lint fixes

.pylintrc

@@ -23,6 +23,7 @@ ignore-paths=/usr/lib/.*$,
              modules/todo,
              modules/unipc,
              modules/xadapter,
+             modules/dcsolver,
              repositories,
              modules/prompt_parser_xhinker.py,
              extensions-builtin/sd-webui-agent-scheduler,
@@ -136,6 +137,7 @@ disable=bad-inline-option,
         consider-using-get,
         consider-using-in,
         consider-using-min-builtin,
+        consider-using-max-builtin,
         consider-using-sys-exit,
         dangerous-default-value,
         deprecated-pragma,

.ruff.toml

@@ -13,6 +13,7 @@ exclude = [
     "modules/todo",
     "modules/unipc",
     "modules/xadapter",
+    "modules/dcsolver",
     "modules/intel/openvino",
     "modules/intel/ipex",
     "modules/segmoe",

installer.py

@@ -530,7 +530,7 @@ def install_rocm_zluda():
             log.info('Using CPU-only torch')
             torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision')
         #else:
-        # TODO TBD after ROCm for Windows is released
+        # TODO after ROCm for Windows is released
     else:
         if rocm.version is None or float(rocm.version) > 6.1: # assume the latest if version check fails
             torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/rocm6.1')

modules/images.py

@@ -221,7 +221,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None, output_type
     def latent(im, w, h, upscaler):
         from modules.processing_vae import vae_encode, vae_decode
         import torch
-        latents = vae_encode(im, shared.sd_model, full_quality=False) # TODO enable full VAE mode
+        latents = vae_encode(im, shared.sd_model, full_quality=False) # TODO enable full VAE mode for resize-latent
         latents = torch.nn.functional.interpolate(latents, size=(int(h // 8), int(w // 8)), mode=upscaler["mode"], antialias=upscaler["antialias"])
         im = vae_decode(latents, shared.sd_model, output_type='pil', full_quality=False)[0]
         return im

modules/processing_diffusers.py

@@ -179,7 +179,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
             shared.state.job_count = 2 * p.n_iter
             shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
             shared.log.info(f'HiRes: class={shared.sd_model.__class__.__name__} sampler="{p.hr_sampler_name}"')
-            if 'Upscale' in shared.sd_model.__class__.__name__ or 'Flux in shared.sd_refiner.__class__.__name__':
+            if 'Upscale' in shared.sd_model.__class__.__name__ or 'Flux' in shared.sd_model.__class__.__name__:
                 output.images = processing_vae.vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
             if p.is_control and hasattr(p, 'task_args') and p.task_args.get('image', None) is not None:
                 if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
@@ -248,7 +248,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
             image = output.images[i]
             noise_level = round(350 * p.denoising_strength)
             output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np'
-            if 'Upscale' in shared.sd_refiner.__class__.__name__ or 'Flux in shared.sd_refiner.__class__.__name__':
+            if 'Upscale' in shared.sd_refiner.__class__.__name__ or 'Flux' in shared.sd_refiner.__class__.__name__:
                 image = processing_vae.vae_decode(latents=image, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
                 p.extra_generation_params['Noise level'] = noise_level
                 output_type = 'np'

modules/processing_vae.py

@@ -124,6 +124,7 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
     t0 = time.time()
     prev_job = shared.state.job
     shared.state.job = 'VAE'
+    decoded = None
     if not torch.is_tensor(latents): # already decoded
         return latents
     if latents.shape[0] == 0:
@@ -134,24 +135,28 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
     if not hasattr(model, 'vae'):
         shared.log.error('VAE not found in model')
         return []
+
     if hasattr(model, "_unpack_latents") and hasattr(model, "vae_scale_factor") and width is not None and height is not None: # FLUX
         latents = model._unpack_latents(latents, height, width, model.vae_scale_factor) # pylint: disable=protected-access
     if len(latents.shape) == 3: # lost a batch dim in hires
         latents = latents.unsqueeze(0)
     if latents.shape[0] == 4 and latents.shape[1] != 4: # likely animatediff latent
         latents = latents.permute(1, 0, 2, 3)
-    if any(s >= 512 for s in latents.shape):
-        imgs = latents.float().cpu().numpy()
+
+    if any(s >= 512 for s in latents.shape): # not a latent, likely an image
+        decoded = latents.float().cpu().numpy()
     elif full_quality and hasattr(shared.sd_model, "vae"):
         decoded = full_vae_decode(latents=latents, model=shared.sd_model)
     else:
         decoded = taesd_vae_decode(latents=latents)
+
     if hasattr(model, 'image_processor'):
         imgs = model.image_processor.postprocess(decoded, output_type=output_type)
     else:
         import diffusers
-        image_processor = diffusers.image_processor.VaeImageProcessor()
-        imgs = image_processor.postprocess(decoded, output_type=output_type)
+        model.image_processor = diffusers.image_processor.VaeImageProcessor()
+        imgs = model.image_processor.postprocess(decoded, output_type=output_type)
+
     shared.state.job = prev_job
     if shared.cmd_opts.profile or debug:
         t1 = time.time()