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file mode 100644 index 0000000..7d17ebf --- /dev/null +++ b/docs/superpowers/plans/2026-05-26-lls-flux2klein-background-workflow.md @@ -0,0 +1,41 @@ +# LLS Flux2Klein Background Workflow Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Add a ready-to-use ComfyUI example workflow that performs e-commerce background replacement with `LLSFlux2KleinEditTextEncode` on a fully matched Flux2/Klein model stack. + +**Architecture:** Keep the backend node implementation as-is and add a new example workflow JSON plus a workflow regression test. The workflow must use `UNETLoader + CLIPLoader(type=flux2) + VAELoader(flux2-vae)` and derive the edit mask from `RMBG -> InvertMask` so the sampler receives a consistent Flux2/Klein edit payload. + +**Tech Stack:** JSON workflow exports, Python `unittest`, existing ComfyUI core nodes, existing LLS custom node registration + +--- + +### Task 1: Lock The Workflow Contract With A Failing Test + +**Files:** +- Modify: `tests/test_example_workflows.py` + +- [ ] **Step 1: Add a test that loads `LLS-Flux2Klein-电商换背景工作流.json`** +- [ ] **Step 2: Assert the workflow includes `LLSFlux2KleinEditTextEncode`, `UNETLoader`, `CLIPLoader`, `VAELoader`, `RMBG`, `InvertMask`, `ConditioningZeroOut`, `KSampler`, and `VAEDecode`** +- [ ] **Step 3: Assert `CLIPLoader` uses `flux2`, `VAELoader` uses `flux2-vae.safetensors`, and the LLS node uses `mask_mode = use_mask` with `resize_mode = longest_edge`** +- [ ] **Step 4: Run the focused workflow test and confirm it fails before the workflow JSON exists** + +### Task 2: Add The New Background-Replacement Workflow + +**Files:** +- Create: `LLS-Flux2Klein-电商换背景工作流.json` + +- [ ] **Step 1: Build a compact workflow with `LoadImage` main product input feeding both `RMBG` and `LLSFlux2KleinEditTextEncode.image1`** +- [ ] **Step 2: Add one connected scene reference image for `image2` and leave `image3` optional** +- [ ] **Step 3: Use `RMBG -> InvertMask -> LLSFlux2KleinEditTextEncode.mask` and route `conditioning`, `ConditioningZeroOut`, `latent`, `KSampler`, and `VAEDecode` according to the edit flow contract** +- [ ] **Step 4: Route the model through a Flux2/Klein-safe sampler chain and add `PreviewImage` plus `SaveImage` outputs** + +### Task 3: Verify The Workflow And Summarize Constraints + +**Files:** +- Modify: files from earlier tasks only if verification exposes gaps + +- [ ] **Step 1: Run `python3 -m unittest tests.test_example_workflows -v`** +- [ ] **Step 2: Run `python3 -m json.tool 'LLS-Flux2Klein-电商换背景工作流.json' > /tmp/lls_flux2klein_background_workflow.pretty.json` to validate JSON syntax** +- [ ] **Step 3: Re-run the targeted Flux2/Klein unit tests if the workflow change touches shared assumptions** +- [ ] **Step 4: Summarize the new workflow file, ComfyUI search name, minimal connection path, and the remaining compatibility-vs-native Flux2 API gaps** diff --git a/flux2klein/lls_flux2klein_edit_text_encode.py b/flux2klein/lls_flux2klein_edit_text_encode.py index 951b193..a176b72 100644 --- a/flux2klein/lls_flux2klein_edit_text_encode.py +++ b/flux2klein/lls_flux2klein_edit_text_encode.py @@ -32,6 +32,16 @@ except Exception as exc: # pragma: no cover - optional runtime dependency else: # pragma: no cover - exercised in ComfyUI runtime/tests when comfy exists _COMFY_UTILS_ERR = None +try: + import comfy_extras.nodes_flux as native_flux_nodes +except Exception: # pragma: no cover - optional ComfyUI runtime dependency + native_flux_nodes = None + +try: + import comfy_extras.nodes_edit_model as native_edit_model_nodes +except Exception: # pragma: no cover - optional ComfyUI runtime dependency + native_edit_model_nodes = None + RESIZE_MODE_CHOICES = ["longest_edge", "keep_original"] MASK_MODE_CHOICES = ["none", "use_mask", "invert_mask"] @@ -71,6 +81,13 @@ class _ConstantMask: ) +class _ShapeOnlyLatent: + """Fallback latent object for tests when torch tensors are unavailable.""" + + def __init__(self, shape): + self.shape = tuple(shape) + + def _extract_latent_samples(latent): if isinstance(latent, dict) and "samples" in latent: return latent["samples"] @@ -87,6 +104,17 @@ def _get_image_batch_size(image) -> int: return batch +def _extract_node_output_value(value): + if hasattr(value, "result"): + value = value.result + elif hasattr(value, "args"): + value = value.args + + if isinstance(value, tuple) and len(value) == 1: + return value[0] + return value + + def _scale_size_by_longest_edge(width: int, height: int, target_longest_edge: int) -> tuple[int, int]: width = int(width) height = int(height) @@ -305,6 +333,70 @@ def _encode_reference_latent(vae, image, target_width: int, target_height: int, return {"samples": samples}, prepared +def _encode_image_latent(vae, image, source_name: str): + try: + samples = vae.encode(image) + except Exception as exc: + raise RuntimeError( + f"[LLS] VAE encode failed for {source_name} / {source_name} 的 VAE 编码失败: {exc}" + ) from exc + return {"samples": samples} + + +def _get_latent_channels(latent_samples) -> int: + shape = tuple(getattr(latent_samples, "shape", ())) + if len(shape) < 2: + raise RuntimeError("[LLS] Invalid latent tensor shape. 无法识别 latent 通道数。") + return int(shape[1]) + + +def _build_empty_like_latent(samples): + shape = tuple(getattr(samples, "shape", ())) + if len(shape) != 4: + raise RuntimeError("[LLS] Invalid latent tensor shape for Empty Flux2 latent fallback.") + + if torch is not None and isinstance(samples, torch.Tensor): + return {"samples": torch.zeros(shape, dtype=samples.dtype, device=samples.device)} + + try: + return {"samples": samples.__class__(shape)} + except Exception: + return {"samples": _ShapeOnlyLatent(shape)} + + +def _build_empty_flux2_latent(main_image, main_latent): + width, height = get_image_size(main_image) + batch_size = _get_image_batch_size(main_image) + + execute = getattr(getattr(native_flux_nodes, "EmptyFlux2LatentImage", None), "execute", None) + if callable(execute): + try: + return _extract_node_output_value(execute(width=width, height=height, batch_size=batch_size)) + except TypeError: + return _extract_node_output_value(execute(width, height, batch_size)) + except Exception: + pass + + return _build_empty_like_latent(_extract_latent_samples(main_latent)) + + +def _attach_reference_latents(conditioning, ref_latents): + if not ref_latents: + return conditioning + + execute = getattr(getattr(native_edit_model_nodes, "ReferenceLatent", None), "execute", None) + if callable(execute): + updated = conditioning + for ref_latent in ref_latents: + try: + updated = _extract_node_output_value(execute(updated, latent=ref_latent)) + except TypeError: + updated = _extract_node_output_value(execute(updated, ref_latent)) + return updated + + return _append_reference_latents(conditioning, [_extract_latent_samples(latent) for latent in ref_latents]) + + def _build_noise_mask(mask, latent_samples): target_width = int(latent_samples.shape[-1]) target_height = int(latent_samples.shape[-2]) @@ -386,25 +478,48 @@ class LLSFlux2KleinEditTextEncode: conditioning, full_prompt = _encode_multivision_conditioning(clip, str(prompt), vision_images) target_width, target_height = get_image_size(main_image) - ref_latents = [] - for index, image in enumerate(images_for_edit, start=1): - ref_latent, _prepared = _encode_reference_latent( - vae, - image, - target_width, - target_height, - source_name=f"image{index}", - ) - ref_latents.append(ref_latent) + main_latent = _encode_image_latent(vae, main_image, source_name="image1") + main_latent_channels = _get_latent_channels(_extract_latent_samples(main_latent)) + use_official_flux2_latent = main_latent_channels >= 128 - conditioning = _append_reference_latents( - conditioning, - [_extract_latent_samples(latent) for latent in ref_latents], - ) + ref_latents = [main_latent] + if reference_image_2 is not None: + if use_official_flux2_latent: + ref_latents.append(_encode_image_latent(vae, reference_image_2, source_name="image2")) + else: + ref_latent, _prepared = _encode_reference_latent( + vae, + reference_image_2, + target_width, + target_height, + source_name="image2", + ) + ref_latents.append(ref_latent) + if reference_image_3 is not None: + if use_official_flux2_latent: + ref_latents.append(_encode_image_latent(vae, reference_image_3, source_name="image3")) + else: + ref_latent, _prepared = _encode_reference_latent( + vae, + reference_image_3, + target_width, + target_height, + source_name="image3", + ) + ref_latents.append(ref_latent) - latent = { - "samples": _extract_latent_samples(ref_latents[0]), - } + conditioning = _attach_reference_latents(conditioning, ref_latents) + + if use_official_flux2_latent: + latent = _build_empty_flux2_latent(main_image, main_latent) + latent_mode = "empty_flux2_latent" + conditioning_backend = "flux2klein_multivision_clip+reference_latent" + else: + latent = { + "samples": _extract_latent_samples(main_latent), + } + latent_mode = "image1_reference_latent" + conditioning_backend = "flux2klein_multivision_clip" if mask is not None and str(mask_mode) != "none": latent["noise_mask"] = _build_noise_mask(output_mask, latent["samples"]) @@ -426,13 +541,14 @@ class LLSFlux2KleinEditTextEncode: "main_image": "image1", "reference_images": reference_names, "num_reference_images": len(reference_names), - "conditioning_backend": "flux2klein_multivision_clip", + "conditioning_backend": conditioning_backend, "native_error": None, - "native_wrapper": "PainterFluxImageEdit-compatible", - "latent_mode": "image1_reference_latent", + "native_wrapper": "official Flux2 ReferenceLatent" if use_official_flux2_latent else "PainterFluxImageEdit-compatible", + "latent_mode": latent_mode, "latent_has_noise_mask": "noise_mask" in latent, "vision_image_count": len(vision_images), "reference_latent_count": len(ref_latents), + "main_latent_channels": main_latent_channels, "target_size": {"width": int(target_width), "height": int(target_height)}, "note": "image1 is main edit image; image2 and image3 are reference images; images are not concatenated", } diff --git a/image.png b/image.png deleted file mode 100644 index bf707a4..0000000 Binary files a/image.png and /dev/null differ diff --git a/model_loader/nodes.py b/model_loader/nodes.py index 2cf4728..07f1d94 100644 --- a/model_loader/nodes.py +++ b/model_loader/nodes.py @@ -41,6 +41,7 @@ from ..utils.model_info import ( get_family_defaults, info_to_json, infer_family_from_name, + is_flux2_family, is_flux_family, is_sdxl_family, tag_lls_object, @@ -62,6 +63,8 @@ _FLUX_T5_PATTERNS = ("t5xxl_fp8_e4m3fn.safetensors", "t5xxl_fp16.safetensors", " _SDXL_CLIP_L_PATTERNS = ("clip_l.safetensors", "clip_l", "clip-l") _SDXL_CLIP_G_PATTERNS = ("clip_g.safetensors", "clip_g", "clip-g") _FLUX_VAE_PATTERNS = ("ae.safetensors", "ae", "vae") +_FLUX2_QWEN_PATTERNS = ("qwen_3_8b.safetensors", "qwen_3_8b_fp8mixed.safetensors", "qwen_3_8b") +_FLUX2_VAE_PATTERNS = ("flux2-vae.safetensors", "flux2-vae", "flux2_vae") def _build_capability_tags(model_name: str, family: str) -> dict[str, object]: @@ -226,6 +229,12 @@ def _load_external_text_encoder(family: str, name1: str, name2: str | None): raise RuntimeError("[LLS] ComfyUI core CLIPLoader class was not found.") return clip_loader_cls().load_clip(name1, type="stable_diffusion")[0] + if is_flux2_family(family): + clip_loader_cls = getattr(comfy_core_nodes, "CLIPLoader", None) + if clip_loader_cls is None: + raise RuntimeError("[LLS] ComfyUI core CLIPLoader class was not found.") + return clip_loader_cls().load_clip(name1, type="flux2")[0] + dual_loader_cls = getattr(comfy_core_nodes, "DualCLIPLoader", None) if dual_loader_cls is None: raise RuntimeError("[LLS] ComfyUI core DualCLIPLoader class was not found.") @@ -255,6 +264,10 @@ def _resolve_external_text_encoder_names( return