Merge branch 'dev' into patch-2

CHANGELOG.md

@@ -1,16 +1,16 @@
 # Change Log for SD.Next
 
-## Update for 2025-07-25
+## Update for 2025-07-26
 
-### Highlights for 2025-07-25
+### Highlights for 2025-07-26
 
 Feature highlights include:  
-- **ModernUI** layout redesign which should make it more user friendly and easier to navigate and several new UI themes!  
+- **ModernUI** layout redesign which should make it more user friendly and easier to navigate plus several new UI themes!  
 - New models [WanAI Wan 2.1](https://wan.video/) for text-to-image workflows, [FreePix F-Lite](https://huggingface.co/Freepik/F-Lite), [Bria 3.2](https://huggingface.co/briaai/BRIA-3.2)
 - Redesigned [LTXVideo](https://vladmandic.github.io/sdnext-docs/Video) interface with support for general video models plus optimized [FramePack](https://vladmandic.github.io/sdnext-docs/FramePack) and [LTXVideo](https://vladmandic.github.io/sdnext-docs/LTX) support  
 - Fully integrated nudity detection and optional censorship with [NudeNet](https://vladmandic.github.io/sdnext-docs/NudeNet)  
 - New background replacement and relightning methods using **Latent Bridge Matching** and new **PixelArt** processing filter  
-- New **LLM/VLM** models available for captioning and prompt enhance  
+- Additional **LLM/VLM** models available for captioning and prompt enhance  
 - Number of workflow and general quality-of-life improvements, especially around **Styles**, **Detailer**, **Preview**, **Batch**, **Control**  
 - Compute improvements  
 - [Wiki](https://github.com/vladmandic/automatic/wiki) & [Docs](https://vladmandic.github.io/sdnext-docs/) updates, especially new end-to-end [Parameters](https://vladmandic.github.io/sdnext-docs/Parameters/) page  
@@ -29,7 +29,7 @@ For details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/master
 
 [ReadMe](https://github.com/vladmandic/automatic/blob/master/README.md) | [ChangeLog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [Docs](https://vladmandic.github.io/sdnext-docs/) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867)
 
-### Details for 2025-07-25
+### Details for 2025-07-26
 
 - **License**  
   - SD.Next [license](https://github.com/vladmandic/sdnext/blob/dev/LICENSE.txt) switched from **aGPL-v3.0** to **Apache-v2.0**  
@@ -81,7 +81,7 @@ For details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/master
   - support for post load quantization  
 - **UI**  
   - major update to modernui layout  
-  - add new Windows-like *Blcoks* UI theme  
+  - add new Windows-like *Blocks* UI theme  
   - redesign of the *Flat* UI theme  
 - **WIKI**  
   - new [Parameters](https://vladmandic.github.io/sdnext-docs/Parameters/) page that lists and explains all generation parameters  
@@ -144,6 +144,8 @@ For details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/master
   - fix modules merge save model  
   - fix torchvision bicubic upsample with ipex  
   - fix instantir pipeline  
+  - fix prompt encoding if prompts within batch have different segment counts  
+  - fix detailer min/max size  
   - cleanup control infotext  
   - allow upscaling with models that have implicit VAE processing  
   - framepack improve offloading  

