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core/pipelines/sd_image_pipeline.py

@@ -1,259 +1,259 @@
-import os
-import random
-import shutil
-import torch
-import gradio as gr
-from PIL import Image
-from typing import List, Dict, Any
-
-from .base_pipeline import BasePipeline
-from core.settings import *
-from utils.app_utils import sanitize_prompt
-from core.workflow_assembler import WorkflowAssembler
-from .workflow_executor import WorkflowExecutor
-from .pipeline_input_processor import process_pipeline_inputs
-
-class SdImagePipeline(BasePipeline):
-    def get_required_models(self, model_display_name: str, **kwargs) -> List[str]:
-        model_info = ALL_MODEL_MAP.get(model_display_name)
-        if not model_info:
-            return [model_display_name]
-
-        path_or_components = model_info[1]
-        if isinstance(path_or_components, dict):
-            return [v for v in path_or_components.values() if v and v != "pixel_space"]
-        else:
-            return [model_display_name]
-
-    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
-        model_display_name = ui_inputs['model_display_name']
-        
-        progress(0.4, desc="Executing workflow...")
-        
-        initial_objects = {}
-
-        decoded_images_tensor = WorkflowExecutor.execute_workflow(workflow, initial_objects=initial_objects)
-        
-        output_images = []
-        start_seed = ui_inputs['seed'] if ui_inputs['seed'] != -1 else random.randint(0, 2**64 - 1)
-        for i in range(decoded_images_tensor.shape[0]):
-            img_tensor = decoded_images_tensor[i]
-            pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
-            current_seed = start_seed + i
-            
-            width_for_meta = ui_inputs.get('width', 'N/A')
-            height_for_meta = ui_inputs.get('height', 'N/A')
-
-            params_string = f"{ui_inputs['positive_prompt']}\nNegative prompt: {ui_inputs['negative_prompt']}\n"
-            params_string += f"Steps: {ui_inputs['num_inference_steps']}, Sampler: {ui_inputs['sampler']}, Scheduler: {ui_inputs['scheduler']}, CFG scale: {ui_inputs['guidance_scale']}, Seed: {current_seed}, Size: {width_for_meta}x{height_for_meta}, Base Model: {model_display_name}"
-            if ui_inputs['task_type'] != 'txt2img': params_string += f", Denoise: {ui_inputs['denoise']}"
-            if ui_inputs.get('clip_skip') and ui_inputs['clip_skip'] != 1: params_string += f", Clip skip: {abs(ui_inputs['clip_skip'])}"
-            if loras_string: params_string += f", {loras_string}"
-            
-            pil_image.info = {'parameters': params_string.strip()}
-            output_images.append(pil_image)
-            
-        return output_images
-
-    def run(self, ui_inputs: Dict, progress):
-        progress(0, desc="Preparing models...")
-        
-        task_type = ui_inputs['task_type']
-        model_display_name = ui_inputs['model_display_name']
-        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl')
-        
-        architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
-        workflow_model_type = architectures_dict.get(model_type, {}).get("model_type", model_type.lower().replace(" ", "").replace(".", ""))
-        
-        ui_inputs['positive_prompt'] = sanitize_prompt(ui_inputs.get('positive_prompt', ''))
-        ui_inputs['negative_prompt'] = sanitize_prompt(ui_inputs.get('negative_prompt', ''))
-        
-        if 'clip_skip' in ui_inputs and ui_inputs['clip_skip'] is not None:
-             ui_inputs['clip_skip'] = -int(ui_inputs['clip_skip'])
-        else:
-             ui_inputs['clip_skip'] = -1
-
-        required_models = self.get_required_models(model_display_name=model_display_name)
-
-        is_pid_enabled = (ui_inputs.get('pid_settings', 'OFF') == 'ON' and task_type == 'txt2img')
-        if is_pid_enabled:
-            import yaml
-            pid_config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'yaml', 'pid.yaml')
-            pid_unet_name = "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
-            try:
-                with open(pid_config_path, 'r', encoding='utf-8') as f:
-                    pid_config = yaml.safe_load(f) or {}
-                pid_items = pid_config.get("PiD", [])
-                for item in pid_items:
-                    archs = item.get("architectures", [])
-                    if workflow_model_type in archs:
-                        pid_unet_name = item.get("filepath")
-                        break
-            except Exception as e:
-                print(f"Error loading PiD config for download: {e}")
-
-            if pid_unet_name not in required_models:
-                required_models.append(pid_unet_name)
-            if "gemma_2_2b_it_elm_fp8_scaled.safetensors" not in required_models:
-                required_models.append("gemma_2_2b_it_elm_fp8_scaled.safetensors")
-
-        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
-        
-        temp_files_to_clean = []
-        try:
-            processed = process_pipeline_inputs(ui_inputs, progress, workflow_model_type)
-            temp_files_to_clean.extend(processed["temp_files_to_clean"])
-            
-            active_loras_for_gpu = processed["active_loras_for_gpu"]
-            active_loras_for_meta = processed["active_loras_for_meta"]
-            active_controlnets = processed["active_controlnets"]
-            active_anima_controlnets = processed["active_anima_controlnets"]
-            active_diffsynth_controlnets = processed["active_diffsynth_controlnets"]
-            active_krea2_controlnets = processed.get("active_krea2_controlnets", [])
-            active_ipadapters = processed["active_ipadapters"]
-            active_flux1_ipadapters = processed["active_flux1_ipadapters"]
-            active_sd3_ipadapters = processed["active_sd3_ipadapters"]
-            active_styles = processed["active_styles"]
-            active_reference_latents = processed["active_reference_latents"]
-            active_hidream_o1_reference = processed["active_hidream_o1_reference"]
-            active_conditioning = processed["active_conditioning"]
-
-            loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
-            
-            progress(0.8, desc="Assembling workflow...")
