From f9768b2e1c2c321ac70209dd8722c765569f0db4 Mon Sep 17 00:00:00 2001 From: Filip Strand Date: Tue, 11 Nov 2025 16:25:58 +0100 Subject: [PATCH] Qwen image edit (#274) Co-authored-by: Filip Strand --- _typos.toml | 2 +- pyproject.toml | 2 + src/mflux/callbacks/instances/memory_saver.py | 6 +- src/mflux/config/model_config.py | 38 +- src/mflux/error/exceptions.py | 4 + src/mflux/generate_qwen_edit.py | 65 ++ src/mflux/generate_qwen_edit_plus.py | 81 ++ .../models/common/lora/mapping/lora_loader.py | 58 +- src/mflux/models/common/weights/__init__.py | 1 + .../models/common/weights/mapping/__init__.py | 6 + .../common/weights/mapping/weight_mapper.py | 215 ++++ .../common/weights/mapping/weight_mapping.py | 33 + .../model/qwen_text_encoder/qwen_attention.py | 30 +- .../model/qwen_text_encoder/qwen_encoder.py | 79 +- .../qwen_text_encoder/qwen_patch_merger.py | 32 + .../qwen_text_encoder/qwen_text_encoder.py | 4 +- .../qwen_vision_attention.py | 83 ++ .../qwen_text_encoder/qwen_vision_block.py | 24 + .../qwen_vision_language_encoder.py | 54 + .../qwen_vision_language_prompt_encoder.py | 93 ++ .../qwen_text_encoder/qwen_vision_mlp.py | 15 + .../qwen_vision_patch_embed.py | 34 + .../qwen_vision_rotary_embedding.py | 16 + .../qwen_vision_transformer.py | 149 +++ .../model/qwen_transformer/qwen_attention.py | 200 ++-- .../qwen_transformer/qwen_feed_forward.py | 6 +- .../model/qwen_transformer/qwen_layer_norm.py | 7 +- .../qwen/model/qwen_transformer/qwen_rope.py | 174 ++-- .../qwen_transformer/qwen_time_text_embed.py | 8 +- .../qwen_timestep_embedding.py | 7 +- .../model/qwen_transformer/qwen_timesteps.py | 25 +- .../qwen_transformer/qwen_transformer.py | 71 +- .../qwen_transformer_block.py | 78 +- .../qwen_transformer_rms_norm.py | 23 + .../qwen_vae/qwen_image_attention_block_3d.py | 4 +- .../qwen_vae/qwen_image_causal_conv_3d.py | 19 +- .../model/qwen_vae/qwen_image_decoder_3d.py | 4 +- .../qwen_vae/qwen_image_down_block_3d.py | 2 +- .../model/qwen_vae/qwen_image_encoder_3d.py | 26 +- .../models/qwen/qwen_edit_initializer.py | 109 +++ src/mflux/models/qwen/qwen_initializer.py | 6 +- .../qwen/tokenizer/qwen_image_processor.py | 162 +++ .../models/qwen/tokenizer/qwen_tokenizer.py | 2 +- .../qwen/tokenizer/qwen_tokenizer_handler.py | 8 - .../qwen_vision_language_processor.py | 103 ++ .../qwen_vision_language_tokenizer.py | 142 +++ .../models/qwen/variants/edit/__init__.py | 5 + .../qwen/variants/edit/qwen_image_edit.py | 263 +++++ .../variants/edit/qwen_image_edit_plus.py | 285 ++++++ .../qwen/variants/edit/utils/__init__.py | 1 + .../variants/edit/utils/qwen_edit_util.py | 106 ++ .../edit/utils/qwen_prompt_rewriter.py | 116 +++ .../qwen/variants/txt2img/qwen_image.py | 10 +- .../qwen/weights/qwen_weight_handler.py | 752 ++------------ .../qwen/weights/qwen_weight_mapping.py | 919 ++++++++++++++++++ .../models/qwen/weights/qwen_weight_util.py | 47 +- src/mflux/schedulers/__init__.py | 6 +- .../flow_match_euler_discrete_scheduler.py | 75 ++ src/mflux/schedulers/linear_scheduler.py | 16 +- src/mflux/utils/image_compare.py | 94 ++ src/mflux/utils/quantization_util.py | 2 +- tests/depth/test_depth_pro.py | 4 +- .../image_generation/helpers/image_compare.py | 46 - .../image_generation_concept_test_helper.py | 7 +- ...image_generation_controlnet_test_helper.py | 5 +- .../image_generation_depth_test_helper.py | 5 +- .../image_generation_edit_test_helper.py | 103 ++ .../image_generation_fill_test_helper.py | 5 +- .../image_generation_ic_edit_test_helper.py | 5 +- ...image_generation_in_context_test_helper.py | 5 +- .../image_generation_kontext_test_helper.py | 65 -- .../image_generation_redux_test_helper.py | 5 +- .../helpers/image_generation_test_helper.py | 5 +- .../test_generate_image_edit.py | 69 ++ .../test_generate_image_kontext.py | 18 - tests/resources/reference_qwen_edit.png | Bin 0 -> 220761 bytes tests/resources/reference_qwen_edit_plus.png | Bin 0 -> 243670 bytes ...ference_qwen_edit_plus_multiple_images.png | Bin 0 -> 227527 bytes tests/resources/reference_qwen_lora.png | Bin 283115 -> 274700 bytes tests/resources/reference_qwen_txt2img.png | Bin 290364 -> 264350 bytes tests/resources/shirt.jpg | Bin 0 -> 95489 bytes 81 files changed, 4101 insertions(+), 1253 deletions(-) create mode 100644 src/mflux/generate_qwen_edit.py create mode 100644 src/mflux/generate_qwen_edit_plus.py create mode 100644 src/mflux/models/common/weights/__init__.py create mode 100644 src/mflux/models/common/weights/mapping/__init__.py create mode 100644 src/mflux/models/common/weights/mapping/weight_mapper.py create mode 100644 src/mflux/models/common/weights/mapping/weight_mapping.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_patch_merger.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_attention.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_block.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_encoder.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_prompt_encoder.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_mlp.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_patch_embed.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_rotary_embedding.py create mode 100644 src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_transformer.py create mode 100644 src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_rms_norm.py create mode 100644 src/mflux/models/qwen/qwen_edit_initializer.py create mode 100644 src/mflux/models/qwen/tokenizer/qwen_image_processor.py create mode 100644 src/mflux/models/qwen/tokenizer/qwen_vision_language_processor.py create mode 100644 src/mflux/models/qwen/tokenizer/qwen_vision_language_tokenizer.py create mode 100644 src/mflux/models/qwen/variants/edit/__init__.py create mode 100644 src/mflux/models/qwen/variants/edit/qwen_image_edit.py create mode 100644 src/mflux/models/qwen/variants/edit/qwen_image_edit_plus.py create mode 100644 src/mflux/models/qwen/variants/edit/utils/__init__.py create mode 100644 src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py create mode 100644 src/mflux/models/qwen/variants/edit/utils/qwen_prompt_rewriter.py create mode 100644 src/mflux/models/qwen/weights/qwen_weight_mapping.py create mode 100644 src/mflux/schedulers/flow_match_euler_discrete_scheduler.py create mode 100644 src/mflux/utils/image_compare.py delete mode 100644 tests/image_generation/helpers/image_compare.py create mode 100644 tests/image_generation/helpers/image_generation_edit_test_helper.py delete mode 100644 tests/image_generation/helpers/image_generation_kontext_test_helper.py create mode 100644 tests/image_generation/test_generate_image_edit.py delete mode 100644 tests/image_generation/test_generate_image_kontext.py create mode 100644 tests/resources/reference_qwen_edit.png create mode 100644 tests/resources/reference_qwen_edit_plus.png create mode 100644 tests/resources/reference_qwen_edit_plus_multiple_images.png create mode 100644 tests/resources/shirt.jpg diff --git a/_typos.toml b/_typos.toml index fb03470..66f9d60 100644 --- a/_typos.toml +++ b/_typos.toml @@ -2,4 +2,4 @@ # use "typos --dump-config" to disocver other configs to override [default] -extend-ignore-identifiers-re = ["EmbedND", "embed_nd", "alle"] +extend-ignore-identifiers-re = ["EmbedND", "embed_nd", "alle", "thw"] diff --git a/pyproject.toml b/pyproject.toml index 3e34f9c..3dfeb0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,6 +84,8 @@ mflux-generate-fill = "mflux.generate_fill:main" mflux-generate-depth = "mflux.generate_depth:main" mflux-generate-redux = "mflux.generate_redux:main" mflux-generate-kontext = "mflux.generate_kontext:main" +mflux-generate-qwen-edit = "mflux.generate_qwen_edit:main" +mflux-generate-qwen-edit-plus = "mflux.generate_qwen_edit_plus:main" mflux-concept = "mflux.concept:main" mflux-concept-from-image = "mflux.concept_from_image:main" mflux-save = "mflux.save:main" diff --git a/src/mflux/callbacks/instances/memory_saver.py b/src/mflux/callbacks/instances/memory_saver.py index 82fbc88..bb93416 100644 --- a/src/mflux/callbacks/instances/memory_saver.py +++ b/src/mflux/callbacks/instances/memory_saver.py @@ -62,8 +62,12 @@ class MemorySaver(BeforeLoopCallback, InLoopCallback, AfterLoopCallback): self.model.clip_image_encoder = None if hasattr(self.model, "t5_image_encoder"): self.model.t5_image_encoder = None - if hasattr(self.model, "text_encoder"): + if hasattr(self.model, "text_encoder") and self.model.text_encoder is not None: self.model.text_encoder = None + if hasattr(self.model, "qwen_vl_encoder") and self.model.qwen_vl_encoder is not None: + self.model.qwen_vl_encoder = None + if hasattr(self.model, "qwen_vl_tokenizer") and self.model.qwen_vl_tokenizer is not None: + self.model.qwen_vl_tokenizer = None gc.collect() mx.clear_cache() diff --git a/src/mflux/config/model_config.py b/src/mflux/config/model_config.py index 87e7e90..e400e25 100644 --- a/src/mflux/config/model_config.py +++ b/src/mflux/config/model_config.py @@ -89,6 +89,16 @@ class ModelConfig: def qwen_image() -> "ModelConfig": return AVAILABLE_MODELS["qwen-image"] + @staticmethod + @lru_cache + def qwen_image_edit() -> "ModelConfig": + return AVAILABLE_MODELS["qwen-image-edit"] + + @staticmethod + @lru_cache + def qwen_image_edit_plus() -> "ModelConfig": + return AVAILABLE_MODELS["qwen-image-edit-plus"] + def x_embedder_input_dim(self) -> int: if "Fill" in self.model_name: return 384 @@ -130,7 +140,9 @@ class ModelConfig: # Infer from model_name substring - prefer longer matches (more specific) # Use case-insensitive matching for better compatibility model_name_lower = model_name.lower() - matching_bases = [(b, alias) for b in base_models for alias in b.aliases if alias and alias.lower() in model_name_lower] + matching_bases = [ + (b, alias) for b in base_models for alias in b.aliases if alias and alias.lower() in model_name_lower + ] if matching_bases: # Sort by alias length descending, then by priority ascending @@ -300,4 +312,28 @@ AVAILABLE_MODELS = { requires_sigma_shift=None, priority=11, ), + "qwen-image-edit": ModelConfig( + aliases=["qwen-image-edit", "qwen-edit"], + model_name="Qwen/Qwen-Image-Edit", + base_model=None, + controlnet_model=None, + custom_transformer_model=None, + num_train_steps=None, + max_sequence_length=None, + supports_guidance=None, + requires_sigma_shift=None, + priority=12, + ), + "qwen-image-edit-plus": ModelConfig( + aliases=["qwen-image-edit-plus", "qwen-edit-plus", "qwen-edit-2509"], + model_name="Qwen/Qwen-Image-Edit-2509", + base_model=None, + controlnet_model=None, + custom_transformer_model=None, + num_train_steps=None, + max_sequence_length=None, + supports_guidance=None, + requires_sigma_shift=None, + priority=13, + ), } diff --git a/src/mflux/error/exceptions.py b/src/mflux/error/exceptions.py index f9239cf..1a35a57 100644 --- a/src/mflux/error/exceptions.py +++ b/src/mflux/error/exceptions.py @@ -37,3 +37,7 @@ class CommandExecutionError(MFluxException): super().__init__( f"Command '{' '.join(cmd)}' exited with code {return_code}. See .stdout / .stderr for details." ) + + +class ReferenceVsOutputImageError(AssertionError): + """Raised when reference and output images don't match within the allowed threshold.""" diff --git a/src/mflux/generate_qwen_edit.py b/src/mflux/generate_qwen_edit.py new file mode 100644 index 0000000..3e7697c --- /dev/null +++ b/src/mflux/generate_qwen_edit.py @@ -0,0 +1,65 @@ +from pathlib import Path + +from mflux.callbacks.callback_manager import CallbackManager +from mflux.config.config import Config +from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException +from mflux.models.qwen.variants.edit.qwen_image_edit import QwenImageEdit +from mflux.ui import defaults as ui_defaults +from mflux.ui.cli.parsers import CommandLineParser +from mflux.ui.prompt_utils import get_effective_prompt + + +def main(): + # 0. Parse command line arguments + parser = CommandLineParser(description="Generate an image using Flux Kontext with image conditioning.") + parser.add_general_arguments() + parser.add_model_arguments(require_model_arg=False) + parser.add_lora_arguments() + parser.add_image_generator_arguments(supports_metadata_config=True) + parser.add_image_to_image_arguments(required=True) + parser.add_output_arguments() + args = parser.parse_args() + + # 0. Set default guidance value if not provided by user + if args.guidance is None: + args.guidance = ui_defaults.GUIDANCE_SCALE_KONTEXT + + # 1. Load the model + qwen = QwenImageEdit( + quantize=args.quantize, + local_path=args.path, + lora_paths=args.lora_paths, + lora_scales=args.lora_scales, + ) + + # 2. Register callbacks + memory_saver = CallbackManager.register_callbacks(args=args, model=qwen) + + try: + for seed in args.seed: + # 3. Generate an image for each seed value + image = qwen.generate_image( + seed=seed, + prompt=get_effective_prompt(args), + config=Config( + num_inference_steps=args.steps, + height=args.height, + width=args.width, + guidance=args.guidance, + image_path=args.image_path, + ), + ) + + # 4. Save the image + output_path = Path(args.output.format(seed=seed)) + image.save(path=output_path, export_json_metadata=args.metadata) + + except (StopImageGenerationException, PromptFileReadError) as exc: + print(exc) + finally: + if memory_saver: + print(memory_saver.memory_stats()) + + +if __name__ == "__main__": + main() diff --git a/src/mflux/generate_qwen_edit_plus.py b/src/mflux/generate_qwen_edit_plus.py new file mode 100644 index 0000000..39b2a59 --- /dev/null +++ b/src/mflux/generate_qwen_edit_plus.py @@ -0,0 +1,81 @@ +from pathlib import Path + +from mflux.callbacks.callback_manager import CallbackManager +from mflux.config.config import Config +from mflux.error.exceptions import PromptFileReadError, StopImageGenerationException +from mflux.models.qwen.variants.edit.qwen_image_edit_plus import QwenImageEditPlus +from mflux.ui import defaults as ui_defaults +from mflux.ui.cli.parsers import CommandLineParser +from mflux.ui.prompt_utils import get_effective_negative_prompt, get_effective_prompt + + +def main(): + # 0. Parse command line arguments + parser = CommandLineParser(description="Generate an image using Qwen Image Edit Plus with image conditioning.") + parser.add_general_arguments() + parser.add_model_arguments(require_model_arg=False) + parser.add_lora_arguments() + parser.add_image_generator_arguments(supports_metadata_config=True) + parser.add_image_to_image_arguments(required=True) + parser.add_argument( + "--image-paths", + type=Path, + nargs="+", + default=None, + help="Local paths to multiple init images (for plus model). If not provided, uses --image-path as single image.", + ) + parser.add_output_arguments() + args = parser.parse_args() + + # 0. Set default guidance value if not provided by user + if args.guidance is None: + args.guidance = ui_defaults.GUIDANCE_SCALE_KONTEXT + + # 1. Load the model + qwen = QwenImageEditPlus( + quantize=args.quantize, + local_path=args.path, + lora_paths=args.lora_paths, + lora_scales=args.lora_scales, + ) + + # 2. Register callbacks + memory_saver = CallbackManager.register_callbacks(args=args, model=qwen) + + try: + for seed in args.seed: + # 3. Prepare image paths: use --image-paths if provided, otherwise fallback to --image-path + image_paths = None + if args.image_paths: + image_paths = [str(p) for p in args.image_paths] + elif args.image_path: + image_paths = [str(args.image_path)] + + # 4. Generate an image for each seed value + image = qwen.generate_image( + seed=seed, + prompt=get_effective_prompt(args), + config=Config( + num_inference_steps=args.steps, + height=args.height, + width=args.width, + guidance=args.guidance, + image_path=args.image_path, + ), + negative_prompt=get_effective_negative_prompt(args), + image_paths=image_paths, + ) + + # 5. Save the image + output_path = Path(args.output.format(seed=seed)) + image.save(path=output_path, export_json_metadata=args.metadata) + + except (StopImageGenerationException, PromptFileReadError) as exc: + print(exc) + finally: + if memory_saver: + print(memory_saver.memory_stats()) + + +if __name__ == "__main__": + main() diff --git a/src/mflux/models/common/lora/mapping/lora_loader.py b/src/mflux/models/common/lora/mapping/lora_loader.py index 4f15826..ef42bc3 100644 --- a/src/mflux/models/common/lora/mapping/lora_loader.py +++ b/src/mflux/models/common/lora/mapping/lora_loader.py @@ -11,7 +11,6 @@ from mflux.models.common.lora.mapping.lora_mapping import LoRATarget class LoRALoader: - @staticmethod def load_and_apply_lora( lora_mapping: list[LoRATarget], @@ -39,10 +38,7 @@ class LoRALoader: @staticmethod def _apply_single_lora( - transformer: nn.Module, - lora_file: str, - scale: float, - lora_mapping: list[LoRATarget] + transformer: nn.Module, lora_file: str, scale: float, lora_mapping: list[LoRATarget] ) -> None: # Load the LoRA weights if not Path(lora_file).exists(): @@ -58,8 +54,6 @@ class LoRALoader: print(f"❌ Failed to load LoRA file: {e}") return - print(f"🔍 DEBUG: Found {len(weights)} LoRA weights") - # Apply LoRA using the provided mapping flat_mapping = LoRALoader._get_flat_mapping(lora_mapping) applied_count = LoRALoader._apply_lora_with_mapping(transformer, weights, scale, flat_mapping) @@ -68,10 +62,7 @@ class LoRALoader: @staticmethod def _apply_lora_with_mapping( - transformer: nn.Module, - weights: dict, - scale: float, - lora_mappings: Dict[str, Tuple[str, str, bool]] + transformer: nn.Module, weights: dict, scale: float, lora_mappings: Dict[str, Tuple[str, str, bool]] ) -> int: applied_count = 0 lora_data_by_target = {} @@ -87,7 +78,7 @@ class LoRALoader: # Extract block number from the weight key - try both . and _ separators # This handles both standard LoRA formats (dot-separated) and other formats (underscore-separated) # Find all numbers in the weight key - numbers_in_key = re.findall(r'\d+', weight_key) + numbers_in_key = re.findall(r"\d+", weight_key) for num_str in numbers_in_key: try: test_block_idx = int(num_str) @@ -119,8 +110,6 @@ class LoRALoader: lora_data_by_target[target_path][matrix_name] = (weight_value, transpose) - print(f"🔍 DEBUG: Found {len(lora_data_by_target)} LoRA target layers") - # Apply LoRA to each target for target_path, lora_data in lora_data_by_target.items(): if LoRALoader._apply_lora_matrices_to_target(transformer, target_path, lora_data, scale): @@ -129,12 +118,7 @@ class LoRALoader: return applied_count @staticmethod - def _apply_lora_matrices_to_target( - transformer: nn.Module, - target_path: str, - lora_data: dict, - scale: float - ) -> bool: + def _apply_lora_matrices_to_target(transformer: nn.Module, target_path: str, lora_data: dict, scale: float) -> bool: # Navigate to the target layer current_module = transformer path_parts = target_path.split(".") @@ -175,40 +159,31 @@ class LoRALoader: # Create new LoRA layer # Check if it's a linear layer (either nn.Linear, LoRALinear, or FusedLoRALinear) - is_linear = hasattr(current_module, 'weight') + is_linear = hasattr(current_module, "weight") is_lora_linear = isinstance(current_module, LoRALinear) is_fused_linear = isinstance(current_module, FusedLoRALinear) - + if is_linear or is_lora_linear or is_fused_linear: # Handle fusion: if the current module is already a LoRA layer, fuse them if is_lora_linear: print(f" 🔀 Fusing with existing LoRA at {target_path}") # Create a temporary LoRA layer from the base linear of the existing LoRA - lora_layer = LoRALinear.from_linear( - current_module.linear, - r=lora_A.shape[1], - scale=effective_scale - ) + lora_layer = LoRALinear.from_linear(current_module.linear, r=lora_A.shape[1], scale=effective_scale) # Set the LoRA matrices lora_layer.lora_A = lora_A lora_layer.lora_B = lora_B # Apply alpha scaling to the matrices if present if "alpha" in lora_data: lora_layer.lora_B = lora_layer.lora_B * alpha_scale - + # Create fused layer with the existing LoRA and the new one - fused_layer = FusedLoRALinear( - base_linear=current_module.linear, - loras=[current_module, lora_layer] - ) + fused_layer = FusedLoRALinear(base_linear=current_module.linear, loras=[current_module, lora_layer]) replacement_layer = fused_layer elif is_fused_linear: print(f" 🔀 Adding to existing fusion at {target_path}") # Create a temporary LoRA layer from the base linear lora_layer = LoRALinear.from_linear( - current_module.base_linear, - r=lora_A.shape[1], - scale=effective_scale + current_module.base_linear, r=lora_A.shape[1], scale=effective_scale ) # Set the LoRA matrices lora_layer.lora_A = lora_A @@ -216,28 +191,23 @@ class LoRALoader: # Apply alpha scaling to the matrices if present if "alpha" in lora_data: lora_layer.lora_B = lora_layer.lora_B * alpha_scale - + # Add to existing fusion fused_layer = FusedLoRALinear( - base_linear=current_module.base_linear, - loras=current_module.loras + [lora_layer] + base_linear=current_module.base_linear, loras=current_module.loras + [lora_layer] ) replacement_layer = fused_layer else: # First LoRA on this layer # Create LoRA layer - lora_layer = LoRALinear.from_linear( - current_module, - r=lora_A.shape[1], - scale=effective_scale - ) + lora_layer = LoRALinear.from_linear(current_module, r=lora_A.shape[1], scale=effective_scale) # Set the LoRA matrices - use the correct dimensions from the LoRA file lora_layer.lora_A = lora_A lora_layer.lora_B = lora_B # Apply alpha scaling to the matrices if present if "alpha" in lora_data: lora_layer.lora_B = lora_layer.lora_B * alpha_scale - + replacement_layer = lora_layer # Replace the layer in the parent module diff --git a/src/mflux/models/common/weights/__init__.py b/src/mflux/models/common/weights/__init__.py new file mode 100644 index 0000000..88528ca --- /dev/null +++ b/src/mflux/models/common/weights/__init__.py @@ -0,0 +1 @@ +"""Common weight mapping utilities.""" diff --git a/src/mflux/models/common/weights/mapping/__init__.py b/src/mflux/models/common/weights/mapping/__init__.py new file mode 100644 index 0000000..ded25f5 --- /dev/null +++ b/src/mflux/models/common/weights/mapping/__init__.py @@ -0,0 +1,6 @@ +"""Weight mapping utilities for transforming HuggingFace weights to MLX structure.""" + +from mflux.models.common.weights.mapping.weight_mapper import WeightMapper +from mflux.models.common.weights.mapping.weight_mapping import WeightMapping, WeightTarget + +__all__ = ["WeightMapping", "WeightTarget", "WeightMapper"] diff --git a/src/mflux/models/common/weights/mapping/weight_mapper.py b/src/mflux/models/common/weights/mapping/weight_mapper.py new file mode 100644 index 0000000..ca71143 --- /dev/null +++ b/src/mflux/models/common/weights/mapping/weight_mapper.py @@ -0,0 +1,215 @@ +""" +Weight Mapper - Applies declarative weight mappings to transform HF weights to MLX structure. + +Similar to LoRALoader, but for weight mapping instead of LoRA application. +""" + +import re +from typing import Callable, Dict, List, Optional + +import mlx.core as mx + +from mflux.models.common.weights.mapping.weight_mapping import WeightTarget + + +class WeightMapper: + """Maps HuggingFace weights to MLX nested structure using declarative mappings.""" + + @staticmethod + def apply_mapping( + hf_weights: Dict[str, mx.array], + mapping: List[WeightTarget], + num_blocks: Optional[int] = None, + num_layers: Optional[int] = None, + ) -> Dict: + """ + Apply weight mapping to transform HF weights to MLX structure. + + Args: + hf_weights: Raw HuggingFace weights (flat dict with dot-notation keys) + mapping: List of WeightTarget mappings + num_blocks: Number of transformer blocks (auto-detected if None) + num_layers: Number of text encoder layers (auto-detected if None) + + Returns: + Nested dict structure matching MLX model + """ + # Auto-detect number of blocks if not provided + if num_blocks is None: + num_blocks = WeightMapper._detect_num_blocks(hf_weights) + + # Auto-detect number of layers if not provided + if num_layers is None: + num_layers = WeightMapper._detect_num_layers(hf_weights) + + # Build flat mapping: HF pattern -> (MLX path, transform) + flat_mapping = WeightMapper._build_flat_mapping(mapping, num_blocks, num_layers) + + # Map weights + mapped_weights = {} + mapped_count = 0 + skipped_count = 0 + + for hf_key, hf_tensor in hf_weights.items(): + # Try to find matching mapping + mlx_path, transform = WeightMapper._find_mapping(hf_key, flat_mapping) + + if mlx_path: + # Apply transform if specified + tensor = hf_tensor + if transform: + tensor = transform(tensor) + + # Build nested structure + WeightMapper._set_nested_value(mapped_weights, mlx_path, tensor) + mapped_count += 1 + else: + # Weight not in mapping - might be intentionally skipped (e.g., lm_head) + # or optional weight (e.g., conv_shortcut) - that's OK + skipped_count += 1 + + # Optional: uncomment for debugging + # print(f"✅ Mapped {mapped_count} weights, skipped {skipped_count}") + + return mapped_weights + + @staticmethod + def _detect_num_blocks(hf_weights: Dict[str, mx.array]) -> int: + """Detect number of transformer blocks from weight keys.""" + block_numbers = set() + for key in hf_weights.keys(): + # Match pattern: transformer_blocks.{number}.something + match = re.search(r"transformer_blocks\.(\d+)\.", key) + if match: + block_numbers.add(int(match.group(1))) + + if block_numbers: + return max(block_numbers) + 1 # Blocks are 0-indexed + return 0 + + @staticmethod + def _detect_num_layers(hf_weights: Dict[str, mx.array]) -> int: + """Detect number of text encoder layers from weight keys.""" + layer_numbers = set() + for key in hf_weights.keys(): + # Match pattern: model.layers.{number}.something + match = re.search(r"model\.layers\.(\d+)\.", key) + if match: + layer_numbers.add(int(match.group(1))) + + if layer_numbers: + return max(layer_numbers) + 1 # Layers are 0-indexed + return 28 # Default 28 layers for Qwen text encoder + + @staticmethod + def _build_flat_mapping( + mapping: List[WeightTarget], num_blocks: int = 0, num_layers: int = 28 + ) -> Dict[str, tuple[str, Optional[Callable[[mx.array], mx.array]]]]: + """ + Build flat mapping from declarative targets. + + Returns: + Dict mapping HF pattern -> (MLX path, transform) + """ + flat = {} + + for target in mapping: + # Expand placeholders for each pattern + for hf_pattern in target.hf_patterns: + # Check which placeholders are present + has_block = "{block}" in hf_pattern or "{block}" in target.mlx_path + has_i = "{i}" in hf_pattern or "{i}" in target.mlx_path + has_res = "{res}" in hf_pattern or "{res}" in target.mlx_path + has_layer = "{layer}" in hf_pattern or "{layer}" in target.mlx_path + + # Handle multiple placeholders together + if has_block and has_res: + # Up blocks: expand both {block} and {res} + max_blocks = num_blocks if num_blocks > 0 else 4 # Default 4 for up_blocks + for block_num in range(max_blocks): + for res in range(3): # 3 resnets per up_block + concrete_hf = hf_pattern.replace("{block}", str(block_num)).replace("{res}", str(res)) + concrete_mlx = target.mlx_path.replace("{block}", str(block_num)).replace("{res}", str(res)) + flat[concrete_hf] = (concrete_mlx, target.transform) + elif has_block: + # Expand {block} only (for transformer blocks or visual blocks) + # Check if this is for visual blocks (32 blocks) or transformer blocks + if "visual.blocks" in hf_pattern or "visual.blocks" in target.mlx_path: + max_blocks = 32 # Visual blocks are always 32 + else: + max_blocks = num_blocks if num_blocks > 0 else 4 # Default 4 for up_blocks + for block_num in range(max_blocks): + concrete_hf = hf_pattern.replace("{block}", str(block_num)) + concrete_mlx = target.mlx_path.replace("{block}", str(block_num)) + flat[concrete_hf] = (concrete_mlx, target.transform) + elif has_layer: + # Expand {layer} for text encoder layers or visual blocks + max_layers = num_layers if num_layers > 0 else 28 # Default 28 for text encoder + for layer_num in range(max_layers): + concrete_hf = hf_pattern.replace("{layer}", str(layer_num)) + concrete_mlx = target.mlx_path.replace("{layer}", str(layer_num)) + flat[concrete_hf] = (concrete_mlx, target.transform) + elif has_i: + # Expand {i} only (for mid_block resnets) + for i in range(2): # 2 resnets in mid_block + concrete_hf = hf_pattern.replace("{i}", str(i)) + concrete_mlx = target.mlx_path.replace("{i}", str(i)) + flat[concrete_hf] = (concrete_mlx, target.transform) + elif has_res: + # This shouldn't happen for VAE (encoder down_blocks are explicit) + # But handle it just in case + if "up_block" in hf_pattern: + for res in range(3): + concrete_hf = hf_pattern.replace("{res}", str(res)) + concrete_mlx = target.mlx_path.replace("{res}", str(res)) + flat[concrete_hf] = (concrete_mlx, target.transform) + else: + # No placeholder, use as-is + flat[hf_pattern] = (target.mlx_path, target.transform) + + return flat + + @staticmethod + def _find_mapping( + hf_key: str, flat_mapping: Dict[str, tuple[str, Optional[Callable[[mx.array], mx.array]]]] + ) -> tuple[Optional[str], Optional[Callable[[mx.array], mx.array]]]: + """Find MLX path and transform for a given HF key.""" + return flat_mapping.get(hf_key, (None, None)) + + @staticmethod + def _set_nested_value(d: Dict, path: str, value: mx.array): + """ + Set value in nested dict using dot-notation path. + + Creates nested structure as needed. + Handles both dict keys and list indices. + """ + parts = path.split(".") + current = d + i = 0 + + while i < len(parts) - 1: + part = parts[i] + + # Check if next part is a digit (list index) + if i + 1 < len(parts) and parts[i + 1].isdigit(): + # This is a list, ensure it exists + if part not in current: + current[part] = [] + # Ensure list is large enough + idx = int(parts[i + 1]) + while len(current[part]) <= idx: + current[part].append({}) + current = current[part][idx] + # Skip both the key and the index + i += 2 + else: + # Regular dict key + if part not in current: + current[part] = {} + current = current[part] + i += 1 + + # Set final value + final_key = parts[-1] + current[final_key] = value diff --git a/src/mflux/models/common/weights/mapping/weight_mapping.py b/src/mflux/models/common/weights/mapping/weight_mapping.py new file mode 100644 index 0000000..906b29e --- /dev/null +++ b/src/mflux/models/common/weights/mapping/weight_mapping.py @@ -0,0 +1,33 @@ +""" +Base classes for declarative weight mapping. + +Similar to LoRA mapping, but for transforming HuggingFace weights to MLX structure. +""" + +from dataclasses import dataclass +from typing import Callable, List, Optional, Protocol + +import mlx.core as mx + + +@dataclass +class WeightTarget: + """ + Declarative weight mapping target. + + Maps HuggingFace weight names to MLX nested structure paths. + """ + + mlx_path: str # MLX nested path, e.g., "transformer_blocks.