Merge pull request #4351 from vladmandic/dev

merge dev

CHANGELOG.md

@@ -1,18 +1,34 @@
 # Change Log for SD.Next
 
-## Update for 2025-11-01
+## Update for 2025-11-02
 
-- feature: add inline wildcards using curly braces syntax  
-- feature: guard against multi-controlnet in hires  
-- feature: add setting to control `cudnn` enable/disable  
-- feature: cleanup `--optional` installer  
-- fix: update diffusers  
-- fix: inpaint  
-- fix: model type detection  
-- fix: version detection when cloned with `.git` suffix  
-- fix: init `sdnq` on video model load  
-- ui: fix full-screen image viewer buttons with non-standard ui theme  
-- ui: control tab show override section  
+- **Features**
+  - **detailer**
+    optional include detection image to output results  
+    optional sort detection objects left-to-right for improved prompt consistency  
+    enable multi-subject and multi-model prompts  
+  - **wildcards**: add inline processing using curly braces syntax  
+  - add setting to control `cudnn` enable/disable  
+  - change `vlm` beams to 1 by default for faster response  
+  - update diffusers  
+- **Fixes**
+  - `chrono` transformers handling  
+  - `chrono` extract last frame  
+  - `chrono` add vae scale override, thanks @CalamitousFelicitousness  
+  - `runai` improve streamer integration  
+  - `transformers` dtype use new syntax  
+  - `rocm` possible endless loop during hip detection  
+  - `rocm` auto-disable miopen for gfx120x
+  - `detailer` better handling of settings, thanks @awsr
+  - `installer` cleanup `--optional`  
+  - `hires` guard against multi-controlnet  
+  - `inpaint` handling
+  - `version` detection when cloned with `.git` suffix, thanks @awsr
+  - `sdnq` init on video model load
+  - `model type` detection
+  - `model type` add tracing to model detection
+  - `ui` fix full-screen image viewer buttons with non-standard ui theme
+  - `ui` control tab show override section
 
 ## Update for 2025-10-31
 

modules/postprocess/yolo.py

@@ -6,7 +6,7 @@ from copy import copy
 import numpy as np
 import gradio as gr
 from PIL import Image, ImageDraw
-from modules import shared, processing, devices, processing_class, ui_common, ui_components, ui_symbols
+from modules import shared, processing, devices, processing_class, ui_common, ui_components, ui_symbols, images
 from modules.detailer import Detailer
 
 
@@ -216,6 +216,16 @@ class YoloRestorer(Detailer):
         )
         return [merged]
 
+    def draw_boxes(self, image: Image.Image, items: list[YoloResult]) -> Image.Image:
+        annotated = image.copy()
+        draw = ImageDraw.Draw(annotated)
+        font = images.get_font(16)
+        for i, item in enumerate(items):
+            draw.rectangle(item.box, outline="#00C8C8", width=3)
+            draw.text((item.box[0]+4, item.box[1]+4), f'{i+1} {item.label} {item.score:.2f}', fill="black", font=font)
+            draw.text((item.box[0]+2, item.box[1]+2), f'{i+1} {item.label} {item.score:.2f}', fill="white", font=font)
+        return np.array(annotated)
+
     def restore(self, np_image, p: processing.StableDiffusionProcessing = None):
         if shared.state.interrupted or shared.state.skipped:
             return np_image
@@ -241,6 +251,7 @@ class YoloRestorer(Detailer):
         orig_p = p.__dict__.copy()
         orig_cls = p.__class__
         models_used = []
+        np_images = []
 
         for i, model_val in enumerate(models):
             if ':' in model_val:
@@ -342,9 +353,18 @@ class YoloRestorer(Detailer):
             shared.opts.schedulers_sigma_adjust = shared.opts.detailer_sigma_adjust
             shared.opts.schedulers_sigma_adjust_max = shared.opts.detailer_sigma_adjust_max
 
-            for item in items:
+            if shared.opts.detailer_sort:
+                items = sorted(items, key=lambda x: x.box[0]) # sort items left-to-right to improve consistency
+            if shared.opts.detailer_save:
+                np_images.append(self.draw_boxes(image, items)) # save debug image with boxes
+
+            for j, item in enumerate(items):
                 if item.mask is None:
                     continue
+                pc.keep_prompts = True
+                pc.prompts = [prompt_lines[(i*len(items)+j) % len(prompt_lines)]]
+                pc.negative_prompts = [negative_lines[(i*len(items)+j) % len(negative_lines)]]
+                shared.log.debug(f'Detail: model="{i+1}:{name}" item={j+1}/{len(items)} box={item.box} score={item.score:.2f} prompt="{pc.prompt}"')
                 pc.init_images = [image]
                 pc.image_mask = [item.mask]
                 pc.overlay_images = []
@@ -372,14 +392,14 @@ class YoloRestorer(Detailer):
             p.state = orig_p.get('state', None)
             p.ops = orig_p.get('ops', [])
             shared.opts.data['mask_apply_overlay'] = orig_apply_overlay
-            np_image = np.array(image)
+            np_images.append(np.array(image))
 
             if len(mask_all) > 0 and shared.opts.include_mask:
                 from modules.control.util import blend
                 p.image_mask = blend([np.array(m) for m in mask_all])
                 p.image_mask = Image.fromarray(p.image_mask)
 
-        return np_image
+        return np_images
 
     def change_mode(self, dropdown, text):
         self.ui_mode = not self.ui_mode
@@ -391,7 +411,7 @@ class YoloRestorer(Detailer):
             return gr.update(visible=False), gr.update(visible=True, value=value), gr.update(visible=False)
 
     def ui(self, tab: str):
-        def ui_settings_change(merge, detailers, text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution):
+        def ui_settings_change(merge, detailers, text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort):
             shared.opts.detailer_merge = merge
             shared.opts.detailer_models = detailers
             shared.opts.detailer_args = text if not self.ui_mode else ''
@@ -405,14 +425,19 @@ class YoloRestorer(Detailer):
             shared.opts.detailer_iou = iou
             shared.opts.detailer_sigma_adjust = renoise_value
             shared.opts.detailer_sigma_adjust_max = renoise_end
+            shared.opts.detailer_save = save
+            shared.opts.detailer_sort = sort
             # shared.opts.detailer_resolution = resolution
             shared.opts.save(shared.config_filename, silent=True)
-            shared.log.debug(f'Detailer settings: models={detailers} classes={classes} strength={strength} conf={min_confidence} max={max_detected} iou={iou} size={min_size}-{max_size} padding={padding} steps={steps} resolution={resolution}')
+            shared.log.debug(f'Detailer settings: models={detailers} classes={classes} strength={strength} conf={min_confidence} max={max_detected} iou={iou} size={min_size}-{max_size} padding={padding} steps={steps} resolution={resolution} save={save} sort={sort}')
 
         with gr.Accordion(open=False, label="Detailer", elem_id=f"{tab}_detailer_accordion", elem_classes=["small-accordion"]):
             with gr.Row():
                 enabled = gr.Checkbox(label="Enable detailer pass", elem_id=f"{tab}_detailer_enabled", value=False)
+                save = gr.Checkbox(label="Include detection results", elem_id=f"{tab}_detailer_save", value=shared.opts.detailer_save, visible=True)
+            with gr.Row():
                 merge = gr.Checkbox(label="Merge detailers", elem_id=f"{tab}_detailer_merge", value=shared.opts.detailer_merge, visible=True)
+                sort = gr.Checkbox(label="Sort detections", elem_id=f"{tab}_detailer_sort", value=shared.opts.detailer_sort, visible=True)
             with gr.Row():
                 detailers = gr.Dropdown(label="Detailer models", elem_id=f"{tab}_detailers", choices=list(self.list), value=shared.opts.detailer_models, multiselect=True, visible=True)
                 detailers_text = gr.Textbox(label="Detailer list", elem_id=f"{tab}_detailers_text", placeholder="Comma separated list of detailer models", lines=2, visible=False, interactive=True)
@@ -446,18 +471,20 @@ class YoloRestorer(Detailer):
                 renoise_value = gr.Slider(minimum=0.5, maximum=1.5, step=0.01, label='Renoise', value=shared.opts.detailer_sigma_adjust, elem_id=f"{tab}_detailer_renoise")
                 renoise_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Renoise end', value=shared.opts.detailer_sigma_adjust_max, elem_id=f"{tab}_detailer_renoise_end")
 
-            merge.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            detailers.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            detailers_text.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            classes.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            padding.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            blur.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            min_confidence.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            max_detected.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            min_size.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            max_size.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            iou.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
-            resolution.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution], outputs=[])
+            merge.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            detailers.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            detailers_text.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            classes.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            padding.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            blur.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            min_confidence.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            max_detected.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            min_size.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            max_size.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            iou.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            resolution.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            save.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
+            sort.change(fn=ui_settings_change, inputs=[merge, detailers, detailers_text, classes, strength, padding, blur, min_confidence, max_detected, min_size, max_size, iou, steps, renoise_value, renoise_end, resolution, save, sort], outputs=[])
             return enabled, prompt, negative, steps, strength, resolution
 