-            
-            if ui_inputs.get('seed') == -1:
-                ui_inputs['seed'] = random.randint(0, 2**32 - 1)
-            
-            model_info = ALL_MODEL_MAP[model_display_name]
-            path_or_components = model_info[1]
-            latent_type = model_info[3] if len(model_info) > 3 and model_info[3] else 'latent'
-            latent_generator_template = "EmptyLatentImage"
-            if latent_type == 'sd3_latent':
-                latent_generator_template = "EmptySD3LatentImage"
-            elif latent_type == 'chroma_radiance_latent':
-                latent_generator_template = "EmptyChromaRadianceLatentImage"
-            elif latent_type == 'hunyuan_latent':
-                latent_generator_template = "EmptyHunyuanImageLatent"
-
-            dynamic_values = {
-                'task_type': ui_inputs['task_type'], 
-                'model_type': workflow_model_type,
-                'latent_type': latent_type,
-                'latent_generator_template': latent_generator_template
-            }
-            
-            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
-            assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
-            
-            hidream_o1_smoothing_data = []
-            if workflow_model_type == 'hidream-o1' and model_display_name == "HiDream-O1-Image":
-                hidream_o1_smoothing_data.append({})
-
-            workflow_inputs = {
-                **ui_inputs,
-                "positive_prompt": ui_inputs['positive_prompt'], "negative_prompt": ui_inputs['negative_prompt'],
-                "seed": ui_inputs['seed'], "steps": ui_inputs['num_inference_steps'], "cfg": ui_inputs['guidance_scale'],
-                "sampler_name": ui_inputs['sampler'], "scheduler": ui_inputs['scheduler'],
-                "batch_size": ui_inputs['batch_size'],
-                "clip_skip": ui_inputs['clip_skip'],
-                "denoise": ui_inputs['denoise'],
-                "vae_name": ui_inputs.get('vae_name'),
-                "guidance": ui_inputs.get('guidance', 3.5),
-                "lora_chain": active_loras_for_gpu,
-                "controlnet_chain": active_controlnets if not active_anima_controlnets else [],
-                "anima_controlnet_lllite_chain": active_anima_controlnets,
-                "diffsynth_controlnet_chain": active_diffsynth_controlnets,
-                "krea2_controlnet_chain": active_krea2_controlnets,
-                "ipadapter_chain": active_ipadapters,
-                "flux1_ipadapter_chain": active_flux1_ipadapters,
-                "sd3_ipadapter_chain": active_sd3_ipadapters,
-                "style_chain": active_styles,
-                "conditioning_chain": active_conditioning,
-                "reference_latent_chain": active_reference_latents,
-                "hidream_o1_reference_chain": active_hidream_o1_reference,
-                "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
-                "hidream_o1_smoothing_chain": hidream_o1_smoothing_data,
-                "pid_chain": [ui_inputs.get('pid_settings', 'OFF')] if is_pid_enabled else [],
-                "scheduler_width": ui_inputs.get('width', 1024),
-                "scheduler_height": ui_inputs.get('height', 1024),
-            }
-
-            if isinstance(path_or_components, dict):
-                workflow_inputs.update({
-                    'unet_name': path_or_components.get('unet'),
-                    'unet_uncond_name': path_or_components.get('unet_uncond'),
-                    'vae_name': ui_inputs.get('vae_name') or path_or_components.get('vae'),
-                    'clip_name': path_or_components.get('clip'),
-                    'clip1_name': path_or_components.get('clip1'),
-                    'clip2_name': path_or_components.get('clip2'),
-                    'clip3_name': path_or_components.get('clip3'),
-                    'clip4_name': path_or_components.get('clip4'),
-                    'lora_name': path_or_components.get('lora'),
-                })
-            else:
-                workflow_inputs['model_name'] = path_or_components
-            
-            if task_type == 'txt2img':
-                workflow_inputs['width'] = ui_inputs['width']
-                workflow_inputs['height'] = ui_inputs['height']
-            
-            workflow = assembler.assemble(workflow_inputs)
-
-            progress(1.0, desc="All models ready. Requesting GPU for generation...")
