[WEB] Update seed calculation and model versions.

Signed-Off-by: Gaurav Shukla <[email protected]>

Author: Shukla-Gaurav

web/index.py

@@ -7,6 +7,9 @@
 from PIL import Image
 import json
 import os
+from random import randint
+from numpy import iinfo
+import numpy as np
 
 """
 def debug_event(debug):
@@ -147,7 +150,12 @@ def debug_event(debug):
                         value="fp16",
                         choices=["fp16", "fp32"],
                     )
-                    seed = gr.Textbox(value="42", max_lines=1, label="Seed")
+                    uint32_info = iinfo(np.uint32)
+                    rand_seed = randint(uint32_info.min, uint32_info.max)
+                    seed = gr.Number(
+                        value=rand_seed,
+                        label="Seed",
+                    )
                 with gr.Row():
                     cache = gr.Checkbox(label="Cache", value=True)
                     debug = gr.Checkbox(

web/models/stable_diffusion/main.py

@@ -7,6 +7,8 @@
 from diffusers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from tqdm.auto import tqdm
 import numpy as np
+from numpy import iinfo
+from random import randint
 from models.stable_diffusion.opt_params import get_unet, get_vae, get_clip
 from models.stable_diffusion.arguments import args, schedulers, cache_obj
 
@@ -23,7 +25,7 @@ def stable_diff_inf(
     guidance: float,
     height: int,
     width: int,
-    seed: str,
+    seed: int,
     precision: str,
     device: str,
     cache: bool,
@@ -35,12 +37,9 @@ def stable_diff_inf(
 
     start = time.time()
     # set seed value
-    try:
-        seed = int(seed)
-        if seed < 0 or seed > 10000:
-            seed = int(torch.randint(low=25, high=100, size=()))
-    except (ValueError, OverflowError) as error:
-        seed = hash(seed)
+    uint32_info = iinfo(np.uint32)
+    if seed < uint32_info.min and seed >= uint32_info.max:
+        seed = randint(uint32_info.min, uint32_info.max)
 
     args.set_params(
         prompt,

web/models/stable_diffusion/opt_params.py

@@ -31,7 +31,7 @@ def get_unet(args):
             return get_shark_model(args, bucket, model_name, iree_flags)
         else:
             bucket = "gs://shark_tank/prashant_nod"
-            model_name = "unet_18nov_fp16"
+            model_name = "unet_22nov_fp16"
             iree_flags += [
                 "--iree-flow-enable-padding-linalg-ops",
                 "--iree-flow-linalg-ops-padding-size=32",
@@ -44,7 +44,7 @@ def get_unet(args):
     # Tuned model is not present for `fp32` case.
     if args.precision == "fp32":
         bucket = "gs://shark_tank/prashant_nod"
-        model_name = "unet_18nov_fp32"
+        model_name = "unet_22nov_fp32"
         iree_flags += [
             "--iree-flow-enable-conv-nchw-to-nhwc-transform",
             "--iree-flow-enable-padding-linalg-ops",
@@ -77,7 +77,7 @@ def get_vae(args):
         )
     if args.precision in ["fp16", "int8"]:
         bucket = "gs://shark_tank/prashant_nod"
-        model_name = "vae_18nov_fp16"
+        model_name = "vae_22nov_fp16"
         iree_flags += [
             "--iree-flow-enable-conv-nchw-to-nhwc-transform",
             "--iree-flow-enable-padding-linalg-ops",
@@ -89,7 +89,7 @@ def get_vae(args):
 
     if args.precision == "fp32":
         bucket = "gs://shark_tank/prashant_nod"
-        model_name = "vae_18nov_fp32"
+        model_name = "vae_22nov_fp32"
         iree_flags += [
             "--iree-flow-enable-conv-nchw-to-nhwc-transform",
             "--iree-flow-enable-padding-linalg-ops",