 

modules/processing.py

@@ -307,7 +307,14 @@ def process_samples(p: StableDiffusionProcessing, samples):
                     info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
                     images.save_image(Image.fromarray(sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-detailer")
                 sample = detailer.detail(sample, p)
-                if sample is not None:
+                if isinstance(sample, list):
+                    if len(sample) > 0:
+                        image = Image.fromarray(sample[0])
+                    if len(sample) > 1:
+                        annotated = Image.fromarray(sample[1])
+                        out_images.append(annotated)
+                        out_infotexts.append("Detailer annotations")
+                elif sample is not None:
                     image = Image.fromarray(sample)
 
             if p.color_corrections is not None and i < len(p.color_corrections):

modules/rocm.py

@@ -276,6 +276,12 @@ if sys.platform == "win32":
         try:
             import torch
             import numpy as np
+            from modules.devices import get_optimal_device
+
+            gfx_version = Agent.parse_gfx_version(getattr(torch.cuda.get_device_properties(get_optimal_device()), "gcnArchName", "gfx0000"))
+            if (gfx_version & 0xFFF0) == 0x1200:
+                # disable MIOpen for gfx120x
+                torch.backends.cudnn.enabled = False
 
             original_cholesky_ex = torch.linalg.cholesky_ex
             @wraps(original_cholesky_ex)

modules/sd_detect.py

@@ -9,121 +9,127 @@ debug_load = os.environ.get('SD_LOAD_DEBUG', None)
 
 
 def guess_by_size(fn, current_guess):
+    new_guess = None
     if os.path.isfile(fn) and fn.endswith('.safetensors'):
         size = round(os.path.getsize(fn) / 1024 / 1024)
         if (size > 0 and size < 128):
             shared.log.warning(f'Model size smaller than expected: file="{fn}" size={size} MB')
         elif (size >= 316 and size <= 324) or (size >= 156 and size <= 164): # 320 or 160
             shared.log.warning(f'Model detected as VAE model, but attempting to load as model: file="{fn}" size={size} MB')
-            return 'VAE'
+            new_guess = 'VAE'
         elif (size >= 2002 and size <= 2038): # 2032
-            return 'Stable Diffusion 1.5'
+            new_guess = 'Stable Diffusion 1.5'
         elif (size >= 3138 and size <= 3142): #3140
-            return 'Stable Diffusion XL'
+            new_guess = 'Stable Diffusion XL'
         elif (size >= 3361 and size <= 3369): # 3368
-            return 'Stable Diffusion Upscale'
+            new_guess = 'Stable Diffusion Upscale'
         elif (size >= 4891 and size <= 4899): # 4897
-            return 'Stable Diffusion XL Inpaint'
+            new_guess = 'Stable Diffusion XL Inpaint'
         elif (size >= 4970 and size <= 4976): # 4973
-            return 'Stable Diffusion 2' # SD v2 but could be eps or v-prediction
+            new_guess = 'Stable Diffusion 2' # SD v2 but could be eps or v-prediction
         elif (size >= 5791 and size <= 5799): # 5795
-            return 'Stable Diffusion XL Refiner'
+            new_guess = 'Stable Diffusion XL Refiner'
         elif (size > 5692 and size < 5698) or (size > 4134 and size < 4138) or (size > 10362 and size < 10366) or (size > 15028 and size < 15228):
-            return 'Stable Diffusion 3'
+            new_guess = 'Stable Diffusion 3'
         elif (size >= 6420 and size <= 7220): # 6420, IustriousRedux is 6541, monkrenRealisticINT_v10 is 7217
-            return 'Stable Diffusion XL'
+            new_guess = 'Stable Diffusion XL'
         elif (size >= 9791 and size <= 9799): # 9794
-            return 'Stable Diffusion XL Instruct'
+            new_guess = 'Stable Diffusion XL Instruct'
         elif (size >= 18414 and size <= 18420): # sd35-large aio
-            return 'Stable Diffusion 3'
+            new_guess = 'Stable Diffusion 3'
         elif (size >= 20000 and size <= 40000):
-            return 'FLUX'
-    return current_guess
+            new_guess = 'FLUX'
+        if debug_load:
+            shared.log.trace(f'Autodetect: method=size file="{fn}" size={size} previous="{current_guess}" current="{new_guess}"')
+    return new_guess or current_guess
 
 
 def guess_by_name(fn, current_guess):
+    new_guess = None
     if 'instaflow' in fn.lower():
-        return 'InstaFlow'
+        new_guess = 'InstaFlow'
     elif 'segmoe' in fn.lower():
-        return 'SegMoE'
+        new_guess = 'SegMoE'
     elif 'hunyuandit' in fn.lower():
-        return 'HunyuanDiT'
+        new_guess = 'HunyuanDiT'
     elif 'hdm-xut' in fn.lower():
-        return 'hdm'
+        new_guess = 'hdm'
     elif 'pixart-xl' in fn.lower():
-        return 'PixArt Alpha'
+        new_guess = 'PixArt Alpha'
     elif 'stable-diffusion-3' in fn.lower():
-        return 'Stable Diffusion 3'
+        new_guess = 'Stable Diffusion 3'
     elif 'stable-cascade' in fn.lower() or 'stablecascade' in fn.lower() or 'wuerstchen3' in fn.lower() or ('sotediffusion' in fn.lower() and "v2" in fn.lower()):
         if devices.dtype == torch.float16:
             shared.log.warning('Stable Cascade does not support Float16')
-        return 'Stable Cascade'
+        new_guess = 'Stable Cascade'
     elif 'pixart-sigma' in fn.lower():
-        return 'PixArt Sigma'
+        new_guess = 'PixArt Sigma'
     elif 'sana' in fn.lower():
-        return 'Sana'
+        new_guess = 'Sana'
     elif 'lumina-next' in fn.lower():
-        return 'Lumina-Next'
+        new_guess = 'Lumina-Next'
     elif 'lumina-image-2' in fn.lower():
-        return 'Lumina 2'
+        new_guess = 'Lumina 2'
     elif 'kolors' in fn.lower():
-        return 'Kolors'
+        new_guess = 'Kolors'
     elif 'auraflow' in fn.lower() or 'pony-v7' in fn.lower():
-        return 'AuraFlow'
+        new_guess = 'AuraFlow'
     elif 'cogview3' in fn.lower():
-        return 'CogView 3'
+        new_guess = 'CogView 3'
     elif 'cogview4' in fn.lower():
-        return 'CogView 4'
+        new_guess = 'CogView 4'
     elif 'meissonic' in fn.lower():
-        return 'Meissonic'
+        new_guess = 'Meissonic'
     elif 'monetico' in fn.lower():
-        return 'Monetico'
+        new_guess = 'Monetico'
     elif 'omnigen2' in fn.lower():
-        return 'OmniGen2'
+        new_guess = 'OmniGen2'
     elif 'omnigen' in fn.lower():
-        return 'OmniGen'
+        new_guess = 'OmniGen'
     elif 'sd3' in fn.lower():
-        return 'Stable Diffusion 3'
+        new_guess = 'Stable Diffusion 3'
     elif 'hidream' in fn.lower():
-        return 'HiDream'
+        new_guess = 'HiDream'
     elif 'chroma' in fn.lower() and 'xl' not in fn.lower():
-        return 'Chroma'
+        new_guess = 'Chroma'
     elif 'flux' in fn.lower() or 'flex.1' in fn.lower():
         size = round(os.path.getsize(fn) / 1024 / 1024) if os.path.isfile(fn) else 0
         if size > 11000 and size < 16000:
             shared.log.warning(f'Model detected as FLUX UNET model, but attempting to load a base model: file="{fn}" size={size} MB')
-        return 'FLUX'
+        new_guess = 'FLUX'
     elif 'flex.2' in fn.lower():
-        return 'FLEX'
+        new_guess = 'FLEX'
     elif 'cosmos-predict2' in fn.lower():
-        return 'Cosmos'
+        new_guess = 'Cosmos'
     elif 'f-lite' in fn.lower():
-        return 'FLite'
+        new_guess = 'FLite'
     elif 'wan' in fn.lower():
-        return 'WanAI'
+        new_guess = 'WanAI'
     if 'chronoedit' in fn.lower():
-        return 'ChronoEdit'
+        new_guess = 'ChronoEdit'
     elif 'bria' in fn.lower():
-        return 'Bria'
+        new_guess = 'Bria'
     elif 'qwen' in fn.lower():
-        return 'Qwen'
+        new_guess = 'Qwen'
     elif 'nextstep' in fn.lower():
-        return 'NextStep'
+        new_guess = 'NextStep'
     elif 'kandinsky-2-1' in fn.lower():
-        return 'Kandinsky 2.1'
+        new_guess = 'Kandinsky 2.1'
     elif 'kandinsky-2-2' in fn.lower():
-        return 'Kandinsky 2.2'
+        new_guess = 'Kandinsky 2.2'
     elif 'kandinsky-3' in fn.lower():
-        return 'Kandinsky 3.0'
+        new_guess = 'Kandinsky 3.0'
     elif 'hunyuanimage3' in fn.lower() or 'hunyuanimage-3' in fn.lower():
-        return 'HunyuanImage3'
+        new_guess = 'HunyuanImage3'
     elif 'hunyuanimage' in fn.lower():
-        return 'HunyuanImage'
+        new_guess = 'HunyuanImage'
     elif 'x-omni' in fn.lower():
-        return 'X-Omni'
+        new_guess = 'X-Omni'
     elif 'sdxl-turbo' in fn.lower() or 'stable-diffusion-xl' in fn.lower():
-        return 'Stable Diffusion XL'
-    return current_guess
+        new_guess = 'Stable Diffusion XL'
+    if debug_load:
+        shared.log.trace(f'Autodetect: method=name file="{fn}" previous="{current_guess}" current="{new_guess}"')
+    return new_guess or current_guess
 