-
-            results = self._execute_gpu_logic(
-                self._gpu_logic,
-                duration=ui_inputs['zero_gpu_duration'],
-                default_duration=60,
-                task_name=f"ImageGen ({task_type})",
-                ui_inputs=ui_inputs,
-                loras_string=loras_string,
-                workflow=workflow,
-                assembler=assembler, 
-                progress=progress
-            )
-            
-            import json
-            import glob
-            from PIL import PngImagePlugin
-            
-            prompt_json = json.dumps(workflow)
-            
-            out_dir = os.path.abspath(OUTPUT_DIR)
-            os.makedirs(out_dir, exist_ok=True)
-            
-            try:
-                existing_files = glob.glob(os.path.join(out_dir, "gen_*.png"))
-                existing_files.sort(key=os.path.getmtime)
-                while len(existing_files) > 50:
-                    os.remove(existing_files.pop(0))
-            except Exception as e:
-                print(f"Warning: Failed to cleanup output dir: {e}")
-
-            final_results = []
-            for img in results:
-                if not isinstance(img, Image.Image):
-                    final_results.append(img)
-                    continue
-                    
-                metadata = PngImagePlugin.PngInfo()
-                params_string = img.info.get("parameters", "")
-                if params_string:
-                    metadata.add_text("parameters", params_string)
-                metadata.add_text("prompt", prompt_json)
-                
-                filename = f"gen_{random.randint(1000000, 9999999)}.png"
-                filepath = os.path.join(out_dir, filename)
-                img.save(filepath, "PNG", pnginfo=metadata)
-                final_results.append(filepath)
-            
-            results = final_results
-
-        finally:
-            for temp_file in temp_files_to_clean:
-                if temp_file and os.path.exists(temp_file):
-                    os.remove(temp_file)
-                    print(f"✅ Cleaned up temp file: {temp_file}")
-
+import os
+import random
+import shutil
+import torch
+import gradio as gr
+from PIL import Image
+from typing import List, Dict, Any
+
+from .base_pipeline import BasePipeline
+from core.settings import *
+from utils.app_utils import sanitize_prompt
+from core.workflow_assembler import WorkflowAssembler
+from .workflow_executor import WorkflowExecutor
+from .pipeline_input_processor import process_pipeline_inputs
+
+class SdImagePipeline(BasePipeline):
+    def get_required_models(self, model_display_name: str, **kwargs) -> List[str]:
+        model_info = ALL_MODEL_MAP.get(model_display_name)
+        if not model_info:
+            return [model_display_name]
+
+        path_or_components = model_info[1]
+        if isinstance(path_or_components, dict):
+            return [v for v in path_or_components.values() if v and v != "pixel_space"]
+        else:
+            return [model_display_name]
+
+    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
+        model_display_name = ui_inputs['model_display_name']
+        
+        progress(0.4, desc="Executing workflow...")
+        
+        initial_objects = {}
+
+        decoded_images_tensor = WorkflowExecutor.execute_workflow(workflow, initial_objects=initial_objects)
+        
+        output_images = []
+        start_seed = ui_inputs['seed'] if ui_inputs['seed'] != -1 else random.randint(0, 2**64 - 1)
+        for i in range(decoded_images_tensor.shape[0]):
+            img_tensor = decoded_images_tensor[i]
+            pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
+            current_seed = start_seed + i
+            
+            width_for_meta = ui_inputs.get('width', 'N/A')
+            height_for_meta = ui_inputs.get('height', 'N/A')
+
+            params_string = f"{ui_inputs['positive_prompt']}\nNegative prompt: {ui_inputs['negative_prompt']}\n"
+            params_string += f"Steps: {ui_inputs['num_inference_steps']}, Sampler: {ui_inputs['sampler']}, Scheduler: {ui_inputs['scheduler']}, CFG scale: {ui_inputs['guidance_scale']}, Seed: {current_seed}, Size: {width_for_meta}x{height_for_meta}, Base Model: {model_display_name}"
+            if ui_inputs['task_type'] != 'txt2img': params_string += f", Denoise: {ui_inputs['denoise']}"
+            if ui_inputs.get('clip_skip') and ui_inputs['clip_skip'] != 1: params_string += f", Clip skip: {abs(ui_inputs['clip_skip'])}"
+            if loras_string: params_string += f", {loras_string}"
+            
+            pil_image.info = {'parameters': params_string.strip()}
+            output_images.append(pil_image)
+            
+        return output_images
+
+    def run(self, ui_inputs: Dict, progress):
+        progress(0, desc="Preparing models...")
+        
+        task_type = ui_inputs['task_type']
+        model_display_name = ui_inputs['model_display_name']
+        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl')
+        
+        architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
+        workflow_model_type = architectures_dict.get(model_type, {}).get("model_type", model_type.lower().replace(" ", "").replace(".", ""))
+        
+        ui_inputs['positive_prompt'] = sanitize_prompt(ui_inputs.get('positive_prompt', ''))
+        ui_inputs['negative_prompt'] = sanitize_prompt(ui_inputs.get('negative_prompt', ''))
+        
+        if 'clip_skip' in ui_inputs and ui_inputs['clip_skip'] is not None:
+             ui_inputs['clip_skip'] = -int(ui_inputs['clip_skip'])
+        else:
+             ui_inputs['clip_skip'] = -1
+
+        required_models = self.get_required_models(model_display_name=model_display_name)
+
+        is_pid_enabled = (ui_inputs.get('pid_settings', 'OFF') == 'ON' and task_type == 'txt2img')
+        if is_pid_enabled:
+            import yaml
+            pid_config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'yaml', 'pid.yaml')
+            pid_unet_name = "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
+            try:
+                with open(pid_config_path, 'r', encoding='utf-8') as f:
+                    pid_config = yaml.safe_load(f) or {}
+                pid_items = pid_config.get("PiD", [])
+                for item in pid_items:
+                    archs = item.get("architectures", [])
+                    if workflow_model_type in archs:
+                        pid_unet_name = item.get("filepath")
+                        break
+            except Exception as e:
+                print(f"Error loading PiD config for download: {e}")
+
+            if pid_unet_name not in required_models:
+                required_models.append(pid_unet_name)
+            if "gemma_2_2b_it_elm_fp8_scaled.safetensors" not in required_models:
+                required_models.append("gemma_2_2b_it_elm_fp8_scaled.safetensors")
+
+        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
+        
+        temp_files_to_clean = []
+        try:
+            processed = process_pipeline_inputs(ui_inputs, progress, workflow_model_type)
+            temp_files_to_clean.extend(processed["temp_files_to_clean"])
+            
+            active_loras_for_gpu = processed["active_loras_for_gpu"]
+            active_loras_for_meta = processed["active_loras_for_meta"]
+            active_controlnets = processed["active_controlnets"]
+            active_anima_controlnets = processed["active_anima_controlnets"]
+            active_diffsynth_controlnets = processed["active_diffsynth_controlnets"]
+            active_krea2_controlnets = processed.get("active_krea2_controlnets", [])
+            active_ipadapters = processed["active_ipadapters"]
+            active_flux1_ipadapters = processed["active_flux1_ipadapters"]
+            active_sd3_ipadapters = processed["active_sd3_ipadapters"]
+            active_styles = processed["active_styles"]
+            active_reference_latents = processed["active_reference_latents"]
+            active_hidream_o1_reference = processed["active_hidream_o1_reference"]
+            active_conditioning = processed["active_conditioning"]
+
+            loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
+            
+            progress(0.8, desc="Assembling workflow...")