{block}.attn.to_q.weight" + hf_patterns: List[str] # HuggingFace naming patterns, e.g., ["transformer_blocks.{block}.attn.to_q.weight"] + transform: Optional[Callable[[mx.array], mx.array]] = None # Optional transform (reshape, transpose, etc.) + required: bool = True # If False, weight is optional (may not exist in all models) + + +class WeightMapping(Protocol): + """Protocol for weight mapping classes.""" + + @staticmethod + def get_mapping() -> List[WeightTarget]: + """Return list of weight mapping targets.""" + return [] diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py index 8ce5ea2..2567eba 100644 --- a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_attention.py @@ -42,18 +42,14 @@ class QwenAttention(nn.Module): position_embeddings: tuple[mx.array, mx.array] | None = None, ) -> mx.array: bsz, q_len, _ = hidden_states.shape - - # Q, K, V projections query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) - # Reshape and transpose query_states = query_states.reshape(bsz, q_len, self.num_attention_heads, self.head_dim).transpose(0, 2, 1, 3) key_states = key_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(0, 2, 1, 3) value_states = value_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(0, 2, 1, 3) - # Apply RoPE query_states, key_states = QwenAttention._apply_multimodal_rotary_pos_emb( q=query_states, k=key_states, @@ -61,15 +57,12 @@ class QwenAttention(nn.Module): mrope_section=self.rope_scaling["mrope_section"], ) - # GQA expansion using repeat_interleave semantics if self.num_key_value_heads != self.num_attention_heads: key_states = QwenAttention._repeat_kv(key_states, self.num_key_value_groups) value_states = QwenAttention._repeat_kv(value_states, self.num_key_value_groups) - # Attention computation attn_weights = mx.matmul(query_states, key_states.transpose(0, 1, 3, 2)) * self.scaling - # Apply attention mask if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask @@ -84,8 +77,6 @@ class QwenAttention(nn.Module): @staticmethod def _repeat_kv(hidden_states: mx.array, n_rep: int) -> mx.array: batch, num_key_value_heads, slen, head_dim = hidden_states.shape - if n_rep == 1: - return hidden_states hidden_states = mx.expand_dims(hidden_states, axis=2) hidden_states = mx.broadcast_to(hidden_states, (batch, num_key_value_heads, n_rep, slen, head_dim)) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) @@ -100,11 +91,10 @@ class QwenAttention(nn.Module): ) -> tuple[mx.array, mx.array]: mrope_section_doubled = [s * 2 for s in mrope_section] + cos, sin = position_embeddings cos_chunks = [] sin_chunks = [] start_idx = 0 - - cos, sin = position_embeddings for section_size in mrope_section_doubled: end_idx = start_idx + section_size cos_chunk = cos[..., start_idx:end_idx] @@ -113,27 +103,29 @@ class QwenAttention(nn.Module): sin_chunks.append(sin_chunk) start_idx = end_idx - # For each chunk position, select the corresponding modality - # chunk 0 -> modality 0, chunk 1 -> modality 1, chunk 2 -> modality 2, etc. cos_selected = [chunk[i % 3] for i, chunk in enumerate(cos_chunks)] sin_selected = [chunk[i % 3] for i, chunk in enumerate(sin_chunks)] - # Concatenate back together cos_combined = mx.concatenate(cos_selected, axis=-1) sin_combined = mx.concatenate(sin_selected, axis=-1) - # Add head dimension if needed if unsqueeze_dim == 1: cos_combined = mx.expand_dims(cos_combined, axis=1) sin_combined = mx.expand_dims(sin_combined, axis=1) - elif unsqueeze_dim == 2: - cos_combined = mx.expand_dims(cos_combined, axis=2) - sin_combined = mx.expand_dims(sin_combined, axis=2) - # Apply rotary embedding with properly processed cos/sin + orig_q_dtype = q.dtype + orig_k_dtype = k.dtype + q = q.astype(mx.float32) + k = k.astype(mx.float32) + cos_combined = cos_combined.astype(mx.float32) + sin_combined = sin_combined.astype(mx.float32) + q_embed = (q * cos_combined) + (QwenAttention._rotate_half(q) * sin_combined) k_embed = (k * cos_combined) + (QwenAttention._rotate_half(k) * sin_combined) + q_embed = q_embed.astype(orig_q_dtype) + k_embed = k_embed.astype(orig_k_dtype) + return q_embed, k_embed @staticmethod diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder.py index b93afa1..7db8c60 100644 --- a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder.py +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_encoder.py @@ -19,6 +19,8 @@ class QwenEncoder(nn.Module): self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers + self.image_token_id = 151655 + self.embed_tokens = nn.Embedding(vocab_size, hidden_size) self.layers = [QwenEncoderLayer() for i in range(num_hidden_layers)] self.norm = QwenRMSNorm(hidden_size, eps=1e-6) @@ -29,27 +31,69 @@ class QwenEncoder(nn.Module): rope_type="default", ) + self.visual = None + + def get_image_features(self, pixel_values: mx.array, image_grid_thw: mx.array) -> mx.array: + if self.visual is None: + raise RuntimeError("Vision transformer not initialized. Call load_visual_weights() first.") + + pixel_values = pixel_values.astype(mx.float32) + image_embeds = self.visual(pixel_values, image_grid_thw) + original_split_sizes = image_grid_thw.prod(axis=-1).astype(mx.int32) + split_sizes = (original_split_sizes // 4).astype(mx.int32) + split_sizes = [int(s) for s in split_sizes.tolist()] + split_sizes = [s for s in split_sizes if s > 0] + image_embeds_split = [] + start_idx = 0 + for split_size in split_sizes: + end_idx = start_idx + split_size + image_embeds_split.append(image_embeds[start_idx:end_idx]) + start_idx = end_idx + return image_embeds_split + def __call__( self, input_ids: mx.array, - attention_mask: mx.array | None = None, + attention_mask: mx.array, + pixel_values: mx.array | None = None, + image_grid_thw: mx.array | None = None, ) -> mx.array: batch_size, seq_len = input_ids.shape inputs_embeds = self.embed_tokens(input_ids) + + if pixel_values is not None and image_grid_thw is not None: + image_embeds_split = self.get_image_features(pixel_values, image_grid_thw) + image_embeds = mx.concatenate(image_embeds_split, axis=0) + + image_positions = input_ids == self.image_token_id + n_image_tokens = mx.sum(image_positions).item() + + if n_image_tokens > 0 and image_embeds.shape[0] >= n_image_tokens: + image_positions_flat = image_positions.flatten() + inputs_embeds_flat = inputs_embeds.reshape(-1, inputs_embeds.shape[-1]) + image_embeds_to_use = image_embeds + + new_embeds_list = [] + image_idx = 0 + for i in range(len(image_positions_flat)): + if image_positions_flat[i] and image_idx < image_embeds_to_use.shape[0]: + new_embeds_list.append(image_embeds_to_use[image_idx]) + image_idx += 1 + else: + new_embeds_list.append(inputs_embeds_flat[i]) + + new_embeds = mx.stack(new_embeds_list, axis=0) + inputs_embeds = new_embeds.reshape(inputs_embeds.shape) cache_position = mx.arange(seq_len, dtype=mx.int32) position_ids = mx.expand_dims(mx.expand_dims(cache_position, axis=0), axis=0) position_ids = mx.broadcast_to(position_ids, (3, batch_size, seq_len)) - if attention_mask is not None: - padding_mask = mx.where( - attention_mask == 1, - mx.zeros_like(attention_mask).astype(mx.float32), - mx.ones_like(attention_mask).astype(mx.float32) * (-float("inf")), - ) - padding_mask = mx.expand_dims(mx.expand_dims(padding_mask, axis=1), axis=1) - else: - padding_mask = None + padding_mask = mx.where( + attention_mask == 1, + mx.zeros_like(attention_mask).astype(mx.float32), + mx.ones_like(attention_mask).astype(mx.float32) * (-float("inf")), + ) + padding_mask = mx.expand_dims(mx.expand_dims(padding_mask, axis=1), axis=1) - # Create causal triangular mask idx = mx.arange(seq_len, dtype=mx.int32) j = mx.expand_dims(idx, axis=0) i = mx.expand_dims(idx, axis=1) @@ -57,18 +101,13 @@ class QwenEncoder(nn.Module): zeros_2d = mx.zeros((seq_len, seq_len)).astype(mx.float32) neginf_2d = mx.ones((seq_len, seq_len)).astype(mx.float32) * (-float("inf")) causal_tri_mask = mx.where(tri_bool, neginf_2d, zeros_2d) - causal_tri_mask = mx.expand_dims(mx.expand_dims(causal_tri_mask, axis=0), axis=0) # [1,1,S,S] + causal_tri_mask = mx.expand_dims(mx.expand_dims(causal_tri_mask, axis=0), axis=0) causal_tri_mask = mx.broadcast_to(causal_tri_mask, (batch_size, 1, seq_len, seq_len)) - - # Final attention mask: causal triangle + padding mask - if padding_mask is not None: - attention_mask_4d = causal_tri_mask + padding_mask - else: - attention_mask_4d = causal_tri_mask - + attention_mask_4d = causal_tri_mask + padding_mask hidden_states = inputs_embeds position_embeddings = self.rotary_emb(hidden_states, position_ids) - for layer in self.layers: + for i, layer in enumerate(self.layers): hidden_states = layer(hidden_states, attention_mask_4d, position_embeddings) + hidden_states = self.norm(hidden_states) return hidden_states diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_patch_merger.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_patch_merger.py new file mode 100644 index 0000000..c1e3a08 --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_patch_merger.py @@ -0,0 +1,32 @@ +import mlx.core as mx +from mlx import nn + + +class PatchMerger(nn.Module): + def __init__(self, context_dim: int, hidden_size: int, spatial_merge_size: int = 2): + super().__init__() + self.spatial_merge_size = spatial_merge_size + self.hidden_size_merged = context_dim * (spatial_merge_size**2) + self.ln_q = nn.RMSNorm(context_dim, eps=1e-6) + self.mlp_0 = nn.Linear(self.hidden_size_merged, self.hidden_size_merged, bias=True) + self.mlp_1 = nn.Linear(self.hidden_size_merged, hidden_size, bias=True) + + def __call__(self, x: mx.array, grid_thw: mx.array) -> mx.array: + if not hasattr(self, "_weights_logged"): + self._weights_logged = True + x = self.ln_q(x) + merged_patches = [] + offset = 0 + for t, h, w in grid_thw: + t, h, w = int(t), int(h), int(w) + num_patches_this_image = t * h * w + x_this_image = x[offset : offset + num_patches_this_image] + x_merged = x_this_image.reshape(-1, self.hidden_size_merged) + merged_patches.append(x_merged) + offset += num_patches_this_image + + x = mx.concatenate(merged_patches, axis=0) + x = self.mlp_0(x) + x = nn.gelu(x) + x = self.mlp_1(x) + return x diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py index fc468fb..7085bf0 100644 --- a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_text_encoder.py @@ -12,15 +12,17 @@ class QwenTextEncoder(nn.Module): def __call__( self, input_ids: mx.array, - attention_mask: mx.array | None = None, + attention_mask: mx.array, ) -> tuple[mx.array, mx.array]: hidden_states = self.encoder(input_ids, attention_mask) + prompt_embeds, encoder_attention_mask = QwenTextEncoder._process_text_embeddings_mlx( hidden_states=hidden_states, attention_mask=attention_mask, drop_idx=34, dtype=mx.bfloat16, ) + return prompt_embeds, encoder_attention_mask @staticmethod diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_attention.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_attention.py new file mode 100644 index 0000000..f73fb8f --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_attention.py @@ -0,0 +1,83 @@ +import mlx.core as mx +from mlx import nn + + +class VisionAttention(nn.Module): + def __init__(self, embed_dim: int = 1280, num_heads: int = 16): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + + self.qkv = nn.Linear(embed_dim, 3 * embed_dim, bias=True) + self.proj = nn.Linear(embed_dim, embed_dim, bias=True) + + def _rotate_half(self, x: mx.array) -> mx.array: + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return mx.concatenate([-x2, x1], axis=-1) + + def _apply_rope(self, x: mx.array, cos: mx.array, sin: mx.array) -> mx.array: + cos_expanded = mx.expand_dims(cos, axis=0) + sin_expanded = mx.expand_dims(sin, axis=0) + rotated = (x * cos_expanded) + (self._rotate_half(x) * sin_expanded) + return rotated + + def __call__(self, x: mx.array, position_embeddings=None, cu_seqlens=None) -> mx.array: + seq_len, embed_dim = x.shape + + qkv = self.qkv(x) + qkv = qkv.reshape(seq_len, 3, self.num_heads, self.head_dim) + q, k, v = mx.split(qkv, 3, axis=1) + q = q.squeeze(1).transpose(1, 0, 2) + k = k.squeeze(1).transpose(1, 0, 2) + v = v.squeeze(1).transpose(1, 0, 2) + + if position_embeddings is not None: + cos_emb, sin_emb = position_embeddings + if cos_emb.shape[0] != seq_len: + cos_emb = cos_emb[:seq_len] + sin_emb = sin_emb[:seq_len] + + q = self._apply_rope(q, cos_emb, sin_emb) + k = self._apply_rope(k, cos_emb, sin_emb) + + # Process attention chunks if cu_seqlens is provided (windowed attention) + if cu_seqlens is not None and len(cu_seqlens) > 2: + # Split Q, K, V into chunks based on cu_seqlens + # cu_seqlens is cumulative, so lengths[i] = cu_seqlens[i+1] - cu_seqlens[i] + lengths = [int((cu_seqlens[i + 1] - cu_seqlens[i]).item()) for i in range(len(cu_seqlens) - 1)] + + # Split tensors (q,k,v are [heads, seq, head_dim]) + q_chunks = [] + k_chunks = [] + v_chunks = [] + offset = 0 + for length in lengths: + q_chunks.append(q[:, offset : offset + length, :]) + k_chunks.append(k[:, offset : offset + length, :]) + v_chunks.append(v[:, offset : offset + length, :]) + offset += length + + # Process each chunk separately + attn_outputs = [] + scale = 1.0 / (self.head_dim**0.5) + for q_chunk, k_chunk, v_chunk in zip(q_chunks, k_chunks, v_chunks): + # Compute attention for this chunk + scores = mx.matmul(q_chunk, k_chunk.transpose(0, 2, 1)) * scale + attn_weights = mx.softmax(scores, axis=-1) + attn_chunk = mx.matmul(attn_weights, v_chunk) # [heads, chunk_len, head_dim] + attn_outputs.append(attn_chunk) + + # Concatenate chunks back together + attn_output = mx.concatenate(attn_outputs, axis=1) # [heads, seq, head_dim] + else: + # Full attention (no chunking) + scale = 1.0 / (self.head_dim**0.5) + scores = mx.matmul(q, k.transpose(0, 2, 1)) * scale # [heads, seq, seq] + attn_weights = mx.softmax(scores, axis=-1) + attn_output = mx.matmul(attn_weights, v) # [heads, seq, head_dim] + + # Reshape and project + attn_output = attn_output.transpose(1, 0, 2).reshape(seq_len, embed_dim) # [seq, embed_dim] + return self.proj(attn_output) diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_block.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_block.py new file mode 100644 index 0000000..81f9281 --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_block.py @@ -0,0 +1,24 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_attention import VisionAttention +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_mlp import VisionMLP + + +class VisionBlock(nn.Module): + def __init__(self, embed_dim: int = 1280, num_heads: int = 16, mlp_ratio: float = 2.671875): + super().__init__() + self.norm1 = nn.RMSNorm(embed_dim, eps=1e-6) + self.norm2 = nn.RMSNorm(embed_dim, eps=1e-6) + self.attn = VisionAttention(embed_dim, num_heads) + mlp_hidden_dim = int(embed_dim * mlp_ratio) + self.mlp = VisionMLP(embed_dim, mlp_hidden_dim) + + def __call__(self, x: mx.array, position_embeddings=None, cu_seqlens=None) -> mx.array: + normed1 = self.norm1(x) + attn_out = self.attn(normed1, position_embeddings, cu_seqlens) + x = x + attn_out + normed2 = self.norm2(x) + mlp_out = self.mlp(normed2) + x = x + mlp_out + return x diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_encoder.py new file mode 100644 index 0000000..6f828cf --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_encoder.py @@ -0,0 +1,54 @@ +import mlx.core as mx +from mlx import nn + +from mflux.models.qwen.model.qwen_text_encoder.qwen_encoder import QwenEncoder + + +class QwenVisionLanguageEncoder(nn.Module): + def __init__(self, encoder=None): + super().__init__() + self.encoder = encoder or QwenEncoder() + self.edit_template_start_idx = 64 + + def __call__( + self, + input_ids: mx.array, + attention_mask: mx.array | None = None, + pixel_values: mx.array | None = None, + image_grid_thw: mx.array | None = None, + ) -> tuple[mx.array, mx.array]: + hidden_states = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + ) + + trimmed = [] + batch = hidden_states.shape[0] + for i in range(batch): + valid_len = int(mx.sum(attention_mask[i]).item()) + trimmed.append(hidden_states[i, :valid_len, :]) + drop_idx = self.edit_template_start_idx + trimmed_after_drop = [t[drop_idx:] if t.shape[0] > drop_idx else t for t in trimmed] + trimmed = trimmed_after_drop + max_len = max(t.shape[0] for t in trimmed) if trimmed else 0 + hidden_dim = hidden_states.shape[2] + padded_embeds = [] + padded_masks = [] + for t in trimmed: + cur_len = t.shape[0] + if cur_len < max_len: + pad_e = mx.zeros((max_len - cur_len, hidden_dim), dtype=t.dtype) + t_pad = mx.concatenate([t, pad_e], axis=0) + pad_m = mx.concatenate( + [mx.ones(cur_len, dtype=mx.int32), mx.zeros(max_len - cur_len, dtype=mx.int32)], axis=0 + ) + else: + t_pad = t + pad_m = mx.ones(cur_len, dtype=mx.int32) + padded_embeds.append(t_pad) + padded_masks.append(pad_m) + prompt_embeds = mx.stack(padded_embeds, axis=0) if padded_embeds else hidden_states + encoder_attention_mask = mx.stack(padded_masks, axis=0) if padded_masks else attention_mask + return prompt_embeds, encoder_attention_mask diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_prompt_encoder.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_prompt_encoder.py new file mode 100644 index 0000000..33ec1ba --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_language_prompt_encoder.py @@ -0,0 +1,93 @@ +from typing import Optional, Union + +import mlx.core as mx +import numpy as np +from PIL import Image + +from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenTextEncoder +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_language_encoder import QwenVisionLanguageEncoder +from mflux.models.qwen.tokenizer.qwen_tokenizer import TokenizerQwen +from mflux.models.qwen.tokenizer.qwen_vision_language_tokenizer import QwenVisionLanguageTokenizer + + +class QwenVisionLanguagePromptEncoder: + @staticmethod + def encode_prompt_with_image( + prompt: str, + image: Union[Image.Image, np.ndarray, str], + negative_prompt: Optional[str] = None, + prompt_cache: Optional[dict] = None, + qwen_vl_tokenizer: Optional[QwenVisionLanguageTokenizer] = None, + qwen_vl_encoder: Optional[QwenVisionLanguageEncoder] = None, + ) -> tuple[mx.array, mx.array, mx.array, mx.array, mx.array]: + if qwen_vl_tokenizer is None or qwen_vl_encoder is None: + raise ValueError("qwen_vl_tokenizer and qwen_vl_encoder are required") + + input_ids, attention_mask, pixel_values, image_grid_thw = qwen_vl_tokenizer.tokenize_with_image( + prompt=prompt, image=image + ) + + prompt_embeds, prompt_mask = qwen_vl_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + ) + + if negative_prompt is not None and negative_prompt != "": + neg_input_ids, neg_attention_mask, neg_pixel_values, neg_image_grid_thw = ( + qwen_vl_tokenizer.tokenize_with_image(prompt=negative_prompt, image=image) + ) + + neg_prompt_embeds, neg_prompt_mask = qwen_vl_encoder( + input_ids=neg_input_ids, + attention_mask=neg_attention_mask, + pixel_values=neg_pixel_values, + image_grid_thw=neg_image_grid_thw, + ) + else: + # If no negative prompt provided, mirror shapes with zeros masks/embeds to disable CFG cleanly + neg_prompt_embeds, neg_prompt_mask = prompt_embeds, prompt_mask + + # 3. Return the result including image_grid_thw for conditioning + result = (prompt_embeds, prompt_mask, neg_prompt_embeds, neg_prompt_mask, image_grid_thw) + return result + + @staticmethod + def encode_prompt_auto( + prompt: str, + negative_prompt: Optional[str] = None, + image: Optional[Union[Image.Image, np.ndarray, str]] = None, + prompt_cache: Optional[dict] = None, + qwen_tokenizer: Optional[TokenizerQwen] = None, + qwen_text_encoder: Optional[QwenTextEncoder] = None, + qwen_vl_tokenizer: Optional[QwenVisionLanguageTokenizer] = None, + qwen_vl_encoder: Optional[QwenVisionLanguageEncoder] = None, + ) -> tuple[mx.array, mx.array, mx.array, mx.array]: + if prompt_cache is None: + prompt_cache = {} + + if image is not None and qwen_vl_tokenizer is not None and qwen_vl_encoder is not None: + # Use vision-language encoding for edit tasks + return QwenVisionLanguagePromptEncoder.encode_prompt_with_image( + prompt=prompt, + image=image, + negative_prompt=negative_prompt, + prompt_cache=prompt_cache, + qwen_vl_tokenizer=qwen_vl_tokenizer, + qwen_vl_encoder=qwen_vl_encoder, + ) + else: + # Fall back to text-only encoding for regular generation + if qwen_tokenizer is None or qwen_text_encoder is None: + raise ValueError( + "Text-only components (qwen_tokenizer, qwen_text_encoder) are required when no image is provided" + ) + + return QwenVisionLanguagePromptEncoder.encode_prompt( + prompt=prompt, + negative_prompt=negative_prompt or "", + prompt_cache=prompt_cache, + qwen_tokenizer=qwen_tokenizer, + qwen_text_encoder=qwen_text_encoder, + ) diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_mlp.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_mlp.py new file mode 100644 index 0000000..a16b2fe --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_mlp.py @@ -0,0 +1,15 @@ +import mlx.core as mx +from mlx import nn + + +class VisionMLP(nn.Module): + def __init__(self, dim: int, hidden_dim: int): + super().__init__() + self.gate_proj = nn.Linear(dim, hidden_dim, bias=True) + self.up_proj = nn.Linear(dim, hidden_dim, bias=True) + self.down_proj = nn.Linear(hidden_dim, dim, bias=True) + + def __call__(self, x: mx.array) -> mx.array: + gate = nn.silu(self.gate_proj(x)) + up = self.up_proj(x) + return self.down_proj(gate * up) diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_patch_embed.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_patch_embed.py new file mode 100644 index 0000000..658d291 --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_patch_embed.py @@ -0,0 +1,34 @@ +import mlx.core as mx +from mlx import nn + + +class VisionPatchEmbed(nn.Module): + def __init__( + self, + patch_size: int = 14, + temporal_patch_size: int = 2, + in_channels: int = 3, + embed_dim: int = 1280, + ): + super().__init__() + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.in_channels = in_channels + self.embed_dim = embed_dim + + self.proj = nn.Conv3d( + in_channels=in_channels, + out_channels=embed_dim, + kernel_size=[temporal_patch_size, patch_size, patch_size], + stride=[temporal_patch_size, patch_size, patch_size], + bias=False, + ) + + def __call__(self, hidden_states: mx.array) -> mx.array: + batch_size = hidden_states.shape[0] + hidden_states = hidden_states.reshape( + batch_size, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size + ) + hidden_states = hidden_states.transpose(0, 2, 3, 4, 1) + output = self.proj(hidden_states) + return output.reshape(batch_size, self.embed_dim) diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_rotary_embedding.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_rotary_embedding.py new file mode 100644 index 0000000..88cb675 --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_rotary_embedding.py @@ -0,0 +1,16 @@ +import mlx.core as mx +from mlx import nn + + +class VisionRotaryEmbedding(nn.Module): + def __init__(self, dim: int, theta: float = 10000.0): + super().__init__() + self.dim = dim + self.theta = theta + inv_freq = 1.0 / (theta ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)) + self.inv_freq = inv_freq + + def __call__(self, max_grid_size: int) -> mx.array: + positions = mx.arange(max_grid_size, dtype=mx.float32) + freqs = mx.outer(positions, self.inv_freq) + return freqs diff --git a/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_transformer.py b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_transformer.py new file mode 100644 index 0000000..f26c965 --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_text_encoder/qwen_vision_transformer.py @@ -0,0 +1,149 @@ +import mlx.core as mx +import numpy as np +from mlx import nn + +from mflux.models.qwen.model.qwen_text_encoder.qwen_patch_merger import PatchMerger +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_block import VisionBlock +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_patch_embed import VisionPatchEmbed +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_rotary_embedding import VisionRotaryEmbedding + + +class VisionTransformer(nn.Module): + def __init__( + self, + patch_size: int = 14, + temporal_patch_size: int = 2, + in_channels: int = 3, + embed_dim: int = 1280, + depth: int = 32, + num_heads: int = 16, + mlp_ratio: float = 2.671875, + hidden_size: int = 3584, + spatial_merge_size: int = 2, + window_size: int = 112, + fullatt_block_indexes: list = None, + ): + super().