 
 def guess_by_diffusers(fn, current_guess):
@@ -160,22 +166,27 @@ def guess_by_diffusers(fn, current_guess):
                 if v is not None and v.__name__ == pipeline.__name__:
                     if is_quant:
                         k = f'{k} SDNQ'
+                    if debug_load:
+                        shared.log.trace(f'Autodetect: method=diffusers file="{fn}" previous="{current_guess}" current="{k}"')
                     return k, v
     return current_guess, None
 
 
 def guess_variant(fn, current_guess):
+    new_guess = None
     if 'inpaint' in fn.lower():
         if current_guess == 'Stable Diffusion':
-            return 'Stable Diffusion Inpaint'
+            new_guess = 'Stable Diffusion Inpaint'
         elif current_guess == 'Stable Diffusion XL':
-            return 'Stable Diffusion XL Inpaint'
+            new_guess = 'Stable Diffusion XL Inpaint'
     elif 'instruct' in fn.lower():
         if current_guess == 'Stable Diffusion':
-            return 'Stable Diffusion Instruct'
+            new_guess = 'Stable Diffusion Instruct'
         elif current_guess == 'Stable Diffusion XL':
-            return 'Stable Diffusion XL Instruct'
-    return current_guess
+            new_guess = 'Stable Diffusion XL Instruct'
+    if debug_load:
+        shared.log.trace(f'Autodetect: method=variant file="{fn}" previous="{current_guess}" current="{new_guess}"')
+    return new_guess or current_guess
 
 
 def detect_pipeline(f: str, op: str = 'model'):

modules/sd_hijack_safetensors.py

@@ -1,7 +1,10 @@
+import io
 import os
+import contextlib
 import safetensors.torch
 import transformers
 from installer import install, log
+from modules import errors
 
 
 orig_load_file = safetensors.torch.load_file
@@ -13,14 +16,20 @@ def hijacked_load_file(checkpoint_file, device="cpu"):
         return orig_load_file(checkpoint_file, device=device)
 
     install('runai_model_streamer')
-    log.trace(f'Loader: method=runai chunk={os.environ["RUNAI_STREAMER_CHUNK_BYTESIZE"]} limit={os.environ["RUNAI_STREAMER_MEMORY_LIMIT"]} device={device}')
+    log.debug(f'Loader: method=runai type=file chunk={os.environ["RUNAI_STREAMER_CHUNK_BYTESIZE"]} limit={os.environ["RUNAI_STREAMER_MEMORY_LIMIT"]} device={device}')
     state_dict = {}
+    stdout = io.StringIO()
     from runai_model_streamer import SafetensorsStreamer
-    with SafetensorsStreamer() as streamer:
-        streamer.stream_file(checkpoint_file)
-        for key, tensor in streamer.get_tensors():
-            state_dict[key] = tensor.to(device)
-
+    with contextlib.redirect_stdout(stdout):
+        try:
+            with SafetensorsStreamer() as streamer:
+                streamer.stream_file(checkpoint_file)
+                for key, tensor in streamer.get_tensors():
+                    state_dict[key] = tensor.to(device)
+        except Exception as e:
+            log.error(f'Loader: {e}')
+            log.error(stdout.getvalue())
+            errors.display(e, 'runai')
     return state_dict
 
 
@@ -29,14 +38,20 @@ def hijacked_load_state_dict(checkpoint_file, is_quantized: bool = False, map_lo
         return orig_load_state_dict(checkpoint_file=checkpoint_file, is_quantized=is_quantized, map_location=map_location, weights_only=weights_only)
 
     install('runai_model_streamer')
-    log.trace(f'Loader: method=runai chunk={os.environ["RUNAI_STREAMER_CHUNK_BYTESIZE"]} limit={os.environ["RUNAI_STREAMER_MEMORY_LIMIT"]} device={map_location} quantized={is_quantized}')
+    log.trace(f'Loader: method=runai type=dict chunk={os.environ["RUNAI_STREAMER_CHUNK_BYTESIZE"]} limit={os.environ["RUNAI_STREAMER_MEMORY_LIMIT"]} device={map_location} quantized={is_quantized}')
     state_dict = {}
+    stdout = io.StringIO()
     from runai_model_streamer import SafetensorsStreamer
-    with SafetensorsStreamer() as streamer:
-        streamer.stream_file(checkpoint_file)
-        for key, tensor in streamer.get_tensors():
-            state_dict[key] = tensor.to(map_location) if map_location != "meta" else tensor
-
+    with contextlib.redirect_stdout(stdout):
+        try:
+            with SafetensorsStreamer() as streamer:
+                streamer.stream_file(checkpoint_file)
+                for key, tensor in streamer.get_tensors():
+                    state_dict[key] = tensor.to(map_location) if map_location != "meta" else tensor
+        except Exception as e:
+            log.error(f'Loader: {e}')
+            log.error(stdout.getvalue())
+            errors.display(e, 'runai')
     return state_dict
 
 

modules/sd_offload.py

@@ -18,7 +18,7 @@ offload_warn = ['sc', 'sd3', 'f1', 'h1', 'hunyuandit', 'auraflow', 'omnigen', 'o
 offload_post = ['h1']
 offload_hook_instance = None
 balanced_offload_exclude = ['CogView4Pipeline', 'MeissonicPipeline']
-no_split_module_classes = ["Linear", "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d"]
+no_split_module_classes = ["Linear", "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d", "WanTransformerBlock"]
 accelerate_dtype_byte_size = None
 move_stream = None
 

modules/sd_vae.py

@@ -14,6 +14,7 @@ debug = os.environ.get('SD_LOAD_DEBUG', None) is not None
 unspecified = object()
 vae_scale_override = {
     'WanPipeline': 16,
+    'ChronoEditPipeline': 16,
 }
 
 

modules/shared.py

@@ -619,20 +619,6 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01, "visible": False}),
 
     "postprocessing_sep_detailer": OptionInfo("<h2>Detailer</h2>", "", gr.HTML),
-    "detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
-    "detailer_classes": OptionInfo("", "Detailer classes", gr.Textbox, { "visible": False}),
-    "detailer_conf": OptionInfo(0.6, "Min confidence", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    "detailer_max": OptionInfo(2, "Max detected", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1, "visible": False}),
-    "detailer_iou": OptionInfo(0.5, "Max overlap", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    "detailer_sigma_adjust": OptionInfo(1.0, "Detailer sigma adjust", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    "detailer_sigma_adjust_max": OptionInfo(1.0, "Detailer sigma end", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    "detailer_min_size": OptionInfo(0.0, "Min object size", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    "detailer_max_size": OptionInfo(1.0, "Max object size", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
-    "detailer_padding": OptionInfo(20, "Item padding", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
-    "detailer_blur": OptionInfo(10, "Item edge blur", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
-    "detailer_models": OptionInfo(['face-yolo8n'], "Detailer models", gr.Dropdown, lambda: {"multiselect":True, "choices": list(yolo.list), "visible": False}),
-    "detailer_args": OptionInfo("", "Detailer args", gr.Textbox, { "visible": False}),
-    "detailer_merge": OptionInfo(False, "Merge multiple results from each detailer model", gr.Checkbox, {"visible": False}),
     "detailer_unload": OptionInfo(False, "Move detailer model to CPU when complete"),
     "detailer_augment": OptionInfo(True, "Detailer use model augment"),
 
