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update sd-webui api parameters to v1.9.3 (#4798)
Co-authored-by: Your Name <chen@krasus.red>
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Krasus.Chen
GitHub
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93d3350c8c
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@ -13,49 +13,76 @@ from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter,
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from core.tools.errors import ToolProviderCredentialValidationError
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from core.tools.tool.builtin_tool import BuiltinTool
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# All commented out parameters default to null
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DRAW_TEXT_OPTIONS = {
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# Prompts
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"prompt": "",
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"negative_prompt": "",
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# "styles": [],
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# Seeds
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"seed": -1,
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"subseed": -1,
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"subseed_strength": 0,
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"seed_resize_from_h": -1,
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'sampler_index': 'DPM++ SDE Karras',
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"seed_resize_from_w": -1,
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# Samplers
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# "sampler_name": "DPM++ 2M",
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# "scheduler": "",
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# "sampler_index": "Automatic",
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# Latent Space Options
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"batch_size": 1,
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"n_iter": 1,
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"steps": 10,
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"cfg_scale": 7,
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"width": 1024,
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"height": 1024,
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"restore_faces": False,
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"width": 512,
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"height": 512,
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# "restore_faces": True,
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# "tiling": True,
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"do_not_save_samples": False,
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"do_not_save_grid": False,
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"eta": 0,
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"denoising_strength": 0,
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"s_min_uncond": 0,
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"s_churn": 0,
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"s_tmax": 0,
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"s_tmin": 0,
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"s_noise": 0,
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# "eta": 0,
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# "denoising_strength": 0.75,
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# "s_min_uncond": 0,
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# "s_churn": 0,
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# "s_tmax": 0,
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# "s_tmin": 0,
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# "s_noise": 0,
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"override_settings": {},
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"override_settings_restore_afterwards": True,
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# Refinement Options
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"refiner_checkpoint": "",
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"refiner_switch_at": 0,
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"disable_extra_networks": False,
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"comments": {},
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# "firstpass_image": "",
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# "comments": "",
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# High-Resolution Options
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"enable_hr": False,
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"firstphase_width": 0,
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"firstphase_height": 0,
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"hr_scale": 2,
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# "hr_upscaler": "",
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"hr_second_pass_steps": 0,
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"hr_resize_x": 0,
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"hr_resize_y": 0,
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# "hr_checkpoint_name": "",
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# "hr_sampler_name": "",
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# "hr_scheduler": "",
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"hr_prompt": "",
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"hr_negative_prompt": "",
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# Task Options
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# "force_task_id": "",
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# Script Options
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# "script_name": "",
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"script_args": [],
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# Output Options
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"send_images": True,
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"save_images": False,
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"alwayson_scripts": {}
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"alwayson_scripts": {},
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# "infotext": "",
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}
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@ -88,60 +115,15 @@ class StableDiffusionTool(BuiltinTool):
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except Exception as e:
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raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
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# prompt
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prompt = tool_parameters.get('prompt', '')
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if not prompt:
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return self.create_text_message('Please input prompt')
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# get negative prompt
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negative_prompt = tool_parameters.get('negative_prompt', '')
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# get size
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width = tool_parameters.get('width', 1024)
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height = tool_parameters.get('height', 1024)
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# get steps
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steps = tool_parameters.get('steps', 1)
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# get lora
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lora = tool_parameters.get('lora', '')
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# get image id
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# get image id and image variable
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image_id = tool_parameters.get('image_id', '')
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if image_id.strip():
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image_variable = self.get_default_image_variable()
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if image_variable:
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image_binary = self.get_variable_file(image_variable.name)
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if not image_binary:
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return self.create_text_message('Image not found, please request user to generate image firstly.')
