From 4bbc3f31d4ce98ca0c951cb74bc95aa1ba75ada4 Mon Sep 17 00:00:00 2001 From: Alahari Prudhvi Akhil Date: Mon, 21 Oct 2024 08:20:20 +0530 Subject: [PATCH] Blender: v1.0 release --- README.md | 3 + .../SnapdragonImageGeneration/README.md | 48 ++ .../SnapdragonImageGeneration/__init__.py | 588 +++++++++++++++++ .../docs/resources/Thumbs.db | Bin 0 -> 11776 bytes .../resources/blender_snapdragon_addon.gif | Bin 0 -> 2068888 bytes .../docs/resources/enable-addon.png | Bin 0 -> 227357 bytes .../docs/resources/main_icon.jpg | Bin 0 -> 19618 bytes .../SnapdragonImageGeneration/install.bat | 24 + .../SnapdragonImageGeneration/install.py | 216 +++++++ .../model_pipelines/ControlNetCanny.py | 602 ++++++++++++++++++ .../qairt_constants.py | 44 ++ .../requirements.txt | 17 + .../SnapdragonImageGeneration/utils.py | 67 ++ 13 files changed, 1609 insertions(+) create mode 100644 plugins/blender/SnapdragonImageGeneration/README.md create mode 100644 plugins/blender/SnapdragonImageGeneration/__init__.py create mode 100644 plugins/blender/SnapdragonImageGeneration/docs/resources/Thumbs.db create mode 100644 plugins/blender/SnapdragonImageGeneration/docs/resources/blender_snapdragon_addon.gif create mode 100644 plugins/blender/SnapdragonImageGeneration/docs/resources/enable-addon.png create mode 100644 plugins/blender/SnapdragonImageGeneration/docs/resources/main_icon.jpg create mode 100644 plugins/blender/SnapdragonImageGeneration/install.bat create mode 100644 plugins/blender/SnapdragonImageGeneration/install.py create mode 100644 plugins/blender/SnapdragonImageGeneration/model_pipelines/ControlNetCanny.py create mode 100644 plugins/blender/SnapdragonImageGeneration/qairt_constants.py create mode 100644 plugins/blender/SnapdragonImageGeneration/requirements.txt create mode 100644 plugins/blender/SnapdragonImageGeneration/utils.py diff --git a/README.md b/README.md index d7cd96c..9617499 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,9 @@ Our AI plugins offer the following key features: ### drawing AUTOMATIC1111 stable-diffusion-webui Extension - [Overview and Installation](plugins/stable-diffusion-webui/qairt_accelerate/README.md) +### drawing Blender ControlNet Plugin +- [Overview and Installation](plugins/blender/SnapdragonImageGeneration/README.md) + ## License Information This project is licensed under the [BSD-3-Clause License](https://spdx.org/licenses/BSD-3-Clause.html). For the full license text, please refer to the [LICENSE](LICENSE) file in this repository. diff --git a/plugins/blender/SnapdragonImageGeneration/README.md b/plugins/blender/SnapdragonImageGeneration/README.md new file mode 100644 index 0000000..971f17e --- /dev/null +++ b/plugins/blender/SnapdragonImageGeneration/README.md @@ -0,0 +1,48 @@ + +# drawing Blender ControlNet Plugin + +Blender ControlNet plugin runs on Qualcomm X-Elite NPU using [Qualcomm AI Runtime (QAIRT) SDK](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk). The plugin uses QAIRT python APIs to run context binaries (.bin) generated by QAIRT SDK. We provide optimal performance by using QAIRT to run AI models on Qualcomm X-Elite NPU. These models are hosted on [Qualcomm AI hub](https://aihub.qualcomm.com/models/controlnet_quantized). + +![controlnet](docs/resources/blender_snapdragon_addon.gif) +This clip is at 2x speed + +## Supported features + +* ControlNet model, 512x512 as input image, 512x512 or 2048x2048 as output image +* Sampling method: DPM++ 2M +* Upscaling method: ESRGAN-x4 + +## Installation Instructions: + +### Step 1: Install Dependencies +Download and install [Python 3.10.6](https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe) + +> **_NOTE:_** The program is tested to work on Python `3.10.6`. Don't use other versions. + +### Step 2: Download Blender Arm64 +Download Blender Arm64 4.3 Alpha version from [Daily build](https://builder.blender.org/download/daily/). The Stable version of Arm64 is expected to be releasing soon. + +### Step 3: Download Blender ControlNet Plugin +Download `blender-controlnet-plugin-v1.0.zip` from the [latest release](https://github.com/quic/wos-ai-plugins/releases/tag/v1.0-blender). +Unzip and place the `SnapdragonImageGeneration` folder under `C:\Users\\AppData\Roaming\Blender Foundation\Blender\4.3\scripts\addons` + +> **_NOTE:_** The path `C:\Users\\AppData\Roaming\Blender Foundation\Blender\4.3` won't be created until the Blender application is opened at least once. + +> **_NOTE:_** Create the `scripts\addons` folder if not already present. + + +### Step 4: Install Plugin + +Run below command to install the plugin. This installs QAIRT SDK and models required for the plugin. This script also creates a python virtual environment for the plugin. +``` +.\install.bat +``` + +### Step 5: Enable Plugin +Run Blender Arm64 4.3 and go to `Edit`->`Preferences`->`Add-ons` and search for `Snapdragon Image Generation` and enable it. +drawing + + +## Troubleshooting + +* To reinstall from scratch, delete directories: `venv`, `qnn_assets` diff --git a/plugins/blender/SnapdragonImageGeneration/__init__.py b/plugins/blender/SnapdragonImageGeneration/__init__.py new file mode 100644 index 0000000..067c8c2 --- /dev/null +++ b/plugins/blender/SnapdragonImageGeneration/__init__.py @@ -0,0 +1,588 @@ +# ============================================================================= +# +# Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# +# ============================================================================= + +import bpy +from bpy.props import ( + StringProperty, + IntProperty, + FloatProperty, + EnumProperty, + BoolProperty, +) +import subprocess +from .utils import * +from .qairt_constants import * +import random +import json +import time +import re + + +bl_info = { + "name": "Snapdragon Image Generation", + "author": "Snapdragon AI contributors", + "description": "Use ControlNet-CannyEdge model to generate new images similar to the given input image.", + "blender": (3, 1, 0), + "version": (1, 0, 0), + "location": "Image Editor -> Sidebar -> SnapdragonAI", + "category": "Paint", +} + + +def log(message): + with open(LOG_FILE, LogMode.RECORDING.value) as