@@ -671,10 +657,10 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
     "interrogate_vlm_model": OptionInfo(vlm_default, "VLM: default model", gr.Dropdown, {"choices": list(vlm_models)}),
     "interrogate_vlm_prompt": OptionInfo(vlm_prompts[2], "VLM: default prompt", DropdownEditable, {"choices": vlm_prompts }),
     "interrogate_vlm_system": OptionInfo(vlm_system, "VLM: default prompt"),
-    "interrogate_vlm_num_beams": OptionInfo(3, "VLM: num beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1, "visible": False}),
+    "interrogate_vlm_num_beams": OptionInfo(1, "VLM: num beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1, "visible": False}),
     "interrogate_vlm_max_length": OptionInfo(512, "VLM: max length", gr.Slider, {"minimum": 1, "maximum": 4096, "step": 1, "visible": False}),
     "interrogate_vlm_do_sample": OptionInfo(False, "VLM: use sample method"),
-    "interrogate_vlm_temperature": OptionInfo(0, "VLM: num beams", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "visible": False}),
+    "interrogate_vlm_temperature": OptionInfo(0, "VLM: temperature", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "visible": False}),
     "interrogate_vlm_top_k": OptionInfo(0, "VLM: top-k", gr.Slider, {"minimum": 0, "maximum": 99, "step": 1, "visible": False}),
     "interrogate_vlm_top_p": OptionInfo(0, "VLM: top-p", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "visible": False}),
 
@@ -787,6 +773,24 @@ options_templates.update(options_section(('hidden_options', "Hidden options"), {
     'schedulers_max_shift': OptionInfo(1.15, "Sampler max shift", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01, "visible": False}),
     'uni_pc_variant': OptionInfo("bh2", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"], "visible": False}),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"], "visible": False}),
+
+    # detailer settings are handled separately
+    "detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
+    "detailer_classes": OptionInfo("", "Detailer classes", gr.Textbox, { "visible": False}),
+    "detailer_conf": OptionInfo(0.6, "Min confidence", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
+    "detailer_max": OptionInfo(2, "Max detected", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1, "visible": False}),
+    "detailer_iou": OptionInfo(0.5, "Max overlap", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
+    "detailer_sigma_adjust": OptionInfo(1.0, "Detailer sigma adjust", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
+    "detailer_sigma_adjust_max": OptionInfo(1.0, "Detailer sigma end", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
+    "detailer_min_size": OptionInfo(0.0, "Min object size", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
+    "detailer_max_size": OptionInfo(1.0, "Max object size", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
+    "detailer_padding": OptionInfo(20, "Item padding", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
+    "detailer_blur": OptionInfo(10, "Item edge blur", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
+    "detailer_models": OptionInfo(['face-yolo8n'], "Detailer models", gr.Dropdown, lambda: {"multiselect":True, "choices": list(yolo.list), "visible": False}),
+    "detailer_args": OptionInfo("", "Detailer args", gr.Textbox, { "visible": False}),
+    "detailer_merge": OptionInfo(False, "Merge multiple results from each detailer model", gr.Checkbox, {"visible": False}),
+    "detailer_sort": OptionInfo(False, "Sort detailer output by location", gr.Checkbox, {"visible": False}),
+    "detailer_save": OptionInfo(False, "Include detection results", gr.Checkbox, {"visible": False}),
 }))
 
 

modules/shared_items.py

@@ -47,7 +47,6 @@ pipelines = {
     'WanAI': getattr(diffusers, 'WanPipeline', None),
     'Qwen': getattr(diffusers, 'QwenImagePipeline', None),
     'HunyuanImage': getattr(diffusers, 'HunyuanImagePipeline', None),
-    'ChronoEdit': getattr(diffusers, 'WanImageToVideoPipeline', None),
     # dynamically imported and redefined later
     'Meissonic': getattr(diffusers, 'DiffusionPipeline', None),
     'Monetico': getattr(diffusers, 'DiffusionPipeline', None),
@@ -59,6 +58,7 @@ pipelines = {
     'hdm': getattr(diffusers, 'DiffusionPipeline', None),
     'X-Omni': getattr(diffusers, 'DiffusionPipeline', None),
     'HunyuanImage3': getattr(diffusers, 'DiffusionPipeline', None),
+    'ChronoEdit': getattr(diffusers, 'DiffusionPipeline', None),
 }
 
 

modules/timer.py

@@ -4,7 +4,7 @@ import sys
 
 
 try:
-    default_min_time = float(os.environ.get('SD_MIN_TIMER', '0.05'))
+    default_min_time = float(os.environ.get('SD_MIN_TIMER', '0.1'))
 except Exception:
     default_min_time = 0.1
 

modules/ui_settings.py

@@ -208,7 +208,6 @@ def create_ui():
                 if (section_id, section_text) not in sections:
                     sections.append((section_id, section_text))
 
-            shared.log.debug(f'Settings: sections={len(sections)} settings={len(shared.opts.list())}/{len(list(shared.opts.data_labels))} quicksettings={len(quicksettings_list)}')
             with gr.Tabs(elem_id="settings"):
                 quicksettings_list.clear()
                 for (section_id, section_text) in sections:
@@ -241,6 +240,8 @@ def create_ui():
                     create_dirty_indicator("show_all_pages", [])
                 request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications", visible=False)
 
+            shared.log.debug(f'Settings: sections={len(sections)} settings={len(shared.opts.list())}/{len(list(shared.opts.data_labels))} quicksettings={len(quicksettings_list)}')
+
         with gr.TabItem("Update", id="system_update", elem_id="tab_update"):
             from modules import update
             update.create_ui()

modules/windows_hip_ffi.py

@@ -72,11 +72,11 @@ if sys.platform == "win32":
                 idx = idx + 2
                 while prop[idx] != 0x00:
                     c = prop[idx]
+                    idx += 1
                     if (c < 0x30 or c > 0x39) and (c < 0x61 or c > 0x66): # hexadecimal
                         name = ""
                         continue
                     name += chr(c)
-                    idx += 1
                 break
 
