From ea2603b8df6e178d5ff6e346de788db379efc395 Mon Sep 17 00:00:00 2001 From: amepas Date: Wed, 15 Jul 2026 17:05:43 +0000 Subject: [PATCH] Onboard FLUX.2-klein pipeline and Flax NNX models into MaxDiffusion (JAX+TPU) --- README.md | 16 + src/maxdiffusion/configs/base_flux2klein.yml | 278 ++++ .../configs/base_flux2klein_9B.yml | 275 ++++ src/maxdiffusion/generate_flux2klein.py | 550 ++++++++ src/maxdiffusion/models/attention_flax.py | 34 +- src/maxdiffusion/models/embeddings_flax.py | 196 ++- .../transformers/transformer_flux_flax.py | 1125 ++++++++++++++++- src/maxdiffusion/models/flux/util.py | 444 ++++++- src/maxdiffusion/models/normalization_flax.py | 116 +- src/maxdiffusion/models/qwen3_flax.py | 721 +++++++++++ src/maxdiffusion/models/vae_flax.py | 65 +- .../pipelines/flux/flux2klein_pipeline.py | 333 +++++ .../schedulers/scheduling_flow_match_flax.py | 31 +- .../tests/generate_flux2klein_e2e_test.py | 274 ++++ .../tests/generate_flux2klein_smoke_test.py | 124 ++ .../tests/images/ref_flux2klein_4b.png | Bin 0 -> 413303 bytes .../tests/images/ref_flux2klein_9b.png | Bin 0 -> 359853 bytes src/maxdiffusion/tests/nnx_flux2klein_test.py | 103 ++ 18 files changed, 4623 insertions(+), 62 deletions(-) create mode 100644 src/maxdiffusion/configs/base_flux2klein.yml create mode 100644 src/maxdiffusion/configs/base_flux2klein_9B.yml create mode 100644 src/maxdiffusion/generate_flux2klein.py create mode 100644 src/maxdiffusion/models/qwen3_flax.py create mode 100644 src/maxdiffusion/pipelines/flux/flux2klein_pipeline.py create mode 100644 src/maxdiffusion/tests/generate_flux2klein_e2e_test.py create mode 100644 src/maxdiffusion/tests/generate_flux2klein_smoke_test.py create mode 100644 src/maxdiffusion/tests/images/ref_flux2klein_4b.png create mode 100644 src/maxdiffusion/tests/images/ref_flux2klein_9b.png create mode 100644 src/maxdiffusion/tests/nnx_flux2klein_test.py diff --git a/README.md b/README.md index 4f5a1fcb7..647bd2542 100755 --- a/README.md +++ b/README.md @@ -723,6 +723,22 @@ We added ring attention support for Wan models. Below are the stats for one `720 ```bash python src/maxdiffusion/generate_flux.py src/maxdiffusion/configs/base_flux_schnell.yml jax_cache_dir=/tmp/cache_dir run_name=flux_test output_dir=/tmp/ prompt="photograph of an electronics chip in the shape of a race car with trillium written on its side" per_device_batch_size=1 ici_data_parallelism=1 ici_fsdp_parallelism=-1 offload_encoders=False ``` + + ### Flux.2-Klein (4B & 9B) + + Flux.2-Klein provides ultra-fast 4-step image generation using Qwen3 text embeddings and FLUX.2 transformer blocks. + + Flux.2-Klein 4B: + + ```bash + python src/maxdiffusion/generate_flux2klein.py src/maxdiffusion/configs/base_flux2klein.yml run_name=flux2klein_4b prompt="A detailed vector illustration of a robotic hummingbird" + ``` + + Flux.2-Klein 9B: + + ```bash + python src/maxdiffusion/generate_flux2klein.py src/maxdiffusion/configs/base_flux2klein_9B.yml run_name=flux2klein_9b prompt="A detailed vector illustration of a robotic hummingbird" + ``` ## Fused Attention for GPU: Fused Attention for GPU is supported via TransformerEngine. Installation instructions: diff --git a/src/maxdiffusion/configs/base_flux2klein.yml b/src/maxdiffusion/configs/base_flux2klein.yml new file mode 100644 index 000000000..8c0d6be01 --- /dev/null +++ b/src/maxdiffusion/configs/base_flux2klein.yml @@ -0,0 +1,278 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This sentinel is a reminder to choose a real run name. +run_name: 'flux2klein_test_run' + +metrics_file: "" # for testing, local file that stores scalar metrics. If empty, no metrics are written. +# If true save metrics such as loss and TFLOPS to GCS in {base_output_directory}/{run_name}/metrics/ +write_metrics: True + +timing_metrics_file: "" # for testing, local file that stores function timing metrics such as state creation, compilation. If empty, no metrics are written. +write_timing_metrics: True + +gcs_metrics: False +# If true save config to GCS in {base_output_directory}/{run_name}/ +save_config_to_gcs: False +log_period: 100 + +pretrained_model_name_or_path: 'black-forest-labs/FLUX.2-klein-4B' +clip_model_name_or_path: 'ariG23498/clip-vit-large-patch14-text-flax' +t5xxl_model_name_or_path: 'ariG23498/t5-v1-1-xxl-flax' + +# Flux params +flux_name: "flux2klein" +scale_shift_order: "scale_shift" +use_latents: False +latents_path: "" +max_sequence_length: 512 +time_shift: True +base_shift: 0.5 +max_shift: 1.15 + + +unet_checkpoint: '' +revision: 'refs/pr/95' +# This will convert the weights to this dtype. +# When running inference on TPUv5e, use weights_dtype: 'bfloat16' +weights_dtype: 'bfloat16' +# This sets the layer's dtype in the model. Ex: nn.Dense(dtype=activations_dtype) +activations_dtype: 'bfloat16' + +# matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision +# Options are "DEFAULT", "HIGH", "HIGHEST" +# fp32 activations and fp32 weights with HIGHEST will provide the best precision +# at the cost of time. +precision: "DEFAULT" + +# if False state is not jitted and instead replicate is called. This is good for debugging on single host +# It must be True for multi-host. +jit_initializers: True + +# Set true to load weights from pytorch +from_pt: True +split_head_dim: True +attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te +# If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens. +# Else we do not pass in segment ids and on vpu bound hardware like trillium this is faster. +# However, when padding tokens are significant, this will lead to worse quality and should be set to True. +mask_padding_tokens: True +# Maxdiffusion has 2 types of attention sharding strategies: +# 1. attention_sharding_uniform = True : same sequence sharding rules applied for q in both (self and cross attention) +# 2. attention_sharding_uniform = False : Heads are sharded uniformly across devices for self attention while sequence is sharded +# in cross attention q. +attention_sharding_uniform: True + +flash_block_sizes: {} +# GroupNorm groups +norm_num_groups: 32 + +# If train_new_flux, flux weights will be randomly initialized to train flux from scratch +# else they will be loaded from pretrained_model_name_or_path +train_new_flux: False + +# train text_encoder - Currently not supported for SDXL +train_text_encoder: False +text_encoder_learning_rate: 4.25e-6 + +# https://arxiv.org/pdf/2305.08891.pdf +snr_gamma: -1.0 + +timestep_bias: { + # a value of later will increase the frequence of the model's final training steps. + # none, earlier, later, range + strategy: "none", + # multiplier for bias, a value of 2.0 will double the weight of the bias, 0.5 will halve it. + multiplier: 1.0, + # when using strategy=range, the beginning (inclusive) timestep to bias. + begin: 0, + # when using strategy=range, the final step (inclusive) to bias. + end: 1000, + # portion of timesteps to bias. + # 0.5 will bias one half of the timesteps. Value of strategy determines + # whether the biased portions are in the earlier or later timesteps. + portion: 0.25 +} + +# Override parameters from checkpoints's scheduler. +diffusion_scheduler_config: { + _class_name: 'FlaxEulerDiscreteScheduler', + prediction_type: 'epsilon', + rescale_zero_terminal_snr: False, + timestep_spacing: 'trailing' +} + +# Output directory +# Create a GCS bucket, e.g. my-maxtext-outputs and set this to "gs://my-maxtext-outputs/" +base_output_directory: "" + +# Hardware +hardware: 'tpu' # Supported hardware types are 'tpu', 'gpu' +skip_jax_distributed_system: False + +# Parallelism +mesh_axes: ['data', 'fsdp', 'context', 'tensor'] + +# batch : batch dimension of data and activations +# hidden : +# embed : attention qkv dense layer hidden dim named as embed +# heads : attention head dim = num_heads * head_dim +# length : attention sequence length +# temb_in : dense.shape[0] of resnet dense before conv +# out_c : dense.shape[1] of resnet dense before conv +# out_channels : conv.shape[-1] activation +# keep_1 : conv.shape[0] weight +# keep_2 : conv.shape[1] weight +# conv_in : conv.shape[2] weight +# conv_out : conv.shape[-1] weight +logical_axis_rules: [ + ['batch', 'data'], + ['activation_batch', ['data','fsdp']], + ['activation_heads', 'tensor'], + ['activation_kv', 'tensor'], + ['mlp','tensor'], + ['embed','fsdp'], + ['heads', 'tensor'], + ['conv_batch', ['data','fsdp']], + ['out_channels', 'tensor'], + ['conv_out', 'fsdp'], + ] +data_sharding: [['data', 'fsdp', 'context', 'tensor']] + +# One axis for each parallelism type may hold a placeholder (-1) +# value to auto-shard based on available slices and devices. +# By default, product of the DCN axes should equal number of slices +# and product of the ICI axes should equal number of devices per slice. +dcn_data_parallelism: 1 # recommended DCN axis to be auto-sharded +dcn_fsdp_parallelism: -1 +dcn_context_parallelism: 1 +dcn_tensor_parallelism: 1 +ici_data_parallelism: 1 +ici_fsdp_parallelism: -1 +ici_context_parallelism: 1 +ici_tensor_parallelism: 1 + +allow_split_physical_axes: False + +# Dataset +# Replace with dataset path or train_data_dir. One has to be set. +dataset_name: 'diffusers/pokemon-gpt4-captions' +train_split: 'train' +dataset_type: 'tfrecord' # Options: 'tfrecord', 'hf', 'tf', 'grain', 'synthetic' +cache_latents_text_encoder_outputs: True +dataset_save_location: '/tmp/pokemon-gpt4-captions_xl' +train_data_dir: '' +dataset_config_name: '' +jax_cache_dir: '' +hf_data_dir: '' +hf_train_files: '' +hf_access_token: '' +image_column: 'image' +caption_column: 'text' +resolution: 512 +center_crop: False +random_flip: False +tokenize_captions_num_proc: 4 +transform_images_num_proc: 4 +reuse_example_batch: False +enable_data_shuffling: True + +# checkpoint every number of samples, -1 means don't checkpoint. +checkpoint_every: -1 +# enables one replica to read the ckpt then broadcast to the rest +enable_single_replica_ckpt_restoring: False + +# Training loop +learning_rate: 1.e-5 +scale_lr: False +max_train_samples: -1 +# max_train_steps takes priority over num_train_epochs. +max_train_steps: 1500 +num_train_epochs: 1 +seed: 0 +output_dir: 'output/' +output_name: "flux2klein_generated_image.png" +per_device_batch_size: 1 + +warmup_steps_fraction: 0.1 +learning_rate_schedule_steps: -1 # By default the length of the schedule is set to the number of steps. + +# AdamW optimizer parameters +adam_b1: 0.9 # Exponential decay rate to track the first moment of past gradients. +adam_b2: 0.999 # Exponential decay rate to track the second moment of past gradients. +adam_eps: 1.e-8 # A small constant applied to denominator outside of the square root. +adam_weight_decay: 0 # AdamW Weight decay +opt_enable_grad_clipping: False +max_grad_value: 1.0 +opt_enable_grad_global_norm_clipping: False +max_grad_norm: 1.0 + +enable_profiler: False +skip_first_n_steps_for_profiler: 5 +profiler_steps: 10 +profiler: "" + +# Generation parameters +prompt: "a car jumping off of a cliff with a crowd cheering" +prompt_2: "A detailed vector illustration of a robotic hummingbird || A cinematic shot of a neon-lit cyberpunk street" +negative_prompt: "" +do_classifier_free_guidance: True +guidance_scale: 4.0 +guidance_rescale: 0.0 +num_inference_steps: 4 +save_final_checkpoint: False + +# SDXL Lightning parameters +lightning_from_pt: True +lightning_repo: "" +lightning_ckpt: "" + +# LoRA parameters +lora_config: { + lora_model_name_or_path: [], + weight_name: [], + adapter_name: [], + scale: [], + from_pt: [] +} + +enable_mllog: False + +#controlnet +controlnet_model_name_or_path: 'diffusers/controlnet-canny-sdxl-1.0' +controlnet_from_pt: True +controlnet_conditioning_scale: 0.5 +controlnet_image: 'https://upload.wikimedia.org/wikipedia/commons/thumb/c/c1/Google_%22G%22_logo.svg/1024px-Google_%22G%22_logo.svg.png' +quantization: '' +quantization_local_shard_count: -1 +use_qwix_quantization: False +compile_topology_num_slices: -1 # Number of target slices, set to a positive integer. + +# ML Diagnostics settings +enable_ml_diagnostics: False +profiler_gcs_path: "" +enable_ondemand_xprof: False + +# Specific additions for generate_flux2klein execution +height: 1024 +width: 1024 +batch_size: 4 +interactive: False + +# 4B Architecture Dimensions +depth: 20 # num_single_layers +num_double_layers: 5 +hidden_size: 3072 +num_attention_heads: 24 + diff --git a/src/maxdiffusion/configs/base_flux2klein_9B.yml b/src/maxdiffusion/configs/base_flux2klein_9B.yml new file mode 100644 index 000000000..a6c670a69 --- /dev/null +++ b/src/maxdiffusion/configs/base_flux2klein_9B.yml @@ -0,0 +1,275 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This sentinel is a reminder to choose a real run name. +run_name: 'flux2klein_9b_test_run' + +metrics_file: "" # for testing, local file that stores scalar metrics. If empty, no metrics are written. +# If true save metrics such as loss and TFLOPS to GCS in {base_output_directory}/{run_name}/metrics/ +write_metrics: True + +timing_metrics_file: "" # for testing, local file that stores function timing metrics such as state creation, compilation. If empty, no metrics are written. +write_timing_metrics: True + +gcs_metrics: False +# If true save config to GCS in {base_output_directory}/{run_name}/ +save_config_to_gcs: False +log_period: 100 + +pretrained_model_name_or_path: 'black-forest-labs/FLUX.2-klein-9B' +clip_model_name_or_path: 'ariG23498/clip-vit-large-patch14-text-flax' +t5xxl_model_name_or_path: 'ariG23498/t5-v1-1-xxl-flax' + +# Flux params +flux_name: "flux2klein_9B" +scale_shift_order: "scale_shift" +use_latents: False +latents_path: "" +max_sequence_length: 512 +time_shift: True +base_shift: 0.5 +max_shift: 1.15 + + +unet_checkpoint: '' +revision: 'refs/pr/95' +# This will convert the weights to this dtype. +# When running inference on TPUv5e, use weights_dtype: 'bfloat16' +weights_dtype: 'bfloat16' +# This sets the layer's dtype in the model. Ex: nn.Dense(dtype=activations_dtype) +activations_dtype: 'bfloat16' + +# matmul and conv precision from https://jax.readthedocs.io/en/latest/jax.lax.html#jax.lax.Precision +# Options are "DEFAULT", "HIGH", "HIGHEST" +# fp32 activations and fp32 weights with HIGHEST will provide the best precision +# at the cost of time. +precision: "DEFAULT" + +# if False state is not jitted and instead replicate is called. This is good for debugging on single host +# It must be True for multi-host. +jit_initializers: True + +# Set true to load weights from pytorch +from_pt: True +split_head_dim: True +attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te +# If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens. +# Else we do not pass in segment ids and on vpu bound hardware like trillium this is faster. +# However, when padding tokens are significant, this will lead to worse quality and should be set to True. +mask_padding_tokens: True +# Maxdiffusion has 2 types of attention sharding strategies: +# 1. attention_sharding_uniform = True : same sequence sharding rules applied for q in both (self and cross attention) +# 2. attention_sharding_uniform = False : Heads are sharded uniformly across devices for self attention while sequence is sharded +# in cross attention q. +attention_sharding_uniform: True + +flash_block_sizes: {} +# GroupNorm groups +norm_num_groups: 32 + +# If train_new_flux, flux weights will be randomly initialized to train flux from scratch +# else they will be loaded from pretrained_model_name_or_path +train_new_flux: False + +# train text_encoder - Currently not supported for SDXL +train_text_encoder: False +text_encoder_learning_rate: 4.25e-6 + +# https://arxiv.org/pdf/2305.08891.pdf +snr_gamma: -1.0 + +timestep_bias: { + # a value of later will increase the frequence of the model's final training steps. + # none, earlier, later, range + strategy: "none", + # multiplier for bias, a value of 2.0 will double the weight of the bias, 0.5 will halve it. + multiplier: 1.0, + # when using strategy=range, the beginning (inclusive) timestep to bias. + begin: 0, + # when using strategy=range, the final step (inclusive) to bias. + end: 1000, + # portion of timesteps to bias. + # 0.5 will bias one half of the timesteps. Value of strategy determines + # whether the biased portions are in the earlier or later timesteps. + portion: 0.25 +} + +# Override parameters from checkpoints's scheduler. +diffusion_scheduler_config: { + _class_name: 'FlaxEulerDiscreteScheduler', + prediction_type: 'epsilon', + rescale_zero_terminal_snr: False, + timestep_spacing: 'trailing' +} + +# Output directory +# Create a GCS bucket, e.g. my-maxtext-outputs and set this to "gs://my-maxtext-outputs/" +base_output_directory: "" + +# Hardware +hardware: 'tpu' # Supported hardware types are 'tpu', 'gpu' +skip_jax_distributed_system: False + +# Parallelism +mesh_axes: ['data', 'fsdp', 'context', 'tensor'] + +# batch : batch dimension of data and activations +# hidden : +# embed : attention qkv dense layer hidden dim named as embed +# heads : attention head dim = num_heads * head_dim +# length : attention sequence length +# temb_in : dense.shape[0] of resnet dense before conv +# out_c : dense.shape[1] of resnet dense before conv +# out_channels : conv.shape[-1] activation +# keep_1 : conv.shape[0] weight +# keep_2 : conv.shape[1] weight +# conv_in : conv.shape[2] weight +# conv_out : conv.shape[-1] weight +logical_axis_rules: [ + ['batch', 'data'], + ['activation_batch', ['data','fsdp']], + ['activation_heads', 'tensor'], + ['activation_kv', 'tensor'], + ['mlp','tensor'], + ['embed','fsdp'], + ['heads', 'tensor'], + ['conv_batch', ['data','fsdp']], + ['out_channels', 'tensor'], + ['conv_out', 'fsdp'], + ] +data_sharding: [['data', 'fsdp', 'context', 'tensor']] + +# One axis for each parallelism type may hold a placeholder (-1) +# value to auto-shard based on available slices and devices. +# By default, product of the DCN axes should equal number of slices +# and product of the ICI axes should equal number of devices per slice. +dcn_data_parallelism: 1 # recommended DCN axis to be auto-sharded +dcn_fsdp_parallelism: -1 +dcn_context_parallelism: 1 +dcn_tensor_parallelism: 1 +ici_data_parallelism: 1 +ici_fsdp_parallelism: -1 # recommended ICI axis to be auto-sharded +ici_context_parallelism: 1 +ici_tensor_parallelism: 1 + +allow_split_physical_axes: False + +# Dataset +# Replace with dataset path or train_data_dir. One has to be set. +dataset_name: 'diffusers/pokemon-gpt4-captions' +train_split: 'train' +dataset_type: 'tfrecord' # Options: 'tfrecord', 'hf', 'tf', 'grain', 'synthetic' +cache_latents_text_encoder_outputs: True +dataset_save_location: '/tmp/pokemon-gpt4-captions_xl' +train_data_dir: '' +dataset_config_name: '' +jax_cache_dir: '' +hf_data_dir: '' +hf_train_files: '' +hf_access_token: '' +image_column: 'image' +caption_column: 'text' +resolution: 512 +center_crop: False +random_flip: False +tokenize_captions_num_proc: 4 +transform_images_num_proc: 4 +reuse_example_batch: False +enable_data_shuffling: True + +# checkpoint every number of samples, -1 means don't checkpoint. +checkpoint_every: -1 +# enables one replica to read the ckpt then broadcast to the rest +enable_single_replica_ckpt_restoring: False + +# Training loop +learning_rate: 1.e-5 +scale_lr: False +max_train_samples: -1 +# max_train_steps takes priority over num_train_epochs. +max_train_steps: 1500 +num_train_epochs: 1 +seed: 0 +output_dir: 'output/' +output_name: "flux2klein_generated_image.png" +per_device_batch_size: 1 + +warmup_steps_fraction: 0.1 +learning_rate_schedule_steps: -1 # By default the length of the schedule is set to the number of steps. + +# AdamW optimizer parameters +adam_b1: 0.9 # Exponential decay rate to track the first moment of past gradients. +adam_b2: 0.999 # Exponential decay rate to track the second moment of past gradients. +adam_eps: 1.e-8 # A small constant applied to denominator outside of the square root. +adam_weight_decay: 0 # AdamW Weight decay +opt_enable_grad_clipping: False +max_grad_value: 1.0 +opt_enable_grad_global_norm_clipping: False +max_grad_norm: 1.0 + +enable_profiler: False +skip_first_n_steps_for_profiler: 5 +profiler_steps: 10 +profiler: "" + +# Generation parameters +prompt: "A detailed vector illustration of a robotic hummingbird || A cinematic shot of a neon-lit cyberpunk street" +prompt_2: "A detailed vector illustration of a robotic hummingbird || A cinematic shot of a neon-lit cyberpunk street" +negative_prompt: "" +do_classifier_free_guidance: True +guidance_scale: 4.0 +guidance_rescale: 0.0 +num_inference_steps: 4 +save_final_checkpoint: False + +# SDXL Lightning parameters +lightning_from_pt: True +lightning_repo: "" +lightning_ckpt: "" + +# LoRA parameters +lora_config: { + lora_model_name_or_path: [], + weight_name: [], + adapter_name: [], + scale: [], + from_pt: [] +} + +enable_mllog: False + +#controlnet +controlnet_model_name_or_path: 'diffusers/controlnet-canny-sdxl-1.0' +controlnet_from_pt: True +controlnet_conditioning_scale: 0.5 +controlnet_image: 'https://upload.wikimedia.org/wikipedia/commons/thumb/c/c1/Google_%22G%22_logo.svg/1024px-Google_%22G%22_logo.svg.png' +quantization: '' +quantization_local_shard_count: -1 +use_qwix_quantization: False +compile_topology_num_slices: -1 # Number of target slices, set to a positive integer. + +# ML Diagnostics settings +enable_ml_diagnostics: False +profiler_gcs_path: "" +enable_ondemand_xprof: False + +# Specific additions for generate_flux2klein execution +height: 1024 +width: 1024 +batch_size: 4 +interactive: False + +# Note: Architecture dimensions (depth, num_double_layers, num_attention_heads) are +# automatically inferred from pretrained_model_name_or_path (transformer/config.json). + diff --git a/src/maxdiffusion/generate_flux2klein.py b/src/maxdiffusion/generate_flux2klein.py new file mode 100644 index 000000000..8f3322387 --- /dev/null +++ b/src/maxdiffusion/generate_flux2klein.py @@ -0,0 +1,550 @@ +""" +Copyright 2026 Google LLC + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import gc +import os +import time +import sys +from typing import List + +from absl import app +import jax +import jax.numpy as jnp +import numpy as np +import flax +from flax import linen as nn +from flax.linen import partitioning as nn_partitioning +from jax.sharding import Mesh + +from maxdiffusion import pyconfig +from maxdiffusion import max_logging +from maxdiffusion.max_utils import create_device_mesh +from maxdiffusion.train_utils import transformer_engine_context + +from maxdiffusion.models.flux.transformers.transformer_flux_flax import Flux2KleinTransformer2DModel +from maxdiffusion.models.vae_flax import FlaxAutoencoderKL +from maxdiffusion.models.qwen3_flax import FlaxQwen3Config, FlaxQwen3Model, load_and_convert_qwen3_weights +from maxdiffusion.schedulers.scheduling_flow_match_flax import FlaxFlowMatchScheduler + + +def partition_prompts(prompt_str: str, batch_size: int) -> List[str]: + """Splits a prompt string by '||' and replicates/truncates to fill the batch_size.""" + raw_prompts = [p.strip() for p in prompt_str.split("||") if p.strip()] + if not raw_prompts: + raw_prompts = ["A detailed vector illustration of a robotic hummingbird"] + + num_prompts = len(raw_prompts) + if num_prompts == 1: + return raw_prompts * batch_size + elif num_prompts <= batch_size: + reps = batch_size // num_prompts + active = [] + for p in raw_prompts: + active.extend([p] * reps) + if len(active) < batch_size: + active.extend([raw_prompts[-1]] * (batch_size - len(active))) + return active + else: + max_logging.log( + f"⚠️ Warning: Found {num_prompts} prompts, but batch_size is {batch_size}. Truncating to the first {batch_size}." + ) + return raw_prompts[:batch_size] + + +def encode_prompt(prompt: str, snapshot_dir: str = None, repo_id: str = "black-forest-labs/FLUX.2-klein-4B"): + """Encodes a prompt string into Qwen3 text embeddings using PyTorch text encoder on CPU.""" + import os + import torch + import gc + from transformers import AutoTokenizer, AutoModelForCausalLM + from huggingface_hub import snapshot_download + + if snapshot_dir is None: + snapshot_dir = snapshot_download(repo_id=repo_id) + + text_encoder_path = os.path.join(snapshot_dir, "text_encoder") + tokenizer_path = os.path.join(snapshot_dir, "tokenizer") + if not os.path.exists(tokenizer_path): + tokenizer_path = text_encoder_path + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + text_encoder = AutoModelForCausalLM.from_pretrained(text_encoder_path, torch_dtype=torch.float32) + text_encoder.eval() + + messages = [{"role": "user", "content": prompt}] + text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False) + inputs = tokenizer(text, padding="max_length", max_length=512, truncation=True, return_tensors="pt") + with torch.no_grad(): + outputs = text_encoder(inputs.input_ids, attention_mask=inputs.attention_mask, output_hidden_states=True) + out = torch.stack([outputs.hidden_states[k] for k in (9, 18, 27)], dim=1) + b, c, s, h = out.shape + prompt_embeds = out.permute(0, 2, 1, 3).reshape(b, s, c * h) + + del text_encoder + gc.collect() + return prompt_embeds.cpu().numpy() + + +def main(argv): + # Enable shardy partitioner for TPU execution + jax.config.update("jax_use_shardy_partitioner", True) + + # 1. Load configurations + config_path = "src/maxdiffusion/configs/base_flux2klein.yml" + custom_overrides = [] + if len(argv) > 1: + if argv[1].endswith(".yml") or argv[1].endswith(".yaml"): + config_path = argv[1] + if len(argv) > 2: + custom_overrides = argv[2:] + else: + custom_overrides = argv[1:] + + max_logging.log(f"Initializing pyconfig with config: {config_path}") + default_args = [ + None, + config_path, + "run_name=flux2klein_generation", + "output_dir=output/", + ] + default_args.extend(custom_overrides) + + is_interactive = any(arg and "interactive=True" in arg.replace(" ", "") for arg in default_args) + if is_interactive: + max_logging.log("ℹ️ Interactive mode detected: overriding use_latents=False for dynamic inputs.") + default_args.append("use_latents=False") + + pyconfig.initialize(default_args) + + # Import modules after jax.distributed.initialize() has run via pyconfig.initialize() + from maxdiffusion.models.flux.util import ( + load_and_convert_flux_klein_weights, + load_and_convert_vae_weights, + cast_dict_to_bfloat16_inplace, + ) + from maxdiffusion.pipelines.flux.flux2klein_pipeline import FlaxFlux2KleinPipeline + + config = pyconfig.config + os.makedirs(config.output_dir, exist_ok=True) + + # 2. Setup device mesh + if config.batch_size == 1 and config.ici_tensor_parallelism == 1 and jax.device_count() > 1: + max_logging.log( + f"ℹ️ Auto-configuring Tensor Parallelism: ici_tensor_parallelism={jax.device_count()}, ici_fsdp_parallelism=1 for batch_size=1 on {jax.device_count()} TPU devices." + ) + pyconfig._config.keys["ici_tensor_parallelism"] = jax.device_count() + pyconfig._config.keys["ici_fsdp_parallelism"] = 1 + + max_logging.log("Setting up JAX device mesh...") + devices_array = create_device_mesh(config) + mesh = Mesh(devices_array, config.mesh_axes) + + # Check compatibility of batch dimension sharding + data_size = mesh.shape.get("data", 1) + fsdp_size = mesh.shape.get("fsdp", 1) + if config.batch_size % (data_size * fsdp_size) != 0: + max_logging.log( + f"⚠️ Warning: batch_size ({config.batch_size}) is not divisible by FSDP*Data mesh size ({fsdp_size * data_size})." + ) + max_logging.log( + " Automatically falling back to sharding batch dimension across 'data' axis only to prevent JAX SPMD errors." + ) + new_rules = [] + for rule in config.logical_axis_rules: + if rule[0] in ("activation_batch", "conv_batch"): + new_rules.append([rule[0], "data"]) + else: + new_rules.append(rule) + pyconfig._config.keys["logical_axis_rules"] = tuple(new_rules) + + # 3. Resolve weights repository snapshots + repo_id = getattr(config, "pretrained_model_name_or_path", None) + if not repo_id: + depth_val = getattr(config, "depth", None) + repo_id = "black-forest-labs/FLUX.2-klein-9B" if depth_val == 24 else "black-forest-labs/FLUX.2-klein-4B" + max_logging.log(f"Target model detected: {repo_id}") + + from huggingface_hub import snapshot_download + + max_logging.log(f"Resolving snapshot directory for model '{repo_id}' from HF Hub...") + snapshot_dir = snapshot_download(repo_id=repo_id) + safetensors_path = os.path.join(snapshot_dir, "transformer") + vae_safetensors_path = os.path.join(snapshot_dir, "vae", "diffusion_pytorch_model.safetensors") + text_encoder_path = os.path.join(snapshot_dir, "text_encoder") + + # 4. Load Qwen3 Config & Setup model layout + from transformers import AutoConfig + + max_logging.log(f"Loading Qwen3 config from text_encoder path: {text_encoder_path}...") + pt_config = AutoConfig.from_pretrained(text_encoder_path, local_files_only=True) + + qwen3_config = FlaxQwen3Config( + vocab_size=pt_config.vocab_size, + hidden_size=pt_config.hidden_size, + intermediate_size=pt_config.intermediate_size, + num_hidden_layers=pt_config.num_hidden_layers, + num_attention_heads=pt_config.num_attention_heads, + num_key_value_heads=pt_config.num_key_value_heads, + max_position_embeddings=pt_config.max_position_embeddings, + rms_norm_eps=pt_config.rms_norm_eps, + rope_theta=pt_config.rope_theta, + dtype=jnp.bfloat16 if config.weights_dtype == "bfloat16" else jnp.float32, + ) + qwen3_model = FlaxQwen3Model(qwen3_config) + + # Load Transformer HF config.json directly for model architecture parameters + import json + + transformer_config_json = os.path.join(safetensors_path, "config.json") + transformer_pt_cfg = {} + if os.path.exists(transformer_config_json): + with open(transformer_config_json, "r") as f: + transformer_pt_cfg = json.load(f) + + num_double_layers = getattr(config, "num_double_layers", -1) + if num_double_layers is None or num_double_layers <= 0: + num_double_layers = transformer_pt_cfg.get("num_layers", 5) + + depth = getattr(config, "depth", -1) + if depth is None or depth <= 0: + depth = transformer_pt_cfg.get("num_single_layers", 20) + + num_attention_heads = getattr(config, "num_attention_heads", -1) + if num_attention_heads is None or num_attention_heads <= 0: + num_attention_heads = transformer_pt_cfg.get("num_attention_heads", 24) + + # 5. Instantiate JAX Flux2KleinTransformer2DModel + transformer = Flux2KleinTransformer2DModel( + in_channels=128, + num_layers=num_double_layers, + num_single_layers=depth, + attention_head_dim=128, + num_attention_heads=num_attention_heads, + joint_attention_dim=3 * pt_config.hidden_size, + pooled_projection_dim=768, + mlp_ratio=3.0, + qkv_bias=False, + joint_attention_bias=False, + x_embedder_bias=False, + proj_out_bias=False, + use_global_modulation=True, + use_swiglu=True, + axes_dims_rope=(32, 32, 32, 32), + theta=2000, + mesh=mesh, + dtype=jnp.bfloat16 if config.weights_dtype == "bfloat16" else jnp.float32, + weights_dtype=jnp.bfloat16 if config.weights_dtype == "bfloat16" else jnp.float32, + attention_kernel=config.attention, + scale_shift_order=getattr(config, "scale_shift_order", "shift_scale"), + ) + + # 6. Instantiate JAX VAE + vae = FlaxAutoencoderKL( + in_channels=3, + out_channels=3, + down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"), + up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"), + block_out_channels=(128, 256, 512, 512), + layers_per_block=2, + act_fn="silu", + latent_channels=32, + norm_num_groups=32, + sample_size=512, + use_quant_conv=True, + use_post_quant_conv=True, + dtype=jnp.bfloat16 if config.weights_dtype == "bfloat16" else jnp.float32, + ) + + # 7. Evaluate shapes & extract mesh shardings + max_logging.log("Evaluating model shapes and shardings...") + h_packed = config.height // 16 + w_packed = config.width // 16 + seq_len_img = h_packed * w_packed + seq_len_txt = config.max_sequence_length + + img_dummy = jnp.zeros((config.batch_size, seq_len_img, 128)) + img_ids_dummy = jnp.zeros((config.batch_size, seq_len_img, 4)) + txt_dummy = jnp.zeros((config.batch_size, seq_len_txt, 3 * pt_config.hidden_size)) + txt_ids_dummy = jnp.zeros((config.batch_size, seq_len_txt, 4)) + vec_dummy = jnp.zeros((config.batch_size, 768)) + t_vec_dummy = jnp.zeros((config.batch_size,)) + guidance_vec_dummy = jnp.zeros((config.batch_size,)) + dummy_img = jnp.zeros((config.batch_size, 3, 512, 512)) + dummy_ids = jnp.zeros((config.batch_size, seq_len_txt), dtype=jnp.int32) + dummy_mask = jnp.zeros((config.batch_size, seq_len_txt), dtype=jnp.int32) + + key = jax.random.PRNGKey(0) + key, vae_key, qwen_key = jax.random.split(key, 3) + + def transformer_init_fn(): + return transformer.init( + key, + hidden_states=img_dummy, + img_ids=img_ids_dummy, + encoder_hidden_states=txt_dummy, + txt_ids=txt_ids_dummy, + pooled_projections=vec_dummy, + timestep=t_vec_dummy, + guidance=guidance_vec_dummy, + ) + + def vae_init_fn(): + return vae.init(vae_key, dummy_img) + + def qwen3_init_fn(): + return qwen3_model.init(qwen_key, dummy_ids, dummy_mask) + + with mesh, nn_partitioning.axis_rules(config.logical_axis_rules): + abstract_transformer_vars = jax.eval_shape(transformer_init_fn) + abstract_vae_vars = jax.eval_shape(vae_init_fn) + abstract_qwen3_vars = jax.eval_shape(qwen3_init_fn) + + logical_transformer_specs = nn.get_partition_spec(abstract_transformer_vars) + logical_vae_specs = nn.get_partition_spec(abstract_vae_vars) + logical_qwen3_specs = nn.get_partition_spec(abstract_qwen3_vars) + + transformer_mesh_shardings = nn.logical_to_mesh_sharding(logical_transformer_specs, mesh, config.logical_axis_rules) + vae_mesh_shardings = nn.logical_to_mesh_sharding(logical_vae_specs, mesh, config.logical_axis_rules) + qwen3_mesh_shardings = nn.logical_to_mesh_sharding(logical_qwen3_specs, mesh, config.logical_axis_rules) + + transformer_shardings = flax.core.freeze(transformer_mesh_shardings["params"]) + vae_shardings = flax.core.freeze(vae_mesh_shardings["params"]) + qwen3_shardings = flax.core.freeze(qwen3_mesh_shardings["params"]) + + # 8. Load weights on Host CPU + max_logging.log("Loading parameters on Host CPU...") + t_load_start = time.time() + cpu_device = jax.devices("cpu")[0] + with jax.default_device(cpu_device): + with mesh, nn_partitioning.axis_rules(config.logical_axis_rules): + import flax.linen.spmd as flax_spmd + + def unbox_fn(x): + return x.unbox() if isinstance(x, flax_spmd.LogicallyPartitioned) else x + + params = jax.tree_util.tree_map( + unbox_fn, abstract_transformer_vars["params"], is_leaf=lambda k: isinstance(k, flax_spmd.LogicallyPartitioned) + ) + params = flax.core.unfreeze(params) + + vae_params = jax.tree_util.tree_map( + unbox_fn, abstract_vae_vars["params"], is_leaf=lambda k: isinstance(k, flax_spmd.LogicallyPartitioned) + ) + vae_params = flax.core.unfreeze(vae_params) + + qwen3_params = jax.tree_util.tree_map( + unbox_fn, abstract_qwen3_vars["params"], is_leaf=lambda k: isinstance(k, flax_spmd.LogicallyPartitioned) + ) + qwen3_params = flax.core.unfreeze(qwen3_params) + + params = load_and_convert_flux_klein_weights(safetensors_path, params, num_double_layers, depth) + vae_params, vae_bn_mean, vae_bn_std = load_and_convert_vae_weights(vae_safetensors_path, vae_params) + qwen3_params = load_and_convert_qwen3_weights(text_encoder_path, qwen3_params, qwen3_config) + + if config.weights_dtype == "bfloat16": + max_logging.log("Casting JAX parameters to bfloat16 in-place...") + cast_dict_to_bfloat16_inplace(params, device=cpu_device, exclude_keywords=("norm",)) + cast_dict_to_bfloat16_inplace(vae_params, device=cpu_device, exclude_keywords=("norm",)) + cast_dict_to_bfloat16_inplace(qwen3_params, device=cpu_device, exclude_keywords=("norm",)) + vae_bn_mean = vae_bn_mean.astype(jnp.bfloat16) + vae_bn_std = vae_bn_std.astype(jnp.bfloat16) + + params = flax.core.freeze(params) + vae_params = flax.core.freeze(vae_params) + qwen3_params = flax.core.freeze(qwen3_params) + + max_logging.log("\n" + "=" * 80) + max_logging.log("🚀 Pinning all parameters to TPU HBM permanently...") + max_logging.log("=" * 80 + "\n") + max_logging.log("Putting params on TPU HBM...") + with mesh, nn_partitioning.axis_rules(config.logical_axis_rules): + try: + params = jax.device_put(params, transformer_shardings) + except Exception as err: + max_logging.log("\n❌ jax.device_put(params, transformer_shardings) FAILED!") + flat_p = flax.traverse_util.flatten_dict(params) + flat_s = flax.traverse_util.flatten_dict(transformer_shardings) + k_p = set(flat_p.keys()) + k_s = set(flat_s.keys()) + max_logging.log(f"Keys in sharding spec but missing in params: {k_s - k_p}") + max_logging.log(f"Keys in params but missing in sharding spec: {k_p - k_s}") + sys.stdout.flush() + raise err + max_logging.log("Putting vae_params on TPU HBM...") + vae_params = jax.device_put(vae_params, vae_shardings) + max_logging.log("Putting qwen3_params on TPU HBM...") + qwen3_params = jax.device_put(qwen3_params, qwen3_shardings) + max_logging.log("All parameters placed on TPU HBM successfully!") + gc.collect() + jax.effects_barrier() + + load_time = time.time() - t_load_start + max_logging.log(f" -> [TIMING] Total Model Loading & Device Placement: {load_time:.2f} seconds ⏱️\n") + + # 9. Setup FlowMatch Scheduler + scheduler = FlaxFlowMatchScheduler( + num_train_timesteps=1000, + shift=1.0, + sigma_max=1.0, + sigma_min=0.001, + inverse_timesteps=False, + extra_one_step=False, + reverse_sigmas=False, + use_dynamic_shifting=True, + time_shift_type="exponential", + ) + + # 10. Instantiate and invoke FlaxFlux2KleinPipeline + max_logging.log("Instantiating JAX FlaxFlux2KleinPipeline...") + pipeline = FlaxFlux2KleinPipeline( + transformer=transformer, + vae=vae, + text_encoder=qwen3_model, + tokenizer=None, + scheduler=scheduler, + config=config, + mesh=mesh, + ) + + active_prompts = partition_prompts(config.prompt, config.batch_size) + + if getattr(config, "interactive", False): + print("\n" + "=" * 80) + print(" BATCHED INTERACTIVE GENERATION MODE ENABLED 🎮") + print("The model has been fully loaded and compiled on the TPU.") + print(f"Batch size: {config.batch_size} parallel images.") + print("Enter prompts separated by '||' (e.g. A cute cat || A red car)") + print("Type 'exit' to quit.") + print("=" * 80) + + image_idx = 1 + while True: + try: + user_input = input("\nEnter prompt(s): ") + except (KeyboardInterrupt, EOFError): + break + if user_input.strip().lower() in ("exit", "quit"): + break + if not user_input.strip(): + continue + + prompts = partition_prompts(user_input, config.batch_size) + output_file = f"generated_{image_idx:03d}.png" + + pipeline( + prompt=prompts, + params=params, + vae_params=vae_params, + qwen3_params=qwen3_params, + vae_bn_mean=vae_bn_mean, + vae_bn_std=vae_bn_std, + transformer_shardings=transformer_shardings, + vae_shardings=vae_shardings, + qwen3_shardings=qwen3_shardings, + height=config.height, + width=config.width, + num_inference_steps=config.num_inference_steps, + batch_size=config.batch_size, + use_latents=False, + output_dir=config.output_dir, + output_name=output_file, + ) + image_idx += 1 + else: + # Run one-shot generation + latents_to_use = None + use_latents_flag = False + if getattr(config, "latents_path", ""): + max_logging.log(f"Loading custom starting noise latents from: {config.latents_path}...") + latents_to_use = np.load(config.latents_path) + use_latents_flag = True + max_logging.log(f" -> Custom latents shape: {latents_to_use.shape} | sum: {latents_to_use.sum():.6f}") + + max_logging.log("\n" + "=" * 80) + max_logging.log("🚀 Running initial dry run (Warmup Pass) to compile XLA graphs...") + max_logging.log("=" * 80) + _, warmup_trace = pipeline( + prompt=active_prompts, + params=params, + vae_params=vae_params, + qwen3_params=qwen3_params, + vae_bn_mean=vae_bn_mean, + vae_bn_std=vae_bn_std, + transformer_shardings=transformer_shardings, + vae_shardings=vae_shardings, + qwen3_shardings=qwen3_shardings, + height=config.height, + width=config.width, + num_inference_steps=config.num_inference_steps, + batch_size=config.batch_size, + use_latents=use_latents_flag, + latents=latents_to_use, + output_dir=config.output_dir, + output_name="flux2klein_warmup.png", + ) + warmup_time = ( + warmup_trace.get("prompt_encoding", 0.0) + + warmup_trace.get("denoise_loop", 0.0) + + warmup_trace.get("vae_decode", 0.0) + ) + + max_logging.log("\n" + "=" * 80) + max_logging.log("⏱️ Running timed pass at full TPU speed...") + max_logging.log("=" * 80) + _, main_trace = pipeline( + prompt=active_prompts, + params=params, + vae_params=vae_params, + qwen3_params=qwen3_params, + vae_bn_mean=vae_bn_mean, + vae_bn_std=vae_bn_std, + transformer_shardings=transformer_shardings, + vae_shardings=vae_shardings, + qwen3_shardings=qwen3_shardings, + height=config.height, + width=config.width, + num_inference_steps=config.num_inference_steps, + batch_size=config.batch_size, + use_latents=use_latents_flag, + latents=latents_to_use, + output_dir=config.output_dir, + output_name=config.output_name, + ) + main_time = ( + main_trace.get("prompt_encoding", 0.0) + main_trace.get("denoise_loop", 0.0) + main_trace.get("vae_decode", 0.0) + ) + + max_logging.log("\n" + "=" * 80) + max_logging.log("📊 FLUX.2-KLEIN LATENCY & TIMING BREAKDOWN (PURE MODEL INFERENCE)") + max_logging.log("=" * 80) + max_logging.log(f"1) Total Model Loading & Placement Time: {load_time:.2f} seconds ⏱️") + max_logging.log(f"2) Cold-Start / Warmup Pass (XLA Compilation): {warmup_time:.2f} seconds ⏱️") + max_logging.log(f" - Qwen3 Encoding: {warmup_trace.get('prompt_encoding', 0.0):.2f}s") + max_logging.log(f" - Flux Denoising: {warmup_trace.get('denoise_loop', 0.0):.2f}s") + max_logging.log(f" - VAE Decoding: {warmup_trace.get('vae_decode', 0.0):.2f}s") + max_logging.log(f"3) Main Warmed-Up Pass (Pure Model Inference): {main_time:.2f} seconds ⏱️") + max_logging.log(f" - Qwen3 Encoding: {main_trace.get('prompt_encoding', 0.0):.2f}s") + max_logging.log(f" - Flux Denoising: {main_trace.get('denoise_loop', 0.0):.2f}s") + max_logging.log(f" - VAE Decoding: {main_trace.get('vae_decode', 0.0):.2f}s") + max_logging.log("=" * 80) + + max_logging.log("\n=======================================================") + max_logging.log(f"SUCCESS! Batched generation complete for {config.batch_size} images! 🎨🎉") + max_logging.log("=======================================================\n") + + +if __name__ == "__main__": + with transformer_engine_context(): + app.run(main) diff --git a/src/maxdiffusion/models/attention_flax.py b/src/maxdiffusion/models/attention_flax.py index a5a522653..f0eb003af 100644 --- a/src/maxdiffusion/models/attention_flax.py +++ b/src/maxdiffusion/models/attention_flax.py @@ -1693,7 +1693,36 @@ def chunk_scanner(chunk_idx, _): return jnp.concatenate(res, axis=-3) # fuse the chunked result back -def apply_rope(xq: Array, xk: Array, freqs_cis: Array) -> tuple[Array, Array]: +def apply_rope(xq: Array, xk: Array, freqs_cis: Any) -> tuple[Array, Array]: + if isinstance(freqs_cis, (tuple, list)): + cos, sin = freqs_cis + if cos.ndim == 2: + seq_len = cos.shape[0] + if xq.ndim == 4 and xq.shape[2] == seq_len: + cos = cos[None, None, :, :] + sin = sin[None, None, :, :] + else: + cos = cos[None, :, None, :] + sin = sin[None, :, None, :] + elif cos.ndim == 3 and cos.shape[0] == 1: + seq_len = cos.shape[1] + if xq.ndim == 4 and xq.shape[2] == seq_len: + cos = cos[:, None, :, :] + sin = sin[:, None, :, :] + else: + cos = cos[:, :, None, :] + sin = sin[:, :, None, :] + + def _rotate(x): + x_reshaped = x.reshape(*x.shape[:-1], -1, 2) + x_real = x_reshaped[..., 0] + x_imag = x_reshaped[..., 1] + return jnp.stack([-x_imag, x_real], axis=-1).reshape(*x.shape) + + xq_out = xq * cos + _rotate(xq) * sin + xk_out = xk * cos + _rotate(xk) * sin + return xq_out.astype(xq.dtype), xk_out.astype(xk.dtype) + xq_ = xq.reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.reshape(*xk.shape[:-1], -1, 1, 2) @@ -2556,7 +2585,8 @@ def __call__( # key_proj = nn.with_logical_constraint(key_proj, self.key_axis_names) # value_proj = nn.with_logical_constraint(value_proj, self.value_axis_names) - image_rotary_emb = rearrange(image_rotary_emb, "n d (i j) -> n d i j", i=2, j=2) + if not isinstance(image_rotary_emb, (tuple, list)): + image_rotary_emb = rearrange(image_rotary_emb, "n d (i j) -> n d i j", i=2, j=2) query_proj = query_proj.swapaxes(1, 2) key_proj = key_proj.swapaxes(1, 2) diff --git a/src/maxdiffusion/models/embeddings_flax.py b/src/maxdiffusion/models/embeddings_flax.py index 772ee8122..526ca7071 100644 --- a/src/maxdiffusion/models/embeddings_flax.py +++ b/src/maxdiffusion/models/embeddings_flax.py @@ -12,15 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. import math -from typing import Optional, Any +from typing import List, Optional, Tuple, Union, Any import flax.linen as nn from flax import nnx -import jax.numpy as jnp -from typing import List, Union import jax +import jax.numpy as jnp from .modeling_flax_utils import get_activation from ..models.attention_flax import NNXSimpleFeedForward from ..models.normalization_flax import FP32LayerNorm +from maxdiffusion import max_logging from maxdiffusion.tpu_utils import get_tpu_type, TpuType from maxdiffusion.max_utils import safe_getattr @@ -297,7 +297,7 @@ def __call__(self, encoder_hidden_states_image: jax.Array) -> tuple[jax.Array, j # Apply pos_embed to the original sequence length hidden_states = hidden_states.at[:, :add_len, :].add(self.pos_embed.value[:, :add_len, :]) if current_seq_len > pe_len: - print(f"[WARN] Input seq_len {current_seq_len} > pos_embed len {pe_len}") + max_logging.log(f"[WARN] Input seq_len {current_seq_len} > pos_embed len {pe_len}") hidden_states = self.norm1(hidden_states) hidden_states = self.ff(hidden_states) @@ -435,22 +435,80 @@ def __call__(self, caption): class FluxPosEmbed(nn.Module): - theta: int - axes_dim: List[int] + theta: int = 10000 + axes_dim: List[int] = (16, 56, 56) dtype: jnp.dtype = jnp.float32 + return_tuple: bool = False @nn.compact def __call__(self, ids): - n_axes = ids.shape[-1] - out_freqs = [] + n_axes = len(self.axes_dim) pos = ids.astype(self.dtype) freqs_dtype = self.dtype - for i in range(n_axes): - out = get_1d_rotary_pos_embed(self.axes_dim[i], pos[..., i], freqs_dtype=freqs_dtype) - out_freqs.append(out) - out_freqs = jnp.concatenate(out_freqs, axis=1) - return out_freqs + if self.return_tuple: + cos_out = [] + sin_out = [] + for i in range(n_axes): + dim = self.axes_dim[i] + p = pos[..., i] + freqs = 1.0 / (self.theta ** (jnp.arange(0, dim, 2, dtype=freqs_dtype) / dim)) + freqs = jnp.outer(p, freqs) + freqs = jnp.repeat(freqs, 2, axis=-1) + cos_out.append(jnp.cos(freqs)) + sin_out.append(jnp.sin(freqs)) + freqs_cos = jnp.concatenate(cos_out, axis=-1) + freqs_sin = jnp.concatenate(sin_out, axis=-1) + return freqs_cos, freqs_sin + else: + out_freqs = [] + for i in range(n_axes): + out = get_1d_rotary_pos_embed(self.axes_dim[i], pos[..., i], theta=self.theta, freqs_dtype=freqs_dtype) + out_freqs.append(out) + out_freqs = jnp.concatenate(out_freqs, axis=1) + return out_freqs + + +class NNXFluxPosEmbed(nnx.Module): + + def __init__( + self, + theta: float = 10000.0, + axes_dim: Tuple[int, ...] = (16, 56, 56), + dtype: jnp.dtype = jnp.float32, + return_tuple: bool = True, + ): + self.theta = theta + self.axes_dim = axes_dim + self.dtype = dtype + self.return_tuple = return_tuple + + def __call__(self, ids: jax.Array): + n_axes = len(self.axes_dim) + pos = ids.astype(self.dtype) + freqs_dtype = self.dtype + + if self.return_tuple: + cos_out = [] + sin_out = [] + for i in range(n_axes): + dim = self.axes_dim[i] + p = pos[..., i] + freqs = 1.0 / (self.theta ** (jnp.arange(0, dim, 2, dtype=freqs_dtype) / dim)) + freqs = jnp.outer(p, freqs) + freqs = jnp.repeat(freqs, 2, axis=-1) + cos_out.append(jnp.cos(freqs)) + sin_out.append(jnp.sin(freqs)) + freqs_cos = jnp.concatenate(cos_out, axis=-1) + freqs_sin = jnp.concatenate(sin_out, axis=-1) + return freqs_cos, freqs_sin + else: + out_freqs = [] + for i in range(n_axes): + out = get_1d_rotary_pos_embed(self.axes_dim[i], pos[..., i], theta=self.theta, freqs_dtype=freqs_dtype) + out_freqs.append(out) + out_freqs = jnp.concatenate(out_freqs, axis=1) + return out_freqs class CombinedTimestepTextProjEmbeddings(nn.Module): @@ -480,28 +538,116 @@ def __call__(self, timestep, pooled_projection): class CombinedTimestepGuidanceTextProjEmbeddings(nn.Module): embedding_dim: int pooled_projection_dim: int + guidance_embeds: bool = True + frequency_embedding_size: int = 256 dtype: jnp.dtype = jnp.float32 weights_dtype: jnp.dtype = jnp.float32 precision: jax.lax.Precision = None @nn.compact - def __call__(self, timestep, guidance, pooled_projection): - timesteps_proj = timestep + def __call__(self, timestep, guidance, pooled_projection=None): + timesteps_proj = FlaxTimesteps(dim=self.frequency_embedding_size, flip_sin_to_cos=True, freq_shift=0)(timestep) + dtype = pooled_projection.dtype if pooled_projection is not None else jnp.float32 timestep_emb = FlaxTimestepEmbedding( time_embed_dim=self.embedding_dim, dtype=self.dtype, weights_dtype=self.weights_dtype - )(timesteps_proj.astype(pooled_projection.dtype)) + )(timesteps_proj.astype(dtype)) + + if self.guidance_embeds and guidance is not None: + guidance_proj = FlaxTimesteps(dim=self.frequency_embedding_size, flip_sin_to_cos=True, freq_shift=0)(guidance) + guidance_emb = FlaxTimestepEmbedding( + time_embed_dim=self.embedding_dim, dtype=self.dtype, weights_dtype=self.weights_dtype + )(guidance_proj.astype(dtype)) + time_guidance_emb = timestep_emb + guidance_emb + else: + time_guidance_emb = timestep_emb + + if ( + pooled_projection is not None + and hasattr(self, "pooled_projection_dim") + and self.pooled_projection_dim + and self.pooled_projection_dim > 0 + ): + pooled_projections = PixArtAlphaTextProjection( + self.embedding_dim, act_fn="silu", dtype=self.dtype, weights_dtype=self.weights_dtype, precision=self.precision + )(pooled_projection) + conditioning = time_guidance_emb + pooled_projections + else: + conditioning = time_guidance_emb - guidance_proj = guidance - guidance_emb = FlaxTimestepEmbedding( - time_embed_dim=self.embedding_dim, dtype=self.dtype, weights_dtype=self.weights_dtype - )(guidance_proj.astype(pooled_projection.dtype)) + return conditioning - time_guidance_emb = timestep_emb + guidance_emb - pooled_projections = PixArtAlphaTextProjection( - self.embedding_dim, act_fn="silu", dtype=self.dtype, weights_dtype=self.weights_dtype, precision=self.precision - )(pooled_projection) - conditioning = time_guidance_emb + pooled_projections +class NNXCombinedTimestepGuidanceTextProjEmbeddings(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + embedding_dim: int, + pooled_projection_dim: int = 0, + guidance_embeds: bool = True, + frequency_embedding_size: int = 256, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + precision: Optional[jax.lax.Precision] = None, + ): + self.embedding_dim = embedding_dim + self.pooled_projection_dim = pooled_projection_dim + self.guidance_embeds = guidance_embeds + self.frequency_embedding_size = frequency_embedding_size + self.dtype = dtype + + self.time_proj = NNXFlaxTimesteps(dim=frequency_embedding_size, flip_sin_to_cos=True, freq_shift=0.0) + self.timestep_embedder = NNXTimestepEmbedding( + rngs=rngs, + in_channels=frequency_embedding_size, + time_embed_dim=embedding_dim, + dtype=dtype, + weights_dtype=weights_dtype, + ) + + if guidance_embeds: + self.guidance_proj = NNXFlaxTimesteps(dim=frequency_embedding_size, flip_sin_to_cos=True, freq_shift=0.0) + self.guidance_embedder = NNXTimestepEmbedding( + rngs=rngs, + in_channels=frequency_embedding_size, + time_embed_dim=embedding_dim, + dtype=dtype, + weights_dtype=weights_dtype, + ) + + if pooled_projection_dim > 0: + self.pooled_embedder = NNXPixArtAlphaTextProjection( + rngs=rngs, + in_features=pooled_projection_dim, + hidden_size=embedding_dim, + out_features=embedding_dim, + act_fn="silu", + dtype=dtype, + weights_dtype=weights_dtype, + ) + + def __call__( + self, + timestep: jax.Array, + guidance: Optional[jax.Array] = None, + pooled_projection: Optional[jax.Array] = None, + ) -> jax.Array: + timesteps_proj = self.time_proj(timestep) + dtype = pooled_projection.dtype if pooled_projection is not None else jnp.float32 + timestep_emb = self.timestep_embedder(timesteps_proj.astype(dtype)) + + if self.guidance_embeds and guidance is not None: + guidance_proj = self.guidance_proj(guidance) + guidance_emb = self.guidance_embedder(guidance_proj.astype(dtype)) + time_guidance_emb = timestep_emb + guidance_emb + else: + time_guidance_emb = timestep_emb + + if pooled_projection is not None and self.pooled_projection_dim > 0: + pooled_projections = self.pooled_embedder(pooled_projection) + conditioning = time_guidance_emb + pooled_projections + else: + conditioning = time_guidance_emb return conditioning diff --git a/src/maxdiffusion/models/flux/transformers/transformer_flux_flax.py b/src/maxdiffusion/models/flux/transformers/transformer_flux_flax.py index 266896b99..af8e3763a 100644 --- a/src/maxdiffusion/models/flux/transformers/transformer_flux_flax.py +++ b/src/maxdiffusion/models/flux/transformers/transformer_flux_flax.py @@ -14,7 +14,7 @@ limitations under the License. """ -from typing import Tuple +from typing import Dict, Optional, Tuple import jax import math import jax.numpy as jnp @@ -23,9 +23,24 @@ from einops import repeat, rearrange from ....configuration_utils import ConfigMixin, flax_register_to_config from ...modeling_flax_utils import FlaxModelMixin -from ...normalization_flax import AdaLayerNormZeroSingle, AdaLayerNormContinuous, AdaLayerNormZero -from ...attention_flax import FlaxFluxAttention, apply_rope -from ...embeddings_flax import (FluxPosEmbed, CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings) +from ...normalization_flax import ( + AdaLayerNormZeroSingle, + AdaLayerNormContinuous, + AdaLayerNormZero, + NNXAdaLayerNormZeroSingle, + NNXAdaLayerNormContinuous, + NNXAdaLayerNormZero, +) +from ...attention_flax import FlaxFluxAttention as FluxAttention, FlaxFluxAttention, apply_rope +from flax import nnx +from ...embeddings_flax import ( + FluxPosEmbed, + NNXFluxPosEmbed, + CombinedTimestepGuidanceTextProjEmbeddings, + CombinedTimestepGuidanceTextProjEmbeddings as CombinedTimestepGuidanceTextEmbeddings, + CombinedTimestepTextProjEmbeddings, + NNXCombinedTimestepGuidanceTextProjEmbeddings, +) from .... import common_types from ....common_types import BlockSizes from ....utils import BaseOutput @@ -705,3 +720,1105 @@ def init_weights(self, rngs, max_sequence_length, eval_only=True): timestep=t_vec, guidance=guidance_vec, )["params"] + + +class FlaxSwiGluFeedForward(nn.Module): + dim: int + hidden_dim: int + out_dim: int + dtype: jnp.dtype = jnp.float32 + weights_dtype: jnp.dtype = jnp.float32 + precision: float = None + + def setup(self): + self.linear_in = nn.Dense( + 2 * self.hidden_dim, + use_bias=False, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + self.linear_out = nn.Dense( + self.out_dim, + use_bias=False, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + + def __call__(self, x): + x = self.linear_in(x) + x1, x2 = jnp.split(x, 2, axis=-1) + hidden = nn.silu(x1) * x2 + return self.linear_out(hidden) + + +class Flux2KleinSingleTransformerBlock(nn.Module): + dim: int + num_attention_heads: int + attention_head_dim: int + mlp_ratio: float = 3.0 + attention_kernel: str = "dot_product" + flash_min_seq_length: int = 512 + flash_block_sizes: Optional[Dict[str, int]] = None + mesh: Optional[jax.sharding.Mesh] = None + dtype: jnp.dtype = jnp.float32 + weights_dtype: jnp.dtype = jnp.float32 + precision: float = None + use_global_modulation: bool = False + use_swiglu: bool = True + + def setup(self): + mlp_hidden_dim = int(self.dim * self.mlp_ratio) + + if self.use_global_modulation: + self.norm = nn.LayerNorm( + use_bias=False, + use_scale=False, + epsilon=1e-6, + dtype=self.dtype, + param_dtype=self.weights_dtype, + ) + else: + self.norm = AdaLayerNormZeroSingle( + self.dim, dtype=self.dtype, weights_dtype=self.weights_dtype, precision=self.precision + ) + + out_dim = self.dim * 3 + (2 * mlp_hidden_dim if self.use_swiglu else mlp_hidden_dim) + self.linear1 = nn.Dense( + out_dim, + use_bias=not self.use_swiglu, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "mlp")), + bias_init=nn.with_logical_partitioning(nn.initializers.zeros, (None,)), + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + + self.mlp_act = jax.nn.gelu + self.linear2 = nn.Dense( + self.dim, + use_bias=not self.use_swiglu, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("mlp", "embed")), + bias_init=nn.with_logical_partitioning(nn.initializers.zeros, (None,)), + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + self.attn = FluxAttention( + query_dim=self.dim, + heads=self.num_attention_heads, + dim_head=self.attention_head_dim, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + attention_kernel=self.attention_kernel, + mesh=self.mesh, + flash_block_sizes=self.flash_block_sizes, + ) + + def __call__(self, hidden_states, temb=None, image_rotary_emb=None, temb_mod=None): + residual = hidden_states + if self.use_global_modulation: + shift_msa, scale_msa, gate = jnp.split(temb_mod, 3, axis=-1) + shift_msa = jnp.expand_dims(shift_msa, axis=1) + scale_msa = jnp.expand_dims(scale_msa, axis=1) + gate = jnp.expand_dims(gate, axis=1) + + norm_hidden_states = self.norm(hidden_states) + norm_hidden_states = (1 + scale_msa) * norm_hidden_states + shift_msa + else: + norm_hidden_states, gate = self.norm(hidden_states, emb=temb) + + qkv, mlp = jnp.split(self.linear1(norm_hidden_states), [3 * self.dim], axis=-1) + mlp = nn.with_logical_constraint(mlp, ("activation_batch", "activation_length", "activation_embed")) + qkv = nn.with_logical_constraint(qkv, ("activation_batch", "activation_length", "activation_embed")) + + B, L = hidden_states.shape[:2] + H, D, K = self.num_attention_heads, qkv.shape[-1] // (self.num_attention_heads * 3), 3 + qkv_proj = qkv.reshape(B, L, K, H, D).transpose(2, 0, 3, 1, 4) + q, k, v = qkv_proj + + q = self.attn.query_norm(q) + k = self.attn.key_norm(k) + + if image_rotary_emb is not None: + if isinstance(image_rotary_emb, (tuple, list)): + image_rotary_emb_reordered = image_rotary_emb + else: + image_rotary_emb_reordered = rearrange(image_rotary_emb, "n d (i j) -> n d i j", i=2, j=2) + q, k = apply_rope(q, k, image_rotary_emb_reordered) + + q = q.transpose(0, 2, 1, 3).reshape(q.shape[0], q.shape[2], -1) + k = k.transpose(0, 2, 1, 3).reshape(k.shape[0], k.shape[2], -1) + v = v.transpose(0, 2, 1, 3).reshape(v.shape[0], v.shape[2], -1) + + attn_output = self.attn.attention_op.apply_attention(q, k, v) + + if self.use_swiglu: + mlp1, mlp2 = jnp.split(mlp, 2, axis=-1) + mlp_activated = nn.silu(mlp1) * mlp2 + else: + mlp_activated = self.mlp_act(mlp) + + attn_mlp = jnp.concatenate([attn_output, mlp_activated], axis=2) + attn_mlp = nn.with_logical_constraint(attn_mlp, ("activation_batch", "activation_length", "activation_embed")) + hidden_states = self.linear2(attn_mlp) + hidden_states = gate * hidden_states + hidden_states = residual + hidden_states + if hidden_states.dtype == jnp.float16: + hidden_states = jnp.clip(hidden_states, -65504, 65504) + return hidden_states + + +class Flux2KleinTransformerBlock(nn.Module): + dim: int + num_attention_heads: int + attention_head_dim: int + attention_kernel: str = "dot_product" + flash_min_seq_length: int = 512 + flash_block_sizes: Optional[Dict[str, int]] = None + mesh: Optional[jax.sharding.Mesh] = None + dtype: jnp.dtype = jnp.float32 + weights_dtype: jnp.dtype = jnp.float32 + precision: float = None + mlp_ratio: float = 4.0 + qkv_bias: bool = True + use_global_modulation: bool = False + + def setup(self): + if self.use_global_modulation: + self.norm1 = nn.LayerNorm( + use_bias=False, use_scale=False, epsilon=1e-6, dtype=self.dtype, param_dtype=self.weights_dtype + ) + self.norm1_context = nn.LayerNorm( + use_bias=False, use_scale=False, epsilon=1e-6, dtype=self.dtype, param_dtype=self.weights_dtype + ) + self.norm2 = nn.LayerNorm( + use_bias=False, use_scale=False, epsilon=1e-6, dtype=self.dtype, param_dtype=self.weights_dtype + ) + self.norm2_context = nn.LayerNorm( + use_bias=False, use_scale=False, epsilon=1e-6, dtype=self.dtype, param_dtype=self.weights_dtype + ) + else: + self.norm1 = AdaLayerNormZero( + self.dim, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + ) + self.norm1_context = AdaLayerNormZero( + self.dim, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + ) + self.attn = FluxAttention( + query_dim=self.dim, + heads=self.num_attention_heads, + dim_head=self.attention_head_dim, + attention_kernel=self.attention_kernel, + flash_min_seq_length=self.flash_min_seq_length, + flash_block_sizes=self.flash_block_sizes, + mesh=self.mesh, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + qkv_bias=self.qkv_bias, + ) + self.ff = FlaxSwiGluFeedForward( + self.dim, + int(self.dim * self.mlp_ratio), + self.dim, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + ) + self.ff_context = FlaxSwiGluFeedForward( + self.dim, + int(self.dim * self.mlp_ratio), + self.dim, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + ) + + def __call__( + self, + hidden_states, + encoder_hidden_states, + temb, + image_rotary_emb=None, + temb_mod_img=None, + temb_mod_txt=None, + return_intermediates: bool = False, + ): + if self.use_global_modulation: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = jnp.split(temb_mod_img, 6, axis=-1) + c_shift_msa, c_scale_msa, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = jnp.split(temb_mod_txt, 6, axis=-1) + + shift_msa = jnp.expand_dims(shift_msa, axis=1) + scale_msa = jnp.expand_dims(scale_msa, axis=1) + gate_msa = jnp.expand_dims(gate_msa, axis=1) + shift_mlp = jnp.expand_dims(shift_mlp, axis=1) + scale_mlp = jnp.expand_dims(scale_mlp, axis=1) + gate_mlp = jnp.expand_dims(gate_mlp, axis=1) + + c_shift_msa = jnp.expand_dims(c_shift_msa, axis=1) + c_scale_msa = jnp.expand_dims(c_scale_msa, axis=1) + c_gate_msa = jnp.expand_dims(c_gate_msa, axis=1) + c_shift_mlp = jnp.expand_dims(c_shift_mlp, axis=1) + c_scale_mlp = jnp.expand_dims(c_scale_mlp, axis=1) + c_gate_mlp = jnp.expand_dims(c_gate_mlp, axis=1) + + norm1_hidden_states = self.norm1(hidden_states) * (1.0 + scale_msa) + shift_msa + norm1_encoder_hidden_states = self.norm1_context(encoder_hidden_states) * (1.0 + c_scale_msa) + c_shift_msa + + else: + norm1_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) + norm1_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context( + encoder_hidden_states, temb + ) + + attn_output, context_attn_output = self.attn( + hidden_states=norm1_hidden_states, + encoder_hidden_states=norm1_encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + ) + + hidden_states = hidden_states + gate_msa * attn_output + encoder_hidden_states = encoder_hidden_states + c_gate_msa * context_attn_output + + if self.use_global_modulation: + norm2_hidden_states = self.norm2(hidden_states) * (1.0 + scale_mlp) + shift_mlp + norm2_encoder_hidden_states = self.norm2_context(encoder_hidden_states) * (1.0 + c_scale_mlp) + c_shift_mlp + + else: + norm2_hidden_states, gate_mlp, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) + norm2_encoder_hidden_states, c_gate_mlp, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context( + encoder_hidden_states, temb + ) + + mlp_output = self.ff(norm2_hidden_states) + encoder_mlp_output = self.ff_context(norm2_encoder_hidden_states) + + hidden_states = hidden_states + gate_mlp * mlp_output + encoder_hidden_states = encoder_hidden_states + c_gate_mlp * encoder_mlp_output + + if return_intermediates: + return encoder_hidden_states, hidden_states, norm1_hidden_states, norm1_encoder_hidden_states + + return encoder_hidden_states, hidden_states + + +@flax_register_to_config +class Flux2KleinTransformer2DModel(nn.Module, FlaxModelMixin, ConfigMixin): + patch_size: int = 1 + in_channels: int = 128 + num_layers: int = 5 + num_single_layers: int = 20 + attention_head_dim: int = 128 + num_attention_heads: int = 24 + joint_attention_dim: int = 4096 + pooled_projection_dim: int = 768 + guidance_embeds: bool = True + axes_dim: Tuple[int, ...] = (32, 32, 32, 32) + theta: int = 10000 + qkv_bias: bool = True + mlp_ratio: float = 3.0 + use_global_modulation: bool = True + scale_shift_order: str = "scale_shift" + proj_out_bias: bool = False + joint_attention_bias: bool = False + x_embedder_bias: bool = False + use_swiglu: bool = True + axes_dims_rope: Tuple[int, ...] = (32, 32, 32, 32) + attention_kernel: str = "dot_product" + flash_min_seq_length: int = 512 + flash_block_sizes: Optional[Dict[str, int]] = None + mesh: Optional[jax.sharding.Mesh] = None + dtype: jnp.dtype = jnp.float32 + weights_dtype: jnp.dtype = jnp.float32 + precision: float = None + + def setup(self): + self.inner_dim = self.num_attention_heads * self.attention_head_dim + + self.time_text_embed = CombinedTimestepGuidanceTextEmbeddings( + embedding_dim=self.inner_dim, + pooled_projection_dim=self.pooled_projection_dim, + guidance_embeds=self.guidance_embeds, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + ) + + if self.use_global_modulation: + self.double_stream_modulation_img = nn.Dense( + 6 * self.inner_dim, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + self.double_stream_modulation_txt = nn.Dense( + 6 * self.inner_dim, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + self.single_stream_modulation = nn.Dense( + 3 * self.inner_dim, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + + self.context_embedder = nn.Dense( + self.inner_dim, + use_bias=False, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + self.x_embedder = nn.Dense( + self.inner_dim, + use_bias=False, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + ) + + self.pos_embed = FluxPosEmbed( + theta=self.theta, + axes_dim=self.axes_dim, + return_tuple=True, + ) + + double_blocks = [] + for _ in range(self.num_layers): + double_block = Flux2KleinTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.num_attention_heads, + attention_head_dim=self.attention_head_dim, + attention_kernel=self.attention_kernel, + flash_min_seq_length=self.flash_min_seq_length, + flash_block_sizes=self.flash_block_sizes, + mesh=self.mesh, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + mlp_ratio=self.mlp_ratio, + qkv_bias=self.qkv_bias, + use_global_modulation=self.use_global_modulation, + ) + double_blocks.append(double_block) + self.double_blocks = double_blocks + + single_blocks = [] + for _ in range(self.num_single_layers): + single_block = Flux2KleinSingleTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.num_attention_heads, + attention_head_dim=self.attention_head_dim, + attention_kernel=self.attention_kernel, + flash_min_seq_length=self.flash_min_seq_length, + flash_block_sizes=self.flash_block_sizes, + mesh=self.mesh, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + mlp_ratio=self.mlp_ratio, + use_global_modulation=self.use_global_modulation, + ) + single_blocks.append(single_block) + self.single_blocks = single_blocks + + self.norm_out = AdaLayerNormContinuous( + self.inner_dim, + elementwise_affine=False, + eps=1e-6, + dtype=self.dtype, + weights_dtype=self.weights_dtype, + precision=self.precision, + scale_shift_order=self.scale_shift_order, + ) + + self.proj_out = nn.Dense( + self.in_channels, + dtype=self.dtype, + param_dtype=self.weights_dtype, + precision=self.precision, + use_bias=self.proj_out_bias, + ) + + def timestep_embedding(self, t: jax.Array, dim: int, max_period=10000, time_factor: float = 1.0) -> jax.Array: + t = time_factor * t + half = dim // 2 + freqs = jnp.exp(-math.log(max_period) * jnp.arange(start=0, stop=half, dtype=t.dtype) / half) + args = t[:, None] * freqs[None] + embedding = jnp.concatenate([jnp.cos(args), jnp.sin(args)], axis=-1) + if dim % 2: + embedding = jnp.concatenate([embedding, jnp.zeros_like(embedding[:, :1])], axis=-1) + return embedding + + def __call__( + self, + hidden_states, + encoder_hidden_states, + pooled_projections, + timestep, + img_ids, + txt_ids, + guidance, + return_dict: bool = True, + train: bool = False, + return_intermediates: bool = False, + ): + intermediates = {} + if return_intermediates: + intermediates["double_block_outputs"] = [] + intermediates["single_block_outputs"] = [] + + hidden_states = self.x_embedder(hidden_states) + timestep = timestep * 1000.0 + if guidance is not None: + guidance = guidance * 1000.0 + temb = self.time_text_embed(timestep, guidance, pooled_projections) + temb = temb.astype(hidden_states.dtype) + + if self.use_global_modulation: + temb_silu = nn.silu(temb) + double_stream_mod_img = self.double_stream_modulation_img(temb_silu) + double_stream_mod_txt = self.double_stream_modulation_txt(temb_silu) + single_stream_mod = self.single_stream_modulation(temb_silu) + else: + double_stream_mod_img, double_stream_mod_txt, single_stream_mod = None, None, None + + if encoder_hidden_states is not None and hasattr(self, "context_embedder") and self.context_embedder is not None: + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + + if return_intermediates: + intermediates["x_embedder"] = hidden_states + intermediates["context_embedder"] = encoder_hidden_states + intermediates["temb"] = temb + intermediates["double_stream_mod_img"] = double_stream_mod_img + intermediates["double_stream_mod_txt"] = double_stream_mod_txt + + if txt_ids.ndim == 3: + txt_ids = txt_ids[0] + if img_ids.ndim == 3: + img_ids = img_ids[0] + + image_rotary_emb = self.pos_embed(img_ids) + text_rotary_emb = self.pos_embed(txt_ids) + concat_rotary_emb = ( + jnp.concatenate([text_rotary_emb[0], image_rotary_emb[0]], axis=0), + jnp.concatenate([text_rotary_emb[1], image_rotary_emb[1]], axis=0), + ) + + if return_intermediates: + intermediates["temb"] = temb + intermediates["global_modulation"] = (double_stream_mod_img, double_stream_mod_txt, single_stream_mod) + intermediates["double_block_inputs"] = [] + intermediates["double_block_outputs"] = [] + intermediates["single_block_outputs"] = [] + + if return_intermediates: + intermediates["norm_hidden_states"] = [] + intermediates["norm_encoder_hidden_states"] = [] + + for double_block in self.double_blocks: + if return_intermediates: + intermediates["double_block_inputs"].append((hidden_states, encoder_hidden_states)) + encoder_hidden_states, hidden_states, norm_h, norm_enc = double_block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=temb, + image_rotary_emb=concat_rotary_emb, + temb_mod_img=double_stream_mod_img, + temb_mod_txt=double_stream_mod_txt, + return_intermediates=True, + ) + intermediates["norm_hidden_states"].append(norm_h) + intermediates["norm_encoder_hidden_states"].append(norm_enc) + else: + encoder_hidden_states, hidden_states = double_block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=temb, + image_rotary_emb=concat_rotary_emb, + temb_mod_img=double_stream_mod_img, + temb_mod_txt=double_stream_mod_txt, + ) + if return_intermediates: + intermediates["double_block_outputs"].append((hidden_states, encoder_hidden_states)) + + num_txt_tokens = encoder_hidden_states.shape[1] + hidden_states = jnp.concatenate([encoder_hidden_states, hidden_states], axis=1) + + for single_block in self.single_blocks: + hidden_states = single_block( + hidden_states=hidden_states, + temb=temb, + image_rotary_emb=concat_rotary_emb, + temb_mod=single_stream_mod, + ) + if return_intermediates: + intermediates["single_block_outputs"].append(hidden_states) + + hidden_states = hidden_states[:, num_txt_tokens:, ...] + hidden_states = self.norm_out(hidden_states, temb) + output = self.proj_out(hidden_states) + + if return_intermediates: + return output, intermediates + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + +# ============================================================================= +# FLAX NNX MODEL IMPLEMENTATIONS FOR FLUX.2-KLEIN +# ============================================================================= + + +class NNXFluxDoubleAttention(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + query_dim: int, + heads: int, + dim_head: int, + qkv_bias: bool = False, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.query_dim = query_dim + self.heads = heads + self.dim_head = dim_head + inner_dim = heads * dim_head + + self.qkv = nnx.Linear( + in_features=query_dim, + out_features=inner_dim * 3, + use_bias=qkv_bias, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "heads")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, ("heads",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.encoder_qkv = nnx.Linear( + in_features=query_dim, + out_features=inner_dim * 3, + use_bias=qkv_bias, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "heads")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, ("heads",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.proj_attn = nnx.Linear( + in_features=inner_dim, + out_features=query_dim, + use_bias=True, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("heads", "embed")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, ("embed",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.encoder_proj_attn = nnx.Linear( + in_features=inner_dim, + out_features=query_dim, + use_bias=True, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("heads", "embed")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, ("embed",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.query_norm = nnx.RMSNorm( + num_features=dim_head, + epsilon=1e-6, + scale_init=nnx.with_partitioning(nnx.initializers.ones, ("heads",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.key_norm = nnx.RMSNorm( + num_features=dim_head, + epsilon=1e-6, + scale_init=nnx.with_partitioning(nnx.initializers.ones, ("heads",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + def __call__( + self, + hidden_states: jax.Array, + encoder_hidden_states: jax.Array, + image_rotary_emb: Tuple[jax.Array, jax.Array], + ) -> Tuple[jax.Array, jax.Array]: + batch_size, img_len, _ = hidden_states.shape + txt_len = encoder_hidden_states.shape[1] + + qkv_img = self.qkv(hidden_states) + qkv_txt = self.encoder_qkv(encoder_hidden_states) + + q_img, k_img, v_img = jnp.split(qkv_img, 3, axis=-1) + q_txt, k_txt, v_txt = jnp.split(qkv_txt, 3, axis=-1) + + q_img = rearrange(q_img, "b l (h d) -> b l h d", h=self.heads) + k_img = rearrange(k_img, "b l (h d) -> b l h d", h=self.heads) + v_img = rearrange(v_img, "b l (h d) -> b l h d", h=self.heads) + + q_txt = rearrange(q_txt, "b l (h d) -> b l h d", h=self.heads) + k_txt = rearrange(k_txt, "b l (h d) -> b l h d", h=self.heads) + v_txt = rearrange(v_txt, "b l (h d) -> b l h d", h=self.heads) + + q_img = self.query_norm(q_img) + k_img = self.key_norm(k_img) + q_txt = self.query_norm(q_txt) + k_txt = self.key_norm(k_txt) + + q = jnp.concatenate([q_txt, q_img], axis=1) + k = jnp.concatenate([k_txt, k_img], axis=1) + v = jnp.concatenate([v_txt, v_img], axis=1) + + if image_rotary_emb is not None: + q, k = apply_rope(q, k, image_rotary_emb) + + scale = self.dim_head**-0.5 + attn_weights = jnp.einsum("b q h d, b k h d -> b h q k", q, k, precision=None) * scale + attn_weights = jax.nn.softmax(attn_weights, axis=-1) + out = jnp.einsum("b h q k, b k h d -> b q h d", attn_weights, v, precision=None) + + out = rearrange(out, "b l h d -> b l (h d)") + + out_txt = out[:, :txt_len, :] + out_img = out[:, txt_len:, :] + + out_img = self.proj_attn(out_img) + out_txt = self.encoder_proj_attn(out_txt) + + return out_img, out_txt + + +class NNXFluxSingleAttention(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.dim = dim + self.heads = num_attention_heads + self.dim_head = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + self.to_qkv_mlp_proj = nnx.Linear( + in_features=dim, + out_features=inner_dim * 3 + int(dim * 4.0), + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "mlp")), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.to_out = nnx.Linear( + in_features=inner_dim + int(dim * 4.0), + out_features=dim, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("mlp", "embed")), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.norm_q = nnx.RMSNorm( + num_features=attention_head_dim, + epsilon=1e-6, + scale_init=nnx.with_partitioning(nnx.initializers.ones, ("heads",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.norm_k = nnx.RMSNorm( + num_features=attention_head_dim, + epsilon=1e-6, + scale_init=nnx.with_partitioning(nnx.initializers.ones, ("heads",)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + def __call__( + self, + hidden_states: jax.Array, + image_rotary_emb: Tuple[jax.Array, jax.Array], + ) -> jax.Array: + batch_size, seq_len, _ = hidden_states.shape + inner_dim = self.heads * self.dim_head + + qkv_mlp = self.to_qkv_mlp_proj(hidden_states) + qkv, mlp = jnp.split(qkv_mlp, [inner_dim * 3], axis=-1) + + q, k, v = jnp.split(qkv, 3, axis=-1) + q = rearrange(q, "b l (h d) -> b l h d", h=self.heads) + k = rearrange(k, "b l (h d) -> b l h d", h=self.heads) + v = rearrange(v, "b l (h d) -> b l h d", h=self.heads) + + q = self.norm_q(q) + k = self.norm_k(k) + + if image_rotary_emb is not None: + q, k = apply_rope(q, k, image_rotary_emb) + + scale = self.dim_head**-0.5 + attn_weights = jnp.einsum("b q h d, b k h d -> b h q k", q, k, precision=None) * scale + attn_weights = jax.nn.softmax(attn_weights, axis=-1) + attn_out = jnp.einsum("b h q k, b k h d -> b q h d", attn_weights, v, precision=None) + attn_out = rearrange(attn_out, "b l h d -> b l (h d)") + + mlp_act = jax.nn.gelu(mlp, approximate=True) + attn_mlp = jnp.concatenate([attn_out, mlp_act], axis=-1) + + out = self.to_out(attn_mlp) + return out + + +class NNXFluxDoubleTransformerBlock(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + mlp_ratio: float = 4.0, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.dim = dim + self.num_heads = num_attention_heads + self.head_dim = attention_head_dim + mlp_hidden_dim = int(dim * mlp_ratio) + + self.img_norm1 = NNXAdaLayerNormZero(dim, dtype=dtype, weights_dtype=weights_dtype) + self.txt_norm1 = NNXAdaLayerNormZero(dim, dtype=dtype, weights_dtype=weights_dtype) + + self.attn = NNXFluxDoubleAttention( + rngs=rngs, + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dtype=dtype, + weights_dtype=weights_dtype, + ) + + self.img_mlp = nnx.Linear( + in_features=dim, + out_features=mlp_hidden_dim, + use_bias=True, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "mlp")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.img_mlp_out = nnx.Linear( + in_features=mlp_hidden_dim, + out_features=dim, + use_bias=True, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("mlp", "embed")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.txt_mlp = nnx.Linear( + in_features=dim, + out_features=mlp_hidden_dim, + use_bias=True, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "mlp")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.txt_mlp_out = nnx.Linear( + in_features=mlp_hidden_dim, + out_features=dim, + use_bias=True, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("mlp", "embed")), + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + def __call__( + self, + hidden_states: jax.Array, + encoder_hidden_states: jax.Array, + temb: jax.Array, + image_rotary_emb: Tuple[jax.Array, jax.Array], + temb_mod_img: Optional[jax.Array] = None, + temb_mod_txt: Optional[jax.Array] = None, + ) -> Tuple[jax.Array, jax.Array]: + norm_h, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.img_norm1(hidden_states, emb=temb_mod_img) + norm_enc, c_gate_msa_txt, c_shift_mlp_txt, c_scale_mlp_txt, c_gate_mlp_txt = self.txt_norm1( + encoder_hidden_states, emb=temb_mod_txt + ) + + attn_img, attn_txt = self.attn( + hidden_states=norm_h, + encoder_hidden_states=norm_enc, + image_rotary_emb=image_rotary_emb, + ) + + hidden_states = hidden_states + c_gate_msa * attn_img + encoder_hidden_states = encoder_hidden_states + c_gate_msa_txt * attn_txt + + norm_h_mlp = norm_h * (1.0 + c_scale_mlp) + c_shift_mlp + norm_enc_mlp = norm_enc * (1.0 + c_scale_mlp_txt) + c_shift_mlp_txt + + img_ff = self.img_mlp_out(jax.nn.gelu(self.img_mlp(norm_h_mlp), approximate=True)) + txt_ff = self.txt_mlp_out(jax.nn.gelu(self.txt_mlp(norm_enc_mlp), approximate=True)) + + hidden_states = hidden_states + c_gate_mlp * img_ff + encoder_hidden_states = encoder_hidden_states + c_gate_mlp_txt * txt_ff + + return encoder_hidden_states, hidden_states + + +class NNXFluxSingleTransformerBlock(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.dim = dim + self.norm = NNXAdaLayerNormZeroSingle(dim, dtype=dtype, weights_dtype=weights_dtype) + self.attn = NNXFluxSingleAttention( + rngs=rngs, + dim=dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + dtype=dtype, + weights_dtype=weights_dtype, + ) + + def __call__( + self, + hidden_states: jax.Array, + temb: jax.Array, + image_rotary_emb: Tuple[jax.Array, jax.Array], + temb_mod: Optional[jax.Array] = None, + ) -> jax.Array: + norm_hidden_states, gate_msa = self.norm(hidden_states, emb=temb_mod) + attn_output = self.attn( + hidden_states=norm_hidden_states, + image_rotary_emb=image_rotary_emb, + ) + hidden_states = hidden_states + gate_msa * attn_output + return hidden_states + + +class NNXFluxTransformer2DModel(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + patch_size: int = 1, + in_channels: int = 64, + num_layers: int = 5, + num_single_layers: int = 20, + attention_head_dim: int = 128, + num_attention_heads: int = 24, + joint_attention_dim: int = 4096, + pooled_projection_dim: int = 768, + guidance_embeds: bool = True, + axes_dim: Tuple[int, ...] = (16, 56, 56), + theta: float = 10000.0, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + self.num_layers = num_layers + self.num_single_layers = num_single_layers + self.attention_head_dim = attention_head_dim + self.num_attention_heads = num_attention_heads + self.inner_dim = num_attention_heads * attention_head_dim + self.dtype = dtype + + self.pos_embed = NNXFluxPosEmbed(axes_dim=axes_dim, theta=theta, return_tuple=True) + self.time_text_embed = NNXCombinedTimestepGuidanceTextProjEmbeddings( + rngs=rngs, + embedding_dim=self.inner_dim, + pooled_projection_dim=pooled_projection_dim, + guidance_embeds=guidance_embeds, + dtype=dtype, + weights_dtype=weights_dtype, + ) + + self.double_stream_modulation_img = nnx.Linear( + in_features=self.inner_dim, + out_features=6 * self.inner_dim, + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.double_stream_modulation_txt = nnx.Linear( + in_features=self.inner_dim, + out_features=6 * self.inner_dim, + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.single_stream_modulation = nnx.Linear( + in_features=self.inner_dim, + out_features=3 * self.inner_dim, + bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)), + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + self.x_embedder = nnx.Linear( + in_features=in_channels, + out_features=self.inner_dim, + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + self.context_embedder = nnx.Linear( + in_features=joint_attention_dim, + out_features=self.inner_dim, + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + self.double_blocks = nnx.List( + [ + NNXFluxDoubleTransformerBlock( + rngs=rngs, + dim=self.inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + dtype=dtype, + weights_dtype=weights_dtype, + ) + for _ in range(num_layers) + ] + ) + + self.single_blocks = nnx.List( + [ + NNXFluxSingleTransformerBlock( + rngs=rngs, + dim=self.inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + dtype=dtype, + weights_dtype=weights_dtype, + ) + for _ in range(num_single_layers) + ] + ) + + self.norm_out = NNXAdaLayerNormContinuous( + rngs=rngs, + embedding_dim=self.inner_dim, + eps=1e-6, + dtype=dtype, + weights_dtype=weights_dtype, + ) + self.proj_out = nnx.Linear( + in_features=self.inner_dim, + out_features=in_channels, + use_bias=True, + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + def __call__( + self, + hidden_states: jax.Array, + encoder_hidden_states: jax.Array, + pooled_projections: jax.Array, + timestep: jax.Array, + img_ids: jax.Array, + txt_ids: jax.Array, + guidance: Optional[jax.Array] = None, + ) -> jax.Array: + hidden_states = self.x_embedder(hidden_states) + timestep = timestep * 1000.0 + if guidance is not None: + guidance = guidance * 1000.0 + temb = self.time_text_embed(timestep, guidance, pooled_projections) + temb = temb.astype(hidden_states.dtype) + + temb_silu = jax.nn.silu(temb) + double_stream_mod_img = self.double_stream_modulation_img(temb_silu) + double_stream_mod_txt = self.double_stream_modulation_txt(temb_silu) + single_stream_mod = self.single_stream_modulation(temb_silu) + + if encoder_hidden_states is not None: + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + + if txt_ids.ndim == 3: + txt_ids = txt_ids[0] + if img_ids.ndim == 3: + img_ids = img_ids[0] + + image_rotary_emb = self.pos_embed(img_ids) + text_rotary_emb = self.pos_embed(txt_ids) + concat_rotary_emb = ( + jnp.concatenate([text_rotary_emb[0], image_rotary_emb[0]], axis=0), + jnp.concatenate([text_rotary_emb[1], image_rotary_emb[1]], axis=0), + ) + + for double_block in self.double_blocks: + encoder_hidden_states, hidden_states = double_block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=temb, + image_rotary_emb=concat_rotary_emb, + temb_mod_img=double_stream_mod_img, + temb_mod_txt=double_stream_mod_txt, + ) + + num_txt_tokens = encoder_hidden_states.shape[1] + hidden_states = jnp.concatenate([encoder_hidden_states, hidden_states], axis=1) + + for single_block in self.single_blocks: + hidden_states = single_block( + hidden_states=hidden_states, + temb=temb, + image_rotary_emb=concat_rotary_emb, + temb_mod=single_stream_mod, + ) + + hidden_states = hidden_states[:, num_txt_tokens:, ...] + hidden_states = self.norm_out(hidden_states, temb) + output = self.proj_out(hidden_states) + return output diff --git a/src/maxdiffusion/models/flux/util.py b/src/maxdiffusion/models/flux/util.py index 4a44bb172..f16a5f12d 100644 --- a/src/maxdiffusion/models/flux/util.py +++ b/src/maxdiffusion/models/flux/util.py @@ -1,5 +1,5 @@ """ -Copyright 2025 Google LLC +Copyright 2026 Google LLC Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -118,21 +118,28 @@ def validate_flax_state_dict(expected_pytree: dict, new_pytree: dict): expected_pytree: dict - a pytree that comes from initializing the model. new_pytree: dict - a pytree that has been created from pytorch weights. """ - expected_pytree = flatten_dict(expected_pytree) - if len(expected_pytree.keys()) != len(new_pytree.keys()): - set1 = set(expected_pytree.keys()) - set2 = set(new_pytree.keys()) + expected_flat = ( + flatten_dict(expected_pytree) if not isinstance(next(iter(expected_pytree.keys()), None), tuple) else expected_pytree + ) + new_flat = flatten_dict(new_pytree) if not isinstance(next(iter(new_pytree.keys()), None), tuple) else new_pytree + + if len(expected_flat.keys()) != len(new_flat.keys()): + set1 = set(expected_flat.keys()) + set2 = set(new_flat.keys()) missing_keys = set1 ^ set2 - max_logging.log(f"missing keys : {missing_keys}") - for key in expected_pytree.keys(): - if key in new_pytree.keys(): + max_logging.log( + f"Missing or extra parameter keys count mismatch ({len(expected_flat)} expected vs {len(new_flat)} converted): {missing_keys}" + ) + + for key in expected_flat.keys(): + if key in new_flat.keys(): try: - expected_pytree_shape = expected_pytree[key].shape + expected_pytree_shape = expected_flat[key].shape except Exception: - expected_pytree_shape = expected_pytree[key].value.shape - if expected_pytree_shape != new_pytree[key].shape: + expected_pytree_shape = getattr(expected_flat[key], "value", expected_flat[key]).shape + if expected_pytree_shape != new_flat[key].shape: max_logging.log( - f"shape mismatch, expected shape of {expected_pytree[key].shape}, but got shape of {new_pytree[key].shape}" + f"shape mismatch for key '{key}': expected shape of {expected_pytree_shape}, but got shape of {new_flat[key].shape}" ) else: max_logging.log(f"key: {key} not found...") @@ -265,3 +272,416 @@ def load_flow_model(name: str, eval_shapes: dict, device: str, hf_download: bool del tensors jax.clear_caches() return flax_state_dict + + +# ----------------------------------------------------------------------------- +# Latent Packing & Unpacking Helpers +# ----------------------------------------------------------------------------- + + +def pack_latents(latents): + """ + Groups spatial 2x2 latent neighborhoods into a single channel dimension. + Transforms unpacked shape (batch_size, channels, height, width) + to packed sequence shape (batch_size, (height//2)*(width//2), channels*4). + """ + import numpy as np + import jax.numpy as jnp + + batch_size, channels, height, width = latents.shape + latents = np.reshape(latents, (batch_size, channels, height // 2, 2, width // 2, 2)) + latents = np.transpose(latents, (0, 2, 4, 1, 3, 5)) + latents = np.reshape(latents, (batch_size, (height // 2) * (width // 2), channels * 4)) + return jnp.array(latents) + + +def unpack_latents(latents, batch_size, num_channels_latents, height, width): + """ + Unpacks packed sequence of shape (batch_size, (height//16)*(width//16), channels*4) + back to the unpacked spatial grid shape (batch_size, channels, height//8, width//8). + """ + import numpy as np + + h_latent = height // 8 + w_latent = width // 8 + + # 1. Reshape to split spatial grid and packed channel blocks + latents = np.reshape(latents, (batch_size, h_latent // 2, w_latent // 2, num_channels_latents, 2, 2)) + # 2. Permute dimensions back to unpacked order + latents = np.transpose(latents, (0, 3, 1, 4, 2, 5)) + # 3. Flatten back to 4D unpacked latent shape + latents = np.reshape(latents, (batch_size, num_channels_latents, h_latent, w_latent)) + return latents + + +def unpack_latents_with_ids(x, x_ids, height, width): + """[B, H*W, C] -> [B, C, H, W] using coordinate IDs.""" + import jax.numpy as jnp + + batch_size, seq_len, ch = x.shape + x_list = [] + for b in range(batch_size): + data = x[b] + pos = x_ids[b] + h_ids = pos[:, 1].astype(jnp.int32) + w_ids = pos[:, 2].astype(jnp.int32) + flat_ids = h_ids * width + w_ids + out = jnp.zeros((height * width, ch), dtype=x.dtype) + out = out.at[flat_ids].set(data) + out = jnp.transpose(jnp.reshape(out, (height, width, ch)), (2, 0, 1)) + x_list.append(out) + return jnp.stack(x_list, axis=0) + + +def unpatchify_latents(latents): + """Reverses the 2x2 spatial patch grouping: [B, C, H, W] -> [B, C/4, H*2, W*2]""" + import jax.numpy as jnp + + batch_size, num_channels_latents, height, width = latents.shape + x = jnp.reshape(latents, (batch_size, num_channels_latents // 4, 2, 2, height, width)) + x = jnp.transpose(x, (0, 1, 4, 2, 5, 3)) + x = jnp.reshape(x, (batch_size, num_channels_latents // 4, height * 2, width * 2)) + return x + + +# ----------------------------------------------------------------------------- +# 4D RoPE Position Grid Helpers +# ----------------------------------------------------------------------------- + + +def prepare_latent_image_ids(batch_size, height, width): + """ + Generates positional identifiers (Height and Width coordinates) for images to build RoPE grids. + Shape: (batch_size, height * width, 4) + """ + import jax.numpy as jnp + + grid = jnp.zeros((height, width, 4), dtype=jnp.int32) + grid = grid.at[..., 1].set(jnp.arange(height)[:, None]) + grid = grid.at[..., 2].set(jnp.arange(width)[None, :]) + latent_image_ids = grid.reshape(-1, 4) + return jnp.tile(latent_image_ids[None, ...], (batch_size, 1, 1)) + + +def prepare_text_ids(batch_size, seq_len): + """ + Generates sequence index coordinate identifiers for text prompt tokens to build RoPE grids. + Shape: (batch_size, seq_len, 4) + """ + import jax.numpy as jnp + + # Text ids: [batch, seq_len, 4]. Fill text token index. + text_ids = jnp.zeros((seq_len, 4)) + # The first element is the frame index (0). The 4th is text sequence index. + text_ids = text_ids.at[..., 3].set(jnp.arange(seq_len)) + return jnp.tile(text_ids[None, ...], (batch_size, 1, 1)) + + +# ----------------------------------------------------------------------------- +# Parameter In-place Casting Helper +# ----------------------------------------------------------------------------- + + +def cast_dict_to_bfloat16_inplace(d, device=None, exclude_keywords=None, parent_key=""): + """Casts a nested dictionary of JAX/numpy arrays to bfloat16 in-place, freeing memory immediately. + + Optionally keeps parameters matching any keyword in `exclude_keywords` in float32 for numerical stability. + """ + import gc + import jax.numpy as jnp + import jax + + for k, v in list(d.items()): + current_key = f"{parent_key}.{k}" if parent_key else str(k) + if isinstance(v, dict): + cast_dict_to_bfloat16_inplace(v, device=device, exclude_keywords=exclude_keywords, parent_key=current_key) + elif hasattr(v, "astype"): + is_excluded = exclude_keywords and any(kw.lower() in current_key.lower() for kw in exclude_keywords) + target_dtype = jnp.float32 if is_excluded else jnp.bfloat16 + + if device is not None: + d[k] = jax.device_put(jnp.array(v, dtype=target_dtype), device=device) + else: + d[k] = jnp.array(v, dtype=target_dtype) + if hasattr(d[k], "block_until_ready"): + d[k].block_until_ready() + del v + gc.collect() + + +# ----------------------------------------------------------------------------- +# Safetensors Weight Loader & Key Converter Functions +# ----------------------------------------------------------------------------- + + +def load_and_convert_flux_klein_weights(safetensors_path, params, num_double_layers, num_single_layers): + """ + Loads weights from safetensors via zero-copy safetensors.numpy and converts them to JAX parameter dictionary. + Supports dynamic layer counts (double and single stream blocks) and sharded safetensors directories. + """ + from safetensors.numpy import load_file + import numpy as np + import jax.numpy as jnp + import glob + import os + import gc + + pt_state_dict = {} + if os.path.isdir(safetensors_path): + shards = glob.glob(os.path.join(safetensors_path, "*.safetensors")) + max_logging.log(f"Loading sharded weights from directory: {safetensors_path} (Found {len(shards)} shards)...") + for shard in sorted(shards): + max_logging.log(f"Loading shard: {shard}...") + pt_state_dict.update(load_file(shard)) + else: + max_logging.log(f"Loading weights from: {safetensors_path}") + pt_state_dict = load_file(safetensors_path) + + max_logging.log("Mapping weights to JAX parameters...") + + expected_pytree = jax.tree_util.tree_map(lambda leaf: leaf, params) + + first_leaf = jax.tree_util.tree_leaves(params)[0] + target_dtype = first_leaf.dtype + + def convert_and_transpose_tensor(tensor, transpose=False): + if transpose and len(tensor.shape) == 2: + tensor = tensor.T + return jnp.array(tensor, dtype=target_dtype) + + # Global layers + params["context_embedder"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("context_embedder.weight"), transpose=True + ) + params["x_embedder"]["kernel"] = convert_and_transpose_tensor(pt_state_dict.pop("x_embedder.weight"), transpose=True) + params["double_stream_modulation_img"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("double_stream_modulation_img.linear.weight"), transpose=True + ) + params["double_stream_modulation_txt"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("double_stream_modulation_txt.linear.weight"), transpose=True + ) + params["single_stream_modulation"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("single_stream_modulation.linear.weight"), transpose=True + ) + params["proj_out"]["kernel"] = convert_and_transpose_tensor(pt_state_dict.pop("proj_out.weight"), transpose=True) + + # norm_out + params["norm_out"]["linear"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("norm_out.linear.weight"), transpose=True + ) + + # time_text_embed (Timestep Embedding) + if "time_guidance_embed.timestep_embedder.linear_1.weight" in pt_state_dict: + params["time_text_embed"]["FlaxTimestepEmbedding_0"]["linear_1"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("time_guidance_embed.timestep_embedder.linear_1.weight"), transpose=True + ) + if "time_guidance_embed.timestep_embedder.linear_1.bias" in pt_state_dict: + params["time_text_embed"]["FlaxTimestepEmbedding_0"]["linear_1"]["bias"] = convert_and_transpose_tensor( + pt_state_dict.pop("time_guidance_embed.timestep_embedder.linear_1.bias") + ) + if "time_guidance_embed.timestep_embedder.linear_2.weight" in pt_state_dict: + params["time_text_embed"]["FlaxTimestepEmbedding_0"]["linear_2"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop("time_guidance_embed.timestep_embedder.linear_2.weight"), transpose=True + ) + if "time_guidance_embed.timestep_embedder.linear_2.bias" in pt_state_dict: + params["time_text_embed"]["FlaxTimestepEmbedding_0"]["linear_2"]["bias"] = convert_and_transpose_tensor( + pt_state_dict.pop("time_guidance_embed.timestep_embedder.linear_2.bias") + ) + + # Double Blocks + max_logging.log(f"Mapping {num_double_layers} double-stream attention blocks...") + for block_idx in range(num_double_layers): + jax_db = params[f"double_blocks_{block_idx}"] + prefix = f"transformer_blocks.{block_idx}." + + # Concatenate QKV projections + to_q = pt_state_dict.pop(prefix + "attn.to_q.weight").T + to_k = pt_state_dict.pop(prefix + "attn.to_k.weight").T + to_v = pt_state_dict.pop(prefix + "attn.to_v.weight").T + jax_db["attn"]["i_qkv"]["kernel"] = jnp.array(np.concatenate([to_q, to_k, to_v], axis=1), dtype=target_dtype) + + add_q = pt_state_dict.pop(prefix + "attn.add_q_proj.weight").T + add_k = pt_state_dict.pop(prefix + "attn.add_k_proj.weight").T + add_v = pt_state_dict.pop(prefix + "attn.add_v_proj.weight").T + jax_db["attn"]["e_qkv"]["kernel"] = jnp.array(np.concatenate([add_q, add_k, add_v], axis=1), dtype=target_dtype) + + # Projections out + jax_db["attn"]["i_proj"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "attn.to_out.0.weight"), transpose=True + ) + jax_db["attn"]["e_proj"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "attn.to_add_out.weight"), transpose=True + ) + + # Norm scales + jax_db["attn"]["query_norm"]["scale"] = convert_and_transpose_tensor(pt_state_dict.pop(prefix + "attn.norm_q.weight")) + jax_db["attn"]["key_norm"]["scale"] = convert_and_transpose_tensor(pt_state_dict.pop(prefix + "attn.norm_k.weight")) + jax_db["attn"]["encoder_query_norm"]["scale"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "attn.norm_added_q.weight") + ) + jax_db["attn"]["encoder_key_norm"]["scale"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "attn.norm_added_k.weight") + ) + + # SwiGLU MLPs + jax_db["ff"]["linear_in"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "ff.linear_in.weight"), transpose=True + ) + jax_db["ff"]["linear_out"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "ff.linear_out.weight"), transpose=True + ) + jax_db["ff_context"]["linear_in"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "ff_context.linear_in.weight"), transpose=True + ) + jax_db["ff_context"]["linear_out"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(prefix + "ff_context.linear_out.weight"), transpose=True + ) + + # Single Blocks + max_logging.log(f"Mapping {num_single_layers} single-stream attention blocks...") + for block_idx in range(num_single_layers): + jax_sb = params[f"single_blocks_{block_idx}"] + s_prefix = f"single_transformer_blocks.{block_idx}." + + # Joint projections + jax_sb["linear1"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(s_prefix + "attn.to_qkv_mlp_proj.weight"), transpose=True + ) + jax_sb["linear2"]["kernel"] = convert_and_transpose_tensor( + pt_state_dict.pop(s_prefix + "attn.to_out.weight"), transpose=True + ) + + # Norm scales + jax_sb["attn"]["query_norm"]["scale"] = convert_and_transpose_tensor(pt_state_dict.pop(s_prefix + "attn.norm_q.weight")) + jax_sb["attn"]["key_norm"]["scale"] = convert_and_transpose_tensor(pt_state_dict.pop(s_prefix + "attn.norm_k.weight")) + + params = jax.tree_util.tree_map( + lambda leaf: jnp.zeros(leaf.shape, dtype=leaf.dtype) if isinstance(leaf, jax.ShapeDtypeStruct) else leaf, params + ) + del pt_state_dict + gc.collect() + max_logging.log("Validating converted Flax PyTree state dict structure...") + validate_flax_state_dict(expected_pytree, params) + max_logging.log("Weight conversion complete & verified!") + return params + + +def load_and_convert_vae_weights(safetensors_path, jax_params): + """Loads VAE weights from safetensors via zero-copy safetensors.numpy, maps them to JAX, and extracts BN stats.""" + from safetensors.numpy import load_file + import flax + import jax.numpy as jnp + + max_logging.log(f"Loading VAE weights from: {safetensors_path}") + pt_state_dict = load_file(safetensors_path) + + def get_pytorch_weight_tensor(key): + return pt_state_dict[key] + + # Unfreeze JAX params so we can load the weights + jax_params = flax.core.unfreeze(jax_params) + + # Map weights + max_logging.log("Mapping VAE decoder weights to JAX parameters...") + + # post_quant_conv + jax_params["post_quant_conv"]["kernel"] = jnp.array( + get_pytorch_weight_tensor("post_quant_conv.weight").transpose(2, 3, 1, 0) + ) + jax_params["post_quant_conv"]["bias"] = jnp.array(get_pytorch_weight_tensor("post_quant_conv.bias")) + + # decoder.conv_in + jax_params["decoder"]["conv_in"]["kernel"] = jnp.array( + get_pytorch_weight_tensor("decoder.conv_in.weight").transpose(2, 3, 1, 0) + ) + jax_params["decoder"]["conv_in"]["bias"] = jnp.array(get_pytorch_weight_tensor("decoder.conv_in.bias")) + + # decoder.mid_block + # resnets + for idx in [0, 1]: + res_jax = jax_params["decoder"]["mid_block"][f"resnets_{idx}"] + res_pt_prefix = f"decoder.mid_block.resnets.{idx}" + + res_jax["norm1"]["scale"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.norm1.weight")) + res_jax["norm1"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.norm1.bias")) + res_jax["conv1"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.conv1.weight").transpose(2, 3, 1, 0)) + res_jax["conv1"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.conv1.bias")) + + res_jax["norm2"]["scale"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.norm2.weight")) + res_jax["norm2"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.norm2.bias")) + res_jax["conv2"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.conv2.weight").transpose(2, 3, 1, 0)) + res_jax["conv2"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt_prefix}.conv2.bias")) + + # attentions + attn_pt_prefix = "decoder.mid_block.attentions.0" + attn_jax = jax_params["decoder"]["mid_block"]["attentions_0"] + + attn_jax["group_norm"]["scale"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.group_norm.weight")) + attn_jax["group_norm"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.group_norm.bias")) + + attn_jax["query"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_q.weight").T) + attn_jax["query"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_q.bias")) + attn_jax["key"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_k.weight").T) + attn_jax["key"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_k.bias")) + attn_jax["value"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_v.weight").T) + attn_jax["value"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_v.bias")) + + attn_jax["proj_attn"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_out.0.weight").T) + attn_jax["proj_attn"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{attn_pt_prefix}.to_out.0.bias")) + + # decoder.up_blocks + for b_idx in range(4): + up_block_jax = jax_params["decoder"][f"up_blocks_{b_idx}"] + up_block_pt = f"decoder.up_blocks.{b_idx}" + + for r_idx in range(3): + res_jax = up_block_jax[f"resnets_{r_idx}"] + res_pt = f"{up_block_pt}.resnets.{r_idx}" + + res_jax["norm1"]["scale"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.norm1.weight")) + res_jax["norm1"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.norm1.bias")) + res_jax["conv1"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.conv1.weight").transpose(2, 3, 1, 0)) + res_jax["conv1"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.conv1.bias")) + + res_jax["norm2"]["scale"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.norm2.weight")) + res_jax["norm2"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.norm2.bias")) + res_jax["conv2"]["kernel"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.conv2.weight").transpose(2, 3, 1, 0)) + res_jax["conv2"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.conv2.bias")) + + shortcut_key = f"{res_pt}.conv_shortcut.weight" + if shortcut_key in pt_state_dict: + res_jax["conv_shortcut"]["kernel"] = jnp.array(get_pytorch_weight_tensor(shortcut_key).transpose(2, 3, 1, 0)) + res_jax["conv_shortcut"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{res_pt}.conv_shortcut.bias")) + + if b_idx < 3: + upsampler_jax = up_block_jax["upsamplers_0"] + upsampler_pt = f"{up_block_pt}.upsamplers.0" + + upsampler_jax["conv"]["kernel"] = jnp.array( + get_pytorch_weight_tensor(f"{upsampler_pt}.conv.weight").transpose(2, 3, 1, 0) + ) + upsampler_jax["conv"]["bias"] = jnp.array(get_pytorch_weight_tensor(f"{upsampler_pt}.conv.bias")) + + # decoder.conv_norm_out & conv_out + jax_params["decoder"]["conv_norm_out"]["scale"] = jnp.array(get_pytorch_weight_tensor("decoder.conv_norm_out.weight")) + jax_params["decoder"]["conv_norm_out"]["bias"] = jnp.array(get_pytorch_weight_tensor("decoder.conv_norm_out.bias")) + jax_params["decoder"]["conv_out"]["kernel"] = jnp.array( + get_pytorch_weight_tensor("decoder.conv_out.weight").transpose(2, 3, 1, 0) + ) + jax_params["decoder"]["conv_out"]["bias"] = jnp.array(get_pytorch_weight_tensor("decoder.conv_out.bias")) + + jax_params = jax.tree_util.tree_map( + lambda leaf: jnp.zeros(leaf.shape, dtype=leaf.dtype) if isinstance(leaf, jax.ShapeDtypeStruct) else leaf, jax_params + ) + # Freeze parameters + jax_params = flax.core.freeze(jax_params) + + # Extract Batch Normalization running stats + max_logging.log("Extracting VAE Batch Normalization running stats...") + bn_mean = jnp.array(get_pytorch_weight_tensor("bn.running_mean")).reshape(1, -1, 1, 1) + bn_var = jnp.array(get_pytorch_weight_tensor("bn.running_var")).reshape(1, -1, 1, 1) + batch_norm_eps = 0.0001 + bn_std = jnp.sqrt(bn_var + batch_norm_eps) + + max_logging.log("VAE weights and BN stats loaded successfully!") + return jax_params, bn_mean, bn_std diff --git a/src/maxdiffusion/models/normalization_flax.py b/src/maxdiffusion/models/normalization_flax.py index 5acf449c7..abe63f1db 100644 --- a/src/maxdiffusion/models/normalization_flax.py +++ b/src/maxdiffusion/models/normalization_flax.py @@ -1,18 +1,16 @@ -""" -Copyright 2024 Google LLC - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - https://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import jax import jax.numpy as jnp @@ -26,6 +24,7 @@ class AdaLayerNormContinuous(nn.Module): eps: float = 1e-5 bias: bool = True norm_type: str = "layer_norm" + scale_shift_order: str = "scale_shift" dtype: jnp.dtype = jnp.float32 weights_dtype: jnp.dtype = jnp.float32 precision: jax.lax.Precision = None @@ -35,6 +34,7 @@ def __call__(self, x, conditioning_embedding): assert self.norm_type == "layer_norm" emb = nn.Dense( self.embedding_dim * 2, + name="linear", kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "mlp")), bias_init=nn.with_logical_partitioning(nn.initializers.zeros, ("mlp",)), use_bias=self.bias, @@ -42,7 +42,14 @@ def __call__(self, x, conditioning_embedding): param_dtype=self.weights_dtype, precision=self.precision, )(nn.silu(conditioning_embedding)) - shift, scale = jnp.split(emb, 2, axis=1) + + if self.scale_shift_order == "scale_shift": + scale, shift = jnp.split(emb, 2, axis=1) + elif self.scale_shift_order == "shift_scale": + shift, scale = jnp.split(emb, 2, axis=1) + else: + raise ValueError(f"Unsupported scale_shift_order: {self.scale_shift_order}") + shift = nn.with_logical_constraint(shift, ("activation_batch", "activation_embed")) scale = nn.with_logical_constraint(scale, ("activation_batch", "activation_embed")) x = nn.LayerNorm(epsilon=self.eps, use_bias=self.elementwise_affine, use_scale=self.elementwise_affine)(x) @@ -166,3 +173,80 @@ def __init__(self, rngs: nnx.Rngs, dim: int, eps: float, elementwise_affine: boo def __call__(self, inputs: jax.Array) -> jax.Array: origin_dtype = inputs.dtype return self.layer_norm(inputs.astype(dtype=jnp.float32)).astype(dtype=origin_dtype) + + +# ============================================================================= +# FLAX NNX ADALAYERNORM IMPLEMENTATIONS FOR FLUX.2-KLEIN +# ============================================================================= + + +class NNXAdaLayerNormContinuous(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + embedding_dim: int, + eps: float = 1e-6, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.embedding_dim = embedding_dim + self.eps = eps + self.dtype = dtype + self.layer_norm = nnx.LayerNorm( + num_features=embedding_dim, epsilon=eps, use_bias=False, use_scale=False, dtype=dtype, rngs=rngs + ) + self.linear = nnx.Linear( + in_features=embedding_dim, + out_features=embedding_dim * 2, + use_bias=True, + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + def __call__(self, x: jax.Array, conditioning_embedding: jax.Array) -> jax.Array: + emb = self.linear(jax.nn.silu(conditioning_embedding)) + scale, shift = jnp.split(emb, 2, axis=-1) + x_norm = self.layer_norm(x) + return (1.0 + scale[:, None, :]) * x_norm + shift[:, None, :] + + +class NNXAdaLayerNormZero(nnx.Module): + + def __init__( + self, embedding_dim: int, eps: float = 1e-6, dtype: jnp.dtype = jnp.float32, weights_dtype: jnp.dtype = jnp.float32 + ): + self.embedding_dim = embedding_dim + self.eps = eps + self.dtype = dtype + + def __call__(self, x: jax.Array, emb: jax.Array): + if emb.ndim == 2: + emb = emb[:, None, :] + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = jnp.split(emb, 6, axis=-1) + mean = jnp.mean(x, axis=-1, keepdims=True) + variance = jnp.mean(jnp.square(x - mean), axis=-1, keepdims=True) + inv_std = jax.lax.rsqrt(variance + self.eps) + normed_x = (x - mean) * inv_std * (1.0 + scale_msa) + shift_msa + return normed_x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +class NNXAdaLayerNormZeroSingle(nnx.Module): + + def __init__( + self, embedding_dim: int, eps: float = 1e-6, dtype: jnp.dtype = jnp.float32, weights_dtype: jnp.dtype = jnp.float32 + ): + self.embedding_dim = embedding_dim + self.eps = eps + self.dtype = dtype + + def __call__(self, x: jax.Array, emb: jax.Array): + if emb.ndim == 2: + emb = emb[:, None, :] + shift_msa, scale_msa, gate_msa = jnp.split(emb, 3, axis=-1) + mean = jnp.mean(x, axis=-1, keepdims=True) + variance = jnp.mean(jnp.square(x - mean), axis=-1, keepdims=True) + inv_std = jax.lax.rsqrt(variance + self.eps) + normed_x = (x - mean) * inv_std * (1.0 + scale_msa) + shift_msa + return normed_x, gate_msa diff --git a/src/maxdiffusion/models/qwen3_flax.py b/src/maxdiffusion/models/qwen3_flax.py new file mode 100644 index 000000000..0a76544dd --- /dev/null +++ b/src/maxdiffusion/models/qwen3_flax.py @@ -0,0 +1,721 @@ +""" +Copyright 2026 Google LLC + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import math +from typing import Any, List, Optional, Tuple +from flax import nnx +import flax.linen as nn +import jax +import jax.numpy as jnp +import numpy as np + +from maxdiffusion import max_logging + +# ----------------------------------------------------------------------------- +# Qwen3 Configuration +# ----------------------------------------------------------------------------- + + +class FlaxQwen3Config: + + def __init__( + self, + vocab_size: int = 151936, + hidden_size: int = 2560, + intermediate_size: int = 9728, + num_hidden_layers: int = 36, + num_attention_heads: int = 32, + num_key_value_heads: int = 8, + head_dim: int = 128, + rms_norm_eps: float = 1e-6, + rope_theta: float = 1000000.0, + max_position_embeddings: int = 40960, + dtype=jnp.float32, + ): + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.head_dim = head_dim + self.rms_norm_eps = rms_norm_eps + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + self.dtype = dtype + + +# ----------------------------------------------------------------------------- +# Core Model Layers +# ----------------------------------------------------------------------------- + + +class FlaxQwen3RMSNorm(nn.Module): + dim: int + eps: float = 1e-6 + dtype: Any = jnp.float32 + + @nn.compact + def __call__(self, x): + x_float = x.astype(jnp.float32) + variance = jnp.mean(jnp.square(x_float), axis=-1, keepdims=True) + scale = self.param("weight", nn.initializers.ones, (self.dim,), self.dtype) + normed = x_float * jax.lax.rsqrt(variance + self.eps) + return (normed.astype(self.dtype)) * scale + + +class FlaxQwen3MLP(nn.Module): + config: FlaxQwen3Config + + @nn.compact + def __call__(self, x): + gate_proj = nn.Dense( + self.config.intermediate_size, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "mlp")), + dtype=self.config.dtype, + name="gate_proj", + ) + up_proj = nn.Dense( + self.config.intermediate_size, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "mlp")), + dtype=self.config.dtype, + name="up_proj", + ) + down_proj = nn.Dense( + self.config.hidden_size, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("mlp", "embed")), + dtype=self.config.dtype, + name="down_proj", + ) + + return down_proj(jax.nn.silu(gate_proj(x)) * up_proj(x)) + + +# ----------------------------------------------------------------------------- +# Rotary Position Embeddings (RoPE) +# ----------------------------------------------------------------------------- + + +def precompute_qwen3_freqs_cis(head_dim: int, max_seq_len: int, theta: float = 1000000.0) -> Tuple[jnp.ndarray, jnp.ndarray]: + """ + Precomputes the cosine and sine tables for RoPE. + Matches standard Llama/Qwen half-half rotation layout. + """ + inv_freq = 1.0 / (theta ** (jnp.arange(0, head_dim, 2, dtype=jnp.float32) / head_dim)) + t = jnp.arange(max_seq_len, dtype=jnp.float32) + freqs = jnp.outer(t, inv_freq) # (max_seq_len, head_dim // 2) + + # Concatenate [freqs, freqs] to match Hugging Face's rotate_half layout + emb = jnp.concatenate([freqs, freqs], axis=-1) # (max_seq_len, head_dim) + + cos = jnp.cos(emb) + sin = jnp.sin(emb) + return cos, sin + + +def apply_qwen3_rotary_pos_emb( + q: jnp.ndarray, k: jnp.ndarray, cos: jnp.ndarray, sin: jnp.ndarray +) -> Tuple[jnp.ndarray, jnp.ndarray]: + """ + Applies RoPE to Q and K tensors. + q shape: (batch, seq_len, num_heads, head_dim) + k shape: (batch, seq_len, num_kv_heads, head_dim) + cos, sin shape: (seq_len, head_dim) + """ + # Reshape cos/sin to (1, seq_len, 1, head_dim) for broadcasting + cos = cos[jnp.newaxis, :, jnp.newaxis, :] + sin = sin[jnp.newaxis, :, jnp.newaxis, :] + + def rotate_half(x): + half = x.shape[-1] // 2 + x1 = x[..., :half] + x2 = x[..., half:] + return jnp.concatenate([-x2, x1], axis=-1) + + q_rot = (q * cos) + (rotate_half(q) * sin) + k_rot = (k * cos) + (rotate_half(k) * sin) + return q_rot, k_rot + + +# ----------------------------------------------------------------------------- +# Self Attention (Grouped Query Attention) +# ----------------------------------------------------------------------------- + + +class FlaxQwen3Attention(nn.Module): + config: FlaxQwen3Config + + @nn.compact + def __call__( + self, + x: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + cos_table: Optional[jnp.ndarray] = None, + sin_table: Optional[jnp.ndarray] = None, + ): + batch_size, seq_len, _ = x.shape + + # 1. Project Q, K, V + # Output sizes: Q: 4096, K: 1024, V: 1024 + q_proj = nn.Dense( + self.config.num_attention_heads * self.config.head_dim, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "heads")), + dtype=self.config.dtype, + name="q_proj", + ) + k_proj = nn.Dense( + self.config.num_key_value_heads * self.config.head_dim, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "heads")), + dtype=self.config.dtype, + name="k_proj", + ) + v_proj = nn.Dense( + self.config.num_key_value_heads * self.config.head_dim, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("embed", "heads")), + dtype=self.config.dtype, + name="v_proj", + ) + o_proj = nn.Dense( + self.config.hidden_size, + use_bias=False, + kernel_init=nn.with_logical_partitioning(nn.initializers.lecun_normal(), ("heads", "embed")), + dtype=self.config.dtype, + name="o_proj", + ) + + # QK-Norm Layers (Head-wise, sharing scale weights of size head_dim = 128) + q_norm = FlaxQwen3RMSNorm( + dim=self.config.head_dim, + eps=self.config.rms_norm_eps, + dtype=self.config.dtype, + name="q_norm", + ) + k_norm = FlaxQwen3RMSNorm( + dim=self.config.head_dim, + eps=self.config.rms_norm_eps, + dtype=self.config.dtype, + name="k_norm", + ) + + q = q_proj(x) + k = k_proj(x) + v = v_proj(x) + + # 2. Reshape to heads first: (batch, seq_len, num_heads, head_dim) + q = q.reshape((batch_size, seq_len, self.config.num_attention_heads, self.config.head_dim)) + k = k.reshape((batch_size, seq_len, self.config.num_key_value_heads, self.config.head_dim)) + v = v.reshape((batch_size, seq_len, self.config.num_key_value_heads, self.config.head_dim)) + + # Apply QK-Norm head-wise (normalizes over the last axis of size 128) + q = q_norm(q) + k = k_norm(k) + + # 3. Apply RoPE + if cos_table is not None and sin_table is not None: + # Extract cos/sin for the current sequence length + cos = cos_table[:seq_len, :] + sin = sin_table[:seq_len, :] + q, k = apply_qwen3_rotary_pos_emb(q, k, cos, sin) + + # 4. Repeat KV heads to match Query heads (GQA) + gqa_ratio = self.config.num_attention_heads // self.config.num_key_value_heads + if gqa_ratio > 1: + k = jnp.repeat(k, gqa_ratio, axis=-2) + v = jnp.repeat(v, gqa_ratio, axis=-2) + + # 5. Transpose to (batch, num_heads, seq_len, head_dim) for attention + q = jnp.transpose(q, (0, 2, 1, 3)) + k = jnp.transpose(k, (0, 2, 1, 3)) + v = jnp.transpose(v, (0, 2, 1, 3)) + + # 6. Compute attention logits in float32 + q_f = q.astype(jnp.float32) + k_f = k.astype(jnp.float32) + v_f = v.astype(jnp.float32) + + scores = jnp.matmul(q_f, jnp.transpose(k_f, (0, 1, 3, 2))) / math.sqrt(self.config.head_dim) + + # 7. Apply causal attention mask + causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=jnp.bool_)) + scores = jnp.where(causal_mask, scores, -1e4) + + # 8. Apply padding attention mask if provided + if attention_mask is not None: + p_mask = attention_mask[:, jnp.newaxis, jnp.newaxis, :].astype(jnp.bool_) + scores = jnp.where(p_mask, scores, -1e4) + + # 9. Softmax & Weighted Sum in float32 + probs = jax.nn.softmax(scores, axis=-1) + out = jnp.matmul(probs, v_f).astype(self.config.dtype) + + # 10. Reshape back and project out: (batch, seq_len, hidden_size) + out = jnp.transpose(out, (0, 2, 1, 3)).reshape((batch_size, seq_len, -1)) + return o_proj(out) + + +# ----------------------------------------------------------------------------- +# Decoder Block Layer +# ----------------------------------------------------------------------------- + + +class FlaxQwen3DecoderLayer(nn.Module): + config: FlaxQwen3Config + + @nn.compact + def __call__( + self, + x: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + cos_table: Optional[jnp.ndarray] = None, + sin_table: Optional[jnp.ndarray] = None, + ): + # input_layernorm + input_layernorm = FlaxQwen3RMSNorm( + dim=self.config.hidden_size, + eps=self.config.rms_norm_eps, + dtype=self.config.dtype, + name="input_layernorm", + ) + # self_attn + self_attn = FlaxQwen3Attention( + config=self.config, + name="self_attn", + ) + # post_attention_layernorm + post_attention_layernorm = FlaxQwen3RMSNorm( + dim=self.config.hidden_size, + eps=self.config.rms_norm_eps, + dtype=self.config.dtype, + name="post_attention_layernorm", + ) + # mlp + mlp = FlaxQwen3MLP( + config=self.config, + name="mlp", + ) + + # Self-Attention block (with residual) + attn_out = self_attn( + input_layernorm(x), + attention_mask=attention_mask, + cos_table=cos_table, + sin_table=sin_table, + ) + x = x + attn_out + + # MLP block (with residual) + mlp_out = mlp(post_attention_layernorm(x)) + x = x + mlp_out + + return x + + +# ----------------------------------------------------------------------------- +# Full Transformer Model +# ----------------------------------------------------------------------------- + + +class FlaxQwen3Model(nn.Module): + config: FlaxQwen3Config + + @nn.compact + def __call__( + self, + input_ids: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + ) -> Tuple[jnp.ndarray, List[jnp.ndarray]]: + """ + Runs the full Qwen3-4B model. + Returns: + last_hidden_state: Output of the final layer (batch, seq_len, 2560) + all_hidden_states: List of activations from every layer, including token embeddings (length 37) + """ + batch_size, seq_len = input_ids.shape + + # 1. Token Embeddings + embed_tokens = nn.Embed( + num_embeddings=self.config.vocab_size, + features=self.config.hidden_size, + embedding_init=nn.with_logical_partitioning( + nn.initializers.normal(stddev=self.config.hidden_size**-0.5), ("vocab", "embed") + ), + dtype=self.config.dtype, + name="embed_tokens", + ) + hidden_states = embed_tokens(input_ids) + + # Track all layer activations (including embedding layer) + all_hidden_states = [hidden_states] + + # 2. Precompute RoPE cos/sin tables + cos_table, sin_table = precompute_qwen3_freqs_cis( + head_dim=self.config.head_dim, + max_seq_len=self.config.max_position_embeddings, + theta=self.config.rope_theta, + ) + + # 3. Stacked Decoder Layers + for i in range(self.config.num_hidden_layers): + layer = FlaxQwen3DecoderLayer( + config=self.config, + name=f"layers_{i}", + ) + hidden_states = layer( + hidden_states, + attention_mask=attention_mask, + cos_table=cos_table, + sin_table=sin_table, + ) + all_hidden_states.append(hidden_states) + + # 4. Final RMSNorm + norm = FlaxQwen3RMSNorm( + dim=self.config.hidden_size, + eps=self.config.rms_norm_eps, + dtype=self.config.dtype, + name="norm", + ) + hidden_states = norm(hidden_states) + + # Keep all_hidden_states as the raw outputs of the layers, do not overwrite with final norm. + + return hidden_states, all_hidden_states + + +# ----------------------------------------------------------------------------- +# NNX Transformer Model Implementations for Qwen3 +# ----------------------------------------------------------------------------- + + +class NNXFlaxQwen3RMSNorm(nnx.Module): + + def __init__(self, rngs: nnx.Rngs, dim: int, eps: float = 1e-6, dtype: jnp.dtype = jnp.float32): + self.eps = eps + self.dtype = dtype + self.weight = nnx.Param(jnp.ones((dim,), dtype=dtype)) + + def __call__(self, x: jnp.ndarray) -> jnp.ndarray: + x_float = x.astype(jnp.float32) + variance = jnp.mean(jnp.square(x_float), axis=-1, keepdims=True) + normed = x_float * jax.lax.rsqrt(variance + self.eps) + return (normed.astype(self.dtype)) * self.weight[...] + + +class NNXFlaxQwen3MLP(nnx.Module): + + def __init__(self, rngs: nnx.Rngs, config: FlaxQwen3Config): + self.config = config + self.gate_proj = nnx.Linear( + in_features=config.hidden_size, + out_features=config.intermediate_size, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "mlp")), + dtype=config.dtype, + rngs=rngs, + ) + self.up_proj = nnx.Linear( + in_features=config.hidden_size, + out_features=config.intermediate_size, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "mlp")), + dtype=config.dtype, + rngs=rngs, + ) + self.down_proj = nnx.Linear( + in_features=config.intermediate_size, + out_features=config.hidden_size, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("mlp", "embed")), + dtype=config.dtype, + rngs=rngs, + ) + + def __call__(self, x: jnp.ndarray) -> jnp.ndarray: + return self.down_proj(jax.nn.silu(self.gate_proj(x)) * self.up_proj(x)) + + +class NNXFlaxQwen3Attention(nnx.Module): + + def __init__(self, rngs: nnx.Rngs, config: FlaxQwen3Config): + self.config = config + self.num_heads = config.num_attention_heads + self.num_kv_heads = config.num_key_value_heads + self.head_dim = config.head_dim + + self.q_proj = nnx.Linear( + in_features=config.hidden_size, + out_features=config.num_attention_heads * config.head_dim, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "heads")), + dtype=config.dtype, + rngs=rngs, + ) + self.k_proj = nnx.Linear( + in_features=config.hidden_size, + out_features=config.num_key_value_heads * config.head_dim, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "heads")), + dtype=config.dtype, + rngs=rngs, + ) + self.v_proj = nnx.Linear( + in_features=config.hidden_size, + out_features=config.num_key_value_heads * config.head_dim, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "heads")), + dtype=config.dtype, + rngs=rngs, + ) + self.o_proj = nnx.Linear( + in_features=config.num_attention_heads * config.head_dim, + out_features=config.hidden_size, + use_bias=False, + kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("heads", "embed")), + dtype=config.dtype, + rngs=rngs, + ) + + self.q_norm = NNXFlaxQwen3RMSNorm(rngs=rngs, dim=config.head_dim, eps=config.rms_norm_eps, dtype=config.dtype) + self.k_norm = NNXFlaxQwen3RMSNorm(rngs=rngs, dim=config.head_dim, eps=config.rms_norm_eps, dtype=config.dtype) + + def __call__( + self, + x: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + cos_table: Optional[jnp.ndarray] = None, + sin_table: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + batch_size, seq_len, _ = x.shape + + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + q = q.reshape(batch_size, seq_len, self.num_heads, self.head_dim) + k = k.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim) + v = v.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim) + + q = self.q_norm(q) + k = self.k_norm(k) + + if cos_table is not None and sin_table is not None: + cos_seq = cos_table[:seq_len, :] + sin_seq = sin_table[:seq_len, :] + q, k = apply_qwen3_rotary_pos_emb(q, k, cos_seq, sin_seq) + + if self.num_kv_heads != self.num_heads: + num_repeats = self.num_heads // self.num_kv_heads + k = jnp.repeat(k, num_repeats, axis=2) + v = jnp.repeat(v, num_repeats, axis=2) + + q = jnp.transpose(q, (0, 2, 1, 3)) + k = jnp.transpose(k, (0, 2, 1, 3)) + v = jnp.transpose(v, (0, 2, 1, 3)) + + scale = 1.0 / math.sqrt(self.head_dim) + scores = jnp.matmul(q, jnp.transpose(k, (0, 1, 3, 2))) * scale + + if attention_mask is not None: + scores = scores + attention_mask + + attn_probs = jax.nn.softmax(scores, axis=-1) + output = jnp.matmul(attn_probs, v) + output = jnp.transpose(output, (0, 2, 1, 3)) + output = output.reshape(batch_size, seq_len, -1) + output = self.o_proj(output) + return output + + +class NNXFlaxQwen3DecoderLayer(nnx.Module): + + def __init__(self, rngs: nnx.Rngs, config: FlaxQwen3Config): + self.config = config + self.input_layernorm = NNXFlaxQwen3RMSNorm( + rngs=rngs, dim=config.hidden_size, eps=config.rms_norm_eps, dtype=config.dtype + ) + self.self_attn = NNXFlaxQwen3Attention(rngs=rngs, config=config) + self.post_attention_layernorm = NNXFlaxQwen3RMSNorm( + rngs=rngs, dim=config.hidden_size, eps=config.rms_norm_eps, dtype=config.dtype + ) + self.mlp = NNXFlaxQwen3MLP(rngs=rngs, config=config) + + def __call__( + self, + x: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + cos_table: Optional[jnp.ndarray] = None, + sin_table: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + residual = x + normed_x = self.input_layernorm(x) + attn_out = self.self_attn( + normed_x, + attention_mask=attention_mask, + cos_table=cos_table, + sin_table=sin_table, + ) + x = residual + attn_out + + mlp_out = self.mlp(self.post_attention_layernorm(x)) + x = x + mlp_out + return x + + +class NNXFlaxQwen3Model(nnx.Module): + + def __init__(self, rngs: nnx.Rngs, config: FlaxQwen3Config): + self.config = config + self.embed_tokens = nnx.Embed( + num_embeddings=config.vocab_size, + features=config.hidden_size, + embedding_init=nnx.with_partitioning(nnx.initializers.normal(stddev=config.hidden_size**-0.5), ("vocab", "embed")), + dtype=config.dtype, + rngs=rngs, + ) + self.layers = nnx.List([NNXFlaxQwen3DecoderLayer(rngs=rngs, config=config) for _ in range(config.num_hidden_layers)]) + self.norm = NNXFlaxQwen3RMSNorm(rngs=rngs, dim=config.hidden_size, eps=config.rms_norm_eps, dtype=config.dtype) + + def __call__( + self, + input_ids: jnp.ndarray, + attention_mask: Optional[jnp.ndarray] = None, + ) -> Tuple[jnp.ndarray, List[jnp.ndarray]]: + hidden_states = self.embed_tokens(input_ids) + all_hidden_states = [hidden_states] + + cos_table, sin_table = precompute_qwen3_freqs_cis( + head_dim=self.config.head_dim, + max_seq_len=self.config.max_position_embeddings, + theta=self.config.rope_theta, + ) + + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=attention_mask, + cos_table=cos_table, + sin_table=sin_table, + ) + all_hidden_states.append(hidden_states) + + hidden_states = self.norm(hidden_states) + return hidden_states, all_hidden_states + + +# ----------------------------------------------------------------------------- +# Weight Mapping & Conversion Utilities +# ----------------------------------------------------------------------------- + + +def load_and_convert_qwen3_weights(safetensors_path: str, jax_params: dict, config: FlaxQwen3Config) -> dict: + """ + Loads weights from safetensors via zero-copy safetensors.numpy and converts them to JAX parameter dictionary. + """ + import glob + import os + from safetensors.numpy import load_file + + torch_weights: dict = {} + if os.path.isdir(safetensors_path): + # Find all safetensors shards + shards = glob.glob(os.path.join(safetensors_path, "*.safetensors")) + max_logging.log(f"Loading sharded Qwen3 weights from directory: {safetensors_path} (Found {len(shards)} shards)...") + for shard in sorted(shards): + max_logging.log(f"Loading shard: {shard}...") + torch_weights.update(load_file(shard)) + else: + # Single file path + max_logging.log(f"Loading Qwen3 weights from file: {safetensors_path}...") + torch_weights = load_file(safetensors_path) + max_logging.log("Safetensors weights loaded successfully. Starting JAX parameter mapping...") + + # Helper to transpose and cast weight + def get_w(name: str, transpose: bool = True) -> np.ndarray: + nonlocal torch_weights + if name not in torch_weights: + raise KeyError(f"Weight '{name}' not found in safetensors!") + t = torch_weights[name] + if len(t.shape) == 2 and transpose: + t = t.T + return t + + # Create mutable copy of JAX params to populate + import flax + + flat_params = flax.traverse_util.flatten_dict(jax_params) + converted_flat = {} + + for k, v in flat_params.items(): + # Reconstruct path string for debugging/matching + path_str = ".".join(k) + + # 1. Token Embeddings + if k[0] == "embed_tokens" and k[1] == "embedding": + converted_flat[k] = get_w("model.embed_tokens.weight", transpose=False) + + # 2. Decoder Layer Normalizations (RMSNorm) + elif "input_layernorm" in path_str and k[-1] == "weight": + layer_idx = k[0].split("_")[1] + converted_flat[k] = get_w(f"model.layers.{layer_idx}.input_layernorm.weight") + + elif "post_attention_layernorm" in path_str and k[-1] == "weight": + layer_idx = k[0].split("_")[1] + converted_flat[k] = get_w(f"model.layers.{layer_idx}.post_attention_layernorm.weight") + + # 3. Attention Projections & QK-Norm + elif "self_attn" in path_str and k[-1] == "kernel": + layer_idx = k[0].split("_")[1] + proj_name = k[2] # q_proj, k_proj, v_proj, o_proj + converted_flat[k] = get_w(f"model.layers.{layer_idx}.self_attn.{proj_name}.weight") + + elif "self_attn" in path_str and "q_norm" in path_str and k[-1] == "weight": + layer_idx = k[0].split("_")[1] + converted_flat[k] = get_w(f"model.layers.{layer_idx}.self_attn.q_norm.weight") + + elif "self_attn" in path_str and "k_norm" in path_str and k[-1] == "weight": + layer_idx = k[0].split("_")[1] + converted_flat[k] = get_w(f"model.layers.{layer_idx}.self_attn.k_norm.weight") + + # 4. MLP Block + elif "mlp" in path_str and k[-1] == "kernel": + layer_idx = k[0].split("_")[1] + proj_name = k[2] # gate_proj, up_proj, down_proj + converted_flat[k] = get_w(f"model.layers.{layer_idx}.mlp.{proj_name}.weight") + + # 5. Final RMSNorm + elif k[0] == "norm" and k[1] == "weight": + converted_flat[k] = get_w("model.norm.weight") + + else: + max_logging.log(f"WARNING: JAX parameter '{path_str}' did not match any PyTorch weights!") + converted_flat[k] = np.zeros(v.shape, dtype=np.float32) if hasattr(v, "shape") and not isinstance(v, np.ndarray) else v + + # Clean up PyTorch memory immediately + del torch_weights + import gc + + gc.collect() + + res = flax.traverse_util.unflatten_dict(converted_flat) + return jax.tree_util.tree_map( + lambda leaf: jnp.zeros(leaf.shape, dtype=leaf.dtype) if isinstance(leaf, jax.ShapeDtypeStruct) else leaf, res + ) diff --git a/src/maxdiffusion/models/vae_flax.py b/src/maxdiffusion/models/vae_flax.py index 042ec2755..4264bc650 100644 --- a/src/maxdiffusion/models/vae_flax.py +++ b/src/maxdiffusion/models/vae_flax.py @@ -16,9 +16,10 @@ import math from functools import partial -from typing import Tuple +from typing import Optional, Tuple import flax +from flax import nnx import flax.linen as nn import jax from jax import tree_util @@ -958,3 +959,65 @@ def _wan_diag_gauss_dist_unflatten(aux, children): _wan_diag_gauss_dist_flatten, _wan_diag_gauss_dist_unflatten, ) + + +class NNXFlaxAutoencoderKL(nnx.Module): + + def __init__( + self, + rngs: nnx.Rngs, + in_channels: int = 3, + out_channels: int = 3, + down_block_types: Tuple[str, ...] = ("FlaxDownEncoderBlock2D",), + up_block_types: Tuple[str, ...] = ("FlaxUpDecoderBlock2D",), + block_out_channels: Tuple[int, ...] = (64,), + layers_per_block: int = 1, + act_fn: str = "silu", + latent_channels: int = 4, + norm_num_groups: int = 32, + sample_size: int = 32, + dtype: jnp.dtype = jnp.float32, + weights_dtype: jnp.dtype = jnp.float32, + ): + self.in_channels = in_channels + self.out_channels = out_channels + self.latent_channels = latent_channels + self.dtype = dtype + + self.decoder = FlaxDecoder( + in_channels=latent_channels, + out_channels=out_channels, + up_block_types=up_block_types, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + norm_num_groups=norm_num_groups, + act_fn=act_fn, + dtype=dtype, + weights_dtype=weights_dtype, + ) + self.post_quant_conv = nnx.Conv( + in_features=latent_channels, + out_features=latent_channels, + kernel_size=(1, 1), + strides=(1, 1), + padding="VALID", + dtype=dtype, + param_dtype=weights_dtype, + rngs=rngs, + ) + + def decode( + self, latents: jax.Array, decoder_params: Optional[dict] = None, deterministic: bool = True, return_dict: bool = True + ): + if latents.shape[-1] != self.latent_channels: + latents = jnp.transpose(latents, (0, 2, 3, 1)) + + hidden_states = self.post_quant_conv(latents) + if decoder_params is not None: + hidden_states = self.decoder.apply({"params": decoder_params}, hidden_states, deterministic=deterministic) + hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2)) + + if not return_dict: + return (hidden_states,) + + return FlaxDecoderOutput(sample=hidden_states) diff --git a/src/maxdiffusion/pipelines/flux/flux2klein_pipeline.py b/src/maxdiffusion/pipelines/flux/flux2klein_pipeline.py new file mode 100644 index 000000000..7341bf8b9 --- /dev/null +++ b/src/maxdiffusion/pipelines/flux/flux2klein_pipeline.py @@ -0,0 +1,333 @@ +""" +Copyright 2026 Google LLC + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import time +from typing import List, Union, Optional, Any +from PIL import Image + +import jax +import jax.numpy as jnp +import numpy as np +from flax.linen import partitioning as nn_partitioning + +from maxdiffusion import max_logging +from ..pipeline_flax_utils import FlaxDiffusionPipeline +from ...models.flux.transformers.transformer_flux_flax import Flux2KleinTransformer2DModel +from ...models.vae_flax import FlaxAutoencoderKL +from ...models.qwen3_flax import FlaxQwen3Model +from ...schedulers.scheduling_flow_match_flax import FlaxFlowMatchScheduler, compute_empirical_mu + +from ...models.flux.util import ( + pack_latents, + unpack_latents, + prepare_latent_image_ids, + prepare_text_ids, +) + + +class FlaxFlux2KleinPipeline(FlaxDiffusionPipeline): + """ + Unified end-to-end inference pipeline for Flux.2-klein-4B and 9B models on JAX+TPU. + Supports dynamic parameter offloading to Host CPU to optimize HBM footprint. + """ + + def __init__( + self, + transformer: Flux2KleinTransformer2DModel, + vae: FlaxAutoencoderKL, + text_encoder: FlaxQwen3Model, + tokenizer, + scheduler: FlaxFlowMatchScheduler, + config, + mesh, + **kwargs, + ): + super().__init__() + self.register_modules( + transformer=transformer, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler, + ) + self._config = config + self.mesh = mesh + + # JIT compilation cache + self._jitted_qwen3_forward = None + self._jitted_transformer_step = None + self._jitted_vae_decode = None + + def _setup_jit_functions(self): + if self._jitted_qwen3_forward is not None: + return + + @jax.jit + def qwen3_forward(q_params, ids, mask): + return self.text_encoder.apply({"params": q_params}, input_ids=ids, attention_mask=mask) + + @jax.jit + def transformer_step(t_params, latents, img_ids, prompt_embeds, txt_ids, vec, timestep, guidance): + return self.transformer.apply( + {"params": t_params}, + hidden_states=latents, + img_ids=img_ids, + encoder_hidden_states=prompt_embeds, + txt_ids=txt_ids, + pooled_projections=vec, + timestep=timestep, + guidance=guidance, + ) + + @jax.jit + def vae_decode(v_params, latents_unpatched): + return self.vae.apply({"params": v_params}, latents=latents_unpatched, method=self.vae.decode) + + self._jitted_qwen3_forward = qwen3_forward + self._jitted_transformer_step = transformer_step + self._jitted_vae_decode = vae_decode + + def _prepare_latents(self, config, batch_size, height, width): + num_channels_latents = 32 + latent_height = height // 8 + latent_width = width // 8 + latent_shape = (batch_size, num_channels_latents, latent_height, latent_width) + + seed_val = getattr(config, "seed", None) + if seed_val is None: + seed_val = int(time.time()) & 0x7FFFFFFF + max_logging.log( + f"Generating random gaussian noise in unpacked space (32 channels) with seed: {seed_val} and shape: {latent_shape}..." + ) + np.random.seed(seed_val) + latents_unpacked = np.random.randn(*latent_shape).astype(np.float32) + + # Pack/patchify noise exactly like PyTorch: + # (batch, 32, H/16, 2, W/16, 2) -> permute(0, 1, 3, 5, 2, 4) -> reshape(batch, 128, H/16, W/16) + B, C, H, W = latents_unpacked.shape + latents_packed = latents_unpacked.reshape(B, C, H // 2, 2, W // 2, 2) + latents_packed = np.transpose(latents_packed, (0, 1, 3, 5, 2, 4)) + latents_packed = latents_packed.reshape(B, 128, H // 2, W // 2) + + return latents_packed + + def __call__( + self, + prompt: Union[str, List[str]], + params, + vae_params, + qwen3_params, + vae_bn_mean, + vae_bn_std, + transformer_shardings, + vae_shardings, + qwen3_shardings, + height: int = 1024, + width: int = 1024, + num_inference_steps: int = 4, + batch_size: int = 1, + use_latents: bool = False, + latents: Optional[Any] = None, + measure_time: bool = False, + output_dir: str = "output/", + output_name: str = "flux2klein_generated_image.png", + ): + # 1. Setup JIT functions + self._setup_jit_functions() + + # 2. Setup prompts and inputs + if isinstance(prompt, str): + prompts = [prompt] * batch_size + else: + prompts = prompt + + seq_len_img = (height // 16) * (width // 16) + seq_len_txt = self._config.max_sequence_length + + # Load or generate latents + if use_latents and latents is not None: + latents_jax = jnp.array(latents) + if latents_jax.ndim == 4: + B, C, H, W = latents_jax.shape + if C == 32: + max_logging.log(" [PIPELINE] Unpacked 32-channel latents detected. Packing using pack_latents...") + latents_jax = pack_latents(latents_jax) + else: + latents_jax = jnp.transpose(jnp.reshape(latents_jax, (B, C, H * W)), (0, 2, 1)) + else: + latents_numpy = self._prepare_latents(self._config, batch_size, height, width) + max_logging.log(f"DEBUG PIPELINE: latents_numpy sum = {latents_numpy.sum():.6f} | mean = {latents_numpy.mean():.6f}") + B, C, H, W = latents_numpy.shape + latents_jax = jnp.transpose(jnp.reshape(latents_numpy, (B, C, H * W)), (0, 2, 1)) + + # RoPE position IDs + txt_ids_val = prepare_text_ids(batch_size, seq_len_txt) + img_ids_val = prepare_latent_image_ids(batch_size, height // 16, width // 16) + + # Scheduler + mu = compute_empirical_mu(seq_len_img, num_inference_steps) + scheduler_state = self.scheduler.create_state() + sigmas_custom = jnp.linspace(1.0, 1.0 / num_inference_steps, num_inference_steps, dtype=jnp.float32) + scheduler_state = self.scheduler.set_timesteps_ltx2( + state=scheduler_state, + num_inference_steps=num_inference_steps, + shift=mu, + sigmas=sigmas_custom, + ) + + trace = {} + + with self.mesh, nn_partitioning.axis_rules(self._config.logical_axis_rules): + # --------------------------------------------------------------------- + # PHASE A: Encode Prompt (Qwen3) + # --------------------------------------------------------------------- + max_logging.log(f"[PHASE A] Encoding {len(prompts)} prompt(s) using JAX Qwen3 on TPU...") + t0 = time.perf_counter() + + # Resolve tokenizer path from config + tokenizer_path = self._config.tokenizer_model_name_or_path + hf_home = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/huggingface")) + repo_cache = os.path.join( + hf_home, "hub", f"models--{self._config.pretrained_model_name_or_path.replace('/', '--')}", "snapshots" + ) + if os.path.exists(repo_cache) and os.listdir(repo_cache): + tokenizer_path = os.path.join(repo_cache, os.listdir(repo_cache)[0]) + + from transformers import Qwen2TokenizerFast + + try: + tokenizer = Qwen2TokenizerFast.from_pretrained(tokenizer_path, local_files_only=True) + except Exception: + tokenizer = Qwen2TokenizerFast.from_pretrained(tokenizer_path, subfolder="tokenizer", local_files_only=True) + + # Tokenize using deterministic explicit template string (version-agnostic across transformers versions) + templated_texts = [f"<|im_start|>user\n{p}<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" for p in prompts] + max_logging.log(f"DEBUG: templated_texts[0] = {repr(templated_texts[0])}") + inputs = tokenizer(templated_texts, return_tensors="np", padding="max_length", truncation=True, max_length=seq_len_txt) + prompt_ids = jnp.array(inputs["input_ids"]) + prompt_mask = jnp.array(inputs["attention_mask"]) + max_logging.log(f"DEBUG: prompt_ids[0][:15] = {prompt_ids[0][:15]}") + + # Run Text Encoding + hidden_states, all_hidden_states = self._jitted_qwen3_forward(qwen3_params, prompt_ids, prompt_mask) + + # Stack layers 9, 18, 27 to form prompt embeddings + h_9 = all_hidden_states[9] + h_18 = all_hidden_states[18] + h_27 = all_hidden_states[27] + out = jnp.stack([h_9, h_18, h_27], axis=1) + # Transpose shape to [B, seq_len, 3*hidden_size] + prompt_embeds_jax = jnp.transpose(out, (0, 2, 1, 3)).reshape((batch_size, seq_len_txt, -1)) + prompt_embeds_jax.block_until_ready() + max_logging.log( + f"DEBUG: prompt_embeds_jax min={float(prompt_embeds_jax.min()):.4f}, max={float(prompt_embeds_jax.max()):.4f}, mean={float(prompt_embeds_jax.mean()):.4f}, sum={float(prompt_embeds_jax.sum()):.4f}" + ) + + trace["prompt_encoding"] = time.perf_counter() - t0 + max_logging.log(f" -> [TIMING] Prompt Encoding (Qwen3): {trace['prompt_encoding']:.4f} seconds ⏱️") + + # --------------------------------------------------------------------- + # PHASE B: Denoising Loop (Flux Transformer) + # --------------------------------------------------------------------- + max_logging.log( + f"[PHASE B] Running {num_inference_steps}-step E2E Denoising Loop on a batch of {batch_size} images..." + ) + t0 = time.perf_counter() + + guidance_vec_val = None + vec_val = None + + for step_idx in range(num_inference_steps): + timestep = scheduler_state.timesteps[step_idx] + t_vec = jnp.array([timestep / 1000.0] * batch_size) + + # Execute transformer forward pass step + model_output = self._jitted_transformer_step( + params, latents_jax, img_ids_val, prompt_embeds_jax, txt_ids_val, vec_val, t_vec, guidance_vec_val + ) + + # Update latents using FlowMatch step + latents_jax = self.scheduler.step( + state=scheduler_state, + model_output=model_output.sample, + timestep=scheduler_state.timesteps[step_idx], + sample=latents_jax, + ).prev_sample + + # Print progress + sigma_val = scheduler_state.sigmas[step_idx] + max_logging.log( + f" -> Step {step_idx}: Timestep = {scheduler_state.timesteps[step_idx]:.4f}, Sigma = {sigma_val:.4f}" + ) + + latents_jax.block_until_ready() + + trace["denoise_loop"] = time.perf_counter() - t0 + max_logging.log(f" -> [TIMING] Denoising Loop (Flux): {trace['denoise_loop']:.4f} seconds ⏱️") + + # --------------------------------------------------------------------- + # PHASE C: Decode Latents (VAE Decoder) + # --------------------------------------------------------------------- + max_logging.log("[PHASE C] Decoding final latents to RGB image using JAX VAE decoder on TPU...") + t0 = time.perf_counter() + + # Apply Channel-wise Batch Normalization Scaling in packed sequence format (denormalize) + vae_bn_mean_seq = vae_bn_mean.reshape(1, 1, 128) + vae_bn_std_seq = vae_bn_std.reshape(1, 1, 128) + latents_bn = latents_jax * vae_bn_std_seq + vae_bn_mean_seq + + # Unpack packed latents back to spatial grid + latents_unpacked = unpack_latents(latents_bn, batch_size, 32, height, width) + + # Decode VAE latents to RGB pixels + decoded_out = self._jitted_vae_decode(vae_params, latents_unpacked) + # VAE output is in decoded_out.sample + images_rgb = decoded_out.sample + images_rgb.block_until_ready() + + trace["vae_decode"] = time.perf_counter() - t0 + max_logging.log(f" -> [TIMING] VAE Decoding: {trace['vae_decode']:.4f} seconds ⏱️") + + # --------------------------------------------------------------------- + # POST-PROCESS: Format and Save Outputs + # --------------------------------------------------------------------- + max_logging.log("Postprocessing and saving generated images...") + saved_paths = [] + # Clamp pixels and scale to [0, 255] + images_rgb = jnp.clip((images_rgb + 1.0) / 2.0, 0.0, 1.0) + images_numpy = np.array(images_rgb) + + for b_idx in range(batch_size): + image_np = np.array(images_numpy[b_idx] * 255.0, dtype=np.uint8) + # Transpose channel dimension if shape is (C, H, W) instead of (H, W, C) + if image_np.shape[0] == 3: + image_np = image_np.transpose(1, 2, 0) + + img = Image.fromarray(image_np) + + # Formulate output filename for this batch index + if batch_size > 1: + batch_output_name = output_name.replace(".png", f"_b{b_idx}.png") + else: + batch_output_name = output_name + + output_png_path = os.path.join(output_dir, batch_output_name) + img.save(output_png_path) + max_logging.log(f" -> Saved image: {output_png_path} | Prompt: '{prompts[b_idx]}'") + saved_paths.append(output_png_path) + + return saved_paths, trace diff --git a/src/maxdiffusion/schedulers/scheduling_flow_match_flax.py b/src/maxdiffusion/schedulers/scheduling_flow_match_flax.py index bf88e8774..8e9f38ff4 100644 --- a/src/maxdiffusion/schedulers/scheduling_flow_match_flax.py +++ b/src/maxdiffusion/schedulers/scheduling_flow_match_flax.py @@ -93,6 +93,8 @@ def __init__( inverse_timesteps: bool = False, extra_one_step: bool = False, reverse_sigmas: bool = False, + use_dynamic_shifting: bool = False, + time_shift_type: str = "linear", dtype: jnp.dtype = jnp.float32, ): self.dtype = dtype @@ -142,7 +144,13 @@ def set_timesteps( if self.config.inverse_timesteps: sigmas = jnp.flip(sigmas, dims=[0]) - sigmas = current_shift * sigmas / (1 + (current_shift - 1) * sigmas) + if getattr(self.config, "use_dynamic_shifting", False): + if getattr(self.config, "time_shift_type", "exponential") == "exponential": + sigmas = jnp.exp(current_shift) / (jnp.exp(current_shift) + (1 / jnp.clip(sigmas, 1e-7, 1.0) - 1)) + else: + sigmas = current_shift * sigmas / (1 + (current_shift - 1) * sigmas) + else: + sigmas = current_shift * sigmas / (1 + (current_shift - 1) * sigmas) if self.config.reverse_sigmas: sigmas = 1 - sigmas @@ -321,7 +329,8 @@ def get_next_sigma(): return state.sigmas[timestep_id + 1] def get_final_sigma(): - return jnp.array(1.0 if (self.config.inverse_timesteps or self.config.reverse_sigmas) else 0.0, dtype=sigma.dtype) + val = 1.0 if (self.config.inverse_timesteps or self.config.reverse_sigmas) else 0.0 + return jnp.full_like(state.sigmas[timestep_id + 1], val, dtype=sigma.dtype) is_final_step = to_final or jnp.all(timestep_id + 1 >= state.timesteps.shape[0]) sigma_next = jax.lax.cond(is_final_step, get_final_sigma, get_next_sigma) @@ -402,3 +411,21 @@ def training_weight(self, state: FlowMatchSchedulerState, timestep: jnp.ndarray) def __len__(self) -> int: return self.config.num_train_timesteps + + +def compute_empirical_mu(image_seq_len: int, num_steps: int) -> float: + """ + Computes the empirical time shift parameter (mu) used by Flux models + to offset sigmas dynamically based on resolution sequence length. + """ + a1, b1 = 8.73809524e-05, 1.89833333 + a2, b2 = 0.00016927, 0.45666666 + if image_seq_len > 4300: + mu = a2 * image_seq_len + b2 + return float(mu) + m_200 = a2 * image_seq_len + b2 + m_10 = a1 * image_seq_len + b1 + a = (m_200 - m_10) / 190.0 + b = m_200 - 200.0 * a + mu = a * num_steps + b + return float(mu) diff --git a/src/maxdiffusion/tests/generate_flux2klein_e2e_test.py b/src/maxdiffusion/tests/generate_flux2klein_e2e_test.py new file mode 100644 index 000000000..38f800e4c --- /dev/null +++ b/src/maxdiffusion/tests/generate_flux2klein_e2e_test.py @@ -0,0 +1,274 @@ +""" +Copyright 2026 Google LLC + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import subprocess +import gc +import numpy as np +import torch +from PIL import Image +from skimage.metrics import structural_similarity as ssim + +# Set HF_HOME cache path early +if not os.environ.get("HF_HOME"): + if os.path.exists("/mnt/data/hf_cache"): + os.environ["HF_HOME"] = "/mnt/data/hf_cache" + + +def compute_psnr(img1, img2): + img1_np = np.array(img1).astype(np.float64) + img2_np = np.array(img2).astype(np.float64) + mse = np.mean((img1_np - img2_np) ** 2) + if mse == 0: + return float("inf") + return 20 * np.log10(255.0 / np.sqrt(mse)) + + +def compute_ssim(img1, img2): + img1_gray = np.array(img1.convert("L")) + img2_gray = np.array(img2.convert("L")) + return ssim(img1_gray, img2_gray) + + +def run_pytorch_pipeline(model_id, prompt, batch_size, width, height, num_inference_steps, seed, latents_pt_packed, prefix): + # Locate cached model files + cache_dir = f"/mnt/data/hf_cache/hub/models--{model_id.replace('/', '--')}/snapshots" + if not os.path.exists(cache_dir): + raise FileNotFoundError(f"Hugging Face cache directory not found: {cache_dir}") + snapshots = os.listdir(cache_dir) + snapshot_dir = os.path.join(cache_dir, snapshots[0]) + print(f"\n[PyTorch] Loading '{model_id}' weights from: {snapshot_dir}") + + from diffusers.pipelines.flux2.pipeline_flux2_klein import Flux2KleinPipeline + + # Run in FP32 + print("[PyTorch] Running Leg 1: FP32...") + pipe_fp32 = Flux2KleinPipeline.from_pretrained(snapshot_dir, torch_dtype=torch.float32, local_files_only=True) + pipe_fp32.to("cpu") + with torch.no_grad(): + pt_images_fp32 = pipe_fp32( + prompt=[prompt] * batch_size, + width=width, + height=height, + latents=latents_pt_packed, + num_inference_steps=num_inference_steps, + output_type="pil", + ).images + + fp32_paths = [] + for idx, img in enumerate(pt_images_fp32): + path = f"/tmp/{prefix}_pt_fp32_b{idx}.png" + img.save(path) + fp32_paths.append(path) + print(f" -> Saved: {path}") + + del pipe_fp32 + gc.collect() + + # Run in BF16 + print("[PyTorch] Running Leg 2: BF16...") + pipe_bf16 = Flux2KleinPipeline.from_pretrained(snapshot_dir, torch_dtype=torch.bfloat16, local_files_only=True) + pipe_bf16.to("cpu") + with torch.no_grad(): + pt_images_bf16 = pipe_bf16( + prompt=[prompt] * batch_size, + width=width, + height=height, + latents=latents_pt_packed.to(torch.bfloat16), + num_inference_steps=num_inference_steps, + output_type="pil", + ).images + + bf16_paths = [] + for idx, img in enumerate(pt_images_bf16): + path = f"/tmp/{prefix}_pt_bf16_b{idx}.png" + img.save(path) + bf16_paths.append(path) + print(f" -> Saved: {path}") + + del pipe_bf16 + gc.collect() + + return fp32_paths, bf16_paths + + +def main(): + prompt = "An animated person dancing in the fields" + width = 512 + height = 512 + num_inference_steps = 4 + batch_size = 4 + seed = 123 + + print("=" * 80) + # 1. Generate identical starting noise on CPU using NumPy + print(f"Generating shared starting noise on CPU (seed={seed}, batch_size={batch_size})...") + np.random.seed(seed) + latents_numpy = np.random.randn(batch_size, 32, height // 8, width // 8).astype(np.float32) + + # Save latents to file for JAX pipeline to read + latents_file_path = "/tmp/shared_noise_b4.npy" + np.save(latents_file_path, latents_numpy) + print(f" -> Saved shared noise to JAX-compatible file: {latents_file_path}") + + # Prepare packed latents for PyTorch + latents_unpacked_pt = torch.from_numpy(latents_numpy) + latents_pt_packed = latents_unpacked_pt.view(batch_size, 32, height // 16, 2, width // 16, 2) + latents_pt_packed = latents_pt_packed.permute(0, 1, 3, 5, 2, 4) + latents_pt_packed = latents_pt_packed.reshape(batch_size, 128, height // 16, width // 16) + + # ========================================================================= + # PART I: FLUX.2-KLEIN-4B PARITY + # ========================================================================= + print("\n" + "#" * 80) + print("🎬 STARTING PARITY EVALUATION FOR FLUX.2-KLEIN-4B") + print("#" * 80) + + # 1. Run PyTorch 4B (FP32 & BF16) + pt_4b_fp32_paths, pt_4b_bf16_paths = run_pytorch_pipeline( + model_id="black-forest-labs/FLUX.2-klein-4B", + prompt=prompt, + batch_size=batch_size, + width=width, + height=height, + num_inference_steps=num_inference_steps, + seed=seed, + latents_pt_packed=latents_pt_packed, + prefix="4b", + ) + + # 2. Run JAX 4B (via generate_flux2klein.py) + print("\n[JAX 4B] Executing pipeline script generate_flux2klein.py...") + cmd_jax_4b = [ + "python3", + "src/maxdiffusion/generate_flux2klein.py", + "src/maxdiffusion/configs/base_flux2klein.yml", + "skip_jax_distributed_system=True", + f"prompt={prompt}", + "output_dir=/tmp/", + f"seed={seed}", + f"height={height}", + f"width={width}", + f"num_inference_steps={num_inference_steps}", + f"batch_size={batch_size}", + f"latents_path={latents_file_path}", + "weights_dtype=bfloat16", + "activations_dtype=bfloat16", + "precision=DEFAULT", + "output_name=jax_4b_dancing.png", + ] + print(f"Executing: {' '.join(cmd_jax_4b)}") + subprocess.run(cmd_jax_4b, check=True) + + jax_4b_paths = [f"/tmp/jax_4b_dancing_b{b}.png" for b in range(batch_size)] + print(f"[JAX 4B] Execution complete! Verifying outputs at: {jax_4b_paths}") + + # ========================================================================= + # ========================================================================= + # PART II: FLUX.2-KLEIN-9B PARITY + # ========================================================================= + print("\n" + "#" * 80) + print("🎬 STARTING PARITY EVALUATION FOR FLUX.2-KLEIN-9B") + print("#" * 80) + pt_9b_fp32_paths, pt_9b_bf16_paths = run_pytorch_pipeline( + model_id="black-forest-labs/FLUX.2-klein-9B", + prompt=prompt, + batch_size=batch_size, + width=width, + height=height, + num_inference_steps=num_inference_steps, + seed=seed, + latents_pt_packed=latents_pt_packed, + prefix="9b", + ) + + print("\n[JAX 9B] Executing pipeline script generate_flux2klein.py...") + cmd_jax_9b = [ + "python3", + "src/maxdiffusion/generate_flux2klein.py", + "src/maxdiffusion/configs/base_flux2klein_9B.yml", + "skip_jax_distributed_system=True", + f"prompt={prompt}", + "output_dir=/tmp/", + f"seed={seed}", + f"height={height}", + f"width={width}", + f"num_inference_steps={num_inference_steps}", + f"batch_size={batch_size}", + f"latents_path={latents_file_path}", + "weights_dtype=bfloat16", + "activations_dtype=bfloat16", + "precision=DEFAULT", + "output_name=jax_9b_dancing.png", + ] + print(f"Executing: {' '.join(cmd_jax_9b)}") + subprocess.run(cmd_jax_9b, check=True) + + jax_9b_paths = [f"/tmp/jax_9b_dancing_b{b}.png" for b in range(batch_size)] + print(f"[JAX 9B] Execution complete! Verifying outputs at: {jax_9b_paths}") + + # ========================================================================= + # PART III: METRICS EVALUATION & COMPARISON REPORT + # ========================================================================= + print("\n" + "=" * 80) + print("📊 BATCHED VISUAL ALIGNMENT COMPARISON REPORT") + print("=" * 80) + + report = [] + report.append("# 📈 Flux.2-klein Batched (Batch-4) E2E Parity Report") + report.append(f"Prompt: '{prompt}'\n") + + report.append("## 4B Model Parity (JAX TPU vs PyTorch CPU)") + report.append("| Batch Index | JAX vs PyTorch FP32 SSIM | JAX vs PyTorch FP32 PSNR | JAX vs PyTorch BF16 SSIM |") + report.append("| :--- | :--- | :--- | :--- |") + for b in range(batch_size): + img_pt_fp32 = Image.open(pt_4b_fp32_paths[b]) + img_pt_bf16 = Image.open(pt_4b_bf16_paths[b]) + img_jax = Image.open(jax_4b_paths[b]) + + ssim_fp32 = compute_ssim(img_jax, img_pt_fp32) + psnr_fp32 = compute_psnr(img_jax, img_pt_fp32) + ssim_bf16 = compute_ssim(img_jax, img_pt_bf16) + + report.append(f"| Batch Element {b} | {ssim_fp32:.6f} | {psnr_fp32:.2f} dB | {ssim_bf16:.6f} |") + print(f" [4B Batch {b}] JAX vs PyTorch FP32 SSIM: {ssim_fp32:.6f} | JAX vs PyTorch BF16 SSIM: {ssim_bf16:.6f}") + + report.append("\n## 9B Model Parity (JAX TPU vs PyTorch CPU)") + report.append("| Batch Index | JAX vs PyTorch FP32 SSIM | JAX vs PyTorch FP32 PSNR | JAX vs PyTorch BF16 SSIM |") + report.append("| :--- | :--- | :--- | :--- |") + for b in range(batch_size): + img_pt_fp32 = Image.open(pt_9b_fp32_paths[b]) + img_pt_bf16 = Image.open(pt_9b_bf16_paths[b]) + img_jax = Image.open(jax_9b_paths[b]) + + ssim_fp32 = compute_ssim(img_jax, img_pt_fp32) + psnr_fp32 = compute_psnr(img_jax, img_pt_fp32) + ssim_bf16 = compute_ssim(img_jax, img_pt_bf16) + + report.append(f"| Batch Element {b} | {ssim_fp32:.6f} | {psnr_fp32:.2f} dB | {ssim_bf16:.6f} |") + print(f" [9B Batch {b}] JAX vs PyTorch FP32 SSIM: {ssim_fp32:.6f} | JAX vs PyTorch BF16 SSIM: {ssim_bf16:.6f}") + + report_path = "/tmp/flux_batched_hummingbird_parity_report.md" + with open(report_path, "w") as rf: + rf.write("\n".join(report)) + + print("\n" + "=" * 80) + print(f"SUCCESS! Batched parity report written to: {report_path}") + print("=" * 80) + + +if __name__ == "__main__": + main() diff --git a/src/maxdiffusion/tests/generate_flux2klein_smoke_test.py b/src/maxdiffusion/tests/generate_flux2klein_smoke_test.py new file mode 100644 index 000000000..24362d35d --- /dev/null +++ b/src/maxdiffusion/tests/generate_flux2klein_smoke_test.py @@ -0,0 +1,124 @@ +""" +Copyright 2026 Google LLC + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import unittest +import pytest + +import numpy as np +from PIL import Image +from skimage.metrics import structural_similarity as ssim + +from maxdiffusion import pyconfig +from maxdiffusion import generate_flux2klein + +IN_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true" +THIS_DIR = os.path.dirname(os.path.abspath(__file__)) +PROMPT = "anime corgi eating sushi in the mountains" + + +class GenerateFlux2KleinSmokeTest(unittest.TestCase): + """End-to-end smoke test for Flux2Klein 4B and 9B.""" + + @pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="Don't run smoke tests on Github Actions (requires TPU HBM)") + def test_flux2klein_4b_smoke(self): + """End-to-end smoke test for Flux.2-klein-4B image generation at 1024x1024.""" + ref_path = os.path.join(THIS_DIR, "images", "ref_flux2klein_4b.png") + self.assertTrue(os.path.exists(ref_path), f"Reference image not found: {ref_path}") + base_image = np.array(Image.open(ref_path)).astype(np.uint8) + + output_dir = "/mnt/data/smoke_test_4b" if os.path.exists("/mnt/data") else "/tmp/smoke_test_4b" + os.makedirs(output_dir, exist_ok=True) + out_path = os.path.join(output_dir, "flux2klein_generated_image.png") + if os.path.exists(out_path): + os.remove(out_path) + + pyconfig._config = None + pyconfig.config = None + args = [ + None, + os.path.join(THIS_DIR, "..", "configs", "base_flux2klein.yml"), + "run_name=smoke_test_4b", + f"output_dir={output_dir}", + "jax_cache_dir=/tmp/cache_dir", + "skip_jax_distributed_system=True", + f"prompt={PROMPT}", + "height=512", + "width=512", + "batch_size=1", + "seed=42", + "ici_fsdp_parallelism=-1", + "weights_dtype=bfloat16", + "activations_dtype=bfloat16", + "precision=DEFAULT", + ] + + generate_flux2klein.main(args) + + self.assertTrue(os.path.exists(out_path), "Smoke test 4B failed to produce output image!") + test_image = np.array(Image.open(out_path)).astype(np.uint8) + + self.assertEqual(base_image.shape, test_image.shape) + ssim_compare = ssim(base_image, test_image, channel_axis=-1, data_range=255) + print(f"\n[SMOKE TEST 4B] SSIM Score: {ssim_compare:.6f}") + self.assertGreaterEqual(ssim_compare, 0.75) + + @pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="Don't run smoke tests on Github Actions (requires TPU HBM)") + def test_flux2klein_9b_smoke(self): + """End-to-end smoke test for Flux.2-klein-9B image generation at 1024x1024.""" + ref_path = os.path.join(THIS_DIR, "images", "ref_flux2klein_9b.png") + self.assertTrue(os.path.exists(ref_path), f"Reference image not found: {ref_path}") + base_image = np.array(Image.open(ref_path)).astype(np.uint8) + + output_dir = "/mnt/data/smoke_test_9b" if os.path.exists("/mnt/data") else "/tmp/smoke_test_9b" + os.makedirs(output_dir, exist_ok=True) + out_path = os.path.join(output_dir, "flux2klein_generated_image.png") + if os.path.exists(out_path): + os.remove(out_path) + + pyconfig._config = None + pyconfig.config = None + args = [ + None, + os.path.join(THIS_DIR, "..", "configs", "base_flux2klein_9B.yml"), + "run_name=smoke_test_9b", + f"output_dir={output_dir}", + "jax_cache_dir=/tmp/cache_dir", + "skip_jax_distributed_system=True", + f"prompt={PROMPT}", + "height=512", + "width=512", + "batch_size=1", + "seed=42", + "ici_fsdp_parallelism=-1", + "weights_dtype=bfloat16", + "activations_dtype=bfloat16", + "precision=DEFAULT", + ] + + generate_flux2klein.main(args) + + self.assertTrue(os.path.exists(out_path), "Smoke test 9B failed to produce output image!") + test_image = np.array(Image.open(out_path)).astype(np.uint8) + + self.assertEqual(base_image.shape, test_image.shape) + ssim_compare = ssim(base_image, test_image, channel_axis=-1, data_range=255) + print(f"\n[SMOKE TEST 9B] SSIM Score: {ssim_compare:.6f}") + self.assertGreaterEqual(ssim_compare, 0.80) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/maxdiffusion/tests/images/ref_flux2klein_4b.png b/src/maxdiffusion/tests/images/ref_flux2klein_4b.png new file mode 100644 index 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+from flax import nnx + +from maxdiffusion.models.flux.transformers.transformer_flux_flax import NNXFluxTransformer2DModel +from maxdiffusion.models.qwen3_flax import FlaxQwen3Config, NNXFlaxQwen3Model +from maxdiffusion.models.vae_flax import NNXFlaxAutoencoderKL +from maxdiffusion.models.embeddings_flax import NNXCombinedTimestepGuidanceTextProjEmbeddings + + +class NNXFlux2KleinTest(unittest.TestCase): + + def test_nnx_combined_timestep_embeddings(self): + rngs = nnx.Rngs(0) + embedder = NNXCombinedTimestepGuidanceTextProjEmbeddings( + rngs=rngs, + embedding_dim=768, + pooled_projection_dim=768, + guidance_embeds=True, + ) + timestep = jnp.array([500.0]) + guidance = jnp.array([3.5]) + pooled_projection = jnp.ones((1, 768)) + + out = embedder(timestep, guidance, pooled_projection) + self.assertEqual(out.shape, (1, 768)) + + def test_nnx_qwen3_text_encoder_forward(self): + rngs = nnx.Rngs(0) + config = FlaxQwen3Config( + vocab_size=1000, + hidden_size=256, + intermediate_size=512, + num_hidden_layers=2, + num_attention_heads=4, + num_key_value_heads=2, + head_dim=64, + max_position_embeddings=128, + ) + model = NNXFlaxQwen3Model(rngs=rngs, config=config) + dummy_ids = jnp.ones((1, 16), dtype=jnp.int32) + last_hidden_state, all_hidden_states = model(dummy_ids) + + self.assertEqual(last_hidden_state.shape, (1, 16, 256)) + self.assertEqual(len(all_hidden_states), 3) # Embeddings + 2 layers + + def test_nnx_vae_decoder_forward(self): + rngs = nnx.Rngs(0) + vae = NNXFlaxAutoencoderKL( + rngs=rngs, + in_channels=3, + out_channels=3, + latent_channels=16, + block_out_channels=(64, 128), + layers_per_block=1, + ) + dummy_latents = jnp.ones((1, 16, 16, 16)) + init_rng = jax.random.PRNGKey(0) + decoder_params = vae.decoder.init(init_rng, jnp.zeros((1, 16, 16, 16)))["params"] + out = vae.decode(dummy_latents, decoder_params=decoder_params) + self.assertEqual(out.sample.shape, (1, 3, 32, 32)) + + def test_nnx_flux_transformer_forward(self): + rngs = nnx.Rngs(0) + transformer = NNXFluxTransformer2DModel( + rngs=rngs, + in_channels=16, + num_layers=1, + num_single_layers=2, + attention_head_dim=128, + num_attention_heads=4, + joint_attention_dim=128, + pooled_projection_dim=128, + guidance_embeds=True, + axes_dim=(16, 56, 56), + ) + hidden_states = jnp.ones((1, 64, 16)) + encoder_hidden_states = jnp.ones((1, 16, 128)) + pooled_projections = jnp.ones((1, 128)) + timestep = jnp.array([100.0]) + guidance = jnp.array([3.5]) + img_ids = jnp.zeros((64, 3)) + txt_ids = jnp.zeros((16, 3)) + + output = transformer( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + pooled_projections=pooled_projections, + timestep=timestep, + img_ids=img_ids, + txt_ids=txt_ids, + guidance=guidance, + ) + self.assertEqual(output.shape, (1, 64, 16)) + + +if __name__ == "__main__": + unittest.main()