log_file: + log_file.write(f"{message}\n") + + +def process_command( + command, + start_marker, + end_marker, + log_file_path, + log_mode, + on_done_callback, + step_callback=None, +): + global process + + def output_callback(output): + with open(log_file_path, log_mode) as log_file: + log_file.write(f"{output}\n") + + is_success = False + if output.find(start_marker) != -1 and output.find(end_marker) != -1: + start_index = output.find(start_marker) + len(start_marker) + end_index = output.find(end_marker) + is_success = output[start_index:end_index].strip() == 'True' + log(f"striped={output[start_index:end_index].strip()} is_success={is_success}") + on_done_callback(is_success) + + + send_command(process, command, output_callback, step_callback) + + +class Model_OT_LoadPanel(bpy.types.Operator): + bl_idname = "server.load_model" + bl_label = "Load Model" + bl_description = "Load the model" + + @classmethod + def poll(cls, context): + return not context.scene.is_model_loading + + def execute(self, context): + global process + + def step_callback(log_line): + total_models = 4 + pattern = "Time\:\ model_initialize" + if log_line and re.search(pattern, log_line): + context.scene.progress += 1 / total_models + + log_file_path = LOG_FILE + if process is None or not context.scene.is_model_loaded: + env = os.environ.copy() + env["PYTHONPATH"] = ( + f"{PLUGIN_DIR}{os.pathsep}{env.get('PYTHONPATH', '')}" + ) + + process = subprocess.Popen( + [VENV_PYTHON, CONTROLNET_PIPELINE], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + env=env, + ) + + load_command = {"action": "load_model", "params": {}} + start_marker = "LOAD_MODEL_RESULT_START" + end_marker = "LOAD_MODEL_RESULT_END" + + def on_done_callback(is_success): + if is_success: + context.scene.is_model_loaded = True + else: + context.scene.is_model_loaded = False + show_error(f"Failed while loading models.\n Look at the generated addon logs for more details {LOG_FILE}") + context.scene.is_model_loading = False + context.scene.progress = 0 + + context.scene.is_model_loading = True + process_command( + load_command, + start_marker, + end_marker, + log_file_path, + LogMode.BEGIN.value, + on_done_callback=on_done_callback, + step_callback=step_callback, + ) + + else: + context.scene.is_model_loaded = True + self.report({"INFO"}, "Model is already loaded.") + return {"FINISHED"} + + +class Model_OT_UnloadPanel(bpy.types.Operator): + bl_idname = "server.unload_model" + bl_label = "Unload Model" + bl_description = "Unload the model" + + @classmethod + def poll(cls, context): + return ( + not context.scene.is_model_unloading and not context.scene.is_model_running + ) + + def execute(self, context): + global process + + def step_callback(log_line): + total_models = 4 + pattern = "Time\:\ model_destroy" + if log_line and re.search(pattern, log_line): + context.scene.progress += 1 / total_models + + log_file_path = LOG_FILE + if process is not None and context.scene.is_model_loaded: + unload_command = {"action": "unload_model", "params": {}} + start_marker = "UNLOAD_MODEL_RESULT_START" + end_marker = "UNLOAD_MODEL_RESULT_END" + + def on_done_callback(is_success): + if is_success: + context.scene.is_model_loaded = False + else: + context.scene.is_model_loaded = True + show_error(f"Failed while unloading models.\n Look at the generated addon logs for more details {LOG_FILE}") + context.scene.is_model_unloading = False + context.scene.progress = 0 + + context.scene.is_model_unloading = True + process_command( + unload_command, + start_marker, + end_marker, + log_file_path, + LogMode.RECORDING.value, + on_done_callback=on_done_callback, + step_callback=step_callback, + ) + else: + context.scene.is_model_loaded = False + self.report({"INFO"}, "Model is already unloaded.") + return {"FINISHED"} + + +class CONVERSION_PT_ConversionPanel(bpy.types.Panel): + bl_label = "3D to 2D Conversion" + bl_idname = "CONVERSION_PT_conversion_panel" + bl_space_type = "VIEW_3D" + bl_region_type = "UI" + bl_category = "Snapdragon Image Generation" + + def draw(self, context): + layout = self.layout + conversion_settings = context.scene.conversion_settings + + layout.prop(conversion_settings, "image_resolution") + layout.operator("conversion.conversion_operator", text="Convert 3D to 2D") + + +class CONVERSION_OT_ConversionOperator(bpy.types.Operator): + bl_idname = "conversion.conversion_operator" + bl_label = "Convert 3D to 2D" + bl_description = "Convert the 3D object scene to a 2D image" + + def execute(self, context): + scene = context.scene + conversion_settings = scene.conversion_settings + output_path = bpy.path.abspath( + os.path.join(OUTPUTS_DIR, f"2d_image_{str(time.time_ns())}.png") + ) + + if not scene.camera: + self.report({"ERROR"}, "No camera found in the scene") + return {"CANCELLED"} + + scene.render.image_settings.file_format = "PNG" + if conversion_settings.image_resolution == "512_512": + scene.render.resolution_x = 512 + scene.render.resolution_y = 512 + else: + show_error("Invalid resolution selected") + scene.render.filepath = output_path + + bpy.ops.render.render("INVOKE_DEFAULT", write_still=True) + + conversion_settings.input_image_path = output_path + + self.report({"INFO"}, f"Image saved at:{output_path}") + return {"FINISHED"} + + +class RENDER_OT_ShowImage(bpy.types.Operator): + bl_idname = "render.show_image" + bl_label = "Show Rendered Image" + bl_description = "Show the image" + + image_path: StringProperty( + name="Image Path", + description="Path to the image file", + default="", + maxlen=512, + subtype="FILE_PATH", + ) + + def execute(self, context): + image = bpy.data.images.load(filepath=self.image_path) + + is_image_loaded = False + for window in bpy.data.window_managers["WinMan"].windows: + for area in window.screen.areas: + if area.type == "IMAGE_EDITOR": + is_image_loaded = True + area.spaces.active.image = image + break + + if not is_image_loaded: + screen = bpy.context.screen + for area in screen.areas: + if area.type == 'VIEW_3D': + area.type = 'IMAGE_EDITOR' + area.spaces.active.image = image + break + return {"FINISHED"} + + def