WIP include flux1 dev

.gitignore

@@ -10,3 +10,4 @@
 .venv
 *.png
 *.jpg
+*.pyc

main.py

@@ -4,9 +4,9 @@ import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'src')))
 
 from flux_1_schnell.config.config import Config
-from flux_1_schnell.flux import Flux1Schnell
+from flux_1_schnell.flux import Flux1
 
-flux = Flux1Schnell("black-forest-labs/FLUX.1-schnell")
+flux = Flux1("black-forest-labs/FLUX.1-schnell")
 
 image = flux.generate_image(
     seed=3,

src/flux_1_schnell/flux.py

@@ -10,14 +10,13 @@ from flux_1_schnell.models.text_encoder.t5_encoder.t5_encoder import T5Encoder
 from flux_1_schnell.models.transformer.transformer import Transformer
 from flux_1_schnell.models.vae.vae import VAE
 from flux_1_schnell.post_processing.image_util import ImageUtil
-from flux_1_schnell.scheduler.scheduler import FlowMatchEulerDiscreteNoiseScheduler
 from flux_1_schnell.tokenizer.clip_tokenizer import TokenizerCLIP
 from flux_1_schnell.tokenizer.t5_tokenizer import TokenizerT5
 from flux_1_schnell.tokenizer.tokenizer_handler import TokenizerHandler
 from flux_1_schnell.weights.weight_handler import WeightHandler
 
 
-class Flux1Schnell:
+class Flux1:
 
     def __init__(self, repo_id: str):
         tokenizers = TokenizerHandler.load_from_disk_or_huggingface(repo_id)
@@ -47,16 +46,12 @@ class Flux1Schnell:
                 config=config
             )
 
-            latents = FlowMatchEulerDiscreteNoiseScheduler.denoise(
-                t=t,
-                noise=noise,
-                latent=latents,
-                config=config
-            )
+            dt = config.sigmas[t + 1] - config.sigmas[t]
+            latents += noise * dt
 
             mx.eval(latents)
 
-        latents = Flux1Schnell._unpack_latents(latents, config.width, config.height)
+        latents = Flux1._unpack_latents(latents, config.width, config.height)
         decoded = self.vae.decode(latents)
         return ImageUtil.to_image(decoded)
 

src/flux_1_schnell/scheduler/scheduler.py

@@ -1,20 +0,0 @@
-import mlx.core as mx
-
-from flux_1_schnell.config.config import Config
-
-
-class FlowMatchEulerDiscreteNoiseScheduler:
-
-    @staticmethod
-    def denoise(
-            t: int,
-            noise: mx.array,
-            latent: mx.array,
-            config: Config,
-    ) -> mx.array:
-        sigma = config.sigmas[t]
-        denoised = latent - noise * sigma
-        derivative = (latent - denoised) / sigma
-        dt = config.sigmas[t + 1] - sigma
-        prev_sample = latent + derivative * dt
-        return prev_sample