             # if name == "", hipDeviceProp does not contain arch name

pipelines/chrono/__init__.py

@@ -0,0 +1,763 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import html
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import PIL
+import regex as re
+import torch
+from transformers import AutoTokenizer, CLIPImageProcessor, CLIPVisionModel, UMT5EncoderModel
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput
+from diffusers.loaders import WanLoraLoaderMixin
+from diffusers.models import AutoencoderKLWan, WanTransformer3DModel
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_ftfy_available():
+    import ftfy
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> import numpy as np
+        >>> from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
+        >>> from diffusers.utils import export_to_video, load_image
+        >>> from transformers import CLIPVisionModel
+
+        >>> # Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+        >>> model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+        >>> image_encoder = CLIPVisionModel.from_pretrained(
+        ...     model_id, subfolder="image_encoder", torch_dtype=torch.float32
+        ... )
+        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+        >>> pipe = WanImageToVideoPipeline.from_pretrained(
+        ...     model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
+        ... )
+        >>> pipe.to("cuda")
+
+        >>> image = load_image(
+        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+        ... )
+        >>> max_area = 480 * 832
+        >>> aspect_ratio = image.height / image.width
+        >>> mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+        >>> height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+        >>> width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+        >>> image = image.resize((width, height))
+        >>> prompt = (
+        ...     "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
+        ...     "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
+        ... )
+        >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+        >>> output = pipe(
+        ...     image=image,
+        ...     prompt=prompt,
+        ...     negative_prompt=negative_prompt,
+        ...     height=height,
+        ...     width=width,
+        ...     num_frames=81,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=16)
+        ```
+"""
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def prompt_clean(text):
+    text = whitespace_clean(basic_clean(text))
+    return text
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class ChronoEditPipeline(DiffusionPipeline, WanLoraLoaderMixin):
+    r"""
+    Pipeline for image-to-video generation using Wan.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        tokenizer ([`T5Tokenizer`]):
+            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
+            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        text_encoder ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        image_encoder ([`CLIPVisionModel`]):
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModel), specifically
+            the
+            [clip-vit-huge-patch14](https://github.com/mlfoundations/open_clip/blob/main/docs/PRETRAINED.md#vit-h14-xlm-roberta-large)
+            variant.
+        transformer ([`WanTransformer3DModel`]):
+            Conditional Transformer to denoise the input latents.
+        scheduler ([`UniPCMultistepScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLWan`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+    """
+
+    model_cpu_offload_seq = "text_encoder->image_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        text_encoder: UMT5EncoderModel,
+        image_encoder: CLIPVisionModel,
+        image_processor: CLIPImageProcessor,
+        transformer: WanTransformer3DModel,
+        vae: AutoencoderKLWan,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            image_encoder=image_encoder,
+            transformer=transformer,
+            scheduler=scheduler,
+            image_processor=image_processor,
+        )
+
+        self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+        self.image_processor = image_processor
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 512,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(u) for u in prompt]
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
+        seq_lens = mask.gt(0).sum(dim=1).long()
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    def encode_image(
+        self,
+        image: PipelineImageInput,
+        device: Optional[torch.device] = None,
+    ):
+        device = device or self._execution_device
+        image = self.image_processor(images=image, return_tensors="pt").to(device)
+        image_embeds = self.image_encoder(**image, output_hidden_states=True)
+        return image_embeds.hidden_states[-2]
+
+    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def check_inputs(
+        self,
+        prompt,
+        negative_prompt,
+        image,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        image_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if image is not None and image_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `image`: {image} and `image_embeds`: {image_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        if image is None and image_embeds is None:
+            raise ValueError(
+                "Provide either `image` or `prompt_embeds`. Cannot leave both `image` and `image_embeds` undefined."
+            )
+        if image is not None and not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image):
+            raise ValueError(f"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is {type(image)}")
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif negative_prompt is not None and (
+            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
+        ):
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+
+    def prepare_latents(
+        self,
+        image: PipelineImageInput,
+        batch_size: int,
+        num_channels_latents: int = 16,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        latent_height = height // self.vae_scale_factor_spatial
+        latent_width = width // self.vae_scale_factor_spatial
+
+        shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        image = image.unsqueeze(2)
+        video_condition = torch.cat(
+            [image, image.new_zeros(image.shape[0], image.shape[1], num_frames - 1, height, width)], dim=2
+        )
+        video_condition = video_condition.to(device=device, dtype=dtype)
+
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.vae.config.z_dim, 1, 1, 1)
+            .to(latents.device, latents.dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            latents.device, latents.dtype
+        )
+
+        if isinstance(generator, list):
+            latent_condition = [
+                retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax") for _ in generator
+            ]
+            latent_condition = torch.cat(latent_condition)
+        else:
+            latent_condition = retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax")
+            latent_condition = latent_condition.repeat(batch_size, 1, 1, 1, 1)
+
+        latent_condition = (latent_condition - latents_mean) * latents_std
+
+        mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width)
+        mask_lat_size[:, :, list(range(1, num_frames))] = 0
+        first_frame_mask = mask_lat_size[:, :, 0:1]
+        first_frame_mask = torch.repeat_interleave(first_frame_mask, dim=2, repeats=self.vae_scale_factor_temporal)
+        mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2)
+        mask_lat_size = mask_lat_size.view(batch_size, -1, self.vae_scale_factor_temporal, latent_height, latent_width)
+        mask_lat_size = mask_lat_size.transpose(1, 2)
+        mask_lat_size = mask_lat_size.to(latent_condition.device)
+
+        return latents, torch.concat([mask_lat_size, latent_condition], dim=1)
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        image_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "np",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        enable_temporal_reasoning: bool = False,
+        num_temporal_reasoning_steps: int = 0,
+        offload_model: bool=False
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            image (`PipelineImageInput`):
+                The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            height (`int`, defaults to `480`):
+                The height of the generated video.
+            width (`int`, defaults to `832`):
+                The width of the generated video.
+            num_frames (`int`, defaults to `81`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, defaults to `50`):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, defaults to `5.0`):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `negative_prompt` input argument.
+            image_embeds (`torch.Tensor`, *optional*):
+                Pre-generated image embeddings. Can be used to easily tweak image inputs (weighting). If not provided,
+                image embeddings are generated from the `image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`WanPipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, *optional*, defaults to `512`):
+                The maximum sequence length of the prompt.
+            shift (`float`, *optional*, defaults to `5.0`):
+                The shift of the flow.
+            autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
+                The dtype to use for the torch.amp.autocast.
+        Examples:
+
+        Returns:
+            [`~WanPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`WanPipelineOutput`] is returned, otherwise a `tuple` is returned where
+                the first element is a list with the generated images and the second element is a list of `bool`s
+                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            negative_prompt,
+            image,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            image_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        if num_frames % self.vae_scale_factor_temporal != 1:
+            logger.warning(
+                f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number."
+            )
+            num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
+        num_frames = max(num_frames, 1)
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        device = self._execution_device
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if offload_model:
+            self.text_encoder.cpu()
+        # Encode image embedding
+        transformer_dtype = self.transformer.dtype
+        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        if negative_prompt_embeds is not None:
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+
+        if image_embeds is None:
+            image_embeds = self.encode_image(image, device)
+        image_embeds = image_embeds.repeat(batch_size, 1, 1)
+        image_embeds = image_embeds.to(transformer_dtype)
+
+        if offload_model:
+            self.image_encoder.cpu()
+
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.vae.config.z_dim
+        image = self.video_processor.preprocess(image, height=height, width=width).to(device, dtype=torch.bfloat16)
+        latents, condition = self.prepare_latents(
+            image,
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_frames,
+            torch.bfloat16,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        if offload_model:
+            torch.cuda.empty_cache()
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+
+                if self.interrupt:
+                    continue
+
+                if enable_temporal_reasoning and i == num_temporal_reasoning_steps:
+                    latents = latents[:, :, [0, -1]]
+                    condition = condition[:, :, [0, -1]]
+
+                    for j in range(len(self.scheduler.model_outputs)):
+                        if self.scheduler.model_outputs[j] is not None:
+                            if latents.shape[-3] != self.scheduler.model_outputs[j].shape[-3]:
+                                self.scheduler.model_outputs[j] = self.scheduler.model_outputs[j][:,:,[0, -1]]
+                    if self.scheduler.last_sample is not None:
+                        self.scheduler.last_sample = self.scheduler.last_sample[:, :, [0, -1]]
+
+                self._current_timestep = t
+                latent_model_input = torch.cat([latents, condition], dim=1).to(transformer_dtype)
+                timestep = t.expand(latents.shape[0])
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    encoder_hidden_states_image=image_embeds,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                if offload_model:
+                    torch.cuda.empty_cache()
+
+                if self.do_classifier_free_guidance:
+                    noise_uncond = self.transformer(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        encoder_hidden_states_image=image_embeds,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if offload_model:
+            self.transformer.cpu()
+            torch.cuda.empty_cache()
+
+        self._current_timestep = None
+
+        if output_type != "latent":
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
+
+            if enable_temporal_reasoning and num_temporal_reasoning_steps > 0:
+                video_edit = self.vae.decode(latents[:, :, [0, -1]], return_dict=False)[0]
+                video_reason = self.vae.decode(latents[:, :, :-1], return_dict=False)[0]
+                video = torch.cat([video_reason, video_edit[:, :, 1:]], dim=2)
+            else:
+                video = self.vae.decode(latents, return_dict=False)[0]
+
+            # video = self.vae.decode(latents, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return WanPipelineOutput(frames=video)

pipelines/chrono/pipeline_chronoedit.py

@@ -1,764 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import html
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import PIL
-import regex as re
-import torch
-from transformers import AutoTokenizer, CLIPImageProcessor, CLIPVisionModel, UMT5EncoderModel
-
-from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
-from diffusers.image_processor import PipelineImageInput
-from diffusers.loaders import WanLoraLoaderMixin
-from diffusers.models import AutoencoderKLWan, WanTransformer3DModel # pylint: disable=unused-import # register to diffusers
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.video_processor import VideoProcessor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput
-from .transformer_chronoedit import ChronoEditTransformer3DModel
-
-
-if is_torch_xla_available():
-    import torch_xla.core.xla_model as xm
-
-    XLA_AVAILABLE = True
-else:
-    XLA_AVAILABLE = False
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-if is_ftfy_available():
-    import ftfy
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```python
-        >>> import torch
-        >>> import numpy as np
-        >>> from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
-        >>> from diffusers.utils import export_to_video, load_image
-        >>> from transformers import CLIPVisionModel
-
-        >>> # Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
-        >>> model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-        >>> image_encoder = CLIPVisionModel.from_pretrained(
-        ...     model_id, subfolder="image_encoder", torch_dtype=torch.float32
-        ... )
-        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-        >>> pipe = WanImageToVideoPipeline.from_pretrained(
-        ...     model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
-        ... )
-        >>> pipe.to("cuda")
-
-        >>> image = load_image(
-        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
-        ... )
-        >>> max_area = 480 * 832
-        >>> aspect_ratio = image.height / image.width
-        >>> mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-        >>> height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-        >>> width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-        >>> image = image.resize((width, height))
-        >>> prompt = (
-        ...     "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
-        ...     "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
-        ... )
-        >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
-
-        >>> output = pipe(
-        ...     image=image,
-        ...     prompt=prompt,
-        ...     negative_prompt=negative_prompt,
-        ...     height=height,
-        ...     width=width,
-        ...     num_frames=81,
-        ...     guidance_scale=5.0,
-        ... ).frames[0]
-        >>> export_to_video(output, "output.mp4", fps=16)
-        ```
-"""
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-    return text
-
-
-def prompt_clean(text):
-    text = whitespace_clean(basic_clean(text))
-    return text
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
-def retrieve_latents(
-    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
-):
-    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
