Update src/pipeline.py

src/pipeline.py

@@ -1,53 +1,43 @@
-from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
+from diffusers import AutoencoderKL
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-
-from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
 import torch
 import torch._dynamo
 import gc
-from PIL import Image as img
 from PIL.Image import Image
 from pipelines.models import TextToImageRequest
 from torch import Generator
-import time
-from diffusers import FluxTransformer2DModel, DiffusionPipeline
+from diffusers import DiffusionPipeline
 from torchao.quantization import quantize_, int8_weight_only
-Pipeline = None
 
-ckpt_id = "black-forest-labs/FLUX.1-schnell"
-def empty_cache():
-    start = time.time()
+Pipeline = None
+MODEL_ID = "black-forest-labs/FLUX.1-schnell"
+def clear():
     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()
-    print(f"Flush took: {time.time() - start}")
 
 def load_pipeline() -> Pipeline:    
-    empty_cache()
+    clear()
     dtype, device = torch.bfloat16, "cuda"
     vae = AutoencoderKL.from_pretrained(
-        ckpt_id, subfolder="vae", torch_dtype=torch.bfloat16
+        MODEL_ID, subfolder="vae", torch_dtype=torch.bfloat16
     )
-    quantize_(vae, int8_weight_only())
+    quantize_(vae, int8_weight_only(), device="cuda")
     pipeline = DiffusionPipeline.from_pretrained(
-        ckpt_id,
+        MODEL_ID,
         vae=vae,
         torch_dtype=dtype,
         )
     pipeline.enable_sequential_cpu_offload()
     for _ in range(2):
-        empty_cache()
-        pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
-    
+        pipeline(prompt="unpervaded, unencumber, froggish, groundneedle, transnatural, fatherhood, outjump, cinerator", width=1024, height=1024, guidance_scale=0.1, num_inference_steps=4, max_sequence_length=256)
+    clear()
     return pipeline
 
-
-from datetime import datetime
 @torch.inference_mode()
 def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
-    empty_cache()
+    clear()
     generator = Generator("cuda").manual_seed(request.seed)
     image=pipeline(request.prompt,generator=generator, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, height=request.height, width=request.width, output_type="pil").images[0]
     return image