+            
+            if ui_inputs.get('seed') == -1:
+                ui_inputs['seed'] = random.randint(0, 2**32 - 1)
+            
+            model_info = ALL_MODEL_MAP[model_display_name]
+            path_or_components = model_info[1]
+            latent_type = model_info[3] if len(model_info) > 3 and model_info[3] else 'latent'
+            latent_generator_template = "EmptyLatentImage"
+            if latent_type == 'sd3_latent':
+                latent_generator_template = "EmptySD3LatentImage"
+            elif latent_type == 'chroma_radiance_latent':
+                latent_generator_template = "EmptyChromaRadianceLatentImage"
+            elif latent_type == 'hunyuan_latent':
+                latent_generator_template = "EmptyHunyuanImageLatent"
+
+            dynamic_values = {
+                'task_type': ui_inputs['task_type'], 
+                'model_type': workflow_model_type,
+                'latent_type': latent_type,
+                'latent_generator_template': latent_generator_template
+            }
+            
+            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "sd_unified_recipe.yaml")
+            assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
+            
+            hidream_o1_smoothing_data = []
+            if workflow_model_type == 'hidream-o1' and model_display_name == "HiDream-O1-Image":
+                hidream_o1_smoothing_data.append({})
+
+            workflow_inputs = {
+                **ui_inputs,
+                "positive_prompt": ui_inputs['positive_prompt'], "negative_prompt": ui_inputs['negative_prompt'],
+                "seed": ui_inputs['seed'], "steps": ui_inputs['num_inference_steps'], "cfg": ui_inputs['guidance_scale'],
+                "sampler_name": ui_inputs['sampler'], "scheduler": ui_inputs['scheduler'],
+                "batch_size": ui_inputs['batch_size'],
+                "clip_skip": ui_inputs['clip_skip'],
+                "denoise": ui_inputs['denoise'],
+                "vae_name": ui_inputs.get('vae_name'),
+                "guidance": ui_inputs.get('guidance', 3.5),
+                "lora_chain": active_loras_for_gpu,
+                "controlnet_chain": active_controlnets if not active_anima_controlnets else [],
+                "anima_controlnet_lllite_chain": active_anima_controlnets,
+                "diffsynth_controlnet_chain": active_diffsynth_controlnets,
+                "krea2_controlnet_chain": active_krea2_controlnets,
+                "ipadapter_chain": active_ipadapters,
+                "flux1_ipadapter_chain": active_flux1_ipadapters,
+                "sd3_ipadapter_chain": active_sd3_ipadapters,
+                "style_chain": active_styles,
+                "conditioning_chain": active_conditioning,
+                "reference_latent_chain": active_reference_latents,
+                "hidream_o1_reference_chain": active_hidream_o1_reference,
+                "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
+                "hidream_o1_smoothing_chain": hidream_o1_smoothing_data,
+                "pid_chain": [ui_inputs.get('pid_settings', 'OFF')] if is_pid_enabled else [],
+                "scheduler_width": ui_inputs.get('width', 1024),
+                "scheduler_height": ui_inputs.get('height', 1024),
+            }
+
+            if isinstance(path_or_components, dict):
+                workflow_inputs.update({
+                    'unet_name': path_or_components.get('unet'),
+                    'unet_uncond_name': path_or_components.get('unet_uncond'),
+                    'vae_name': ui_inputs.get('vae_name') or path_or_components.get('vae'),
+                    'clip_name': path_or_components.get('clip'),
+                    'clip1_name': path_or_components.get('clip1'),
+                    'clip2_name': path_or_components.get('clip2'),
+                    'clip3_name': path_or_components.get('clip3'),
+                    'clip4_name': path_or_components.get('clip4'),
+                    'lora_name': path_or_components.get('lora'),
+                })
+            else:
+                workflow_inputs['model_name'] = path_or_components
+            
+            if task_type == 'txt2img':
+                workflow_inputs['width'] = ui_inputs['width']
+                workflow_inputs['height'] = ui_inputs['height']
+            
+            workflow = assembler.assemble(workflow_inputs)
+
+            progress(1.0, desc="All models ready. Requesting GPU for generation...")