name1, None return (_match_file(available, _SDXL_CLIP_L_PATTERNS) or available[0] if available else None, None) + if is_flux2_family(family): + resolved_1 = name1 or _match_file(available, _FLUX2_QWEN_PATTERNS) + return resolved_1, None + if is_sdxl_family(family): resolved_1 = name1 or _match_file(available, _SDXL_CLIP_L_PATTERNS) resolved_2 = name2 or _match_file(available, _SDXL_CLIP_G_PATTERNS) @@ -269,6 +282,8 @@ def _resolve_external_vae_name(family: str, name: str | None) -> str | None: if name: return name available = _get_filename_list("vae") + if is_flux2_family(family): + return _match_file(available, _FLUX2_VAE_PATTERNS) if is_flux_family(family): return _match_file(available, _FLUX_VAE_PATTERNS) return available[0] if available else None @@ -285,6 +300,11 @@ def _missing_text_encoder_error(family: str, missing_role: str) -> RuntimeError: f"[LLS] Missing SDXL text encoder {filename}. " "Place it in ComfyUI/models/text_encoders/." ) + if is_flux2_family(family): + return RuntimeError( + "[LLS] Missing Flux2/Klein text encoder qwen_3_8b.safetensors " + "or qwen_3_8b_fp8mixed.safetensors. Place one of them in ComfyUI/models/text_encoders/." + ) filename = "clip_l.safetensors" if missing_role == "clip_l" else "t5xxl_fp8_e4m3fn.safetensors" fallback = ( "[LLS] Missing FLUX text encoder t5xxl_fp8_e4m3fn.safetensors or t5xxl_fp16.safetensors. " @@ -325,6 +345,11 @@ def _resolve_text_encoder( raise _missing_text_encoder_error(family, "clip") return _load_external_text_encoder(family, resolved_1, None), "external", resolved_1, None + if is_flux2_family(family): + if not resolved_1: + raise _missing_text_encoder_error(family, "qwen_3_8b") + return _load_external_text_encoder(family, resolved_1, None), "external", resolved_1, None + required_role_1 = "clip_l" required_role_2 = "clip_g" if is_sdxl_family(family) else "t5xxl" if not resolved_1: @@ -367,6 +392,11 @@ def _resolve_vae( if resolved_vae_name: return _load_external_vae(resolved_vae_name), "external", resolved_vae_name + if is_flux2_family(family): + raise RuntimeError( + "[LLS] Missing Flux2/Klein VAE flux2-vae.safetensors. Place it in ComfyUI/models/vae/." + ) + if is_flux_family(family): raise RuntimeError( "[LLS] Missing FLUX AE/VAE ae.safetensors. Place it in ComfyUI/models/vae/." diff --git a/tests/test_example_workflows.py b/tests/test_example_workflows.py index 760364c..1585041 100644 --- a/tests/test_example_workflows.py +++ b/tests/test_example_workflows.py @@ -16,6 +16,12 @@ class TestExampleWorkflows(unittest.TestCase): return node self.fail(f"Missing node type {node_type!r} in workflow") + def _find_input(self, node: dict, name: str) -> dict: + for item in node.get("inputs", []): + if item.get("name") == name: + return item + self.fail(f"Missing input {name!r} in node {node.get('type')!r}") + def test_universal_txt2img_ksampler_keeps_seed_control_widget(self): workflow = self._load("LLS-universal-txt2img-workflow.json") node = self._find_node(workflow, "LLSSimpleKSampler") @@ -36,6 +42,33 @@ class TestExampleWorkflows(unittest.TestCase): self.assertEqual(widgets[2], "randomize") self.assertEqual(widgets[3], 20) + def test_flux2klein_background_replace_workflow_uses_lls_edit_and_flux2_stack(self): + workflow = self._load("LLS-Flux2Klein-电商换背景工作流.json") + + encode = self._find_node(workflow, "LLSFlux2KleinEditTextEncode") + unet = self._find_node(workflow, "UNETLoader") + clip = self._find_node(workflow, "CLIPLoader") + vae = self._find_node(workflow, "VAELoader") + self._find_node(workflow, "RMBG") + self._find_node(workflow, "InvertMask") + self._find_node(workflow, "ConditioningZeroOut") + self._find_node(workflow, "KSampler") + self._find_node(workflow, "VAEDecode") + + self.assertEqual(unet.get("widgets_values", [None])[0], "flux-2-klein-9b.safetensors") + self.assertEqual(clip.get("widgets_values", [None, None])[1], "flux2") + self.assertEqual(vae.get("widgets_values", [None])[0], "flux2-vae.safetensors") + + widgets = encode.get("widgets_values", []) + self.assertGreaterEqual(len(widgets), 4) + self.assertEqual(widgets[2], "longest_edge") + self.assertEqual(widgets[3], "use_mask") + + self.assertIsNotNone(self._find_input(encode, "image1").get("link")) + self.assertIsNotNone(self._find_input(encode, "image2").get("link")) + self.assertIsNone(self._find_input(encode, "image3").get("link")) + self.assertIsNotNone(self._find_input(encode, "mask").get("link")) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_flux2klein_node.py b/tests/test_flux2klein_node.py index 849b8d1..e97e156 100644 --- a/tests/test_flux2klein_node.py +++ b/tests/test_flux2klein_node.py @@ -12,6 +12,8 @@ try: FakeMask, FakeVAE, FakeTensor, + NativeEditModelStub, + NativeFluxStub, StandardClipStub, import_plugin_submodule, load_plugin_package, @@ -22,6 +24,8 @@ except ImportError: FakeMask, FakeVAE, FakeTensor, + NativeEditModelStub, + NativeFluxStub, StandardClipStub, import_plugin_submodule, load_plugin_package, @@ -137,6 +141,39 @@ class TestFlux2KleinNode(unittest.TestCase): self.assertNotIn("noise_mask", latent) self.assertEqual(custom_output["latent_has_noise_mask"], False) + def test_flux2_official_path_uses_empty_latent_and_reference_latent_nodes(self): + NativeFluxStub.reset() + NativeEditModelStub.reset() + + with mock.patch.object(self.module, "native_flux_nodes", NativeFluxStub, create=True), mock.patch.object( + self.module, + "native_edit_model_nodes", + NativeEditModelStub, + create=True, + ): + conditioning, latent, custom_output, main_image, output_mask = self.node.encode( + clip=StandardClipStub(), + vae=FakeVAE(latent_channels=128, downscale_ratio=16), + image1=FakeTensor((1, 832, 1248, 3), label="main"), + prompt="replace background with a clean kitchen scene", + ref_longest_edge=1248, + resize_mode="keep_original", + mask_mode="use_mask", + image2=FakeTensor((1, 640, 640, 3), label="ref2"), + image3=None, + mask=FakeMask((1, 832, 1248), mask_bbox=(50, 40, 500, 700), mask_area_ratio=0.35, label="mask"), + ) + + self.assertEqual(NativeFluxStub.last_empty_call, {"width": 1248, "height": 832, "batch_size": 1}) + self.assertEqual(len(NativeEditModelStub.last_calls), 2) + self.assertEqual(latent["samples"].shape, (1, 128, 52, 78)) + self.assertEqual(latent["noise_mask"].shape, (1, 52, 78)) + self.assertEqual(custom_output["latent_mode"], "empty_flux2_latent") + self.assertEqual(custom_output["conditioning_backend"], "flux2klein_multivision_clip+reference_latent") + self.assertEqual(custom_output["reference_latent_count"], 2) + self.assertEqual(main_image.shape, (1, 832, 1248, 3)) + self.assertEqual(output_mask.shape, (1, 832, 1248)) + def test_missing_clip_backend_raises_clear_error(self): with self.assertRaisesRegex(RuntimeError, "vision-aware|text encoding backend"): self.node.encode( diff --git a/tests/test_loader_prompt_refactor.py b/tests/test_loader_prompt_refactor.py index b3a889d..95b13d4 100644 --- a/tests/test_loader_prompt_refactor.py +++ b/tests/test_loader_prompt_refactor.py @@ -448,6 +448,43 @@ class TestLoaderPromptRefactor(unittest.TestCase): self.assertEqual(payload["text_encoder_name"], "clip_l.safetensors, t5xxl_fp16.safetensors") self.assertEqual(payload["vae_name"], "ae.safetensors") + def test_universal_loader_loads_flux2_klein_with_qwen_and_flux2_vae(self): + load_plugin_package() + from lls_node_test_refactor.model_loader import nodes as loader_nodes + + folder_paths = FolderPathsStub() + folder_paths._files["diffusion_models"].append("flux-2-klein-9b.safetensors") + folder_paths._files["text_encoders"].append("qwen_3_8b.safetensors") + folder_paths._files["vae"].append("flux2-vae.safetensors") + + with mock.patch.object(loader_nodes, "folder_paths", folder_paths), mock.patch.object( + loader_nodes, + "comfy_sd", + ComfySDStub(), + ), mock.patch.object(loader_nodes, "comfy_core_nodes", CoreNodesStub()): + node = loader_nodes.LLSUniversalModelLoader() + model, text_encoder, vae, model_info = node.load( + "diffusion_models/flux-2-klein-9b.safetensors", + "Auto", + "advanced", + "external", + "external", + "qwen_3_8b.safetensors", + "(auto)", + "flux2-vae.safetensors", + ) + + payload = json.loads(model_info) + self.assertEqual(model.label, "MODEL::flux-2-klein-9b.safetensors") + self.assertEqual(text_encoder.label, "CLIP::flux2::qwen_3_8b.safetensors") + self.assertEqual(vae.label, "VAE::flux2-vae.safetensors") + self.assertEqual(payload["model_family"], "FLUX2_KLEIN") + self.assertEqual(payload["text_encoder_name_1"], "qwen_3_8b.safetensors") + self.assertEqual(payload["text_encoder_name_2"], "") + self.assertEqual(payload["text_encoder_name"], "qwen_3_8b.safetensors") + self.assertEqual(payload["vae_name"], "flux2-vae.safetensors") + self.assertEqual(text_encoder._lls_text_encoder_type, "flux2_qwen") + def test_loader_raises_clear_error_when_flux_text_encoder_is_missing(self): load_plugin_package() from lls_node_test_refactor.model_loader import nodes as loader_nodes diff --git a/utils/model_info.py b/utils/model_info.py index acaf493..f86f98b 100644 --- a/utils/model_info.py +++ b/utils/model_info.py @@ -19,6 +19,9 @@ LEGACY_FAMILY_ALIASES: dict[str, str] = { "FLUX": "FLUX_DEV", "FLUX_SCHNELL": "FLUX_SCHNELL", "FLUX_DEV": "FLUX_DEV", + "FLUX2": "FLUX2_DEV", + "FLUX2_DEV": "FLUX2_DEV", + "FLUX2_KLEIN": "FLUX2_KLEIN", } MODEL_FAMILY_CHOICES: list[str] = [ @@ -29,6 +32,8 @@ MODEL_FAMILY_CHOICES: list[str] = [ "SDXL_TURBO", "FLUX_SCHNELL", "FLUX_DEV", + "FLUX2_DEV", + "FLUX2_KLEIN", ] FAMILY_DEFAULTS: dict[str, dict[str, Any]] = { @@ -122,6 +127,42 @@ FAMILY_DEFAULTS: dict[str, dict[str, Any]] = { "is_turbo": False, "is_flux": True, }, + "FLUX2_DEV": { + "text_encoder_type": "flux2_qwen", + "required_text_encoders": ["qwen_3_8b"], + "required_vae": "required", + "latent_channels": 128, + "downscale_ratio": 16, + "default_width": 1024, + "default_height": 1024, + "default_steps": 20, + "default_cfg": 1.0, + "default_guidance": 3.5, + "default_sampler": "euler", + "default_scheduler": "simple", + "default_denoise": 1.0, + "guidance_embed": True, + "is_turbo": False, + "is_flux": True, + }, + "FLUX2_KLEIN": { + "text_encoder_type": "flux2_qwen", + "required_text_encoders": ["qwen_3_8b"], + "required_vae": "required", + "latent_channels": 128, + "downscale_ratio": 16, + "default_width": 1024, + "default_height": 1024, + "default_steps": 20, + "default_cfg": 1.0, + "default_guidance": 3.5, + "default_sampler": "euler", + "default_scheduler": "simple", + "default_denoise": 1.0, + "guidance_embed": True, + "is_turbo": False, + "is_flux": True, + }, } SAMPLING_PRESETS: dict[str, dict[str, dict[str, float | int | None]]] = { @@ -190,6 +231,32 @@ SAMPLING_PRESETS: dict[str, dict[str, dict[str, float | int | None]]] = { "Balanced": {"steps": 20, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, "High Quality": {"steps": 28, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, }, + "FLUX2_DEV": { + FAMILY_DEFAULT_PRESET: { + "steps": 20, + "cfg": 1.0, + "guidance": 3.5, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0, + }, + "Fast": {"steps": 12, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, + "Balanced": {"steps": 20, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, + "High Quality": {"steps": 28, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, + }, + "FLUX2_KLEIN": { + FAMILY_DEFAULT_PRESET: { + "steps": 20, + "cfg": 1.0, + "guidance": 3.5, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0, + }, + "Fast": {"steps": 12, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, + "Balanced": {"steps": 20, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, + "High Quality": {"steps": 28, "cfg": 1.0, "guidance": 3.5, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}, + }, } 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