-        return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        return encoder_output.latent_dist.mode()
-    elif hasattr(encoder_output, "latents"):
-        return encoder_output.latents
-    else:
-        raise AttributeError("Could not access latents of provided encoder_output")
-
-
-class ChronoEditPipeline(DiffusionPipeline, WanLoraLoaderMixin):
-    r"""
-    Pipeline for image-to-video generation using Wan.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        tokenizer ([`T5Tokenizer`]):
-            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
-            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
-        text_encoder ([`T5EncoderModel`]):
-            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
-            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
-        image_encoder ([`CLIPVisionModel`]):
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModel), specifically
-            the
-            [clip-vit-huge-patch14](https://github.com/mlfoundations/open_clip/blob/main/docs/PRETRAINED.md#vit-h14-xlm-roberta-large)
-            variant.
-        transformer ([`WanTransformer3DModel`]):
-            Conditional Transformer to denoise the input latents.
-        scheduler ([`UniPCMultistepScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKLWan`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
-    """
-
-    model_cpu_offload_seq = "text_encoder->image_encoder->transformer->vae"
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
-
-    def __init__(
-        self,
-        tokenizer: AutoTokenizer,
-        text_encoder: UMT5EncoderModel,
-        image_encoder: CLIPVisionModel,
-        image_processor: CLIPImageProcessor,
-        transformer: ChronoEditTransformer3DModel,
-        vae: AutoencoderKLWan,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            image_encoder=image_encoder,
-            transformer=transformer,
-            scheduler=scheduler,
-            image_processor=image_processor,
-        )
-
-        self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
-        self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
-        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
-        self.image_processor = image_processor
-
-    def _get_t5_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]] = None,
-        num_videos_per_prompt: int = 1,
-        max_sequence_length: int = 512,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        device = device or self._execution_device
-        dtype = dtype or self.text_encoder.dtype
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        prompt = [prompt_clean(u) for u in prompt]
-        batch_size = len(prompt)
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            add_special_tokens=True,
-            return_attention_mask=True,
-            return_tensors="pt",
-        )
-        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
-        seq_lens = mask.gt(0).sum(dim=1).long()
-
-        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
-        prompt_embeds = torch.stack(
-            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
-        )
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        _, seq_len, _ = prompt_embeds.shape
-        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
-
-        return prompt_embeds
-
-    def encode_image(
-        self,
-        image: PipelineImageInput,
-        device: Optional[torch.device] = None,
-    ):
-        device = device or self._execution_device
-        image = self.image_processor(images=image, return_tensors="pt").to(device)
-        image_embeds = self.image_encoder(**image, output_hidden_states=True)
-        return image_embeds.hidden_states[-2]
-
-    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        do_classifier_free_guidance: bool = True,
-        num_videos_per_prompt: int = 1,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        max_sequence_length: int = 226,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                Whether to use classifier free guidance or not.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
-            prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            device: (`torch.device`, *optional*):
-                torch device
-            dtype: (`torch.dtype`, *optional*):
-                torch dtype
-        """
-        device = device or self._execution_device
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            prompt_embeds = self._get_t5_prompt_embeds(
-                prompt=prompt,
-                num_videos_per_prompt=num_videos_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-                dtype=dtype,
-            )
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-
-            negative_prompt_embeds = self._get_t5_prompt_embeds(
-                prompt=negative_prompt,
-                num_videos_per_prompt=num_videos_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-                dtype=dtype,
-            )
-
-        return prompt_embeds, negative_prompt_embeds
-
-    def check_inputs(
-        self,
-        prompt,
-        negative_prompt,
-        image,
-        height,
-        width,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        image_embeds=None,
-        callback_on_step_end_tensor_inputs=None,
-    ):
-        if image is not None and image_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `image`: {image} and `image_embeds`: {image_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        if image is None and image_embeds is None:
-            raise ValueError(
-                "Provide either `image` or `prompt_embeds`. Cannot leave both `image` and `image_embeds` undefined."
-            )
-        if image is not None and not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image):
-            raise ValueError(f"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is {type(image)}")
-        if height % 16 != 0 or width % 16 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
-
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        elif negative_prompt is not None and (
-            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
-        ):
-            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
-
-    def prepare_latents(
-        self,
-        image: PipelineImageInput,
-        batch_size: int,
-        num_channels_latents: int = 16,
-        height: int = 480,
-        width: int = 832,
-        num_frames: int = 81,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
-        latent_height = height // self.vae_scale_factor_spatial
-        latent_width = width // self.vae_scale_factor_spatial
-
-        shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device=device, dtype=dtype)
-
-        image = image.unsqueeze(2)
-        video_condition = torch.cat(
-            [image, image.new_zeros(image.shape[0], image.shape[1], num_frames - 1, height, width)], dim=2
-        )
-        video_condition = video_condition.to(device=device, dtype=dtype)
-
-        latents_mean = (
-            torch.tensor(self.vae.config.latents_mean)
-            .view(1, self.vae.config.z_dim, 1, 1, 1)
-            .to(latents.device, latents.dtype)
-        )
-        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
-            latents.device, latents.dtype
-        )
-
-        if isinstance(generator, list):
-            latent_condition = [
-                retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax") for _ in generator
-            ]
-            latent_condition = torch.cat(latent_condition)
-        else:
-            latent_condition = retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax")
-            latent_condition = latent_condition.repeat(batch_size, 1, 1, 1, 1)
-
-        latent_condition = (latent_condition - latents_mean) * latents_std
-
-        mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width)
-        mask_lat_size[:, :, list(range(1, num_frames))] = 0
-        first_frame_mask = mask_lat_size[:, :, 0:1]
-        first_frame_mask = torch.repeat_interleave(first_frame_mask, dim=2, repeats=self.vae_scale_factor_temporal)
-        mask_lat_size = torch.concat([first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2)
-        mask_lat_size = mask_lat_size.view(batch_size, -1, self.vae_scale_factor_temporal, latent_height, latent_width)
-        mask_lat_size = mask_lat_size.transpose(1, 2)
-        mask_lat_size = mask_lat_size.to(latent_condition.device)
-
-        return latents, torch.concat([mask_lat_size, latent_condition], dim=1)
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def current_timestep(self):
-        return self._current_timestep
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @property
-    def attention_kwargs(self):
-        return self._attention_kwargs
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image: PipelineImageInput,
-        prompt: Union[str, List[str]] = None,
-        negative_prompt: Union[str, List[str]] = None,
-        height: int = 480,
-        width: int = 832,
-        num_frames: int = 81,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 5.0,
-        num_videos_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        image_embeds: Optional[torch.Tensor] = None,
-        output_type: Optional[str] = "np",
-        return_dict: bool = True,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[
-            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
-        ] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 512,
-        enable_temporal_reasoning: bool = False,
-        num_temporal_reasoning_steps: int = 0,
-        offload_model: bool=False
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            image (`PipelineImageInput`):
-                The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            height (`int`, defaults to `480`):
-                The height of the generated video.
-            width (`int`, defaults to `832`):
-                The width of the generated video.
-            num_frames (`int`, defaults to `81`):
-                The number of frames in the generated video.
-            num_inference_steps (`int`, defaults to `50`):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, defaults to `5.0`):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.Tensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `negative_prompt` input argument.
-            image_embeds (`torch.Tensor`, *optional*):
-                Pre-generated image embeddings. Can be used to easily tweak image inputs (weighting). If not provided,
-                image embeddings are generated from the `image` input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`WanPipelineOutput`] instead of a plain tuple.
-            attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
-                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
-                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
-                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
-                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int`, *optional*, defaults to `512`):
-                The maximum sequence length of the prompt.
-            shift (`float`, *optional*, defaults to `5.0`):
-                The shift of the flow.
-            autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
-                The dtype to use for the torch.amp.autocast.
-        Examples:
-
-        Returns:
-            [`~WanPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`WanPipelineOutput`] is returned, otherwise a `tuple` is returned where
-                the first element is a list with the generated images and the second element is a list of `bool`s
-                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
-        """
-
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
-            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            negative_prompt,
-            image,
-            height,
-            width,
-            prompt_embeds,
-            negative_prompt_embeds,
-            image_embeds,
-            callback_on_step_end_tensor_inputs,
-        )
-
-        if num_frames % self.vae_scale_factor_temporal != 1:
-            logger.warning(
-                f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number."
-            )
-            num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
-        num_frames = max(num_frames, 1)
-
-        self._guidance_scale = guidance_scale
-        self._attention_kwargs = attention_kwargs
-        self._current_timestep = None
-        self._interrupt = False
-
-        device = self._execution_device
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            num_videos_per_prompt=num_videos_per_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            max_sequence_length=max_sequence_length,
-            device=device,
-        )
-        if offload_model:
-            self.text_encoder.cpu()
-        # Encode image embedding
-        transformer_dtype = self.transformer.dtype
-        prompt_embeds = prompt_embeds.to(transformer_dtype)
-        if negative_prompt_embeds is not None:
-            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
-
-        if image_embeds is None:
-            image_embeds = self.encode_image(image, device)
-        image_embeds = image_embeds.repeat(batch_size, 1, 1)
-        image_embeds = image_embeds.to(transformer_dtype)
-
-        if offload_model:
-            self.image_encoder.cpu()
-
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.vae.config.z_dim
-        image = self.video_processor.preprocess(image, height=height, width=width).to(device, dtype=torch.bfloat16)
-        latents, condition = self.prepare_latents(
-            image,
-            batch_size * num_videos_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            num_frames,
-            torch.bfloat16,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        self._num_timesteps = len(timesteps)
-
-        if offload_model:
-            torch.cuda.empty_cache()
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-
-                if self.interrupt:
-                    continue
-
-                if enable_temporal_reasoning and i == num_temporal_reasoning_steps:
-                    latents = latents[:, :, [0, -1]]
-                    condition = condition[:, :, [0, -1]]
-
-                    for j in range(len(self.scheduler.model_outputs)):
-                        if self.scheduler.model_outputs[j] is not None:
-                            if latents.shape[-3] != self.scheduler.model_outputs[j].shape[-3]:
-                                self.scheduler.model_outputs[j] = self.scheduler.model_outputs[j][:,:,[0, -1]]
-                    if self.scheduler.last_sample is not None:
-                        self.scheduler.last_sample = self.scheduler.last_sample[:, :, [0, -1]]
-
-                self._current_timestep = t
-                latent_model_input = torch.cat([latents, condition], dim=1).to(transformer_dtype)
-                timestep = t.expand(latents.shape[0])
-
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    encoder_hidden_states_image=image_embeds,
-                    attention_kwargs=attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                if offload_model:
-                    torch.cuda.empty_cache()
-
-                if self.do_classifier_free_guidance:
-                    noise_uncond = self.transformer(
-                        hidden_states=latent_model_input,
-                        timestep=timestep,
-                        encoder_hidden_states=negative_prompt_embeds,
-                        encoder_hidden_states_image=image_embeds,
-                        attention_kwargs=attention_kwargs,
-                        return_dict=False,
-                    )[0]
-                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-                if XLA_AVAILABLE:
-                    xm.mark_step()
-
-        if offload_model:
-            self.transformer.cpu()
-            torch.cuda.empty_cache()
-
-        self._current_timestep = None
-
-        if output_type != "latent":
-            latents = latents.to(self.vae.dtype)
-            latents_mean = (
-                torch.tensor(self.vae.config.latents_mean)
-                .view(1, self.vae.config.z_dim, 1, 1, 1)
-                .to(latents.device, latents.dtype)
-            )
-            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
-                latents.device, latents.dtype
-            )
-            latents = latents / latents_std + latents_mean
-
-            if enable_temporal_reasoning and num_temporal_reasoning_steps > 0:
-                video_edit = self.vae.decode(latents[:, :, [0, -1]], return_dict=False)[0]
-                video_reason = self.vae.decode(latents[:, :, :-1], return_dict=False)[0]
-                video = torch.cat([video_reason, video_edit[:, :, 1:]], dim=2)
-            else:
-                video = self.vae.decode(latents, return_dict=False)[0]
-
-            # video = self.vae.decode(latents, return_dict=False)[0]
-            video = self.video_processor.postprocess_video(video, output_type=output_type)
-        else:
-            video = latents
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (video,)
-
-        return WanPipelineOutput(frames=video)