invoke(self, context, event): + wm = context.window_manager + return wm.invoke_props_dialog(self) + + +class ConversionSettings(bpy.types.PropertyGroup): + image_resolution: EnumProperty( + name="Image Resolution", + description="Size of the input image to the ControlNet model", + items=[ + ("512_512", "512x512", "Generate image at 512x512 resolution"), + ], + default="512_512", + ) + + input_image_path: StringProperty( + name="Input Image Path", + description="Path of the last converted image", + default="", + subtype="FILE_PATH", + update=lambda self, context: self.update_output_path(), + ) + + output_img_path: StringProperty( + name="Output Image Path", + description="Path of the generated image", + default=get_generated_img_default_path(), + subtype="FILE_PATH", + ) + + def update_output_path(self): + if self.input_image_path: + self.output_img_path = get_output_img_path(self.input_image_path) + + +class IMAGE_PT_GeneratorPanel(bpy.types.Panel): + bl_label = "Image Generation" + bl_idname = "IMAGE_PT_generator_panel" + bl_space_type = "VIEW_3D" + bl_region_type = "UI" + bl_category = "Snapdragon Image Generation" + + def draw(self, context): + layout = self.layout + generator = context.scene.image_generator + conversion_settings = context.scene.conversion_settings + + layout.prop(generator, "model") + layout.prop(generator, "device") + layout.prop(generator, "prompt") + layout.prop(generator, "negative_prompt") + layout.prop(conversion_settings, "input_image_path") + layout.prop(generator, "seed") + layout.prop(generator, "guidance_scale") + layout.prop(generator, "steps") + layout.prop(generator, "output_resolution") + layout.prop(conversion_settings, "output_img_path") + + col = layout.column(align=True) + row1 = col.row(align=True) + row1.scale_y = 1.5 + row2 = col.row(align=True) + row2.scale_y = 1.5 + if not context.scene.is_model_loaded: + row1.operator("server.load_model", icon="PLAY", text="Load Models") + else: + row1.operator("server.unload_model", icon="X", text="Unload Models") + row1.operator("image.generate", icon="PLAY", text="Generate") + if ( + context.scene.is_model_running + or context.scene.is_model_loading + or context.scene.is_model_unloading + ): + row2.progress(factor=context.scene.progress, type="BAR") + + +class IMAGE_OT_GenerateOperator(bpy.types.Operator): + bl_label = "Generate Image" + bl_idname = "image.generate" + bl_description = "Generate an image using the model" + + @classmethod + def poll(cls, context): + return ( + context.scene.is_model_loaded + and not context.scene.is_model_running + and not context.scene.is_model_unloading + ) + + def execute(self, context): + scene = context.scene + generate = scene.image_generator + conversion_settings = scene.conversion_settings + + def step_callback(log_line): + pattern = "Step\ ([0-9]+)\ Running\.\.\." + if log_line: + match = re.search(pattern, log_line) + if match: + total_steps = int(generate.steps) + current_step = int(match.group(1)) + 0.5 + context.scene.progress = current_step / total_steps + + if process is None or not context.scene.is_model_loaded: + self.report({"INFO"}, "Model is not loaded.") + else: + input_path = conversion_settings.input_image_path + if os.path.isfile(conversion_settings.output_img_path): + conversion_settings.output_img_path = utils.get_random_output_img_path( + conversion_settings.output_img_path + ) + output_path = conversion_settings.output_img_path + prompt = generate.prompt + negative_prompt = generate.negative_prompt + seed = generate.seed + steps = generate.steps + cfg_scale = generate.guidance_scale + output_resolution = generate.output_resolution + + context.scene.is_model_running = True + + def on_done_callback(is_success): + if is_success: + bpy.ops.render.show_image(image_path=output_path) + else: + show_error(f"Image generation failed.\n Look at the generated addon logs for more details {LOG_FILE}") + context.scene.is_model_running = False + context.scene.progress = 0 + + self.generate_image( + input_path, + output_path, + prompt, + negative_prompt, + seed, + steps, + cfg_scale, + output_resolution, + step_callback, + on_done_callback=on_done_callback, + ) + + return {"FINISHED"} + + def generate_image( + self, + input_path, + output_path, + prompt, + negative_prompt, + seed, + steps, + cfg_scale, + output_resolution, + step_callback, + on_done_callback, + ): + + log_file_path = LOG_FILE + is_high_resolution = 1 if output_resolution == "2048" else 0 + if seed == -1: + seed = random.randrange(4294967294) + data = { + "input_path": input_path, + "output_path": output_path, + "prompt": prompt, + "negative_prompt": negative_prompt, + "seed": seed, + "steps": steps, + "cfg_scale": cfg_scale, + "output_resolution": is_high_resolution, + } + + exec_command = {"action": "run_model", "params": data} + log(json.dumps(exec_command, indent=2)) + start_marker = "RUN_MODEL_RESULT_START" + end_marker = "RUN_MODEL_RESULT_END" + process_command( + exec_command, + start_marker, + end_marker, + log_file_path, + LogMode.RECORDING.value, + on_done_callback=on_done_callback, + step_callback=step_callback, + ) + + +class ImageGeneratorProperties(bpy.types.PropertyGroup): + model: EnumProperty( + name="Model", + description="Model to run", + items=[ + ( + "controlnet_canny", + "ControlNet Canny Quantized", + "Generating visual arts from text prompt and input guiding image.", + ), + ], + default="controlnet_canny", + ) + + device: EnumProperty( + name="Device", + description="Device to use", + items=[ + ("NPU", "NPU", "Qualcomm X-Elite NPU"), + ], + default="NPU", + ) + + prompt: StringProperty( + name="Prompt", + description="Text prompt to guide the image generation", + default="", + ) + + negative_prompt: StringProperty( + name="Negative Prompt", + description="Text prompt to avoid during the image generation", + default="", + ) + + seed: IntProperty( + name="Seed", description="Seed value for random number generation", default=10 + ) + + guidance_scale: FloatProperty( + name="Guidance Scale", + description="Scale for guidance during image generation", + default=9, + min=5.0, + max=15.0, + ) + + steps: EnumProperty( + name="Steps", + description="Number of steps for the image generation process", + items=[ + ("10", "10 Steps", "10 steps for Unet"), + ("15", "15 Steps", "15 steps for Unet"), + ("20", "20 Steps", "20 steps for Unet"), + ("25", "25 Steps", "25 steps for Unet"), + ("30", "30 Steps", "30 steps for Unet"), + ("35", "35 Steps", "35 steps for Unet"), + ("40", "40 Steps", "40 steps for Unet"), + ("50", "50 Steps", "50 steps for Unet"), + ], + default="20", + ) + + output_resolution: EnumProperty( + name="Output Image Size", + description="Size of the output image", + items=[ + ("512", "512x512", "Generate image at 512x512 resolution"), + ("2048", "2048x2048", "Generate image at 2048x2048 resolution"), + ], + default="512", + ) + + +def register(): + bpy.utils.register_class(CONVERSION_PT_ConversionPanel) + bpy.utils.register_class(CONVERSION_OT_ConversionOperator) + bpy.utils.register_class(RENDER_OT_ShowImage) + bpy.utils.register_class(Model_OT_LoadPanel) + bpy.utils.register_class(Model_OT_UnloadPanel) + bpy.utils.register_class(ConversionSettings) + + bpy.types.Scene.conversion_settings = bpy.props.PointerProperty( + type=ConversionSettings + ) + + bpy.utils.register_class(IMAGE_PT_GeneratorPanel) + bpy.utils.register_class(IMAGE_OT_GenerateOperator) + bpy.utils.register_class(ImageGeneratorProperties) + + bpy.types.Scene.image_generator = bpy.props.PointerProperty( + type=ImageGeneratorProperties + ) + + def step_progress_update(self, context): + if hasattr(context.area, "regions"): + for region in context.area.regions: + if region.type == "UI": + region.tag_redraw() + return None + + bpy.types.Scene.progress = bpy.props.FloatProperty( + name="", default=0, min=0, max=1, update=step_progress_update + ) + bpy.types.Scene.is_model_loaded = bpy.props.BoolProperty( + name="Model Loaded", default=False + ) + bpy.types.Scene.is_model_loading = bpy.props.BoolProperty( + name="Is model loading", default=False + ) + bpy.types.Scene.is_model_unloading = bpy.props.BoolProperty( + name="Is model loading", default=False + ) + bpy.types.Scene.is_model_running = bpy.props.BoolProperty( + name="Is model running", default=False + ) + + +def unregister(): + bpy.utils.unregister_class(CONVERSION_PT_ConversionPanel) + bpy.utils.unregister_class(CONVERSION_OT_ConversionOperator) + bpy.utils.unregister_class(RENDER_OT_ShowImage) + bpy.utils.unregister_class(Model_OT_LoadPanel) + bpy.utils.unregister_class(Model_OT_UnloadPanel) + bpy.utils.unregister_class(ConversionSettings) + + del bpy.types.Scene.conversion_settings + + bpy.utils.unregister_class(IMAGE_PT_GeneratorPanel) + bpy.utils.unregister_class(IMAGE_OT_GenerateOperator) + bpy.utils.unregister_class(ImageGeneratorProperties) + + del bpy.types.Scene.image_generator + + +if __name__ == "__main__": + register() diff --git 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All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# +# ============================================================================= + +import os +import qairt_constants as consts +import zipfile +import shutil +import zipfile +from huggingface_hub import hf_hub_download +import torch +import numpy as np +from diffusers.models.embeddings import get_timestep_embedding, TimestepEmbedding +from diffusers import UNet2DConditionModel +from diffusers import DPMSolverMultistepScheduler +import qai_hub +import requests +import subprocess + + +def run(command, desc=None, errdesc=None, custom_env=None, live: bool = True) -> str: + if desc is not None: + print(desc) + + run_kwargs = { + "args": command, + "shell": True, + "env": os.environ if custom_env is None else custom_env, + "errors": "ignore", + } + + if not live: + run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE + + result = subprocess.run(**run_kwargs) + + if result.returncode != 0: + error_bits = [ + f"{errdesc or 'Error running command'}.", + f"Command: {command}", + f"Error code: {result.returncode}", + ] + if result.stdout: + error_bits.append(f"stdout: {result.stdout}") + if result.stderr: + error_bits.append(f"stderr: {result.stderr}") + raise RuntimeError("\n".join(error_bits)) + + return result.stdout or "" + + +def download_url(url, save_path, chunk_size=128): + r = requests.get(url, stream=True) + with open(save_path, "wb") as fd: + for chunk in r.iter_content(chunk_size=chunk_size): + fd.write(chunk) + + +def download_qairt_sdk(): + # Setup QAIRT SDK + if not os.path.isdir(consts.QNN_SDK_ROOT): + os.makedirs(consts.QAIRT_DIR, exist_ok=True) + print(f"Downloading QAIRT SDK...") + download_url(consts.QNN_SDK_DOWNLOAD_URL, consts.SDK_SAVE_PATH) + print(f"QAIRT SDK downloaded.") + + with zipfile.ZipFile(consts.SDK_SAVE_PATH, "r") as zip_ref: + zip_ref.extractall(consts.PLUGIN_DIR) + shutil.move( + os.path.join(consts.PLUGIN_DIR, "qairt", consts.QAIRT_VERSION), + os.path.join(consts.QNN_SDK_ROOT, ".."), + ) + shutil.rmtree(os.path.join(consts.PLUGIN_DIR, "qairt")) + os.remove(consts.SDK_SAVE_PATH) + + +def setup_qairt_env(): + # Preparing all the binaries and libraries for execution. + SDK_lib_dir = consts.QNN_SDK_ROOT + "\\lib\\arm64x-windows-msvc" + SDK_hexagon_dir = consts.QNN_SDK_ROOT + "\\lib\\hexagon-v{}\\unsigned".format( + consts.DSP_ARCH + ) + + # Copy necessary libraries to a common location + libs = [ + "QnnHtp.dll", + "QnnSystem.dll", + "QnnHtpPrepare.dll", + "QnnHtpV{}Stub.dll".format(consts.DSP_ARCH), + ] + hexagon_libs = [ + "libQnnHtpV{}Skel.so".format(consts.DSP_ARCH), + # "libqnnhtpv73.cat", + ] + for lib in libs: + if not os.path.isfile(os.path.join(consts.QNN_LIBS_DIR, lib)): + shutil.copy(os.path.join(SDK_lib_dir, lib), consts.QNN_LIBS_DIR) + + for lib in hexagon_libs: + if not os.path.isfile(os.path.join(consts.QNN_LIBS_DIR, lib)): + shutil.copy(os.path.join(SDK_hexagon_dir, lib), consts.QNN_LIBS_DIR) + +def install_qai_hub(): + if os.path.isfile(consts.QAI_HUB_CONFIG): + shutil.copy(consts.QAI_HUB_CONFIG, consts.QAI_HUB_CONFIG_BACKUP) + run(f"\"{consts.VENV_PATH}\\Scripts\\qai-hub.exe\" configure --api_token {consts.HUB_ID} > NUL", False) + +def restore_qai_hub_key(): + if