cleanup logging and update requirements

Signed-off-by: vladmandic <mandic00@live.com>

installer.py

@@ -495,12 +495,14 @@ def check_transformers():
         return
     pkg_transformers = package_spec('transformers')
     pkg_tokenizers = package_spec('tokenizers')
-    target_commit = '753d61104116eefc8ffc977327b441ee0c8d599f' # transformers commit hash == 4.57.6
+    # target_commit = '753d61104116eefc8ffc977327b441ee0c8d599f' # transformers commit hash == 4.57.6
+    target_commit = 'a28c974c7ac74c83dbf379e93ceecc2661730f63' # transformers commit hash == 4.57.6
     if args.use_directml:
         target_transformers = '4.52.4'
         target_tokenizers = '0.21.4'
     else:
-        target_transformers = '4.57.6'
+        # target_transformers = '4.57.6'
+        target_transformers = None
         target_tokenizers = '0.22.2'
     if target_transformers is not None:
         # Pinned release version (e.g. DirectML)

modules/api/control.py

@@ -179,7 +179,7 @@ class APIControl:
             u = req.control[i]
             ut = u.unit_type if u.unit_type is not None else default_type
             if ut not in unit_types:
-                shared.log.error(f'Control unknown unit type: type={ut} available={unit_types}')
+                log.error(f'Control unknown unit type: type={ut} available={unit_types}')
                 continue
             if (len(self.units) > i) and (self.units[i].process_id == u.process) and (self.units[i].model_id == u.model) and (self.units[i].type == ut):
                 unit = self.units[i]

modules/processing_diffusers.py

@@ -510,7 +510,7 @@ def update_pipeline(sd_model, p: processing.StableDiffusionProcessing):
     if sd_model is None:
         sd_model = shared.sd_model
     if sd_model is None:
-        shared.log.warning('Processing: op=update model not loaded')
+        log.warning('Processing: op=update model not loaded')
         return None
     updated_model = sd_model
     if sd_models.get_diffusers_task(sd_model) == sd_models.DiffusersTaskType.INPAINTING and getattr(p, 'image_mask', None) is None and p.task_args.get('image_mask', None) is None and getattr(p, 'mask', None) is None:

modules/sd_offload.py

@@ -7,7 +7,7 @@ import torch
 import accelerate.hooks
 import accelerate.utils.modeling
 from modules.logger import log
-from modules import shared, devices, errors, model_quant, sd_models, sd_models_aux
+from modules import shared, devices, errors, model_quant, sd_models, sd_offload_aux
 from modules.timer import process as process_timer
 
 
@@ -244,7 +244,7 @@ class OffloadHook(accelerate.hooks.ModelHook):
             if shared.opts.diffusers_offload_pre:
                 t0 = time.time()
                 debug_move(f'Offload: type=balanced op=pre module={module.__class__.__name__}')
-                sd_models_aux.evict_aux(reason=f'pre:{module.__class__.__name__}')
+                sd_offload_aux.evict_aux(reason=f'pre:{module.__class__.__name__}')
                 for pipe in get_pipe_variants():
                     for module_name in get_module_names(pipe):
                         module_instance = getattr(pipe, module_name, None)

modules/sd_offload_aux.py

@@ -3,7 +3,6 @@ import dataclasses
 import torch
 from modules.logger import log
 from modules import shared, devices
-from modules.timer import process as process_timer
 
 
 move_stream = None

modules/sd_samplers_diffusers.py

@@ -486,7 +486,7 @@ class DiffusionSampler:
             try:
                 cls_source = inspect.getsource(constructor)
                 if '"flow_prediction"' not in cls_source and "'flow_prediction'" not in cls_source:
-                    shared.log.warning(f'Sampler: "{name}" does not support flow_prediction')
+                    log.warning(f'Sampler: "{name}" does not support flow_prediction')
                     self.sampler = None
                     return
             except (TypeError, OSError):

modules/token_merge.py

@@ -1,3 +1,4 @@
+from modules.logger import log
 from modules import shared
 
 
@@ -20,7 +21,7 @@ def apply_token_merging(sd_model, p=None):
         if current_tome == tome:
             return
         if hypertile and not shared.cmd_opts.experimental:
-            shared.log.warning('Token merging not supported with HyperTile for UNet')
+            log.warning('Token merging not supported with HyperTile for UNet')
             return
         try:
             import installer
@@ -34,10 +35,10 @@ def apply_token_merging(sd_model, p=None):
                 merge_crossattn=False,
                 merge_mlp=False
             )
-            shared.log.info(f'Applying ToMe: ratio={tome}')
+            log.info(f'Applying ToMe: ratio={tome}')
             sd_model.applied_tome = tome
         except Exception:
-            shared.log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}')
+            log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}')
     else:
         sd_model.applied_tome = 0
 
