Update src/pipeline.py

src/pipeline.py

@@ -1,56 +1,48 @@
 #6
+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 typing import TypeAlias
-
-import torch
+from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
 from PIL.Image import Image
-from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, AutoencoderTiny
-from huggingface_hub.constants import HF_HUB_CACHE
 from pipelines.models import TextToImageRequest
 from torch import Generator
-from transformers import T5EncoderModel, CLIPTextModel
-
-Pipeline: TypeAlias = FluxPipeline
+from diffusers import FluxTransformer2DModel, DiffusionPipeline
+from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
 
-CHECKPOINT = "black-forest-labs/FLUX.1-schnell"
-REVISION = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
+os.environ["TOKENIZERS_PARALLELISM"] = "True"
+torch._dynamo.config.suppress_errors = True
 
+Pipeline = None
+ids = "black-forest-labs/FLUX.1-schnell"
+Revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
 
 def load_pipeline() -> Pipeline:
-    text_encoder = CLIPTextModel.from_pretrained(CHECKPOINT, revision=REVISION, subfolder="text_encoder", local_files_only=True, torch_dtype=torch.bfloat16,)
-
-    path2 = os.path.join(HF_HUB_CACHE, "models--city96--t5-v1_1-xxl-encoder-bf16/snapshots/1b9c856aadb864af93c1dcdc226c2774fa67bc86")
-
-    text_encoder_2 = T5EncoderModel.from_pretrained(path2, torch_dtype=torch.bfloat16,)
-
-    pathV = os.path.join(HF_HUB_CACHE, "models--madebyollin--taef1/snapshots/5463ee684fd9131a724bea777a2f50d89b0b6b24")
-
-    vae = AutoencoderTiny.from_pretrained(pathV, torch_dtype=torch.bfloat16,)
-
-    pathT = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a")
-
-    transformer = FluxTransformer2DModel.from_pretrained(pathT, torch_dtype=torch.bfloat16, use_safetensors=False,)
-
-    pipeline = FluxPipeline.from_pretrained(CHECKPOINT, revision=REVISION, local_files_only=True, text_encoder=text_encoder, text_encoder_2=text_encoder_2, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16,).to("cuda")
-
-    pipeline("")
-
+    vae = AutoencoderKL.from_pretrained(ids,revision=Revision, subfolder="vae", local_files_only=True, torch_dtype=torch.bfloat16,)
+    quantize_(vae, int8_weight_only())
+    text_encoder_2 = T5EncoderModel.from_pretrained("city96/t5-v1_1-xxl-encoder-bf16", revision = "1b9c856aadb864af93c1dcdc226c2774fa67bc86", torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last)
+    path = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a")
+    transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False).to(memory_format=torch.channels_last)
+    pipeline = DiffusionPipeline.from_pretrained(ids, revision=Revision, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,)
+    pipeline.to("cuda")
+
+    for _ in range(3):
+        pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
     return pipeline
 
+@torch.no_grad()
 def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
-    gc.collect()
-    torch.cuda.empty_cache()
-    torch.cuda.reset_peak_memory_stats()
-
     generator = Generator(pipeline.device).manual_seed(request.seed)
 
     return pipeline(
         request.prompt,
         generator=generator,
-        guidance_scale=0.5,
+        guidance_scale=0.0,
         num_inference_steps=4,
         max_sequence_length=256,
         height=request.height,
         width=request.width,
-    ).images[0]
+    ).images[0]