modules/devices.py

@@ -455,7 +455,7 @@ def set_sdpa_params():
                 from flash_attn import flash_attn_func
                 sdpa_pre_flash_atten = torch.nn.functional.scaled_dot_product_attention
                 @wraps(sdpa_pre_flash_atten)
-                def sdpa_flash_atten(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
+                def sdpa_flash_atten(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False, **kwargs):
                     if query.shape[-1] <= 128 and attn_mask is None and query.dtype != torch.float32:
                         is_unsqueezed = False
                         if query.dim() == 3:
@@ -465,6 +465,9 @@ def set_sdpa_params():
                                 key = key.unsqueeze(0)
                             if value.dim() == 3:
                                 value = value.unsqueeze(0)
+                        if enable_gqa:
+                            key = key.repeat_interleave(query.size(-3)//key.size(-3), -3)
+                            value = value.repeat_interleave(query.size(-3)//value.size(-3), -3)
                         query = query.transpose(1, 2)
                         key = key.transpose(1, 2)
                         value = value.transpose(1, 2)
@@ -473,7 +476,9 @@ def set_sdpa_params():
                             attn_output = attn_output.squeeze(0)
                         return attn_output
                     else:
-                        return sdpa_pre_flash_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
+                        if enable_gqa:
+                            kwargs["enable_gqa"] = enable_gqa
+                        return sdpa_pre_flash_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
                 torch.nn.functional.scaled_dot_product_attention = sdpa_flash_atten
                 log.debug('Torch attention: type="ck flash attention"')
             except Exception as err:
@@ -485,11 +490,16 @@ def set_sdpa_params():
                 from sageattention import sageattn
                 sdpa_pre_sage_atten = torch.nn.functional.scaled_dot_product_attention
                 @wraps(sdpa_pre_sage_atten)
-                def sdpa_sage_atten(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
+                def sdpa_sage_atten(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False, **kwargs):
                     if (query.shape[-1] in {128, 96, 64}) and (attn_mask is None) and (query.dtype != torch.float32):
+                        if enable_gqa:
+                            key = key.repeat_interleave(query.size(-3)//key.size(-3), -3)
+                            value = value.repeat_interleave(query.size(-3)//value.size(-3), -3)
                         return sageattn(q=query, k=key, v=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
                     else:
-                        return sdpa_pre_sage_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
+                        if enable_gqa:
+                            kwargs["enable_gqa"] = enable_gqa
+                        return sdpa_pre_sage_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
                 torch.nn.functional.scaled_dot_product_attention = sdpa_sage_atten
                 log.debug('Torch attention: type="sage attention"')
             except Exception as err:

modules/images.py

@@ -242,7 +242,7 @@ def parse_comfy_metadata(data: dict):
             version = dct.get('extra', {}).get('frontendVersion', 'unknown')
             if version is not None:
                 res = f" | Version: {version} | Nodes: {nodes}"
-        except:
+        except Exception:
             pass
         return res
 
@@ -257,7 +257,7 @@ def parse_comfy_metadata(data: dict):
                     model = inp.get('model', None)
                     if isinstance(model, str) and len(model) > 0:
                         res += f" | Model: {model} | Class: {val.get('class_type', '')}"
-        except:
+        except Exception:
             pass
         return res
 
@@ -280,7 +280,7 @@ def parse_invoke_metadata(data: dict):
                 version = dct['app_version']
                 if isinstance(version, str) and len(version) > 0:
                     res += f" | Version: {version}"
-        except:
+        except Exception:
             pass
         return res
 
@@ -299,7 +299,7 @@ def parse_novelai_metadata(data: dict):
             dct = json.loads(data["Comment"])
             sampler = sd_samplers.samplers_map.get(dct["sampler"], "Euler a")
             geninfo = f'{data["Description"]} Negative prompt: {dct["uc"]} Steps: {dct["steps"]}, Sampler: {sampler}, CFG scale: {dct["scale"]}, Seed: {dct["seed"]}, Clip skip: 2, ENSD: 31337'
-        except Exception as e:
+        except Exception:
             pass
     return geninfo
 

modules/intel/ipex/attention.py

@@ -57,9 +57,11 @@ def find_sdpa_slice_sizes(query_shape, key_shape, query_element_size, slice_rate
 
 original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
 @wraps(torch.nn.functional.scaled_dot_product_attention)
-def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs):
+def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False, **kwargs):
     if query.device.type != "xpu":
-        return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs)
+        if enable_gqa:
+            kwargs["enable_gqa"] = enable_gqa
+        return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
     is_unsqueezed = False
     if query.dim() == 3:
         query = query.unsqueeze(0)
@@ -68,6 +70,9 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
             key = key.unsqueeze(0)
         if value.dim() == 3:
             value = value.unsqueeze(0)
+    if enable_gqa:
+        key = key.repeat_interleave(query.size(-3)//key.size(-3), -3)
+        value = value.repeat_interleave(query.size(-3)//value.size(-3), -3)
     do_batch_split, do_head_split, do_query_split, split_batch_size, split_head_size, split_query_size = find_sdpa_slice_sizes(query.shape, key.shape, query.element_size(), slice_rate=attention_slice_rate, trigger_rate=sdpa_slice_trigger_rate)
 
