combo patch

TODO.md

@@ -7,8 +7,7 @@ Stuff to be fixed...
 - ClipSkip not updated on read gen info
 - Usage of `sd_vae` in quick settings
 - Run VAE with hires at 1280
-- Make TensorFlow optional
-
+- Transformers version
 
 ## Features
 
@@ -58,11 +57,7 @@ Tech that can be integrated as part of the core workflow...
 
 ### Pending Code Updates
 
-- fix VAE dtype  
-  should fix most issues with NaN or black images  
-- add built-in Gradio themes  
-- fix setup race conditions
-- reduce requirements  
-- more AMD specific work
-- initial work on Apple platform support
-- additional PR merges
+- Use samples format for live preview
+- Identify race condition where generate locks up while fetching preview
+- Use **Approx NN** for live preview
+- Create default `styles.csv`

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -89,7 +89,7 @@ class UpscalerSwinIR(Upscaler):
 
         with progress.open(filename, 'rb', description=f'Loading weights: [cyan]{filename}', auto_refresh=True) as f:
             pretrained_model = torch.load(filename)
-        if params is not None:
+        if params is not None and params in pretrained_model:
             model.load_state_dict(pretrained_model[params], strict=True)
         else:
             model.load_state_dict(pretrained_model, strict=True)

modules/processing.py

@@ -442,9 +442,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
 
 
 def decode_first_stage(model, x):
-    with devices.autocast(disable=x.dtype == devices.dtype_vae):
+    with devices.autocast(disable = x.dtype==devices.dtype_vae):
         x = model.decode_first_stage(x)
-
     return x
 
 

modules/progress.py

@@ -1,11 +1,7 @@
 import base64
 import io
 import time
-
-from pydantic import BaseModel, Field
-
-from modules.shared import opts
-
+from pydantic import BaseModel, Field # pylint: disable=no-name-in-module
 import modules.shared as shared
 
 
@@ -15,18 +11,15 @@ finished_tasks = []
 
 
 def start_task(id_task):
-    global current_task
-
+    global current_task # pylint: disable=global-statement
     current_task = id_task
     pending_tasks.pop(id_task, None)
 
 
 def finish_task(id_task):
-    global current_task
-
+    global current_task # pylint: disable=global-statement
     if current_task == id_task:
         current_task = None
-
     finished_tasks.append(id_task)
     if len(finished_tasks) > 16:
         finished_tasks.pop(0)
@@ -60,39 +53,31 @@ def progressapi(req: ProgressRequest):
     active = req.id_task == current_task
     queued = req.id_task in pending_tasks
     completed = req.id_task in finished_tasks
-
     if not active:
         return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo="In queue..." if queued else "Waiting...")
-
     progress = 0
-
     job_count, job_no = shared.state.job_count, shared.state.job_no
     sampling_steps, sampling_step = shared.state.sampling_steps, shared.state.sampling_step
-
     if job_count > 0:
         progress += job_no / job_count
     if sampling_steps > 0 and job_count > 0:
         progress += 1 / job_count * sampling_step / sampling_steps
-
     progress = min(progress, 1)
-
     elapsed_since_start = time.time() - shared.state.time_start
     predicted_duration = elapsed_since_start / progress if progress > 0 else None
     eta = predicted_duration - elapsed_since_start if predicted_duration is not None else None
-
     id_live_preview = req.id_live_preview
     shared.state.set_current_image()
-    if opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview:
+    if shared.opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview:
         image = shared.state.current_image
         if image is not None:
             buffered = io.BytesIO()
-            image.save(buffered, format="png")
-            live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
+            fmt = 'jpeg' if shared.opts.samples_format == 'jpg' else shared.opts.samples_format
+            image.save(buffered, format=fmt)
+            live_preview = f'data:image/{fmt};base64,{base64.b64encode(buffered.getvalue()).decode("ascii")}'
             id_live_preview = shared.state.id_live_preview
         else:
             live_preview = None
     else:
         live_preview = None
-
     return ProgressResponse(active=active, queued=queued, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)
-

modules/sd_samplers_common.py

@@ -28,14 +28,12 @@ approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
 def single_sample_to_image(sample, approximation=None):
     if approximation is None:
         approximation = approximation_indexes.get(opts.show_progress_type, 0)
-
     if approximation == 2:
         x_sample = sd_vae_approx.cheap_approximation(sample)
     elif approximation == 1:
         x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
     else:
         x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)

modules/shared.py

@@ -112,7 +112,6 @@ class State:
             "sampling_step": self.sampling_step,
             "sampling_steps": self.sampling_steps,
         }
-
         return obj
 
     def begin(self):
@@ -142,20 +141,17 @@ class State:
         """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
         if not parallel_processing_allowed:
             return
-
         if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps != -1:
             self.do_set_current_image()
 