+
+            results = self._execute_gpu_logic(
+                self._gpu_logic,
+                duration=ui_inputs['zero_gpu_duration'],
+                default_duration=60,
+                task_name=f"ImageGen ({task_type})",
+                ui_inputs=ui_inputs,
+                loras_string=loras_string,
+                workflow=workflow,
+                assembler=assembler, 
+                progress=progress
+            )
+            
+            import json
+            import glob
+            from PIL import PngImagePlugin
+            
+            prompt_json = json.dumps(workflow)
+            
+            out_dir = os.path.abspath(OUTPUT_DIR)
+            os.makedirs(out_dir, exist_ok=True)
+            
+            try:
+                existing_files = glob.glob(os.path.join(out_dir, "gen_*.png"))
+                existing_files.sort(key=os.path.getmtime)
+                while len(existing_files) > 50:
+                    os.remove(existing_files.pop(0))
+            except Exception as e:
+                print(f"Warning: Failed to cleanup output dir: {e}")
+
+            final_results = []
+            for img in results:
+                if not isinstance(img, Image.Image):
+                    final_results.append(img)
+                    continue
+                    
+                metadata = PngImagePlugin.PngInfo()
+                params_string = img.info.get("parameters", "")
+                if params_string:
+                    metadata.add_text("parameters", params_string)
+                metadata.add_text("prompt", prompt_json)
+                
+                filename = f"gen_{random.randint(1000000, 9999999)}.png"
+                filepath = os.path.join(out_dir, filename)
+                img.save(filepath, "PNG", pnginfo=metadata)
+                final_results.append(filepath)
+            
+            results = final_results
+
+        finally:
+            for temp_file in temp_files_to_clean:
+                if temp_file and os.path.exists(temp_file):
+                    os.remove(temp_file)
+                    print(f"✅ Cleaned up temp file: {temp_file}")
+
         return results

ui/events/run_handlers.py

@@ -1,105 +1,105 @@
-import gradio as gr
-from core.generation_logic import generate_image_wrapper
-
-def create_run_event(prefix: str, task_type: str, ui_components: dict):
-    run_inputs_map = {
-        'model_display_name': ui_components[f'base_model_{prefix}'],
-        'positive_prompt': ui_components.get(f'prompt_{prefix}') or ui_components.get(f'{prefix}_positive_prompt'),
-        'negative_prompt': ui_components.get(f'neg_prompt_{prefix}') or ui_components.get(f'{prefix}_negative_prompt'),
-        'seed': ui_components.get(f'seed_{prefix}') or ui_components.get(f'{prefix}_seed'),
-        'batch_size': ui_components.get(f'batch_size_{prefix}') or ui_components.get(f'{prefix}_batch_size'),
-        'guidance_scale': ui_components.get(f'cfg_{prefix}') or ui_components.get(f'{prefix}_cfg'),
-        'num_inference_steps': ui_components.get(f'steps_{prefix}') or ui_components.get(f'{prefix}_steps'),
-        'sampler': ui_components.get(f'sampler_{prefix}') or ui_components.get(f'{prefix}_sampler_name'),
-        'scheduler': ui_components.get(f'scheduler_{prefix}') or ui_components.get(f'{prefix}_scheduler'),
-        'zero_gpu_duration': ui_components.get(f'zero_gpu_{prefix}'),
-
-        'clip_skip': ui_components.get(f'clip_skip_{prefix}'),
-        'guidance': ui_components.get(f'guidance_{prefix}'),
-        'task_type': gr.State(task_type)
-    }
-    
-    if ui_components.get(f'pid_settings_{prefix}'):
-        run_inputs_map['pid_settings'] = ui_components[f'pid_settings_{prefix}']
-    
-    if task_type not in ['img2img', 'inpaint']:
-        run_inputs_map.update({
-            'width': ui_components.get(f'width_{prefix}') or ui_components.get(f'{prefix}_width'), 
-            'height': ui_components.get(f'height_{prefix}') or ui_components.get(f'{prefix}_height')
-        })
-    
-    task_specific_map = {
-        'img2img': {'img2img_image': f'input_image_{prefix}', 'img2img_denoise': f'denoise_{prefix}'},
-        'inpaint': {'inpaint_image_dict': f'input_image_dict_{prefix}', 'grow_mask_by': f'grow_mask_by_{prefix}', 'inpaint_denoise': f'denoise_{prefix}'},
-        'outpaint': {'outpaint_image': f'input_image_{prefix}', 'left': f'left_{prefix}', 'top': f'top_{prefix}', 'right': f'right_{prefix}', 'bottom': f'bottom_{prefix}', 'feathering': f'feathering_{prefix}'},
-        'hires_fix': {'hires_image': f'input_image_{prefix}', 'hires_upscaler': f'hires_upscaler_{prefix}', 'hires_scale_by': f'hires_scale_by_{prefix}', 'hires_denoise': f'denoise_{prefix}'}
-    }
-    if task_type in task_specific_map:
-        for key, comp_name in task_specific_map[task_type].items():