     # Slice SDPA
@@ -93,7 +98,7 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
                                 key[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                                 value[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                                 attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :] if attn_mask is not None else attn_mask,
-                                dropout_p=dropout_p, is_causal=is_causal, **kwargs
+                                dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs
                             )
                     else:
                         hidden_states[start_idx:end_idx, start_idx_h:end_idx_h, :, :] = original_scaled_dot_product_attention(
@@ -101,7 +106,7 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
                             key[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                             value[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                             attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, :, :] if attn_mask is not None else attn_mask,
-                            dropout_p=dropout_p, is_causal=is_causal, **kwargs
+                            dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs
                         )
             else:
                 hidden_states[start_idx:end_idx, :, :, :] = original_scaled_dot_product_attention(
@@ -109,11 +114,11 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
                     key[start_idx:end_idx, :, :, :],
                     value[start_idx:end_idx, :, :, :],
                     attn_mask=attn_mask[start_idx:end_idx, :, :, :] if attn_mask is not None else attn_mask,
-                    dropout_p=dropout_p, is_causal=is_causal, **kwargs
+                    dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs
                 )
         torch.xpu.synchronize(query.device)
     else:
-        hidden_states = original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs)
+        hidden_states = original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
     if is_unsqueezed:
         hidden_states = hidden_states.squeeze(0)
     return hidden_states

modules/postprocess/yolo.py

@@ -144,8 +144,8 @@ class YoloRestorer(Detailer):
                 mask_image = None
                 w, h = box[2] - box[0], box[3] - box[1]
                 x_size, y_size = w/image.width, h/image.height
-                min_size = shared.opts.detailer_min_size if shared.opts.detailer_min_size > 0 and shared.opts.detailer_min_size < 1 else 0
-                max_size = shared.opts.detailer_max_size if shared.opts.detailer_max_size > 0 and shared.opts.detailer_max_size < 1 else 1
+                min_size = shared.opts.detailer_min_size if shared.opts.detailer_min_size >= 0 and shared.opts.detailer_min_size <= 1 else 0
+                max_size = shared.opts.detailer_max_size if shared.opts.detailer_max_size >= 0 and shared.opts.detailer_max_size <= 1 else 1
                 if x_size >= min_size and y_size >=min_size and x_size <= max_size and y_size <= max_size:
                     if mask:
                         mask_image = image.copy()

