Update src/pipeline.py

src/pipeline.py

@@ -1,51 +1,38 @@
-#5.1
+#5.2
 from huggingface_hub.constants import HF_HUB_CACHE
 from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
 import torch
 import torch._dynamo
 import gc
 import os
-from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
+from diffusers import FluxPipeline, AutoencoderKL
 from PIL.Image import Image
 from pipelines.models import TextToImageRequest
 from torch import Generator
 from diffusers import FluxTransformer2DModel, DiffusionPipeline
-from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
-
+from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
 os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
 os.environ["TOKENIZERS_PARALLELISM"] = "True"
 torch._dynamo.config.suppress_errors = True
-
 Pipeline = None
 ids = "slobers/Flux.1.Schnella"
 Revision = "e34d670e44cecbbc90e4962e7aada2ac5ce8b55b"
-
-def empty_cache():
-    gc.collect()
-    torch.cuda.empty_cache()
-    torch.cuda.reset_max_memory_allocated()
-    torch.cuda.reset_peak_memory_stats()
-
 def load_pipeline() -> Pipeline:
-    empty_cache()
     path = os.path.join(HF_HUB_CACHE, "models--slobers--Flux.1.Schnella/snapshots/e34d670e44cecbbc90e4962e7aada2ac5ce8b55b/transformer")
     transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False)
-    pipeline = FluxPipeline.from_pretrained(ids, revision=Revision, transformer=transformer, local_files_only=True, torch_dtype=torch.bfloat16,)
+    vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-schnell", revision="741f7c3ce8b383c54771c7003378a50191e9efe9", subfolder="vae", torch_dtype=torch.bfloat16)
+    pipeline = FluxPipeline.from_pretrained(ids, revision=Revision, transformer=transformer, vae=vae, local_files_only=True, torch_dtype=torch.bfloat16)
     pipeline.to("cuda")
-    pipeline.vae = torch.compile(pipeline.vae, mode="max-autotune", fullgraph=True, dynamic=True)
-    for _ in range(3):
-        pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", width=1024, height=1024, guidance_scale=5.0, num_inference_steps=4, max_sequence_length=256)
+    pipeline = apply_cache_on_pipe(pipeline, residual_diff_threshold=0.888)
+    pipeline("")
     return pipeline
-    empty_cache()
-
 @torch.no_grad()
 def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
     generator = Generator(pipeline.device).manual_seed(request.seed)
-
     return pipeline(
         request.prompt,
         generator=generator,
-        guidance_scale=6.5,
+        guidance_scale=0.0,
         num_inference_steps=4,
         max_sequence_length=256,
         height=request.height,