-            if comp_name in ui_components:
-                run_inputs_map[key] = ui_components[comp_name]
-    
-    lora_data_components = ui_components.get(f'all_lora_components_flat_{prefix}', [])
-    controlnet_data_components = ui_components.get(f'all_controlnet_components_flat_{prefix}', [])
-    anima_controlnet_lllite_data_components = ui_components.get(f'all_anima_controlnet_lllite_components_flat_{prefix}', [])
-    diffsynth_controlnet_data_components = ui_components.get(f'all_diffsynth_controlnet_components_flat_{prefix}', [])
-    krea2_controlnet_data_components = ui_components.get(f'all_krea2_controlnet_components_flat_{prefix}', [])
-    ipadapter_data_components = ui_components.get(f'all_ipadapter_components_flat_{prefix}', [])
-    sd3_ipadapter_data_components = ui_components.get(f'all_sd3_ipadapter_components_flat_{prefix}', [])
-    flux1_ipadapter_data_components = ui_components.get(f'all_flux1_ipadapter_components_flat_{prefix}', [])
-    style_data_components = ui_components.get(f'all_style_components_flat_{prefix}', [])
-    embedding_data_components = ui_components.get(f'all_embedding_components_flat_{prefix}', [])
-    conditioning_data_components = ui_components.get(f'all_conditioning_components_flat_{prefix}', [])
-    reference_latent_data_components = ui_components.get(f'all_reference_latent_components_flat_{prefix}', [])
-    hidream_o1_reference_data_components = ui_components.get(f'all_hidream_o1_reference_components_flat_{prefix}', [])
-    
-    run_inputs_map['vae_source'] = ui_components.get(f'vae_source_{prefix}')
-    run_inputs_map['vae_id'] = ui_components.get(f'vae_id_{prefix}')
-    run_inputs_map['vae_file'] = ui_components.get(f'vae_file_{prefix}')
-
-    input_keys = list(run_inputs_map.keys())
-    input_list_flat = [v for v in run_inputs_map.values() if v is not None]
-    all_chains = [
-        lora_data_components, controlnet_data_components, anima_controlnet_lllite_data_components, diffsynth_controlnet_data_components, krea2_controlnet_data_components, ipadapter_data_components,
-        sd3_ipadapter_data_components, flux1_ipadapter_data_components, style_data_components,
-        embedding_data_components, conditioning_data_components, reference_latent_data_components, hidream_o1_reference_data_components
-    ]
-    for chain in all_chains:
-        if chain:
-            input_list_flat.extend(chain)
-
-    def create_ui_inputs_dict(*args):
-        valid_keys = [k for k in input_keys if run_inputs_map[k] is not None]
-        ui_dict = dict(zip(valid_keys, args[:len(valid_keys)]))
-        arg_idx = len(valid_keys)
-
-        def assign_chain_data(chain_key, components_list):
-            nonlocal arg_idx
-            if components_list:
-                ui_dict[chain_key] = list(args[arg_idx : arg_idx + len(components_list)])
-                arg_idx += len(components_list)
-
-        assign_chain_data('lora_data', lora_data_components)
-        assign_chain_data('controlnet_data', controlnet_data_components)
-        assign_chain_data('anima_controlnet_lllite_data', anima_controlnet_lllite_data_components)
-        assign_chain_data('diffsynth_controlnet_data', diffsynth_controlnet_data_components)
-        assign_chain_data('krea2_controlnet_data', krea2_controlnet_data_components)
-        assign_chain_data('ipadapter_data', ipadapter_data_components)
-        assign_chain_data('sd3_ipadapter_chain', sd3_ipadapter_data_components)
-        assign_chain_data('flux1_ipadapter_data', flux1_ipadapter_data_components)
-        assign_chain_data('style_data', style_data_components)
-        assign_chain_data('embedding_data', embedding_data_components)
-        assign_chain_data('conditioning_data', conditioning_data_components)
-        assign_chain_data('reference_latent_data', reference_latent_data_components)
-        assign_chain_data('hidream_o1_reference_data', hidream_o1_reference_data_components)
-
-        return ui_dict
-
-    run_btn = ui_components.get(f'run_{prefix}') or ui_components.get(f'{prefix}_run_button')
-    res_gal = ui_components.get(f'result_{prefix}') or ui_components.get(f'{prefix}_output_gallery')
-    if run_btn and res_gal:
-        run_btn.click(
-            fn=lambda *args, progress=gr.Progress(track_tqdm=True): generate_image_wrapper(create_ui_inputs_dict(*args), progress),
-            inputs=input_list_flat,
-            outputs=[res_gal]
+import gradio as gr
+from core.generation_logic import generate_image_wrapper
+
+def create_run_event(prefix: str, task_type: str, ui_components: dict):
+    run_inputs_map = {
+        'model_display_name': ui_components[f'base_model_{prefix}'],
+        'positive_prompt': ui_components.get(f'prompt_{prefix}') or ui_components.get(f'{prefix}_positive_prompt'),
+        'negative_prompt': ui_components.get(f'neg_prompt_{prefix}') or ui_components.get(f'{prefix}_negative_prompt'),