os.path.isfile(consts.QAI_HUB_CONFIG_BACKUP): + shutil.copy(consts.QAI_HUB_CONFIG_BACKUP, consts.QAI_HUB_CONFIG) + +def convert_and_download_models(): + os.makedirs(consts.CONVERTION_DIR, exist_ok=True) + if not os.path.isfile(consts.TIMESTEP_EMBEDDING_1_5_MODEL_PATH): + model = qai_hub.get_model(consts.TIMESTEP_EMBEDDING_1_5_MODEL_ID) + model.download(filename=consts.TIMESTEP_EMBEDDING_1_5_MODEL_PATH) + if not os.path.isfile(consts.ESRGAN_X4_MODEL_PATH): + try: + model = qai_hub.get_model(consts.ESRGAN_X4_MODEL_ID) + model.download(filename=consts.ESRGAN_X4_MODEL_PATH) + except Exception: + run( + f'cmd /c "{consts.VENV_PYTHON}" -m qai_hub_models.models.esrgan.export ' + + f'--device "Snapdragon X Elite CRD" --height 512 --width 512 --target-runtime qnn ' + + f"--skip-profiling --skip-inferencing --skip-summary --output-dir {consts.CONVERTION_DIR}" + ) + shutil.move( + os.path.join(consts.CONVERTION_DIR, "esrgan.so"), + consts.ESRGAN_X4_MODEL_PATH, + ) + +print(f"Downloading QAIRT model bin files...") + +os.makedirs(consts.QNN_LIBS_DIR, exist_ok=True) +os.makedirs(consts.CONTROLNET_DIR, exist_ok=True) +os.makedirs(consts.TIME_EMBEDDING_PATH, exist_ok=True) +os.makedirs(consts.LOGS_DIR, exist_ok=True) + +download_qairt_sdk() +setup_qairt_env() + +# Download required models +CONTROLNET_MODEL_REVISION = "f71ca660a2a5f77edccdd4aca27187e94292123d" +hf_hub_download( + repo_id="qualcomm/ControlNet", + filename="ControlNet_Quantized.bin", + local_dir=consts.CONTROLNET_DIR, + revision=CONTROLNET_MODEL_REVISION, +) +hf_hub_download( + repo_id="qualcomm/ControlNet", + filename="TextEncoder_Quantized.bin", + local_dir=consts.CONTROLNET_DIR, + revision=CONTROLNET_MODEL_REVISION, +) +hf_hub_download( + repo_id="qualcomm/ControlNet", + filename="UNet_Quantized.bin", + local_dir=consts.CONTROLNET_DIR, + revision=CONTROLNET_MODEL_REVISION, +) +hf_hub_download( + repo_id="qualcomm/ControlNet", + filename="VAEDecoder_Quantized.bin", + local_dir=consts.CONTROLNET_DIR, + revision=CONTROLNET_MODEL_REVISION, +) + +install_qai_hub() +print("Downloading required models using qai-hub...") +convert_and_download_models() +print("Downloaded required models.") +restore_qai_hub_key() + +time_embeddings = TimestepEmbedding(320, 1280) +time_embeddings.load_state_dict(torch.load(consts.TIMESTEP_EMBEDDING_1_5_MODEL_PATH)) +scheduler = DPMSolverMultistepScheduler( + num_train_timesteps=1000, + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", +) + + +def get_timestep(step): + return np.int32(scheduler.timesteps.numpy()[step]) + +def get_time_embedding(timestep): + timestep = torch.tensor([timestep]) + t_emb = get_timestep_embedding(timestep, 320, True, 0) + emb = time_embeddings(t_emb).detach().numpy() + return emb + +def gen_time_embedding(user_step): + scheduler.set_timesteps(user_step) + + time_emb_path = os.path.join(consts.TIME_EMBEDDING_PATH, str(user_step)) + os.makedirs(time_emb_path, exist_ok=True) + for step in range(user_step): + file_path = os.path.join(time_emb_path, str(step) + ".raw") + timestep = get_timestep(step) + time_embedding = get_time_embedding(timestep) + time_embedding.tofile(file_path) + +gen_time_embedding(10) +gen_time_embedding(15) +gen_time_embedding(20) +gen_time_embedding(25) +gen_time_embedding(30) +gen_time_embedding(35) +gen_time_embedding(40) +gen_time_embedding(50) diff --git a/plugins/blender/SnapdragonImageGeneration/model_pipelines/ControlNetCanny.py b/plugins/blender/SnapdragonImageGeneration/model_pipelines/ControlNetCanny.py new file mode 100644 index 0000000..2630db0 --- /dev/null +++ b/plugins/blender/SnapdragonImageGeneration/model_pipelines/ControlNetCanny.py @@ -0,0 +1,602 @@ +# ============================================================================= +# +# Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# +# ============================================================================= + +import time +from PIL import Image +import os +import shutil +import cv2 +import numpy as np +import torch +from transformers import CLIPTokenizer +from diffusers import DPMSolverMultistepScheduler +from diffusers.utils import load_image +from diffusers.utils import PIL_INTERPOLATION +import sys +from torchvision import transforms +import json +import qairt_constants as consts +import traceback + +from qai_appbuilder import ( + QNNContext, + QNNContextProc, + QNNShareMemory, + Runtime, + LogLevel, + ProfilingLevel, + PerfProfile, + QNNConfig, + timer, +) + +#################################################################### + +OUT_H, OUT_W = 512, 512 + +tokenizer = None +scheduler = None +tokenizer_max_length = 77 # Define Tokenizer output max length (must be 77) + +# model objects. +text_encoder = None +unet = None +vae_decoder = None +realesrgan = None +realesrgan_mem = None + +# Any user defined prompt +user_prompt = "" +uncond_prompt = "" +user_seed = np.int64(1) +user_step = 20 # User defined step value, any integer value in {20, 30, 50} +user_text_guidance = 9.0 # User define text guidance, any float value in [5.0, 15.0] +user_high_resolution = False +input_image_path = None +output_image_path = None + +#################################################################### + + +def reset(): + global scheduler + global tokenizer + global text_encoder + global unet + global vae_decoder + global controlnet + global realesrgan + global realesrgan_mem + + global user_prompt + global uncond_prompt + global user_seed + global user_step + global user_text_guidance + global user_high_resolution + global input_image_path + global output_image_path + + tokenizer = None + scheduler = None + + # model objects. + text_encoder = None + unet = None + vae_decoder = None + realesrgan = None + realesrgan_mem = None + + # Any user defined prompt + user_prompt = "" + uncond_prompt = "" + user_seed = np.int64(1) + user_step = 20 # User defined step value, any integer value in {20, 30, 50} + user_text_guidance = ( + 9.0 # User define text guidance, any float value in [5.0, 15.0] + ) + input_image_path = None + user_high_resolution = True + output_image_path = None + + +#################################################################### + + +class TextEncoder(QNNContext): + # @timer + def Inference(self, input_data): + input_datas = [input_data] + output_data = super().Inference(input_datas)[0] + + # Output of Text encoder should be of shape (1, 77, 768) + output_data = output_data.reshape((1, 77, 768)) + return output_data + + +class Unet(QNNContext): + # @timer + def Inference( + self, + input_data_1, + input_data_2, + input_data_3, + input_data_4, + input_data_5, + input_data_6, + input_data_7, + input_data_8, + input_data_9, + input_data_10, + input_data_11, + input_data_12, + input_data_13, + input_data_14, + input_data_15, + input_data_16, + ): + # We need to reshape the array to 1 dimensionality before send it to the network. 'input_data_2' already is 1 dimensionality, so doesn't need to reshape. + input_data_1 = input_data_1.reshape(input_data_1.size) + input_data_3 = input_data_3.reshape(input_data_3.size) + input_data_4 = input_data_4.reshape(input_data_4.size) + input_data_5 = input_data_5.reshape(input_data_5.size) + input_data_6 = input_data_6.reshape(input_data_6.size) + + input_data_7 = input_data_7.reshape(input_data_7.size) + input_data_8 = input_data_8.reshape(input_data_8.size) + input_data_9 = input_data_9.reshape(input_data_9.size) + input_data_10 = input_data_10.reshape(input_data_10.size) + input_data_11 = input_data_11.reshape(input_data_11.size) + + input_data_12 = input_data_12.reshape(input_data_12.size) + input_data_13 = input_data_13.reshape(input_data_13.size) + input_data_14 = input_data_14.reshape(input_data_14.size) + input_data_15 = input_data_15.reshape(input_data_15.size) + input_data_16 = input_data_16.reshape(input_data_16.size) + + input_datas = [ + input_data_1, + input_data_2, + input_data_3, + input_data_4, + input_data_5, + input_data_6, + input_data_7, + input_data_8, + input_data_9, + input_data_10, + input_data_11, + input_data_12, + input_data_13, + input_data_14, + input_data_15, + input_data_16, + ] + + output_data = super().Inference(input_datas)[0] + output_data = output_data.reshape(1, 64, 64, 4) + return output_data + + +class VaeDecoder(QNNContext): + # @timer + def Inference(self, input_data): + input_data = input_data.reshape(input_data.size) + input_datas = [input_data] + + output_data = super().Inference(input_datas)[0] + + return output_data + + +class RealESRGan(QNNContextProc): + # @timer + def Inference(self, realesrgan_mem, input_data): + input_datas = [input_data] + + output_data = super().Inference(realesrgan_mem, input_datas)[0] + + return output_data + + +class ControlNet(QNNContext): + # @timer + def Inference(self, input_data_1, input_data_2, input_data_3, input_data_4): + # We need to reshape the array to 1 dimensionality before send it to the network. 'input_data_2' already is 1 dimensionality, so doesn't need to reshape. + + input_data_1 = input_data_1.reshape(input_data_1.size) + input_data_3 = input_data_3.reshape(input_data_3.size) + input_data_4 = input_data_4.reshape(input_data_4.size) + + input_datas = [input_data_1, input_data_2, input_data_3, input_data_4] + output_data = super().Inference(input_datas) + return output_data + + +#################################################################### + + +def model_initialize(): + global scheduler + global tokenizer + global text_encoder + global unet + global vae_decoder + global controlnet + global realesrgan + global realesrgan_mem + + result = True + + # model names + model_text_encoder = "text_encoder" + model_unet = "model_unet" + model_vae_decoder = "vae_decoder" + model_realesrgan = "realesrgan" + model_controlnet = "controlnet" + + # process names + model_realesrgan_proc = "~realesrgan" + + # share memory names. + model_realesrgan_mem = model_realesrgan + "~memory" + + # models' path. + text_encoder_model = "{}\\{}_Quantized.bin".format( + consts.CONTROLNET_DIR, "TextEncoder" + ) + unet_model = "{}\\{}_Quantized.bin".format(consts.CONTROLNET_DIR, "UNet") + vae_decoder_model = "{}\\{}_Quantized.bin".format( + consts.CONTROLNET_DIR, "VAEDecoder" + ) + controlnet_model = "{}\\{}_Quantized.bin".format( + consts.CONTROLNET_DIR, "ControlNet" + ) + realesrgan_model = consts.ESRGAN_X4_MODEL_PATH + + # Instance for Unet + unet = Unet(model_unet, unet_model) + + # Instance for TextEncoder + text_encoder = TextEncoder(model_text_encoder, text_encoder_model) + + # Instance for VaeDecoder + vae_decoder = VaeDecoder(model_vae_decoder, vae_decoder_model) + + # Instance for ControlNet + controlnet = ControlNet(model_controlnet, controlnet_model) + + # Instance for RealESRGan which inherited from the class QNNContextProc, the model will be loaded into a separate process. + realesrgan = RealESRGan(model_realesrgan, model_realesrgan_proc, realesrgan_model) + realesrgan_mem = QNNShareMemory(model_realesrgan_mem, 1024 * 1024 * 50) # 50M + + # Initializing the Tokenizer + tokenizer = CLIPTokenizer.from_pretrained( + "openai/clip-vit-base-patch32", cache_dir=consts.CACHE_DIR + ) + + # Scheduler - initializing the Scheduler. + scheduler = DPMSolverMultistepScheduler( + num_train_timesteps=1000, + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + ) + + torch.from_numpy( + np.array([1]) + ) # Let LazyImport to import the torch & numpy lib here. + + return result + + +def run_tokenizer(prompt): + text_input = tokenizer( + prompt, padding="max_length", max_length=tokenizer_max_length, truncation=True + ) + text_input = np.array(text_input.input_ids, dtype=np.float32) + return text_input + + +# These parameters can be configured through GUI 'settings'. +def setup_parameters( + input_img_path, + output_img_path, + prompt, + un_prompt, + seed, + step, + text_guidance, + high_resolution, +): + + global user_prompt + global uncond_prompt + global user_seed + global user_step + global user_text_guidance + global user_high_resolution + global input_image_path + global output_image_path + + user_prompt = prompt + uncond_prompt = un_prompt + user_seed = seed + user_step = step + user_text_guidance = text_guidance + user_high_resolution = high_resolution + input_image_path = input_img_path + output_image_path = output_img_path + + assert isinstance(user_seed, np.int64) == True, "user_seed