTextToSVGRequest, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + sync_op, + upload_image_to_comfyapi, + validate_string, +) +from comfy_extras.nodes_images import SVG + + +class QuiverTextToSVGNode(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="QuiverTextToSVGNode", + display_name="Quiver Text to SVG", + category="api node/image/Quiver", + description="Generate an SVG from a text prompt using Quiver AI.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text description of the desired SVG output.", + ), + IO.String.Input( + "instructions", + multiline=True, + default="", + tooltip="Additional style or formatting guidance.", + optional=True, + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplatePrefix( + IO.Image.Input("image"), + prefix="ref_", + min=0, + max=4, + ), + tooltip="Up to 4 reference images to guide the generation.", + optional=True, + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "arrow-preview", + [ + IO.Float.Input( + "temperature", + default=1.0, + min=0.0, + max=2.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Randomness control. Higher values increase randomness.", + advanced=True, + ), + IO.Float.Input( + "top_p", + default=1.0, + min=0.05, + max=1.0, + step=0.05, + display_mode=IO.NumberDisplay.slider, + tooltip="Nucleus sampling parameter.", + advanced=True, + ), + IO.Float.Input( + "presence_penalty", + default=0.0, + min=-2.0, + max=2.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Token presence penalty.", + advanced=True, + ), + ], + ), + ], + tooltip="Model to use for SVG generation.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; " + "actual results are nondeterministic regardless of seed.", + ), + ], + outputs=[ + IO.SVG.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + expr="""{"type":"usd","usd":0.429}""", + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int, + instructions: str = None, + reference_images: IO.Autogrow.Type = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False, min_length=1) + + references = None + if reference_images: + references = [] + for key in reference_images: + url = await upload_image_to_comfyapi(cls, reference_images[key]) + references.append(QuiverImageObject(url=url)) + if len(references) > 4: + raise ValueError("Maximum 4 reference images are allowed.") + + instructions_val = instructions.strip() if instructions else None + if instructions_val == "": + instructions_val = None + + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/quiver/v1/svgs/generations", method="POST"), + response_model=QuiverSVGResponse, + data=QuiverTextToSVGRequest( + model=model["model"], + prompt=prompt, + instructions=instructions_val, + references=references, + temperature=model.get("temperature"), + top_p=model.get("top_p"), + presence_penalty=model.get("presence_penalty"), + ), + ) + + svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data] + return IO.NodeOutput(SVG(svg_data)) + + +class QuiverImageToSVGNode(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="QuiverImageToSVGNode", + display_name="Quiver Image to SVG", + category="api node/image/Quiver", + description="Vectorize a raster image into SVG using Quiver AI.", + inputs=[ + IO.Image.Input( + "image", + tooltip="Input image to vectorize.", + ), + IO.Boolean.Input( + "auto_crop", + default=False, + tooltip="Automatically crop to the dominant subject.", + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "arrow-preview", + [ + IO.Int.Input( + "target_size", + default=1024, + min=128, + max=4096, + tooltip="Square resize target in pixels.", + ), + IO.Float.Input( + "temperature", + default=1.0, + min=0.0, + max=2.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Randomness control. Higher values increase randomness.", + advanced=True, + ), + IO.Float.Input( + "top_p", + default=1.0, + min=0.05, + max=1.0, + step=0.05, + display_mode=IO.NumberDisplay.slider, + tooltip="Nucleus sampling parameter.", + advanced=True, + ), + IO.Float.Input( + "presence_penalty", + default=0.0, + min=-2.0, + max=2.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Token presence penalty.", + advanced=True, + ), + ], + ), + ], + tooltip="Model to use for SVG vectorization.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; " + "actual results are nondeterministic regardless of seed.", + ), + ], + outputs=[ + IO.SVG.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + expr="""{"type":"usd","usd":0.429}""", + ), + ) + + @classmethod + async def execute( + cls, + image, + auto_crop: bool, + model: dict, + seed: int, + ) -> IO.NodeOutput: + image_url = await upload_image_to_comfyapi(cls, image) + + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/quiver/v1/svgs/vectorizations", method="POST"), + response_model=QuiverSVGResponse, + data=QuiverImageToSVGRequest( + model=model["model"], + image=QuiverImageObject(url=image_url), + auto_crop=auto_crop if auto_crop else None, + target_size=model.get("target_size"), + temperature=model.get("temperature"), + top_p=model.get("top_p"), + presence_penalty=model.get("presence_penalty"), + ), + ) + + svg_data = [BytesIO(item.svg.encode("utf-8")) for item in response.data] + return IO.NodeOutput(SVG(svg_data)) + + +class QuiverExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + QuiverTextToSVGNode, + QuiverImageToSVGNode, + ] + + +async def comfy_entrypoint() -> QuiverExtension: + return QuiverExtension() From 45d5c83a3005e7fc28ce9e4ff04b77875052eb51 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 20 Mar 2026 13:08:26 -0700 Subject: [PATCH 45/65] Make EmptyImage node follow intermediate device/dtype. (#13079) --- nodes.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/nodes.py b/nodes.py index e93fa9767..2c4650a20 100644 --- a/nodes.py +++ b/nodes.py @@ -1966,9 +1966,11 @@ class EmptyImage: CATEGORY = "image" def generate(self, width, height, batch_size=1, color=0): - r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) - g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) - b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + dtype = comfy.model_management.intermediate_dtype() + device = comfy.model_management.intermediate_device() + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF, device=device, dtype=dtype) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF, device=device, dtype=dtype) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF, device=device, dtype=dtype) return (torch.cat((r, g, b), dim=-1), ) class ImagePadForOutpaint: From 87cda1fc25ca11a55ede88bf264cfe0a20d340ce Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Fri, 20 Mar 2026 17:03:42 -0700 Subject: [PATCH 46/65] Move inline comfy.context_windows imports to top-level in model_base.py (#13083) The recent PR that added resize_cond_for_context_window methods to model classes used inline 'import comfy.context_windows' in each method body. This moves that import to the top-level import section, replacing 4 duplicate inline imports with a single top-level one. --- comfy/model_base.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 88905e191..43ec93324 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -21,6 +21,7 @@ import comfy.ldm.hunyuan3dv2_1.hunyuandit import torch import logging import comfy.ldm.lightricks.av_model +import comfy.context_windows from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from comfy.ldm.cascade.stage_c import StageC from comfy.ldm.cascade.stage_b import StageB @@ -1383,7 +1384,6 @@ class WAN21_Vace(WAN21): def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): if cond_key == "vace_context": - import comfy.context_windows return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=3, retain_index_list=retain_index_list) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) @@ -1441,7 +1441,6 @@ class WAN21_HuMo(WAN21): def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): if cond_key == "audio_embed": - import comfy.context_windows return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) @@ -1463,7 +1462,6 @@ class WAN22_Animate(WAN21): return out def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): - import comfy.context_windows if cond_key == "face_pixel_values": return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_scale=4, temporal_offset=1) if cond_key == "pose_latents": @@ -1508,7 +1506,6 @@ class WAN22_S2V(WAN21): def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): if cond_key == "audio_embed": - import comfy.context_windows return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) From dc719cde9c448c65242ae2d4ba400ba18c36846f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 20 Mar 2026 20:09:15 -0400 Subject: [PATCH 47/65] ComfyUI version 0.18.0 --- 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 701f4d66a..a3b7204dc 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__ = "0.17.0" +__version__ = "0.18.0" diff --git a/pyproject.toml b/pyproject.toml index e2ca79be7..6db9b1267 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.17.0" +version = "0.18.0" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From a11f68dd3b5393b6afc37e01c91fa84963d2668a Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 20 Mar 2026 20:15:50 -0700 Subject: [PATCH 48/65] Fix canny node not working with fp16. (#13085) --- comfy_extras/nodes_canny.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index 5e7c4eabb..648b4279d 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -3,6 +3,7 @@ from typing_extensions import override import comfy.model_management from comfy_api.latest import ComfyExtension, io +import torch class Canny(io.ComfyNode): @@ -29,8 +30,8 @@ class Canny(io.ComfyNode): @classmethod def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput: - output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) - img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) + output = canny(image.to(device=comfy.model_management.get_torch_device(), dtype=torch.float32).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()).repeat(1, 3, 1, 1).movedim(1, -1) return io.NodeOutput(img_out) From b5d32e6ad23f3deb0cd16b5f2afa81ff92d89e6e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 21 Mar 2026 14:47:42 -0700 Subject: [PATCH 49/65] Fix sampling issue with fp16 intermediates. (#13099) --- comfy/samplers.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 8be449ef7..0a4d062db 100755 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -985,8 +985,8 @@ class CFGGuider: self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) device = self.model_patcher.load_device - noise = noise.to(device) - latent_image = latent_image.to(device) + noise = noise.to(device=device, dtype=torch.float32) + latent_image = latent_image.to(device=device, dtype=torch.float32) sigmas = sigmas.to(device) cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) @@ -1028,6 +1028,7 @@ class CFGGuider: denoise_mask, _ = comfy.utils.pack_latents(denoise_masks) else: denoise_mask = denoise_masks[0] + denoise_mask = denoise_mask.float() self.conds = {} for k in self.original_conds: From 11c15d8832ab8a95ebe31f85c131429978668c76 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 21 Mar 2026 14:53:25 -0700 Subject: [PATCH 50/65] Fix fp16 intermediates giving different results. (#13100) --- comfy/sample.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/sample.py b/comfy/sample.py index e9c2259ab..653829582 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -8,12 +8,12 @@ import comfy.nested_tensor def prepare_noise_inner(latent_image, generator, noise_inds=None): if noise_inds is None: - return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype) unique_inds, inverse = np.unique(noise_inds, return_inverse=True) noises = [] for i in range(unique_inds[-1]+1): - noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype) if i in unique_inds: noises.append(noise) noises = [noises[i] for i in inverse] From 25b6d1d6298c380c1d4de90ff9f38484a84ada19 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Sat, 21 Mar 2026 15:44:35 -0700 Subject: [PATCH 51/65] wan: vae: Fix light/color change (#13101) There was an issue where the resample split was too early and dropped one of the rolling convolutions a frame early. This is most noticable as a lighting/color change between pixel frames 5->6 (latent 2->3), or as a lighting change between the first and last frame in an FLF wan flow. --- comfy/ldm/wan/vae.