+        'seed': ui_components.get(f'seed_{prefix}') or ui_components.get(f'{prefix}_seed'),
+        'batch_size': ui_components.get(f'batch_size_{prefix}') or ui_components.get(f'{prefix}_batch_size'),
+        'guidance_scale': ui_components.get(f'cfg_{prefix}') or ui_components.get(f'{prefix}_cfg'),
+        'num_inference_steps': ui_components.get(f'steps_{prefix}') or ui_components.get(f'{prefix}_steps'),
+        'sampler': ui_components.get(f'sampler_{prefix}') or ui_components.get(f'{prefix}_sampler_name'),
+        'scheduler': ui_components.get(f'scheduler_{prefix}') or ui_components.get(f'{prefix}_scheduler'),
+        'zero_gpu_duration': ui_components.get(f'zero_gpu_{prefix}'),
+
+        'clip_skip': ui_components.get(f'clip_skip_{prefix}'),
+        'guidance': ui_components.get(f'guidance_{prefix}'),
+        'task_type': gr.State(task_type)
+    }
+    
+    if ui_components.get(f'pid_settings_{prefix}'):
+        run_inputs_map['pid_settings'] = ui_components[f'pid_settings_{prefix}']
+    
+    if task_type not in ['img2img', 'inpaint']:
+        run_inputs_map.update({
+            'width': ui_components.get(f'width_{prefix}') or ui_components.get(f'{prefix}_width'), 
+            'height': ui_components.get(f'height_{prefix}') or ui_components.get(f'{prefix}_height')
+        })
+    
+    task_specific_map = {
+        'img2img': {'img2img_image': f'input_image_{prefix}', 'img2img_denoise': f'denoise_{prefix}'},
+        'inpaint': {'inpaint_image_dict': f'input_image_dict_{prefix}', 'grow_mask_by': f'grow_mask_by_{prefix}', 'inpaint_denoise': f'denoise_{prefix}'},
+        'outpaint': {'outpaint_image': f'input_image_{prefix}', 'left': f'left_{prefix}', 'top': f'top_{prefix}', 'right': f'right_{prefix}', 'bottom': f'bottom_{prefix}', 'feathering': f'feathering_{prefix}'},
+        'hires_fix': {'hires_image': f'input_image_{prefix}', 'hires_upscaler': f'hires_upscaler_{prefix}', 'hires_scale_by': f'hires_scale_by_{prefix}', 'hires_denoise': f'denoise_{prefix}'}
+    }
+    if task_type in task_specific_map:
+        for key, comp_name in task_specific_map[task_type].items():
+            if comp_name in ui_components:
+                run_inputs_map[key] = ui_components[comp_name]
+    
+    lora_data_components = ui_components.get(f'all_lora_components_flat_{prefix}', [])
+    controlnet_data_components = ui_components.get(f'all_controlnet_components_flat_{prefix}', [])
+    anima_controlnet_lllite_data_components = ui_components.get(f'all_anima_controlnet_lllite_components_flat_{prefix}', [])
+    diffsynth_controlnet_data_components = ui_components.get(f'all_diffsynth_controlnet_components_flat_{prefix}', [])
+    krea2_controlnet_data_components = ui_components.get(f'all_krea2_controlnet_components_flat_{prefix}', [])
+    ipadapter_data_components = ui_components.get(f'all_ipadapter_components_flat_{prefix}', [])
+    sd3_ipadapter_data_components = ui_components.get(f'all_sd3_ipadapter_components_flat_{prefix}', [])
+    flux1_ipadapter_data_components = ui_components.get(f'all_flux1_ipadapter_components_flat_{prefix}', [])
+    style_data_components = ui_components.get(f'all_style_components_flat_{prefix}', [])
+    embedding_data_components = ui_components.get(f'all_embedding_components_flat_{prefix}', [])
+    conditioning_data_components = ui_components.get(f'all_conditioning_components_flat_{prefix}', [])
+    reference_latent_data_components = ui_components.get(f'all_reference_latent_components_flat_{prefix}', [])
+    hidream_o1_reference_data_components = ui_components.get(f'all_hidream_o1_reference_components_flat_{prefix}', [])
+    
+    run_inputs_map['vae_source'] = ui_components.get(f'vae_source_{prefix}')
+    run_inputs_map['vae_id'] = ui_components.get(f'vae_id_{prefix}')
+    run_inputs_map['vae_file'] = ui_components.get(f'vae_file_{prefix}')
+
+    input_keys = list(run_inputs_map.keys())
+    input_list_flat = [v for v in run_inputs_map.values() if v is not None]
+    all_chains = [
+        lora_data_components, controlnet_data_components, anima_controlnet_lllite_data_components, diffsynth_controlnet_data_components, krea2_controlnet_data_components, ipadapter_data_components,
+        sd3_ipadapter_data_components, flux1_ipadapter_data_components, style_data_components,
+        embedding_data_components, conditioning_data_components, reference_latent_data_components, hidream_o1_reference_data_components
+    ]
+    for chain in all_chains:
+        if chain:
+            input_list_flat.extend(chain)
+
+    def create_ui_inputs_dict(*args):
+        valid_keys = [k for k in input_keys if run_inputs_map[k] is not None]
+        ui_dict = dict(zip(valid_keys, args[:len(valid_keys)]))
+        arg_idx = len(valid_keys)
+
+        def assign_chain_data(chain_key, components_list):
+            nonlocal arg_idx
