fix(api): switch RNG based on LPW parameter

api/onnx_web/chain/blend_img2img.py

@@ -1,6 +1,7 @@
 from logging import getLogger
 
 import torch
+import numpy as np
 from diffusers import OnnxStableDiffusionImg2ImgPipeline
 from PIL import Image
 
@@ -35,8 +36,9 @@ def blend_img2img(
     )
     if params.lpw:
         pipe = pipe.img2img
-
+        rng = torch.manual_seed(params.seed)
-    rng = torch.manual_seed(params.seed)
+    else:
+        rng = np.random.RandomState(params.seed)
 
     result = pipe(
         prompt,

api/onnx_web/chain/blend_inpaint.py

@@ -2,6 +2,7 @@ from logging import getLogger
 from typing import Callable, Tuple
 
 import torch
+import numpy as np
 from diffusers import OnnxStableDiffusionInpaintPipeline
 from PIL import Image
 
@@ -68,9 +69,11 @@ def blend_inpaint(
         )
         if params.lpw:
             pipe = pipe.inpaint
+            rng = torch.manual_seed(params.seed)
+        else:
+            rng = np.random.RandomState(params.seed)
 
         latents = get_latents_from_seed(params.seed, size)
-        rng = torch.manual_seed(params.seed)
 
         result = pipe(
             params.prompt,

api/onnx_web/chain/source_txt2img.py

@@ -1,6 +1,7 @@
 from logging import getLogger
 
 import torch
+import numpy as np
 from diffusers import OnnxStableDiffusionPipeline
 from PIL import Image
 
@@ -36,9 +37,11 @@ def source_txt2img(
     )
     if params.lpw:
         pipe = pipe.text2img
+        rng = torch.manual_seed(params.seed)
+    else:
+        rng = np.random.RandomState(params.seed)
 
     latents = get_latents_from_seed(params.seed, size)
-    rng = torch.manual_seed(params.seed)
 
     result = pipe(
         prompt,

api/onnx_web/chain/upscale_outpaint.py

@@ -2,6 +2,7 @@ from logging import getLogger
 from typing import Callable, Tuple
 
 import torch
+import numpy as np
 from diffusers import OnnxStableDiffusionInpaintPipeline
 from PIL import Image, ImageDraw
 
@@ -73,9 +74,11 @@ def upscale_outpaint(
         )
         if params.lpw:
             pipe = pipe.inpaint
+            rng = torch.manual_seed(params.seed)
+        else:
+            rng = np.random.RandomState(params.seed)
 
         latents = get_tile_latents(full_latents, dims)
-        rng = torch.manual_seed(params.seed)
 
         result = pipe(
             image,

api/onnx_web/diffusion/run.py

@@ -2,6 +2,7 @@ from logging import getLogger
 from typing import Any
 
 import torch
+import numpy as np
 from diffusers import OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionPipeline
 from PIL import Image, ImageChops
 
@@ -29,9 +30,11 @@ def run_txt2img_pipeline(
     )
     if params.lpw:
         pipe = pipe.text2img
+        rng = torch.manual_seed(params.seed)
+    else:
+        rng = np.random.RandomState(params.seed)
 
     latents = get_latents_from_seed(params.seed, size)
-    rng = torch.manual_seed(params.seed)
 
     progress = job.get_progress_callback()
     result = pipe(
@@ -78,8 +81,10 @@ def run_img2img_pipeline(
     )
     if params.lpw:
         pipe = pipe.img2img
+        rng = torch.manual_seed(params.seed)
+    else:
+        rng = np.random.RandomState(params.seed)
 
-    rng = torch.manual_seed(params.seed)
 
     progress = job.get_progress_callback()
     result = pipe(