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py index deeb8695b..57b0dabf7 100644 --- a/comfy/ldm/wan/vae.py +++ b/comfy/ldm/wan/vae.py @@ -376,11 +376,16 @@ class Decoder3d(nn.Module): return layer = self.upsamples[layer_idx] - if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1: - for frame_idx in range(x.shape[2]): + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 2: + for frame_idx in range(0, x.shape[2], 2): self.run_up( - layer_idx, - [x[:, :, frame_idx:frame_idx + 1, :, :]], + layer_idx + 1, + [x[:, :, frame_idx:frame_idx + 2, :, :]], feat_cache, feat_idx.copy(), out_chunks, @@ -388,11 +393,6 @@ class Decoder3d(nn.Module): del x return - if feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - next_x_ref = [x] del x self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks) From ebf6b52e322664af91fcdc8b8848d31d5fb98f66 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 21 Mar 2026 22:32:16 -0400 Subject: [PATCH 52/65] ComfyUI v0.18.1 --- 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 a3b7204dc..61d7672ca 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__ = "0.18.0" +__version__ = "0.18.1" diff --git a/pyproject.toml b/pyproject.toml index 6db9b1267..1fc9402a1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.18.0" +version = "0.18.1" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From d49420b3c7daf86cae1d7419e37848a974e1b7be Mon Sep 17 00:00:00 2001 From: Talmaj Date: Sun, 22 Mar 2026 04:51:05 +0100 Subject: [PATCH 53/65] LongCat-Image edit (#13003) --- comfy/ldm/flux/model.py | 2 +- comfy/model_base.py | 5 +++-- comfy/text_encoders/llama.py | 11 +++++++++-- comfy/text_encoders/longcat_image.py | 25 ++++++++++++++++++++----- comfy/text_encoders/qwen_vl.py | 3 +++ 5 files changed, 36 insertions(+), 10 deletions(-) diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py index 8e7912e6d..2020326c2 100644 --- a/comfy/ldm/flux/model.py +++ b/comfy/ldm/flux/model.py @@ -386,7 +386,7 @@ class Flux(nn.Module): h = max(h, ref.shape[-2] + h_offset) w = max(w, ref.shape[-1] + w_offset) - kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) + kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset, transformer_options=transformer_options) img = torch.cat([img, kontext], dim=1) img_ids = torch.cat([img_ids, kontext_ids], dim=1) ref_num_tokens.append(kontext.shape[1]) diff --git a/comfy/model_base.py b/comfy/model_base.py index 43ec93324..bfffe2402 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -937,9 +937,10 @@ class LongCatImage(Flux): transformer_options = transformer_options.copy() rope_opts = transformer_options.get("rope_options", {}) rope_opts = dict(rope_opts) + pe_len = float(c_crossattn.shape[1]) if c_crossattn is not None else 512.0 rope_opts.setdefault("shift_t", 1.0) - rope_opts.setdefault("shift_y", 512.0) - rope_opts.setdefault("shift_x", 512.0) + rope_opts.setdefault("shift_y", pe_len) + rope_opts.setdefault("shift_x", pe_len) transformer_options["rope_options"] = rope_opts return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index ccc200b7a..9fdea999c 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -1028,12 +1028,19 @@ class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module): grid = e.get("extra", None) start = e.get("index") if position_ids is None: - position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device) + position_ids = torch.ones((3, embeds.shape[1]), device=embeds.device, dtype=torch.long) position_ids[:, :start] = torch.arange(0, start, device=embeds.device) end = e.get("size") + start len_max = int(grid.max()) // 2 start_next = len_max + start - position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device) + if attention_mask is not None: + # Assign compact sequential positions to attended tokens only, + # skipping over padding so post-padding tokens aren't inflated. + after_mask = attention_mask[0, end:] + text_positions = after_mask.cumsum(0) - 1 + start_next + offset + position_ids[:, end:] = torch.where(after_mask.bool(), text_positions, position_ids[0, end:]) + else: + position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device) position_ids[0, start:end] = start + offset max_d = int(grid[0][1]) // 2 position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] diff --git a/comfy/text_encoders/longcat_image.py b/comfy/text_encoders/longcat_image.py index 882d80901..0962779e3 100644 --- a/comfy/text_encoders/longcat_image.py +++ b/comfy/text_encoders/longcat_image.py @@ -64,7 +64,13 @@ class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer): return [output] +IMAGE_PAD_TOKEN_ID = 151655 + class LongCatImageTokenizer(sd1_clip.SD1Tokenizer): + T2I_PREFIX = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n" + EDIT_PREFIX = "<|im_start|>system\nAs an image editing expert, first analyze the content and attributes of the input image(s). Then, based on the user's editing instructions, clearly and precisely determine how to modify the given image(s), ensuring that only the specified parts are altered and all other aspects remain consistent with the original(s).<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>" + SUFFIX = "<|im_end|>\n<|im_start|>assistant\n" + def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__( embedding_directory=embedding_directory, @@ -72,10 +78,8 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer): name="qwen25_7b", tokenizer=LongCatImageBaseTokenizer, ) - self.longcat_template_prefix = "<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n" - self.longcat_template_suffix = "<|im_end|>\n<|im_start|>assistant\n" - def tokenize_with_weights(self, text, return_word_ids=False, **kwargs): + def tokenize_with_weights(self, text, return_word_ids=False, images=None, **kwargs): skip_template = False if text.startswith("<|im_start|>"): skip_template = True @@ -90,11 +94,14 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer): text, return_word_ids=return_word_ids, disable_weights=True, **kwargs ) else: + has_images = images is not None and len(images) > 0 + template_prefix = self.EDIT_PREFIX if has_images else self.T2I_PREFIX + prefix_ids = base_tok.tokenizer( - self.longcat_template_prefix, add_special_tokens=False + template_prefix, add_special_tokens=False )["input_ids"] suffix_ids = base_tok.tokenizer( - self.longcat_template_suffix, add_special_tokens=False + self.SUFFIX, add_special_tokens=False )["input_ids"] prompt_tokens = base_tok.tokenize_with_weights( @@ -106,6 +113,14 @@ class LongCatImageTokenizer(sd1_clip.SD1Tokenizer): suffix_pairs = [(t, 1.0) for t in suffix_ids] combined = prefix_pairs + prompt_pairs + suffix_pairs + + if has_images: + embed_count = 0 + for i in range(len(combined)): + if combined[i][0] == IMAGE_PAD_TOKEN_ID and embed_count < len(images): + combined[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"}, combined[i][1]) + embed_count += 1 + tokens = {"qwen25_7b": [combined]} return tokens diff --git a/comfy/text_encoders/qwen_vl.py b/comfy/text_encoders/qwen_vl.py index 3b18ce730..98c350a12 100644 --- a/comfy/text_encoders/qwen_vl.py +++ b/comfy/text_encoders/qwen_vl.py @@ -425,4 +425,7 @@ class Qwen2VLVisionTransformer(nn.Module): hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention) hidden_states = self.merger(hidden_states) + # Potentially important for spatially precise edits. This is present in the HF implementation. + reverse_indices = torch.argsort(window_index) + hidden_states = hidden_states[reverse_indices, :] return hidden_states From 6265a239f379f1a5cf2bfdcd3a9631d4c11e50fb Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 22 Mar 2026 15:46:18 -0700 Subject: [PATCH 54/65] Add warning for users who disable dynamic vram. (#13113) --- main.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/main.py b/main.py index f99aee38e..cd4483c67 100644 --- a/main.py +++ b/main.py @@ -471,6 +471,9 @@ if __name__ == "__main__": if sys.version_info.major == 3 and sys.version_info.minor < 10: logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.") + if args.disable_dynamic_vram: + logging.warning("Dynamic vram disabled with argument. If you have any issues with dynamic vram enabled please give us a detailed reports as this argument will be removed soon.") + event_loop, _, start_all_func = start_comfyui() try: x = start_all_func() From da6edb5a4e5745869d64ae05b96263da42d5364e Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Tue, 24 Mar 2026 01:59:21 +0900 Subject: [PATCH 55/65] bump manager version to 4.1b8 (#13108) --- manager_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manager_requirements.txt b/manager_requirements.txt index 5b06b56f6..90a2be84e 100644 --- a/manager_requirements.txt +++ b/manager_requirements.txt @@ -1 +1 @@ -comfyui_manager==4.1b6 \ No newline at end of file +comfyui_manager==4.1b8 From e87858e9743f92222cdb478f1f835135750b6a0b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Tue, 24 Mar 2026 00:22:24 +0200 Subject: [PATCH 56/65] feat: LTX2: Support reference audio (ID-LoRA) (#13111) --- comfy/ldm/lightricks/av_model.py | 42 +++++++++++++++++ comfy/model_base.py | 4 ++ comfy_extras/nodes_lt.py | 80 ++++++++++++++++++++++++++++++++ 3 files changed, 126 insertions(+) diff --git a/comfy/ldm/lightricks/av_model.py b/comfy/ldm/lightricks/av_model.py index 08d686b7b..6f2ba41ef 100644 --- a/comfy/ldm/lightricks/av_model.py +++ b/comfy/ldm/lightricks/av_model.py @@ -681,6 +681,33 @@ class LTXAVModel(LTXVModel): additional_args["has_spatial_mask"] = has_spatial_mask ax, a_latent_coords = self.a_patchifier.patchify(ax) + + # Inject reference audio for ID-LoRA in-context conditioning + ref_audio = kwargs.get("ref_audio", None) + ref_audio_seq_len = 0 + if ref_audio is not None: + ref_tokens = ref_audio["tokens"].to(dtype=ax.dtype, device=ax.device) + if ref_tokens.shape[0] < ax.shape[0]: + ref_tokens = ref_tokens.expand(ax.shape[0], -1, -1) + ref_audio_seq_len = ref_tokens.shape[1] + B = ax.shape[0] + + # Compute negative temporal positions matching ID-LoRA convention: + # offset by -(end_of_last_token + time_per_latent) so reference ends just before t=0 + p = self.a_patchifier + tpl = p.hop_length * p.audio_latent_downsample_factor / p.sample_rate + ref_start = p._get_audio_latent_time_in_sec(0, ref_audio_seq_len, torch.float32, ax.device) + ref_end = p._get_audio_latent_time_in_sec(1, ref_audio_seq_len + 1, torch.float32, ax.device) + time_offset = ref_end[-1].item() + tpl + ref_start = (ref_start - time_offset).unsqueeze(0).expand(B, -1).unsqueeze(1) + ref_end = (ref_end - time_offset).unsqueeze(0).expand(B, -1).unsqueeze(1) + ref_pos = torch.stack([ref_start, ref_end], dim=-1) + + additional_args["ref_audio_seq_len"] = ref_audio_seq_len + additional_args["target_audio_seq_len"] = ax.shape[1] + ax = torch.cat([ref_tokens, ax], dim=1) + a_latent_coords = torch.cat([ref_pos.to(a_latent_coords), a_latent_coords], dim=2) + ax = self.audio_patchify_proj(ax) # additional_args.update({"av_orig_shape": list(x.shape)}) @@ -721,6 +748,14 @@ class LTXAVModel(LTXVModel): # Prepare audio timestep a_timestep = kwargs.get("a_timestep") + ref_audio_seq_len = kwargs.get("ref_audio_seq_len", 0) + if ref_audio_seq_len > 0 and a_timestep is not None: + # Reference tokens must have timestep=0, expand scalar/1D timestep to per-token so ref=0 and target=sigma. + target_len = kwargs.get("target_audio_seq_len") + if a_timestep.dim() <= 1: + a_timestep = a_timestep.view(-1, 1).expand(batch_size, target_len) + ref_ts = torch.zeros(batch_size, ref_audio_seq_len, *a_timestep.shape[2:], device=a_timestep.device, dtype=a_timestep.dtype) + a_timestep = torch.cat([ref_ts, a_timestep], dim=1) if a_timestep is not None: a_timestep_scaled = a_timestep * self.timestep_scale_multiplier a_timestep_flat = a_timestep_scaled.flatten() @@ -955,6 +990,13 @@ class LTXAVModel(LTXVModel): v_embedded_timestep = embedded_timestep[0] a_embedded_timestep = embedded_timestep[1] + # Trim reference audio tokens before unpatchification + ref_audio_seq_len = kwargs.get("ref_audio_seq_len", 0) + if ref_audio_seq_len > 0: + ax = ax[:, ref_audio_seq_len:] + if a_embedded_timestep.shape[1] > 1: + a_embedded_timestep = a_embedded_timestep[:, ref_audio_seq_len:] + # Expand compressed video timestep if needed if isinstance(v_embedded_timestep, CompressedTimestep): v_embedded_timestep = v_embedded_timestep.expand() diff --git a/comfy/model_base.py b/comfy/model_base.py index bfffe2402..70aff886e 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1061,6 +1061,10 @@ class LTXAV(BaseModel): if guide_attention_entries is not None: out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries) + ref_audio = kwargs.get("ref_audio", None) + if ref_audio is not None: + out['ref_audio'] = comfy.conds.CONDConstant(ref_audio) + return out def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs): diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index c05571143..d7c2e8744 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -3,6 +3,7 @@ import node_helpers import torch import comfy.model_management import comfy.model_sampling +import comfy.samplers import comfy.utils import math import numpy as np @@ -682,6 +683,84 @@ class LTXVSeparateAVLatent(io.ComfyNode): return io.NodeOutput(video_latent, audio_latent) +class LTXVReferenceAudio(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="LTXVReferenceAudio", + display_name="LTXV Reference Audio (ID-LoRA)", + category="conditioning/audio", + description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).", + inputs=[ + io.Model.Input("model"), + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Audio.Input("reference_audio", tooltip="Reference audio clip whose speaker identity to transfer. ~5 seconds recommended (training duration). Shorter or longer clips may degrade voice identity transfer."), + io.Vae.Input(id="audio_vae", display_name="Audio VAE", tooltip="LTXV Audio VAE for encoding."), + io.Float.Input("identity_guidance_scale", default=3.0, min=0.0, max=100.0, step=0.01, round=0.01, tooltip="Strength of identity guidance. Runs an extra forward pass without reference each step to amplify speaker identity. Set to 0 to disable (no extra pass)."), + io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True, tooltip="Start of the sigma range where identity guidance is active."), + io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True, tooltip="End of the sigma range where identity guidance is active."), + ], + outputs=[ + io.Model.Output(), + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + ], + ) + + @classmethod + def execute(cls, model, positive, negative, reference_audio, audio_vae, identity_guidance_scale, start_percent, end_percent) -> io.NodeOutput: + # Encode reference audio to latents and patchify + audio_latents = audio_vae.encode(reference_audio) + b, c, t, f = audio_latents.shape + ref_tokens = audio_latents.permute(0, 2, 1, 3).reshape(b, t, c * f) + ref_audio = {"tokens": ref_tokens} + + positive = node_helpers.conditioning_set_values(positive, {"ref_audio": ref_audio}) + negative = node_helpers.conditioning_set_values(negative, {"ref_audio": ref_audio}) + + # Patch model with identity guidance + m = model.clone() + scale = identity_guidance_scale + model_sampling = m.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) + + def post_cfg_function(args): + if scale == 0: + return args["denoised"] + + sigma = args["sigma"] + sigma_ = sigma[0].item() + if sigma_ > sigma_start or sigma_ < sigma_end: + return args["denoised"] + + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + model_options = args["model_options"].copy() + x = args["input"] + + # Strip ref_audio from conditioning for the no-reference pass + noref_cond = [] + for entry in cond: + new_entry = entry.copy() + mc = new_entry.get("model_conds", {}).copy() + mc.pop("ref_audio", None) + new_entry["model_conds"] = mc + noref_cond.append(new_entry) + + (pred_noref,) = comfy.samplers.calc_cond_batch( + args["model"], [noref_cond], x, sigma, model_options + ) + + return cfg_result + (cond_pred - pred_noref) * scale + + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return io.NodeOutput(m, positive, negative) + + class LtxvExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: @@ -697,6 +776,7 @@ class LtxvExtension(ComfyExtension): LTXVCropGuides, LTXVConcatAVLatent, LTXVSeparateAVLatent, + LTXVReferenceAudio, ] From 2d4970ff677970fbca9f9f562296eda46de8aa4c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 23 Mar 2026 17:43:41 -0700 Subject: [PATCH 57/65] Update frontend version to 1.42.8 (#13126) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index ad0344ed4..26cc94354 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.41.21 +comfyui-frontend-package==1.42.8 comfyui-workflow-templates==0.9.26 comfyui-embedded-docs==0.4.3 torch From 2d5fd3f5dde51574d77601dbe4c163a95a56121a Mon Sep 17 00:00:00 2001 From: Kelly Yang <124ykl@gmail.com> Date: Tue, 24 Mar 2026 11:22:30 -0700 Subject: [PATCH 58/65] fix: set default values of Color Adjustment node to zero (#13084) Co-authored-by: Jedrzej Kosinski --- blueprints/Color Adjustment.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blueprints/Color Adjustment.json b/blueprints/Color Adjustment.json index c599f7213..47f3df783 100644 --- a/blueprints/Color Adjustment.json +++ b/blueprints/Color Adjustment.json @@ -1 +1 @@ -{"revision": 0, "last_node_id": 14, "last_link_id": 0, "nodes": [{"id": 14, "type": "36677b92-5dd8-47a5-9380-4da982c1894f", "pos": [3610, -2630], "size": [270, 150], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "image", "localized_name": 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\n // Tint (green/magenta): positive = green, negative = magenta\n color.g += tint * TEMP_TINT_PRIMARY;\n color.r -= tint * TEMP_TINT_SECONDARY;\n color.b -= tint * TEMP_TINT_SECONDARY;\n \n // Single clamp after temperature/tint\n color = clamp(color, 0.0, 1.0);\n \n // Vibrance with skin protection\n if (vibrance != 0.0) {\n float maxC = max(color.r, max(color.g, color.b));\n float minC = min(color.r, min(color.g, color.b));\n float sat = maxC - minC;\n float gray = dot(color, LUMA_WEIGHTS);\n \n if (vibrance < 0.0) {\n // Desaturate: -100 \u2192 gray\n color = mix(vec3(gray), color, 1.0 + vibrance);\n } else {\n // Boost less saturated colors more\n float vibranceAmt = vibrance * (1.0 - sat);\n \n // Branchless skin tone protection\n float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n float warmth = (color.r - color.b) / max(maxC, EPSILON);\n float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n \n color = 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"widgets": [], "nodes": [{"id": 15, "type": "GLSLShader", "pos": [3590, -3940], "size": [420, 252], "flags": {}, "order": 4, "mode": 0, "inputs": [{"label": "image0", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 29}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}, {"label": "u_float0", "localized_name": "floats.u_float0", "name": "floats.u_float0", "shape": 7, "type": "FLOAT", "link": 34}, {"label": "u_float1", "localized_name": "floats.u_float1", "name": "floats.u_float1", "shape": 7, "type": "FLOAT", "link": 30}, {"label": "u_float2", "localized_name": "floats.u_float2", "name": "floats.u_float2", "shape": 7, "type": "FLOAT", "link": 31}, {"label": "u_float3", "localized_name": "floats.u_float3", "name": "floats.u_float3", "shape": 7, "type": "FLOAT", "link": 33}, {"label": "u_float4", "localized_name": "floats.u_float4", "name": "floats.u_float4", "shape": 7, "type": "FLOAT", "link": null}, {"label": "u_int0", "localized_name": "ints.u_int0", "name": "ints.u_int0", "shape": 7, "type": "INT", "link": null}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}], "outputs": [{"localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [28]}, {"localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": null}, {"localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": null}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n vec4 tex = texture(u_image0, v_texCoord);\n vec3 color = tex.rgb;\n \n // Scale inputs: -100/100 \u2192 -1/1\n float temperature = u_float0 * INPUT_SCALE;\n float tint = u_float1 * INPUT_SCALE;\n float vibrance = u_float2 * INPUT_SCALE;\n float saturation = u_float3 * INPUT_SCALE;\n \n // Temperature (warm/cool): positive = warm, negative = cool\n color.r += temperature * TEMP_TINT_PRIMARY;\n color.b -= temperature * TEMP_TINT_PRIMARY;\n \n // Tint (green/magenta): positive = green, negative = magenta\n color.g += tint * TEMP_TINT_PRIMARY;\n color.r -= tint * TEMP_TINT_SECONDARY;\n color.b -= tint * TEMP_TINT_SECONDARY;\n \n // Single clamp after temperature/tint\n color = clamp(color, 0.0, 1.0);\n \n // Vibrance with skin protection\n if (vibrance != 0.0) {\n float maxC = max(color.r, max(color.g, color.b));\n float minC = min(color.r, min(color.g, color.b));\n float sat = maxC - minC;\n float gray = dot(color, LUMA_WEIGHTS);\n \n if (vibrance < 0.0) {\n // Desaturate: -100 \u2192 gray\n color = mix(vec3(gray), color, 1.0 + vibrance);\n } else {\n // Boost less saturated colors more\n float vibranceAmt = vibrance * (1.0 - sat);\n \n // Branchless skin tone protection\n float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n float warmth = (color.r - color.b) / max(maxC, EPSILON);\n float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n \n color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n }\n }\n \n // Saturation\n if (saturation != 0.0) {\n float gray = dot(color, LUMA_WEIGHTS);\n float satMix = saturation < 0.0\n ? 1.0 + saturation // -100 \u2192 gray\n : 1.0 + saturation * SATURATION_BOOST; // +100 \u2192 3x boost\n color = mix(vec3(gray), color, satMix);\n }\n \n fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);\n}", "from_input"]}, {"id": 6, "type": "PrimitiveFloat", "pos": [3290, -3610], "size": [270, 58], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "vibrance", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [26, 31]}], "title": "Vibrance", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [128, 128, 128]}, {"offset": 1, "color": [255, 0, 0]}]}, "widgets_values": [0]}, {"id": 7, "type": "PrimitiveFloat", "pos": [3290, 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"step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [0, 255, 0]}, {"offset": 0.5, "color": [255, 255, 255]}, {"offset": 1, "color": [255, 0, 255]}]}, "widgets_values": [0]}, {"id": 4, "type": "PrimitiveFloat", "pos": [3290, -3940], "size": [270, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"label": "temperature", "localized_name": "value", "name": "value", "type": "FLOAT", "widget": {"name": "value"}, "link": null}], "outputs": [{"localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT", "links": [34]}], "title": "Temperature", "properties": {"Node name for S&R": "PrimitiveFloat", "max": 100, "min": -100, "step": 1, "display": "gradientslider", "gradient_stops": [{"offset": 0, "color": [68, 136, 255]}, {"offset": 0.5, "color": [255, 255, 255]}, {"offset": 1, "color": [255, 136, 0]}]}, "widgets_values": [0]}], "groups": [], "links": [{"id": 34, "origin_id": 4, "origin_slot": 0, "target_id": 15, "target_slot": 2, "type": "FLOAT"}, {"id": 30, "origin_id": 5, "origin_slot": 0, "target_id": 15, "target_slot": 3, "type": "FLOAT"}, {"id": 31, "origin_id": 6, "origin_slot": 0, "target_id": 15, "target_slot": 4, "type": "FLOAT"}, {"id": 33, "origin_id": 7, "origin_slot": 0, "target_id": 15, "target_slot": 5, "type": "FLOAT"}, {"id": 29, "origin_id": -10, "origin_slot": 0, "target_id": 15, "target_slot": 0, "type": "IMAGE"}, {"id": 28, "origin_id": 15, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}} From f9ec85f739aeab3fbc0f89baaa1e0fc196f2ff2c Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Tue, 24 Mar 2026 22:27:39 +0200 Subject: [PATCH 59/65] feat(api-nodes): update xAI Grok nodes (#13140) --- comfy_api_nodes/apis/grok.py | 10 +- comfy_api_nodes/nodes_grok.py | 251 ++++++++++++++++++++++++++++++++++ 2 files changed, 260 insertions(+), 1 deletion(-) diff --git a/comfy_api_nodes/apis/grok.py b/comfy_api_nodes/apis/grok.py index c56c8aecc..fbedb53e0 100644 --- a/comfy_api_nodes/apis/grok.py +++ b/comfy_api_nodes/apis/grok.py @@ -29,13 +29,21 @@ class ImageEditRequest(BaseModel): class VideoGenerationRequest(BaseModel): model: str = Field(...) prompt: str = Field(...) - image: InputUrlObject | None = Field(...) + image: InputUrlObject | None = Field(None) + reference_images: list[InputUrlObject] | None = Field(None) duration: int = Field(...) aspect_ratio: str | None = Field(...) resolution: str = Field(...) seed: int = Field(...) +class VideoExtensionRequest(BaseModel): + prompt: str = Field(...) + video: InputUrlObject = Field(...) + duration: int = Field(default=6) + model: str | None = Field(default=None) + + class VideoEditRequest(BaseModel): model: str = Field(...) prompt: str = Field(...) diff --git a/comfy_api_nodes/nodes_grok.py b/comfy_api_nodes/nodes_grok.py index 0716d6239..dabc899d6 100644 --- a/comfy_api_nodes/nodes_grok.py +++ b/comfy_api_nodes/nodes_grok.py @@ -8,6 +8,7 @@ from comfy_api_nodes.apis.grok import ( ImageGenerationResponse, InputUrlObject, VideoEditRequest, + VideoExtensionRequest, VideoGenerationRequest, VideoGenerationResponse, VideoStatusResponse, @@ -21,6 +22,7 @@ from comfy_api_nodes.util import ( poll_op, sync_op, tensor_to_base64_string, + upload_images_to_comfyapi, upload_video_to_comfyapi, validate_string, validate_video_duration, @@ -33,6 +35,13 @@ def _extract_grok_price(response) -> float | None: return None +def _extract_grok_video_price(response) -> float | None: + price = _extract_grok_price(response) + if price is not None: + return price * 1.43 + return None + + class GrokImageNode(IO.ComfyNode): @classmethod @@ -354,6 +363,8 @@ class GrokVideoNode(IO.ComfyNode): seed: int, image: Input.Image | None = None, ) -> IO.NodeOutput: + if model == "grok-imagine-video-beta": + model = "grok-imagine-video" image_url = None if image is not None: if get_number_of_images(image) != 1: @@ -462,6 +473,244 @@ class GrokVideoEditNode(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(response.video.url)) +class GrokVideoReferenceNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GrokVideoReferenceNode", + display_name="Grok Reference-to-Video", + category="api node/video/Grok", + description="Generate video guided by reference images as style and content references.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + tooltip="Text description of the desired video.", + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "grok-imagine-video", + [ + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplatePrefix( + IO.Image.Input("image"), + prefix="reference_", + min=1, + max=7, + ), + tooltip="Up to 7 reference images to guide the video generation.", + ), + IO.Combo.Input( + "resolution", + options=["480p", "720p"], + tooltip="The resolution of the output video.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"], + tooltip="The aspect ratio of the output video.", + ), + IO.Int.Input( + "duration", + default=6, + min=2, + max=10, + step=1, + tooltip="The duration of the output video in seconds.", + display_mode=IO.NumberDisplay.slider, + ), + ], + ), + ], + tooltip="The model to use for video generation.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; " + "actual results are nondeterministic regardless of seed.", + ), + ], + 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, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends( + widgets=["model.duration", "model.resolution"], + input_groups=["model.reference_images"], + ), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $refs := inputGroups["model.reference_images"]; + $rate := $res = "720p" ? 