@@ -45,7 +46,7 @@ def apply_token_merging(sd_model, p=None):
         if current_todo == todo:
             return
         if hypertile and not shared.cmd_opts.experimental:
-            shared.log.warning('Token merging not supported with HyperTile for UNet')
+            log.warning('Token merging not supported with HyperTile for UNet')
             return
         try:
             from modules.todo.todo_utils import patch_attention_proc
@@ -61,10 +62,10 @@ def apply_token_merging(sd_model, p=None):
                         "ratio_level_2": 0.0,
                         }
             patch_attention_proc(sd_model.unet, token_merge_args=token_merge_args)
-            shared.log.info(f'Applying ToDo: ratio={todo}')
+            log.info(f'Applying ToDo: ratio={todo}')
             sd_model.applied_todo = todo
         except Exception:
-            shared.log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}')
+            log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}')
     else:
         sd_model.applied_todo = 0
 

modules/ui_control.py

@@ -197,11 +197,7 @@ def create_ui(_blocks: gr.Blocks=None):
 
                 guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_guidance.create_guidance_inputs('control')
                 vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('control')
-                hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio, hdr_apply_hires, \
-                    grading_brightness, grading_contrast, grading_saturation, grading_hue, grading_gamma, grading_sharpness, grading_color_temp, \
-                    grading_shadows, grading_midtones, grading_highlights, grading_clahe_clip, grading_clahe_grid, \
-                    grading_shadows_tint, grading_highlights_tint, grading_split_tone_balance, \
-                    grading_vignette, grading_grain, grading_lut_file, grading_lut_strength = ui_sections.create_color_inputs('control')
+                hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio, hdr_apply_hires, grading_brightness, grading_contrast, grading_saturation, grading_hue, grading_gamma, grading_sharpness, grading_color_temp, grading_shadows, grading_midtones, grading_highlights, grading_clahe_clip, grading_clahe_grid, grading_shadows_tint, grading_highlights_tint, grading_split_tone_balance, grading_vignette, grading_grain, grading_lut_file, grading_lut_strength = ui_sections.create_color_inputs('control')
 
                 with gr.Accordion(open=False, label="Video", elem_id="control_video", elem_classes=["small-accordion"]):
                     with gr.Row():

modules/ui_img2img.py

@@ -135,11 +135,7 @@ def create_ui():
 
                     guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_guidance.create_guidance_inputs('img2img')
                     vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('img2img')
-                    hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio, hdr_apply_hires, \
-                        grading_brightness, grading_contrast, grading_saturation, grading_hue, grading_gamma, grading_sharpness, grading_color_temp, \
-                        grading_shadows, grading_midtones, grading_highlights, grading_clahe_clip, grading_clahe_grid, \
-                        grading_shadows_tint, grading_highlights_tint, grading_split_tone_balance, \
-                        grading_vignette, grading_grain, grading_lut_file, grading_lut_strength = ui_sections.create_color_inputs('img2img')
+                    hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio, hdr_apply_hires, grading_brightness, grading_contrast, grading_saturation, grading_hue, grading_gamma, grading_sharpness, grading_color_temp, grading_shadows, grading_midtones, grading_highlights, grading_clahe_clip, grading_clahe_grid, grading_shadows_tint, grading_highlights_tint, grading_split_tone_balance, grading_vignette, grading_grain, grading_lut_file, grading_lut_strength = ui_sections.create_color_inputs('img2img')
                     enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, hr_refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('img2img')
                     detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution = shared.yolo.ui('img2img')
 

modules/ui_txt2img.py

@@ -35,11 +35,7 @@ def create_ui():
                     seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = ui_sections.create_seed_inputs('txt2img')
                     guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, cfg_end = ui_guidance.create_guidance_inputs('txt2img')
                     vae_type, tiling, hidiffusion, clip_skip = ui_sections.create_advanced_inputs('txt2img')
-                    hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio, hdr_apply_hires, \
-                        grading_brightness, grading_contrast, grading_saturation, grading_hue, grading_gamma, grading_sharpness, grading_color_temp, \
-                        grading_shadows, grading_midtones, grading_highlights, grading_clahe_clip, grading_clahe_grid, \
-                        grading_shadows_tint, grading_highlights_tint, grading_split_tone_balance, \
-                        grading_vignette, grading_grain, grading_lut_file, grading_lut_strength = ui_sections.create_color_inputs('txt2img')
+                    hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundary, hdr_color_picker, hdr_tint_ratio, hdr_apply_hires, grading_brightness, grading_contrast, grading_saturation, grading_hue, grading_gamma, grading_sharpness, grading_color_temp, grading_shadows, grading_midtones, grading_highlights, grading_clahe_clip, grading_clahe_grid, grading_shadows_tint, grading_highlights_tint, grading_split_tone_balance, grading_vignette, grading_grain, grading_lut_file, grading_lut_strength = ui_sections.create_color_inputs('txt2img')
                     enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('txt2img')
                     detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution  = shared.yolo.ui('txt2img')
                     override_settings = ui_common.create_override_inputs('txt2img')

