162 lines
6.6 KiB
Python
162 lines
6.6 KiB
Python
from pathlib import Path
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import mlx.core as mx
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from mlx import nn
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from tqdm import tqdm
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from mflux.config.config import Config
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from mflux.config.model_config import ModelConfig
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from mflux.config.runtime_config import RuntimeConfig
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from mflux.error.exceptions import StopImageGenerationException
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from mflux.models.text_encoder.clip_encoder.clip_encoder import CLIPEncoder
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from mflux.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
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from mflux.models.transformer.transformer import Transformer
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from mflux.models.vae.vae import VAE
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from mflux.post_processing.array_util import ArrayUtil
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from mflux.post_processing.generated_image import GeneratedImage
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from mflux.post_processing.image_util import ImageUtil
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from mflux.post_processing.stepwise_handler import StepwiseHandler
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from mflux.tokenizer.clip_tokenizer import TokenizerCLIP
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from mflux.tokenizer.t5_tokenizer import TokenizerT5
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from mflux.tokenizer.tokenizer_handler import TokenizerHandler
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from mflux.weights.model_saver import ModelSaver
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from mflux.weights.weight_handler import WeightHandler
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class Flux1:
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def __init__(
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self,
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model_config: ModelConfig,
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quantize: int | None = None,
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local_path: str | None = None,
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lora_paths: list[str] | None = None,
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lora_scales: list[float] | None = None,
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):
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self.lora_paths = lora_paths
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self.lora_scales = lora_scales
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self.model_config = model_config
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# Load and initialize the tokenizers from disk, huggingface cache, or download from huggingface
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tokenizers = TokenizerHandler(model_config.model_name, self.model_config.max_sequence_length, local_path)
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self.t5_tokenizer = TokenizerT5(tokenizers.t5, max_length=self.model_config.max_sequence_length)
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self.clip_tokenizer = TokenizerCLIP(tokenizers.clip)
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# Initialize the models
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self.vae = VAE()
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self.transformer = Transformer(model_config)
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self.t5_text_encoder = T5Encoder()
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self.clip_text_encoder = CLIPEncoder()
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# Load the weights from disk, huggingface cache, or download from huggingface
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weights = WeightHandler(
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repo_id=model_config.model_name,
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local_path=local_path,
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lora_paths=lora_paths,
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lora_scales=lora_scales
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) # fmt: off
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# Set the loaded weights if they are not quantized
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if weights.quantization_level is None:
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self._set_model_weights(weights)
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# Optionally quantize the model here at initialization (also required if about to load quantized weights)
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self.bits = None
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if quantize is not None or weights.quantization_level is not None:
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self.bits = weights.quantization_level if weights.quantization_level is not None else quantize
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# fmt: off
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nn.quantize(self.vae, class_predicate=lambda _, m: isinstance(m, nn.Linear), group_size=64, bits=self.bits)
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nn.quantize(self.transformer, class_predicate=lambda _, m: isinstance(m, nn.Linear) and len(m.weight[1]) > 64, group_size=64, bits=self.bits)
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nn.quantize(self.t5_text_encoder, class_predicate=lambda _, m: isinstance(m, nn.Linear), group_size=64, bits=self.bits)
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nn.quantize(self.clip_text_encoder, class_predicate=lambda _, m: isinstance(m, nn.Linear), group_size=64, bits=self.bits)
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# fmt: on
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# If loading previously saved quantized weights, the weights must be set after modules have been quantized
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if weights.quantization_level is not None:
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self._set_model_weights(weights)
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def generate_image(
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self,
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seed: int,
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prompt: str,
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config: Config = Config(),
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stepwise_output_dir: Path = None,
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) -> GeneratedImage:
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# Create a new runtime config based on the model type and input parameters
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config = RuntimeConfig(config, self.model_config)
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time_steps = tqdm(range(config.num_inference_steps))
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stepwise_handler = StepwiseHandler(
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flux=self,
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config=config,
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seed=seed,
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prompt=prompt,
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time_steps=time_steps,
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output_dir=stepwise_output_dir,
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)
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# 1. Create the initial latents
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latents = mx.random.normal(
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shape=[1, (config.height // 16) * (config.width // 16), 64],
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key=mx.random.key(seed)
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) # fmt: off
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# 2. Embed the prompt
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t5_tokens = self.t5_tokenizer.tokenize(prompt)
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clip_tokens = self.clip_tokenizer.tokenize(prompt)
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prompt_embeds = self.t5_text_encoder.forward(t5_tokens)
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pooled_prompt_embeds = self.clip_text_encoder.forward(clip_tokens)
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for t in time_steps:
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try:
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# 3.t Predict the noise
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noise = self.transformer.predict(
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t=t,
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prompt_embeds=prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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hidden_states=latents,
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config=config,
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)
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# 4.t Take one denoise step
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dt = config.sigmas[t + 1] - config.sigmas[t]
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latents += noise * dt
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# Handle stepwise output if enabled
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stepwise_handler.process_step(t, latents)
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# Evaluate to enable progress tracking
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mx.eval(latents)
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except KeyboardInterrupt: # noqa: PERF203
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stepwise_handler.handle_interruption()
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raise StopImageGenerationException(f"Stopping image generation at step {t + 1}/{len(time_steps)}")
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# 5. Decode the latent array and return the image
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latents = ArrayUtil.unpack_latents(latents, config.height, config.width)
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decoded = self.vae.decode(latents)
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return ImageUtil.to_image(
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decoded_latents=decoded,
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seed=seed,
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prompt=prompt,
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quantization=self.bits,
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generation_time=time_steps.format_dict["elapsed"],
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lora_paths=self.lora_paths,
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lora_scales=self.lora_scales,
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config=config,
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)
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@staticmethod
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def from_alias(alias: str, quantize: int | None = None) -> "Flux1":
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return Flux1(
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model_config=ModelConfig.from_alias(alias),
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quantize=quantize,
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)
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def _set_model_weights(self, weights):
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self.vae.update(weights.vae)
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self.transformer.update(weights.transformer)
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self.t5_text_encoder.update(weights.t5_encoder)
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self.clip_text_encoder.update(weights.clip_encoder)
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def save_model(self, base_path: str) -> None:
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ModelSaver.save_model(self, self.bits, base_path)
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