pipelines/chrono/transformer_chronoedit.py

@@ -1,476 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from typing import Any, Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
-from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
-from diffusers.models.attention import FeedForward
-from diffusers.models.attention_processor import Attention
-from diffusers.models.cache_utils import CacheMixin
-from diffusers.models.embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import FP32LayerNorm
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class ChronoEditAttnProcessor2_0:
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("ChronoEditAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        encoder_hidden_states_img = None
-        if attn.add_k_proj is not None:
-            encoder_hidden_states_img = encoder_hidden_states[:, :257]
-            encoder_hidden_states = encoder_hidden_states[:, 257:]
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-
-        if rotary_emb is not None:
-
-            def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):
-                x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2)))
-                x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4)
-                return x_out.type_as(hidden_states)
-
-            query = apply_rotary_emb(query, rotary_emb)
-            key = apply_rotary_emb(key, rotary_emb)
-
-        # I2V task
-        hidden_states_img = None
-        if encoder_hidden_states_img is not None:
-            key_img = attn.add_k_proj(encoder_hidden_states_img)
-            key_img = attn.norm_added_k(key_img)
-            value_img = attn.add_v_proj(encoder_hidden_states_img)
-
-            key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-            value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-
-            hidden_states_img = F.scaled_dot_product_attention(
-                query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False
-            )
-            hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3)
-            hidden_states_img = hidden_states_img.type_as(query)
-
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
-        hidden_states = hidden_states.type_as(query)
-
-        if hidden_states_img is not None:
-            hidden_states = hidden_states + hidden_states_img
-
-        hidden_states = attn.to_out[0](hidden_states)
-        hidden_states = attn.to_out[1](hidden_states)
-        return hidden_states
-
-
-class ChronoEditImageEmbedding(torch.nn.Module):
-    def __init__(self, in_features: int, out_features: int):
-        super().__init__()
-
-        self.norm1 = FP32LayerNorm(in_features)
-        self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu")
-        self.norm2 = FP32LayerNorm(out_features)
-
-    def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.norm1(encoder_hidden_states_image)
-        hidden_states = self.ff(hidden_states)
-        hidden_states = self.norm2(hidden_states)
-        return hidden_states
-
-
-class ChronoEditTimeTextImageEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        time_freq_dim: int,
-        time_proj_dim: int,
-        text_embed_dim: int,
-        image_embed_dim: Optional[int] = None,
-    ):
-        super().__init__()
-
-        self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim)
-        self.act_fn = nn.SiLU()
-        self.time_proj = nn.Linear(dim, time_proj_dim)
-        self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh")
-
-        self.image_embedder = None
-        if image_embed_dim is not None:
-            self.image_embedder = ChronoEditImageEmbedding(image_embed_dim, dim)
-
-    def forward(
-        self,
-        timestep: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_hidden_states_image: Optional[torch.Tensor] = None,
-    ):
-        timestep = self.timesteps_proj(timestep)
-
-        time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
-        if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:
-            timestep = timestep.to(time_embedder_dtype)
-        temb = self.time_embedder(timestep).type_as(encoder_hidden_states)
-        timestep_proj = self.time_proj(self.act_fn(temb))
-
-        encoder_hidden_states = self.text_embedder(encoder_hidden_states)
-        if encoder_hidden_states_image is not None:
-            encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image)
-
-        return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
-
-
-class ChronoEditRotaryPosEmbed(nn.Module):
-    def __init__(
-        self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0, temporal_skip_len: int = 8
-    ):
-        super().__init__()
-
-        self.attention_head_dim = attention_head_dim
-        self.patch_size = patch_size
-        self.max_seq_len = max_seq_len
-        self.temporal_skip_len = temporal_skip_len
-
-        h_dim = w_dim = 2 * (attention_head_dim // 6)
-        t_dim = attention_head_dim - h_dim - w_dim
-
-        freqs = []
-        for dim in [t_dim, h_dim, w_dim]:
-            freq = get_1d_rotary_pos_embed(
-                dim, max_seq_len, theta, use_real=False, repeat_interleave_real=False, freqs_dtype=torch.float64
-            )
-            freqs.append(freq)
-        self.freqs = torch.cat(freqs, dim=1)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        _batch_size, _num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.patch_size
-        ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w
-
-        self.freqs = self.freqs.to(hidden_states.device)
-        freqs = self.freqs.split_with_sizes(
-            [
-                self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6),
-                self.attention_head_dim // 6,
-                self.attention_head_dim // 6,
-            ],
-            dim=1,
-        )
-
-        assert num_frames == 2 or num_frames == self.temporal_skip_len, f"num_frames must be 2 or {self.temporal_skip_len}, but got {num_frames}"
-        if num_frames == 2:
-            freqs_f = freqs[0][:self.temporal_skip_len][[0, -1]].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
-        else:
-            freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
-        freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1).reshape(1, 1, ppf * pph * ppw, -1)
-        return freqs
-
-
-class ChronoEditTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        ffn_dim: int,
-        num_heads: int,
-        qk_norm: str = "rms_norm_across_heads",
-        cross_attn_norm: bool = False,
-        eps: float = 1e-6,
-        added_kv_proj_dim: Optional[int] = None,
-    ):
-        super().__init__()
-
-        # 1. Self-attention
-        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_heads,
-            kv_heads=num_heads,
-            dim_head=dim // num_heads,
-            qk_norm=qk_norm,
-            eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
-            processor=ChronoEditAttnProcessor2_0(),
-        )
-
-        # 2. Cross-attention
-        self.attn2 = Attention(
-            query_dim=dim,
-            heads=num_heads,
-            kv_heads=num_heads,
-            dim_head=dim // num_heads,
-            qk_norm=qk_norm,
-            eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
-            added_kv_proj_dim=added_kv_proj_dim,
-            added_proj_bias=True,
-            processor=ChronoEditAttnProcessor2_0(),
-        )
-        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
-
-        # 3. Feed-forward
-        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
-        self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)
-
-        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        rotary_emb: torch.Tensor,
-    ) -> torch.Tensor:
-        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-            self.scale_shift_table + temb.float()
-        ).chunk(6, dim=1)
-
-        # 1. Self-attention
-        norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
-        attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
-        hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)
-
-        # 2. Cross-attention
-        norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
-        attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
-        hidden_states = hidden_states + attn_output
-
-        # 3. Feed-forward
-        norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
-            hidden_states
-        )
-        ff_output = self.ffn(norm_hidden_states)
-        hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states)
-
-        return hidden_states
-
-
-class ChronoEditTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
-    r"""
-    A Transformer model for video-like data used in the ChronoEdit model.
-
-    Args:
-        patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
-            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
-        num_attention_heads (`int`, defaults to `40`):
-            Fixed length for text embeddings.
-        attention_head_dim (`int`, defaults to `128`):
-            The number of channels in each head.
-        in_channels (`int`, defaults to `16`):
-            The number of channels in the input.
-        out_channels (`int`, defaults to `16`):
-            The number of channels in the output.
-        text_dim (`int`, defaults to `512`):
-            Input dimension for text embeddings.
-        freq_dim (`int`, defaults to `256`):
-            Dimension for sinusoidal time embeddings.
-        ffn_dim (`int`, defaults to `13824`):
-            Intermediate dimension in feed-forward network.
-        num_layers (`int`, defaults to `40`):
-            The number of layers of transformer blocks to use.
-        window_size (`Tuple[int]`, defaults to `(-1, -1)`):
-            Window size for local attention (-1 indicates global attention).
-        cross_attn_norm (`bool`, defaults to `True`):
-            Enable cross-attention normalization.
-        qk_norm (`bool`, defaults to `True`):
-            Enable query/key normalization.
-        eps (`float`, defaults to `1e-6`):
-            Epsilon value for normalization layers.
-        add_img_emb (`bool`, defaults to `False`):
-            Whether to use img_emb.
-        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
-            The number of channels to use for the added key and value projections. If `None`, no projection is used.
-    """
-
-    _supports_gradient_checkpointing = True
-    _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
-    _no_split_modules = ["ChronoEditTransformerBlock"]
-    _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
-    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
-
-    @register_to_config
-    def __init__(
-        self,
-        patch_size: Tuple[int] = (1, 2, 2),
-        num_attention_heads: int = 40,
-        attention_head_dim: int = 128,
-        in_channels: int = 16,
-        out_channels: int = 16,
-        text_dim: int = 4096,
-        freq_dim: int = 256,
-        ffn_dim: int = 13824,
-        num_layers: int = 40,
-        cross_attn_norm: bool = True,
-        qk_norm: Optional[str] = "rms_norm_across_heads",
-        eps: float = 1e-6,
-        image_dim: Optional[int] = None,
-        added_kv_proj_dim: Optional[int] = None,
-        rope_max_seq_len: int = 1024,
-        rope_temporal_skip_len: int = 8,
-    ) -> None:
-        super().__init__()
-
-        inner_dim = num_attention_heads * attention_head_dim
-        out_channels = out_channels or in_channels
-
-        # 1. Patch & position embedding
-        self.rope = ChronoEditRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len, temporal_skip_len=rope_temporal_skip_len)
-        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
-
-        # 2. Condition embeddings
-        # image_embedding_dim=1280 for I2V model
-        self.condition_embedder = ChronoEditTimeTextImageEmbedding(
-            dim=inner_dim,
-            time_freq_dim=freq_dim,
-            time_proj_dim=inner_dim * 6,
-            text_embed_dim=text_dim,
-            image_embed_dim=image_dim,
-        )
-
-        # 3. Transformer blocks
-        self.blocks = nn.ModuleList(
-            [
-                ChronoEditTransformerBlock(
-                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-        # 4. Output norm & projection
-        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
-        self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
-        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.LongTensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_hidden_states_image: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
-        batch_size, _num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.config.patch_size # pylint: disable=no-member
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        rotary_emb = self.rope(hidden_states)
-
-        hidden_states = self.patch_embedding(hidden_states)
-        hidden_states = hidden_states.flatten(2).transpose(1, 2)
-
-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image
-        )
-        timestep_proj = timestep_proj.unflatten(1, (6, -1))
-
-        if encoder_hidden_states_image is not None:
-            encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
-
-        # 4. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block in self.blocks:
-                hidden_states = self._gradient_checkpointing_func(
-                    block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb
-                )
-        else:
-            for block in self.blocks:
-                hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)
-
-        # 5. Output norm, projection & unpatchify
-        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
-
-        # Move the shift and scale tensors to the same device as hidden_states.
-        # When using multi-GPU inference via accelerate these will be on the
-        # first device rather than the last device, which hidden_states ends up
-        # on.
-        shift = shift.to(hidden_states.device)
-        scale = scale.to(hidden_states.device)
-
-        hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
-        hidden_states = self.proj_out(hidden_states)
-
-        hidden_states = hidden_states.reshape(
-            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
-        )
-        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
-        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)