modules/processing_args.py

@@ -157,7 +157,7 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
         'StableCascade' in model.__class__.__name__ or
         'Flux' in model.__class__.__name__ or
         'Chroma' in model.__class__.__name__ or
-        'HiDreamImagePipeline' in model.__class__.__name__ # hidream-e1 has different embeds
+        'HiDreamImagePipeline' in model.__class__.__name__
     ):
         try:
             prompt_parser_diffusers.embedder = prompt_parser_diffusers.PromptEmbedder(prompts, negative_prompts, steps, clip_skip, p)
@@ -173,23 +173,28 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
     if 'prompt' in possible:
         if 'OmniGen' in model.__class__.__name__:
             prompts = [p.replace('|image|', '<img><|image_1|></img>') for p in prompts]
-        if 'HiDreamImage' in model.__class__.__name__  and prompt_parser_diffusers.embedder is not None:
+        if ('HiDreamImage' in model.__class__.__name__) and (prompt_parser_diffusers.embedder is not None):
             args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
             prompt_embeds = prompt_parser_diffusers.embedder('prompt_embeds')
             args['prompt_embeds_t5'] = prompt_embeds[0]
             args['prompt_embeds_llama3'] = prompt_embeds[1]
-        elif hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'prompt_embeds' in possible and prompt_parser_diffusers.embedder is not None:
-            args['prompt_embeds'] = prompt_parser_diffusers.embedder('prompt_embeds')
-            if 'StableCascade' in model.__class__.__name__:
-                args['prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('positive_pooleds').unsqueeze(0)
-            elif 'XL' in model.__class__.__name__:
-                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
-            elif 'StableDiffusion3' in model.__class__.__name__:
-                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
-            elif 'Flux' in model.__class__.__name__:
-                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
-            elif 'Chroma' in model.__class__.__name__:
-                args['prompt_attention_mask'] = prompt_parser_diffusers.embedder('prompt_attention_masks')
+        elif hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and ('prompt_embeds' in possible) and (prompt_parser_diffusers.embedder is not None):
+            embeds = prompt_parser_diffusers.embedder('prompt_embeds')
+            if embeds is None:
+                shared.log.warning('Prompt parser encode: empty prompt embeds')
+                args['prompt'] = prompts
+            else:
+                args['prompt_embeds'] = embeds
+                if 'StableCascade' in model.__class__.__name__:
+                    args['prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('positive_pooleds').unsqueeze(0)
+                elif 'XL' in model.__class__.__name__:
+                    args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+                elif 'StableDiffusion3' in model.__class__.__name__:
+                    args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+                elif 'Flux' in model.__class__.__name__:
+                    args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+                elif 'Chroma' in model.__class__.__name__:
+                    args['prompt_attention_mask'] = prompt_parser_diffusers.embedder('prompt_attention_masks')
         else:
             args['prompt'] = prompts
     if 'negative_prompt' in possible:
@@ -406,11 +411,13 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
         clean['generator'] = f'{generator[0].device}:{[g.initial_seed() for g in generator]}'
     clean['parser'] = parser
     for k, v in clean.copy().items():
-        if isinstance(v, torch.Tensor) or isinstance(v, np.ndarray):
+        if v is None:
+            clean[k] = None
+        elif isinstance(v, torch.Tensor) or isinstance(v, np.ndarray):
             clean[k] = v.shape
-        if isinstance(v, list) and len(v) > 0 and (isinstance(v[0], torch.Tensor) or isinstance(v[0], np.ndarray)):
+        elif isinstance(v, list) and len(v) > 0 and (isinstance(v[0], torch.Tensor) or isinstance(v[0], np.ndarray)):
             clean[k] = [x.shape for x in v]
-        if not debug_enabled and k.endswith('_embeds'):
+        elif not debug_enabled and k.endswith('_embeds'):
             del clean[k]
             clean['prompt'] = 'embeds'
     task = str(sd_models.get_diffusers_task(model)).replace('DiffusersTaskType.', '')

modules/prompt_parser_diffusers.py

@@ -73,17 +73,17 @@ class PromptEmbedder:
         earlyout = self.checkcache(p)
         if earlyout:
             return
-        pipe = prepare_model(p.sd_model)
-        if pipe is None:
+        self.pipe = prepare_model(p.sd_model)
+        if self.pipe is None:
             shared.log.error("Prompt encode: cannot find text encoder in model")
             return
         # per prompt in batch
         for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts)):
             self.prepare_schedule(prompt, negative_prompt)
             if self.scheduled_prompt:
-                self.scheduled_encode(pipe, batchidx)
+                self.scheduled_encode(self.pipe, batchidx)
             else:
-                self.encode(pipe, prompt, negative_prompt, batchidx)
+                self.encode(self.pipe, prompt, negative_prompt, batchidx)
         self.checkcache(p)
         debug(f"Prompt encode: time={(time.time() - t0):.3f}")
 
@@ -223,6 +223,8 @@ class PromptEmbedder:
         batch = getattr(self, key)
         res = []
         try:
+            if len(batch) == 0 or len(batch[0]) == 0:
+                return None # flux has no negative prompts
             if isinstance(batch[0][0], list) and len(batch[0][0]) == 2 and isinstance(batch[0][0][1], torch.Tensor) and batch[0][0][1].shape[0] == 32:
                 # hidream uses a list of t5 + llama prompt embeds: [t5_embeds, llama_embeds]
                 # t5_embeds shape: [batch_size, seq_len, dim]
@@ -248,9 +250,11 @@ class PromptEmbedder:
                         res.append(batch[i][step])
                     except IndexError:
                         res.append(batch[i][0])  # if not scheduled, return default
+                if any(res[0].shape[1] != r.shape[1] for r in res):
+                    res = pad_to_same_length(self.pipe, res)
                 return torch.cat(res)
-        except Exception:
-            pass
+        except Exception as e:
+            shared.log.error(f"Prompt encode: {e}")
         return None
 
 
@@ -472,7 +476,7 @@ def prepare_embedding_providers(pipe, clip_skip) -> list[EmbeddingsProvider]:
 
 
 def pad_to_same_length(pipe, embeds, empty_embedding_providers=None):
-    if not hasattr(pipe, 'encode_prompt') and 'StableCascade' not in pipe.__class__.__name__:
+    if not hasattr(pipe, 'encode_prompt') and ('StableCascade' not in pipe.__class__.__name__):
         return embeds
     device = devices.device
     if shared.opts.diffusers_zeros_prompt_pad or 'StableDiffusion3' in pipe.__class__.__name__:

modules/sd_hijack_dynamic_atten.py

@@ -54,7 +54,7 @@ def find_sdpa_slice_sizes(query_shape, key_shape, query_element_size, slice_rate
 if devices.sdpa_pre_dyanmic_atten is None:
     devices.sdpa_pre_dyanmic_atten = torch.nn.functional.scaled_dot_product_attention
 @wraps(devices.sdpa_pre_dyanmic_atten)
-def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs):
+def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False, **kwargs):
     is_unsqueezed = False
     if query.dim() == 3:
         query = query.unsqueeze(0)
@@ -63,6 +63,9 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
             key = key.unsqueeze(0)
         if value.dim() == 3:
             value = value.unsqueeze(0)
+    if enable_gqa:
+        key = key.repeat_interleave(query.size(-3)//key.size(-3), -3)
+        value = value.repeat_interleave(query.size(-3)//value.size(-3), -3)
     do_batch_split, do_head_split, do_query_split, split_batch_size, split_head_size, split_query_size = find_sdpa_slice_sizes(query.shape, key.shape, query.element_size(), slice_rate=shared.opts.dynamic_attention_slice_rate, trigger_rate=shared.opts.dynamic_attention_trigger_rate)
 
     # Slice SDPA
@@ -88,7 +91,7 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
                                 key[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                                 value[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                                 attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :] if attn_mask is not None else attn_mask,
-                                dropout_p=dropout_p, is_causal=is_causal, **kwargs
+                                dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs
                             )
                     else:
                         hidden_states[start_idx:end_idx, start_idx_h:end_idx_h, :, :] = devices.sdpa_pre_dyanmic_atten(
@@ -96,7 +99,7 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
                             key[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                             value[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
                             attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, :, :] if attn_mask is not None else attn_mask,
-                            dropout_p=dropout_p, is_causal=is_causal, **kwargs
+                            dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs
                         )
             else:
                 hidden_states[start_idx:end_idx, :, :, :] = devices.sdpa_pre_dyanmic_atten(
@@ -104,12 +107,12 @@ def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, drop
                     key[start_idx:end_idx, :, :, :],
                     value[start_idx:end_idx, :, :, :],
                     attn_mask=attn_mask[start_idx:end_idx, :, :, :] if attn_mask is not None else attn_mask,
-                    dropout_p=dropout_p, is_causal=is_causal, **kwargs
+                    dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs
                 )
         if devices.backend != "directml":
             getattr(torch, query.device.type).synchronize()
     else:
-        hidden_states = devices.sdpa_pre_dyanmic_atten(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs)
+        hidden_states = devices.sdpa_pre_dyanmic_atten(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
     if is_unsqueezed:
         hidden_states = hidden_states.squeeze(0)
     return hidden_states

modules/shared.py

@@ -618,8 +618,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
     "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": 0.1, "maximum": 1, "step": 0.05, "visible": False}),
-    "detailer_max_size": OptionInfo(1.0, "Max object size", gr.Slider, {"minimum": 0.1, "maximum": 1, "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}),

modules/ui_common.py

@@ -174,10 +174,11 @@ def save_files(js_data, files, html_info, index):
                 items = infotext.parse(geninfo)
                 p = PObject(items)
             try:
-                seed = p.all_seeds[i]
-                prompt = p.all_prompts[i]
+                seed = p.all_seeds[i] if i < len(p.all_seeds) else p.seed
+                prompt = p.all_prompts[i] if i < len(p.all_prompts) else p.prompt
                 fullfn, txt_fullfn, _exif = images.save_image(image, shared.opts.outdir_save, "", seed=seed, prompt=prompt, info=info, extension=shared.opts.samples_format, grid=is_grid, p=p)
             except Exception as e:
+                fullfn, txt_fullfn = None, None
                 shared.log.error(f'Save: image={image} i={i} seeds={p.all_seeds} prompts={p.all_prompts}')
                 errors.display(e, 'save')
             if fullfn is None: