Spaces:
Running
on
Zero
Running
on
Zero
refactor gui.py
Browse files- app.py +9 -402
- gui.py +432 -0
- service/gemini_service.py +11 -0
- utils/model_utils.py +1 -1
app.py
CHANGED
@@ -184,6 +184,7 @@ import diffusers
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diffusers.utils.logging.set_verbosity(40)
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import warnings
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warnings.filterwarnings(
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action="ignore",
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@@ -204,407 +205,12 @@ warnings.filterwarnings(
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logger.setLevel(logging.DEBUG)
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base_model_id="cagliostrolab/animagine-xl-3.1",
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task_name="txt2img",
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vae_model=None,
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type_model_precision=torch.float16,
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retain_task_model_in_cache=False,
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)
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def load_new_model(
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self,
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model_name,
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vae_model,
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task,
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progress=gr.Progress(track_tqdm=True)):
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"""
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:param model_name:
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:param vae_model:
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:param task:
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:param progress:
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"""
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yield f"Loading model: {model_name}"
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vae_model = vae_model if vae_model != "None" else None
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if model_name in model_list:
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model_is_xl = "xl" in model_name.lower()
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sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
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model_type = "SDXL" if model_is_xl else "SD 1.5"
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incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
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if incompatible_vae:
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vae_model = None
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self.model.load_pipe(
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model_name,
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task_name=task_stablepy[task],
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vae_model=vae_model if vae_model != "None" else None,
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type_model_precision=torch.float16,
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retain_task_model_in_cache=False,
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)
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yield f"Model loaded: {model_name}"
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@spaces.GPU
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def generate_pipeline(
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self,
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prompt,
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neg_prompt,
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num_images,
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steps,
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cfg,
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clip_skip,
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seed,
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lora1,
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lora_scale1,
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lora2,
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lora_scale2,
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lora3,
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lora_scale3,
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lora4,
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lora_scale4,
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lora5,
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lora_scale5,
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sampler,
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img_height,
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img_width,
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model_name,
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vae_model,
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task,
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image_control,
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preprocessor_name,
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preprocess_resolution,
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image_resolution,
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style_prompt, # list []
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style_json_file,
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image_mask,
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strength,
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low_threshold,
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high_threshold,
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value_threshold,
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distance_threshold,
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controlnet_output_scaling_in_unet,
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controlnet_start_threshold,
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controlnet_stop_threshold,
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textual_inversion,
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syntax_weights,
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upscaler_model_path,
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upscaler_increases_size,
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esrgan_tile,
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esrgan_tile_overlap,
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hires_steps,
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hires_denoising_strength,
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hires_sampler,
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hires_prompt,
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hires_negative_prompt,
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hires_before_adetailer,
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hires_after_adetailer,
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loop_generation,
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leave_progress_bar,
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disable_progress_bar,
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image_previews,
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display_images,
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save_generated_images,
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image_storage_location,
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retain_compel_previous_load,
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retain_detailfix_model_previous_load,
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retain_hires_model_previous_load,
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t2i_adapter_preprocessor,
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t2i_adapter_conditioning_scale,
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t2i_adapter_conditioning_factor,
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xformers_memory_efficient_attention,
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freeu,
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generator_in_cpu,
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adetailer_inpaint_only,
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adetailer_verbose,
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adetailer_sampler,
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adetailer_active_a,
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prompt_ad_a,
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negative_prompt_ad_a,
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strength_ad_a,
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face_detector_ad_a,
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person_detector_ad_a,
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hand_detector_ad_a,
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mask_dilation_a,
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mask_blur_a,
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mask_padding_a,
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adetailer_active_b,
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prompt_ad_b,
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negative_prompt_ad_b,
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strength_ad_b,
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face_detector_ad_b,
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person_detector_ad_b,
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hand_detector_ad_b,
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mask_dilation_b,
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mask_blur_b,
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mask_padding_b,
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retain_task_cache_gui,
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image_ip1,
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mask_ip1,
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model_ip1,
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mode_ip1,
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scale_ip1,
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image_ip2,
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mask_ip2,
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model_ip2,
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mode_ip2,
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scale_ip2):
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vae_model = vae_model if vae_model != "None" else None
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loras_list: list = [lora1, lora2, lora3, lora4, lora5]
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vae_msg: str = f"VAE: {vae_model}" if vae_model else ""
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msg_lora: list = []
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if model_name in model_list:
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model_is_xl = "xl" in model_name.lower()
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sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
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model_type = "SDXL" if model_is_xl else "SD 1.5"
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incompatible_vae = ((model_is_xl and
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vae_model and
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not sdxl_in_vae) or
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(not model_is_xl and
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sdxl_in_vae))
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if incompatible_vae:
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msg_inc_vae = (
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f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you"
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f" are using a {model_type} model. The default VAE "
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"will be used."