__init__() + + self.patch_embed = VisionPatchEmbed(patch_size, temporal_patch_size, in_channels, embed_dim) + self.spatial_merge_size = spatial_merge_size + self.window_size = window_size + self.fullatt_block_indexes = fullatt_block_indexes or [7, 15, 23, 31] + self.spatial_merge_unit = spatial_merge_size * spatial_merge_size + self.patch_size = patch_size + + head_dim = embed_dim // num_heads + self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2) + + self.blocks = [VisionBlock(embed_dim, num_heads, mlp_ratio) for _ in range(depth)] + self.merger = PatchMerger(embed_dim, hidden_size, spatial_merge_size) + + def get_window_index(self, grid_thw: mx.array): + window_index = [] + cu_window_seqlens = [0] + window_index_id = 0 + vit_merger_window_size = self.window_size // self.patch_size // self.spatial_merge_size + + for t, grid_h, grid_w in grid_thw: + t, grid_h, grid_w = int(t), int(grid_h), int(grid_w) + llm_grid_h = grid_h // self.spatial_merge_size + llm_grid_w = grid_w // self.spatial_merge_size + + index = mx.arange(t * llm_grid_h * llm_grid_w).reshape(t, llm_grid_h, llm_grid_w) + + pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size + pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size + num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size + num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size + + index_padded = mx.pad(index, ((0, 0), (0, pad_h), (0, pad_w)), constant_values=-100) + + index_padded = index_padded.reshape( + t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size + ) + index_padded = mx.transpose(index_padded, (0, 1, 3, 2, 4)).reshape( + t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size + ) + + seqlens = mx.sum((index_padded != -100).astype(mx.int32), axis=(2, 3)).reshape(-1) + index_padded_flat = index_padded.reshape(-1) + + index_padded_np = np.array(index_padded_flat) + index_new = mx.array(index_padded_np[index_padded_np != -100]) + + window_index.append(index_new + window_index_id) + cu_seqlens_tmp = mx.cumsum(seqlens) * self.spatial_merge_unit + cu_window_seqlens[-1] + cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) + window_index_id += t * llm_grid_h * llm_grid_w + + window_index = mx.concatenate(window_index, axis=0) + cu_window_seqlens = mx.array(cu_window_seqlens, dtype=mx.int32) + + return window_index, cu_window_seqlens + + def rot_pos_emb(self, grid_thw: mx.array) -> mx.array: + pos_ids = [] + for t, h, w in grid_thw: + t, h, w = int(t), int(h), int(w) + hpos_ids = mx.repeat(mx.arange(h)[..., None], w, axis=1) + wpos_ids = mx.repeat(mx.arange(w)[None, ...], h, axis=0) + merge_h = h // self.spatial_merge_size + merge_w = w // self.spatial_merge_size + hpos_ids = hpos_ids.reshape(merge_h, self.spatial_merge_size, merge_w, self.spatial_merge_size) + wpos_ids = wpos_ids.reshape(merge_h, self.spatial_merge_size, merge_w, self.spatial_merge_size) + hpos_ids = mx.transpose(hpos_ids, (0, 2, 1, 3)) + wpos_ids = mx.transpose(wpos_ids, (0, 2, 1, 3)) + hpos_ids = hpos_ids.reshape(-1) + wpos_ids = wpos_ids.reshape(-1) + pos_id_pair = mx.stack([hpos_ids, wpos_ids], axis=-1) + pos_id_pair = mx.tile(pos_id_pair, (t, 1)) + pos_ids.append(pos_id_pair) + pos_ids = mx.concatenate(pos_ids, axis=0) + max_grid_size = int(mx.max(grid_thw[:, 1:]).item()) + rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) + h_indices = pos_ids[:, 0].astype(mx.int32) + w_indices = pos_ids[:, 1].astype(mx.int32) + h_emb = rotary_pos_emb_full[h_indices] + w_emb = rotary_pos_emb_full[w_indices] + rotary_pos_emb = mx.stack([h_emb, w_emb], axis=1) + rotary_pos_emb = rotary_pos_emb.reshape(rotary_pos_emb.shape[0], -1) + return rotary_pos_emb + + def __call__(self, pixel_values: mx.array, grid_thw: mx.array) -> mx.array: + hidden_states = self.patch_embed(pixel_values) + rotary_pos_emb = self.rot_pos_emb(grid_thw) + window_index, cu_window_seqlens = self.get_window_index(grid_thw) + cu_window_seqlens_unique = [cu_window_seqlens[0].item()] + for i in range(1, len(cu_window_seqlens)): + if cu_window_seqlens[i].item() != cu_window_seqlens_unique[-1]: + cu_window_seqlens_unique.append(cu_window_seqlens[i].item()) + cu_window_seqlens = mx.array(cu_window_seqlens_unique, dtype=mx.int32) + seq_len = hidden_states.shape[0] + cu_seqlens = [] + offset = 0 + for t, h, w in grid_thw: + t, h, w = int(t), int(h), int(w) + length = t * h * w + offset += length + cu_seqlens.append(offset) + cu_seqlens = mx.array([0] + cu_seqlens, dtype=mx.int32) + seq_len = hidden_states.shape[0] + num_groups = seq_len // self.spatial_merge_unit + hidden_states_grouped = hidden_states.reshape(num_groups, self.spatial_merge_unit, -1) + hidden_states_grouped = hidden_states_grouped[window_index.astype(mx.int32), :, :] + hidden_states = hidden_states_grouped.reshape(seq_len, -1) + rotary_pos_emb_grouped = rotary_pos_emb.reshape(num_groups, self.spatial_merge_unit, -1) + rotary_pos_emb_grouped = rotary_pos_emb_grouped[window_index.astype(mx.int32), :, :] + rotary_pos_emb = rotary_pos_emb_grouped.reshape(seq_len, -1) + emb = mx.concatenate([rotary_pos_emb, rotary_pos_emb], axis=-1) + position_embeddings = (mx.cos(emb), mx.sin(emb)) + for layer_num, block in enumerate(self.blocks): + if layer_num in self.fullatt_block_indexes: + cu_seqlens_now = cu_seqlens + else: + cu_seqlens_now = cu_window_seqlens + hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now) + hidden_states = self.merger(hidden_states, grid_thw) + reverse_indices = mx.argsort(window_index.astype(mx.int32)) + hidden_states = hidden_states[reverse_indices.astype(mx.int32), :] + return hidden_states diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py index 11a638c..bbb3a93 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_attention.py @@ -2,8 +2,7 @@ from __future__ import annotations import mlx.core as mx from mlx import nn - -from mflux.models.flux.model.flux_transformer.common.attention_utils import AttentionUtils +from mlx.core.fast import scaled_dot_product_attention class QwenAttention(nn.Module): @@ -12,24 +11,16 @@ class QwenAttention(nn.Module): self.dim = dim self.num_heads = num_heads self.head_dim = head_dim - - # Attention projections for image stream self.to_q = nn.Linear(dim, dim) self.to_k = nn.Linear(dim, dim) self.to_v = nn.Linear(dim, dim) - - # Attention projections for text stream self.add_q_proj = nn.Linear(dim, dim) self.add_k_proj = nn.Linear(dim, dim) self.add_v_proj = nn.Linear(dim, dim) - - # Query/Key normalization self.norm_q = nn.RMSNorm(self.head_dim, eps=1e-6) self.norm_k = nn.RMSNorm(self.head_dim, eps=1e-6) self.norm_added_q = nn.RMSNorm(self.head_dim, eps=1e-6) self.norm_added_k = nn.RMSNorm(self.head_dim, eps=1e-6) - - # Output projections self.attn_to_out = [nn.Linear(dim, dim)] self.to_add_out = nn.Linear(dim, dim) @@ -39,112 +30,129 @@ class QwenAttention(nn.Module): txt_modulated: mx.array, encoder_hidden_states_mask: mx.array | None, image_rotary_emb: tuple[mx.array, mx.array], + block_idx: int | None = None, ) -> tuple[mx.array, mx.array]: - # 1a. Compute Q,K,V for image stream (hidden_states) - img_q, img_k, img_v = AttentionUtils.process_qkv( - hidden_states=img_modulated, - to_q=self.to_q, - to_k=self.to_k, - to_v=self.to_v, - norm_q=self.norm_q, - norm_k=self.norm_k, - num_heads=self.num_heads, - head_dim=self.head_dim, - ) + img_query = self.to_q(img_modulated) + img_key = self.to_k(img_modulated) + img_value = self.to_v(img_modulated) - # 1b. Compute Q,K,V for text stream (encoder_hidden_states) - txt_q, txt_k, txt_v = AttentionUtils.process_qkv( - hidden_states=txt_modulated, - to_q=self.add_q_proj, - to_k=self.add_k_proj, - to_v=self.add_v_proj, - norm_q=self.norm_added_q, - norm_k=self.norm_added_k, - num_heads=self.num_heads, - head_dim=self.head_dim, - ) + txt_query = self.add_q_proj(txt_modulated) + txt_key = self.add_k_proj(txt_modulated) + txt_value = self.add_v_proj(txt_modulated) - # 1c. Concatenate results [text, image] like Flux - joint_q = mx.concatenate([txt_q, img_q], axis=2) - joint_k = mx.concatenate([txt_k, img_k], axis=2) - joint_v = mx.concatenate([txt_v, img_v], axis=2) + img_query = mx.reshape(img_query, (img_query.shape[0], img_query.shape[1], self.num_heads, self.head_dim)) + img_key = mx.reshape(img_key, (img_key.shape[0], img_key.shape[1], self.num_heads, self.head_dim)) + img_value = mx.reshape(img_value, (img_value.shape[0], img_value.shape[1], self.num_heads, self.head_dim)) - # 1d. Apply RoPE to concatenated Q,K - joint_q, joint_k = QwenAttention._apply_rotary_embeddings_joint( - joint_q=joint_q, - joint_k=joint_k, - txt_seq_len=txt_q.shape[2], - image_rotary_emb=image_rotary_emb, - ) + txt_query = mx.reshape(txt_query, (txt_query.shape[0], txt_query.shape[1], self.num_heads, self.head_dim)) + txt_key = mx.reshape(txt_key, (txt_key.shape[0], txt_key.shape[1], self.num_heads, self.head_dim)) + txt_value = mx.reshape(txt_value, (txt_value.shape[0], txt_value.shape[1], self.num_heads, self.head_dim)) - # 2. Compute attention with optional masking - mask = AttentionUtils.convert_key_padding_mask_to_additive_mask( + if self.norm_q is not None: + img_query = self.norm_q(img_query) + if self.norm_k is not None: + img_key = self.norm_k(img_key) + if self.norm_added_q is not None: + txt_query = self.norm_added_q(txt_query) + if self.norm_added_k is not None: + txt_key = self.norm_added_k(txt_key) + + if image_rotary_emb is not None: + (img_cos, img_sin), (txt_cos, txt_sin) = image_rotary_emb + img_query = QwenAttention._apply_rope_qwen(img_query, img_cos, img_sin) + img_key = QwenAttention._apply_rope_qwen(img_key, img_cos, img_sin) + txt_query = QwenAttention._apply_rope_qwen(txt_query, txt_cos, txt_sin) + txt_key = QwenAttention._apply_rope_qwen(txt_key, txt_cos, txt_sin) + + joint_query = mx.concatenate([txt_query, img_query], axis=1) + joint_key = mx.concatenate([txt_key, img_key], axis=1) + joint_value = mx.concatenate([txt_value, img_value], axis=1) + + seq_txt = txt_modulated.shape[1] + mask = self._convert_mask_for_qwen( mask=encoder_hidden_states_mask, - joint_seq_len=joint_q.shape[2], - txt_seq_len=txt_q.shape[2], + joint_seq_len=joint_query.shape[1], + txt_seq_len=seq_txt, ) - # 3. Compute attention - hidden_states = AttentionUtils.compute_attention( - query=joint_q, - key=joint_k, - value=joint_v, - batch_size=1, - num_heads=self.num_heads, - head_dim=self.head_dim, + hidden_states = self._compute_attention_qwen( + query=joint_query, + key=joint_key, + value=joint_value, mask=mask, + block_idx=block_idx, ) - # 4. Separate the results - txt_seq_len = txt_modulated.shape[1] - txt_attn_output = hidden_states[:, :txt_seq_len] - img_attn_output = hidden_states[:, txt_seq_len:] - - # 5. Project the output (Flux-style) - img_attn_output = self.attn_to_out[0](img_attn_output.astype(mx.float32)).astype(img_modulated.dtype) - txt_attn_output = self.to_add_out(txt_attn_output.astype(mx.float32)).astype(txt_modulated.dtype) + txt_attn_output = hidden_states[:, :seq_txt, :] + img_attn_output = hidden_states[:, seq_txt:, :] + img_attn_output = self.attn_to_out[0](img_attn_output) + txt_attn_output = self.to_add_out(txt_attn_output) return img_attn_output, txt_attn_output + def _compute_attention_qwen( + self, + query: mx.array, + key: mx.array, + value: mx.array, + mask: mx.array | None = None, + block_idx: int | None = None, + ) -> mx.array: + query_bhsd = mx.transpose(query, (0, 2, 1, 3)) + key_bhsd = mx.transpose(key, (0, 2, 1, 3)) + value_bhsd = mx.transpose(value, (0, 2, 1, 3)) + head_dim = query.shape[-1] + scale_value = 1.0 / (head_dim**0.5) + hidden_states_bhsd = scaled_dot_product_attention( + query_bhsd, key_bhsd, value_bhsd, scale=scale_value, mask=mask + ) + hidden_states = mx.transpose(hidden_states_bhsd, (0, 2, 1, 3)) + batch_size = hidden_states.shape[0] + seq_len = hidden_states.shape[1] + hidden_states = mx.reshape(hidden_states, (batch_size, seq_len, self.num_heads * self.head_dim)) + hidden_states = hidden_states.astype(query.dtype) + return hidden_states + @staticmethod - def _apply_rotary_embeddings_joint( - joint_q: mx.array, - joint_k: mx.array, + def _convert_mask_for_qwen( + mask: mx.array | None, + joint_seq_len: int, txt_seq_len: int, - image_rotary_emb: tuple[mx.array, mx.array], - ) -> tuple[mx.array, mx.array]: - img_rot, txt_rot = image_rotary_emb + ) -> mx.array | None: + if mask is None: + return None - # Extract separate parts - txt_q = joint_q[:, :, :txt_seq_len, :] - img_q = joint_q[:, :, txt_seq_len:, :] - txt_k = joint_k[:, :, :txt_seq_len, :] - img_k = joint_k[:, :, txt_seq_len:, :] + bsz = mask.shape[0] + img_seq_len = joint_seq_len - txt_seq_len - # Prepare cos/sin for text and image - img_cos = img_rot[..., 0, 0].reshape(img_rot.shape[2], img_rot.shape[3]) - img_sin = img_rot[..., 1, 0].reshape(img_rot.shape[2], img_rot.shape[3]) - txt_cos = txt_rot[..., 0, 0].reshape(txt_rot.shape[2], txt_rot.shape[3]) - txt_sin = txt_rot[..., 1, 0].reshape(txt_rot.shape[2], txt_rot.shape[3]) + ones_img = mx.ones((bsz, img_seq_len), dtype=mx.float32) + joint_mask = mx.concatenate([mask.astype(mx.float32), ones_img], axis=1) - # Transpose [B,H,S,D] -> [B,S,H,D] for RoPE application - img_q_bshd = mx.transpose(img_q, (0, 2, 1, 3)) - img_k_bshd = mx.transpose(img_k, (0, 2, 1, 3)) - txt_q_bshd = mx.transpose(txt_q, (0, 2, 1, 3)) - txt_k_bshd = mx.transpose(txt_k, (0, 2, 1, 3)) + if mx.all(joint_mask >= 0.999): + return None - # Apply RoPE - img_q_bshd, img_k_bshd = AttentionUtils.apply_rope_bshd(img_q_bshd, img_k_bshd, img_cos, img_sin) - txt_q_bshd, txt_k_bshd = AttentionUtils.apply_rope_bshd(txt_q_bshd, txt_k_bshd, txt_cos, txt_sin) + additive = (1.0 - joint_mask) * (-1e9) + return additive.reshape((additive.shape[0], 1, 1, additive.shape[1])) - # Back to [B,H,S,D] - img_q = mx.transpose(img_q_bshd, (0, 2, 1, 3)) - img_k = mx.transpose(img_k_bshd, (0, 2, 1, 3)) - txt_q = mx.transpose(txt_q_bshd, (0, 2, 1, 3)) - txt_k = mx.transpose(txt_k_bshd, (0, 2, 1, 3)) + @staticmethod + def _apply_rope_qwen(x: mx.array, cos_vals: mx.array, sin_vals: mx.array) -> mx.array: + x_float = x.astype(mx.float32) + x_reshaped = mx.reshape(x_float, (*x.shape[:-1], -1, 2)) - # Concatenate back [text, image] - joint_q = mx.concatenate([txt_q, img_q], axis=2) - joint_k = mx.concatenate([txt_k, img_k], axis=2) + x_real = x_reshaped[..., 0] + x_imag = x_reshaped[..., 1] - return joint_q, joint_k + freqs_cos = cos_vals[None, :, None, :] + freqs_sin = sin_vals[None, :, None, :] + + if freqs_cos.shape[-1] != x_real.shape[-1]: + freqs_cos = freqs_cos[..., : x_real.shape[-1]] + freqs_sin = freqs_sin[..., : x_real.shape[-1]] + + out_real = x_real * freqs_cos - x_imag * freqs_sin + out_imag = x_real * freqs_sin + x_imag * freqs_cos + + out_pairs = mx.stack([out_real, out_imag], axis=-1) + x_out = mx.reshape(out_pairs, (*x.shape[:-1], -1)) + + return x_out.astype(x.dtype) diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py index 02a906b..576c68b 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_feed_forward.py @@ -5,11 +5,11 @@ from mlx import nn class QwenFeedForward(nn.Module): def __init__(self, dim: int = 3072): super().__init__() - self.mlp_in = nn.Linear(dim, 4 * dim) - self.mlp_out = nn.Linear(4 * dim, dim) + self.mlp_in = nn.Linear(dim, 4 * dim, bias=True) + self.mlp_out = nn.Linear(4 * dim, dim, bias=True) def __call__(self, hidden_states: mx.array) -> mx.array: - hidden_states = self.mlp_in(hidden_states.astype(mx.float32)) + hidden_states = self.mlp_in(hidden_states) hidden_states = nn.gelu_approx(hidden_states) hidden_states = self.mlp_out(hidden_states) return hidden_states diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py index d3d81ab..882d4ff 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_layer_norm.py @@ -9,13 +9,10 @@ class QwenLayerNorm(nn.Module): self.norm1 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False) def __call__(self, hidden_states: mx.array, text_embeddings: mx.array) -> tuple[mx.array, mx.array, mx.array]: - # Compute modulation parameters from text embeddings - mod_params = self.mod_linear(nn.silu(text_embeddings)) + temb_silu = nn.silu(text_embeddings) + mod_params = self.mod_linear(temb_silu) mod1, mod2 = mx.split(mod_params, 2, axis=-1) - - # Stage 1: Apply normalization and modulation for attention normed1 = self.norm1(hidden_states) shift1, scale1, gate1 = mx.split(mod1, 3, axis=-1) normed_stage1 = normed1 * (1 + scale1[:, None, :]) + shift1[:, None, :] - return normed_stage1, gate1, mod2 diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py index 9a78cfa..ee5c241 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_rope.py @@ -6,114 +6,108 @@ from mlx import nn class QwenEmbedRopeMLX(nn.Module): - """ - Faithful MLX port of Qwen's RoPE embedding helper used in the flux_transformer. - - Exposes a forward(video_fhw, txt_seq_lens) API that returns rotation matrices - for image and text streams with shape [1, 1, S, D/2, 2, 2]. - """ - - def __init__(self, theta: int, axes_dim: list[int], scale_rope: bool = True): + def __init__(self, theta: int, axes_dim: list[int], scale_rope: bool = False): super().__init__() self.theta = theta self.axes_dim = axes_dim self.scale_rope = scale_rope - # Precompute positive and negative index caches up to 1024 as in reference - pos_index = np.arange(1024, dtype=np.int32) - neg_index = (np.arange(1024, dtype=np.int32)[::-1] * -1) - 1 + pos_index = np.arange(4096, dtype=np.int32) + neg_index = (np.arange(4096, dtype=np.int32)[::-1] * -1) - 1 - self._pos_cos = [] - self._pos_sin = [] - self._neg_cos = [] - self._neg_sin = [] - for dim in axes_dim: - cos_p, sin_p = self._rope_params(pos_index, dim) - cos_n, sin_n = self._rope_params(neg_index, dim) - self._pos_cos.append(cos_p) - self._pos_sin.append(sin_p) - self._neg_cos.append(cos_n) - self._neg_sin.append(sin_n) + self.pos_freqs = np.concatenate( + [ + self._rope_params(pos_index, self.axes_dim[0], self.theta), + self._rope_params(pos_index, self.axes_dim[1], self.theta), + self._rope_params(pos_index, self.axes_dim[2], self.theta), + ], + axis=1, + ) + self.neg_freqs = np.concatenate( + [ + self._rope_params(neg_index, self.axes_dim[0], self.theta), + self._rope_params(neg_index, self.axes_dim[1], self.theta), + self._rope_params(neg_index, self.axes_dim[2], self.theta), + ], + axis=1, + ) - @staticmethod - def _rope_params(index: np.ndarray, dim: int, theta: int = 10000) -> tuple[np.ndarray, np.ndarray]: + def _rope_params(self, index: np.ndarray, dim: int, theta: int) -> np.ndarray: assert dim % 2 == 0 scales = np.arange(0, dim, 2, dtype=np.float32) / dim omega = 1.0 / (theta**scales) - out = np.outer(index.astype(np.float32), omega).astype(np.float32) - return np.cos(out), np.sin(out) + freqs = np.outer(index.astype(np.float32), omega) - def _build_video_freqs(self, frame: int, height: int, width: int) -> tuple[np.ndarray, np.ndarray]: - dim_f, dim_h, dim_w = self.axes_dim + cos_freqs = np.cos(freqs) + sin_freqs = np.sin(freqs) - cos_f = self._pos_cos[0][:frame].reshape(frame, 1, 1, -1) - sin_f = self._pos_sin[0][:frame].reshape(frame, 1, 1, -1) + return np.stack([cos_freqs, sin_freqs], axis=-1) + + def _compute_video_freqs(self, frame: int, height: int, width: int, idx: int = 0) -> tuple[np.ndarray, np.ndarray]: + seq_lens = frame * height * width + + axes_splits = [x // 2 for x in self.axes_dim] + freqs_pos = np.split(self.pos_freqs, np.cumsum(axes_splits)[:-1], axis=1) + freqs_neg = np.split(self.neg_freqs, np.cumsum(axes_splits)[:-1], axis=1) + + freqs_frame_raw = freqs_pos[0][idx : idx + frame] + freqs_frame = freqs_frame_raw.reshape(frame, 1, 1, -1, 2) + freqs_frame = np.broadcast_to(freqs_frame, (frame, height, width, freqs_frame.shape[-2], 2)) if self.scale_rope: - cos_h = np.concatenate( - [self._neg_cos[1][-(height - height // 2) :], self._pos_cos[1][: height // 2]], axis=0 - ) - sin_h = np.concatenate( - [self._neg_sin[1][-(height - height // 2) :], self._pos_sin[1][: height // 2]], axis=0 + freqs_height = np.concatenate( + [freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], axis=0 ) else: - cos_h = self._pos_cos[1][:height] - sin_h = self._pos_sin[1][:height] - cos_h = cos_h.reshape(1, height, 1, -1) - sin_h = sin_h.reshape(1, height, 1, -1) + freqs_height = freqs_pos[1][:height] + freqs_height = freqs_height.reshape(1, height, 1, -1, 2) + freqs_height = np.broadcast_to(freqs_height, (frame, height, width, freqs_height.shape[-2], 2)) if self.scale_rope: - cos_w = np.concatenate([self._neg_cos[2][-(width - width // 2) :], self._pos_cos[2][: width // 2]], axis=0) - sin_w = np.concatenate([self._neg_sin[2][-(width - width // 2) :], self._pos_sin[2][: width // 2]], axis=0) + freqs_width = np.concatenate([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], axis=0) else: - cos_w = self._pos_cos[2][:width] - sin_w = self._pos_sin[2][:width] - cos_w = cos_w.reshape(1, 1, width, -1) - sin_w = sin_w.reshape(1, 1, width, -1) + freqs_width = freqs_pos[2][:width] + freqs_width = freqs_width.reshape(1, 1, width, -1, 2) + freqs_width = np.broadcast_to(freqs_width, (frame, height, width, freqs_width.shape[-2], 2)) - cos = np.concatenate( - [ - np.broadcast_to(cos_f, (frame, height, width, cos_f.shape[-1])), - np.broadcast_to(cos_h, (frame, height, width, cos_h.shape[-1])), - np.broadcast_to(cos_w, (frame, height, width, cos_w.shape[-1])), - ], - axis=-1, + freqs = np.concatenate([freqs_frame, freqs_height, freqs_width], axis=-2) + freqs = freqs.reshape(seq_lens, -1, 2) + + cos_freqs = freqs[..., 0] + sin_freqs = freqs[..., 1] + + return cos_freqs, sin_freqs + + def __call__( + self, + video_fhw: tuple[int, int, int] | list[tuple[int, int, int]], + txt_seq_lens: list[int], + ) -> tuple[tuple[mx.array, mx.array], tuple[mx.array, mx.array]]: + if not isinstance(video_fhw, list): + video_fhw = [video_fhw] + + vid_cos_list = [] + vid_sin_list = [] + max_vid_index = 0 + for idx, fhw in enumerate(video_fhw): + frame, height, width = fhw + cos_v, sin_v = self._compute_video_freqs(frame, height, width, idx) + vid_cos_list.append(cos_v) + vid_sin_list.append(sin_v) + + if self.scale_rope: + max_vid_index = max(height // 2, width // 2, max_vid_index) + else: + max_vid_index = max(height, width, max_vid_index) + + vid_cos = np.concatenate(vid_cos_list, axis=0) + vid_sin = np.concatenate(vid_sin_list, axis=0) + + max_len = max(txt_seq_lens) + txt_cos = self.pos_freqs[max_vid_index : max_vid_index + max_len, :, 0] + txt_sin = self.pos_freqs[max_vid_index : max_vid_index + max_len, :, 1] + + return ( + (mx.array(vid_cos.astype(np.float32)), mx.array(vid_sin.astype(np.float32))), + (mx.array(txt_cos.astype(np.float32)), mx.array(txt_sin.astype(np.float32))), ) - sin = np.concatenate( - [ - np.broadcast_to(sin_f, (frame, height, width, sin_f.shape[-1])), - np.broadcast_to(sin_h, (frame, height, width, sin_h.shape[-1])), - np.broadcast_to(sin_w, (frame, height, width, sin_w.shape[-1])), - ], - axis=-1, - ) - - # Flatten to [S, D/2] - cos = cos.reshape(-1, cos.shape[-1]) - sin = sin.reshape(-1, sin.shape[-1]) - return cos, sin - - def __call__(self, video_fhw: tuple[int, int, int] | list[tuple[int, int, int]], txt_seq_lens: list[int]): - if isinstance(video_fhw, list): - video_fhw = video_fhw[0] - frame, height, width = video_fhw - - cos_v, sin_v = self._build_video_freqs(frame, height, width) - - # Build text freqs using max_vid_index rule - max_vid_index = max(height // 2, width // 2) if self.scale_rope else max(height, width) - txt_len = max(txt_seq_lens) - # Combine axes cos/sin for positions starting at max_vid_index - cos_full = np.concatenate(self._pos_cos, axis=1) # [1024, 128] - sin_full = np.concatenate(self._pos_sin, axis=1) - cos_t = cos_full[max_vid_index : max_vid_index + txt_len] - sin_t = sin_full[max_vid_index : max_vid_index + txt_len] - - def to_rot(cos: np.ndarray, sin: np.ndarray) -> mx.array: - row0 = np.stack([cos, -sin], axis=-1) - row1 = np.stack([sin, cos], axis=-1) - rot = np.stack([row0, row1], axis=-2) - rot = rot[None, None, ...].astype(np.float32) - return mx.array(rot) - - return to_rot(cos_v, sin_v), to_rot(cos_t, sin_t) diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py index 8423fe0..3c7d185 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_time_text_embed.py @@ -8,10 +8,10 @@ from mflux.models.qwen.model.qwen_transformer.qwen_timesteps import QwenTimestep class QwenTimeTextEmbed(nn.Module): def __init__(self, timestep_proj_dim: int = 256, inner_dim: int = 3072): super().__init__() - self.time_proj = QwenTimesteps(proj_dim=timestep_proj_dim) + self.time_proj = QwenTimesteps(proj_dim=timestep_proj_dim, scale=1000.0) self.timestep_embedder = QwenTimestepEmbedding(proj_dim=timestep_proj_dim, inner_dim=inner_dim) def __call__(self, timestep: mx.array, hidden_states: mx.array) -> mx.array: - time_proj = self.time_proj(timestep) - time_emb = self.timestep_embedder(time_proj.astype(hidden_states.dtype)) - return time_emb + timesteps_proj = self.time_proj(timestep) + timesteps_emb = self.timestep_embedder(timesteps_proj.astype(hidden_states.dtype)) + return timesteps_emb diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py index 875e4fe..1e4e999 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_timestep_embedding.py @@ -5,10 +5,11 @@ from mlx import nn class QwenTimestepEmbedding(nn.Module): def __init__(self, proj_dim: int, inner_dim: int): super().__init__() - self.linear_1 = nn.Linear(proj_dim, inner_dim) - self.linear_2 = nn.Linear(inner_dim, inner_dim) + self.linear_1 = nn.Linear(proj_dim, inner_dim, bias=True) + self.linear_2 = nn.Linear(inner_dim, inner_dim, bias=True) def __call__(self, x: mx.array) -> mx.array: - x = nn.silu(self.linear_1(x)) + x = self.linear_1(x) + x = nn.silu(x) x = self.linear_2(x) return x diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py index 05184be..59a344d 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_timesteps.py @@ -1,3 +1,5 @@ +import math + import mlx.core as mx from mlx import nn @@ -6,16 +8,27 @@ class QwenTimesteps(nn.Module): def __init__(self, proj_dim: int = 256, scale: float = 1000.0): super().__init__() self.proj_dim = proj_dim + self.flip_sin_to_cos = True + self.downscale_freq_shift = 0.0 self.scale = scale def __call__(self, timesteps: mx.array) -> mx.array: + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + half_dim = self.proj_dim // 2 - max_period = 10000.0 - exponent = -mx.log(mx.array(max_period)) * mx.arange(0, half_dim, dtype=mx.float32) - exponent = exponent / (half_dim - 0.0) - freqs = mx.exp(exponent) - emb = timesteps.astype(mx.float32)[:, None] * freqs[None, :] + max_period = 10000 + + exponent = -math.log(max_period) * mx.arange(0, half_dim, dtype=mx.float32) + exponent = exponent / (half_dim - self.downscale_freq_shift) + + timesteps_dtype = timesteps.dtype + emb = mx.exp(exponent).astype(timesteps_dtype) + + emb = timesteps[:, None].astype(mx.float32) * emb[None, :] emb = self.scale * emb emb = mx.concatenate([mx.sin(emb), mx.cos(emb)], axis=-1) - emb = mx.concatenate([emb[:, half_dim:], emb[:, :half_dim]], axis=-1) + + if self.flip_sin_to_cos: + emb = mx.concatenate([emb[:, half_dim:], emb[:, :half_dim]], axis=-1) + return emb diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py index 2bfbbfc..740eded 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer.py @@ -9,6 +9,7 @@ from mflux.models.flux.model.flux_transformer.ada_layer_norm_continuous import A from mflux.models.qwen.model.qwen_transformer.qwen_rope import QwenEmbedRopeMLX from mflux.models.qwen.model.qwen_transformer.qwen_time_text_embed import QwenTimeTextEmbed from mflux.models.qwen.model.qwen_transformer.qwen_transformer_block import QwenTransformerBlock +from mflux.models.qwen.model.qwen_transformer.qwen_transformer_rms_norm import QwenTransformerRMSNorm class QwenTransformer(nn.Module): @@ -25,7 +26,7 @@ class QwenTransformer(nn.Module): super().__init__() self.inner_dim = num_attention_heads * attention_head_dim self.img_in = nn.Linear(in_channels, self.inner_dim) - self.txt_norm = nn.RMSNorm(joint_attention_dim, eps=1e-6) + self.txt_norm = QwenTransformerRMSNorm(joint_attention_dim, eps=1e-6) self.txt_in = nn.Linear(joint_attention_dim, self.inner_dim) self.time_text_embed = QwenTimeTextEmbed(timestep_proj_dim=256, inner_dim=self.inner_dim) self.pos_embed = QwenEmbedRopeMLX(theta=10000, axes_dim=[16, 56, 56], scale_rope=True) @@ -40,14 +41,22 @@ class QwenTransformer(nn.Module): hidden_states: mx.array, encoder_hidden_states: mx.array, encoder_hidden_states_mask: mx.array, + qwen_image_ids: mx.array | None = None, + cond_image_grid: tuple[int, int, int] | None = None, ) -> mx.array: - # 1. Create embeddings hidden_states = self.img_in(hidden_states) - encoder_hidden_states = self.txt_in(self.txt_norm(encoder_hidden_states)) - text_embeddings = QwenTransformer._compute_text_embeddings(t, hidden_states, self.time_text_embed, config) - image_rotary_embeddings = QwenTransformer._compute_rotary_embeddings(encoder_hidden_states_mask, self.pos_embed, config) # fmt: off - - # 2. Run the transformer blocks + batch_size = hidden_states.shape[0] + timestep = QwenTransformer._compute_timestep(t, config) + timestep = mx.broadcast_to(timestep, (batch_size,)).astype(hidden_states.dtype) + encoder_hidden_states = self.txt_norm(encoder_hidden_states) + encoder_hidden_states = self.txt_in(encoder_hidden_states) + text_embeddings = self.time_text_embed(timestep, hidden_states) + image_rotary_embeddings = QwenTransformer._compute_rotary_embeddings( + encoder_hidden_states_mask=encoder_hidden_states_mask, + pos_embed=self.pos_embed, + config=config, + cond_image_grid=cond_image_grid, + ) for idx, block in enumerate(self.transformer_blocks): encoder_hidden_states, hidden_states = QwenTransformer._apply_transformer_block( idx=idx, @@ -58,8 +67,6 @@ class QwenTransformer(nn.Module): text_embeddings=text_embeddings, image_rotary_embeddings=image_rotary_embeddings, ) - - # 3. Apply output normalization and projection hidden_states = self.norm_out(hidden_states, text_embeddings) hidden_states = self.proj_out(hidden_states) return hidden_states @@ -80,30 +87,56 @@ class QwenTransformer(nn.Module): encoder_hidden_states_mask=encoder_hidden_states_mask, text_embeddings=text_embeddings, image_rotary_emb=image_rotary_embeddings, + block_idx=idx, ) @staticmethod - def _compute_text_embeddings( - t: int, - hidden_states: mx.array, - time_text_embed: QwenTimeTextEmbed, + def _compute_timestep( + t: int | float, config: RuntimeConfig, ) -> mx.array: - time_step = config.scheduler.sigmas[t] - batch = hidden_states.shape[0] - timestep = mx.array(np.full((batch,), time_step, dtype=np.float32)) - text_embeddings = time_text_embed(timestep, hidden_states) - return text_embeddings + """ + Compute timestep tensor from step index or value. + """ + if isinstance(t, int): + if t < len(config.scheduler.sigmas): + timestep_idx = t + time_step = config.scheduler.sigmas[timestep_idx] + else: + timestep_idx = None + for idx, ts in enumerate(config.scheduler.timesteps): + if abs(int(ts.item()) - t) < 1: + timestep_idx = idx + break + if timestep_idx is None: + time_step = t / 1000.0 + else: + time_step = config.scheduler.sigmas[timestep_idx] + else: + timestep_idx = None + time_step = t + + timestep = mx.array(np.full((1,), time_step, dtype=np.float32)) + return timestep @staticmethod def _compute_rotary_embeddings( encoder_hidden_states_mask: mx.array, pos_embed: QwenEmbedRopeMLX, config: RuntimeConfig, + cond_image_grid: tuple[int, int, int] | list[tuple[int, int, int]] | None = None, ) -> tuple[mx.array, mx.array]: latent_height = config.height // 16 latent_width = config.width // 16 - img_shapes = [(1, latent_height, latent_width)] + + if cond_image_grid is None: + img_shapes = [(1, latent_height, latent_width)] + else: + if isinstance(cond_image_grid, list): + img_shapes = [(1, latent_height, latent_width)] + cond_image_grid + else: + img_shapes = [(1, latent_height, latent_width), cond_image_grid] + txt_seq_lens = [int(mx.sum(encoder_hidden_states_mask[i]).item()) for i in range(encoder_hidden_states_mask.shape[0])] # fmt: off img_rotary_emb, txt_rotary_emb = pos_embed(video_fhw=img_shapes, txt_seq_lens=txt_seq_lens) return img_rotary_emb, txt_rotary_emb diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py index ddaf46f..942d5b4 100644 --- a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_block.py @@ -5,18 +5,23 @@ from mlx import nn from mflux.models.qwen.model.qwen_transformer.qwen_attention import QwenAttention from mflux.models.qwen.model.qwen_transformer.qwen_feed_forward import QwenFeedForward -from mflux.models.qwen.model.qwen_transformer.qwen_layer_norm import QwenLayerNorm class QwenTransformerBlock(nn.Module): def __init__(self, dim: int = 3072, num_heads: int = 24, head_dim: int = 128): super().__init__() - self.img_norm1 = QwenLayerNorm(dim=dim) - self.txt_norm1 = QwenLayerNorm(dim=dim) + + self.img_mod_silu = nn.SiLU() + self.img_mod_linear = nn.Linear(dim, 6 * dim, bias=True) + self.img_norm1 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False) self.attn = QwenAttention(dim=dim, num_heads=num_heads, head_dim=head_dim) self.img_norm2 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False) - self.txt_norm2 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False) self.img_ff = QwenFeedForward(dim=dim) + + self.txt_mod_silu = nn.SiLU() + self.txt_mod_linear = nn.Linear(dim, 6 * dim, bias=True) + self.txt_norm1 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False) + self.txt_norm2 = nn.LayerNorm(dims=dim, eps=1e-6, affine=False) self.txt_ff = QwenFeedForward(dim=dim) def __call__( @@ -26,51 +31,46 @@ class QwenTransformerBlock(nn.Module): encoder_hidden_states_mask: mx.array | None, text_embeddings: mx.array, image_rotary_emb: tuple[mx.array, mx.array], + block_idx: int | None = None, ) -> tuple[mx.array, mx.array]: - # 1. Compute stage 1 normalization and modulation - img_modulated, img_gate1, img_mod2 = self.img_norm1(hidden_states, text_embeddings) - txt_modulated, txt_gate1, txt_mod2 = self.txt_norm1(encoder_hidden_states, text_embeddings) + img_mod_params = self.img_mod_linear(self.img_mod_silu(text_embeddings)) + txt_mod_params = self.txt_mod_linear(self.txt_mod_silu(text_embeddings)) + + img_mod1, img_mod2 = mx.split(img_mod_params, 2, axis=-1) + txt_mod1, txt_mod2 = mx.split(txt_mod_params, 2, axis=-1) + + img_normed = self.img_norm1(hidden_states) + img_modulated, img_gate1 = QwenTransformerBlock._modulate(img_normed, img_mod1) + + txt_normed = self.txt_norm1(encoder_hidden_states) + txt_modulated, txt_gate1 = QwenTransformerBlock._modulate(txt_normed, txt_mod1) - # 2. Compute attention img_attn_output, txt_attn_output = self.attn( img_modulated=img_modulated, txt_modulated=txt_modulated, encoder_hidden_states_mask=encoder_hidden_states_mask, image_rotary_emb=image_rotary_emb, + block_idx=block_idx, ) - # 3. Apply attention residual and feed forward - hidden_states = QwenTransformerBlock._apply_residual_and_feed_forward( - hidden_states=hidden_states, - output=img_attn_output, - gate_attn=img_gate1, - mod_params=img_mod2, - norm2=self.img_norm2, - ff_module=self.img_ff, - ) - encoder_hidden_states = QwenTransformerBlock._apply_residual_and_feed_forward( - hidden_states=encoder_hidden_states, - output=txt_attn_output, - gate_attn=txt_gate1, - mod_params=txt_mod2, - norm2=self.txt_norm2, - ff_module=self.txt_ff, - ) + hidden_states = hidden_states + img_gate1 * img_attn_output + encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output + + img_normed2 = self.img_norm2(hidden_states) + img_modulated2, img_gate2 = QwenTransformerBlock._modulate(img_normed2, img_mod2) + + img_mlp_output = self.img_ff(img_modulated2) + + hidden_states = hidden_states + img_gate2 * img_mlp_output + + txt_normed2 = self.txt_norm2(encoder_hidden_states) + txt_modulated2, txt_gate2 = QwenTransformerBlock._modulate(txt_normed2, txt_mod2) + txt_mlp_output = self.txt_ff(txt_modulated2) + encoder_hidden_states = encoder_hidden_states + txt_gate2 * txt_mlp_output return encoder_hidden_states, hidden_states @staticmethod - def _apply_residual_and_feed_forward( - hidden_states: mx.array, - output: mx.array, - gate_attn: mx.array, - mod_params: mx.array, - norm2: nn.LayerNorm, - ff_module: QwenFeedForward, - ) -> mx.array: - hidden_states = hidden_states + gate_attn[:, None, :] * output - shift2, scale2, gate_ff = mx.split(mod_params, 3, axis=-1) - normed = norm2(hidden_states) - modulated = normed * (1 + scale2[:, None, :]) + shift2[:, None, :] - ff_output = ff_module(modulated) - return hidden_states + gate_ff[:, None, :] * ff_output.astype(hidden_states.dtype) + def _modulate(x: mx.array, mod_params: mx.array) -> tuple[mx.array, mx.array]: + shift, scale, gate = mx.split(mod_params, 3, axis=-1) + return x * (1 + scale[:, None, :]) + shift[:, None, :], gate[:, None, :] diff --git a/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_rms_norm.py b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_rms_norm.py new file mode 100644 index 0000000..fbfb86d --- /dev/null +++ b/src/mflux/models/qwen/model/qwen_transformer/qwen_transformer_rms_norm.py @@ -0,0 +1,23 @@ +import mlx.core as mx +from mlx import nn + + +class QwenTransformerRMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.weight = mx.ones((dim,)) + self.eps = eps + + def __call__(self, hidden_states: mx.array) -> mx.array: + input_dtype = hidden_states.dtype + variance = mx.power(hidden_states.astype(mx.float32), 2).mean(axis=-1, keepdims=True) + hidden_states = hidden_states * mx.rsqrt(variance + self.eps) + + if self.weight is not None: + if self.weight.dtype in [mx.bfloat16, mx.float16]: + hidden_states = hidden_states.astype(self.weight.dtype) + hidden_states = hidden_states * self.weight + if hidden_states.dtype != input_dtype: + hidden_states = hidden_states.astype(input_dtype) + + return hidden_states diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py index cab0d3c..9b23da4 100644 --- a/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py +++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_attention_block_3d.py @@ -8,7 +8,7 @@ class QwenImageAttentionBlock3D(nn.Module): def __init__(self, dim: int): super().__init__() self.dim = dim - self.norm = QwenImageRMSNorm(dim, images=True) # Attention blocks use images=True + self.norm = QwenImageRMSNorm(dim, images=True) self.to_qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, stride=1, padding=0) self.proj = nn.Conv2d(dim, dim, kernel_size=1, stride=1, padding=0) @@ -21,7 +21,7 @@ class QwenImageAttentionBlock3D(nn.Module): x_5d = self.norm(x_5d) x = mx.squeeze(x_5d, axis=2) x = mx.transpose(x, (0, 2, 3, 1)) - qkv = self.to_qkv(x) # [bt, h, w, 3c] + qkv = self.to_qkv(x) qkv = mx.transpose(qkv, (0, 3, 1, 2)) qkv = mx.reshape(qkv, (batch_size * time, 1, channels * 3, height * width)) qkv = mx.transpose(qkv, (0, 1, 3, 2)) diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py index 6a3d0f1..f754e8c 100644 --- a/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py +++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_causal_conv_3d.py @@ -24,21 +24,14 @@ class QwenImageCausalConv3D(nn.Module): self.kernel_size = kernel_size def __call__(self, x: mx.array) -> mx.array: - if isinstance(self.padding, int): - pad_t = pad_h = pad_w = self.padding - elif isinstance(self.padding, (tuple, list)) and len(self.padding) == 3: - pad_t, pad_h, pad_w = self.padding - else: - pad_t = pad_h = pad_w = self.padding - - # Apply causal padding in channels-first format (B, C, T, H, W) + pad_t = pad_h = pad_w = self.padding if pad_t > 0 or pad_h > 0 or pad_w > 0: pad_spec = [ - (0, 0), # Batch dimension (B) - (0, 0), # Channel dimension (C) - (2 * pad_t, 0), # Time dimension (T) - causal: pad past, not future - (pad_h, pad_h), # Height dimension (H) - (pad_w, pad_w), # Width dimension (W) + (0, 0), + (0, 0), + (2 * pad_t, 0), + (pad_h, pad_h), + (pad_w, pad_w), ] x = mx.pad(x, pad_spec) diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_decoder_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_decoder_3d.py index aea0240..a9c278b 100644 --- a/src/mflux/models/qwen/model/qwen_vae/qwen_image_decoder_3d.py +++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_decoder_3d.py @@ -16,8 +16,8 @@ class QwenImageDecoder3D(nn.Module): self.up_block1 = QwenImageUpBlock3D(192, 384, num_res_blocks=2, upsample_mode="upsample3d") self.up_block2 = QwenImageUpBlock3D(192, 192, num_res_blocks=2, upsample_mode="upsample2d") self.up_block3 = QwenImageUpBlock3D(96, 96, num_res_blocks=2, upsample_mode=None) - self.norm_out = QwenImageRMSNorm(96, images=False) # Decoder norm_out uses images=False - self.conv_out = QwenImageCausalConv3D(96, 3, 3, 1, 1) # RGB output + self.norm_out = QwenImageRMSNorm(96, images=False) + self.conv_out = QwenImageCausalConv3D(96, 3, 3, 1, 1) def __call__(self, x: mx.array) -> mx.array: x = self.conv_in(x) diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py index a9b8955..e8e4923 100644 --- a/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py +++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_down_block_3d.py @@ -10,7 +10,7 @@ class QwenImageDownBlock3D(nn.Module): super().__init__() self.resnets = [] current_dim = in_channels - for _ in range(num_res_blocks): # FIXED: Use num_res_blocks, not num_res_blocks + 1 + for _ in range(num_res_blocks): self.resnets.append(QwenImageResBlock3D(current_dim, out_channels)) current_dim = out_channels diff --git a/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py b/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py index a4ef063..a1d8e92 100644 --- a/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py +++ b/src/mflux/models/qwen/model/qwen_vae/qwen_image_encoder_3d.py @@ -23,10 +23,7 @@ class QwenImageEncoder3D(nn.Module): down_blocks = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): - if i < len(self.temporal_downsample): - downsample_mode = "downsample3d" if self.temporal_downsample[i] else "downsample2d" - else: - downsample_mode = "downsample2d" + downsample_mode = "downsample3d" if self.temporal_downsample[i] else "downsample2d" # Do not downsample on the final stage only if i == len(dims) - 2: downsample_mode = None @@ -39,14 +36,14 @@ class QwenImageEncoder3D(nn.Module): self.down_blocks = down_blocks self.mid_block = QwenImageMidBlock3D(dims[-1], num_layers=1) - self.norm_out = QwenImageRMSNorm(dims[-1], images=False) # Encoder norm_out uses images=False - self.conv_out = QwenImageCausalConv3D(dims[-1], 32, 3, 1, 1) # Changed z_dim to 32 + self.norm_out = QwenImageRMSNorm(dims[-1], images=False) + self.conv_out = QwenImageCausalConv3D(dims[-1], 32, 3, 1, 1) def __call__(self, x: mx.array) -> mx.array: x = self.conv_in(x) for stage_idx, down_block in enumerate(self.down_blocks): - for res_idx, resnet in enumerate(down_block.resnets): - if stage_idx == 3: + if stage_idx == 3: + for resnet in down_block.resnets: residual = x n1 = resnet.norm1(x) a1 = nn.silu(n1) @@ -54,14 +51,11 @@ class QwenImageEncoder3D(nn.Module): n2 = resnet.norm2(c1) a2 = nn.silu(n2) c2 = resnet.conv2(a2) - if resnet.skip_conv is not None: - residual = resnet.skip_conv(residual) - y = c2 + residual - x = y - else: - x = resnet(x) - if down_block.downsamplers is not None: - x = down_block.downsamplers[0](x) + x = c2 + residual + if down_block.downsamplers is not None: + x = down_block.downsamplers[0](x) + else: + x = down_block(x) x = self.mid_block(x) norm_in = x diff --git a/src/mflux/models/qwen/qwen_edit_initializer.py b/src/mflux/models/qwen/qwen_edit_initializer.py new file mode 100644 index 0000000..d792c47 --- /dev/null +++ b/src/mflux/models/qwen/qwen_edit_initializer.py @@ -0,0 +1,109 @@ +from pathlib import Path + +from transformers import Qwen2TokenizerFast + +from mflux.config.model_config import ModelConfig +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_language_encoder import QwenVisionLanguageEncoder +from mflux.models.qwen.qwen_initializer import QwenImageInitializer +from mflux.models.qwen.tokenizer.qwen_vision_language_processor import QwenVisionLanguageProcessor +from mflux.models.qwen.tokenizer.qwen_vision_language_tokenizer import QwenVisionLanguageTokenizer +from mflux.utils.download import snapshot_download + + +class QwenImageEditInitializer: + @staticmethod + def init( + qwen_model, + model_config: ModelConfig, + quantize: int | None, + local_path: str | None, + lora_paths: list[str] | None = None, + lora_scales: list[float] | None = None, + lora_names: list[str] | None = None, + lora_repo_id: str | None = None, + ) -> None: + # 1. Initialize the base Qwen Image model (VAE, transformer, text encoder, etc.) + QwenImageInitializer.init( + qwen_model=qwen_model, + model_config=model_config, + quantize=quantize, + local_path=local_path, + lora_paths=lora_paths, + lora_scales=lora_scales, + lora_names=lora_names, + lora_repo_id=lora_repo_id, + ) + + # 2. Add vision-language components for edit functionality + QwenImageEditInitializer._init_vision_language_components( + qwen_model=qwen_model, + repo_id=model_config.model_name, + local_path=local_path, + model_config=model_config, + ) + + @staticmethod + def _init_vision_language_components( + qwen_model, + repo_id: str, + local_path: str | None, + model_config: ModelConfig | None = None, + ) -> None: + # 1. Download or get cached tokenizer + root_path = Path(local_path) if local_path else QwenImageEditInitializer._download_vl_processor(repo_id) + + # 2. Load only the tokenizer (we implement image processing ourselves) + tokenizer_path = root_path / "tokenizer" + if not tokenizer_path.exists(): + tokenizer_path = root_path + + try: + tokenizer = Qwen2TokenizerFast.from_pretrained( + pretrained_model_name_or_path=tokenizer_path, + local_files_only=True, + ) + except OSError: + tokenizer = Qwen2TokenizerFast.from_pretrained(repo_id) + + # 3. Create our MLX processor with the tokenizer + processor = QwenVisionLanguageProcessor(tokenizer=tokenizer) + + # 4. Initialize vision-language tokenizer wrapper + if model_config is not None: + model_name_lower = model_config.model_name.lower() + use_picture_prefix = ( + "plus" in model_name_lower + or "2509" in model_config.model_name # Check original case for "2509" + or (hasattr(model_config, "use_picture_prefix") and model_config.use_picture_prefix) + ) + else: + # Fallback: check repo_id + use_picture_prefix = "plus" in repo_id.lower() or "2509" in repo_id + + qwen_model.qwen_vl_tokenizer = QwenVisionLanguageTokenizer( + processor=processor, + max_length=1024, + use_picture_prefix=use_picture_prefix, + ) + + # 5. Initialize vision-language encoder (integrated approach like Diffusers) + qwen_model.qwen_vl_encoder = QwenVisionLanguageEncoder(encoder=qwen_model.text_encoder.encoder) + + @staticmethod + def _download_vl_processor(repo_id: str) -> Path: + return Path( + snapshot_download( + repo_id=repo_id, + allow_patterns=[ + "processor/**", + "preprocessor_config.json", + "tokenizer/**", + "added_tokens.json", + "chat_template.jinja", + "tokenizer.json", + "tokenizer_config.json", + "vocab.json", + "merges.txt", + ], + ) + ) diff --git a/src/mflux/models/qwen/qwen_initializer.py b/src/mflux/models/qwen/qwen_initializer.py index 855d78f..99718a2 100644 --- a/src/mflux/models/qwen/qwen_initializer.py +++ b/src/mflux/models/qwen/qwen_initializer.py @@ -5,8 +5,8 @@ from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenText from mflux.models.qwen.model.qwen_transformer.qwen_transformer import QwenTransformer from mflux.models.qwen.model.qwen_vae.qwen_vae import QwenVAE from mflux.models.qwen.tokenizer.qwen_tokenizer_handler import QwenTokenizerHandler -from mflux.models.qwen.weights.qwen_weight_handler import QwenWeightHandler from mflux.models.qwen.weights.qwen_lora_mapping import QwenLoRAMapping +from mflux.models.qwen.weights.qwen_weight_handler import QwenWeightHandler from mflux.models.qwen.weights.qwen_weight_util import QwenWeightUtil @@ -68,7 +68,3 @@ class QwenImageInitializer: lora_files=qwen_model.lora_paths, lora_scales=qwen_model.lora_scales, ) - else: - print("⚠️ No LoRA paths provided, skipping LoRA setup") - - print("🔧 === END QWEN LORA WEIGHT SETUP DEBUG ===\n") diff --git a/src/mflux/models/qwen/tokenizer/qwen_image_processor.py b/src/mflux/models/qwen/tokenizer/qwen_image_processor.py new file mode 100644 index 0000000..70afcfc --- /dev/null +++ b/src/mflux/models/qwen/tokenizer/qwen_image_processor.py @@ -0,0 +1,162 @@ +import math +from typing import Optional, Union + +import numpy as np +from PIL import Image + +OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073] +OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711] + + +def smart_resize( + height: int, + width: int, + factor: int = 28, + min_pixels: int = 56 * 56, + max_pixels: int = 14 * 14 * 4 * 1280, +) -> tuple[int, int]: + if max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + + +class QwenImageProcessor: + def __init__( + self, + min_pixels: int = 56 * 56, + max_pixels: int = 28 * 28 * 1280, + patch_size: int = 14, + temporal_patch_size: int = 2, + merge_size: int = 2, + image_mean: Optional[list[float]] = None, + image_std: Optional[list[float]] = None, + ): + self.min_pixels = min_pixels + self.max_pixels = max_pixels + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.merge_size = merge_size + self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN + self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD + + def _preprocess( + self, + image: Image.Image, + resized_height: Optional[int] = None, + resized_width: Optional[int] = None, + ) -> tuple[np.ndarray, tuple[int, int, int]]: + if image.mode != "RGB": + image = image.convert("RGB") + + height, width = image.size[1], image.size[0] + + if resized_height is None or resized_width is None: + resized_height, resized_width = smart_resize( + height, + width, + factor=self.patch_size * self.merge_size, + min_pixels=self.min_pixels, + max_pixels=self.max_pixels, + ) + if (height, width) != (resized_height, resized_width): + image = image.resize((resized_width, resized_height), Image.BICUBIC) + + image_np = np.array(image).astype(np.float32) + image_np = image_np / 255.0 + + mean_np = np.array(self.image_mean, dtype=np.float32) + std_np = np.array(self.image_std, dtype=np.float32) + image_np = (image_np - mean_np) / std_np + + image_np = image_np.transpose(2, 0, 1) + patches = image_np[np.newaxis] # Shape: (1, channel, height, width) + + if patches.shape[0] % self.temporal_patch_size != 0: + repeats = np.repeat( + patches[-1][np.newaxis], + self.temporal_patch_size - (patches.shape[0] % self.temporal_patch_size), + axis=0, + ) + patches = np.concatenate([patches, repeats], axis=0) + + channel = patches.shape[1] + grid_t = patches.shape[0] // self.temporal_patch_size + grid_h = resized_height // self.patch_size + grid_w = resized_width // self.patch_size + + patches = patches.reshape( + grid_t, + self.temporal_patch_size, + channel, + grid_h // self.merge_size, + self.merge_size, + self.patch_size, + grid_w // self.merge_size, + self.merge_size, + self.patch_size, + ) + + patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8) + + flatten_patches = patches.reshape( + grid_t * grid_h * grid_w, + channel * self.temporal_patch_size * self.patch_size * self.patch_size, + ) + + return flatten_patches, (grid_t, grid_h, grid_w) + + def preprocess( + self, + images: Union[Image.Image, list[Image.Image]], + ) -> tuple[np.ndarray, np.ndarray]: + if not isinstance(images, list): + images = [images] + + pixel_values_list = [] + vision_grid_thws = [] + + for image in images: + patches, image_grid_thw = self._preprocess(image) + pixel_values_list.append(patches) + vision_grid_thws.append([image_grid_thw[0], image_grid_thw[1], image_grid_thw[2]]) + + # Concatenate all patches from all images along the patch dimension + pixel_values = np.concatenate(pixel_values_list, axis=0) if pixel_values_list else np.array([]) + + vision_grid_thws = np.array(vision_grid_thws) + + return pixel_values, vision_grid_thws + + def get_number_of_image_patches( + self, + height: int, + width: int, + min_pixels: Optional[int] = None, + max_pixels: Optional[int] = None, + ) -> int: + min_pixels = min_pixels if min_pixels is not None else self.min_pixels + max_pixels = max_pixels if max_pixels is not None else self.max_pixels + + factor = self.patch_size * self.merge_size + resized_height, resized_width = smart_resize( + height, + width, + factor, + min_pixels=min_pixels, + max_pixels=max_pixels, + ) + grid_h = resized_height // self.patch_size + grid_w = resized_width // self.patch_size + return grid_h * grid_w diff --git a/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py b/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py index 29bbbb1..a3589c1 100644 --- a/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py +++ b/src/mflux/models/qwen/tokenizer/qwen_tokenizer.py @@ -14,7 +14,7 @@ class TokenizerQwen: tokens = self.tokenizer( formatted_text, max_length=self.max_length + self.template_start_idx, - padding=True, + padding=False, truncation=True, return_tensors="np", ) diff --git a/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py b/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py index c7711a7..848df74 100644 --- a/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py +++ b/src/mflux/models/qwen/tokenizer/qwen_tokenizer_handler.py @@ -7,13 +7,6 @@ from mflux.utils.download import snapshot_download class QwenTokenizerHandler: - """ - Handler for Qwen tokenizer, following the same pattern as TokenizerHandler. - - This loads the Qwen2Tokenizer from the model repository and wraps it - in our TokenizerQwen class for consistent interface. - """ - def __init__( self, repo_id: str, @@ -34,7 +27,6 @@ class QwenTokenizerHandler: @staticmethod def _download_or_get_cached_tokenizer(repo_id: str) -> Path: - """Download or get cached tokenizer files.""" return Path( snapshot_download( repo_id=repo_id, diff --git a/src/mflux/models/qwen/tokenizer/qwen_vision_language_processor.py b/src/mflux/models/qwen/tokenizer/qwen_vision_language_processor.py new file mode 100644 index 0000000..47ed51f --- /dev/null +++ b/src/mflux/models/qwen/tokenizer/qwen_vision_language_processor.py @@ -0,0 +1,103 @@ +from typing import Optional, Union + +import mlx.core as mx +import numpy as np +from PIL import Image + +from mflux.models.qwen.tokenizer.qwen_image_processor import QwenImageProcessor + + +class QwenVisionLanguageProcessor: + def __init__( + self, + tokenizer, + image_processor: Optional[QwenImageProcessor] = None, + image_token: str = "<|image_pad|>", + video_token: str = "<|video_pad|>", + ): + self.tokenizer = tokenizer + self.image_processor = image_processor or QwenImageProcessor() + + self.image_token = image_token + self.video_token = video_token + self.image_token_id = ( + tokenizer.image_token_id + if hasattr(tokenizer, "image_token_id") + else tokenizer.convert_tokens_to_ids(self.image_token) + ) + self.video_token_id = ( + tokenizer.video_token_id + if hasattr(tokenizer, "video_token_id") + else tokenizer.convert_tokens_to_ids(self.video_token) + ) + + def __call__( + self, + images: Optional[Union[Image.Image, list[Image.Image]]] = None, + text: Optional[Union[str, list[str]]] = None, + padding: bool = True, + return_tensors: Optional[str] = None, + ) -> dict: + image_inputs = {} + if images is not None: + pixel_values, image_grid_thw = self.image_processor.preprocess(images) + image_inputs = { + "pixel_values": pixel_values, + "image_grid_thw": image_grid_thw, + } + + if text is not None: + if not isinstance(text, list): + text = [text] + text = text.copy() + + if images is not None: + merge_length = self.image_processor.merge_size**2 + index = 0 + for i in range(len(text)): + while self.image_token in text[i]: + if index < len(image_grid_thw): + num_image_tokens = int(np.prod(image_grid_thw[index])) // merge_length + text[i] = text[i].replace( + self.image_token, + "<|placeholder|>" * num_image_tokens, + 1, + ) + index += 1 + else: + break + text[i] = text[i].replace("<|placeholder|>", self.image_token) + + text_inputs = self.tokenizer( + text, + padding=padding, + return_tensors="pt" if return_tensors == "pt" else "np", + ) + + if return_tensors == "pt": + import torch + + if isinstance(text_inputs["input_ids"], torch.Tensor): + input_ids = mx.array(text_inputs["input_ids"].numpy()) + attention_mask = mx.array(text_inputs["attention_mask"].numpy()) + else: + input_ids = mx.array(text_inputs["input_ids"]) + attention_mask = mx.array(text_inputs["attention_mask"]) + else: + if isinstance(text_inputs["input_ids"], np.ndarray): + input_ids = mx.array(text_inputs["input_ids"]) + attention_mask = mx.array(text_inputs["attention_mask"]) + else: + input_ids = mx.array(np.array(text_inputs["input_ids"])) + attention_mask = mx.array(np.array(text_inputs["attention_mask"])) + else: + input_ids = None + attention_mask = None + + result = {**image_inputs} + if input_ids is not None: + result["input_ids"] = input_ids + if attention_mask is not None: + result["attention_mask"] = attention_mask + + return result diff --git a/src/mflux/models/qwen/tokenizer/qwen_vision_language_tokenizer.py b/src/mflux/models/qwen/tokenizer/qwen_vision_language_tokenizer.py new file mode 100644 index 0000000..519c5be --- /dev/null +++ b/src/mflux/models/qwen/tokenizer/qwen_vision_language_tokenizer.py @@ -0,0 +1,142 @@ +import math +from pathlib import Path +from typing import Union + +import mlx.core as mx +import numpy as np +from PIL import Image + +from mflux.models.qwen.tokenizer.qwen_vision_language_processor import QwenVisionLanguageProcessor + + +class QwenVisionLanguageTokenizer: + def __init__( + self, + processor: QwenVisionLanguageProcessor, + max_length: int = 1024, + use_picture_prefix: bool = True, + ): + self.processor = processor + self.max_length = max_length + self.use_picture_prefix = use_picture_prefix + + if use_picture_prefix: + self.edit_template = ( + "<|im_start|>system\n" + "Describe the key features of the input image (color, shape, size, texture, objects, background), " + "then explain how the user's text instruction should alter or modify the image. " + "Generate a new image that meets the user's requirements while maintaining consistency " + "with the original input where appropriate.<|im_end|>\n" + "<|im_start|>user\n" + "{}<|im_end|>\n" + "<|im_start|>assistant\n" + ) + else: + self.edit_template = ( + "<|im_start|>system\n" + "Describe the key features of the input image (color, shape, size, texture, objects, background), " + "then explain how the user's text instruction should alter or modify the image. " + "Generate a new image that meets the user's requirements while maintaining consistency " + "with the original input where appropriate.