Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 17:24:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

explicit seedvr implementation

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-07-18 10:35:07 +02:00
parent 5c47e557c4
commit 42cb4cf489
16 changed files with 154 additions and 972 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/postprocess/seedvr_model.py
+18 -6
View File
@@ -28,6 +28,8 @@ class UpscalerSeedVR(Upscaler):
]
self.model = None
self.model_loaded = None
self.tile_size = 1024
self.tile_overlap = 0.25
self.device = devices.device
def load_model(self, path: str):
@@ -60,8 +62,9 @@ class UpscalerSeedVR(Upscaler):
}
t1 = time.time()
self.model.dit.config = self.model.config.dit
self.model.vae.tile_sample_min_size = 1024
self.model.vae.tile_latent_min_size = 128
self.model.vae.tile_sample_min_size = self.tile_size
self.model.vae.tile_latent_min_size = self.tile_size // 8
self.model.vae.tile_overlap_factor = self.tile_overlap
self.model = do_post_load_quant(self.model, allow=True)
@@ -136,26 +139,34 @@ class UpscalerSeedVR(Upscaler):
devices.torch_gc()
return result
def do_upscale(self, img: Image.Image, selected_file):
def do_upscale(self, img: Image.Image, selected_file, cfg_scale: float = 3.5, cfg_rescale: float = 0.0, steps: int = 1, seed: int = -1, scale: float | None = None, tile_size: int = 1024, tile_overlap: float = 0.25):
self.load_model(selected_file)
if self.model is None:
return img
from modules.seedvr.src.core import generation
self.scale = self.scale if scale is None else scale
self.tile_size = tile_size if tile_size is not None else self.tile_size
self.tile_overlap = tile_overlap if tile_overlap is not None else self.tile_overlap
self.model.vae.tile_sample_min_size = self.tile_size
self.model.vae.tile_latent_min_size = self.tile_size // 8
self.model.vae.tile_overlap_factor = self.tile_overlap
width = int(self.scale * img.width) // 8 * 8
image_tensor = np.array(img)
image_tensor = torch.from_numpy(image_tensor).to(device=devices.device, dtype=devices.dtype).unsqueeze(0) / 255.0
random.seed()
seed = int(random.randrange(4294967294))
seed = int(random.randrange(4294967294)) if seed == -1 else int(seed)
t0 = time.time()
with devices.inference_context():
result_tensor = generation.generation_loop(
runner=self.model,
images=image_tensor,
cfg_scale=opts.seedvr_cfg_scale,
cfg_scale=cfg_scale,
cfg_rescale=cfg_rescale,
steps=steps,
seed=seed,
res_w=width,
batch_size=1,
@@ -163,7 +174,8 @@ class UpscalerSeedVR(Upscaler):
device=devices.device,
)
t1 = time.time()
log.info(f'Upscaler: type="{self.name}" model="{selected_file}" scale={self.scale} cfg={opts.seedvr_cfg_scale} seed={seed} time={t1 - t0:.2f}')
tiles = getattr(self.model.vae, "tiles", None)
log.info(f'Upscaler: type="{self.name}" model="{selected_file}" scale={self.scale} cfg={cfg_scale} seed={seed} tiles={tiles} time={t1 - t0:.2f}')
img = convert.to_pil(result_tensor.squeeze())
if opts.upscaler_unload: