fix(api): allow random seed in reseed regions

api/onnx_web/chain/source_txt2img.py

@@ -7,6 +7,7 @@
 
 from ..diffusers.load import load_pipeline
 from ..diffusers.utils import (
+    LATENT_FACTOR,
     encode_prompt,
     get_latents_from_seed,
     get_tile_latents,
@@ -78,8 +79,12 @@ def run(
             latents = get_tile_latents(latents, int(params.seed), latent_size, dims)
 
         # reseed latents as needed
+        reseed_rng = np.random.default_rng(params.seed)
         prompt, reseed = parse_reseed(prompt)
         for top, left, bottom, right, region_seed in reseed:
+            if region_seed == -1:
+                region_seed = reseed_rng.integers(2**32)
+
             logger.debug(
                 "reseed latent region: [:, :, %s:%s, %s:%s] with %s",
                 top,
@@ -89,8 +94,13 @@ def run(
                 region_seed,
             )
             latents[
-                :, :, top // 8 : bottom // 8, left // 8 : right // 8
-            ] = get_latents_from_seed(region_seed, Size(right - left, bottom - top), params.batch)
+                :,
+                :,
+                top // LATENT_FACTOR : bottom // LATENT_FACTOR,
+                left // LATENT_FACTOR : right // LATENT_FACTOR,
+            ] = get_latents_from_seed(
+                region_seed, Size(right - left, bottom - top), params.batch
+            )
 
         pipe_type = params.get_valid_pipeline("txt2img")
         pipe = load_pipeline(

api/onnx_web/diffusers/load.py

@@ -19,7 +19,7 @@
 from ..convert.diffusion.lora import blend_loras, buffer_external_data_tensors
 from ..convert.diffusion.textual_inversion import blend_textual_inversions
 from ..diffusers.pipelines.upscale import OnnxStableDiffusionUpscalePipeline
-from ..diffusers.utils import expand_prompt
+from ..diffusers.utils import LATENT_FACTOR, expand_prompt
 from ..params import DeviceParams, ImageParams
 from ..server import ModelTypes, ServerContext
 from ..torch_before_ort import InferenceSession
@@ -264,19 +264,21 @@ def load_pipeline(
         if hasattr(pipe, vae):
             vae_model = getattr(pipe, vae)
             vae_model.set_tiled(tiled=params.tiled_vae)
-            vae_model.set_window_size(params.vae_tile // 8, params.vae_overlap)
+            vae_model.set_window_size(
+                params.vae_tile // LATENT_FACTOR, params.vae_overlap
+            )
 
     # update panorama params
     if params.is_panorama():
-        unet_stride = (params.unet_tile * (1 - params.unet_overlap)) // 8
+        unet_stride = (params.unet_tile * (1 - params.unet_overlap)) // LATENT_FACTOR
         logger.debug(
             "setting panorama window parameters: %s/%s for UNet, %s/%s for VAE",
             params.unet_tile,
             unet_stride,
             params.vae_tile,
             params.vae_overlap,
         )
-        pipe.set_window_size(params.unet_tile // 8, unet_stride)
+        pipe.set_window_size(params.unet_tile // LATENT_FACTOR, unet_stride)
 
     run_gc([device])
 

api/onnx_web/diffusers/pipelines/panorama.py

@@ -28,7 +28,7 @@
 
 from onnx_web.chain.tile import make_tile_mask
 
-from ..utils import parse_regions
+from ..utils import LATENT_CHANNELS, LATENT_FACTOR, parse_regions
 
 logger = logging.get_logger(__name__)
 
@@ -512,7 +512,12 @@ def text2img(
 
         # get the initial random noise unless the user supplied it
         latents_dtype = prompt_embeds.dtype
-        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        latents_shape = (
+            batch_size * num_images_per_prompt,
+            LATENT_CHANNELS,
+            height // LATENT_FACTOR,
+            width // LATENT_FACTOR,
+        )
         if latents is None:
             latents = generator.randn(*latents_shape).astype(latents_dtype)
         elif latents.shape != latents_shape:
@@ -612,10 +617,10 @@ def text2img(
                 )
 
                 # convert coordinates to latent space
-                h_start = top // 8
-                h_end = bottom // 8
-                w_start = left // 8
-                w_end = right // 8
+                h_start = top // LATENT_FACTOR
+                h_end = bottom // LATENT_FACTOR
+                w_start = left // LATENT_FACTOR
+                w_end = right // LATENT_FACTOR
 
                 # get the latents corresponding to the current view coordinates
                 latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]
@@ -1170,8 +1175,8 @@ def inpaint(
         latents_shape = (
             batch_size * num_images_per_prompt,
             num_channels_latents,
-            height // 8,
-            width // 8,
+            height // LATENT_FACTOR,
+            width // LATENT_FACTOR,
         )
         latents_dtype = prompt_embeds.dtype
         if latents is None:

api/onnx_web/diffusers/pipelines/panorama_xl.py

@@ -14,7 +14,7 @@
 
 from onnx_web.chain.tile import make_tile_mask
 
-from ..utils import parse_regions
+from ..utils import LATENT_FACTOR, parse_regions
 
 logger = logging.getLogger(__name__)
 
@@ -457,10 +457,10 @@ def text2img(
                 )
 
                 # convert coordinates to latent space
-                h_start = top // 8
-                h_end = bottom // 8
-                w_start = left // 8
-                w_end = right // 8
+                h_start = top // LATENT_FACTOR
+                h_end = bottom // LATENT_FACTOR
+                w_start = left // LATENT_FACTOR
+                w_end = right // LATENT_FACTOR
 
                 # get the latents corresponding to the current view coordinates
                 latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]

api/onnx_web/diffusers/utils.py

@@ -24,7 +24,7 @@
 REGION_TOKEN = compile(
     r"\<region:(\d+):(\d+):(\d+):(\d+):(-?[\.|\d]+):(-?[\.|\d]+):([^\>]+)\>"
 )
-RESEED_TOKEN = compile(r"\<reseed:(\d+):(\d+):(\d+):(\d+):(\d+)\>")
+RESEED_TOKEN = compile(r"\<reseed:(\d+):(\d+):(\d+):(\d+):(-?\d+)\>")
 
 INTERVAL_RANGE = compile(r"(\w+)-{(\d+),(\d+)(?:,(\d+))?}")
 ALTERNATIVE_RANGE = compile(r"\(([^\)]+)\)")