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2025-03-14 12:14:21 -06:00
commit 7aa734a482
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+# Byte-compiled / optimized / DLL files
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+*.py[cod]
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+.Python
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+.installed.cfg
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+#.idea/
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+MIT License
+
+Copyright (c) 2023 Ostris, LLC
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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+# Flex.1 tools
+
+Some tools to help with [Flex.1-alpha](https://huggingface.co/ostris/Flex.1-alpha) inference on Comfy UI.
+
+## Installation
+
+Clone this repo into your `custom_nodes` directory.
+
+## Nodes
+
+- **Flex Guidance**: Allows you to set the guidance for the Flex.1 guidance embedder, or bypass it completly to use true CFG.
+- **Flex LoRA Loader**: Loads LoRAs and automatically prunes them to Flex.1 layers. It will not be perfect as Flex is heavily diverged from Flux dev and is not a direct ancenstor of it, but it should be good enough for most purposes.
+- **Flex LoRA Loader (Model Only)**: Same as Flex LoRA Loader, but only loads the model and not the text encoder. Most Flux LoRAs do not train the text encoder.
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+import folder_paths
+import node_helpers
+import comfy.sd
+import comfy.utils
+
+
+class FlexGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "conditioning": ("CONDITIONING", ),
+                "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+                "bypass_guidance_embedder": (["yes", "no"], {"default": "no"}),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "do_it"
+
+    CATEGORY = "advanced/conditioning/flux"
+
+    def do_it(self, conditioning, guidance, bypass_guidance_embedder):
+        bypass_guidance_embedder = bypass_guidance_embedder == "yes"
+        guidance_value = guidance
+        if bypass_guidance_embedder:
+            guidance_value = None
+        cond = node_helpers.conditioning_set_values(
+            conditioning, {"guidance": guidance_value}
+        )
+        return (cond, )
+
+
+class FlexLoraLoader:
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
+                "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
+                "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.",
+                       "The modified CLIP model.")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "loaders"
+    DESCRIPTION = "Loads Loras and automatically converts Flux loras to Flex loras."
+
+    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                self.loaded_lora = None
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            # convert it to Flex LoRA
+            # the pruning squashed double idx 5-15 into idx 4
+            # making idx 16, 17, 18 become 5, 6, 7
+            # we will drop double blocks with idx 5-15
+            # and move idx 16, 17, 18 to 5, 6, 7
+            # it is best to drop idx 4 as well since it is so divergent due to pruning
+
+            # loras have different naming patterns, the ones I know about are below
+            block_test_targets = [
+                "double_blocks.{idx}.",
+                "transformer.transformer_blocks.{idx}.",
+                "lora_unet_double_blocks_{idx}_",
+                "lycoris_unet_double_blocks_{idx}_",
+                "lycoris_transformer_blocks_{idx}_",
+                "lora_transformer_blocks_{idx}_",
+            ]
+
+            # we trained the guidance embedder from scratch, the weights will not match at all
+            # loras will destroy it, so we will ignore it
+            ignore_if_contains = [
+                "guidance_in",
+                "guidance_embedder"
+            ]
+
+            # check if any of the keys start with the block_test_targets with idx 8-18,
+            # if they do, then this it is a Flux lora
+
+            is_flux_lora = False
+            for idx in range(8, 19):
+                for target in block_test_targets:
+                    if any(k.startswith(target.format(idx=idx)) for k in lora.keys()):
+                        is_flux_lora = True
+                        break
+                if is_flux_lora:
+                    break
+
+            if is_flux_lora:
+                flex_lora = {}
+                drop_idxs = list(range(4, 16))
+                move_idxs = {16: 5, 17: 6, 18: 7}
+                for k, v in lora.items():
+                    if any(k.startswith(target.format(idx=idx)) for target in block_test_targets for idx in drop_idxs):
+                        # drop it
+                        continue
+                    if any(target in k for target in ignore_if_contains):
+                        continue
+                    for old_idx, new_idx in move_idxs.items():
+                        replaced = False
+                        for target in block_test_targets:
+                            formatted_target = target.format(idx=old_idx)
+                            if k.startswith(formatted_target):
+                                k = k.replace(formatted_target,
+                                              target.format(idx=new_idx))
+                                replaced = True
+                                break
+                        if replaced:
+                            break
+                    flex_lora[k] = v
+                lora = flex_lora
+
+            self.loaded_lora = (lora_path, lora)
+
+        model_lora, clip_lora = comfy.sd.load_lora_for_models(
+            model, clip, lora, strength_model, strength_clip)
+        return (model_lora, clip_lora)
+
+
+class FlexLoraLoaderModelOnly(FlexLoraLoader):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "lora_name": (folder_paths.get_filename_list("loras"), ),
+                             "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_lora_model_only"
+
+    def load_lora_model_only(self, model, lora_name, strength_model):
+        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
+
+
+NODE_CLASS_MAPPINGS = {
+    "FlexGuidance": FlexGuidance,
+    "FlexLoraLoader": FlexLoraLoader,
+    "FlexLoraLoaderModelOnly": FlexLoraLoaderModelOnly,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "FlexGuidance": "Flex Guidance",
+    "FlexLoraLoader": "Flex LoRA Loader",
+    "FlexLoraLoaderModelOnly": "Flex LoRA Loader (Model Only)",
+}