requirements.txt

@@ -29,7 +29,7 @@ requests==2.32.3
 tqdm==4.67.3
 accelerate==1.12.0
 einops==0.8.1
-huggingface_hub==0.36.2
+huggingface_hub==1.5.0
 numpy==2.1.2
 pandas==2.3.1
 protobuf==6.33.5

scripts/lut.py

@@ -1,69 +0,0 @@
-"""
-downloads: https://luts.iwltbap.com/
-lib: https://github.com/homm/pillow-lut-tools
-"""
-import os
-import gradio as gr
-from installer import install
-from modules import scripts_manager, processing
-from modules.logger import log
-
-
-class Script(scripts_manager.Script):
-    def title(self):
-        return 'LUT Color grading'
-
-    def show(self, is_img2img): # pylint: disable=unused-argument
-        return True
-
-    def ui(self, _is_img2img):
-        with gr.Row():
-            gr.HTML("<span>&nbsp LUT Color grading</span><br>")
-        with gr.Row():
-            original = gr.Checkbox(label='Include original image', value=True)
-        with gr.Row():
-            cube_file = gr.File(label='LUT .cube file', help='Download LUTs from https://luts.iwltbap.com/')
-            # cube_file = gr.File(label='LUT .cube file')
-        with gr.Row():
-            gr.HTML("<br>Enhance LUT")
-        with gr.Row():
-            cube_scale = gr.Slider(label='Amplify LUT', minimum=0.0, maximum=5.0, step=0.05, value=1.0)
-            brightness = gr.Slider(label='Brightness', minimum=-1, maximum=1, step=0.05, value=0)
-            exposure = gr.Slider(label='Exposure', minimum=-5, maximum=5, step=0.05, value=0)
-            contrast = gr.Slider(label='Contrast', minimum=-1, maximum=1, step=0.05, value=0)
-            warmth = gr.Slider(label='Warmth', minimum=-1, maximum=1, step=0.05, value=0)
-            saturation = gr.Slider(label='Saturation', minimum=-1, maximum=5, step=0.05, value=0)
-            vibrance = gr.Slider(label='Vibrance', minimum=-1, maximum=5, step=0.05, value=0)
-            hue = gr.Slider(label='Hue', minimum=0, maximum=1, step=0.05, value=0)
-            gamma = gr.Slider(label='Gamma', minimum=0, maximum=10.0, step=0.1, value=1.0)
-        return [original, cube_file, cube_scale, brightness, exposure, contrast, warmth, saturation, vibrance, hue, gamma]
-
-    # auto-executed by the script-callback
-    def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, original, cube_file, cube_scale, brightness, exposure, contrast, warmth, saturation, vibrance, hue, gamma): # pylint: disable=arguments-differ, unused-argument
-        install('pillow_lut', quiet=True)
-        import pillow_lut
-
-        cube = None
-        name = os.path.splitext(os.path.basename(cube_file.name))[0] if cube_file is not None else None
-        log.info(f'Color grading: cube="{name}" scale={cube_scale} brightness={brightness} exposure={exposure} contrast={contrast} warmth={warmth} saturation={saturation} vibrance={vibrance} hue={hue} gamma={gamma}')
-        if cube_file is not None:
-            try:
-                cube = pillow_lut.load_cube_file(cube_file.name)
-                cube = pillow_lut.amplify_lut(cube, cube_scale)
-                cube = pillow_lut.rgb_color_enhance(source=cube, brightness=brightness, exposure=exposure, contrast=contrast, warmth=warmth, saturation=saturation, vibrance=vibrance, hue=hue, gamma=gamma)
-            except Exception as e:
-                log.error(f'Color grading: {e}')
-
-        images = []
-        if processed is not None and len(processed.images) > 0:
-            for image in processed.images:
-                info = image.info.get('parameters', '')
-                if original:
-                    images.append(image)
-                if cube is not None:
-                    filtered = image.filter(cube)
-                    filtered.info['parameters'] = f'{info}, LUT: {name}'
-                    images.append(filtered)
-        processed.images = images
-
-        return processed