<|im_end|>\n" + "<|im_start|>user\n" + "<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n" + "<|im_start|>assistant\n" + ) + self.edit_template_start_idx = 64 + + def tokenize_with_image( + self, + prompt: str, + image: Union[Image.Image, np.ndarray, str, list], + vl_width: int | None = None, + vl_height: int | None = None, + ) -> tuple[mx.array, mx.array, mx.array, mx.array]: + # Normalize image to list format + if not isinstance(image, list): + images = [image] + else: + images = image + + # Format prompt based on tokenizer mode + if self.use_picture_prefix: + # Edit Plus format: Add "Picture N:" prefix for each image + # For multiple images: "Picture 1: ... Picture 2: ... Picture N: ..." + img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>" + base_img_prompt = "" + for i in range(len(images)): + base_img_prompt += img_prompt_template.format(i + 1) + formatted_text = self.edit_template.format(base_img_prompt + prompt) + else: + # Regular Edit format: Vision tokens already in template + # Just format with user prompt directly + formatted_text = self.edit_template.format(prompt) + + # Process images: convert to PIL Images and resize to CONDITION_IMAGE_SIZE + CONDITION_IMAGE_SIZE = 384 * 384 + + processed_images = [] + for img in images: + # Convert to PIL Image + if isinstance(img, (str, Path)): + img = Image.open(img).convert("RGB") + elif isinstance(img, np.ndarray): + img = Image.fromarray(img) + elif not isinstance(img, Image.Image): + raise ValueError(f"Unsupported image type: {type(img)}") + + # Resize to CONDITION_IMAGE_SIZE (384×384) maintaining aspect ratio + img_w, img_h = img.size + ratio = img_w / img_h + condition_width = math.sqrt(CONDITION_IMAGE_SIZE * ratio) + condition_height = condition_width / ratio + condition_width = round(condition_width / 32) * 32 + condition_height = round(condition_height / 32) * 32 + + img = img.resize((int(condition_width), int(condition_height)), Image.BICUBIC) + processed_images.append(img) + + # Use our MLX processor for both text and images + model_inputs = self.processor( + text=[formatted_text], + images=processed_images, + padding=True, + return_tensors=None, # Return numpy/MLX arrays, not PyTorch + ) + + grid_thw = model_inputs["image_grid_thw"][0] + factor = 14 * 2 + self._vl_image_width = int(grid_thw[2]) * factor + self._vl_image_height = int(grid_thw[1]) * factor + + # Convert to MLX arrays if needed + input_ids = model_inputs["input_ids"] + attention_mask = model_inputs["attention_mask"] + pixel_values = mx.array(model_inputs["pixel_values"]) + image_grid_thw = mx.array(model_inputs["image_grid_thw"]) + + return input_ids, attention_mask, pixel_values, image_grid_thw + + def tokenize_text_only(self, prompt: str) -> tuple[mx.array, mx.array]: + # Use the regular text-only template + text_template = ( + "<|im_start|>system\n" + "Describe the image by detailing the color, shape, size, texture, quantity, text, " + "spatial relationships of the objects and background:<|im_end|>\n" + "<|im_start|>user\n{}<|im_end|>\n" + "<|im_start|>assistant\n" + ) + + formatted_text = text_template.format(prompt) + tokens = self.processor.tokenizer( + formatted_text, + max_length=self.max_length + 34, + padding=True, + truncation=True, + return_tensors="pt", + ) + + # Convert PyTorch tensors to MLX arrays + input_ids = mx.array(tokens["input_ids"].numpy()) + attention_mask = mx.array(tokens["attention_mask"].numpy()) + + return input_ids, attention_mask diff --git a/src/mflux/models/qwen/variants/edit/__init__.py b/src/mflux/models/qwen/variants/edit/__init__.py new file mode 100644 index 0000000..8833a8b --- /dev/null +++ b/src/mflux/models/qwen/variants/edit/__init__.py @@ -0,0 +1,5 @@ +# Qwen Image Edit variant +from mflux.models.qwen.variants.edit.qwen_image_edit import QwenImageEdit +from mflux.models.qwen.variants.edit.qwen_image_edit_plus import QwenImageEditPlus + +__all__ = ["QwenImageEdit", "QwenImageEditPlus"] diff --git a/src/mflux/models/qwen/variants/edit/qwen_image_edit.py b/src/mflux/models/qwen/variants/edit/qwen_image_edit.py new file mode 100644 index 0000000..b453f55 --- /dev/null +++ b/src/mflux/models/qwen/variants/edit/qwen_image_edit.py @@ -0,0 +1,263 @@ +import math + +import mlx.core as mx +from mlx import nn +from PIL import Image +from tqdm import tqdm + +from mflux.callbacks.callbacks import Callbacks +from mflux.config.config import Config +from mflux.config.model_config import ModelConfig +from mflux.config.runtime_config import RuntimeConfig +from mflux.error.exceptions import StopImageGenerationException +from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenTextEncoder +from mflux.models.qwen.model.qwen_transformer.qwen_transformer import QwenTransformer +from mflux.models.qwen.model.qwen_vae.qwen_vae import QwenVAE +from mflux.models.qwen.qwen_edit_initializer import QwenImageEditInitializer +from mflux.models.qwen.variants.edit.utils.qwen_edit_util import QwenEditUtil +from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage +from mflux.post_processing.array_util import ArrayUtil +from mflux.post_processing.generated_image import GeneratedImage +from mflux.post_processing.image_util import ImageUtil + + +class QwenImageEdit(nn.Module): + vae: QwenVAE + transformer: QwenTransformer + text_encoder: QwenTextEncoder + + def __init__( + self, + quantize: int | None = None, + local_path: str | None = None, + lora_paths: list[str] | None = None, + lora_scales: list[float] | None = None, + lora_names: list[str] | None = None, + lora_repo_id: str | None = None, + ): + super().__init__() + QwenImageEditInitializer.init( + qwen_model=self, + model_config=ModelConfig.qwen_image_edit(), + quantize=quantize, + local_path=local_path, + lora_paths=lora_paths, + lora_scales=lora_scales, + lora_names=lora_names, + lora_repo_id=lora_repo_id, + ) + + def generate_image( + self, + seed: int, + prompt: str, + config: Config, + negative_prompt: str | None = None, + ) -> GeneratedImage: + runtime_config, vl_width, vl_height = self._compute_dimensions(config) + timesteps = runtime_config.scheduler.timesteps + time_steps = tqdm(range(len(timesteps))) + + # 1. Create initial latents + latents = LatentCreator.create( + seed=seed, + height=runtime_config.height, + width=runtime_config.width, + ) + + # 2. Encode the prompt + prompt_embeds, prompt_mask, negative_prompt_embeds, negative_prompt_mask = ( + self._debug_encode_prompts_with_image( + prompt=prompt, + negative_prompt=negative_prompt, + image_path=runtime_config.image_path, + runtime_config=runtime_config, + vl_width=vl_width, + vl_height=vl_height, + ) + ) + + # 3. Generate image conditioning latents + image_path = str(runtime_config.image_path) if runtime_config.image_path else None + static_image_latents, qwen_image_ids, cond_h_patches, cond_w_patches, num_images = ( + QwenEditUtil.create_image_conditioning_latents( + vae=self.vae, + height=runtime_config.height, + width=runtime_config.width, + image_paths=image_path, + vl_width=vl_width, + vl_height=vl_height, + ) + ) + + # (Optional) Call subscribers for beginning of loop + Callbacks.before_loop( + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + ) + + for t in time_steps: + try: + # 4.t Concatenate the updated latents with the static image latents + hidden_states = mx.concatenate([latents, static_image_latents], axis=1) + hidden_states_neg = mx.concatenate([latents, static_image_latents], axis=1) # noqa: F841 + + # 5.t Predict the noise + noise = self.transformer( + t=t, + config=runtime_config, + hidden_states=hidden_states, + encoder_hidden_states=prompt_embeds, + encoder_hidden_states_mask=prompt_mask, + qwen_image_ids=qwen_image_ids, + cond_image_grid=(1, cond_h_patches, cond_w_patches), + )[:, : latents.shape[1]] + + noise_negative = self.transformer( + t=t, + config=runtime_config, + hidden_states=hidden_states_neg, + encoder_hidden_states=negative_prompt_embeds, + encoder_hidden_states_mask=negative_prompt_mask, + qwen_image_ids=qwen_image_ids, + cond_image_grid=(1, cond_h_patches, cond_w_patches), + )[:, : latents.shape[1]] + + guided_noise = QwenImage.compute_guided_noise(noise, noise_negative, runtime_config.guidance) + + # 6.t Take one denoise step + latents = runtime_config.scheduler.step( + model_output=guided_noise, + timestep=t, + sample=latents, + ) + + # (Optional) Call subscribers in-loop + Callbacks.in_loop( + t=t, + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + time_steps=time_steps, + ) + + # (Optional) Evaluate to enable progress tracking + mx.eval(latents) + + except KeyboardInterrupt: # noqa: PERF203 + Callbacks.interruption( + t=t, + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + time_steps=time_steps, + ) + raise StopImageGenerationException(f"Stopping image generation at step {t + 1}/{len(timesteps)}") + + # (Optional) Call subscribers after loop + Callbacks.after_loop( + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + ) + + # 7. Decode the latent array and return the image + latents = ArrayUtil.unpack_latents(latents=latents, height=runtime_config.height, width=runtime_config.width) + decoded = self.vae.decode(latents) + return ImageUtil.to_image( + decoded_latents=decoded, + config=runtime_config, + seed=seed, + prompt=prompt, + quantization=self.bits, + lora_paths=self.lora_paths, + lora_scales=self.lora_scales, + image_path=runtime_config.image_path, + generation_time=time_steps.format_dict["elapsed"], + negative_prompt=negative_prompt, + ) + + def _debug_encode_prompts_with_image( + self, + prompt: str, + negative_prompt: str, + image_path: str, + runtime_config, + vl_width: int | None = None, + vl_height: int | None = None, + ) -> tuple[mx.array, mx.array, mx.array, mx.array]: + tokenizer = self.qwen_vl_tokenizer + image = Image.open(image_path).convert("RGB") + + pos_input_ids, pos_attention_mask, pos_pixel_values, pos_image_grid_thw = tokenizer.tokenize_with_image( + prompt, image, vl_width=vl_width, vl_height=vl_height + ) + + pos_hidden_states = self.qwen_vl_encoder( + input_ids=pos_input_ids, + attention_mask=pos_attention_mask, + pixel_values=pos_pixel_values, + image_grid_thw=pos_image_grid_thw, + ) + + neg_prompt = negative_prompt if negative_prompt is not None else "" + neg_input_ids, neg_attention_mask, neg_pixel_values, neg_image_grid_thw = tokenizer.tokenize_with_image( + neg_prompt, image, vl_width=vl_width, vl_height=vl_height + ) + + neg_hidden_states = self.qwen_vl_encoder( + input_ids=neg_input_ids, + attention_mask=neg_attention_mask, + pixel_values=neg_pixel_values, + image_grid_thw=neg_image_grid_thw, + ) + + return ( + pos_hidden_states[0].astype(mx.float16), # prompt_embeds + pos_hidden_states[1].astype(mx.float16), # prompt_mask + neg_hidden_states[0].astype(mx.float16), # negative_prompt_embeds + neg_hidden_states[1].astype(mx.float16), # negative_prompt_mask + ) + + def _compute_dimensions( + self, + config: Config, + ) -> tuple[RuntimeConfig, int, int]: + image = ImageUtil.load_image(config.image_path).convert("RGB") + image_size = image.size + + target_area = 1024 * 1024 + ratio = image_size[0] / image_size[1] + calculated_width = math.sqrt(target_area * ratio) + calculated_height = calculated_width / ratio + calculated_width = round(calculated_width / 32) * 32 + calculated_height = round(calculated_height / 32) * 32 + + use_height = config.height or calculated_height + use_width = config.width or calculated_width + + vae_scale_factor = 8 + multiple_of = vae_scale_factor * 2 + use_width = use_width // multiple_of * multiple_of + use_height = use_height // multiple_of * multiple_of + + final_config = Config( + height=use_height, + width=use_width, + image_path=config.image_path, + num_inference_steps=config.num_inference_steps, + guidance=config.guidance, + scheduler=config.scheduler_str, + ) + runtime_config = RuntimeConfig(final_config, self.model_config) + + vl_width = calculated_width + vl_height = calculated_height + + return runtime_config, int(vl_width), int(vl_height) diff --git a/src/mflux/models/qwen/variants/edit/qwen_image_edit_plus.py b/src/mflux/models/qwen/variants/edit/qwen_image_edit_plus.py new file mode 100644 index 0000000..cc510c0 --- /dev/null +++ b/src/mflux/models/qwen/variants/edit/qwen_image_edit_plus.py @@ -0,0 +1,285 @@ +import math + +import mlx.core as mx +from mlx import nn +from tqdm import tqdm + +from mflux.callbacks.callbacks import Callbacks +from mflux.config.config import Config +from mflux.config.model_config import ModelConfig +from mflux.config.runtime_config import RuntimeConfig +from mflux.error.exceptions import StopImageGenerationException +from mflux.latent_creator.latent_creator import LatentCreator +from mflux.models.qwen.model.qwen_text_encoder.qwen_text_encoder import QwenTextEncoder +from mflux.models.qwen.model.qwen_transformer.qwen_transformer import QwenTransformer +from mflux.models.qwen.model.qwen_vae.qwen_vae import QwenVAE +from mflux.models.qwen.qwen_edit_initializer import QwenImageEditInitializer +from mflux.models.qwen.variants.edit.utils.qwen_edit_util import QwenEditUtil +from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage +from mflux.post_processing.array_util import ArrayUtil +from mflux.post_processing.generated_image import GeneratedImage +from mflux.post_processing.image_util import ImageUtil + + +class QwenImageEditPlus(nn.Module): + vae: QwenVAE + transformer: QwenTransformer + text_encoder: QwenTextEncoder + + def __init__( + self, + quantize: int | None = None, + local_path: str | None = None, + lora_paths: list[str] | None = None, + lora_scales: list[float] | None = None, + lora_names: list[str] | None = None, + lora_repo_id: str | None = None, + ): + super().__init__() + QwenImageEditInitializer.init( + qwen_model=self, + model_config=ModelConfig.qwen_image_edit_plus(), + quantize=quantize, + local_path=local_path, + lora_paths=lora_paths, + lora_scales=lora_scales, + lora_names=lora_names, + lora_repo_id=lora_repo_id, + ) + + def generate_image( + self, + seed: int, + prompt: str, + config: Config, + negative_prompt: str | None = None, + image_paths: list[str] | None = None, + ) -> GeneratedImage: + if image_paths is None: + image_paths = [config.image_path] + + runtime_config, vl_width, vl_height, vae_width, vae_height = self._compute_dimensions(config, image_paths) + timesteps = runtime_config.scheduler.timesteps + time_steps = tqdm(range(len(timesteps))) + + # 1. Create initial latents + latents = LatentCreator.create( + seed=seed, + height=runtime_config.height, + width=runtime_config.width, + ) + + # 2. Encode the prompt + prompt_embeds, prompt_mask, negative_prompt_embeds, negative_prompt_mask = self._encode_prompts_with_image_plus( + prompt=prompt, + negative_prompt=negative_prompt, + image_paths=image_paths, + runtime_config=runtime_config, + vl_width=vl_width, + vl_height=vl_height, + ) + + # 3. Generate image conditioning latents + static_image_latents, qwen_image_ids, cond_h_patches, cond_w_patches, num_images = ( + QwenEditUtil.create_image_conditioning_latents( + vae=self.vae, + height=vae_height, + width=vae_width, + image_paths=image_paths, + vl_width=vl_width, + vl_height=vl_height, + ) + ) + + # (Optional) Call subscribers for beginning of loop + Callbacks.before_loop( + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + ) + + for t in time_steps: + try: + # 4.t Concatenate the updated latents with the static image latents + hidden_states = mx.concatenate([latents, static_image_latents], axis=1) + hidden_states_neg = mx.concatenate([latents, static_image_latents], axis=1) + + # 5.t Predict the noise + if num_images > 1: + cond_image_grid = [(1, cond_h_patches, cond_w_patches) for _ in range(num_images)] + else: + cond_image_grid = (1, cond_h_patches, cond_w_patches) + + noise = self.transformer( + t=t, + config=runtime_config, + hidden_states=hidden_states, + encoder_hidden_states=prompt_embeds, + encoder_hidden_states_mask=prompt_mask, + qwen_image_ids=qwen_image_ids, + cond_image_grid=cond_image_grid, + )[:, : latents.shape[1]] + noise_negative = self.transformer( + t=t, + config=runtime_config, + hidden_states=hidden_states_neg, + encoder_hidden_states=negative_prompt_embeds, + encoder_hidden_states_mask=negative_prompt_mask, + qwen_image_ids=qwen_image_ids, + cond_image_grid=cond_image_grid, + )[:, : latents.shape[1]] + guided_noise = QwenImage.compute_guided_noise(noise, noise_negative, runtime_config.guidance) + + # 6.t Take one denoise step + latents = runtime_config.scheduler.step( + model_output=guided_noise, + timestep=t, + sample=latents, + ) + + # (Optional) Call subscribers in-loop + Callbacks.in_loop( + t=t, + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + time_steps=time_steps, + ) + + # (Optional) Evaluate to enable progress tracking + mx.eval(latents) + + except KeyboardInterrupt: # noqa: PERF203 + Callbacks.interruption( + t=t, + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + time_steps=time_steps, + ) + raise StopImageGenerationException(f"Stopping image generation at step {t + 1}/{len(timesteps)}") + + # (Optional) Call subscribers after loop + Callbacks.after_loop( + seed=seed, + prompt=prompt, + latents=latents, + config=runtime_config, + ) + + # 7. Decode the latent array and return the image + latents = ArrayUtil.unpack_latents(latents=latents, height=runtime_config.height, width=runtime_config.width) + decoded = self.vae.decode(latents) + return ImageUtil.to_image( + decoded_latents=decoded, + config=runtime_config, + seed=seed, + prompt=prompt, + quantization=self.bits, + lora_paths=self.lora_paths, + lora_scales=self.lora_scales, + image_path=runtime_config.image_path, + generation_time=time_steps.format_dict["elapsed"], + negative_prompt=negative_prompt, + ) + + def _encode_prompts_with_image_plus( + self, + prompt: str, + negative_prompt: str, + image_paths: list[str], + runtime_config, + vl_width: int | None = None, + vl_height: int | None = None, + ) -> tuple[mx.array, mx.array, mx.array, mx.array]: + tokenizer = self.qwen_vl_tokenizer + pos_input_ids, pos_attention_mask, pos_pixel_values, pos_image_grid_thw = tokenizer.tokenize_with_image( + prompt, image_paths, vl_width=vl_width, vl_height=vl_height + ) + + pos_hidden_states = self.qwen_vl_encoder( + input_ids=pos_input_ids, + attention_mask=pos_attention_mask, + pixel_values=pos_pixel_values, + image_grid_thw=pos_image_grid_thw, + ) + mx.eval(pos_hidden_states[0]) + mx.eval(pos_hidden_states[1]) + + neg_prompt = negative_prompt if negative_prompt is not None else "" + + # Use real MLX tokenizer for negative prompt + neg_input_ids, neg_attention_mask, neg_pixel_values, neg_image_grid_thw = tokenizer.tokenize_with_image( + neg_prompt, image_paths, vl_width=vl_width, vl_height=vl_height + ) + + neg_hidden_states = self.qwen_vl_encoder( + input_ids=neg_input_ids, + attention_mask=neg_attention_mask, + pixel_values=neg_pixel_values, + image_grid_thw=neg_image_grid_thw, + ) + mx.eval(neg_hidden_states[0]) + mx.eval(neg_hidden_states[1]) + + final_prompt_embeds = pos_hidden_states[0].astype(mx.float16) + final_prompt_mask = pos_hidden_states[1].astype(mx.float16) + + return ( + final_prompt_embeds, # prompt_embeds + final_prompt_mask, # prompt_mask + neg_hidden_states[0].astype(mx.float16), # negative_prompt_embeds + neg_hidden_states[1].astype(mx.float16), # negative_prompt_mask + ) + + def _compute_dimensions( + self, + config: Config, + image_paths: list[str], + ) -> tuple[RuntimeConfig, int, int, int, int]: + last_image = ImageUtil.load_image(image_paths[-1]).convert("RGB") + image_size = last_image.size + + target_area = 1024 * 1024 + ratio = image_size[0] / image_size[1] + calculated_width = math.sqrt(target_area * ratio) + calculated_height = calculated_width / ratio + calculated_width = round(calculated_width / 32) * 32 + calculated_height = round(calculated_height / 32) * 32 + + use_height = config.height or calculated_height + use_width = config.width or calculated_width + + vae_scale_factor = 8 + multiple_of = vae_scale_factor * 2 + use_width = use_width // multiple_of * multiple_of + use_height = use_height // multiple_of * multiple_of + + final_config = Config( + height=use_height, + width=use_width, + image_path=config.image_path, + num_inference_steps=config.num_inference_steps, + guidance=config.guidance, + scheduler=config.scheduler_str, + ) + runtime_config = RuntimeConfig(final_config, self.model_config) + + CONDITION_IMAGE_SIZE = 384 * 384 + condition_ratio = image_size[0] / image_size[1] + vl_width = math.sqrt(CONDITION_IMAGE_SIZE * condition_ratio) + vl_height = vl_width / condition_ratio + vl_width = round(vl_width / 32) * 32 + vl_height = round(vl_height / 32) * 32 + + VAE_IMAGE_SIZE = 1024 * 1024 + vae_ratio = image_size[0] / image_size[1] + vae_width = math.sqrt(VAE_IMAGE_SIZE * vae_ratio) + vae_height = vae_width / vae_ratio + vae_width = round(vae_width / 32) * 32 + vae_height = round(vae_height / 32) * 32 + + return runtime_config, int(vl_width), int(vl_height), int(vae_width), int(vae_height) diff --git a/src/mflux/models/qwen/variants/edit/utils/__init__.py b/src/mflux/models/qwen/variants/edit/utils/__init__.py new file mode 100644 index 0000000..71bf0a9 --- /dev/null +++ b/src/mflux/models/qwen/variants/edit/utils/__init__.py @@ -0,0 +1 @@ +# Qwen Edit utilities diff --git a/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py b/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py new file mode 100644 index 0000000..1763c90 --- /dev/null +++ b/src/mflux/models/qwen/variants/edit/utils/qwen_edit_util.py @@ -0,0 +1,106 @@ +import os + +import mlx.core as mx + +from mflux.latent_creator.latent_creator import LatentCreator +from mflux.post_processing.array_util import ArrayUtil + + +class QwenEditUtil: + @staticmethod + def create_image_conditioning_latents( + vae, + height: int, + width: int, + image_paths: list[str] | str, + vl_width: int | None = None, + vl_height: int | None = None, + ) -> tuple[mx.array, mx.array, int, int, int]: + if not isinstance(image_paths, list): + image_paths = [str(image_paths)] + + if vl_width is not None and vl_height is not None: + calc_w, calc_h = vl_width, vl_height + else: + from mflux.post_processing.image_util import ImageUtil + + pil_image = ImageUtil.load_image(image_paths[-1]).convert("RGB") + img_w, img_h = pil_image.size + target_area_env = os.getenv("MFLUX_QWEN_VL_TARGET_AREA") + target_area = int(target_area_env) if target_area_env is not None else 1024 * 1024 + ratio = img_w / img_h + calc_w = int(round(((target_area * ratio) ** 0.5) / 32) * 32) + calc_h = int(round((calc_w / ratio) / 32) * 32) + + all_image_latents = [] + for image_path in image_paths: + input_image = LatentCreator.encode_image( + vae=vae, + image_path=image_path, + height=calc_h, + width=calc_w, + ) + + image_latents = ArrayUtil.pack_latents( + latents=input_image, + height=calc_h, + width=calc_w, + num_channels_latents=16, + ) + all_image_latents.append(image_latents) + + image_latents = mx.concatenate(all_image_latents, axis=1) + + all_image_ids = [] + for _ in image_paths: + image_ids = QwenEditUtil._create_image_ids( + height=calc_h, + width=calc_w, + ) + all_image_ids.append(image_ids) + image_ids = mx.concatenate(all_image_ids, axis=1) + + cond_h_patches = calc_h // 16 + cond_w_patches = calc_w // 16 + num_images = len(image_paths) + return image_latents, image_ids, cond_h_patches, cond_w_patches, num_images + + @staticmethod + def _create_image_ids( + height: int, + width: int, + ) -> mx.array: + latent_height = height // 16 + latent_width = width // 16 + + image_ids = mx.zeros((latent_height, latent_width, 3)) + + row_coords = mx.arange(0, latent_height)[:, None] + row_coords = mx.broadcast_to(row_coords, (latent_height, latent_width)) + image_ids = mx.concatenate( + [ + image_ids[:, :, :1], + row_coords[:, :, None], + image_ids[:, :, 2:], + ], + axis=2, + ) + + col_coords = mx.arange(0, latent_width)[None, :] + col_coords = mx.broadcast_to(col_coords, (latent_height, latent_width)) + image_ids = mx.concatenate( + [ + image_ids[:, :, :2], + col_coords[:, :, None], + ], + axis=2, + ) + + image_ids = mx.reshape(image_ids, (latent_height * latent_width, 3)) + + first_dim = mx.ones((image_ids.shape[0], 1)) + image_ids = mx.concatenate([first_dim, image_ids[:, 1:]], axis=1) + + image_ids = mx.expand_dims(image_ids, axis=0) + + return image_ids diff --git a/src/mflux/models/qwen/variants/edit/utils/qwen_prompt_rewriter.py b/src/mflux/models/qwen/variants/edit/utils/qwen_prompt_rewriter.py new file mode 100644 index 0000000..02ecf81 --- /dev/null +++ b/src/mflux/models/qwen/variants/edit/utils/qwen_prompt_rewriter.py @@ -0,0 +1,116 @@ +from typing import Optional + + +class QwenPromptRewriter: + @staticmethod + def detect_language(prompt: str) -> str: + chinese_ranges = [ + ("\u4e00", "\u9fff"), + ] + for char in prompt: + if any(start <= char <= end for start, end in chinese_ranges): + return "zh" + return "en" + + @staticmethod + def enhance_prompt_en(original_prompt: str) -> str: + # Remove extra whitespace and normalize + prompt = original_prompt.strip() + + # Add style and quality enhancers if not present + quality_terms = ["ultra hd", "4k", "cinematic", "high quality", "detailed"] + has_quality = any(term in prompt.lower() for term in quality_terms) + + if not has_quality: + prompt += ", ultra HD, 4K, cinematic composition, high quality, detailed" + + # Enhance editing-specific instructions + if any(word in prompt.lower() for word in ["change", "replace", "modify", "edit"]): + if "maintain" not in prompt.lower() and "keep" not in prompt.lower(): + prompt += ", maintain original composition and lighting" + + # Add style consistency for artistic transformations + art_styles = ["watercolor", "oil painting", "sketch", "digital art", "anime", "cartoon"] + if any(style in prompt.lower() for style in art_styles): + prompt += ", consistent artistic style throughout" + + return prompt + + @staticmethod + def enhance_prompt_zh(original_prompt: str) -> str: + # Remove extra whitespace and normalize + prompt = original_prompt.strip() + + # Add quality enhancers for Chinese prompts + quality_terms = ["超清", "4k", "高质量", "精细", "电影级"] + has_quality = any(term in prompt for term in quality_terms) + + if not has_quality: + prompt += "，超清，4K，电影级构图，高质量，精细" + + # Enhance editing-specific instructions + edit_terms = ["改变", "替换", "修改", "编辑", "变成"] + if any(term in prompt for term in edit_terms): + if "保持" not in prompt and "维持" not in prompt: + prompt += "，保持原有构图和光线" + + return prompt + + @staticmethod + def enhance_edit_prompt(prompt: str, context: Optional[str] = None) -> str: + language = QwenPromptRewriter.detect_language(prompt) + + if language == "zh": + enhanced = QwenPromptRewriter.enhance_prompt_zh(prompt) + else: + enhanced = QwenPromptRewriter.enhance_prompt_en(prompt) + + # Add edit-specific stability improvements + edit_keywords = { + "en": ["transform", "change", "modify", "edit", "replace", "add", "remove"], + "zh": ["转换", "改变", "修改", "编辑", "替换", "添加", "移除", "变成"], + } + + current_keywords = edit_keywords[language] + has_edit_instruction = any(keyword in enhanced.lower() for keyword in current_keywords) + + if has_edit_instruction: + if language == "zh": + if "保持细节" not in enhanced: + enhanced += "，保持细节准确" + else: + if "preserve details" not in enhanced.lower(): + enhanced += ", preserve fine details" + + return enhanced + + @staticmethod + def create_edit_template( + action: str, target: str, replacement: Optional[str] = None, style: Optional[str] = None, language: str = "en" + ) -> str: + if language == "zh": + templates = { + "replace": f"将{target}替换为{replacement}" if replacement else f"替换{target}", + "add": f"在图像中添加{target}", + "remove": f"从图像中移除{target}", + "transform": f"将图像转换为{target}风格", + } + quality_suffix = "，保持原有构图，超清，4K，精细细节" + else: + templates = { + "replace": f"Replace {target} with {replacement}" if replacement else f"Replace {target}", + "add": f"Add {target} to the image", + "remove": f"Remove {target} from the image", + "transform": f"Transform the image into {target} style", + } + quality_suffix = ", maintain original composition, ultra HD, 4K, fine details" + + base_prompt = templates.get(action, target) + + if style: + if language == "zh": + base_prompt += f"，{style}风格" + else: + base_prompt += f", {style} style" + + return base_prompt + quality_suffix diff --git a/src/mflux/models/qwen/variants/txt2img/qwen_image.py b/src/mflux/models/qwen/variants/txt2img/qwen_image.py index 546fde5..3365bc2 100644 --- a/src/mflux/models/qwen/variants/txt2img/qwen_image.py +++ b/src/mflux/models/qwen/variants/txt2img/qwen_image.py @@ -52,10 +52,6 @@ class QwenImage(nn.Module): prompt: str, config: Config, negative_prompt: str | None = None, - prompt_embeds: mx.array | None = None, - prompt_mask: mx.array | None = None, - negative_prompt_embeds: mx.array | None = None, - negative_prompt_mask: mx.array | None = None, ) -> GeneratedImage: # 0. Create a new runtime config based on the model type and input parameters runtime_config = RuntimeConfig(config, self.model_config) @@ -74,7 +70,7 @@ class QwenImage(nn.Module): ), ) - # 2. Encode the prompt + # 2. Encode the prompt (using native MLX encoding) prompt_embeds, prompt_mask, negative_prompt_embeds, negative_prompt_mask = QwenPromptEncoder.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, @@ -111,7 +107,7 @@ class QwenImage(nn.Module): encoder_hidden_states=negative_prompt_embeds, encoder_hidden_states_mask=negative_prompt_mask, ) - guided_noise = QwenImage._compute_guided_noise(noise, noise_negative, runtime_config.guidance) + guided_noise = QwenImage.compute_guided_noise(noise, noise_negative, runtime_config.guidance) # 4.t Take one denoise step latents = runtime_config.scheduler.step( @@ -173,7 +169,7 @@ class QwenImage(nn.Module): QwenModelSaver.save_model(self, self.bits, base_path) @staticmethod - def _compute_guided_noise( + def compute_guided_noise( noise: mx.array, noise_negative: mx.array, guidance: float, diff --git a/src/mflux/models/qwen/weights/qwen_weight_handler.py b/src/mflux/models/qwen/weights/qwen_weight_handler.py index 39b3235..8097666 100644 --- a/src/mflux/models/qwen/weights/qwen_weight_handler.py +++ b/src/mflux/models/qwen/weights/qwen_weight_handler.py @@ -1,11 +1,17 @@ import json +from collections.abc import Callable from pathlib import Path import mlx.core as mx +import torch +from mlx.utils import tree_unflatten from safetensors.mlx import load_file as mlx_load_file +from safetensors.torch import load_file as torch_load_file +from mflux.models.common.weights.mapping.weight_mapper import WeightMapper +from mflux.models.common.weights.mapping.weight_mapping import WeightTarget from mflux.models.flux.weights.weight_handler import MetaData, WeightHandler -from mflux.models.qwen.weights.qwen_weight_util import QwenWeightUtil +from mflux.models.qwen.weights.qwen_weight_mapping import QwenWeightMapping class QwenWeightHandler: @@ -21,13 +27,6 @@ class QwenWeightHandler: self.vae = vae self.meta_data = meta_data - def num_transformer_blocks(self) -> int: - return ( - len(self.transformer["transformer_blocks"]) - if self.transformer and "transformer_blocks" in self.transformer - else 0 - ) - @staticmethod def load_regular_weights( repo_id: str | None = None, @@ -36,9 +35,12 @@ class QwenWeightHandler: # Load the weights from disk, huggingface cache, or download from huggingface root_path = Path(local_path) if local_path else WeightHandler.download_or_get_cached_weights(repo_id) + # Determine if we should load visual weights (for Edit model) + load_visual_weights = repo_id and "edit" in repo_id.lower() + # Load the weights - transformer, quantization_level, mflux_version = QwenWeightHandler.load_transformer(root_path=root_path) - qwen_text_encoder, _, _ = QwenWeightHandler._load_qwen_text_encoder(root_path=root_path) + transformer, quantization_level, mflux_version = QwenWeightHandler._load_transformer(root_path=root_path) + qwen_text_encoder, _, _ = QwenWeightHandler._load_qwen_text_encoder(root_path=root_path, load_visual_weights=load_visual_weights) # fmt: off vae, _, _ = QwenWeightHandler._load_vae(root_path=root_path) return QwenWeightHandler( @@ -54,612 +56,54 @@ class QwenWeightHandler: ) @staticmethod - def load_transformer(root_path: Path) -> tuple[dict, int | None, str | None]: - flat = QwenWeightHandler._load_safetensors_shards(root_path / "transformer", loading_mode="multi_glob") - - # Check if this is a saved quantized model (with metadata) or HuggingFace weights - # Saved models from mflux-save are already in MLX structure and don't need manual mapping - quantization_level = None - mflux_version = None - - # Try to get metadata from the first weight shard - import mlx.core as mx - from mlx.utils import tree_unflatten - - file_glob = sorted((root_path / "transformer").glob("*.safetensors")) - if file_glob: - data = mx.load(str(file_glob[0]), return_metadata=True) - if len(data) > 1: - quantization_level = data[1].get("quantization_level") - mflux_version = data[1].get("mflux_version") - - # If this is a saved model (has metadata), use tree_unflatten directly - if quantization_level is not None or mflux_version is not None: - return tree_unflatten(list(flat.items())), quantization_level, mflux_version - - # Otherwise, it's HuggingFace weights that need manual mapping - mapped_weights = QwenWeightHandler._manual_transformer_mapping(flat) - return mapped_weights, None, None + def _load_transformer(root_path: Path) -> tuple[dict, int | None, str | None]: + return QwenWeightHandler._load_component( + root_path=root_path, + component_name="transformer", + loading_mode="multi_glob", + mapping_getter=QwenWeightMapping.get_transformer_mapping, + ) @staticmethod - def _load_qwen_text_encoder(root_path: Path) -> tuple[dict, int | None, str | None]: - import mlx.core as mx - from mlx.utils import tree_unflatten - - # Check for saved model metadata FIRST to determine loading mode - quantization_level = None - mflux_version = None - file_glob = sorted((root_path / "text_encoder").glob("*.safetensors")) - if file_glob: - data = mx.load(str(file_glob[0]), return_metadata=True) - if len(data) > 1: - quantization_level = data[1].get("quantization_level") - mflux_version = data[1].get("mflux_version") - - # If this is a saved model, load without expecting index.json - if quantization_level is not None or mflux_version is not None: - all_weights = QwenWeightHandler._load_safetensors_shards( - root_path / "text_encoder", loading_mode="multi_glob" - ) - return tree_unflatten(list(all_weights.items())), quantization_level, mflux_version - - # Otherwise, it's HuggingFace weights that need manual mapping - all_weights = QwenWeightHandler._load_safetensors_shards(root_path / "text_encoder", loading_mode="multi_json") - mapped_weights = QwenWeightHandler._manual_text_encoder_mapping(all_weights) - return mapped_weights, None, None + def _load_qwen_text_encoder( + root_path: Path, load_visual_weights: bool = False + ) -> tuple[dict, int | None, str | None]: + return QwenWeightHandler._load_component( + root_path=root_path, + component_name="text_encoder", + loading_mode="multi_json", + mapping_getter=QwenWeightMapping.get_text_encoder_mapping, + ) @staticmethod def _load_vae(root_path: Path) -> tuple[dict, int | None, str | None]: - import mlx.core as mx - from mlx.utils import tree_unflatten + return QwenWeightHandler._load_component( + root_path=root_path, + component_name="vae", + loading_mode="single", + mapping_getter=QwenWeightMapping.get_vae_mapping, + ) - weights = QwenWeightHandler._load_safetensors_shards(root_path / "vae", loading_mode="single") + @staticmethod + def _load_component( + root_path: Path, + component_name: str, + loading_mode: str, + mapping_getter: Callable[[], list[WeightTarget]], + ) -> tuple[dict, int | None, str | None]: + component_path = root_path / component_name + weights = QwenWeightHandler._load_safetensors_shards(component_path, loading_mode=loading_mode) - # Check for saved model metadata - quantization_level = None - mflux_version = None - file_glob = sorted((root_path / "vae").glob("*.safetensors")) - if file_glob: - data = mx.load(str(file_glob[0]), return_metadata=True) - if len(data) > 1: - quantization_level = data[1].get("quantization_level") - mflux_version = data[1].get("mflux_version") - - # If this is a saved model, use tree_unflatten directly + # Check if this is a saved model (has metadata) + quantization_level, mflux_version = QwenWeightHandler._detect_metadata(component_path) if quantization_level is not None or mflux_version is not None: - return tree_unflatten(list(weights.items())), quantization_level, mflux_version + return QwenWeightHandler._load_saved_model_weights(weights, None, quantization_level, mflux_version) - # Otherwise, it's HuggingFace weights that need manual mapping and reshaping - reshaped_weights = [QwenWeightUtil.reshape_weights(k, v) for k, v in weights.items()] - reshaped_weights = QwenWeightUtil.flatten(reshaped_weights) - weights = dict(reshaped_weights) - mapped_weights = QwenWeightHandler._manual_flux_style_mapping(weights) + # Otherwise, it's HuggingFace weights that need mapping + mapping = mapping_getter() + mapped_weights = WeightMapper.apply_mapping(weights, mapping) return mapped_weights, None, None - @staticmethod - def _manual_flux_style_mapping(diffusers_weights: dict) -> dict: - weights = {} - - # 1. Simple direct mappings (like Flux does) - weights["decoder"] = {} - - # conv_in: decoder.conv_in.weight -> decoder.conv_in.conv3d.weight - weights["decoder"]["conv_in"] = { - "conv3d": { - "weight": diffusers_weights["decoder.conv_in.weight"], - "bias": diffusers_weights["decoder.conv_in.bias"], - } - } - - # conv_out: decoder.conv_out.weight -> decoder.conv_out.conv3d.weight - weights["decoder"]["conv_out"] = { - "conv3d": { - "weight": diffusers_weights["decoder.conv_out.weight"], - "bias": diffusers_weights["decoder.conv_out.bias"], - } - } - - # norm_out: decoder.norm_out.gamma -> decoder.norm_out.weight (flatten to 1D like MLX expects) - dec_gamma = diffusers_weights["decoder.norm_out.gamma"] - if len(dec_gamma.shape) > 1: - dec_gamma = mx.reshape(dec_gamma, (dec_gamma.shape[0],)) - weights["decoder"]["norm_out"] = {"weight": dec_gamma} - - # post_quant_conv: post_quant_conv.weight -> post_quant_conv.conv3d.weight - weights["post_quant_conv"] = { - "conv3d": { - "weight": diffusers_weights["post_quant_conv.weight"], - "bias": diffusers_weights["post_quant_conv.bias"], - } - } - - # 2. Mid block (manual structure building) - weights["decoder"]["mid_block"] = {} - - # mid_block.resnets (list of 2 resnets) - weights["decoder"]["mid_block"]["resnets"] = [{}, {}] - for i in range(2): - resnet = weights["decoder"]["mid_block"]["resnets"][i] - # conv1.weight -> conv1.conv3d.weight - resnet["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv1.weight"], - "bias": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv1.bias"], - } - } - # conv2.weight -> conv2.conv3d.weight - resnet["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv2.weight"], - "bias": diffusers_weights[f"decoder.mid_block.resnets.{i}.conv2.bias"], - } - } - # norm gammas -> 1D weights - g1 = diffusers_weights[f"decoder.mid_block.resnets.{i}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"decoder.mid_block.resnets.{i}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - resnet["norm1"] = {"weight": g1} - resnet["norm2"] = {"weight": g2} - - # mid_block.attentions (list of 1 attention) - weights["decoder"]["mid_block"]["attentions"] = [{}] - attn = weights["decoder"]["mid_block"]["attentions"][0] - g = diffusers_weights["decoder.mid_block.attentions.0.norm.gamma"] - if len(g.shape) > 1: - g = mx.reshape(g, (g.shape[0],)) - attn["norm"] = {"weight": g} - # Note: to_qkv and proj are Conv2d, not Conv3d - no conv3d wrapper - attn["to_qkv"] = { - "weight": diffusers_weights["decoder.mid_block.attentions.0.to_qkv.weight"], - "bias": diffusers_weights["decoder.mid_block.attentions.0.to_qkv.bias"], - } - attn["proj"] = { - "weight": diffusers_weights["decoder.mid_block.attentions.0.proj.weight"], - "bias": diffusers_weights["decoder.mid_block.attentions.0.proj.bias"], - } - - # 3. Up blocks (manual structure building) - for block_idx in range(4): - up_block_key = f"up_block{block_idx}" - weights["decoder"][up_block_key] = {} - - # resnets (list of 3 resnets) - weights["decoder"][up_block_key]["resnets"] = [{}, {}, {}] - for res_idx in range(3): - resnet = weights["decoder"][up_block_key]["resnets"][res_idx] - - # conv1.weight -> conv1.conv3d.weight - resnet["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv1.weight"], - "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv1.bias"], - } - } - # conv2.weight -> conv2.conv3d.weight - resnet["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv2.weight"], - "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv2.bias"], - } - } - # norm gammas -> 1D weights - g1 = diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - resnet["norm1"] = {"weight": g1} - resnet["norm2"] = {"weight": g2} - - # Handle optional conv_shortcut -> skip_conv (only exists for some resnets) - shortcut_key = f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv_shortcut.weight" - if shortcut_key in diffusers_weights: - resnet["skip_conv"] = { - "conv3d": { - "weight": diffusers_weights[shortcut_key], - "bias": diffusers_weights[ - f"decoder.up_blocks.{block_idx}.resnets.{res_idx}.conv_shortcut.bias" - ], - } - } - - # upsamplers (only for blocks 0, 1, 2) - if block_idx <= 2: - weights["decoder"][up_block_key]["upsamplers"] = [{}] - upsampler = weights["decoder"][up_block_key]["upsamplers"][0] - - # resample.1.weight -> resample_conv.weight (Conv2d, no conv3d wrapper) - upsampler["resample_conv"] = { - "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.resample.1.weight"], - "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.resample.1.bias"], - } - - # time_conv (only for blocks 0, 1) - if block_idx <= 1: - upsampler["time_conv"] = { - "conv3d": { - "weight": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.time_conv.weight"], - "bias": diffusers_weights[f"decoder.up_blocks.{block_idx}.upsamplers.0.time_conv.bias"], - } - } - - # 4. Encoder mappings (mirror structure of model parameter names) - weights["encoder"] = {} - - # conv_in: encoder.conv_in.weight -> encoder.conv_in.conv3d.weight - weights["encoder"]["conv_in"] = { - "conv3d": { - "weight": diffusers_weights["encoder.conv_in.weight"], - "bias": diffusers_weights["encoder.conv_in.bias"], - } - } - - # conv_out: encoder.conv_out.weight -> encoder.conv_out.conv3d.weight - weights["encoder"]["conv_out"] = { - "conv3d": { - "weight": diffusers_weights["encoder.conv_out.weight"], - "bias": diffusers_weights["encoder.conv_out.bias"], - } - } - - # norm_out: encoder.norm_out.gamma -> encoder.norm_out.weight (flatten to 1D) - enc_gamma = diffusers_weights["encoder.norm_out.gamma"] - if len(enc_gamma.shape) > 1: - enc_gamma = mx.reshape(enc_gamma, (enc_gamma.shape[0],)) - weights["encoder"]["norm_out"] = {"weight": enc_gamma} - - # mid_block - weights["encoder"]["mid_block"] = {} - # mid_block.attentions (list of 1) - weights["encoder"]["mid_block"]["attentions"] = [{}] - enc_attn = weights["encoder"]["mid_block"]["attentions"][0] - g = diffusers_weights["encoder.mid_block.attentions.0.norm.gamma"] - if len(g.shape) > 1: - g = mx.reshape(g, (g.shape[0],)) - enc_attn["norm"] = {"weight": g} - enc_attn["to_qkv"] = { - "weight": diffusers_weights["encoder.mid_block.attentions.0.to_qkv.weight"], - "bias": diffusers_weights["encoder.mid_block.attentions.0.to_qkv.bias"], - } - enc_attn["proj"] = { - "weight": diffusers_weights["encoder.mid_block.attentions.0.proj.weight"], - "bias": diffusers_weights["encoder.mid_block.attentions.0.proj.bias"], - } - # mid_block.resnets (list of 2) - weights["encoder"]["mid_block"]["resnets"] = [{}, {}] - for i in range(2): - res = weights["encoder"]["mid_block"]["resnets"][i] - g1 = diffusers_weights[f"encoder.mid_block.resnets.{i}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"encoder.mid_block.resnets.{i}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - res["norm1"] = {"weight": g1} - res["norm2"] = {"weight": g2} - res["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv1.weight"], - "bias": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv1.bias"], - } - } - res["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv2.weight"], - "bias": diffusers_weights[f"encoder.mid_block.resnets.{i}.conv2.bias"], - } - } - - # down_blocks - simplified mapping like decoder - # From safetensor keys, encoder has flattened indices 0-9 that need to be grouped into 4 stages - # Stage 0: indices 0,1 (2 resnets, no downsampler) - # Stage 1: indices 2,3,4 (downsampler at 2, resnets at 3,4) - # Stage 2: indices 5,6,7 (downsampler at 5, resnets at 6,7) - # Stage 3: indices 8,9 (downsampler at 8, resnet at 9) - - weights["encoder"]["down_blocks"] = [{}, {}, {}, {}] - - # Stage 0: 2 resnets + downsampler (2D) - weights["encoder"]["down_blocks"][0]["resnets"] = [{}, {}] - for res_idx in range(2): - flat_idx = res_idx # 0, 1 - resnet = weights["encoder"]["down_blocks"][0]["resnets"][res_idx] - g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - resnet["norm1"] = {"weight": g1} - resnet["norm2"] = {"weight": g2} - resnet["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"], - } - } - resnet["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"], - } - } - # Downsampler appears after indices 0,1 at flattened index 2 - weights["encoder"]["down_blocks"][0]["downsamplers"] = [{}] - d0 = weights["encoder"]["down_blocks"][0]["downsamplers"][0] - d0["resample_conv"] = { - "weight": diffusers_weights["encoder.down_blocks.2.resample.1.weight"], - "bias": diffusers_weights["encoder.down_blocks.2.resample.1.bias"], - } - - # Stage 1: downsampler + 2 resnets - weights["encoder"]["down_blocks"][1]["resnets"] = [{}, {}] - weights["encoder"]["down_blocks"][1]["downsamplers"] = [{}] - # Downsampler at flattened index 5 (after stage0's 0,1,2 and stage1's 3,4) - d = weights["encoder"]["down_blocks"][1]["downsamplers"][0] - d["resample_conv"] = { - "weight": diffusers_weights["encoder.down_blocks.5.resample.1.weight"], - "bias": diffusers_weights["encoder.down_blocks.5.resample.1.bias"], - } - # Resnets at indices 3, 4 - for res_idx in range(2): - flat_idx = 3 + res_idx # 3, 4 - resnet = weights["encoder"]["down_blocks"][1]["resnets"][res_idx] - g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - resnet["norm1"] = {"weight": g1} - resnet["norm2"] = {"weight": g2} - resnet["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"], - } - } - resnet["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"], - } - } - # Skip conv only for first resnet (index 3) - if flat_idx == 3: - resnet["skip_conv"] = { - "conv3d": { - "weight": diffusers_weights["encoder.down_blocks.3.conv_shortcut.weight"], - "bias": diffusers_weights["encoder.down_blocks.3.conv_shortcut.bias"], - } - } - - # Stage 2: downsampler + 2 resnets - weights["encoder"]["down_blocks"][2]["resnets"] = [{}, {}] - weights["encoder"]["down_blocks"][2]["downsamplers"] = [{}] - # Downsampler at flattened index 8 - d = weights["encoder"]["down_blocks"][2]["downsamplers"][0] - d["resample_conv"] = { - "weight": diffusers_weights["encoder.down_blocks.8.resample.1.weight"], - "bias": diffusers_weights["encoder.down_blocks.8.resample.1.bias"], - } - d["time_conv"] = { - "conv3d": { - "weight": diffusers_weights["encoder.down_blocks.8.time_conv.weight"], - "bias": diffusers_weights["encoder.down_blocks.8.time_conv.bias"], - } - } - # Resnets at indices 6, 7 - for res_idx in range(2): - flat_idx = 6 + res_idx # 6, 7 - resnet = weights["encoder"]["down_blocks"][2]["resnets"][res_idx] - g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - resnet["norm1"] = {"weight": g1} - resnet["norm2"] = {"weight": g2} - resnet["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"], - } - } - resnet["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"], - } - } - # Skip conv only for first resnet (index 6) - if flat_idx == 6: - resnet["skip_conv"] = { - "conv3d": { - "weight": diffusers_weights["encoder.down_blocks.6.conv_shortcut.weight"], - "bias": diffusers_weights["encoder.down_blocks.6.conv_shortcut.bias"], - } - } - - # Stage 3: no downsampler + 2 resnets (like Diffusers) - weights["encoder"]["down_blocks"][3]["resnets"] = [{}, {}] - # No downsampler in final stage (mirrors diffusers) - # Resnets at indices 9, 10 - for res_idx in range(2): - flat_idx = 9 + res_idx # 9, 10 - resnet = weights["encoder"]["down_blocks"][3]["resnets"][res_idx] - g1 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm1.gamma"] - if len(g1.shape) > 1: - g1 = mx.reshape(g1, (g1.shape[0],)) - g2 = diffusers_weights[f"encoder.down_blocks.{flat_idx}.norm2.gamma"] - if len(g2.shape) > 1: - g2 = mx.reshape(g2, (g2.shape[0],)) - resnet["norm1"] = {"weight": g1} - resnet["norm2"] = {"weight": g2} - resnet["conv1"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv1.bias"], - } - } - resnet["conv2"] = { - "conv3d": { - "weight": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.weight"], - "bias": diffusers_weights[f"encoder.down_blocks.{flat_idx}.conv2.bias"], - } - } - # No skip_conv for stage 3 (channels don't change: 384->384) - - # 5. Quant conv - weights["quant_conv"] = { - "conv3d": { - "weight": diffusers_weights["quant_conv.weight"], - "bias": diffusers_weights["quant_conv.bias"], - } - } - - return weights - - @staticmethod - def _manual_transformer_mapping(diffusers_weights: dict) -> dict: - weights = {} - - # 1. Top-level mappings (exact MLX parameter names) - weights["img_in"] = {"weight": diffusers_weights["img_in.weight"], "bias": diffusers_weights["img_in.bias"]} - weights["txt_norm"] = {"weight": diffusers_weights["txt_norm.weight"]} - weights["txt_in"] = {"weight": diffusers_weights["txt_in.weight"], "bias": diffusers_weights["txt_in.bias"]} - - # 2. Time text embedder (exact MLX structure) - weights["time_text_embed"] = { - "timestep_embedder": { - "linear_1": { - "weight": diffusers_weights["time_text_embed.timestep_embedder.linear_1.weight"], - "bias": diffusers_weights["time_text_embed.timestep_embedder.linear_1.bias"], - }, - "linear_2": { - "weight": diffusers_weights["time_text_embed.timestep_embedder.linear_2.weight"], - "bias": diffusers_weights["time_text_embed.timestep_embedder.linear_2.bias"], - }, - } - } - - # 3. Output head (exact MLX structure) - weights["norm_out"] = { - "linear": { - "weight": diffusers_weights["norm_out.linear.weight"], - "bias": diffusers_weights["norm_out.linear.bias"], - } - } - weights["proj_out"] = { - "weight": diffusers_weights["proj_out.weight"], - "bias": diffusers_weights["proj_out.bias"], - } - - # 4. Transformer blocks (exact MLX parameter names - no applier needed!) - transformer_blocks = [] - for block_idx in range(60): # 60 blocks based on debug output - block = {} - - # Stage 1: Normalization + modulation modules (QwenLayerNorm) - block["img_norm1"] = { - "mod_linear": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.img_mod.1.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.img_mod.1.bias"], - }, - "norm1": {}, # LayerNorm with affine=False has no weights - "norm2": {}, # LayerNorm with affine=False has no weights - } - block["txt_norm1"] = { - "mod_linear": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mod.1.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mod.1.bias"], - }, - "norm1": {}, # LayerNorm with affine=False has no weights - "norm2": {}, # LayerNorm with affine=False has no weights - } - - # Stage 2: Separate normalization modules (Flux-style) - block["img_norm2"] = {} # LayerNorm with affine=False has no weights - block["txt_norm2"] = {} # LayerNorm with affine=False has no weights - - # Attention module (nested under "attn" like Flux) - block["attn"] = { - "to_q": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_q.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_q.bias"], - }, - "to_k": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_k.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_k.bias"], - }, - "to_v": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_v.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_v.bias"], - }, - "add_q_proj": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_q_proj.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_q_proj.bias"], - }, - "add_k_proj": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_k_proj.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_k_proj.bias"], - }, - "add_v_proj": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_v_proj.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.add_v_proj.bias"], - }, - "norm_q": {"weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_q.weight"]}, - "norm_k": {"weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_k.weight"]}, - "norm_added_q": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_added_q.weight"] - }, - "norm_added_k": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.norm_added_k.weight"] - }, - "attn_to_out": [ # type: ignore[dict-item] - { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_out.0.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_out.0.bias"], - } - ], - "to_add_out": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_add_out.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.attn.to_add_out.bias"], - }, - } - - # Feed Forward modules (nested under img_ff/txt_ff) - block["img_ff"] = { - "mlp_in": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.0.proj.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.0.proj.bias"], - }, - "mlp_out": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.2.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.img_mlp.net.2.bias"], - }, - } - block["txt_ff"] = { - "mlp_in": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.0.proj.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.0.proj.bias"], - }, - "mlp_out": { - "weight": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.2.weight"], - "bias": diffusers_weights[f"transformer_blocks.{block_idx}.txt_mlp.net.2.bias"], - }, - } - - transformer_blocks.append(block) - - weights["transformer_blocks"] = transformer_blocks - return weights - @staticmethod def _load_safetensors_shards(path: Path, loading_mode: str = "multi_glob") -> dict[str, mx.array]: all_weights = {} @@ -696,9 +140,6 @@ class QwenWeightHandler: file_weights = mlx_load_file(str(file_path)) except Exception: # noqa: BLE001 # If MLX can't load directly, try with torch and convert - import torch - from safetensors.torch import load_file as torch_load_file - torch_weights = torch_load_file(str(file_path)) file_weights = {} for name, tensor in torch_weights.items(): @@ -724,79 +165,26 @@ class QwenWeightHandler: return all_weights @staticmethod - def _manual_text_encoder_mapping(hf_weights: dict[str, mx.array]) -> dict: - weights = {} - converted_count = 0 - skipped_count = 0 + def _load_saved_model_weights( + weights: dict[str, mx.array] | None, + path: Path | None, + quantization_level: int | None, + mflux_version: str | None, + ) -> tuple[dict, int | None, str | None]: + # If weights already loaded, use them; otherwise load from path + if weights is None: + # For saved models, always use multi_glob (no index.json needed) + weights = QwenWeightHandler._load_safetensors_shards(path, loading_mode="multi_glob") - # Skip LM head and vision encoder weights - we only need the text encoder - filtered_weights = {} - for hf_name, weight in hf_weights.items(): - if hf_name.startswith("lm_head") or hf_name.startswith("visual."): - skipped_count += 1 - continue - filtered_weights[hf_name] = weight + return tree_unflatten(list(weights.items())), quantization_level, mflux_version - # 1. Top-level embeddings (exact MLX parameter names) - weights["encoder"] = {} - weights["encoder"]["embed_tokens"] = {"weight": filtered_weights["model.embed_tokens.weight"]} - converted_count += 1 - - # 2. Final norm (exact MLX parameter names) - weights["encoder"]["norm"] = {"weight": filtered_weights["model.norm.weight"]} - converted_count += 1 - - # 3. Encoder layers (exact MLX parameter names - 28 layers) - layers = [] - for layer_idx in range(28): # 28 text encoder layers - layer = {} - - # Layer norms - layer["input_layernorm"] = {"weight": filtered_weights[f"model.layers.{layer_idx}.input_layernorm.weight"]} - layer["post_attention_layernorm"] = { - "weight": filtered_weights[f"model.layers.{layer_idx}.post_attention_layernorm.weight"] - } - converted_count += 2 - - # Self attention (exact MLX parameter names) - layer["self_attn"] = { - "q_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.q_proj.weight"], - "bias": filtered_weights[f"model.layers.{layer_idx}.self_attn.q_proj.bias"], - }, - "k_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.k_proj.weight"], - "bias": filtered_weights[f"model.layers.{layer_idx}.self_attn.k_proj.bias"], - }, - "v_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.v_proj.weight"], - "bias": filtered_weights[f"model.layers.