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)
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gr.Info(msg_inc_vae)
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vae_msg = msg_inc_vae
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vae_model = None
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for la in loras_list:
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if la is None or la == "None" or la not in lora_model_list:
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continue
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print(la)
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lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
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if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
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msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}."
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gr.Info(msg_inc_lora)
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msg_lora.append(msg_inc_lora)
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task = task_stablepy[task]
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params_ip_img: list = []
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params_ip_msk: list = []
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params_ip_model: list = []
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params_ip_mode: list = []
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params_ip_scale: list = []
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all_adapters = [
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(image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
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(image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
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]
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for (imgip,
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mskip,
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modelip,
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modeip,
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scaleip) in all_adapters:
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if imgip:
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params_ip_img.append(imgip)
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if mskip:
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params_ip_msk.append(mskip)
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params_ip_model.append(modelip)
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params_ip_mode.append(modeip)
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params_ip_scale.append(scaleip)
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# First load
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model_precision = torch.float16
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if not self.model:
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from modelstream import Model_Diffusers2
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print("Loading model...")
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self.model = Model_Diffusers2(
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base_model_id=model_name,
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task_name=task,
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vae_model=vae_model if vae_model != "None" else None,
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type_model_precision=model_precision,
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retain_task_model_in_cache=retain_task_cache_gui,
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)
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if task != "txt2img" and not image_control:
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raise ValueError(
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"No control image found: To use this function, "
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"you have to upload an image in 'Image ControlNet/Inpaint/Img2img'"
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)
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if task == "inpaint" and not image_mask:
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raise ValueError("No mask image found: Specify one in 'Image Mask'")
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if upscaler_model_path in [None, "Lanczos", "Nearest"]:
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upscaler_model = upscaler_model_path
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else:
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directory_upscalers = 'upscalers'
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os.makedirs(directory_upscalers, exist_ok=True)
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url_upscaler = upscaler_dict_gui[upscaler_model_path]
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if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
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download_things(
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directory_upscalers,
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url_upscaler,
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hf_token
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)
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upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
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logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
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print("Config model:", model_name, vae_model, loras_list)
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self.model.load_pipe(
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model_name,
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task_name=task,
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vae_model=vae_model if vae_model != "None" else None,
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type_model_precision=model_precision,
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retain_task_model_in_cache=retain_task_cache_gui,
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)
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if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
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print("No Textual inversion for SDXL")
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adetailer_params_A = {
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"face_detector_ad": face_detector_ad_a,
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"person_detector_ad": person_detector_ad_a,
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"hand_detector_ad": hand_detector_ad_a,
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"prompt": prompt_ad_a,
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"negative_prompt": negative_prompt_ad_a,
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"strength": strength_ad_a,
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# "image_list_task" : None,
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"mask_dilation": mask_dilation_a,
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"mask_blur": mask_blur_a,
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"mask_padding": mask_padding_a,
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"inpaint_only": adetailer_inpaint_only,
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"sampler": adetailer_sampler,
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}
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adetailer_params_B = {
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"face_detector_ad": face_detector_ad_b,
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"person_detector_ad": person_detector_ad_b,
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"hand_detector_ad": hand_detector_ad_b,
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"prompt": prompt_ad_b,
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"negative_prompt": negative_prompt_ad_b,
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"strength": strength_ad_b,
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# "image_list_task" : None,
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"mask_dilation": mask_dilation_b,
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"mask_blur": mask_blur_b,
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"mask_padding": mask_padding_b,
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}
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pipe_params = {
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"prompt": prompt,
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"negative_prompt": neg_prompt,
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"img_height": img_height,
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"img_width": img_width,
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"num_images": num_images,
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508 |
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"num_steps": steps,
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"guidance_scale": cfg,
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"clip_skip": clip_skip,
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"seed": seed,
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"image": image_control,
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"preprocessor_name": preprocessor_name,
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514 |
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"preprocess_resolution": preprocess_resolution,
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515 |
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"image_resolution": image_resolution,
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516 |
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"style_prompt": style_prompt if style_prompt else "",
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517 |
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"style_json_file": "",
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518 |
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"image_mask": image_mask, # only for Inpaint
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519 |
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"strength": strength, # only for Inpaint or ...
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520 |
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"low_threshold": low_threshold,
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521 |
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"high_threshold": high_threshold,
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522 |
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"value_threshold": value_threshold,
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523 |
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"distance_threshold": distance_threshold,
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524 |
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"lora_A": lora1 if lora1 != "None" else None,
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525 |
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"lora_scale_A": lora_scale1,
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526 |
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"lora_B": lora2 if lora2 != "None" else None,
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527 |
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"lora_scale_B": lora_scale2,
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528 |
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"lora_C": lora3 if lora3 != "None" else None,
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529 |
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"lora_scale_C": lora_scale3,
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530 |
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"lora_D": lora4 if lora4 != "None" else None,
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"lora_scale_D": lora_scale4,
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532 |
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"lora_E": lora5 if lora5 != "None" else None,
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533 |
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"lora_scale_E": lora_scale5,
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534 |
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"textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [],
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535 |
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"syntax_weights": syntax_weights, # "Classic"
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536 |
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"sampler": sampler,
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537 |
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"xformers_memory_efficient_attention": xformers_memory_efficient_attention,
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538 |
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"gui_active": True,
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539 |
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"loop_generation": loop_generation,
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540 |
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"controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
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541 |
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"control_guidance_start": float(controlnet_start_threshold),
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542 |
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"control_guidance_end": float(controlnet_stop_threshold),
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543 |
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"generator_in_cpu": generator_in_cpu,
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544 |
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"FreeU": freeu,
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545 |
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"adetailer_A": adetailer_active_a,
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546 |
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"adetailer_A_params": adetailer_params_A,
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547 |
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"adetailer_B": adetailer_active_b,
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548 |
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"adetailer_B_params": adetailer_params_B,
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549 |
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"leave_progress_bar": leave_progress_bar,
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550 |
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"disable_progress_bar": disable_progress_bar,
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551 |
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"image_previews": image_previews,
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552 |
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"display_images": display_images,
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553 |
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"save_generated_images": save_generated_images,
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554 |
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"image_storage_location": image_storage_location,
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555 |
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"retain_compel_previous_load": retain_compel_previous_load,
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556 |
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"retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
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557 |
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"retain_hires_model_previous_load": retain_hires_model_previous_load,
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558 |
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"t2i_adapter_preprocessor": t2i_adapter_preprocessor,
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559 |
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"t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
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560 |
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"t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
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561 |
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"upscaler_model_path": upscaler_model,
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562 |
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"upscaler_increases_size": upscaler_increases_size,
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563 |
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"esrgan_tile": esrgan_tile,
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564 |
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"esrgan_tile_overlap": esrgan_tile_overlap,
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565 |
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"hires_steps": hires_steps,
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566 |
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"hires_denoising_strength": hires_denoising_strength,
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567 |
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"hires_prompt": hires_prompt,
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568 |
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"hires_negative_prompt": hires_negative_prompt,
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569 |
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"hires_sampler": hires_sampler,
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570 |
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"hires_before_adetailer": hires_before_adetailer,
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571 |
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"hires_after_adetailer": hires_after_adetailer,
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572 |
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"ip_adapter_image": params_ip_img,
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573 |
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"ip_adapter_mask": params_ip_msk,
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574 |
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"ip_adapter_model": params_ip_model,
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575 |
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"ip_adapter_mode": params_ip_mode,
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576 |
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"ip_adapter_scale": params_ip_scale,
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577 |
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}
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578 |
-
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# print(pipe_params)
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580 |
-
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581 |
-
random_number = random.randint(1, 100)
|
582 |
-
if random_number < 25 and num_images < 3:
|
583 |
-
if (not upscaler_model and
|
584 |
-
steps < 45 and
|
585 |
-
task in ["txt2img", "img2img"] and
|
586 |
-
not adetailer_active_a and
|
587 |
-
not adetailer_active_b):
|
588 |
-
num_images *= 2
|
589 |
-
pipe_params["num_images"] = num_images
|
590 |
-
gr.Info("Num images x 2 🎉")
|
591 |
-
|
592 |
-
# Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
|
593 |
-
self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
|
594 |
-
|
595 |
-
info_state = f"PROCESSING"
|
596 |
-
for img, seed, data in self.model(**pipe_params):
|
597 |
-
info_state += "."
|
598 |
-
if data:
|
599 |
-
info_state = f"COMPLETED. Seeds: {str(seed)}"
|
600 |
-
if vae_msg:
|
601 |
-
info_state = info_state + "<br>" + vae_msg
|
602 |
-
if msg_lora:
|
603 |
-
info_state = info_state + "<br>" + "<br>".join(msg_lora)
|
604 |
-
yield img, info_state
|
605 |
-
|
606 |
-
|
607 |
-
sd_gen = GuiSD()
|
608 |
|
609 |
with open("app.css", "r") as f:
|
610 |
CSS: str = f.read()
|
@@ -649,7 +255,7 @@ with gr.Blocks(css=CSS) as app:
|
|
649 |
model_name_gui = gr.Dropdown(
|
650 |
label="Model",
|
651 |
choices=model_list,
|
652 |
-
value=
|
653 |
allow_custom_value=True
|
654 |
)
|
655 |
prompt_gui = gr.Textbox(
|
@@ -1104,6 +710,7 @@ with gr.Blocks(css=CSS) as app:
|
|
1104 |
value=choices_task[0]
|
1105 |
)
|
1106 |
|
|
|
1107 |
task_gui.change(
|
1108 |
change_preprocessor_choices,
|
1109 |
[task_gui],
|
|
|
184 |
|
185 |
diffusers.utils.logging.set_verbosity(40)
|
186 |
import warnings
|
187 |
+
from gui import GuiSD
|
188 |
|
189 |
warnings.filterwarnings(
|
190 |
action="ignore",
|
|
|
205 |
logger.setLevel(logging.DEBUG)
|
206 |
|
207 |
|
208 |
+
# init GuiSD
|
209 |
+
sd_gen = GuiSD(
|
210 |
+
model_list=model_list,
|
211 |
+
task_stablepy=task_stablepy,
|
212 |
+
lora_model_list=lora_model_list
|
213 |
+
)
|
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|
|
214 |
|
215 |
with open("app.css", "r") as f:
|
216 |
CSS: str = f.read()
|
|
|
255 |
model_name_gui = gr.Dropdown(
|
256 |
label="Model",
|
257 |
choices=model_list,
|
258 |
+
value="models/animaPencilXL_v500.safetensors" or model_list[0],
|
259 |
allow_custom_value=True
|
260 |
)
|
261 |
prompt_gui = gr.Textbox(
|
|
|
710 |
value=choices_task[0]
|
711 |
)
|
712 |
|
713 |
+
|
714 |
task_gui.change(
|
715 |
change_preprocessor_choices,
|
716 |
[task_gui],
|
gui.py
ADDED
@@ -0,0 +1,432 @@
|
|
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|
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|
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|
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|
|
|
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|
|
|
|
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|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import spaces
|
2 |
+
import os
|
3 |
+
from stablepy import Model_Diffusers
|
4 |
+
from stablepy.diffusers_vanilla.model import scheduler_names
|
5 |
+
from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
|
6 |
+
import torch
|
7 |
+
import re
|
8 |
+
import shutil
|
9 |
+
import random
|
10 |
+
import spaces
|
11 |
+
import gradio as gr
|
12 |
+
from PIL import Image
|
13 |
+
import IPython.display
|
14 |
+
import time, json
|
15 |
+
from IPython.utils import capture
|
16 |
+
import logging
|
17 |
+
from utils.string_utils import extract_parameters
|
18 |
+
