use enhancer generator

https://huggingface.co/spaces/finegrain/finegrain-image-enhancer/discussions/6

requirements.lock

@@ -117,7 +117,7 @@ nvidia-nvjitlink-cu12==12.6.85
     # via torch
 nvidia-nvtx-cu12==12.6.77
     # via torch
-orjson==3.10.16
+orjson==3.10.17
     # via gradio
 packaging==25.0
     # via gradio

src/app.py

@@ -6,7 +6,6 @@ import spaces
 import torch
 from huggingface_hub import hf_hub_download
 from PIL import Image
-from refiners.fluxion.utils import manual_seed
 from refiners.foundationals.latent_diffusion import Solver, solvers
 
 from enhancer import ESRGANUpscaler, ESRGANUpscalerCheckpoints
@@ -114,10 +113,11 @@ def process(
     num_inference_steps: int = 18,
     solver: str = "DDIM",
 ) -> tuple[Image.Image, Image.Image]:
-    manual_seed(seed)
-
     solver_type: type[Solver] = getattr(solvers, solver)
 
+    generator = torch.Generator(device=DEVICE)
+    generator.manual_seed(seed)
+
     enhanced_image = enhancer.upscale(
         image=input_image,
         prompt=prompt,
@@ -131,6 +131,7 @@ def process(
         num_inference_steps=num_inference_steps,
         loras_scale={"more_details": 0.5, "sdxl_render": 1.0},
         solver_type=solver_type,
+        generator=generator,
     )
 
     return (input_image, enhanced_image)