{layer_idx}.self_attn.v_proj.bias"], - }, - "o_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.self_attn.o_proj.weight"] - # Note: o_proj has no bias in MLX structure - }, - } - converted_count += 7 - - # MLP (exact MLX parameter names) - layer["mlp"] = { - "gate_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.mlp.gate_proj.weight"] - # Note: gate_proj has no bias in MLX structure - }, - "up_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.mlp.up_proj.weight"] - # Note: up_proj has no bias in MLX structure - }, - "down_proj": { - "weight": filtered_weights[f"model.layers.{layer_idx}.mlp.down_proj.weight"] - # Note: down_proj has no bias in MLX structure - }, - } - converted_count += 3 - - layers.append(layer) - - weights["encoder"]["layers"] = layers - return weights + @staticmethod + def _detect_metadata(path: Path) -> tuple[int | None, str | None]: + file_glob = sorted(path.glob("*.safetensors")) + if file_glob: + data = mx.load(str(file_glob[0]), return_metadata=True) + if len(data) > 1: + quantization_level = data[1].get("quantization_level") + mflux_version = data[1].get("mflux_version") + return quantization_level, mflux_version + return None, None diff --git a/src/mflux/models/qwen/weights/qwen_weight_mapping.py b/src/mflux/models/qwen/weights/qwen_weight_mapping.py new file mode 100644 index 0000000..c3b477f --- /dev/null +++ b/src/mflux/models/qwen/weights/qwen_weight_mapping.py @@ -0,0 +1,919 @@ +from typing import List + +import mlx.core as mx + +from mflux.models.common.weights.mapping.weight_mapping import WeightMapping, WeightTarget + + +def reshape_gamma_to_1d(tensor: mx.array) -> mx.array: + if len(tensor.shape) > 1: + return mx.reshape(tensor, (tensor.shape[0],)) + return tensor + + +def transpose_patch_embed(tensor: mx.array) -> mx.array: + if len(tensor.shape) == 5: + return tensor.transpose(0, 2, 3, 4, 1) + return tensor + + +def transpose_conv3d_weight(tensor: mx.array) -> mx.array: + if len(tensor.shape) == 5: + return tensor.transpose(0, 2, 3, 4, 1) + return tensor + + +def transpose_conv2d_weight(tensor: mx.array) -> mx.array: + if len(tensor.shape) == 4: + return tensor.transpose(0, 2, 3, 1) + return tensor + + +class QwenWeightMapping(WeightMapping): + @staticmethod + def get_transformer_mapping() -> List[WeightTarget]: + return [ + # Top-level mappings + WeightTarget( + mlx_path="img_in.weight", + hf_patterns=["img_in.weight"], + ), + WeightTarget( + mlx_path="img_in.bias", + hf_patterns=["img_in.bias"], + ), + WeightTarget( + mlx_path="txt_norm.weight", + hf_patterns=["txt_norm.weight"], + ), + WeightTarget( + mlx_path="txt_in.weight", + hf_patterns=["txt_in.weight"], + ), + WeightTarget( + mlx_path="txt_in.bias", + hf_patterns=["txt_in.bias"], + ), + # Time text embedder + WeightTarget( + mlx_path="time_text_embed.timestep_embedder.linear_1.weight", + hf_patterns=["time_text_embed.timestep_embedder.linear_1.weight"], + ), + WeightTarget( + mlx_path="time_text_embed.timestep_embedder.linear_1.bias", + hf_patterns=["time_text_embed.timestep_embedder.linear_1.bias"], + ), + WeightTarget( + mlx_path="time_text_embed.timestep_embedder.linear_2.weight", + hf_patterns=["time_text_embed.timestep_embedder.linear_2.weight"], + ), + WeightTarget( + mlx_path="time_text_embed.timestep_embedder.linear_2.bias", + hf_patterns=["time_text_embed.timestep_embedder.linear_2.bias"], + ), + # Output head + WeightTarget( + mlx_path="norm_out.linear.weight", + hf_patterns=["norm_out.linear.weight"], + ), + WeightTarget( + mlx_path="norm_out.linear.bias", + hf_patterns=["norm_out.linear.bias"], + ), + WeightTarget( + mlx_path="proj_out.weight", + hf_patterns=["proj_out.weight"], + ), + WeightTarget( + mlx_path="proj_out.bias", + hf_patterns=["proj_out.bias"], + ), + # Transformer blocks - Attention + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_q.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_q.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_q.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_q.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_k.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_k.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_k.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_k.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_v.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_v.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_v.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_v.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_q_proj.weight", + hf_patterns=["transformer_blocks.{block}.attn.add_q_proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_q_proj.bias", + hf_patterns=["transformer_blocks.{block}.attn.add_q_proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_k_proj.weight", + hf_patterns=["transformer_blocks.{block}.attn.add_k_proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_k_proj.bias", + hf_patterns=["transformer_blocks.{block}.attn.add_k_proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_v_proj.weight", + hf_patterns=["transformer_blocks.{block}.attn.add_v_proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.add_v_proj.bias", + hf_patterns=["transformer_blocks.{block}.attn.add_v_proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_q.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_q.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_k.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_k.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_added_q.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_added_q.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.norm_added_k.weight", + hf_patterns=["transformer_blocks.{block}.attn.norm_added_k.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.attn_to_out.0.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_out.0.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.attn_to_out.0.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_out.0.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_add_out.weight", + hf_patterns=["transformer_blocks.{block}.attn.to_add_out.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.attn.to_add_out.bias", + hf_patterns=["transformer_blocks.{block}.attn.to_add_out.bias"], + ), + # Transformer blocks - Modulation + WeightTarget( + mlx_path="transformer_blocks.{block}.img_mod_linear.weight", + hf_patterns=["transformer_blocks.{block}.img_mod.1.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.img_mod_linear.bias", + hf_patterns=["transformer_blocks.{block}.img_mod.1.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.txt_mod_linear.weight", + hf_patterns=["transformer_blocks.{block}.txt_mod.1.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.txt_mod_linear.bias", + hf_patterns=["transformer_blocks.{block}.txt_mod.1.bias"], + ), + # Transformer blocks - Feed Forward + WeightTarget( + mlx_path="transformer_blocks.{block}.img_ff.mlp_in.weight", + hf_patterns=["transformer_blocks.{block}.img_mlp.net.0.proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.img_ff.mlp_in.bias", + hf_patterns=["transformer_blocks.{block}.img_mlp.net.0.proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.img_ff.mlp_out.weight", + hf_patterns=["transformer_blocks.{block}.img_mlp.net.2.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.img_ff.mlp_out.bias", + hf_patterns=["transformer_blocks.{block}.img_mlp.net.2.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.txt_ff.mlp_in.weight", + hf_patterns=["transformer_blocks.{block}.txt_mlp.net.0.proj.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.txt_ff.mlp_in.bias", + hf_patterns=["transformer_blocks.{block}.txt_mlp.net.0.proj.bias"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.txt_ff.mlp_out.weight", + hf_patterns=["transformer_blocks.{block}.txt_mlp.net.2.weight"], + ), + WeightTarget( + mlx_path="transformer_blocks.{block}.txt_ff.mlp_out.bias", + hf_patterns=["transformer_blocks.{block}.txt_mlp.net.2.bias"], + ), + ] + + @staticmethod + def get_vae_mapping() -> List[WeightTarget]: + return [ + # ========== Decoder ========== + # Decoder conv_in + WeightTarget( + mlx_path="decoder.conv_in.conv3d.weight", + hf_patterns=["decoder.conv_in.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.conv_in.conv3d.bias", + hf_patterns=["decoder.conv_in.bias"], + ), + # Decoder conv_out + WeightTarget( + mlx_path="decoder.conv_out.conv3d.weight", + hf_patterns=["decoder.conv_out.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.conv_out.conv3d.bias", + hf_patterns=["decoder.conv_out.bias"], + ), + # Decoder norm_out (gamma -> weight with reshape) + WeightTarget( + mlx_path="decoder.norm_out.weight", + hf_patterns=["decoder.norm_out.gamma"], + transform=reshape_gamma_to_1d, + ), + # Post quant conv + WeightTarget( + mlx_path="post_quant_conv.conv3d.weight", + hf_patterns=["post_quant_conv.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="post_quant_conv.conv3d.bias", + hf_patterns=["post_quant_conv.bias"], + ), + # Decoder mid_block resnets + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv1.conv3d.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv1.conv3d.bias", + hf_patterns=["decoder.mid_block.resnets.{i}.conv1.bias"], + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv2.conv3d.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.conv2.conv3d.bias", + hf_patterns=["decoder.mid_block.resnets.{i}.conv2.bias"], + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.norm1.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.mid_block.resnets.{i}.norm2.weight", + hf_patterns=["decoder.mid_block.resnets.{i}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + # Decoder mid_block attention + WeightTarget( + mlx_path="decoder.mid_block.attentions.0.norm.weight", + hf_patterns=["decoder.mid_block.attentions.0.norm.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.0.to_qkv.weight", + hf_patterns=["decoder.mid_block.attentions.0.to_qkv.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.0.to_qkv.bias", + hf_patterns=["decoder.mid_block.attentions.0.to_qkv.bias"], + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.0.proj.weight", + hf_patterns=["decoder.mid_block.attentions.0.proj.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="decoder.mid_block.attentions.0.proj.bias", + hf_patterns=["decoder.mid_block.attentions.0.proj.bias"], + ), + # Decoder up_blocks resnets + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.conv1.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.conv1.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv1.bias"], + ), + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.conv2.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.conv2.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv2.bias"], + ), + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.norm1.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.norm2.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + # Decoder up_blocks optional conv_shortcut -> skip_conv + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.skip_conv.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv_shortcut.weight"], + transform=transpose_conv3d_weight, + required=False, # Optional weight + ), + WeightTarget( + mlx_path="decoder.up_block{block}.resnets.{res}.skip_conv.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.resnets.{res}.conv_shortcut.bias"], + required=False, # Optional weight + ), + # Decoder up_blocks upsamplers (blocks 0, 1, 2) + WeightTarget( + mlx_path="decoder.up_block{block}.upsamplers.0.resample_conv.weight", + hf_patterns=["decoder.up_blocks.{block}.upsamplers.0.resample.1.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="decoder.up_block{block}.upsamplers.0.resample_conv.bias", + hf_patterns=["decoder.up_blocks.{block}.upsamplers.0.resample.1.bias"], + ), + # Decoder up_blocks time_conv (blocks 0, 1) + WeightTarget( + mlx_path="decoder.up_block{block}.upsamplers.0.time_conv.conv3d.weight", + hf_patterns=["decoder.up_blocks.{block}.upsamplers.0.time_conv.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="decoder.up_block{block}.upsamplers.0.time_conv.conv3d.bias", + hf_patterns=["decoder.up_blocks.{block}.upsamplers.0.time_conv.bias"], + ), + # ========== Encoder ========== + # Encoder conv_in + WeightTarget( + mlx_path="encoder.conv_in.conv3d.weight", + hf_patterns=["encoder.conv_in.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.conv_in.conv3d.bias", + hf_patterns=["encoder.conv_in.bias"], + ), + # Encoder conv_out + WeightTarget( + mlx_path="encoder.conv_out.conv3d.weight", + hf_patterns=["encoder.conv_out.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.conv_out.conv3d.bias", + hf_patterns=["encoder.conv_out.bias"], + ), + # Encoder norm_out + WeightTarget( + mlx_path="encoder.norm_out.weight", + hf_patterns=["encoder.norm_out.gamma"], + transform=reshape_gamma_to_1d, + ), + # Encoder mid_block attention + WeightTarget( + mlx_path="encoder.mid_block.attentions.0.norm.weight", + hf_patterns=["encoder.mid_block.attentions.0.norm.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.0.to_qkv.weight", + hf_patterns=["encoder.mid_block.attentions.0.to_qkv.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.0.to_qkv.bias", + hf_patterns=["encoder.mid_block.attentions.0.to_qkv.bias"], + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.0.proj.weight", + hf_patterns=["encoder.mid_block.attentions.0.proj.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.attentions.0.proj.bias", + hf_patterns=["encoder.mid_block.attentions.0.proj.bias"], + ), + # Encoder mid_block resnets + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv1.conv3d.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv1.conv3d.bias", + hf_patterns=["encoder.mid_block.resnets.{i}.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv2.conv3d.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.conv2.conv3d.bias", + hf_patterns=["encoder.mid_block.resnets.{i}.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.norm1.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.mid_block.resnets.{i}.norm2.weight", + hf_patterns=["encoder.mid_block.resnets.{i}.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + # Encoder down_blocks - Stage 0 (flat_idx 0,1 -> resnets[0,1]; flat_idx 2 -> downsampler) + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.0.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.0.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.0.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.0.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.0.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.0.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.0.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.0.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.0.norm1.weight", + hf_patterns=["encoder.down_blocks.0.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.0.norm2.weight", + hf_patterns=["encoder.down_blocks.0.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.1.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.1.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.1.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.1.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.1.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.1.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.1.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.1.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.1.norm1.weight", + hf_patterns=["encoder.down_blocks.1.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.resnets.1.norm2.weight", + hf_patterns=["encoder.down_blocks.1.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.downsamplers.0.resample_conv.weight", + hf_patterns=["encoder.down_blocks.2.resample.1.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.0.downsamplers.0.resample_conv.bias", + hf_patterns=["encoder.down_blocks.2.resample.1.bias"], + ), + # Encoder down_blocks - Stage 1 (flat_idx 3,4 -> resnets[0,1]; flat_idx 5 -> downsampler) + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.3.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.3.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.3.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.3.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.norm1.weight", + hf_patterns=["encoder.down_blocks.3.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.norm2.weight", + hf_patterns=["encoder.down_blocks.3.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.skip_conv.conv3d.weight", + hf_patterns=["encoder.down_blocks.3.conv_shortcut.weight"], + transform=transpose_conv3d_weight, + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.0.skip_conv.conv3d.bias", + hf_patterns=["encoder.down_blocks.3.conv_shortcut.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.1.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.4.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.1.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.4.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.1.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.4.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.1.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.4.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.1.norm1.weight", + hf_patterns=["encoder.down_blocks.4.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.resnets.1.norm2.weight", + hf_patterns=["encoder.down_blocks.4.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.downsamplers.0.resample_conv.weight", + hf_patterns=["encoder.down_blocks.5.resample.1.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.1.downsamplers.0.resample_conv.bias", + hf_patterns=["encoder.down_blocks.5.resample.1.bias"], + ), + # Encoder down_blocks - Stage 2 (flat_idx 6,7 -> resnets[0,1]; flat_idx 8 -> downsampler) + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.6.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.6.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.6.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.6.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.norm1.weight", + hf_patterns=["encoder.down_blocks.6.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.norm2.weight", + hf_patterns=["encoder.down_blocks.6.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.skip_conv.conv3d.weight", + hf_patterns=["encoder.down_blocks.6.conv_shortcut.weight"], + transform=transpose_conv3d_weight, + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.0.skip_conv.conv3d.bias", + hf_patterns=["encoder.down_blocks.6.conv_shortcut.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.1.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.7.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.1.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.7.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.1.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.7.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.1.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.7.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.1.norm1.weight", + hf_patterns=["encoder.down_blocks.7.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.resnets.1.norm2.weight", + hf_patterns=["encoder.down_blocks.7.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.downsamplers.0.resample_conv.weight", + hf_patterns=["encoder.down_blocks.8.resample.1.weight"], + transform=transpose_conv2d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.downsamplers.0.resample_conv.bias", + hf_patterns=["encoder.down_blocks.8.resample.1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.downsamplers.0.time_conv.conv3d.weight", + hf_patterns=["encoder.down_blocks.8.time_conv.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.2.downsamplers.0.time_conv.conv3d.bias", + hf_patterns=["encoder.down_blocks.8.time_conv.bias"], + ), + # Encoder down_blocks - Stage 3 (flat_idx 9,10 -> resnets[0,1]) + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.0.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.9.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.0.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.9.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.0.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.9.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.0.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.9.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.0.norm1.weight", + hf_patterns=["encoder.down_blocks.9.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.0.norm2.weight", + hf_patterns=["encoder.down_blocks.9.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.1.conv1.conv3d.weight", + hf_patterns=["encoder.down_blocks.10.conv1.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.1.conv1.conv3d.bias", + hf_patterns=["encoder.down_blocks.10.conv1.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.1.conv2.conv3d.weight", + hf_patterns=["encoder.down_blocks.10.conv2.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.1.conv2.conv3d.bias", + hf_patterns=["encoder.down_blocks.10.conv2.bias"], + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.1.norm1.weight", + hf_patterns=["encoder.down_blocks.10.norm1.gamma"], + transform=reshape_gamma_to_1d, + ), + WeightTarget( + mlx_path="encoder.down_blocks.3.resnets.1.norm2.weight", + hf_patterns=["encoder.down_blocks.10.norm2.gamma"], + transform=reshape_gamma_to_1d, + ), + # Quant conv + WeightTarget( + mlx_path="quant_conv.conv3d.weight", + hf_patterns=["quant_conv.weight"], + transform=transpose_conv3d_weight, + ), + WeightTarget( + mlx_path="quant_conv.conv3d.bias", + hf_patterns=["quant_conv.bias"], + ), + ] + + @staticmethod + def get_text_encoder_mapping() -> List[WeightTarget]: + return [ + # Top-level embeddings + WeightTarget( + mlx_path="encoder.embed_tokens.weight", + hf_patterns=["model.embed_tokens.weight"], + ), + # Final norm + WeightTarget( + mlx_path="encoder.norm.weight", + hf_patterns=["model.norm.weight"], + ), + # Encoder layers (28 layers) + WeightTarget( + mlx_path="encoder.layers.{layer}.input_layernorm.weight", + hf_patterns=["model.layers.{layer}.input_layernorm.weight"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.post_attention_layernorm.weight", + hf_patterns=["model.layers.{layer}.post_attention_layernorm.weight"], + ), + # Self attention + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.q_proj.weight", + hf_patterns=["model.layers.{layer}.self_attn.q_proj.weight"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.q_proj.bias", + hf_patterns=["model.layers.{layer}.self_attn.q_proj.bias"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.k_proj.weight", + hf_patterns=["model.layers.{layer}.self_attn.k_proj.weight"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.k_proj.bias", + hf_patterns=["model.layers.{layer}.self_attn.k_proj.bias"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.v_proj.weight", + hf_patterns=["model.layers.{layer}.self_attn.v_proj.weight"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.v_proj.bias", + hf_patterns=["model.layers.{layer}.self_attn.v_proj.bias"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.self_attn.o_proj.weight", + hf_patterns=["model.layers.{layer}.self_attn.o_proj.weight"], + ), + # MLP + WeightTarget( + mlx_path="encoder.layers.{layer}.mlp.gate_proj.weight", + hf_patterns=["model.layers.{layer}.mlp.gate_proj.weight"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.mlp.up_proj.weight", + hf_patterns=["model.layers.{layer}.mlp.up_proj.weight"], + ), + WeightTarget( + mlx_path="encoder.layers.{layer}.mlp.down_proj.weight", + hf_patterns=["model.layers.{layer}.mlp.down_proj.weight"], + ), + # Visual weights (optional, only present in Edit models) + # Patch embedding (with transpose transform) + WeightTarget( + mlx_path="encoder.visual.patch_embed.proj.weight", + hf_patterns=["visual.patch_embed.proj.weight"], + transform=transpose_patch_embed, + required=False, + ), + # Vision transformer blocks (32 blocks) - use {block} placeholder + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.attn.qkv.weight", + hf_patterns=["visual.blocks.{block}.attn.qkv.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.attn.qkv.bias", + hf_patterns=["visual.blocks.{block}.attn.qkv.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.attn.proj.weight", + hf_patterns=["visual.blocks.{block}.attn.proj.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.attn.proj.bias", + hf_patterns=["visual.blocks.{block}.attn.proj.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.mlp.gate_proj.weight", + hf_patterns=["visual.blocks.{block}.mlp.gate_proj.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.mlp.gate_proj.bias", + hf_patterns=["visual.blocks.{block}.mlp.gate_proj.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.mlp.up_proj.weight", + hf_patterns=["visual.blocks.{block}.mlp.up_proj.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.mlp.up_proj.bias", + hf_patterns=["visual.blocks.{block}.mlp.up_proj.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.mlp.down_proj.weight", + hf_patterns=["visual.blocks.{block}.mlp.down_proj.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.mlp.down_proj.bias", + hf_patterns=["visual.blocks.{block}.mlp.down_proj.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.norm1.weight", + hf_patterns=["visual.blocks.{block}.norm1.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.blocks.{block}.norm2.weight", + hf_patterns=["visual.blocks.{block}.norm2.weight"], + required=False, + ), + # Patch merger + WeightTarget( + mlx_path="encoder.visual.merger.ln_q.weight", + hf_patterns=["visual.merger.ln_q.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.merger.mlp_0.weight", + hf_patterns=["visual.merger.mlp.0.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.merger.mlp_0.bias", + hf_patterns=["visual.merger.mlp.0.bias"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.merger.mlp_1.weight", + hf_patterns=["visual.merger.mlp.2.weight"], + required=False, + ), + WeightTarget( + mlx_path="encoder.visual.merger.mlp_1.bias", + hf_patterns=["visual.merger.mlp.2.bias"], + required=False, + ), + ] + + @staticmethod + def get_mapping() -> List[WeightTarget]: + return QwenWeightMapping.get_transformer_mapping() diff --git a/src/mflux/models/qwen/weights/qwen_weight_util.py b/src/mflux/models/qwen/weights/qwen_weight_util.py index e7454fd..2ea99f3 100644 --- a/src/mflux/models/qwen/weights/qwen_weight_util.py +++ b/src/mflux/models/qwen/weights/qwen_weight_util.py @@ -1,9 +1,11 @@ from typing import TYPE_CHECKING +import mlx.core as mx import mlx.nn as nn from mlx.utils import tree_flatten # noqa: F401 from mflux.config.config import Config +from mflux.models.qwen.model.qwen_text_encoder.qwen_vision_transformer import VisionTransformer from mflux.utils.quantization_util import QuantizationUtil if TYPE_CHECKING: @@ -50,6 +52,25 @@ class QwenWeightUtil: raise Exception("Error setting weights") + @staticmethod + def _convert_weights_to_bf16(weights_dict: dict): + """ + Recursively convert all weight tensors to BF16. + This matches PyTorch's behavior where the text encoder model is loaded in BF16. + """ + + def convert_recursive(obj): + if isinstance(obj, mx.array): + return obj.astype(mx.bfloat16) + elif isinstance(obj, dict): + return {k: convert_recursive(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [convert_recursive(item) for item in obj] + else: + return obj + + return convert_recursive(weights_dict) + @staticmethod def _set_model_weights( weights: "QwenWeightHandler", @@ -59,4 +80,28 @@ class QwenWeightUtil: ): vae.update(weights.vae, strict=False) transformer.update(weights.transformer, strict=False) - text_encoder.update(weights.qwen_text_encoder, strict=False) + + # Check if visual weights are present and create visual transformer if needed + if text_encoder is not None: + has_visual_weights = ( + "encoder" in weights.qwen_text_encoder and "visual" in weights.qwen_text_encoder["encoder"] + ) + if has_visual_weights and text_encoder.encoder.visual is None: + text_encoder.encoder.visual = VisionTransformer( + patch_size=14, + temporal_patch_size=2, + in_channels=3, + embed_dim=1280, + depth=32, + num_heads=16, + mlp_ratio=2.671875, + hidden_size=text_encoder.encoder.hidden_size, + spatial_merge_size=2, # Match HF's spatial merging + ) + + # Convert all text encoder weights to BF16 to match PyTorch's behavior + # PyTorch loads the model in BF16 and performs all computations in BF16 + # This ensures numerical consistency between MLX and PyTorch + weights.qwen_text_encoder = QwenWeightUtil._convert_weights_to_bf16(weights.qwen_text_encoder) + + text_encoder.update(weights.qwen_text_encoder, strict=False) diff --git a/src/mflux/schedulers/__init__.py b/src/mflux/schedulers/__init__.py index 8f3549f..bb20416 100644 --- a/src/mflux/schedulers/__init__.py +++ b/src/mflux/schedulers/__init__.py @@ -1,7 +1,9 @@ +from .flow_match_euler_discrete_scheduler import FlowMatchEulerDiscreteScheduler from .linear_scheduler import LinearScheduler __all__ = [ "LinearScheduler", + "FlowMatchEulerDiscreteScheduler", ] @@ -17,6 +19,8 @@ class InvalidSchedulerType(TypeError): ... SCHEDULER_REGISTRY = { "linear": LinearScheduler, "LinearScheduler": LinearScheduler, + "flow_match_euler_discrete": FlowMatchEulerDiscreteScheduler, + "FlowMatchEulerDiscreteScheduler": FlowMatchEulerDiscreteScheduler, } @@ -56,7 +60,7 @@ def try_import_external_scheduler(scheduler_object_path: str): # Step 3: Validate that it's a class and inherits from BaseScheduler if not inspect.isclass(SchedulerClass): raise InvalidSchedulerType( - f"{scheduler_object_path!r} is not a class. " f"Schedulers must be classes inheriting from BaseScheduler." + f"{scheduler_object_path!r} is not a class. Schedulers must be classes inheriting from BaseScheduler." ) if not issubclass(SchedulerClass, BaseScheduler): diff --git a/src/mflux/schedulers/flow_match_euler_discrete_scheduler.py b/src/mflux/schedulers/flow_match_euler_discrete_scheduler.py new file mode 100644 index 0000000..065c42f --- /dev/null +++ b/src/mflux/schedulers/flow_match_euler_discrete_scheduler.py @@ -0,0 +1,75 @@ +import math +from typing import TYPE_CHECKING + +import mlx.core as mx + +if TYPE_CHECKING: + from mflux.config.runtime_config import RuntimeConfig + +from mflux.schedulers.base_scheduler import BaseScheduler + + +class FlowMatchEulerDiscreteScheduler(BaseScheduler): + def __init__(self, runtime_config: "RuntimeConfig"): + self.runtime_config = runtime_config + self.model_config = runtime_config.model_config + self.num_train_timesteps = 1000 + self.shift_terminal = 0.02 + self.base_shift = 0.5 + self.max_shift = 0.9 + self.base_image_seq_len = 256 + self.max_image_seq_len = 8192 + self._sigmas, self._timesteps = self._compute_timesteps_and_sigmas() + + @property + def sigmas(self) -> mx.array: + return self._sigmas + + @property + def timesteps(self) -> mx.array: + return self._timesteps + + def _compute_mu(self) -> float: + h_patches = self.runtime_config.height // 16 + w_patches = self.runtime_config.width // 16 + seq_len = h_patches * w_patches + m = (self.max_shift - self.base_shift) / (self.max_image_seq_len - self.base_image_seq_len) + b = self.base_shift - m * self.base_image_seq_len + mu = m * seq_len + b + return mu + + @staticmethod + def _time_shift_exponential(mu: float, sigma_power: float, t: float) -> float: + return math.exp(mu) / (math.exp(mu) + ((1.0 / t - 1.0) ** sigma_power)) + + def _stretch_to_terminal(self, sigmas: list[float]) -> list[float]: + one_minus_sigmas = [1.0 - s for s in sigmas] + scale_factor = one_minus_sigmas[-1] / (1.0 - self.shift_terminal) + stretched = [1.0 - (oms / scale_factor) for oms in one_minus_sigmas] + return stretched + + def _compute_timesteps_and_sigmas(self) -> tuple[mx.array, mx.array]: + num_steps = self.runtime_config.num_inference_steps + sigma_min = 1.0 / self.num_train_timesteps + sigma_max = 1.0 + timesteps_linear = [ + sigma_max * self.num_train_timesteps + - i * (sigma_max - sigma_min) * self.num_train_timesteps / (num_steps - 1) + for i in range(num_steps) + ] + sigmas_linear = [t / self.num_train_timesteps for t in timesteps_linear] + sigmas_shifted = [FlowMatchEulerDiscreteScheduler._time_shift_exponential(1.0, 1.0, s) for s in sigmas_linear] + sigmas_final = self._stretch_to_terminal(sigmas_shifted) + timesteps = [s * self.num_train_timesteps for s in sigmas_final] + sigmas_with_zero = sigmas_final + [0.0] + sigmas_arr = mx.array(sigmas_with_zero, dtype=mx.float32) + timesteps_arr = mx.array(timesteps, dtype=mx.float32) + return sigmas_arr, timesteps_arr + + def step(self, model_output: mx.array, timestep: int, sample: mx.array, **kwargs) -> mx.array: + dt = self._sigmas[timestep + 1] - self._sigmas[timestep] + prev_sample = sample + dt * model_output + return prev_sample + + def scale_model_input(self, latents: mx.array, t: int) -> mx.array: + return latents diff --git a/src/mflux/schedulers/linear_scheduler.py b/src/mflux/schedulers/linear_scheduler.py index 6500e46..66a42a7 100644 --- a/src/mflux/schedulers/linear_scheduler.py +++ b/src/mflux/schedulers/linear_scheduler.py @@ -9,19 +9,19 @@ from mflux.schedulers.base_scheduler import BaseScheduler class LinearScheduler(BaseScheduler): - """ - Linear scheduler - the default/classic scheduler used in mflux. - Creates a linear schedule from 1.0 to 1/num_steps. - """ - def __init__(self, runtime_config: "RuntimeConfig"): self.runtime_config = runtime_config self._sigmas = self._get_sigmas() + self._timesteps = self._get_timesteps() @property def sigmas(self) -> mx.array: return self._sigmas + @property + def timesteps(self) -> mx.array: + return self._timesteps + def _get_sigmas(self) -> mx.array: model_config = self.runtime_config.model_config sigmas = mx.linspace( @@ -44,6 +44,12 @@ class LinearScheduler(BaseScheduler): else: return sigmas + def _get_timesteps(self) -> mx.array: + num_steps = self.runtime_config.num_inference_steps + timesteps = mx.arange(num_steps, dtype=mx.float32) + + return timesteps + def step(self, model_output: mx.array, timestep: int, sample: mx.array, **kwargs) -> mx.array: dt = self._sigmas[timestep + 1] - self._sigmas[timestep] return sample + model_output * dt diff --git a/src/mflux/utils/image_compare.py b/src/mflux/utils/image_compare.py new file mode 100644 index 0000000..a898f91 --- /dev/null +++ b/src/mflux/utils/image_compare.py @@ -0,0 +1,94 @@ +import os +from pathlib import Path + +import cv2 +import numpy as np +from PIL import Image + + +class ImageCompare: + # How we determined DEFAULT_MISMATCH_THRESHOLD value: by eye test + # successive mlx/mflux versions have generated images that are visually close enough + # to consider as a valid upgrade. Minor visual differences are attributable to mlx updates + DEFAULT_MISMATCH_THRESHOLD = 0.15 + ENV_MISMATCH_THRESHOLD = float(os.environ.get("MFLUX_IMAGE_MISMATCH_THRESHOLD", DEFAULT_MISMATCH_THRESHOLD)) + + @staticmethod + def check_images_close_enough( + image1_path: str | Path, + image2_path: str | Path, + error_message_prefix: str, + mismatch_threshold: float | None = None, + ) -> float: + if mismatch_threshold is None: + mismatch_threshold = ImageCompare.ENV_MISMATCH_THRESHOLD + + image1_path = Path(image1_path) + image2_path = Path(image2_path) + image1_data = np.array(Image.open(image1_path)) + image2_data = np.array(Image.open(image2_path)) + closeness_array = np.isclose( + image1_data, + image2_data, + rtol=float(os.environ.get("MFLUX_IMAGE_ALLCLOSE_RTOL", 0.1)), + atol=0, # ignore absolute tolerance + ) + num_mismatched = np.count_nonzero(~closeness_array) + total_elements = image1_data.size + mismatch_ratio = num_mismatched / total_elements + if mismatch_ratio > mismatch_threshold: + diff = image1_data - image2_data + # Scale the difference so it's visible. A difference of 50 will become bright. + # We'll scale it so that a difference of 50 or more becomes pure white (255). + diff_visual = (np.abs(diff) / 50 * 255).clip(0, 255).astype(np.uint8) + cv2.imwrite(image2_path.with_stem(image1_path.stem + "_diff"), diff_visual) + raise AssertionError( + f"{error_message_prefix} Check {image1_path} vs {image2_path} :: their elements are {mismatch_ratio:.1%} different. Fails assertion for {mismatch_threshold=}" + ) + return mismatch_ratio + + @staticmethod + def compare_images( + image1_path: str | Path, + image2_path: str | Path, + mismatch_threshold: float | None = None, + create_diff: bool = True, + ) -> dict: + image1_path = Path(image1_path) + image2_path = Path(image2_path) + image1_data = np.array(Image.open(image1_path)) + image2_data = np.array(Image.open(image2_path)) + + rtol = float(os.environ.get("MFLUX_IMAGE_ALLCLOSE_RTOL", 0.1)) + closeness_array = np.isclose( + image1_data, + image2_data, + rtol=rtol, + atol=0, + ) + + num_mismatched = np.count_nonzero(~closeness_array) + total_elements = image1_data.size + mismatch_ratio = num_mismatched / total_elements + + diff_path = None + if create_diff: + diff = image1_data - image2_data + diff_visual = (np.abs(diff) / 50 * 255).clip(0, 255).astype(np.uint8) + diff_path = image2_path.with_stem(image1_path.stem + "_diff") + cv2.imwrite(str(diff_path), diff_visual) + + result = { + "mismatch_ratio": mismatch_ratio, + "total_pixels": total_elements, + "mismatched_pixels": num_mismatched, + "image1_shape": image1_data.shape, + "image2_shape": image2_data.shape, + "diff_path": str(diff_path) if diff_path else None, + } + + if mismatch_threshold is not None: + result["passes_threshold"] = mismatch_ratio <= mismatch_threshold + result["threshold"] = mismatch_threshold + + return result diff --git a/src/mflux/utils/quantization_util.py b/src/mflux/utils/quantization_util.py index 6863207..a94c758 100644 --- a/src/mflux/utils/quantization_util.py +++ b/src/mflux/utils/quantization_util.py @@ -91,4 +91,4 @@ class QuantizationUtil: bits = int(q_level) if q_level is not None else quantize nn.quantize(vae, bits=bits) nn.quantize(transformer, bits=bits) - nn.quantize(text_encoder, bits=bits) + # nn.quantize(text_encoder, bits=bits) # Quantization of text encoder causes significant semantic degradation diff --git a/tests/depth/test_depth_pro.py b/tests/depth/test_depth_pro.py index cafc7fc..0ac10c1 100644 --- a/tests/depth/test_depth_pro.py +++ b/tests/depth/test_depth_pro.py @@ -2,7 +2,7 @@ import os from pathlib import Path from mflux.models.depth_pro.depth_pro import DepthPro -from tests.image_generation.helpers.image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class TestDepthPro: @@ -24,7 +24,7 @@ class TestDepthPro: depth_result.depth_image.save(output_image_path) # Assert that the generated depth image matches the reference image - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated image doesn't match reference image.", diff --git a/tests/image_generation/helpers/image_compare.py b/tests/image_generation/helpers/image_compare.py deleted file mode 100644 index eb71676..0000000 --- a/tests/image_generation/helpers/image_compare.py +++ /dev/null @@ -1,46 +0,0 @@ -import os -from pathlib import Path - -import cv2 -import numpy as np -from PIL import Image - - -class ReferenceVsOutputImageError(AssertionError): ... - - -# How we determined DEFAULT_MISMATCH_THRESHOLD value: by eye test -# successive mlx/mflux versions have generated images that are visually close enough -# to consider as a valid upgrade. Minor visual differences are attributable to mlx updates -DEFAULT_MISMATCH_THRESHOLD = 0.15 -ENV_MISMATCH_THRESHOLD = float(os.environ.get("MFLUX_IMAGE_MISMATCH_THRESHOLD", DEFAULT_MISMATCH_THRESHOLD)) - - -def check_images_close_enough( - image1_path: str | Path, - image2_path: str | Path, - error_message_prefix: str, - mismatch_threshold: float = ENV_MISMATCH_THRESHOLD, -): - image1_path = Path(image1_path) - image2_path = Path(image2_path) - image1_data = np.array(Image.open(image1_path)) - image2_data = np.array(Image.open(image2_path)) - closeness_array = np.isclose( - image1_data, - image2_data, - rtol=float(os.environ.get("MFLUX_IMAGE_ALLCLOSE_RTOL", 0.1)), - atol=0, # ignore absolute tolerance - ) - num_mismatched = np.count_nonzero(~closeness_array) - total_elements = image1_data.size - mismatch_ratio = num_mismatched / total_elements - if mismatch_ratio > mismatch_threshold: - diff = image1_data - image2_data - # Scale the difference so it's visible. A difference of 50 will become bright. - # We'll scale it so that a difference of 50 or more becomes pure white (255). - diff_visual = (diff / 50 * 255).clip(0, 255).astype(np.uint8) - cv2.imwrite(image2_path.with_stem(image1_path.stem + "_diff"), diff_visual) - raise AssertionError( - f"{error_message_prefix} Check {image1_path} vs {image2_path} :: their elements are {mismatch_ratio:.1%} different. Fails assertion for {mismatch_threshold=}" - ) diff --git a/tests/image_generation/helpers/image_generation_concept_test_helper.py b/tests/image_generation/helpers/image_generation_concept_test_helper.py index 5402375..28657d1 100644 --- a/tests/image_generation/helpers/image_generation_concept_test_helper.py +++ b/tests/image_generation/helpers/image_generation_concept_test_helper.py @@ -5,8 +5,7 @@ from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.concept_attention.flux_concept import Flux1Concept from mflux.models.flux.variants.concept_attention.flux_concept_from_image import Flux1ConceptFromImage - -from .image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class ImageGenerationConceptTestHelper: @@ -56,7 +55,7 @@ class ImageGenerationConceptTestHelper: image.save_concept_heatmap(path=output_heatmap_path, overwrite=True) # then - verify the heatmap matches reference - check_images_close_enough( + ImageCompare.check_images_close_enough( output_heatmap_path, reference_heatmap_path, "Generated concept heatmap doesn't match reference heatmap.", @@ -117,7 +116,7 @@ class ImageGenerationConceptTestHelper: image.save_concept_heatmap(path=output_heatmap_path, overwrite=True) # then - verify the heatmap matches reference - check_images_close_enough( + ImageCompare.check_images_close_enough( output_heatmap_path, reference_heatmap_path, "Generated concept from image heatmap doesn't match reference heatmap.", diff --git a/tests/image_generation/helpers/image_generation_controlnet_test_helper.py b/tests/image_generation/helpers/image_generation_controlnet_test_helper.py index aec0c30..5967772 100644 --- a/tests/image_generation/helpers/image_generation_controlnet_test_helper.py +++ b/tests/image_generation/helpers/image_generation_controlnet_test_helper.py @@ -3,10 +3,9 @@ import os from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.controlnet.flux_controlnet import Flux1Controlnet +from mflux.utils.image_compare import ImageCompare from tests.image_generation.helpers.image_generation_test_helper import ImageGeneratorTestHelper -from .image_compare import check_images_close_enough - class ImageGeneratorControlnetTestHelper: @staticmethod @@ -54,7 +53,7 @@ class ImageGeneratorControlnetTestHelper: image.save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated controlnet image doesn't match reference image", diff --git a/tests/image_generation/helpers/image_generation_depth_test_helper.py b/tests/image_generation/helpers/image_generation_depth_test_helper.py index 52da724..546bb9e 100644 --- a/tests/image_generation/helpers/image_generation_depth_test_helper.py +++ b/tests/image_generation/helpers/image_generation_depth_test_helper.py @@ -4,8 +4,7 @@ from pathlib import Path from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.depth.flux_depth import Flux1Depth - -from .image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class ImageGeneratorDepthTestHelper: @@ -46,7 +45,7 @@ class ImageGeneratorDepthTestHelper: image.save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated depth image doesn't match reference image.", diff --git a/tests/image_generation/helpers/image_generation_edit_test_helper.py b/tests/image_generation/helpers/image_generation_edit_test_helper.py new file mode 100644 index 0000000..9b4a448 --- /dev/null +++ b/tests/image_generation/helpers/image_generation_edit_test_helper.py @@ -0,0 +1,103 @@ +import os +from pathlib import Path +from typing import Any, Type + +from mflux.callbacks.callback_registry import CallbackRegistry +from mflux.callbacks.instances.stepwise_handler import StepwiseHandler +from mflux.config.config import Config +from mflux.config.model_config import ModelConfig +from mflux.utils.image_compare import ImageCompare + + +class ImageGeneratorEditTestHelper: + @staticmethod + def assert_matches_reference_image( + reference_image_path: str, + output_image_path: str, + model_class: Type[Any], + model_config: ModelConfig, + steps: int, + seed: int, + height: int, + width: int, + prompt: str, + image_path: str | None = None, + guidance: float = 2.5, + negative_prompt: str | None = None, + quantize: int = 8, + image_paths: list[str] | None = None, + ): + # resolve paths + reference_image_path = ImageGeneratorEditTestHelper.resolve_path(reference_image_path) + output_image_path = ImageGeneratorEditTestHelper.resolve_path(output_image_path) + + # For Edit Plus, if image_paths is provided but image_path is not, use first element of image_paths + is_edit_plus = "Plus" in model_class.__name__ + if is_edit_plus and image_paths and not image_path: + image_path = image_paths[0] + + if image_path: + image_path = ImageGeneratorEditTestHelper.resolve_path(image_path) + if image_paths: + image_paths = [str(ImageGeneratorEditTestHelper.resolve_path(p)) for p in image_paths] + + try: + # given + model_kwargs = { + "quantize": quantize, + } + model = model_class(**model_kwargs) + + # when + config_kwargs = { + "num_inference_steps": steps, + "height": height, + "width": width, + "guidance": guidance, + "image_path": image_path, + "scheduler": "flow_match_euler_discrete", # Match debug script + } + generate_kwargs = { + "seed": seed, + "prompt": prompt, + "negative_prompt": negative_prompt, + "config": Config(**config_kwargs), + } + + # Edit Plus uses image_paths instead of image_path in config + # Check if it's Edit Plus by checking the class name + if "Plus" in model_class.__name__: + if image_paths: + generate_kwargs["image_paths"] = image_paths + else: + generate_kwargs["image_paths"] = [str(image_path)] + + # Use a temporary directory for stepwise handler output + import tempfile + + temp_dir = tempfile.mkdtemp() + handler = StepwiseHandler(model=model, output_dir=temp_dir) + CallbackRegistry.register_before_loop(handler) + CallbackRegistry.register_in_loop(handler) + CallbackRegistry.register_interrupt(handler) + + image = model.generate_image(**generate_kwargs) + image.save(path=output_image_path, overwrite=True) + + # then + model_name = "qwen edit" + ImageCompare.check_images_close_enough( + output_image_path, + reference_image_path, + f"Generated {model_name} image doesn't match reference image.", + ) + finally: + # cleanup + if os.path.exists(output_image_path) and "MFLUX_PRESERVE_TEST_OUTPUT" not in os.environ: + os.remove(output_image_path) + + @staticmethod + def resolve_path(path) -> Path | None: + if path is None: + return None + return Path(__file__).parent.parent.parent / "resources" / path diff --git a/tests/image_generation/helpers/image_generation_fill_test_helper.py b/tests/image_generation/helpers/image_generation_fill_test_helper.py index 544251d..db4942b 100644 --- a/tests/image_generation/helpers/image_generation_fill_test_helper.py +++ b/tests/image_generation/helpers/image_generation_fill_test_helper.py @@ -4,10 +4,9 @@ from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.fill.flux_fill import Flux1Fill from mflux.ui import defaults as ui_defaults +from mflux.utils.image_compare import ImageCompare from tests.image_generation.helpers.image_generation_test_helper import ImageGeneratorTestHelper -from .image_compare import check_images_close_enough - class ImageGeneratorFillTestHelper: @staticmethod @@ -56,7 +55,7 @@ class ImageGeneratorFillTestHelper: image.save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated fill image doesn't match reference image.", diff --git a/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py b/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py index c244198..4931f85 100644 --- a/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py +++ b/tests/image_generation/helpers/image_generation_ic_edit_test_helper.py @@ -5,8 +5,7 @@ from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.in_context.flux_in_context_fill import Flux1InContextFill from mflux.models.flux.variants.in_context.utils.in_context_loras import prepare_ic_edit_loras - -from .image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class ImageGeneratorICEditTestHelper: @@ -55,7 +54,7 @@ class ImageGeneratorICEditTestHelper: image.get_right_half().save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated ic-edit image doesn't match reference image.", diff --git a/tests/image_generation/helpers/image_generation_in_context_test_helper.py b/tests/image_generation/helpers/image_generation_in_context_test_helper.py index 09269b6..5129ced 100644 --- a/tests/image_generation/helpers/image_generation_in_context_test_helper.py +++ b/tests/image_generation/helpers/image_generation_in_context_test_helper.py @@ -5,8 +5,7 @@ from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.in_context.flux_in_context_dev import Flux1InContextDev from mflux.models.flux.variants.in_context.utils.in_context_loras import LORA_REPO_ID, get_lora_filename - -from .image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class ImageGeneratorInContextTestHelper: @@ -58,7 +57,7 @@ class ImageGeneratorInContextTestHelper: image.get_right_half().save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated in-context image doesn't match reference image.", diff --git a/tests/image_generation/helpers/image_generation_kontext_test_helper.py b/tests/image_generation/helpers/image_generation_kontext_test_helper.py deleted file mode 100644 index 195094b..0000000 --- a/tests/image_generation/helpers/image_generation_kontext_test_helper.py +++ /dev/null @@ -1,65 +0,0 @@ -import os -from pathlib import Path - -from mflux.config.config import Config -from mflux.config.model_config import ModelConfig -from mflux.models.flux.variants.kontext.flux_kontext import Flux1Kontext - -from .image_compare import check_images_close_enough - - -class ImageGeneratorKontextTestHelper: - @staticmethod - def assert_matches_reference_image( - reference_image_path: str, - output_image_path: str, - model_config: ModelConfig, - steps: int, - seed: int, - height: int, - width: int, - prompt: str, - kontext_image_path: str, - guidance: float = 2.5, - ): - # resolve paths - reference_image_path = ImageGeneratorKontextTestHelper.resolve_path(reference_image_path) - output_image_path = ImageGeneratorKontextTestHelper.resolve_path(output_image_path) - kontext_image_path = ImageGeneratorKontextTestHelper.resolve_path(kontext_image_path) - - try: - # given - flux = Flux1Kontext( - quantize=8, - ) - - # when - image = flux.generate_image( - seed=seed, - prompt=prompt, - config=Config( - num_inference_steps=steps, - height=height, - width=width, - guidance=guidance, - image_path=kontext_image_path, - ), - ) - image.save(path=output_image_path, overwrite=True) - - # then - check_images_close_enough( - output_image_path, - reference_image_path, - "Generated kontext image doesn't match reference image.", - ) - finally: - # cleanup - if os.path.exists(output_image_path) and "MFLUX_PRESERVE_TEST_OUTPUT" not in os.environ: - os.remove(output_image_path) - - @staticmethod - def resolve_path(path) -> Path | None: - if path is None: - return None - return Path(__file__).parent.parent.parent / "resources" / path diff --git a/tests/image_generation/helpers/image_generation_redux_test_helper.py b/tests/image_generation/helpers/image_generation_redux_test_helper.py index b3ae0f5..5bd789c 100644 --- a/tests/image_generation/helpers/image_generation_redux_test_helper.py +++ b/tests/image_generation/helpers/image_generation_redux_test_helper.py @@ -4,8 +4,7 @@ from pathlib import Path from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.redux.flux_redux import Flux1Redux - -from .image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class ImageGeneratorReduxTestHelper: @@ -47,7 +46,7 @@ class ImageGeneratorReduxTestHelper: image.save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated redux image doesn't match reference image.", diff --git a/tests/image_generation/helpers/image_generation_test_helper.py b/tests/image_generation/helpers/image_generation_test_helper.py index c0be630..c79cd47 100644 --- a/tests/image_generation/helpers/image_generation_test_helper.py +++ b/tests/image_generation/helpers/image_generation_test_helper.py @@ -6,8 +6,7 @@ from mflux.config.config import Config from mflux.config.model_config import ModelConfig from mflux.models.flux.variants.txt2img.flux import Flux1 from mflux.models.qwen.variants.txt2img.qwen_image import QwenImage - -from .image_compare import check_images_close_enough +from mflux.utils.image_compare import ImageCompare class ImageGeneratorTestHelper: @@ -80,7 +79,7 @@ class ImageGeneratorTestHelper: image.save(path=output_image_path, overwrite=True) # then - check_images_close_enough( + ImageCompare.check_images_close_enough( output_image_path, reference_image_path, "Generated image doesn't match reference image.", diff --git a/tests/image_generation/test_generate_image_edit.py b/tests/image_generation/test_generate_image_edit.py new file mode 100644 index 0000000..47070fb --- /dev/null +++ b/tests/image_generation/test_generate_image_edit.py @@ -0,0 +1,69 @@ +from mflux.config.model_config import ModelConfig +from mflux.models.flux.variants.kontext.flux_kontext import Flux1Kontext +from mflux.models.qwen.variants.edit import QwenImageEdit, QwenImageEditPlus +from tests.image_generation.helpers.image_generation_edit_test_helper import ImageGeneratorEditTestHelper + + +class TestImageGeneratorEdit: + def test_image_generation_flux_kontext(self): + ImageGeneratorEditTestHelper.assert_matches_reference_image( + reference_image_path="reference_dev_kontext.png", + output_image_path="output_dev_kontext.png", + model_class=Flux1Kontext, + model_config=ModelConfig.dev_kontext(), + steps=20, + seed=4869845, + height=384, + width=640, + guidance=2.5, + prompt="Make the hand fistbump the camera instead of showing a flat palm", + image_path="reference_upscaled.png", + ) + + def test_image_generation_qwen_edit(self): + ImageGeneratorEditTestHelper.assert_matches_reference_image( + reference_image_path="reference_qwen_edit.png", + output_image_path="output_qwen_edit.png", + model_class=QwenImageEdit, + model_config=ModelConfig.qwen_image_edit(), + steps=20, + seed=4869845, + height=384, + width=640, + guidance=2.5, + quantize=6, + prompt="Make the hand fistbump the camera instead of showing a flat palm", + image_path="reference_upscaled.png", + ) + + def test_image_generation_qwen_edit_plus(self): + ImageGeneratorEditTestHelper.assert_matches_reference_image( + reference_image_path="reference_qwen_edit_plus.png", + output_image_path="output_qwen_edit_plus.png", + model_class=QwenImageEditPlus, + model_config=ModelConfig.qwen_image_edit_plus(), + steps=20, + seed=4869845, + height=384, + width=640, + guidance=2.5, + quantize=6, + prompt="Make the hand fistbump the camera instead of showing a flat palm", + image_path="reference_upscaled.png", + ) + + def test_image_generation_qwen_edit_plus_multiple_images(self): + ImageGeneratorEditTestHelper.assert_matches_reference_image( + reference_image_path="reference_qwen_edit_plus_multiple_images.png", + output_image_path="output_qwen_edit_plus_multiple_images.png", + model_class=QwenImageEditPlus, + model_config=ModelConfig.qwen_image_edit_plus(), + steps=20, + seed=4869845, + height=384, + width=640, + guidance=2.5, + quantize=8, + prompt="Make the hand fistbump the camera instead of showing a flat palm, and the man should wear this shirt. Maintain the original pose, body position, and overall stance.", + image_paths=["reference_upscaled.png", "shirt.jpg"], + ) diff --git a/tests/image_generation/test_generate_image_kontext.py b/tests/image_generation/test_generate_image_kontext.py deleted file mode 100644 index 71556a1..0000000 --- a/tests/image_generation/test_generate_image_kontext.py +++ /dev/null @@ -1,18 +0,0 @@ -from mflux.config.model_config import ModelConfig -from tests.image_generation.helpers.image_generation_kontext_test_helper import ImageGeneratorKontextTestHelper - - -class TestImageGeneratorKontext: - def test_image_generation_kontext(self): - ImageGeneratorKontextTestHelper.assert_matches_reference_image( - reference_image_path="reference_dev_kontext.png", - output_image_path="output_dev_kontext.png", - model_config=ModelConfig.dev_kontext(), - steps=20, - seed=4869845, - height=384, - width=640, - guidance=2.5, - prompt="Make the hand fistbump the camera instead of showing a flat palm", - 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