should be of type int64" + assert isinstance(user_step, int) == True, "user_step should be of type int" + assert ( + isinstance(user_text_guidance, float) == True + ), "user_text_guidance should be of type float" + assert ( + user_text_guidance >= 5.0 and user_text_guidance <= 15.0 + ), "user_text_guidance should be a float from [5.0, 15.0]" + + +def run_scheduler(noise_pred_uncond, noise_pred_text, latent_in, timestep): + # Convert all inputs from NHWC to NCHW + noise_pred_uncond = np.transpose(noise_pred_uncond, (0, 3, 1, 2)).copy() + noise_pred_text = np.transpose(noise_pred_text, (0, 3, 1, 2)).copy() + latent_in = np.transpose(latent_in, (0, 3, 1, 2)).copy() + + # Convert all inputs to torch tensors + noise_pred_uncond = torch.from_numpy(noise_pred_uncond) + noise_pred_text = torch.from_numpy(noise_pred_text) + latent_in = torch.from_numpy(latent_in) + + # Merge noise_pred_uncond and noise_pred_text based on user_text_guidance + noise_pred = noise_pred_uncond + user_text_guidance * ( + noise_pred_text - noise_pred_uncond + ) + + # Run Scheduler step + latent_out = scheduler.step(noise_pred, timestep, latent_in).prev_sample.numpy() + + # Convert latent_out from NCHW to NHWC + latent_out = np.transpose(latent_out, (0, 2, 3, 1)) + + return latent_out + + +# Function to get timesteps +def get_timestep(step): + return np.int32(scheduler.timesteps.numpy()[step]) + + +def make_canny_image(input_image: Image): + image = np.asarray(input_image) + + # Get edges for input with Canny Edge Detection + low_threshold = 100 + high_threshold = 200 + + image = cv2.Canny(image, low_threshold, high_threshold) + cv2.imwrite(os.path.join(consts.OUTPUTS_DIR, "canny.png"), image) + image = image[:, :, None] + image = np.concatenate([image, image, image], axis=2) + + image = Image.fromarray(image) + + image = np.array(image) + image = image[None, :] + + image = image.astype(np.float32) / 255.0 + return image + + +# Execute the Stable Diffusion pipeline +def model_execute(callback): + PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST) + + scheduler.set_timesteps( + user_step + ) # Setting up user provided time steps for Scheduler + + # Run Tokenizer + cond_tokens = run_tokenizer(user_prompt) + uncond_tokens = run_tokenizer(uncond_prompt) + + # Run Text Encoder on Tokens + uncond_text_embedding = text_encoder.Inference(uncond_tokens) + user_text_embedding = text_encoder.Inference(cond_tokens) + + # Initialize the latent input with random initial latent + latents_shape = (1, 4, OUT_H // 8, OUT_W // 8) + random_init_latent = torch.randn( + latents_shape, generator=torch.manual_seed(user_seed) + ).numpy() + latent_in = random_init_latent.transpose(0, 2, 3, 1) + + image = load_image(input_image_path) + canny_image = make_canny_image(image) + + time_emb_path = os.path.join(consts.TIME_EMBEDDING_PATH, str(user_step)) + + # Run the loop for user_step times + for step in range(user_step): + print(f"Step {step} Running...") + sys.stdout.flush() + + timestep = get_timestep(step) # time_embedding = get_time_embedding(timestep) + file_path = os.path.join(time_emb_path, str(step) + ".raw") + time_embedding = np.fromfile(file_path, dtype=np.float32) + + controlnet_out = controlnet.Inference( + latent_in, time_embedding, user_text_embedding, canny_image + ) + + conditional_noise_pred = unet.Inference( + latent_in, time_embedding, user_text_embedding, *controlnet_out + ) + + controlnet_out = controlnet.Inference( + latent_in, time_embedding, uncond_text_embedding, canny_image + ) + + unconditional_noise_pred = unet.Inference( + latent_in, time_embedding, uncond_text_embedding, *controlnet_out + ) + + latent_in = run_scheduler( + unconditional_noise_pred, conditional_noise_pred, latent_in, timestep + ) + callback(step) + + # Run VAE + output_image = vae_decoder.Inference(latent_in) + output_image = output_image.reshape(OUT_H, OUT_W, -1) + + PerfProfile.RelPerfProfileGlobal() + + if len(output_image) == 0: + callback(None) + return False + else: + image_size = 512 + + # Run RealESRGan + if user_high_resolution: + output_image = realesrgan.Inference(realesrgan_mem, [output_image]) + image_size = 2048 + + image_path = output_image_path + output_image = np.clip(output_image * 255.0, 0.0, 255.0).astype(np.uint8) + output_image = output_image.reshape(image_size, image_size, -1) + Image.fromarray(output_image, mode="RGB").save(image_path) + callback(image_path) + return True + + +# Release all the models. +def model_destroy(): + global text_encoder + global unet + global vae_decoder + global realesrgan + global realesrgan_mem + + del text_encoder + del unet + del vae_decoder + del realesrgan + del realesrgan_mem + + +def SetQNNConfig(): + QNNConfig.Config( + consts.QNN_LIBS_DIR, Runtime.HTP, LogLevel.WARN, ProfilingLevel.BASIC + ) + + +def modelExecuteCallback(result): + if (None == result) or isinstance( + result, str + ): # None == Image generates failed. 'str' == image_path: generated new image path. + if None == result: + result = "None" + print("modelExecuteCallback result: " + result) + + else: + result = (result + 1) * 100 + result = int(result / user_step) + result = str(result) + print("modelExecuteCallback result: " + result) + + +def load_model(): + reset() + SetQNNConfig() + model_initialize() + return True + + +def unload_model(): + model_destroy() + return True + + +def run_model( + input_img_path, + output_img_path, + user_prompt, + uncond_prompt, + user_seed, + user_step, + user_text_guidance, + user_high_resolution, +): + + user_seed = np.int64(int(user_seed)) + user_step = int(user_step) + user_text_guidance = float(user_text_guidance) + user_high_resolution = bool(user_high_resolution) + + setup_parameters( + input_img_path, + output_img_path, + user_prompt, + uncond_prompt, + user_seed, + user_step, + user_text_guidance, + user_high_resolution, + ) + + is_generated = model_execute(modelExecuteCallback) + return is_generated + + +def handle_command(command): + + action = command.get("action") + params = command.get("params", {}) + start_time = time.time() + if action == "load_model": + result = load_model() + result_str = f"LOAD_MODEL_RESULT_START\n{result}\nLOAD_MODEL_RESULT_END" + elapsed_time = time.time() - start_time + print(f"Time taken for loading model :{elapsed_time}\n") + return result_str + + elif action == "run_model": + args = list(params.values()) + result = run_model(*args) + result_str = f"RUN_MODEL_RESULT_START\n{result}\nRUN_MODEL_RESULT_END" + elapsed_time = time.time() - start_time + print(f"Time taken for running the model :{elapsed_time}\n") + return result_str + + elif action == "unload_model": + result = unload_model() + result_str = f"UNLOAD_MODEL_RESULT_START\n{result}\nUNLOAD_MODEL_RESULT_END" + elapsed_time = time.time() - start_time + print(f"Time taken for unloading the model :{elapsed_time}\n") + return result_str + + +if __name__ == "__main__": + try: + while True: + command_line = sys.stdin.readline().strip() + if not command_line: + break + + command = json.loads(command_line) + response = handle_command(command) + print(response) + print("\nEND_DATA\n") + sys.stdout.flush() + except Exception as e: + print(f"\n{str(e)}\n{traceback.format_exc()}") + print("\nEND_DATA\n") + sys.stdout.flush() diff --git a/plugins/blender/SnapdragonImageGeneration/qairt_constants.py b/plugins/blender/SnapdragonImageGeneration/qairt_constants.py new file mode 100644 index 0000000..0dfdbcf --- /dev/null +++ b/plugins/blender/SnapdragonImageGeneration/qairt_constants.py @@ -0,0 +1,44 @@ +# ============================================================================= +# +# Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# +# ============================================================================= + + +import os +import pathlib + +PLUGIN_DIR=pathlib.Path(__file__).parent.resolve() +VENV_PATH=os.path.join(PLUGIN_DIR, "venv") +VENV_PYTHON=os.path.join(VENV_PATH, "Scripts", "python.exe") + +CONTROLNET_PIPELINE=os.path.join(PLUGIN_DIR, "model_pipelines", "ControlNetCanny.py") +OUTPUTS_DIR=os.path.join(PLUGIN_DIR, "Outputs") +LOGS_DIR=os.path.join(PLUGIN_DIR, "logs") +LOG_FILE=os.path.join(LOGS_DIR, "model_log.txt") + +QNN_SDK_DOWNLOAD_URL="https://softwarecenter.qualcomm.com/api/download/software/qualcomm_neural_processing_sdk/v2.24.0.240626.zip" + +QAIRT_VERSION = "2.24.0.240626" +DSP_ARCH = "73" # For X-Elite device. + +QNN_LIBS_DIR = os.path.join(PLUGIN_DIR, "qnn_assets", "qnn_libs") +SDK_SAVE_PATH= os.path.join(PLUGIN_DIR, f"{QAIRT_VERSION}.zip") +QAIRT_DIR=f"C:\\Qualcomm\\AIStack\\QAIRT" +QNN_SDK_ROOT=f"C:\\Qualcomm\\AIStack\\QAIRT\\{QAIRT_VERSION}" + +CONTROLNET_DIR=os.path.join(PLUGIN_DIR, "qnn_assets", "models", "controlnet") +CACHE_DIR = os.path.join(PLUGIN_DIR, "qnn_assets", "models", "cache") +TIME_EMBEDDING_PATH=os.path.join(CACHE_DIR, "time-embedding") + +HUB_ID="aac24f12d047e7f558d8effe4b2fdad0f5c2c341" +QAI_HUB_CONFIG = os.path.join(pathlib.Path.home(), ".qai_hub", "client.ini") +QAI_HUB_CONFIG_BACKUP = os.path.join(pathlib.Path.home(), ".qai_hub", "client.ini.bk") +CONVERTION_DIR = os.path.join(PLUGIN_DIR, "model_conversion") +ESRGAN_X4_MODEL_ID="monl8r2wq" +ESRGAN_X4_MODEL_PATH=os.path.join(CONVERTION_DIR,"esrgan.bin") + +TIMESTEP_EMBEDDING_1_5_MODEL_ID="m7mrzdgxn" +TIMESTEP_EMBEDDING_1_5_MODEL_PATH=os.path.join(CONVERTION_DIR,"time_embedding_sd_1_5.pt") \ No newline at end of file diff --git a/plugins/blender/SnapdragonImageGeneration/requirements.txt b/plugins/blender/SnapdragonImageGeneration/requirements.txt new file mode 100644 index 0000000..b693cd0 --- /dev/null +++ b/plugins/blender/SnapdragonImageGeneration/requirements.txt @@ -0,0 +1,17 @@ +scipy +diffusers==0.21.4 +Flask +numpy +opencv-python +pillow +pybind11 +requests==2.32.3 +tokenizers==0.19.1 +torch==1.13.1 +torchvision==0.14.1 +transformers==4.41.2 +accelerate +huggingface_hub +https://github.com/quic/ai-engine-direct-helper/releases/download/v2.24.0/qai_appbuilder-2.24.0-cp310-cp310-win_amd64.whl +qai-hub==0.13.0 +qai_hub_models[esrgan]==0.11.2 \ No newline at end of file diff --git a/plugins/blender/SnapdragonImageGeneration/utils.py b/plugins/blender/SnapdragonImageGeneration/utils.py new file mode 100644 index 0000000..0cd3083 --- /dev/null +++ b/plugins/blender/SnapdragonImageGeneration/utils.py @@ -0,0 +1,67 @@ +# ============================================================================= +# +# Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +# +# ============================================================================= + +import os +import json +from enum import Enum +from .qairt_constants import * +import traceback +import time +import functools +import bpy + +class LogMode(Enum): + BEGIN = "w" + RECORDING = "a" + +process = None + +def get_output_img_path(input_path): + base_dir, file = os.path.split(input_path) + file_name = file.split('.')[0] + "_controlnet" + return os.path.join(PLUGIN_DIR, f"Outputs\\{file_name}.png") + +def get_random_output_img_path(input_path): + base_dir, file = os.path.split(input_path) + file_name = file.split('.')[0] + "_" + str(time.time_ns()) + return os.path.join(PLUGIN_DIR, f"Outputs\\{file_name}.png") + +def get_generated_img_default_path(): + return os.path.join(PLUGIN_DIR, "Outputs\\generated_image.png") + + +def read_pipeline_output(process, output_lines, output_callback, step_callback): + try: + line = process.stdout.readline().strip() + output_lines.append(line) + if step_callback is not None: + step_callback(line) + if "END_DATA" in line: + response = "\n".join(output_lines) + output_callback(response) + return None + return 0.05 + except Exception as e: + response = "\n".join(output_lines) + output_callback(f"{response}\n{str(e)}\n{traceback.format_exc()}") + return None + +def send_command(process, command, output_callback, step_callback=None): + output_lines = [] + process.stdin.write(json.dumps(command) + '\n') + process.stdin.flush() + + bpy.app.timers.register(functools.partial(read_pipeline_output, process, output_lines, output_callback, step_callback)) + + +def show_error(message=""): + def draw(self, context): + self.layout.label(text=message) + + bpy.context.window_manager.popup_menu(draw, title="Error", icon="ERROR") +

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