0.07 : 0.05; + $price := ($rate * $dur + 0.002 * $refs) * 1.43; + {"type":"usd","usd": $price} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + ref_image_urls = await upload_images_to_comfyapi( + cls, + list(model["reference_images"].values()), + mime_type="image/png", + wait_label="Uploading base images", + max_images=7, + ) + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"), + data=VideoGenerationRequest( + model=model["model"], + reference_images=[InputUrlObject(url=i) for i in ref_image_urls], + prompt=prompt, + resolution=model["resolution"], + duration=model["duration"], + aspect_ratio=model["aspect_ratio"], + seed=seed, + ), + response_model=VideoGenerationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"), + status_extractor=lambda r: r.status if r.status is not None else "complete", + response_model=VideoStatusResponse, + price_extractor=_extract_grok_video_price, + ) + return IO.NodeOutput(await download_url_to_video_output(response.video.url)) + + +class GrokVideoExtendNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GrokVideoExtendNode", + display_name="Grok Video Extend", + category="api node/video/Grok", + description="Extend an existing video with a seamless continuation based on a text prompt.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + tooltip="Text description of what should happen next in the video.", + ), + IO.Video.Input("video", tooltip="Source video to extend. MP4 format, 2-15 seconds."), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "grok-imagine-video", + [ + IO.Int.Input( + "duration", + default=8, + min=2, + max=10, + step=1, + tooltip="Length of the extension in seconds.", + display_mode=IO.NumberDisplay.slider, + ), + ], + ), + ], + tooltip="The model to use for video extension.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; " + "actual results are nondeterministic regardless of seed.", + ), + ], + 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, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model.duration"]), + expr=""" + ( + $dur := $lookup(widgets, "model.duration"); + { + "type": "range_usd", + "min_usd": (0.02 + 0.05 * $dur) * 1.43, + "max_usd": (0.15 + 0.05 * $dur) * 1.43 + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + video: Input.Video, + model: dict, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + validate_video_duration(video, min_duration=2, max_duration=15) + video_size = get_fs_object_size(video.get_stream_source()) + if video_size > 50 * 1024 * 1024: + raise ValueError(f"Video size ({video_size / 1024 / 1024:.1f}MB) exceeds 50MB limit.") + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/xai/v1/videos/extensions", method="POST"), + data=VideoExtensionRequest( + prompt=prompt, + video=InputUrlObject(url=await upload_video_to_comfyapi(cls, video)), + duration=model["duration"], + ), + response_model=VideoGenerationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"), + status_extractor=lambda r: r.status if r.status is not None else "complete", + response_model=VideoStatusResponse, + price_extractor=_extract_grok_video_price, + ) + return IO.NodeOutput(await download_url_to_video_output(response.video.url)) + + class GrokExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -469,7 +718,9 @@ class GrokExtension(ComfyExtension): GrokImageNode, GrokImageEditNode, GrokVideoNode, + GrokVideoReferenceNode, GrokVideoEditNode, + GrokVideoExtendNode, ] From c2862b24af49ff40b251ea2a4e22b92af9e92982 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 24 Mar 2026 14:36:12 -0700 Subject: [PATCH 60/65] Update templates package version. (#13141) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 26cc94354..76f824906 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.8 -comfyui-workflow-templates==0.9.26 +comfyui-workflow-templates==0.9.36 comfyui-embedded-docs==0.4.3 torch torchsde From 8e73678dae6e5763bc860d6f98566243a494f9c2 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Tue, 24 Mar 2026 17:47:28 -0400 Subject: [PATCH 61/65] CURVE node (#12757) * CURVE node * remove curve to sigmas node * feat: add CurveInput ABC with MonotoneCubicCurve implementation (#12986) CurveInput is an abstract base class so future curve representations (bezier, LUT-based, analytical functions) can be added without breaking downstream nodes that type-check against CurveInput. MonotoneCubicCurve is the concrete implementation that: - Mirrors frontend createMonotoneInterpolator (curveUtils.ts) exactly - Pre-computes slopes as numpy arrays at construction time - Provides vectorised interp_array() using numpy for batch evaluation - interp() for single-value evaluation - to_lut() for generating lookup tables CurveEditor node wraps raw widget points in MonotoneCubicCurve. * linear curve * refactor: move CurveEditor to comfy_extras/nodes_curve.py with V3 schema * feat: add HISTOGRAM type and histogram support to CurveEditor * code improve --------- Co-authored-by: Christian Byrne --- comfy_api/input/__init__.py | 8 + comfy_api/latest/_input/__init__.py | 5 + comfy_api/latest/_input/curve_types.py | 219 +++++++++++++++++++++++++ comfy_api/latest/_io.py | 20 ++- comfy_extras/nodes_curve.py | 42 +++++ nodes.py | 1 + 6 files changed, 292 insertions(+), 3 deletions(-) create mode 100644 comfy_api/latest/_input/curve_types.py create mode 100644 comfy_extras/nodes_curve.py diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py index 68ff78270..16d4acfd1 100644 --- a/comfy_api/input/__init__.py +++ b/comfy_api/input/__init__.py @@ -5,6 +5,10 @@ from comfy_api.latest._input import ( MaskInput, LatentInput, VideoInput, + CurvePoint, + CurveInput, + MonotoneCubicCurve, + LinearCurve, ) __all__ = [ @@ -13,4 +17,8 @@ __all__ = [ "MaskInput", "LatentInput", "VideoInput", + "CurvePoint", + "CurveInput", + "MonotoneCubicCurve", + "LinearCurve", ] diff --git a/comfy_api/latest/_input/__init__.py b/comfy_api/latest/_input/__init__.py index 14f0e72f4..05cd3d40a 100644 --- a/comfy_api/latest/_input/__init__.py +++ b/comfy_api/latest/_input/__init__.py @@ -1,4 +1,5 @@ from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput +from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve from .video_types import VideoInput __all__ = [ @@ -7,4 +8,8 @@ __all__ = [ "VideoInput", "MaskInput", "LatentInput", + "CurvePoint", + "CurveInput", + "MonotoneCubicCurve", + "LinearCurve", ] diff --git a/comfy_api/latest/_input/curve_types.py b/comfy_api/latest/_input/curve_types.py new file mode 100644 index 000000000..b6dd7adf9 --- /dev/null +++ b/comfy_api/latest/_input/curve_types.py @@ -0,0 +1,219 @@ +from __future__ import annotations + +import logging +import math +from abc import ABC, abstractmethod +import numpy as np + +logger = logging.getLogger(__name__) + + +CurvePoint = tuple[float, float] + + +class CurveInput(ABC): + """Abstract base class for curve inputs. + + Subclasses represent different curve representations (control-point + interpolation, analytical functions, LUT-based, etc.) while exposing a + uniform evaluation interface to downstream nodes. + """ + + @property + @abstractmethod + def points(self) -> list[CurvePoint]: + """The control points that define this curve.""" + + @abstractmethod + def interp(self, x: float) -> float: + """Evaluate the curve at a single *x* value in [0, 1].""" + + def interp_array(self, xs: np.ndarray) -> np.ndarray: + """Vectorised evaluation over a numpy array of x values. + + Subclasses should override this for better performance. The default + falls back to scalar ``interp`` calls. + """ + return np.fromiter((self.interp(float(x)) for x in xs), dtype=np.float64, count=len(xs)) + + def to_lut(self, size: int = 256) -> np.ndarray: + """Generate a float64 lookup table of *size* evenly-spaced samples in [0, 1].""" + return self.interp_array(np.linspace(0.0, 1.0, size)) + + @staticmethod + def from_raw(data) -> CurveInput: + """Convert raw curve data (dict or point list) to a CurveInput instance. + + Accepts: + - A ``CurveInput`` instance (returned as-is). + - A dict with ``"points"`` and optional ``"interpolation"`` keys. + - A bare list/sequence of ``(x, y)`` pairs (defaults to monotone cubic). + """ + if isinstance(data, CurveInput): + return data + if isinstance(data, dict): + raw_points = data["points"] + interpolation = data.get("interpolation", "monotone_cubic") + else: + raw_points = data + interpolation = "monotone_cubic" + points = [(float(x), float(y)) for x, y in raw_points] + if interpolation == "linear": + return LinearCurve(points) + if interpolation != "monotone_cubic": + logger.warning("Unknown curve interpolation %r, falling back to monotone_cubic", interpolation) + return MonotoneCubicCurve(points) + + +class MonotoneCubicCurve(CurveInput): + """Monotone cubic Hermite interpolation over control points. + + Mirrors the frontend ``createMonotoneInterpolator`` in + ``ComfyUI_frontend/src/components/curve/curveUtils.ts`` so that + backend evaluation matches the editor preview exactly. + + All heavy work (sorting, slope computation) happens once at construction. + ``interp_array`` is fully vectorised with numpy. + """ + + def __init__(self, control_points: list[CurvePoint]): + sorted_pts = sorted(control_points, key=lambda p: p[0]) + self._points = [(float(x), float(y)) for x, y in sorted_pts] + self._xs = np.array([p[0] for p in self._points], dtype=np.float64) + self._ys = np.array([p[1] for p in self._points], dtype=np.float64) + self._slopes = self._compute_slopes() + + @property + def points(self) -> list[CurvePoint]: + return list(self._points) + + def _compute_slopes(self) -> np.ndarray: + xs, ys = self._xs, self._ys + n = len(xs) + if n < 2: + return np.zeros(n, dtype=np.float64) + + dx = np.diff(xs) + dy = np.diff(ys) + dx_safe = np.where(dx == 0, 1.0, dx) + deltas = np.where(dx == 0, 0.0, dy / dx_safe) + + slopes = np.empty(n, dtype=np.float64) + slopes[0] = deltas[0] + slopes[-1] = deltas[-1] + for i in range(1, n - 1): + if deltas[i - 1] * deltas[i] <= 0: + slopes[i] = 0.0 + else: + slopes[i] = (deltas[i - 1] + deltas[i]) / 2 + + for i in range(n - 1): + if deltas[i] == 0: + slopes[i] = 0.0 + slopes[i + 1] = 0.0 + else: + alpha = slopes[i] / deltas[i] + beta = slopes[i + 1] / deltas[i] + s = alpha * alpha + beta * beta + if s > 9: + t = 3 / math.sqrt(s) + slopes[i] = t * alpha * deltas[i] + slopes[i + 1] = t * beta * deltas[i] + return slopes + + def interp(self, x: float) -> float: + xs, ys, slopes = self._xs, self._ys, self._slopes + n = len(xs) + if n == 0: + return 0.0 + if n == 1: + return float(ys[0]) + if x <= xs[0]: + return float(ys[0]) + if x >= xs[-1]: + return float(ys[-1]) + + hi = int(np.searchsorted(xs, x, side='right')) + hi = min(hi, n - 1) + lo = hi - 1 + + dx = xs[hi] - xs[lo] + if dx == 0: + return float(ys[lo]) + + t = (x - xs[lo]) / dx + t2 = t * t + t3 = t2 * t + h00 = 2 * t3 - 3 * t2 + 1 + h10 = t3 - 2 * t2 + t + h01 = -2 * t3 + 3 * t2 + h11 = t3 - t2 + return float(h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi]) + + def interp_array(self, xs_in: np.ndarray) -> np.ndarray: + """Fully vectorised evaluation using numpy.""" + xs, ys, slopes = self._xs, self._ys, self._slopes + n = len(xs) + if n == 0: + return np.zeros_like(xs_in, dtype=np.float64) + if n == 1: + return np.full_like(xs_in, ys[0], dtype=np.float64) + + hi = np.searchsorted(xs, xs_in, side='right').clip(1, n - 1) + lo = hi - 1 + + dx = xs[hi] - xs[lo] + dx_safe = np.where(dx == 0, 1.0, dx) + t = np.where(dx == 0, 0.0, (xs_in - xs[lo]) / dx_safe) + t2 = t * t + t3 = t2 * t + + h00 = 2 * t3 - 3 * t2 + 1 + h10 = t3 - 2 * t2 + t + h01 = -2 * t3 + 3 * t2 + h11 = t3 - t2 + + result = h00 * ys[lo] + h10 * dx * slopes[lo] + h01 * ys[hi] + h11 * dx * slopes[hi] + result = np.where(xs_in <= xs[0], ys[0], result) + result = np.where(xs_in >= xs[-1], ys[-1], result) + return result + + def __repr__(self) -> str: + return f"MonotoneCubicCurve(points={self._points})" + + +class LinearCurve(CurveInput): + """Piecewise linear interpolation over control points. + + Mirrors the frontend ``createLinearInterpolator`` in + ``ComfyUI_frontend/src/components/curve/curveUtils.ts``. + """ + + def __init__(self, control_points: list[CurvePoint]): + sorted_pts = sorted(control_points, key=lambda p: p[0]) + self._points = [(float(x), float(y)) for x, y in sorted_pts] + self._xs = np.array([p[0] for p in self._points], dtype=np.float64) + self._ys = np.array([p[1] for p in self._points], dtype=np.float64) + + @property + def points(self) -> list[CurvePoint]: + return list(self._points) + + def interp(self, x: float) -> float: + xs, ys = self._xs, self._ys + n = len(xs) + if n == 0: + return 0.0 + if n == 1: + return float(ys[0]) + return float(np.interp(x, xs, ys)) + + def interp_array(self, xs_in: np.ndarray) -> np.ndarray: + if len(self._xs) == 0: + return np.zeros_like(xs_in, dtype=np.float64) + if len(self._xs) == 1: + return np.full_like(xs_in, self._ys[0], dtype=np.float64) + return np.interp(xs_in, self._xs, self._ys) + + def __repr__(self) -> str: + return f"LinearCurve(points={self._points})" diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py index 7ca8f4e0c..1cbc8ed26 100644 --- a/comfy_api/latest/_io.py +++ b/comfy_api/latest/_io.py @@ -23,7 +23,7 @@ if TYPE_CHECKING: from comfy.samplers import CFGGuider, Sampler from comfy.sd import CLIP, VAE from comfy.sd import StyleModel as StyleModel_ - from comfy_api.input import VideoInput + from comfy_api.input import VideoInput, CurveInput as CurveInput_ from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class, prune_dict, shallow_clone_class) from comfy_execution.graph_utils import ExecutionBlocker @@ -1242,8 +1242,9 @@ class BoundingBox(ComfyTypeIO): @comfytype(io_type="CURVE") class Curve(ComfyTypeIO): - CurvePoint = tuple[float, float] - Type = list[CurvePoint] + from comfy_api.input import CurvePoint + if TYPE_CHECKING: + Type = CurveInput_ class Input(WidgetInput): def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, @@ -1252,6 +1253,18 @@ class Curve(ComfyTypeIO): if default is None: self.default = [(0.0, 0.0), (1.0, 1.0)] + def as_dict(self): + d = super().as_dict() + if self.default is not None: + d["default"] = {"points": [list(p) for p in self.default], "interpolation": "monotone_cubic"} + return d + + +@comfytype(io_type="HISTOGRAM") +class Histogram(ComfyTypeIO): + """A histogram represented as a list of bin counts.""" + Type = list[int] + DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {} def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]): @@ -2240,5 +2253,6 @@ __all__ = [ "PriceBadge", "BoundingBox", "Curve", + "Histogram", "NodeReplace", ] diff --git a/comfy_extras/nodes_curve.py b/comfy_extras/nodes_curve.py new file mode 100644 index 000000000..9016a84f9 --- /dev/null +++ b/comfy_extras/nodes_curve.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +from comfy_api.latest import ComfyExtension, io +from comfy_api.input import CurveInput +from typing_extensions import override + + +class CurveEditor(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CurveEditor", + display_name="Curve Editor", + category="utils", + inputs=[ + io.Curve.Input("curve"), + io.Histogram.Input("histogram", optional=True), + ], + outputs=[ + io.Curve.Output("curve"), + ], + ) + + @classmethod + def execute(cls, curve, histogram=None) -> io.NodeOutput: + result = CurveInput.from_raw(curve) + + ui = {} + if histogram is not None: + ui["histogram"] = histogram if isinstance(histogram, list) else list(histogram) + + return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result) + + +class CurveExtension(ComfyExtension): + @override + async def get_node_list(self): + return [CurveEditor] + + +async def comfy_entrypoint(): + return CurveExtension() diff --git a/nodes.py b/nodes.py index 2c4650a20..79874d051 100644 --- a/nodes.py +++ b/nodes.py @@ -2455,6 +2455,7 @@ async def init_builtin_extra_nodes(): "nodes_sdpose.py", "nodes_math.py", "nodes_painter.py", + "nodes_curve.py", ] import_failed = [] From a0a64c679fca700a087d0cdfa419912a3e8b3bf8 Mon Sep 17 00:00:00 2001 From: Dante Date: Wed, 25 Mar 2026 07:38:08 +0900 Subject: [PATCH 62/65] Add Number Convert node (#13041) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add Number Convert node for unified numeric type conversion Consolidates fragmented IntToFloat/FloatToInt nodes (previously only available via third-party packs like ComfyMath, FillNodes, etc.) into a single core node. - Single input accepting INT, FLOAT, STRING, and BOOL types - Two outputs: FLOAT and INT - Conversion: bool→0/1, string→parsed number, float↔int standard cast - Follows Math Expression node patterns (comfy_api, io.Schema, etc.) Refs: COM-16925 * Register nodes_number_convert.py in extras_files list Without this entry in nodes.py, the Number Convert node file would not be discovered and loaded at startup. * Add isfinite guard, exception chaining, and unit tests for Number Convert node - Add math.isfinite() check to prevent int() crash on inf/nan string inputs - Use 'from None' for cleaner exception chaining on string parse failure - Add 21 unit tests covering all input types and error paths --- comfy_extras/nodes_number_convert.py | 79 +++++++++++ nodes.py | 1 + .../nodes_number_convert_test.py | 123 ++++++++++++++++++ 3 files changed, 203 insertions(+) create mode 100644 comfy_extras/nodes_number_convert.py create mode 100644 tests-unit/comfy_extras_test/nodes_number_convert_test.py diff --git a/comfy_extras/nodes_number_convert.py b/comfy_extras/nodes_number_convert.py new file mode 100644 index 000000000..b2822c856 --- /dev/null +++ b/comfy_extras/nodes_number_convert.py @@ -0,0 +1,79 @@ +"""Number Convert node for unified numeric type conversion. + +Provides a single node that converts INT, FLOAT, STRING, and BOOL +inputs into FLOAT and INT outputs. +""" + +from __future__ import annotations + +import math + +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + + +class NumberConvertNode(io.ComfyNode): + """Converts various types to numeric FLOAT and INT outputs.""" + + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="ComfyNumberConvert", + display_name="Number Convert", + category="math", + search_aliases=[ + "int to float", "float to int", "number convert", + "int2float", "float2int", "cast", "parse number", + "string to number", "bool to int", + ], + inputs=[ + io.MultiType.Input( + "value", + [io.Int, io.Float, io.String, io.Boolean], + display_name="value", + ), + ], + outputs=[ + io.Float.Output(display_name="FLOAT"), + io.Int.Output(display_name="INT"), + ], + ) + + @classmethod + def execute(cls, value) -> io.NodeOutput: + if isinstance(value, bool): + float_val = 1.0 if value else 0.0 + elif isinstance(value, (int, float)): + float_val = float(value) + elif isinstance(value, str): + text = value.strip() + if not text: + raise ValueError("Cannot convert empty string to number.") + try: + float_val = float(text) + except ValueError: + raise ValueError( + f"Cannot convert string to number: {value!r}" + ) from None + else: + raise TypeError( + f"Unsupported input type: {type(value).__name__}" + ) + + if not math.isfinite(float_val): + raise ValueError( + f"Cannot convert non-finite value to number: {float_val}" + ) + + return io.NodeOutput(float_val, int(float_val)) + + +class NumberConvertExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [NumberConvertNode] + + +async def comfy_entrypoint() -> NumberConvertExtension: + return NumberConvertExtension() diff --git a/nodes.py b/nodes.py index 79874d051..37ceac2fc 100644 --- a/nodes.py +++ b/nodes.py @@ -2454,6 +2454,7 @@ async def init_builtin_extra_nodes(): "nodes_nag.py", "nodes_sdpose.py", "nodes_math.py", + "nodes_number_convert.py", "nodes_painter.py", "nodes_curve.py", ] diff --git a/tests-unit/comfy_extras_test/nodes_number_convert_test.py b/tests-unit/comfy_extras_test/nodes_number_convert_test.py new file mode 100644 index 000000000..0046fa8f4 --- /dev/null +++ b/tests-unit/comfy_extras_test/nodes_number_convert_test.py @@ -0,0 +1,123 @@ +import pytest +from unittest.mock import patch, MagicMock + +mock_nodes = MagicMock() +mock_nodes.MAX_RESOLUTION = 16384 +mock_server = MagicMock() + +with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}): + from comfy_extras.nodes_number_convert import NumberConvertNode + + +class TestNumberConvertExecute: + @staticmethod + def _exec(value) -> object: + return NumberConvertNode.execute(value) + + # --- INT input --- + + def test_int_input(self): + result = self._exec(42) + assert result[0] == 42.0 + assert result[1] == 42 + + def test_int_zero(self): + result = self._exec(0) + assert result[0] == 0.0 + assert result[1] == 0 + + def test_int_negative(self): + result = self._exec(-7) + assert result[0] == -7.0 + assert result[1] == -7 + + # --- FLOAT input --- + + def test_float_input(self): + result = self._exec(3.14) + assert result[0] == 3.14 + assert result[1] == 3 + + def test_float_truncation_toward_zero(self): + result = self._exec(-2.9) + assert result[0] == -2.9 + assert result[1] == -2 # int() truncates toward zero, not floor + + def test_float_output_type(self): + result = self._exec(5) + assert isinstance(result[0], float) + + def test_int_output_type(self): + result = self._exec(5.7) + assert isinstance(result[1], int) + + # --- BOOL input --- + + def test_bool_true(self): + result = self._exec(True) + assert result[0] == 1.0 + assert result[1] == 1 + + def test_bool_false(self): + result = self._exec(False) + assert result[0] == 0.0 + assert result[1] == 0 + + # --- STRING input --- + + def test_string_integer(self): + result = self._exec("42") + assert result[0] == 42.0 + assert result[1] == 42 + + def test_string_float(self): + result = self._exec("3.14") + assert result[0] == 3.14 + assert result[1] == 3 + + def test_string_negative(self): + result = self._exec("-5.5") + assert result[0] == -5.5 + assert result[1] == -5 + + def test_string_with_whitespace(self): + result = self._exec(" 7.0 ") + assert result[0] == 7.0 + assert result[1] == 7 + + def test_string_scientific_notation(self): + result = self._exec("1e3") + assert result[0] == 1000.0 + assert result[1] == 1000 + + # --- STRING error paths --- + + def test_empty_string_raises(self): + with pytest.raises(ValueError, match="Cannot convert empty string"): + self._exec("") + + def test_whitespace_only_string_raises(self): + with pytest.raises(ValueError, match="Cannot convert empty string"): + self._exec(" ") + + def test_non_numeric_string_raises(self): + with pytest.raises(ValueError, match="Cannot convert string to number"): + self._exec("abc") + + def test_string_inf_raises(self): + with pytest.raises(ValueError, match="non-finite"): + self._exec("inf") + + def test_string_nan_raises(self): + with pytest.raises(ValueError, match="non-finite"): + self._exec("nan") + + def test_string_negative_inf_raises(self): + with pytest.raises(ValueError, match="non-finite"): + self._exec("-inf") + + # --- Unsupported type --- + + def test_unsupported_type_raises(self): + with pytest.raises(TypeError, match="Unsupported input type"): + self._exec([1, 2, 3]) From 5ebb0c2e0b72945c271a2fb4db749585aa32a13c Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Wed, 25 Mar 2026 08:39:04 +0800 Subject: [PATCH 63/65] FP8 bwd training (#13121) --- comfy/model_management.py | 1 + comfy/ops.py | 65 ++++++++++++++++++++++++++++--------- comfy_extras/nodes_train.py | 9 +++++ 3 files changed, 59 insertions(+), 16 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 2c250dacc..9617d8388 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -55,6 +55,7 @@ total_vram = 0 # Training Related State in_training = False +training_fp8_bwd = False def get_supported_float8_types(): diff --git a/comfy/ops.py b/comfy/ops.py index 1518ec9de..ca25693db 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -777,8 +777,16 @@ from .quant_ops import ( class QuantLinearFunc(torch.autograd.Function): - """Custom autograd function for quantized linear: quantized forward, compute_dtype backward. - Handles any input rank by flattening to 2D for matmul and restoring shape after. + """Custom autograd function for quantized linear: quantized forward, optionally FP8 backward. + + When training_fp8_bwd is enabled: + - Forward: quantize input per layout (FP8/NVFP4), use quantized matmul + - Backward: all matmuls use FP8 tensor cores via torch.mm dispatch + - Cached input is FP8 (half the memory of bf16) + + When training_fp8_bwd is disabled: + - Forward: quantize input per layout, use quantized matmul + - Backward: dequantize weight to compute_dtype, use standard matmul """ @staticmethod @@ -786,7 +794,7 @@ class QuantLinearFunc(torch.autograd.Function): input_shape = input_float.shape inp = input_float.detach().flatten(0, -2) # zero-cost view to 2D - # Quantize input (same as inference path) + # Quantize input for forward (same layout as weight) if layout_type is not None: q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale) else: @@ -797,43 +805,68 @@ class QuantLinearFunc(torch.autograd.Function): output = torch.nn.functional.linear(q_input, w, b) - # Restore original input shape + # Unflatten output to match original input shape if len(input_shape) > 2: output = output.unflatten(0, input_shape[:-1]) - ctx.save_for_backward(input_float, weight) + # Save for backward ctx.input_shape = input_shape ctx.has_bias = bias is not None ctx.compute_dtype = compute_dtype ctx.weight_requires_grad = weight.requires_grad + ctx.fp8_bwd = comfy.model_management.training_fp8_bwd + + if ctx.fp8_bwd: + # Cache FP8 quantized input — half the memory of bf16 + if isinstance(q_input, QuantizedTensor) and layout_type.startswith('TensorCoreFP8'): + ctx.q_input = q_input # already FP8, reuse + else: + # NVFP4 or other layout — quantize input to FP8 for backward + ctx.q_input = QuantizedTensor.from_float(inp, "TensorCoreFP8E4M3Layout") + ctx.save_for_backward(weight) + else: + ctx.q_input = None + ctx.save_for_backward(input_float, weight) return output @staticmethod @torch.autograd.function.once_differentiable def backward(ctx, grad_output): - input_float, weight = ctx.saved_tensors compute_dtype = ctx.compute_dtype grad_2d = grad_output.flatten(0, -2).to(compute_dtype) - # Dequantize weight to compute dtype for backward matmul - if isinstance(weight, QuantizedTensor): - weight_f = weight.dequantize().to(compute_dtype) + # Value casting — only difference between fp8 and non-fp8 paths + if ctx.fp8_bwd: + weight, = ctx.saved_tensors + # Wrap as FP8 QuantizedTensors → torch.mm dispatches to _scaled_mm + grad_mm = QuantizedTensor.from_float(grad_2d, "TensorCoreFP8E5M2Layout") + if isinstance(weight, QuantizedTensor) and weight._layout_cls.startswith("TensorCoreFP8"): + weight_mm = weight + elif isinstance(weight, QuantizedTensor): + weight_mm = QuantizedTensor.from_float(weight.dequantize().to(compute_dtype), "TensorCoreFP8E4M3Layout") + else: + weight_mm = QuantizedTensor.from_float(weight.to(compute_dtype), "TensorCoreFP8E4M3Layout") + input_mm = ctx.q_input else: - weight_f = weight.to(compute_dtype) + input_float, weight = ctx.saved_tensors + # Standard tensors → torch.mm does regular matmul + grad_mm = grad_2d + if isinstance(weight, QuantizedTensor): + weight_mm = weight.dequantize().to(compute_dtype) + else: + weight_mm = weight.to(compute_dtype) + input_mm = input_float.flatten(0, -2).to(compute_dtype) if ctx.weight_requires_grad else None - # grad_input = grad_output @ weight - grad_input = torch.mm(grad_2d, weight_f) + # Computation — same for both paths, dispatch handles the rest + grad_input = torch.mm(grad_mm, weight_mm) if len(ctx.input_shape) > 2: grad_input = grad_input.unflatten(0, ctx.input_shape[:-1]) - # grad_weight (only if weight requires grad, typically frozen for quantized training) grad_weight = None if ctx.weight_requires_grad: - input_f = input_float.flatten(0, -2).to(compute_dtype) - grad_weight = torch.mm(grad_2d.t(), input_f) + grad_weight = torch.mm(grad_mm.t(), input_mm) - # grad_bias grad_bias = None if ctx.has_bias: grad_bias = grad_2d.sum(dim=0) diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py index 0ad0acee6..df1b39fd5 100644 --- a/comfy_extras/nodes_train.py +++ b/comfy_extras/nodes_train.py @@ -1030,6 +1030,11 @@ class TrainLoraNode(io.ComfyNode): default="bf16", tooltip="The dtype to use for lora.", ), + io.Boolean.Input( + "quantized_backward", + default=False, + tooltip="When using training_dtype 'none' and training on quantized model, doing backward with quantized matmul when enabled.", + ), io.Combo.Input( "algorithm", options=list(adapter_maps.keys()), @@ -1097,6 +1102,7 @@ class TrainLoraNode(io.ComfyNode): seed, training_dtype, lora_dtype, + quantized_backward, algorithm, gradient_checkpointing, checkpoint_depth, @@ -1117,6 +1123,7 @@ class TrainLoraNode(io.ComfyNode): seed = seed[0] training_dtype = training_dtype[0] lora_dtype = lora_dtype[0] + quantized_backward = quantized_backward[0] algorithm = algorithm[0] gradient_checkpointing = gradient_checkpointing[0] offloading = offloading[0] @@ -1125,6 +1132,8 @@ class TrainLoraNode(io.ComfyNode): bucket_mode = bucket_mode[0] bypass_mode = bypass_mode[0] + comfy.model_management.training_fp8_bwd = quantized_backward + # Process latents based on mode if bucket_mode: latents = _process_latents_bucket_mode(latents) From 7d5534d8e516e0d4cd53d6abcdcb7f1f6d51ea97 Mon Sep 17 00:00:00 2001 From: Luke Mino-Altherr Date: Tue, 24 Mar 2026 20:48:55 -0700 Subject: [PATCH 64/65] feat(assets): register output files as assets after prompt execution (#12812) --- app/assets/database/queries/__init__.py | 4 + app/assets/database/queries/asset.py | 12 + .../database/queries/asset_reference.py | 17 ++ app/assets/scanner.py | 15 ++ app/assets/seeder.py | 66 ++++- app/assets/services/__init__.py | 4 + app/assets/services/bulk_ingest.py | 3 + app/assets/services/ingest.py | 102 ++++++- main.py | 43 ++- tests-unit/assets_test/services/conftest.py | 17 +- .../assets_test/services/test_enrich.py | 11 +- .../assets_test/services/test_ingest.py | 51 +++- tests-unit/seeder_test/test_seeder.py | 183 +++++++++++++ tests/test_asset_seeder.py | 250 ++++++++++++++++++ 14 files changed, 764 insertions(+), 14 deletions(-) create mode 100644 tests/test_asset_seeder.py diff --git a/app/assets/database/queries/__init__.py b/app/assets/database/queries/__init__.py index 1632937b2..9949e84e1 100644 --- a/app/assets/database/queries/__init__.py +++ b/app/assets/database/queries/__init__.py @@ -1,6 +1,7 @@ from app.assets.database.queries.asset import ( asset_exists_by_hash, bulk_insert_assets, + create_stub_asset, get_asset_by_hash, get_existing_asset_ids, reassign_asset_references, @@ -12,6 +13,7 @@ from app.assets.database.queries.asset_reference import ( UnenrichedReferenceRow, bulk_insert_references_ignore_conflicts, bulk_update_enrichment_level, + count_active_siblings, bulk_update_is_missing, bulk_update_needs_verify, convert_metadata_to_rows, @@ -80,6 +82,8 @@ __all__ = [ "bulk_insert_references_ignore_conflicts", "bulk_insert_tags_and_meta", "bulk_update_enrichment_level", + "count_active_siblings", + "create_stub_asset", "bulk_update_is_missing", "bulk_update_needs_verify", "convert_metadata_to_rows", diff --git a/app/assets/database/queries/asset.py b/app/assets/database/queries/asset.py index 594d1f1b2..cc7168431 100644 --- a/app/assets/database/queries/asset.py +++ b/app/assets/database/queries/asset.py @@ -78,6 +78,18 @@ def upsert_asset( return asset, created, updated +def create_stub_asset( + session: Session, + size_bytes: int, + mime_type: str | None = None, +) -> Asset: + """Create a new asset with no hash (stub for later enrichment).""" + asset = Asset(size_bytes=size_bytes, mime_type=mime_type, hash=None) + session.add(asset) + session.flush() + return asset + + def bulk_insert_assets( session: Session, rows: list[dict], diff --git a/app/assets/database/queries/asset_reference.py b/app/assets/database/queries/asset_reference.py index 084a32512..8b90ae511 100644 --- a/app/assets/database/queries/asset_reference.py +++ b/app/assets/database/queries/asset_reference.py @@ -114,6 +114,23 @@ def get_reference_by_file_path( ) +def count_active_siblings( + session: Session, + asset_id: str, + exclude_reference_id: str, +) -> int: + """Count active (non-deleted) references to an asset, excluding one reference.""" + return ( + session.query(AssetReference) + .filter( + AssetReference.asset_id == asset_id, + AssetReference.id != exclude_reference_id, + AssetReference.deleted_at.is_(None), + ) + .count() + ) + + def reference_exists_for_asset_id( session: Session, asset_id: str, diff --git a/app/assets/scanner.py b/app/assets/scanner.py index 4e05a97b5..ebb6869af 100644 --- a/app/assets/scanner.py +++ b/app/assets/scanner.py @@ -13,6 +13,7 @@ from app.assets.database.queries import ( delete_references_by_ids, ensure_tags_exist, get_asset_by_hash, + get_reference_by_id, get_references_for_prefixes, get_unenriched_references, mark_references_missing_outside_prefixes, @@ -338,6 +339,7 @@ def build_asset_specs( "metadata": metadata, "hash": asset_hash, "mime_type": mime_type, + "job_id": None, } ) tag_pool.update(tags) @@ -426,6 +428,7 @@ def enrich_asset( except OSError: return new_level + initial_mtime_ns = get_mtime_ns(stat_p) rel_fname = compute_relative_filename(file_path) mime_type: str | None = None metadata = None @@ -489,6 +492,18 @@ def enrich_asset( except Exception as e: logging.warning("Failed to hash %s: %s", file_path, e) + # Optimistic guard: if the reference's mtime_ns changed since we + # started (e.g. ingest_existing_file updated it), our results are + # stale — discard them to avoid overwriting fresh registration data. + ref = get_reference_by_id(session, reference_id) + if ref is None or ref.mtime_ns != initial_mtime_ns: + session.rollback() + logging.info( + "Ref %s mtime changed during enrichment, discarding stale result", + reference_id, + ) + return ENRICHMENT_STUB + if extract_metadata and metadata: system_metadata = metadata.to_user_metadata() set_reference_system_metadata(session, reference_id, system_metadata) diff --git a/app/assets/seeder.py b/app/assets/seeder.py index 029448464..2262928e5 100644 --- a/app/assets/seeder.py +++ b/app/assets/seeder.py @@ -77,7 +77,9 @@ class _AssetSeeder: """ def __init__(self) -> None: - self._lock = threading.Lock() + # RLock is required because _run_scan() drains pending work while + # holding _lock and re-enters start() which also acquires _lock. + self._lock = threading.RLock() self._state = State.IDLE self._progress: Progress | None = None self._last_progress: Progress | None = None @@ -92,6 +94,7 @@ class _AssetSeeder: self._prune_first: bool = False self._progress_callback: ProgressCallback | None = None self._disabled: bool = False + self._pending_enrich: dict | None = None def disable(self) -> None: """Disable the asset seeder, preventing any scans from starting.""" @@ -196,6 +199,42 @@ class _AssetSeeder: compute_hashes=compute_hashes, ) + def enqueue_enrich( + self, + roots: tuple[RootType, ...] = ("models", "input", "output"), + compute_hashes: bool = False, + ) -> bool: + """Start an enrichment scan now, or queue it for after the current scan. + + If the seeder is idle, starts immediately. Otherwise, the enrich + request is stored and will run automatically when the current scan + finishes. + + Args: + roots: Tuple of root types to scan + compute_hashes: If True, compute blake3 hashes + + Returns: + True if started immediately, False if queued for later + """ + with self._lock: + if self.start_enrich(roots=roots, compute_hashes=compute_hashes): + return True + if self._pending_enrich is not None: + existing_roots = set(self._pending_enrich["roots"]) + existing_roots.update(roots) + self._pending_enrich["roots"] = tuple(existing_roots) + self._pending_enrich["compute_hashes"] = ( + self._pending_enrich["compute_hashes"] or compute_hashes + ) + else: + self._pending_enrich = { + "roots": roots, + "compute_hashes": compute_hashes, + } + logging.info("Enrich scan queued (roots=%s)", self._pending_enrich["roots"]) + return False + def cancel(self) -> bool: """Request cancellation of the current scan. @@ -381,9 +420,13 @@ class _AssetSeeder: return marked finally: with self._lock: - self._last_progress = self._progress - self._state = State.IDLE - self._progress = None + self._reset_to_idle() + + def _reset_to_idle(self) -> None: + """Reset state to IDLE, preserving last progress. Caller must hold _lock.""" + self._last_progress = self._progress + self._state = State.IDLE + self._progress = None def _is_cancelled(self) -> bool: """Check if cancellation has been requested.""" @@ -594,9 +637,18 @@ class _AssetSeeder: }, ) with self._lock: - self._last_progress = self._progress - self._state = State.IDLE - self._progress = None + self._reset_to_idle() + pending = self._pending_enrich + if pending is not None: + self._pending_enrich = None + if not self.start_enrich( + roots=pending["roots"], + compute_hashes=pending["compute_hashes"], + ): + logging.warning( + "Pending enrich scan could not start (roots=%s)", + pending["roots"], + ) def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]: """Run phase 1: fast scan to create stub records. diff --git a/app/assets/services/__init__.py b/app/assets/services/__init__.py index 11fcb4122..03990966b 100644 --- a/app/assets/services/__init__.py +++ b/app/assets/services/__init__.py @@ -23,6 +23,8 @@ from app.assets.services.ingest import ( DependencyMissingError, HashMismatchError, create_from_hash, + ingest_existing_file, + register_output_files, upload_from_temp_path, ) from app.assets.database.queries import ( @@ -72,6 +74,8 @@ __all__ = [ "delete_asset_reference", "get_asset_by_hash", "get_asset_detail", + "ingest_existing_file", + "register_output_files", "get_mtime_ns", "get_size_and_mtime_ns", "list_assets_page", diff --git a/app/assets/services/bulk_ingest.py b/app/assets/services/bulk_ingest.py index 54e72730c..67aad838f 100644 --- a/app/assets/services/bulk_ingest.py +++ b/app/assets/services/bulk_ingest.py @@ -37,6 +37,7 @@ class SeedAssetSpec(TypedDict): metadata: ExtractedMetadata | None hash: str | None mime_type: str | None + job_id: str | None class AssetRow(TypedDict): @@ -60,6 +61,7 @@ class ReferenceRow(TypedDict): name: str preview_id: str | None user_metadata: dict[str, Any] | None + job_id: str | None created_at: datetime updated_at: datetime last_access_time: datetime @@ -167,6 +169,7 @@ def batch_insert_seed_assets( "name": spec["info_name"], "preview_id": None, "user_metadata": user_metadata, + "job_id": spec.get("job_id"), "created_at": current_time, "updated_at": current_time, "last_access_time": current_time, diff --git a/app/assets/services/ingest.py b/app/assets/services/ingest.py index 90c51994f..f0b070517 100644 --- a/app/assets/services/ingest.py +++ b/app/assets/services/ingest.py @@ -9,6 +9,9 @@ from sqlalchemy.orm import Session import app.assets.services.hashing as hashing from app.assets.database.queries import ( add_tags_to_reference, + count_active_siblings, + create_stub_asset, + ensure_tags_exist, fetch_reference_and_asset, get_asset_by_hash, get_reference_by_file_path, @@ -23,7 +26,8 @@ from app.assets.database.queries import ( upsert_reference, validate_tags_exist, ) -from app.assets.helpers import normalize_tags +from app.assets.helpers import get_utc_now, normalize_tags +from app.assets.services.bulk_ingest import batch_insert_seed_assets from app.assets.services.file_utils import get_size_and_mtime_ns from app.assets.services.path_utils import ( compute_relative_filename, @@ -130,6 +134,102 @@ def _ingest_file_from_path( ) +def register_output_files( + file_paths: Sequence[str], + user_metadata: UserMetadata = None, + job_id: str | None = None, +) -> int: + """Register a batch of output file paths as assets. + + Returns the number of files successfully registered. + """ + registered = 0 + for abs_path in file_paths: + if not os.path.isfile(abs_path): + continue + try: + if ingest_existing_file( + abs_path, user_metadata=user_metadata, job_id=job_id + ): + registered += 1 + except Exception: + logging.exception("Failed to register output: %s", abs_path) + return registered + + +def ingest_existing_file( + abs_path: str, + user_metadata: UserMetadata = None, + extra_tags: Sequence[str] = (), + owner_id: str = "", + job_id: str | None = None, +) -> bool: + """Register an existing on-disk file as an asset stub. + + If a reference already exists for this path, updates mtime_ns, job_id, + size_bytes, and resets enrichment so the enricher will re-hash it. + + For brand-new paths, inserts a stub record (hash=NULL) for immediate + UX visibility. + + Returns True if a row was inserted or updated, False otherwise. + """ + locator = os.path.abspath(abs_path) + size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path) + mime_type = mimetypes.guess_type(abs_path, strict=False)[0] + name, path_tags = get_name_and_tags_from_asset_path(abs_path) + tags = list(dict.fromkeys(path_tags + list(extra_tags))) + + with create_session() as session: + existing_ref = get_reference_by_file_path(session, locator) + if existing_ref is not None: + now = get_utc_now() + existing_ref.mtime_ns = mtime_ns + existing_ref.job_id = job_id + existing_ref.is_missing = False + existing_ref.deleted_at = None + existing_ref.updated_at = now + existing_ref.enrichment_level = 0 + + asset = existing_ref.asset + if asset: + # If other refs share this asset, detach to a new stub + # instead of mutating the shared row. + siblings = count_active_siblings(session, asset.id, existing_ref.id) + if siblings > 0: + new_asset = create_stub_asset( + session, + size_bytes=size_bytes, + mime_type=mime_type or asset.mime_type, + ) + existing_ref.asset_id = new_asset.id + else: + asset.hash = None + asset.size_bytes = size_bytes + if mime_type: + asset.mime_type = mime_type + session.commit() + return True + + spec = { + "abs_path": abs_path, + "size_bytes": size_bytes, + "mtime_ns": mtime_ns, + "info_name": name, + "tags": tags, + "fname": os.path.basename(abs_path), + "metadata": None, + "hash": None, + "mime_type": mime_type, + "job_id": job_id, + } + if tags: + ensure_tags_exist(session, tags) + result = batch_insert_seed_assets(session, [spec], owner_id=owner_id) + session.commit() + return result.won_paths > 0 + + def _register_existing_asset( asset_hash: str, name: str, diff --git a/main.py b/main.py index cd4483c67..058e8e2de 100644 --- a/main.py +++ b/main.py @@ -9,6 +9,8 @@ import folder_paths import time from comfy.cli_args import args, enables_dynamic_vram from app.logger import setup_logger +from app.assets.seeder import asset_seeder +from app.assets.services import register_output_files import itertools import utils.extra_config from utils.mime_types import init_mime_types @@ -192,7 +194,6 @@ if 'torch' in sys.modules: import comfy.utils -from app.assets.seeder import asset_seeder import execution import server @@ -240,6 +241,38 @@ def cuda_malloc_warning(): logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") +def _collect_output_absolute_paths(history_result: dict) -> list[str]: + """Extract absolute file paths for output items from a history result.""" + paths: list[str] = [] + seen: set[str] = set() + for node_output in history_result.get("outputs", {}).values(): + for items in node_output.values(): + if not isinstance(items, list): + continue + for item in items: + if not isinstance(item, dict): + continue + item_type = item.get("type") + if item_type not in ("output", "temp"): + continue + base_dir = folder_paths.get_directory_by_type(item_type) + if base_dir is None: + continue + base_dir = os.path.abspath(base_dir) + filename = item.get("filename") + if not filename: + continue + abs_path = os.path.abspath( + os.path.join(base_dir, item.get("subfolder", ""), filename) + ) + if not abs_path.startswith(base_dir + os.sep) and abs_path != base_dir: + continue + if abs_path not in seen: + seen.add(abs_path) + paths.append(abs_path) + return paths + + def prompt_worker(q, server_instance): current_time: float = 0.0 cache_type = execution.CacheType.CLASSIC @@ -274,6 +307,7 @@ def prompt_worker(q, server_instance): asset_seeder.pause() e.execute(item[2], prompt_id, extra_data, item[4]) + need_gc = True remove_sensitive = lambda prompt: prompt[:5] + prompt[6:] @@ -296,6 +330,10 @@ def prompt_worker(q, server_instance): else: logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) + if not asset_seeder.is_disabled(): + paths = _collect_output_absolute_paths(e.history_result) + register_output_files(paths, job_id=prompt_id) + flags = q.get_flags() free_memory = flags.get("free_memory", False) @@ -317,6 +355,9 @@ def prompt_worker(q, server_instance): last_gc_collect = current_time need_gc = False hook_breaker_ac10a0.restore_functions() + + if not asset_seeder.is_disabled(): + asset_seeder.enqueue_enrich(roots=("output",), compute_hashes=True) asset_seeder.resume() diff --git a/tests-unit/assets_test/services/conftest.py b/tests-unit/assets_test/services/conftest.py index 31c763d48..bc0723e61 100644 --- a/tests-unit/assets_test/services/conftest.py +++ b/tests-unit/assets_test/services/conftest.py @@ -3,7 +3,7 @@ from pathlib import Path from unittest.mock import patch import pytest -from sqlalchemy import create_engine +from sqlalchemy import create_engine, event from sqlalchemy.orm import Session from app.assets.database.models import Base @@ -23,6 +23,21 @@ def db_engine(): return engine +@pytest.fixture +def db_engine_fk(): + """In-memory SQLite engine with foreign key enforcement enabled.""" + engine = create_engine("sqlite:///:memory:") + + @event.listens_for(engine, "connect") + def _set_pragma(dbapi_connection, connection_record): + cursor = dbapi_connection.cursor() + cursor.execute("PRAGMA foreign_keys=ON") + cursor.close() + + Base.metadata.create_all(engine) + return engine + + @pytest.fixture def session(db_engine): """Session fixture for tests that need direct DB access.""" diff --git a/tests-unit/assets_test/services/test_enrich.py b/tests-unit/assets_test/services/test_enrich.py index 2bd79a01a..6a6561f7f 100644 --- a/tests-unit/assets_test/services/test_enrich.py +++ b/tests-unit/assets_test/services/test_enrich.py @@ -1,9 +1,11 @@ """Tests for asset enrichment (mime_type and hash population).""" +import os from pathlib import Path from sqlalchemy.orm import Session from app.assets.database.models import Asset, AssetReference +from app.assets.services.file_utils import get_mtime_ns from app.assets.scanner import ( ENRICHMENT_HASHED, ENRICHMENT_METADATA, @@ -20,6 +22,13 @@ def _create_stub_asset( name: str | None = None, ) -> tuple[Asset, AssetReference]: """Create a stub asset with reference for testing enrichment.""" + # Use the real file's mtime so the optimistic guard in enrich_asset passes + try: + stat_result = os.stat(file_path, follow_symlinks=True) + mtime_ns = get_mtime_ns(stat_result) + except OSError: + mtime_ns = 1234567890000000000 + asset = Asset( id=asset_id, hash=None, @@ -35,7 +44,7 @@ def _create_stub_asset( name=name or f"test-asset-{asset_id}", owner_id="system", file_path=file_path, - mtime_ns=1234567890000000000, + mtime_ns=mtime_ns, enrichment_level=ENRICHMENT_STUB, ) session.add(ref) diff --git a/tests-unit/assets_test/services/test_ingest.py b/tests-unit/assets_test/services/test_ingest.py index dbb8441c2..b153f9795 100644 --- a/tests-unit/assets_test/services/test_ingest.py +++ b/tests-unit/assets_test/services/test_ingest.py @@ -1,12 +1,18 @@ """Tests for ingest services.""" +from contextlib import contextmanager from pathlib import Path +from unittest.mock import patch import pytest -from sqlalchemy.orm import Session +from sqlalchemy.orm import Session as SASession, Session -from app.assets.database.models import Asset, AssetReference, Tag +from app.assets.database.models import Asset, AssetReference, AssetReferenceTag, Tag from app.assets.database.queries import get_reference_tags -from app.assets.services.ingest import _ingest_file_from_path, _register_existing_asset +from app.assets.services.ingest import ( + _ingest_file_from_path, + _register_existing_asset, + ingest_existing_file, +) class TestIngestFileFromPath: @@ -235,3 +241,42 @@ class TestRegisterExistingAsset: assert result.created is True assert set(result.tags) == {"alpha", "beta"} + + +class TestIngestExistingFileTagFK: + """Regression: ingest_existing_file must seed Tag rows before inserting + AssetReferenceTag rows, otherwise FK enforcement raises IntegrityError.""" + + def test_creates_tag_rows_before_reference_tags(self, db_engine_fk, temp_dir: Path): + """With PRAGMA foreign_keys=ON, tags must exist in the tags table + before they can be referenced in asset_reference_tags.""" + + @contextmanager + def _create_session(): + with SASession(db_engine_fk) as sess: + yield sess + + file_path = temp_dir / "output.png" + file_path.write_bytes(b"image data") + + with patch("app.assets.services.ingest.create_session", _create_session), \ + patch( + "app.assets.services.ingest.get_name_and_tags_from_asset_path", + return_value=("output.png", ["output"]), + ): + result = ingest_existing_file( + abs_path=str(file_path), + extra_tags=["my-job"], + ) + + assert result is True + + with SASession(db_engine_fk) as sess: + tag_names = {t.name for t in sess.query(Tag).all()} + assert "output" in tag_names + assert "my-job" in tag_names + + ref_tags = sess.query(AssetReferenceTag).all() + ref_tag_names = {rt.tag_name for rt in ref_tags} + assert "output" in ref_tag_names + assert "my-job" in ref_tag_names diff --git a/tests-unit/seeder_test/test_seeder.py b/tests-unit/seeder_test/test_seeder.py index db3795e48..6aed6d6f3 100644 --- a/tests-unit/seeder_test/test_seeder.py +++ b/tests-unit/seeder_test/test_seeder.py @@ -1,6 +1,7 @@ """Unit tests for the _AssetSeeder background scanning class.""" import threading +import time from unittest.mock import patch import pytest @@ -771,6 +772,188 @@ class TestSeederStopRestart: assert collected_roots[1] == ("input",) +class TestEnqueueEnrichHandoff: + """Test that the drain of _pending_enrich is atomic with start_enrich.""" + + def test_pending_enrich_runs_after_scan_completes( + self, fresh_seeder: _AssetSeeder, mock_dependencies + ): + """A queued enrich request runs automatically when a scan finishes.""" + enrich_roots_seen: list[tuple] = [] + original_start = fresh_seeder.start + + def tracking_start(*args, **kwargs): + phase = kwargs.get("phase") + roots = kwargs.get("roots", args[0] if args else None) + result = original_start(*args, **kwargs) + if phase == ScanPhase.ENRICH and result: + enrich_roots_seen.append(roots) + return result + + fresh_seeder.start = tracking_start + + # Start a fast scan, then enqueue an enrich while it's running + barrier = threading.Event() + reached = threading.Event() + + def slow_collect(*args): + reached.set() + barrier.wait(timeout=5.0) + return [] + + with patch( + "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect + ): + fresh_seeder.start(roots=("models",), phase=ScanPhase.FAST) + assert reached.wait(timeout=2.0) + + queued = fresh_seeder.enqueue_enrich( + roots=("input",), compute_hashes=True + ) + assert queued is False # queued, not started immediately + + barrier.set() + + # Wait for the original scan + the auto-started enrich scan + deadline = time.monotonic() + 5.0 + while fresh_seeder.get_status().state != State.IDLE and time.monotonic() < deadline: + time.sleep(0.05) + + assert enrich_roots_seen == [("input",)] + + def test_enqueue_enrich_during_drain_does_not_lose_work( + self, fresh_seeder: _AssetSeeder, mock_dependencies + ): + """enqueue_enrich called concurrently with drain cannot drop work. + + Simulates the race: another thread calls enqueue_enrich right as the + scan thread is draining _pending_enrich. The enqueue must either be + picked up by the draining scan or successfully start its own scan. + """ + barrier = threading.Event() + reached = threading.Event() + enrich_started = threading.Event() + + enrich_call_count = 0 + + def slow_collect(*args): + reached.set() + barrier.wait(timeout=5.0) + return [] + + # Track how many times start_enrich actually fires + real_start_enrich = fresh_seeder.start_enrich + enrich_roots_seen: list[tuple] = [] + + def tracking_start_enrich(**kwargs): + nonlocal enrich_call_count + enrich_call_count += 1 + enrich_roots_seen.append(kwargs.get("roots")) + result = real_start_enrich(**kwargs) + if result: + enrich_started.set() + return result + + fresh_seeder.start_enrich = tracking_start_enrich + + with patch( + "app.assets.seeder.collect_paths_for_roots", side_effect=slow_collect + ): + # Start a scan + fresh_seeder.start(roots=("models",), phase=ScanPhase.FAST) + assert reached.wait(timeout=2.0) + + # Queue an enrich while scan is running + fresh_seeder.enqueue_enrich(roots=("output",), compute_hashes=False) + + # Let scan finish — drain will fire start_enrich atomically + barrier.set() + + # Wait for drain to complete and the enrich scan to start + assert enrich_started.wait(timeout=5.0), "Enrich scan was never started from drain" + assert ("output",) in enrich_roots_seen + + def test_concurrent_enqueue_during_drain_not_lost( + self, fresh_seeder: _AssetSeeder, + ): + """A second enqueue_enrich arriving while drain is in progress is not lost. + + Because the drain now holds _lock through the start_enrich call, + a concurrent enqueue_enrich will block until start_enrich has + transitioned state to RUNNING, then the enqueue will queue its + payload as _pending_enrich for the *next* drain. + """ + scan_barrier = threading.Event() + scan_reached = threading.Event() + enrich_barrier = threading.Event() + enrich_reached = threading.Event() + + collect_call = 0 + + def gated_collect(*args): + nonlocal collect_call + collect_call += 1 + if collect_call == 1: + # First call: the initial fast scan + scan_reached.set() + scan_barrier.wait(timeout=5.0) + return [] + + enrich_call = 0 + + def gated_get_unenriched(*args, **kwargs): + nonlocal enrich_call + enrich_call += 1 + if enrich_call == 1: + # First enrich batch: signal and block + enrich_reached.set() + enrich_barrier.wait(timeout=5.0) + return [] + + with ( + patch("app.assets.seeder.dependencies_available", return_value=True), + patch("app.assets.seeder.sync_root_safely", return_value=set()), + patch("app.assets.seeder.collect_paths_for_roots", side_effect=gated_collect), + patch("app.assets.seeder.build_asset_specs", return_value=([], set(), 0)), + patch("app.assets.seeder.insert_asset_specs", return_value=0), + patch("app.assets.seeder.get_unenriched_assets_for_roots", side_effect=gated_get_unenriched), + patch("app.assets.seeder.enrich_assets_batch", return_value=(0, 0)), + ): + # 1. Start fast scan + fresh_seeder.start(roots=("models",), phase=ScanPhase.FAST) + assert scan_reached.wait(timeout=2.0) + + # 2. Queue enrich while fast scan is running + queued = fresh_seeder.enqueue_enrich( + roots=("input",), compute_hashes=False + ) + assert queued is False + + # 3. Let the fast scan finish — drain will start the enrich scan + scan_barrier.set() + + # 4. Wait until the drained enrich scan is running + assert enrich_reached.wait(timeout=5.0) + + # 5. Now enqueue another enrich while the drained scan is running + queued2 = fresh_seeder.enqueue_enrich( + roots=("output",), compute_hashes=True + ) + assert queued2 is False # should be queued, not started + + # Verify _pending_enrich was set (the second enqueue was captured) + with fresh_seeder._lock: + assert fresh_seeder._pending_enrich is not None + assert "output" in fresh_seeder._pending_enrich["roots"] + + # Let the enrich scan finish + enrich_barrier.set() + + deadline = time.monotonic() + 5.0 + while fresh_seeder.get_status().state != State.IDLE and time.monotonic() < deadline: + time.sleep(0.05) + + def _make_row(ref_id: str, asset_id: str = "a1") -> UnenrichedReferenceRow: return UnenrichedReferenceRow( reference_id=ref_id, asset_id=asset_id, diff --git a/tests/test_asset_seeder.py b/tests/test_asset_seeder.py new file mode 100644 index 000000000..4274dab8e --- /dev/null +++ b/tests/test_asset_seeder.py @@ -0,0 +1,250 @@ +"""Tests for app.assets.seeder – enqueue_enrich and pending-queue behaviour.""" + +import threading +from unittest.mock import patch + +import pytest + +from app.assets.seeder import Progress, _AssetSeeder, State + + +@pytest.fixture() +def seeder(): + """Fresh seeder instance for each test.""" + return _AssetSeeder() + + +# --------------------------------------------------------------------------- +# _reset_to_idle +# --------------------------------------------------------------------------- + + +class TestResetToIdle: + def test_sets_idle_and_clears_progress(self, seeder): + """_reset_to_idle should move state to IDLE and snapshot progress.""" + progress = Progress(scanned=10, total=20, created=5, skipped=3) + seeder._state = State.RUNNING + seeder._progress = progress + + with seeder._lock: + seeder._reset_to_idle() + + assert seeder._state is State.IDLE + assert seeder._progress is None + assert seeder._last_progress is progress + + def test_noop_when_progress_already_none(self, seeder): + """_reset_to_idle should handle None progress gracefully.""" + seeder._state = State.CANCELLING + seeder._progress = None + + with seeder._lock: + seeder._reset_to_idle() + + assert seeder._state is State.IDLE + assert seeder._progress is None + assert seeder._last_progress is None + + +# --------------------------------------------------------------------------- +# enqueue_enrich – immediate start when idle +# --------------------------------------------------------------------------- + + +class TestEnqueueEnrichStartsImmediately: + def test_starts_when_idle(self, seeder): + """enqueue_enrich should delegate to start_enrich and return True when idle.""" + with patch.object(seeder, "start_enrich", return_value=True) as mock: + assert seeder.enqueue_enrich(roots=("output",), compute_hashes=True) is True + mock.assert_called_once_with(roots=("output",), compute_hashes=True) + + def test_no_pending_when_started_immediately(self, seeder): + """No pending request should be stored when start_enrich succeeds.""" + with patch.object(seeder, "start_enrich", return_value=True): + seeder.enqueue_enrich(roots=("output",)) + assert seeder._pending_enrich is None + + +# --------------------------------------------------------------------------- +# enqueue_enrich – queuing when busy +# --------------------------------------------------------------------------- + + +class TestEnqueueEnrichQueuesWhenBusy: + def test_queues_when_busy(self, seeder): + """enqueue_enrich should store a pending request when seeder is busy.""" + with patch.object(seeder, "start_enrich", return_value=False): + result = seeder.enqueue_enrich(roots=("models",), compute_hashes=False) + + assert result is False + assert seeder._pending_enrich == { + "roots": ("models",), + "compute_hashes": False, + } + + def test_queues_preserves_compute_hashes_true(self, seeder): + with patch.object(seeder, "start_enrich", return_value=False): + seeder.enqueue_enrich(roots=("input",), compute_hashes=True) + + assert seeder._pending_enrich["compute_hashes"] is True + + +# --------------------------------------------------------------------------- +# enqueue_enrich – merging when a pending request already exists +# --------------------------------------------------------------------------- + + +class TestEnqueueEnrichMergesPending: + def _make_busy(self, seeder): + """Patch start_enrich to always return False (seeder busy).""" + return patch.object(seeder, "start_enrich", return_value=False) + + def test_merges_roots(self, seeder): + """A second enqueue should merge roots with the existing pending request.""" + with self._make_busy(seeder): + seeder.enqueue_enrich(roots=("models",)) + seeder.enqueue_enrich(roots=("output",)) + + merged = set(seeder._pending_enrich["roots"]) + assert merged == {"models", "output"} + + def test_merges_overlapping_roots(self, seeder): + """Duplicate roots should be deduplicated.""" + with self._make_busy(seeder): + seeder.enqueue_enrich(roots=("models", "input")) + seeder.enqueue_enrich(roots=("input", "output")) + + merged = set(seeder._pending_enrich["roots"]) + assert merged == {"models", "input", "output"} + + def test_compute_hashes_sticky_true(self, seeder): + """Once compute_hashes is True it should stay True after merging.""" + with self._make_busy(seeder): + seeder.enqueue_enrich(roots=("models",), compute_hashes=True) + seeder.enqueue_enrich(roots=("output",), compute_hashes=False) + + assert seeder._pending_enrich["compute_hashes"] is True + + def test_compute_hashes_upgrades_to_true(self, seeder): + """A later enqueue with compute_hashes=True should upgrade the pending request.""" + with self._make_busy(seeder): + seeder.enqueue_enrich(roots=("models",), compute_hashes=False) + seeder.enqueue_enrich(roots=("output",), compute_hashes=True) + + assert seeder._pending_enrich["compute_hashes"] is True + + def test_compute_hashes_stays_false(self, seeder): + """If both enqueues have compute_hashes=False it stays False.""" + with self._make_busy(seeder): + seeder.enqueue_enrich(roots=("models",), compute_hashes=False) + seeder.enqueue_enrich(roots=("output",), compute_hashes=False) + + assert seeder._pending_enrich["compute_hashes"] is False + + def test_triple_merge(self, seeder): + """Three successive enqueues should all merge correctly.""" + with self._make_busy(seeder): + seeder.enqueue_enrich(roots=("models",), compute_hashes=False) + seeder.enqueue_enrich(roots=("input",), compute_hashes=False) + seeder.enqueue_enrich(roots=("output",), compute_hashes=True) + + merged = set(seeder._pending_enrich["roots"]) + assert merged == {"models", "input", "output"} + assert seeder._pending_enrich["compute_hashes"] is True + + +# --------------------------------------------------------------------------- +# Pending enrich drains after scan completes +# --------------------------------------------------------------------------- + + +class TestPendingEnrichDrain: + """Verify that _run_scan drains _pending_enrich via start_enrich.""" + + @patch("app.assets.seeder.dependencies_available", return_value=True) + @patch("app.assets.seeder.get_all_known_prefixes", return_value=[]) + @patch("app.assets.seeder.sync_root_safely", return_value=set()) + @patch("app.assets.seeder.collect_paths_for_roots", return_value=[]) + @patch("app.assets.seeder.build_asset_specs", return_value=([], {}, 0)) + def test_pending_enrich_starts_after_scan(self, *_mocks): + """After a fast scan finishes, the pending enrich should be started.""" + seeder = _AssetSeeder() + + seeder._pending_enrich = { + "roots": ("output",), + "compute_hashes": True, + } + + with patch.object(seeder, "start_enrich", return_value=True) as mock_start: + seeder.start_fast(roots=("models",)) + seeder.wait(timeout=5) + + mock_start.assert_called_once_with( + roots=("output",), + compute_hashes=True, + ) + + assert seeder._pending_enrich is None + + @patch("app.assets.seeder.dependencies_available", return_value=True) + @patch("app.assets.seeder.get_all_known_prefixes", return_value=[]) + @patch("app.assets.seeder.sync_root_safely", return_value=set()) + @patch("app.assets.seeder.collect_paths_for_roots", return_value=[]) + @patch("app.assets.seeder.build_asset_specs", return_value=([], {}, 0)) + def test_pending_cleared_even_when_start_fails(self, *_mocks): + """_pending_enrich should be cleared even if start_enrich returns False.""" + seeder = _AssetSeeder() + seeder._pending_enrich = { + "roots": ("output",), + "compute_hashes": False, + } + + with patch.object(seeder, "start_enrich", return_value=False): + seeder.start_fast(roots=("models",)) + seeder.wait(timeout=5) + + assert seeder._pending_enrich is None + + @patch("app.assets.seeder.dependencies_available", return_value=True) + @patch("app.assets.seeder.get_all_known_prefixes", return_value=[]) + @patch("app.assets.seeder.sync_root_safely", return_value=set()) + @patch("app.assets.seeder.collect_paths_for_roots", return_value=[]) + @patch("app.assets.seeder.build_asset_specs", return_value=([], {}, 0)) + def test_no_drain_when_no_pending(self, *_mocks): + """start_enrich should not be called when there is no pending request.""" + seeder = _AssetSeeder() + assert seeder._pending_enrich is None + + with patch.object(seeder, "start_enrich", return_value=True) as mock_start: + seeder.start_fast(roots=("models",)) + seeder.wait(timeout=5) + + mock_start.assert_not_called() + + +# --------------------------------------------------------------------------- +# Thread-safety of enqueue_enrich +# --------------------------------------------------------------------------- + + +class TestEnqueueEnrichThreadSafety: + def test_concurrent_enqueues(self, seeder): + """Multiple threads enqueuing should not lose roots.""" + with patch.object(seeder, "start_enrich", return_value=False): + barrier = threading.Barrier(3) + + def enqueue(root): + barrier.wait() + seeder.enqueue_enrich(roots=(root,), compute_hashes=False) + + threads = [ + threading.Thread(target=enqueue, args=(r,)) + for r in ("models", "input", "output") + ] + for t in threads: + t.start() + for t in threads: + t.join(timeout=5) + + merged = set(seeder._pending_enrich["roots"]) + assert merged == {"models", "input", "output"} From b53b10ea61ef7fc54fbde7c1e7b7c36565bacf82 Mon Sep 17 00:00:00 2001 From: Krishna Chaitanya Date: Tue, 24 Mar 2026 20:53:44 -0700 Subject: [PATCH 65/65] Fix Train LoRA crash when training_dtype is "none" with bfloat16 LoRA weights (#13145) When training_dtype is set to "none" and the model's native dtype is float16, GradScaler was unconditionally enabled. However, GradScaler does not support bfloat16 gradients (only float16/float32), causing a NotImplementedError when lora_dtype is "bf16" (the default). Fix by only enabling GradScaler when LoRA parameters are not in bfloat16, since bfloat16 has the same exponent range as float32 and does not need gradient scaling to avoid underflow. Fixes #13124 --- comfy_extras/nodes_train.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py index df1b39fd5..0616dfc2d 100644 --- a/comfy_extras/nodes_train.py +++ b/comfy_extras/nodes_train.py @@ -1146,6 +1146,7 @@ class TrainLoraNode(io.ComfyNode): # Setup model and dtype mp = model.clone() use_grad_scaler = False + lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) if training_dtype != "none": dtype = node_helpers.string_to_torch_dtype(training_dtype) mp.set_model_compute_dtype(dtype) @@ -1154,7 +1155,10 @@ class TrainLoraNode(io.ComfyNode): model_dtype = mp.model.get_dtype() if model_dtype == torch.float16: dtype = torch.float16 - use_grad_scaler = True + # GradScaler only supports float16 gradients, not bfloat16. + # Only enable it when lora params will also be in float16. + if lora_dtype != torch.bfloat16: + use_grad_scaler = True # Warn about fp16 accumulation instability during training if PerformanceFeature.Fp16Accumulation in args.fast: logging.warning( @@ -1165,7 +1169,6 @@ class TrainLoraNode(io.ComfyNode): else: # For fp8, bf16, or other dtypes, use bf16 autocast dtype = torch.bfloat16 - lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) # Prepare latents and compute counts latents_dtype = dtype if dtype not in (None,) else torch.bfloat16