pipelines/generic.py

@@ -101,7 +101,9 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
             from modules import sdnq # pylint: disable=unused-import # register to diffusers and transformers
         load_args, quant_args = model_quant.get_dit_args(load_config, module='TE', device_map=True, allow_quant=allow_quant, modules_to_not_convert=modules_to_not_convert, modules_dtype_dict=modules_dtype_dict)
         quant_type = model_quant.get_quant_type(quant_args)
+        load_args.pop('torch_dtype', None)
         dtype = dtype or devices.dtype
+        load_args['dtype'] = dtype
 
         # load from local file if specified
         local_file = None
@@ -128,12 +130,14 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
             """
             text_encoder = model_te.load_t5(local_file)
             text_encoder = model_quant.do_post_load_quant(text_encoder, allow=quant_type is not None)
+
         # load from local file safetensors
         elif local_file is not None and local_file.lower().endswith('.safetensors'):
             shared.log.debug(f'Load model: text_encoder="{local_file}" cls={cls_name.__name__} quant="{quant_type}"')
             from modules import model_te
             text_encoder = model_te.load_t5(local_file)
             text_encoder = model_quant.do_post_load_quant(text_encoder, allow=quant_type is not None)
+
         # use shared t5 if possible
         elif cls_name == transformers.T5EncoderModel and allow_shared and shared.opts.te_shared_t5:
             if model_quant.check_nunchaku('TE'):
@@ -155,8 +159,6 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
                     with open(os.path.join('configs', 'flux', 'text_encoder_2', 'config.json'), encoding='utf8') as f:
                         load_args['config'] = transformers.T5Config(**json.load(f))
                 shared.log.debug(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} quant="{quant_type}" shared={shared.opts.te_shared_t5}')
-                if dtype is not None:
-                    load_args['torch_dtype'] = dtype
                 text_encoder = cls_name.from_pretrained(
                     repo_id,
                     cache_dir=shared.opts.hfcache_dir,
@@ -170,8 +172,6 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
                 repo_id = 'Wan-AI/Wan2.1-T2V-1.3B-Diffusers'
             subfolder = 'text_encoder'
             shared.log.debug(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} quant="{quant_type}" shared={shared.opts.te_shared_t5}')
-            if dtype is not None:
-                load_args['torch_dtype'] = dtype
             text_encoder = cls_name.from_pretrained(
                 repo_id,
                 cache_dir=shared.opts.hfcache_dir,
@@ -183,8 +183,6 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
             repo_id = 'hunyuanvideo-community/HunyuanImage-2.1-Diffusers'
             subfolder = 'text_encoder'
             shared.log.debug(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} quant="{quant_type}" shared={shared.opts.te_shared_t5}')
-            if dtype is not None:
-                load_args['torch_dtype'] = dtype
             text_encoder = cls_name.from_pretrained(
                 repo_id,
                 cache_dir=shared.opts.hfcache_dir,
@@ -196,8 +194,6 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
         # load from repo
         if text_encoder is None:
             shared.log.debug(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} quant="{quant_type}" shared={shared.opts.te_shared_t5}')
-            if dtype is not None:
-                load_args['torch_dtype'] = dtype
             if subfolder is not None:
                 load_args['subfolder'] = subfolder
             if variant is not None:

pipelines/model_chrono.py

@@ -1,4 +1,5 @@
 import sys
+import diffusers
 import transformers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
 from pipelines import generic
@@ -20,20 +21,12 @@ def load_chrono(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
     shared.log.debug(f'Load model: type=ChronoEdit repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
-    from pipelines.chrono import pipeline_chronoedit
-    from pipelines.chrono import transformer_chronoedit
-
-    # monkey patch for <https://huggingface.co/Disty0/ChronoEdit-14B-SDNQ-uint4-svd-r32/blob/main/model_index.json>
-    import pipelines.chrono
-    sys.modules['chronoedit_diffusers'] = pipelines.chrono
-    from diffusers.pipelines import pipeline_loading_utils
-    pipeline_loading_utils.LOADABLE_CLASSES['chronoedit_diffusers.transformer_chronoedit'] = {}
-
-    transformer = generic.load_transformer(repo_id, cls_name=transformer_chronoedit.ChronoEditTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer")
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.WanTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer")
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.UMT5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder")
 
     try:
-        pipe = pipeline_chronoedit.ChronoEditPipeline.from_pretrained(
+        from pipelines.chrono import ChronoEditPipeline
+        pipe = ChronoEditPipeline.from_pretrained(
             repo_id,
             transformer=transformer,
             text_encoder=text_encoder,