from stablepy import logger
|
19 |
+
|
20 |
+
logging.getLogger("diffusers").setLevel(logging.ERROR)
|
21 |
+
import diffusers
|
22 |
+
|
23 |
+
diffusers.utils.logging.set_verbosity(40)
|
24 |
+
import warnings
|
25 |
+
|
26 |
+
|
27 |
+
class GuiSD:
|
28 |
+
def __init__(self,
|
29 |
+
model_list,
|
30 |
+
task_stablepy,
|
31 |
+
lora_model_list,
|
32 |
+
stream=True):
|
33 |
+
self.model = None
|
34 |
+
|
35 |
+
print("Loading model...")
|
36 |
+
self.model = Model_Diffusers(
|
37 |
+
base_model_id="cagliostrolab/animagine-xl-3.1",
|
38 |
+
task_name="txt2img",
|
39 |
+
vae_model=None,
|
40 |
+
type_model_precision=torch.float16,
|
41 |
+
retain_task_model_in_cache=False,
|
42 |
+
)
|
43 |
+
self.model_list = model_list
|
44 |
+
self.task_stablepy = task_stablepy
|
45 |
+
self.lora_model_list = lora_model_list
|
46 |
+
self.stream = stream
|
47 |
+
|
48 |
+
def load_new_model(
|
49 |
+
self,
|
50 |
+
model_name,
|
51 |
+
vae_model,
|
52 |
+
task,
|
53 |
+
progress=gr.Progress(track_tqdm=True)):
|
54 |
+
"""
|
55 |
+
:param model_name:
|
56 |
+
:param vae_model:
|
57 |
+
:param task:
|
58 |
+
:param progress:
|
59 |
+
"""
|
60 |
+
yield f"Loading model: {model_name}"
|
61 |
+
|
62 |
+
vae_model = vae_model if vae_model != "None" else None
|
63 |
+
|
64 |
+
if model_name in self.model_list:
|
65 |
+
model_is_xl = "xl" in model_name.lower()
|
66 |
+
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
|
67 |
+
model_type = "SDXL" if model_is_xl else "SD 1.5"
|
68 |
+
incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
|
69 |
+
|
70 |
+
if incompatible_vae:
|
71 |
+
vae_model = None
|
72 |
+
|
73 |
+
self.model.load_pipe(
|
74 |
+
model_name,
|
75 |
+
task_name=self.task_stablepy[task],
|
76 |
+
vae_model=vae_model if vae_model != "None" else None,
|
77 |
+
type_model_precision=torch.float16,
|
78 |
+
retain_task_model_in_cache=False,
|
79 |
+
)
|
80 |
+
yield f"Model loaded: {model_name}"
|
81 |
+
|
82 |
+
@spaces.GPU
|
83 |
+
def generate_pipeline(
|
84 |
+
self,
|
85 |
+
prompt,
|
86 |
+
neg_prompt,
|
87 |
+
num_images,
|
88 |
+
steps,
|
89 |
+
cfg,
|
90 |
+
clip_skip,
|
91 |
+
seed,
|
92 |
+
lora1,
|
93 |
+
lora_scale1,
|
94 |
+
lora2,
|
95 |
+
lora_scale2,
|
96 |
+
lora3,
|
97 |
+
lora_scale3,
|
98 |
+
lora4,
|
99 |
+
lora_scale4,
|
100 |
+
lora5,
|
101 |
+
lora_scale5,
|
102 |
+
sampler,
|
103 |
+
img_height,
|
104 |
+
img_width,
|
105 |
+
model_name,
|
106 |
+
vae_model,
|
107 |
+
task,
|
108 |
+
image_control,
|
109 |
+
preprocessor_name,
|
110 |
+
preprocess_resolution,
|
111 |
+
image_resolution,
|
112 |
+
style_prompt, # list []
|
113 |
+
style_json_file,
|
114 |
+
image_mask,
|
115 |
+
strength,
|
116 |
+
low_threshold,
|
117 |
+
high_threshold,
|
118 |
+
value_threshold,
|
119 |
+
distance_threshold,
|
120 |
