SD - Implement seed arrays for batch runs (#1690)

* SD Scripts and UI tabs that support batch_count can now take a
string containing a JSON array, or a list of integers, as their seed
input.
* Each batch in a run will now take the seed specified at the
corresponding array index if one exists. If there is no seed at
that index, the seed value will be treated as -1 and a random
seed will be assigned at that position. If an integer rather than
a list or json array has been, everything works as before.
* UI seed input controls are now Textboxes with info lines about
the seed formats allowed.
* UI error handling updated to be more helpful if the seed input is
invalid.

apps/stable_diffusion/src/utils/__init__.py

@@ -28,6 +28,7 @@
     fetch_and_update_base_model_id,
     get_path_to_diffusers_checkpoint,
     sanitize_seed,
+    parse_seed_input,
     batch_seeds,
     get_path_stem,
     get_extended_name,

apps/stable_diffusion/src/utils/stable_args.py

@@ -66,9 +66,9 @@ def is_valid_file(arg):
 
 p.add_argument(
     "--seed",
-    type=int,
+    type=str,
     default=-1,
-    help="The seed to use. -1 for a random one.",
+    help="The seed or list of seeds to use. -1 for a random one.",
 )
 
 p.add_argument(

apps/stable_diffusion/src/utils/utils.py

@@ -727,28 +727,57 @@ def fetch_and_update_base_model_id(model_to_run, base_model=""):
 
 # Generate and return a new seed if the provided one is not in the
 # supported range (including -1)
-def sanitize_seed(seed):
+def sanitize_seed(seed: int | str):
+    seed = int(seed)
     uint32_info = np.iinfo(np.uint32)
     uint32_min, uint32_max = uint32_info.min, uint32_info.max
     if seed < uint32_min or seed >= uint32_max:
         seed = randint(uint32_min, uint32_max)
     return seed
 
 
-# Generate a set of seeds, using as the first seed of the set,
-# optionally using it as the rng seed for subsequent seeds in the set
-def batch_seeds(seed, batch_count, repeatable=False):
-    # use the passed seed as the initial seed of the batch
-    seeds = [sanitize_seed(seed)]
+# take a seed expression in an input format and convert it to
+# a list of integers, where possible
+def parse_seed_input(seed_input: str | list | int):
+    if isinstance(seed_input, str):
+        try:
+            seed_input = json.loads(seed_input)
+        except (ValueError, TypeError):
+            seed_input = None
+
+    if isinstance(seed_input, int):
+        return [seed_input]
+
+    if isinstance(seed_input, list) and all(
+        type(seed) is int for seed in seed_input
+    ):
+        return seed_input
+
+    raise TypeError(
+        "Seed input must be an integer or an array of integers in JSON format"
+    )
+
+
+# Generate a set of seeds from an input expression for batch_count batches,
+# optionally using that input as the rng seed for any randomly generated seeds.
+def batch_seeds(
+    seed_input: str | list | int, batch_count: int, repeatable=False
+):
+    # turn the input into a list if possible
+    seeds = parse_seed_input(seed_input)
+
+    # slice or pad the list to be of batch_count length
+    seeds = seeds[:batch_count] + [-1] * (batch_count - len(seeds))
 
     if repeatable:
-        # use the initial seed as the rng generator seed
+        # set seed for the rng based on what we have so far
         saved_random_state = random_getstate()
-        seed_random(seed)
+        if all(seed < 0 for seed in seeds):
+            seeds[0] = sanitize_seed(seeds[0])
+        seed_random(str(seeds))
 
-    # generate the additional seeds
-    for i in range(1, batch_count):
-        seeds.append(sanitize_seed(-1))
+    # generate any seeds that are unspecified
+    seeds = [sanitize_seed(seed) for seed in seeds]
 
     if repeatable:
         # reset the rng back to normal

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -50,7 +50,7 @@ def img2img_inf(
     steps: int,
     strength: float,
     guidance_scale: float,
-    seed: int,
+    seed: str | int,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -230,10 +230,12 @@ def img2img_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     extra_info = {"STRENGTH": strength}
     text_output = ""
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         out_imgs = global_obj.get_sd_obj().generate_images(
@@ -617,8 +619,10 @@ def create_canvas(w, h):
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/inpaint_ui.py

@@ -49,7 +49,7 @@ def inpaint_inf(
     inpaint_full_res_padding: int,
     steps: int,
     guidance_scale: float,
-    seed: int,
+    seed: str | int,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -181,10 +181,13 @@ def inpaint_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     image = image_dict["image"]
     mask_image = image_dict["mask"]
     text_output = ""
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         out_imgs = global_obj.get_sd_obj().generate_images(
@@ -514,8 +517,10 @@ def inpaint_api(
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/lora_train_ui.py

@@ -3,7 +3,7 @@
 import gradio as gr
 from PIL import Image
 from apps.stable_diffusion.scripts import lora_train
-from apps.stable_diffusion.src import prompt_examples, args
+from apps.stable_diffusion.src import prompt_examples, args, utils
 from apps.stable_diffusion.web.ui.utils import (
     available_devices,
     nodlogo_loc,
@@ -168,7 +168,9 @@
                         stop_batch = gr.Button("Stop Batch")
                 with gr.Row():
                     seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                        value=utils.parse_seed_input(args.seed)[0],
+                        precision=0,
+                        label="Seed",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/outpaint_ui.py

@@ -49,7 +49,7 @@ def outpaint_inf(
     width: int,
     steps: int,
     guidance_scale: float,
-    seed: int,
+    seed: str,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -178,7 +178,10 @@ def outpaint_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     left = True if "left" in directions else False
     right = True if "right" in directions else False
@@ -542,8 +545,10 @@ def outpaint_api(
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -46,7 +46,7 @@ def txt2img_inf(
     width: int,
     steps: int,
     guidance_scale: float,
-    seed: int,
+    seed: str | int,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -178,8 +178,11 @@ def txt2img_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     text_output = ""
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         out_imgs = global_obj.get_sd_obj().generate_images(
@@ -481,8 +484,10 @@ def txt2img_api(
                             label="Repeatable Seeds",
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/upscaler_ui.py

@@ -42,7 +42,7 @@ def upscaler_inf(
     steps: int,
     noise_level: int,
     guidance_scale: float,
-    seed: int,
+    seed: str,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -177,8 +177,11 @@ def upscaler_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     extra_info = {"NOISE LEVEL": noise_level}
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         low_res_img = image
@@ -534,8 +537,10 @@ def upscaler_api(
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",