+            if components_list:
+                ui_dict[chain_key] = list(args[arg_idx : arg_idx + len(components_list)])
+                arg_idx += len(components_list)
+
+        assign_chain_data('lora_data', lora_data_components)
+        assign_chain_data('controlnet_data', controlnet_data_components)
+        assign_chain_data('anima_controlnet_lllite_data', anima_controlnet_lllite_data_components)
+        assign_chain_data('diffsynth_controlnet_data', diffsynth_controlnet_data_components)
+        assign_chain_data('krea2_controlnet_data', krea2_controlnet_data_components)
+        assign_chain_data('ipadapter_data', ipadapter_data_components)
+        assign_chain_data('sd3_ipadapter_chain', sd3_ipadapter_data_components)
+        assign_chain_data('flux1_ipadapter_data', flux1_ipadapter_data_components)
+        assign_chain_data('style_data', style_data_components)
+        assign_chain_data('embedding_data', embedding_data_components)
+        assign_chain_data('conditioning_data', conditioning_data_components)
+        assign_chain_data('reference_latent_data', reference_latent_data_components)
+        assign_chain_data('hidream_o1_reference_data', hidream_o1_reference_data_components)
+
+        return ui_dict
+
+    run_btn = ui_components.get(f'run_{prefix}') or ui_components.get(f'{prefix}_run_button')
+    res_gal = ui_components.get(f'result_{prefix}') or ui_components.get(f'{prefix}_output_gallery')
+    if run_btn and res_gal:
+        run_btn.click(
+            fn=lambda *args, progress=gr.Progress(track_tqdm=True): generate_image_wrapper(create_ui_inputs_dict(*args), progress),
+            inputs=input_list_flat,
+            outputs=[res_gal]
         )

yaml/file_list.yaml

@@ -444,18 +444,18 @@ file:
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
       repository_file_path: "diffusion_models/pid_sd3_1024_to_4096_4step_bf16.safetensors"
-    - filename: "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
+    - filename: "pid_1.5_flux1_1024_to_4096_4step_int8_convrot.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
-      repository_file_path: "diffusion_models/pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
-    - filename: "pid_qwenimage_1024_to_4096_4step_bf16.safetensors"
+      repository_file_path: "diffusion_models/1.5/pid_1.5_flux1_1024_to_4096_4step_int8_convrot.safetensors"
+    - filename: "pid_1.5_qwenimage_1024_to_4096_4step_int8_convrot.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
-      repository_file_path: "diffusion_models/pid_qwenimage_1024_to_4096_4step_bf16.safetensors"
-    - filename: "pid_flux2_1024_to_4096_4step_mxfp8.safetensors"
+      repository_file_path: "diffusion_models/1.5/pid_1.5_qwenimage_1024_to_4096_4step_int8_convrot.safetensors"
+    - filename: "pid_1.5_flux2_1024_to_4096_4step_int8_convrot.safetensors"
       source: "hf"
       repo_id: "Comfy-Org/PixelDiT"
-      repository_file_path: "diffusion_models/pid_flux2_1024_to_4096_4step_mxfp8.safetensors"
+      repository_file_path: "diffusion_models/1.5/pid_1.5_flux2_1024_to_4096_4step_int8_convrot.safetensors"
     # Lens
     - filename: "lens_mxfp8.safetensors"
       source: "hf"

yaml/pid.yaml

@@ -1,16 +1,16 @@
-PiD:
-  - filepath: "pid_flux2_1024_to_4096_4step_mxfp8.safetensors"
-    latent_format: "flux"
-    architectures: ["flux2", "flux2-kv", "ideogram-4", "lens", "ernie-image"]
-  - filepath: "pid_qwenimage_1024_to_4096_4step_bf16.safetensors"
-    latent_format: "qwenimage"
-    architectures: ["anima", "qwen-image", "krea-2"]
-  - filepath: "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
-    latent_format: "flux"
-    architectures: ["longcat-image", "newbie-image", "z-image", "kandinsky-5", "ovis-image", "omnigen2", "chroma1", "hidream-i1", "flux1", "boogu-image"]
-  - filepath: "pid_sd3_1024_to_4096_4step_bf16.safetensors"
-    latent_format: "sd3"
-    architectures: ["sd35"]
-  - filepath: "pid_sdxl_1024_to_4096_4step_bf16.safetensors"
-    latent_format: "sdxl"
+PiD:
+  - filepath: "pid_1.5_flux2_1024_to_4096_4step_int8_convrot.safetensors"
+    latent_format: "flux"
+    architectures: ["flux2", "flux2-kv", "ideogram-4", "lens", "ernie-image"]
+  - filepath: "pid_1.5_qwenimage_1024_to_4096_4step_int8_convrot.safetensors"
+    latent_format: "qwenimage"
+    architectures: ["anima", "qwen-image", "krea-2"]
+  - filepath: "pid_1.5_flux1_1024_to_4096_4step_int8_convrot.safetensors"
+    latent_format: "flux"
+    architectures: ["longcat-image", "newbie-image", "z-image", "kandinsky-5", "ovis-image", "omnigen2", "chroma1", "hidream-i1", "flux1", "boogu-image"]
+  - filepath: "pid_sd3_1024_to_4096_4step_bf16.safetensors"
+    latent_format: "sd3"
+    architectures: ["sd35"]
+  - filepath: "pid_sdxl_1024_to_4096_4step_bf16.safetensors"
+    latent_format: "sdxl"
     architectures: ["sdxl"]