+
controlnet_output_scaling_in_unet,
|
121 |
+
controlnet_start_threshold,
|
122 |
+
controlnet_stop_threshold,
|
123 |
+
textual_inversion,
|
124 |
+
syntax_weights,
|
125 |
+
upscaler_model_path,
|
126 |
+
upscaler_increases_size,
|
127 |
+
esrgan_tile,
|
128 |
+
esrgan_tile_overlap,
|
129 |
+
hires_steps,
|
130 |
+
hires_denoising_strength,
|
131 |
+
hires_sampler,
|
132 |
+
hires_prompt,
|
133 |
+
hires_negative_prompt,
|
134 |
+
hires_before_adetailer,
|
135 |
+
hires_after_adetailer,
|
136 |
+
loop_generation,
|
137 |
+
leave_progress_bar,
|
138 |
+
disable_progress_bar,
|
139 |
+
image_previews,
|
140 |
+
display_images,
|
141 |
+
save_generated_images,
|
142 |
+
image_storage_location,
|
143 |
+
retain_compel_previous_load,
|
144 |
+
retain_detailfix_model_previous_load,
|
145 |
+
retain_hires_model_previous_load,
|
146 |
+
t2i_adapter_preprocessor,
|
147 |
+
t2i_adapter_conditioning_scale,
|
148 |
+
t2i_adapter_conditioning_factor,
|
149 |
+
xformers_memory_efficient_attention,
|
150 |
+
freeu,
|
151 |
+
generator_in_cpu,
|
152 |
+
adetailer_inpaint_only,
|
153 |
+
adetailer_verbose,
|
154 |
+
adetailer_sampler,
|
155 |
+
adetailer_active_a,
|
156 |
+
prompt_ad_a,
|
157 |
+
negative_prompt_ad_a,
|
158 |
+
strength_ad_a,
|
159 |
+
face_detector_ad_a,
|
160 |
+
person_detector_ad_a,
|
161 |
+
hand_detector_ad_a,
|
162 |
+
mask_dilation_a,
|
163 |
+
mask_blur_a,
|
164 |
+
mask_padding_a,
|
165 |
+
adetailer_active_b,
|
166 |
+
prompt_ad_b,
|
167 |
+
negative_prompt_ad_b,
|
168 |
+
strength_ad_b,
|
169 |
+
face_detector_ad_b,
|
170 |
+
person_detector_ad_b,
|
171 |
+
hand_detector_ad_b,
|
172 |
+
mask_dilation_b,
|
173 |
+
mask_blur_b,
|
174 |
+
mask_padding_b,
|
175 |
+
retain_task_cache_gui,
|
176 |
+
image_ip1,
|
177 |
+
mask_ip1,
|
178 |
+
model_ip1,
|
179 |
+
mode_ip1,
|
180 |
+
scale_ip1,
|
181 |
+
image_ip2,
|
182 |
+
mask_ip2,
|
183 |
+
model_ip2,
|
184 |
+
mode_ip2,
|
185 |
+
scale_ip2):
|
186 |
+
vae_model = vae_model if vae_model != "None" else None
|
187 |
+
loras_list: list = [lora1, lora2, lora3, lora4, lora5]
|
188 |
+
vae_msg: str = f"VAE: {vae_model}" if vae_model else ""
|
189 |
+
msg_lora: list = []
|
190 |
+
|
191 |
+
if model_name in self.model_list:
|
192 |
+
model_is_xl = "xl" in model_name.lower()
|
193 |
+
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
|
194 |
+
model_type = "SDXL" if model_is_xl else "SD 1.5"
|
195 |
+
incompatible_vae = ((model_is_xl and
|
196 |
+
vae_model and
|
197 |
+
not sdxl_in_vae) or
|
198 |
+
(not model_is_xl and
|
199 |
+
sdxl_in_vae))
|
200 |
+
|
201 |
+
if incompatible_vae:
|
202 |
+
msg_inc_vae = (
|
203 |
+
f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you"
|
204 |
+
f" are using a {model_type} model. The default VAE "
|
205 |
+
"will be used."
|
206 |
+
)
|
207 |
+
gr.Info(msg_inc_vae)
|
208 |
+
vae_msg = msg_inc_vae
|
209 |
+
vae_model = None
|
210 |
+
|
211 |
+
for la in loras_list:
|
212 |
+
if la is None or la == "None" or la not in self.lora_model_list:
|
213 |
+
continue
|
214 |
+
|
215 |
+
print(la)
|
216 |
+
lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
|
217 |
+
if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
|
218 |
+
msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}."
|
219 |
+
gr.Info(msg_inc_lora)
|
220 |
+
msg_lora.append(msg_inc_lora)
|
221 |
+
|
222 |
+
task = self.task_stablepy[task]
|
223 |
+
|
224 |
+
params_ip_img: list = []
|
225 |
+
params_ip_msk: list = []
|
226 |
+
params_ip_model: list = []
|
227 |
+
params_ip_mode: list = []
|
228 |
+
params_ip_scale: list = []
|
229 |
+
|
230 |
+
all_adapters = [
|
231 |
+
(image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
|
232 |
+
(image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
|
233 |
+
]
|
234 |
+
|
235 |
+
for (imgip,
|
236 |
+
mskip,
|
237 |
+
modelip,
|
238 |
+
modeip,
|
239 |
+
scaleip) in all_adapters:
|
240 |
+
if imgip:
|
241 |
+
params_ip_img.append(imgip)
|
242 |
+
if mskip:
|
243 |
+
params_ip_msk.append(mskip)
|
244 |
+
params_ip_model.append(modelip)
|
245 |
+
params_ip_mode.append(modeip)
|
246 |
+
params_ip_scale.append(scaleip)
|
247 |
+
|
248 |
+
# First load
|
249 |
+
model_precision = torch.float16
|
250 |
+
if not self.model:
|
251 |
+
from modelstream import Model_Diffusers2
|
252 |
+
|
253 |
+
print("Loading model...")
|
254 |
+
self.model = Model_Diffusers2(
|
255 |
+
base_model_id=model_name,
|
256 |
+
task_name=task,
|
257 |
+
vae_model=vae_model if vae_model != "None" else None,
|
258 |
+
type_model_precision=model_precision,
|
259 |
+
retain_task_model_in_cache=retain_task_cache_gui,
|
260 |
+
)
|
261 |
+
|
262 |
+
if task != "txt2img" and not image_control:
|
263 |
+
raise ValueError(
|
264 |
+
"No control image found: To use this function, "
|
265 |
+
"you have to upload an image in 'Image ControlNet/Inpaint/Img2img'"
|
266 |
+
)
|
267 |
+
|
268 |
+
if task == "inpaint" and not image_mask:
|
269 |
+
raise ValueError("No mask image found: Specify one in 'Image Mask'")
|
270 |
+
|
271 |
+
if upscaler_model_path in [None, "Lanczos", "Nearest"]:
|
272 |
+
upscaler_model = upscaler_model_path
|
273 |
+
else:
|
274 |
+
directory_upscalers = 'upscalers'
|
275 |
+
os.makedirs(directory_upscalers, exist_ok=True)
|
276 |
+
|
277 |
+
url_upscaler = upscaler_dict_gui[upscaler_model_path]
|
278 |
+
|
279 |
+
if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
|
280 |
+
download_things(
|
281 |
+
directory_upscalers,
|
282 |
+
url_upscaler,
|
283 |
+
hf_token
|
284 |
+
)
|
285 |
+
|
286 |
+
upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
|
287 |
+
|
288 |
+
logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
|
289 |
+
|
290 |
+
print("Config model:", model_name, vae_model, loras_list)
|
291 |
+
|
292 |
+
self.model.load_pipe(
|
293 |
+
model_name,
|
294 |
+
task_name=task,
|
295 |
+
vae_model=vae_model if vae_model != "None" else None,
|
296 |
+
type_model_precision=model_precision,
|
297 |
+
retain_task_model_in_cache=retain_task_cache_gui,
|
298 |
+
)
|
299 |
+
|
300 |
+
if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
|
301 |
+
print("No Textual inversion for SDXL")
|
302 |
+
|
303 |
+
adetailer_params_A: dict = {
|
304 |
+
"face_detector_ad": face_detector_ad_a,
|
305 |
+
"person_detector_ad": person_detector_ad_a,
|
306 |
+
"hand_detector_ad": hand_detector_ad_a,
|
307 |
+
"prompt": prompt_ad_a,
|
308 |
+
"negative_prompt": negative_prompt_ad_a,
|
309 |
+
"strength": strength_ad_a,
|
310 |
+
# "image_list_task" : None,
|
311 |
+
"mask_dilation": mask_dilation_a,
|
312 |
+
"mask_blur": mask_blur_a,
|
313 |
+
"mask_padding": mask_padding_a,
|
314 |
+
"inpaint_only": adetailer_inpaint_only,
|
315 |
+
"sampler": adetailer_sampler,
|
316 |
+
}
|
317 |
+
|
318 |
+
adetailer_params_B: dict = {
|
319 |
+
"face_detector_ad": face_detector_ad_b,
|
320 |
+
"person_detector_ad": person_detector_ad_b,
|
321 |
+
"hand_detector_ad": hand_detector_ad_b,
|
322 |
+
"prompt": prompt_ad_b,
|
323 |
+
"negative_prompt": negative_prompt_ad_b,
|
324 |
+
"strength": strength_ad_b,
|
325 |
+
# "image_list_task" : None,
|
326 |
+
"mask_dilation": mask_dilation_b,
|
327 |
+
"mask_blur": mask_blur_b,
|
328 |
+
"mask_padding": mask_padding_b,
|
329 |
+
}
|
330 |
+
pipe_params: dict = {
|
331 |
+
"prompt": prompt,
|
332 |
+
"negative_prompt": neg_prompt,
|
333 |
+
"img_height": img_height,
|
334 |
+
"img_width": img_width,
|
335 |
+
"num_images": num_images,
|
336 |
+
"num_steps": steps,
|
337 |
+
"guidance_scale": cfg,
|
338 |
+
"clip_skip": clip_skip,
|
339 |
+
"seed": seed,
|
340 |
+
"image": image_control,
|
341 |
+
"preprocessor_name": preprocessor_name,
|
342 |
+
"preprocess_resolution": preprocess_resolution,
|
343 |
+
"image_resolution": image_resolution,
|
344 |
+
"style_prompt": style_prompt if style_prompt else "",
|
345 |
+
"style_json_file": "",
|
346 |
+
"image_mask": image_mask, # only for Inpaint
|
347 |
+
"strength": strength, # only for Inpaint or ...
|
348 |
+
"low_threshold": low_threshold,
|
349 |
+
"high_threshold": high_threshold,
|
350 |
+
"value_threshold": value_threshold,
|
351 |
+
"distance_threshold": distance_threshold,
|
352 |
+
"lora_A": lora1 if lora1 != "None" else None,
|
353 |
+
"lora_scale_A": lora_scale1,
|
354 |
+
"lora_B": lora2 if lora2 != "None" else None,
|
355 |
+
"lora_scale_B": lora_scale2,
|
356 |
+
"lora_C": lora3 if lora3 != "None" else None,
|
357 |
+
"lora_scale_C": lora_scale3,
|
358 |
+
"lora_D": lora4 if lora4 != "None" else None,
|
359 |
+
"lora_scale_D": lora_scale4,
|
360 |
+
"lora_E": lora5 if lora5 != "None" else None,
|
361 |
+
"lora_scale_E": lora_scale5,
|
362 |
+
"textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [],
|
363 |
+
"syntax_weights": syntax_weights, # "Classic"
|
364 |
+
"sampler": sampler,
|
365 |
+
"xformers_memory_efficient_attention": xformers_memory_efficient_attention,
|
366 |
+
"gui_active": True,
|
367 |
+
"loop_generation": loop_generation,
|
368 |
+
"controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
|
369 |
+
"control_guidance_start": float(controlnet_start_threshold),
|
370 |
+
"control_guidance_end": float(controlnet_stop_threshold),
|
371 |
+
"generator_in_cpu": generator_in_cpu,
|
372 |
+
"FreeU": freeu,
|
373 |
+
"adetailer_A": adetailer_active_a,
|
374 |
+
"adetailer_A_params": adetailer_params_A,
|
375 |
+
"adetailer_B": adetailer_active_b,
|
376 |
+
"adetailer_B_params": adetailer_params_B,
|
377 |
+
"leave_progress_bar": leave_progress_bar,
|
378 |
+
"disable_progress_bar": disable_progress_bar,
|
379 |
+
"image_previews": image_previews,
|
380 |
+
"display_images": display_images,
|
381 |
+
"save_generated_images": save_generated_images,
|
382 |
+
"image_storage_location": image_storage_location,
|
383 |
+
"retain_compel_previous_load": retain_compel_previous_load,
|
384 |
+
"retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
|
385 |
+
"retain_hires_model_previous_load": retain_hires_model_previous_load,
|
386 |
+
"t2i_adapter_preprocessor": t2i_adapter_preprocessor,
|
387 |
+
"t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
|
388 |
+
"t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
|
389 |
+
"upscaler_model_path": upscaler_model,
|
390 |
+
"upscaler_increases_size": upscaler_increases_size,
|
391 |
+
"esrgan_tile": esrgan_tile,
|
392 |
+
"esrgan_tile_overlap": esrgan_tile_overlap,
|
393 |
+
"hires_steps": hires_steps,
|
394 |
+
"hires_denoising_strength": hires_denoising_strength,
|
395 |
+
"hires_prompt": hires_prompt,
|
396 |
+
"hires_negative_prompt": hires_negative_prompt,
|
397 |
+
"hires_sampler": hires_sampler,
|
398 |
+
"hires_before_adetailer": hires_before_adetailer,
|
399 |
+
"hires_after_adetailer": hires_after_adetailer,
|
400 |
+
"ip_adapter_image": params_ip_img,
|
401 |
+
"ip_adapter_mask": params_ip_msk,
|
402 |
+
"ip_adapter_model": params_ip_model,
|
403 |
+
"ip_adapter_mode": params_ip_mode,
|
404 |
+
"ip_adapter_scale": params_ip_scale,
|
405 |
+
}
|
406 |
+
|
407 |
+
# print(pipe_params)
|
408 |
+
|
409 |
+
random_number = random.randint(1, 100)
|
410 |
+
if random_number < 25 and num_images < 3:
|
411 |
+
if (not upscaler_model and
|
412 |
+
steps < 45 and
|
413 |
+
task in ["txt2img", "img2img"] and
|
414 |
+
not adetailer_active_a and
|
415 |
+
not adetailer_active_b):
|
416 |
+
num_images *= 2
|
417 |
+
pipe_params["num_images"] = num_images
|
418 |
+
gr.Info("Num images x 2 🎉")
|
419 |
+
|
420 |
+
# Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
|
421 |
+
self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
|
422 |
+
|
423 |
+
info_state = f"PROCESSING"
|
424 |
+
for img, seed, data in self.model(**pipe_params):
|
425 |
+
info_state += "."
|
426 |
+
if data:
|
427 |
+
info_state = f"COMPLETED. Seeds: {str(seed)}"
|
428 |
+
if vae_msg:
|
429 |
+
info_state = info_state + "<br>" + vae_msg
|
430 |
+
if msg_lora:
|
431 |
+
info_state = info_state + "<br>" + "<br>".join(msg_lora)
|
432 |
+
yield img, info_state
|
service/gemini_service.py
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
def prompt_gemini(prompt: str):
|
2 |
+
"""
|
3 |
+
:param prompt:
|
4 |
+
:return:
|
5 |
+
"""
|
6 |
+
import os
|
7 |
+
gemini_api_key: str = os.environ.get("GEMINI_API_KEY")
|
8 |
+
if not gemini_api_key:
|
9 |
+
print("\033[91mYou need an API key to download Gemini models.\033[0m")
|
10 |
+
|
11 |
+
return prompt
|
utils/model_utils.py
CHANGED
@@ -20,4 +20,4 @@ def get_model_list(directory_path):
|
|
20 |
# model_list.append((name_without_extension, file_path))
|
21 |
model_list.append(file_path)
|
22 |
print('\033[34mFILE: ' + file_path + '\033[0m')
|
23 |
-
return model_list
|
|
|
20 |
# model_list.append((name_without_extension, file_path))
|
21 |
model_list.append(file_path)
|
22 |
print('\033[34mFILE: ' + file_path + '\033[0m')
|
23 |
+
return model_list
|