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# convert image to RGB
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image = Image.open(io.BytesIO(image_binary))
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image = image.convert("RGB")
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buffer = io.BytesIO()
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image.save(buffer, format="PNG")
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image_binary = buffer.getvalue()
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image.close()
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image_variable = self.get_default_image_variable()
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# Return text2img if there's no image ID or no image variable
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if not image_id or not image_variable:
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return self.text2img(base_url=base_url,tool_parameters=tool_parameters)
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return self.img2img(base_url=base_url,
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lora=lora,
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image_binary=image_binary,
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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steps=steps,
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model=model)
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return self.text2img(base_url=base_url,
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lora=lora,
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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steps=steps,
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model=model)
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# Proceed with image-to-image generation
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return self.img2img(base_url=base_url,tool_parameters=tool_parameters)
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def validate_models(self) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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@ -197,35 +179,67 @@ class StableDiffusionTool(BuiltinTool):
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except Exception as e:
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return []
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def img2img(self, base_url: str, lora: str, image_binary: bytes,
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prompt: str, negative_prompt: str,
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width: int, height: int, steps: int, model: str) \
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def img2img(self, base_url: str, tool_parameters: dict[str, Any]) \
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-> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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generate image
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"""
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draw_options = {
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"init_images": [b64encode(image_binary).decode('utf-8')],
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"prompt": "",
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"negative_prompt": negative_prompt,
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"denoising_strength": 0.9,
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"width": width,
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"height": height,
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"cfg_scale": 7,
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"sampler_name": "Euler a",
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"restore_faces": False,
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"steps": steps,
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"script_args": ["outpainting mk2"],
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"override_settings": {"sd_model_checkpoint": model}
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}
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# Fetch the binary data of the image
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image_variable = self.get_default_image_variable()
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image_binary = self.get_variable_file(image_variable.name)
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if not image_binary:
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return self.create_text_message('Image not found, please request user to generate image firstly.')
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# Convert image to RGB and save as PNG
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try:
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with Image.open(io.BytesIO(image_binary)) as image:
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with io.BytesIO() as buffer:
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image.convert("RGB").save(buffer, format="PNG")
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image_binary = buffer.getvalue()
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except Exception as e:
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return self.create_text_message(f"Failed to process the image: {str(e)}")
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# copy draw options
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draw_options = deepcopy(DRAW_TEXT_OPTIONS)
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# set image options
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model = tool_parameters.get('model', '')
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draw_options_image = {
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"init_images": [b64encode(image_binary).decode('utf-8')],
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"denoising_strength": 0.9,
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"restore_faces": False,
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"script_args": [],
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"override_settings": {"sd_model_checkpoint": model},
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"resize_mode":0,
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"image_cfg_scale": 0,
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# "mask": None,
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"mask_blur_x": 4,
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"mask_blur_y": 4,
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"mask_blur": 0,
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"mask_round": True,
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"inpainting_fill": 0,
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"inpaint_full_res": True,
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"inpaint_full_res_padding": 0,
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"inpainting_mask_invert": 0,
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"initial_noise_multiplier": 0,
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# "latent_mask": None,
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"include_init_images": True,
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}
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# update key and values
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draw_options.update(draw_options_image)
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draw_options.update(tool_parameters)
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# get prompt lora model
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prompt = tool_parameters.get('prompt', '')
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lora = tool_parameters.get('lora', '')
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model = tool_parameters.get('model', '')
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if lora:
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draw_options['prompt'] = f'{lora},{prompt}'
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else:
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draw_options['prompt'] = prompt
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try:
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url = str(URL(base_url) / 'sdapi' / 'v1' / 'img2img')
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url = str(URL(base_url) / 'sdapi' / 'v1' / 'img2img')
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response = post(url, data=json.dumps(draw_options), timeout=120)
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if response.status_code != 200:
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return self.create_text_message('Failed to generate image')
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@ -239,24 +253,24 @@ class StableDiffusionTool(BuiltinTool):
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except Exception as e:
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return self.create_text_message('Failed to generate image')
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def text2img(self, base_url: str, lora: str, prompt: str, negative_prompt: str, width: int, height: int, steps: int, model: str) \
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def text2img(self, base_url: str, tool_parameters: dict[str, Any]) \
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-> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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generate image
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"""
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# copy draw options
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draw_options = deepcopy(DRAW_TEXT_OPTIONS)
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draw_options.update(tool_parameters)
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# get prompt lora model
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prompt = tool_parameters.get('prompt', '')
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lora = tool_parameters.get('lora', '')
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model = tool_parameters.get('model', '')
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if lora:
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draw_options['prompt'] = f'{lora},{prompt}'
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else:
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draw_options['prompt'] = prompt
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draw_options['width'] = width
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draw_options['height'] = height
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draw_options['steps'] = steps
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draw_options['negative_prompt'] = negative_prompt
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draw_options['override_settings']['sd_model_checkpoint'] = model
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try:
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url = str(URL(base_url) / 'sdapi' / 'v1' / 'txt2img')
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