     def do_set_current_image(self):
         if self.current_latent is None:
             return
-
         import modules.sd_samplers # pylint: disable=W0621
         if opts.show_progress_grid:
             self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
         else:
             self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
-
         self.current_image_sampling_step = self.sampling_step
 
     def assign_current_image(self, image):
@@ -425,8 +421,8 @@ options_templates.update(options_section(('ui', "Live previews"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
-    "show_progress_every_n_steps": OptionInfo(-1, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+    "show_progress_every_n_steps": OptionInfo(1, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
     "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
     "live_preview_refresh_period": OptionInfo(250, "Progressbar/preview update period, in milliseconds")
 }))

modules/styles.py

@@ -49,7 +49,8 @@ class StyleDatabase:
         self.styles.clear()
 
         if not os.path.exists(self.path):
-            return
+            print(f'Creating styles database: {self.path}')
+            self.save_styles(self.path)
 
         with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
             reader = csv.DictReader(file)
@@ -79,9 +80,5 @@ class StyleDatabase:
             # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
             writer.writeheader()
-            writer.writerows(style._asdict() for k,     style in self.styles.items())
-
-        # Always keep a backup file around
-        if os.path.exists(path):
-            shutil.move(path, path + ".bak")
+            writer.writerows(style._asdict() for k, style in self.styles.items())
         shutil.move(temp_path, path)

requirements.txt

@@ -49,7 +49,6 @@ tqdm
 voluptuous
 yapf
 scikit-image
-
 accelerate==0.18.0
 opencv-python==4.7.0.72
 diffusers==0.15.0

setup.py

@@ -217,7 +217,7 @@ def check_torch():
         log.debug(f'Cannot install xformers package: {e}')
     try:
         tensorflow_package = os.environ.get('TENSORFLOW_PACKAGE', 'tensorflow==2.12.0')
-        install(f'--no-deps {tensorflow_package}', ignore=True)
+        install(tensorflow_package, ignore=True)
     except Exception as e:
         log.debug(f'Cannot install tensorflow package: {e}')
 
@@ -237,7 +237,6 @@ def install_packages():
 def install_repositories():
     def d(name):
         return os.path.join(os.path.dirname(__file__), 'repositories', name)
-
     log.info('Installing repositories')
     os.makedirs(os.path.join(os.path.dirname(__file__), 'repositories'), exist_ok=True)
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
@@ -263,7 +262,7 @@ def run_extension_installer(folder):
     if not os.path.isfile(path_installer):
         return
     try:
-        log.debug(f"Running extension installer: {path_installer}")
+        log.debug(f"Running extension installer: {folder} / {path_installer}")
         env = os.environ.copy()
         env['PYTHONPATH'] = os.path.abspath(".")
         result = subprocess.run(f'"{sys.executable}" "{path_installer}"', shell=True, env=env, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=folder)
@@ -334,7 +333,7 @@ def install_submodules():
 
 def ensure_package(pkg):
     try:
-        import pkg
+        import pkg # type: ignore
     except ImportError:
         install(pkg)
 
@@ -394,6 +393,13 @@ def check_extensions():
 
 # check version of the main repo and optionally upgrade it
 def check_version():
+    if not os.path.exists('.git'):
+        log.error('Not a git repository')
+        exit(1)
+    status = git('status')
+    if 'branch' not in status:
+        log.error('Cannot get git repository status')
+        exit(1)
     ver = git('log -1 --pretty=format:"%h %ad"')
     log.info(f'Version: {ver}')
     commit = git('rev-parse HEAD')
@@ -420,17 +426,20 @@ def check_version():
                     log.error('Error upgrading repository')
             else:
                 log.info(f'Latest published version: {commits["commit"]["sha"]} {commits["commit"]["commit"]["author"]["date"]}')
-        if not args.noupdate:
-            log.info('Updating Wiki')
-            try:
-                update(os.path.join(os.path.dirname(__file__), "wiki"))
-                update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
-            except:
-                log.error('Error updating wiki')
     except Exception as e:
         log.error(f'Failed to check version: {e} {commits}')
 
 
+def update_wiki():
+    if not args.noupdate:
+        log.info('Updating Wiki')
+        try:
+            update(os.path.join(os.path.dirname(__file__), "wiki"))
+            update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
+        except:
+            log.error('Error updating wiki')
+
+
 # check if we can run setup in quick mode
 def check_timestamp():
     if not quick_allowed or not os.path.isfile('setup.log'):
@@ -535,6 +544,7 @@ def run_setup():
     install_repositories()
     install_submodules()
     install_extensions()
+    update_wiki()
     if errors == 0:
         log.debug(f'Setup complete without errors: {round(time.time())}')
     else: