mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
289f983f41
* SD Scripts and UI tabs that support batch_count can now take a string containing a JSON array, or a list of integers, as their seed input. * Each batch in a run will now take the seed specified at the corresponding array index if one exists. If there is no seed at that index, the seed value will be treated as -1 and a random seed will be assigned at that position. If an integer rather than a list or json array has been, everything works as before. * UI seed input controls are now Textboxes with info lines about the seed formats allowed. * UI error handling updated to be more helpful if the seed input is invalid.
718 lines
26 KiB
Python
718 lines
26 KiB
Python
import os
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import torch
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import time
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import gradio as gr
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import PIL
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from PIL import Image
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import base64
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from io import BytesIO
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from fastapi.exceptions import HTTPException
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from apps.stable_diffusion.web.ui.utils import (
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available_devices,
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nodlogo_loc,
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get_custom_model_path,
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get_custom_model_files,
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scheduler_list_cpu_only,
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predefined_models,
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cancel_sd,
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)
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from apps.stable_diffusion.src import (
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args,
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Image2ImagePipeline,
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StencilPipeline,
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resize_stencil,
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get_schedulers,
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set_init_device_flags,
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utils,
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save_output_img,
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)
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from apps.stable_diffusion.src.utils import (
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get_generated_imgs_path,
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get_generation_text_info,
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)
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from apps.stable_diffusion.web.utils.common_label_calc import status_label
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import numpy as np
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# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
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init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
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init_use_tuned = args.use_tuned
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init_import_mlir = args.import_mlir
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# Exposed to UI.
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def img2img_inf(
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prompt: str,
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negative_prompt: str,
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image_dict,
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height: int,
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width: int,
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steps: int,
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strength: float,
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guidance_scale: float,
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seed: str | int,
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batch_count: int,
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batch_size: int,
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scheduler: str,
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custom_model: str,
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hf_model_id: str,
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custom_vae: str,
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precision: str,
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device: str,
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max_length: int,
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use_stencil: str,
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save_metadata_to_json: bool,
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save_metadata_to_png: bool,
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lora_weights: str,
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lora_hf_id: str,
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ondemand: bool,
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repeatable_seeds: bool,
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):
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from apps.stable_diffusion.web.ui.utils import (
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get_custom_model_pathfile,
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get_custom_vae_or_lora_weights,
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Config,
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)
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import apps.stable_diffusion.web.utils.global_obj as global_obj
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from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
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SD_STATE_CANCEL,
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)
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args.prompts = [prompt]
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args.negative_prompts = [negative_prompt]
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args.guidance_scale = guidance_scale
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args.seed = seed
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args.steps = steps
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args.strength = strength
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args.scheduler = scheduler
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args.img_path = "not none"
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args.ondemand = ondemand
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if image_dict is None:
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return None, "An Initial Image is required"
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if use_stencil == "scribble":
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image = image_dict["mask"].convert("RGB")
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elif isinstance(image_dict, PIL.Image.Image):
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image = image_dict.convert("RGB")
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else:
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image = image_dict["image"].convert("RGB")
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# set ckpt_loc and hf_model_id.
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args.ckpt_loc = ""
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args.hf_model_id = ""
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args.custom_vae = ""
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if custom_model == "None":
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if not hf_model_id:
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return (
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None,
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"Please provide either custom model or huggingface model ID, "
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"both must not be empty.",
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)
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if "civitai" in hf_model_id:
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args.ckpt_loc = hf_model_id
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else:
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args.hf_model_id = hf_model_id
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elif ".ckpt" in custom_model or ".safetensors" in custom_model:
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args.ckpt_loc = get_custom_model_pathfile(custom_model)
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else:
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args.hf_model_id = custom_model
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if custom_vae != "None":
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args.custom_vae = get_custom_model_pathfile(custom_vae, model="vae")
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args.use_lora = get_custom_vae_or_lora_weights(
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lora_weights, lora_hf_id, "lora"
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)
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args.save_metadata_to_json = save_metadata_to_json
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args.write_metadata_to_png = save_metadata_to_png
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use_stencil = None if use_stencil == "None" else use_stencil
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args.use_stencil = use_stencil
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if use_stencil is not None:
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args.scheduler = "DDIM"
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args.hf_model_id = "runwayml/stable-diffusion-v1-5"
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image, width, height = resize_stencil(image)
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elif "Shark" in args.scheduler:
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print(
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f"Shark schedulers are not supported. Switching to EulerDiscrete "
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f"scheduler"
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)
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args.scheduler = "EulerDiscrete"
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cpu_scheduling = not args.scheduler.startswith("Shark")
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args.precision = precision
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dtype = torch.float32 if precision == "fp32" else torch.half
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new_config_obj = Config(
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"img2img",
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args.hf_model_id,
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args.ckpt_loc,
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args.custom_vae,
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precision,
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batch_size,
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max_length,
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height,
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width,
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device,
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use_lora=args.use_lora,
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use_stencil=use_stencil,
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ondemand=ondemand,
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)
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if (
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not global_obj.get_sd_obj()
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or global_obj.get_cfg_obj() != new_config_obj
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):
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global_obj.clear_cache()
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global_obj.set_cfg_obj(new_config_obj)
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args.batch_count = batch_count
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args.batch_size = batch_size
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args.max_length = max_length
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args.height = height
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args.width = width
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args.device = device.split("=>", 1)[1].strip()
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args.iree_vulkan_target_triple = init_iree_vulkan_target_triple
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args.use_tuned = init_use_tuned
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args.import_mlir = init_import_mlir
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set_init_device_flags()
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model_id = (
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args.hf_model_id
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if args.hf_model_id
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else "stabilityai/stable-diffusion-2-1-base"
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)
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global_obj.set_schedulers(get_schedulers(model_id))
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scheduler_obj = global_obj.get_scheduler(args.scheduler)
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if use_stencil is not None:
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args.use_tuned = False
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global_obj.set_sd_obj(
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StencilPipeline.from_pretrained(
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scheduler_obj,
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args.import_mlir,
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args.hf_model_id,
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args.ckpt_loc,
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args.custom_vae,
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args.precision,
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args.max_length,
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args.batch_size,
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args.height,
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args.width,
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args.use_base_vae,
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args.use_tuned,
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low_cpu_mem_usage=args.low_cpu_mem_usage,
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use_stencil=use_stencil,
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debug=args.import_debug if args.import_mlir else False,
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use_lora=args.use_lora,
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ondemand=args.ondemand,
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)
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)
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else:
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global_obj.set_sd_obj(
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Image2ImagePipeline.from_pretrained(
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scheduler_obj,
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args.import_mlir,
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args.hf_model_id,
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args.ckpt_loc,
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args.custom_vae,
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args.precision,
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args.max_length,
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args.batch_size,
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args.height,
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args.width,
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args.use_base_vae,
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args.use_tuned,
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low_cpu_mem_usage=args.low_cpu_mem_usage,
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debug=args.import_debug if args.import_mlir else False,
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use_lora=args.use_lora,
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ondemand=args.ondemand,
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)
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)
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global_obj.set_sd_scheduler(args.scheduler)
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start_time = time.time()
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global_obj.get_sd_obj().log = ""
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generated_imgs = []
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extra_info = {"STRENGTH": strength}
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text_output = ""
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try:
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seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
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except TypeError as error:
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raise gr.Error(str(error)) from None
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for current_batch in range(batch_count):
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out_imgs = global_obj.get_sd_obj().generate_images(
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prompt,
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negative_prompt,
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image,
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batch_size,
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height,
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width,
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steps,
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strength,
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guidance_scale,
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seeds[current_batch],
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args.max_length,
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dtype,
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args.use_base_vae,
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cpu_scheduling,
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args.max_embeddings_multiples,
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use_stencil=use_stencil,
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)
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total_time = time.time() - start_time
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text_output = get_generation_text_info(
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seeds[: current_batch + 1], device
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)
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text_output += "\n" + global_obj.get_sd_obj().log
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text_output += f"\nTotal image(s) generation time: {total_time:.4f}sec"
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if global_obj.get_sd_status() == SD_STATE_CANCEL:
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break
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else:
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save_output_img(
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out_imgs[0],
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seeds[current_batch],
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extra_info,
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)
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generated_imgs.extend(out_imgs)
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yield generated_imgs, text_output, status_label(
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"Image-to-Image", current_batch + 1, batch_count, batch_size
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)
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return generated_imgs, text_output, ""
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def decode_base64_to_image(encoding):
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if encoding.startswith("data:image/"):
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encoding = encoding.split(";", 1)[1].split(",", 1)[1]
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try:
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image = Image.open(BytesIO(base64.b64decode(encoding)))
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return image
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except Exception as err:
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print(err)
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raise HTTPException(status_code=500, detail="Invalid encoded image")
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def encode_pil_to_base64(images):
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encoded_imgs = []
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for image in images:
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with BytesIO() as output_bytes:
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if args.output_img_format.lower() == "png":
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image.save(output_bytes, format="PNG")
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elif args.output_img_format.lower() in ("jpg", "jpeg"):
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image.save(output_bytes, format="JPEG")
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else:
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raise HTTPException(
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status_code=500, detail="Invalid image format"
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)
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bytes_data = output_bytes.getvalue()
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encoded_imgs.append(base64.b64encode(bytes_data))
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return encoded_imgs
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# Img2Img Rest API.
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def img2img_api(
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InputData: dict,
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):
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print(
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f'Prompt: {InputData["prompt"]}, '
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f'Negative Prompt: {InputData["negative_prompt"]}, '
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f'Seed: {InputData["seed"]}.'
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)
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init_image = decode_base64_to_image(InputData["init_images"][0])
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res = img2img_inf(
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InputData["prompt"],
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InputData["negative_prompt"],
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init_image,
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InputData["height"],
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InputData["width"],
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InputData["steps"],
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InputData["denoising_strength"],
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InputData["cfg_scale"],
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InputData["seed"],
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batch_count=1,
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batch_size=1,
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scheduler="EulerDiscrete",
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custom_model="None",
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hf_model_id=InputData["hf_model_id"]
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if "hf_model_id" in InputData.keys()
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else "stabilityai/stable-diffusion-2-1-base",
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custom_vae="None",
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precision="fp16",
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device=available_devices[0],
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max_length=64,
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use_stencil=InputData["use_stencil"]
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if "use_stencil" in InputData.keys()
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else "None",
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save_metadata_to_json=False,
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save_metadata_to_png=False,
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lora_weights="None",
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lora_hf_id="",
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ondemand=False,
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repeatable_seeds=False,
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)
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# Converts generator type to subscriptable
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res = next(res)
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return {
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"images": encode_pil_to_base64(res[0]),
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"parameters": {},
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"info": res[1],
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}
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with gr.Blocks(title="Image-to-Image") as img2img_web:
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with gr.Row(elem_id="ui_title"):
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nod_logo = Image.open(nodlogo_loc)
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with gr.Row():
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with gr.Column(scale=1, elem_id="demo_title_outer"):
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gr.Image(
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value=nod_logo,
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show_label=False,
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interactive=False,
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elem_id="top_logo",
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width=150,
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height=50,
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)
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with gr.Row(elem_id="ui_body"):
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with gr.Row():
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with gr.Column(scale=1, min_width=600):
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with gr.Row():
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# janky fix for overflowing text
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i2i_model_info = (str(get_custom_model_path())).replace(
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"\\", "\n\\"
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)
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i2i_model_info = f"Custom Model Path: {i2i_model_info}"
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img2img_custom_model = gr.Dropdown(
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label=f"Models",
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info=i2i_model_info,
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elem_id="custom_model",
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value=os.path.basename(args.ckpt_loc)
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if args.ckpt_loc
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else "stabilityai/stable-diffusion-2-1-base",
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choices=["None"]
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+ get_custom_model_files()
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+ predefined_models,
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)
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img2img_hf_model_id = gr.Textbox(
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elem_id="hf_model_id",
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placeholder="Select 'None' in the Models dropdown "
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"on the left and enter model ID here "
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"e.g: SG161222/Realistic_Vision_V1.3, "
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"https://civitai.com/api/download/models/15236",
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value="",
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label="HuggingFace Model ID or Civitai model "
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"download URL",
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lines=3,
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)
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# janky fix for overflowing text
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i2i_vae_info = (str(get_custom_model_path("vae"))).replace(
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"\\", "\n\\"
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)
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i2i_vae_info = f"VAE Path: {i2i_vae_info}"
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custom_vae = gr.Dropdown(
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label=f"Custom VAE Models",
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info=i2i_vae_info,
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elem_id="custom_model",
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value=os.path.basename(args.custom_vae)
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if args.custom_vae
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else "None",
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choices=["None"] + get_custom_model_files("vae"),
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)
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with gr.Group(elem_id="prompt_box_outer"):
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prompt = gr.Textbox(
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label="Prompt",
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value=args.prompts[0],
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lines=2,
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elem_id="prompt_box",
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)
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negative_prompt = gr.Textbox(
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label="Negative Prompt",
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value=args.negative_prompts[0],
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lines=2,
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elem_id="negative_prompt_box",
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)
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img2img_init_image = gr.Image(
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label="Input Image",
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source="upload",
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tool="sketch",
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type="pil",
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height=300,
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)
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with gr.Accordion(label="Stencil Options", open=False):
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with gr.Row():
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use_stencil = gr.Dropdown(
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elem_id="stencil_model",
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label="Stencil model",
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value="None",
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choices=["None", "canny", "openpose", "scribble"],
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)
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def show_canvas(choice):
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if choice == "scribble":
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return (
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gr.Slider.update(visible=True),
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gr.Slider.update(visible=True),
|
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gr.Button.update(visible=True),
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)
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else:
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return (
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gr.Slider.update(visible=False),
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gr.Slider.update(visible=False),
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gr.Button.update(visible=False),
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)
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def create_canvas(w, h):
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return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
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|
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with gr.Row():
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|
canvas_width = gr.Slider(
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label="Canvas Width",
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minimum=256,
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maximum=1024,
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value=512,
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step=1,
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visible=False,
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)
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canvas_height = gr.Slider(
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label="Canvas Height",
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minimum=256,
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maximum=1024,
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value=512,
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step=1,
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visible=False,
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)
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create_button = gr.Button(
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label="Start",
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value="Open drawing canvas!",
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visible=False,
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)
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create_button.click(
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fn=create_canvas,
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inputs=[canvas_width, canvas_height],
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outputs=[img2img_init_image],
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)
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use_stencil.change(
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fn=show_canvas,
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inputs=use_stencil,
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outputs=[canvas_width, canvas_height, create_button],
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)
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with gr.Accordion(label="LoRA Options", open=False):
|
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with gr.Row():
|
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# janky fix for overflowing text
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i2i_lora_info = (
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str(get_custom_model_path("lora"))
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).replace("\\", "\n\\")
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i2i_lora_info = f"LoRA Path: {i2i_lora_info}"
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lora_weights = gr.Dropdown(
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label=f"Standalone LoRA Weights",
|
|
info=i2i_lora_info,
|
|
elem_id="lora_weights",
|
|
value="None",
|
|
choices=["None"] + get_custom_model_files("lora"),
|
|
)
|
|
lora_hf_id = gr.Textbox(
|
|
elem_id="lora_hf_id",
|
|
placeholder="Select 'None' in the Standalone LoRA "
|
|
"weights dropdown on the left if you want to use "
|
|
"a standalone HuggingFace model ID for LoRA here "
|
|
"e.g: sayakpaul/sd-model-finetuned-lora-t4",
|
|
value="",
|
|
label="HuggingFace Model ID",
|
|
lines=3,
|
|
)
|
|
with gr.Accordion(label="Advanced Options", open=False):
|
|
with gr.Row():
|
|
scheduler = gr.Dropdown(
|
|
elem_id="scheduler",
|
|
label="Scheduler",
|
|
value="EulerDiscrete",
|
|
choices=scheduler_list_cpu_only,
|
|
)
|
|
with gr.Group():
|
|
save_metadata_to_png = gr.Checkbox(
|
|
label="Save prompt information to PNG",
|
|
value=args.write_metadata_to_png,
|
|
interactive=True,
|
|
)
|
|
save_metadata_to_json = gr.Checkbox(
|
|
label="Save prompt information to JSON file",
|
|
value=args.save_metadata_to_json,
|
|
interactive=True,
|
|
)
|
|
with gr.Row():
|
|
height = gr.Slider(
|
|
384, 768, value=args.height, step=8, label="Height"
|
|
)
|
|
width = gr.Slider(
|
|
384, 768, value=args.width, step=8, label="Width"
|
|
)
|
|
precision = gr.Radio(
|
|
label="Precision",
|
|
value=args.precision,
|
|
choices=[
|
|
"fp16",
|
|
"fp32",
|
|
],
|
|
visible=True,
|
|
)
|
|
max_length = gr.Radio(
|
|
label="Max Length",
|
|
value=args.max_length,
|
|
choices=[
|
|
64,
|
|
77,
|
|
],
|
|
visible=False,
|
|
)
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
steps = gr.Slider(
|
|
1, 100, value=args.steps, step=1, label="Steps"
|
|
)
|
|
with gr.Column(scale=3):
|
|
strength = gr.Slider(
|
|
0,
|
|
1,
|
|
value=args.strength,
|
|
step=0.01,
|
|
label="Denoising Strength",
|
|
)
|
|
ondemand = gr.Checkbox(
|
|
value=args.ondemand,
|
|
label="Low VRAM",
|
|
interactive=True,
|
|
)
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
guidance_scale = gr.Slider(
|
|
0,
|
|
50,
|
|
value=args.guidance_scale,
|
|
step=0.1,
|
|
label="CFG Scale",
|
|
)
|
|
with gr.Column(scale=3):
|
|
batch_count = gr.Slider(
|
|
1,
|
|
100,
|
|
value=args.batch_count,
|
|
step=1,
|
|
label="Batch Count",
|
|
interactive=True,
|
|
)
|
|
repeatable_seeds = gr.Checkbox(
|
|
args.repeatable_seeds,
|
|
label="Repeatable Seeds",
|
|
)
|
|
with gr.Row():
|
|
batch_size = gr.Slider(
|
|
1,
|
|
4,
|
|
value=args.batch_size,
|
|
step=1,
|
|
label="Batch Size",
|
|
interactive=False,
|
|
visible=False,
|
|
)
|
|
with gr.Row():
|
|
seed = gr.Textbox(
|
|
value=args.seed,
|
|
label="Seed",
|
|
info="An integer or a JSON list of integers, -1 for random",
|
|
)
|
|
device = gr.Dropdown(
|
|
elem_id="device",
|
|
label="Device",
|
|
value=available_devices[0],
|
|
choices=available_devices,
|
|
)
|
|
with gr.Row():
|
|
random_seed = gr.Button("Randomize Seed")
|
|
random_seed.click(
|
|
lambda: -1,
|
|
inputs=[],
|
|
outputs=[seed],
|
|
queue=False,
|
|
)
|
|
stop_batch = gr.Button("Stop Batch")
|
|
stable_diffusion = gr.Button("Generate Image(s)")
|
|
|
|
with gr.Column(scale=1, min_width=600):
|
|
with gr.Group():
|
|
img2img_gallery = gr.Gallery(
|
|
label="Generated images",
|
|
show_label=False,
|
|
elem_id="gallery",
|
|
columns=2,
|
|
object_fit="contain",
|
|
)
|
|
std_output = gr.Textbox(
|
|
value=f"Images will be saved at "
|
|
f"{get_generated_imgs_path()}",
|
|
lines=1,
|
|
elem_id="std_output",
|
|
show_label=False,
|
|
)
|
|
img2img_status = gr.Textbox(visible=False)
|
|
with gr.Row():
|
|
img2img_sendto_inpaint = gr.Button(value="SendTo Inpaint")
|
|
img2img_sendto_outpaint = gr.Button(
|
|
value="SendTo Outpaint"
|
|
)
|
|
img2img_sendto_upscaler = gr.Button(
|
|
value="SendTo Upscaler"
|
|
)
|
|
|
|
kwargs = dict(
|
|
fn=img2img_inf,
|
|
inputs=[
|
|
prompt,
|
|
negative_prompt,
|
|
img2img_init_image,
|
|
height,
|
|
width,
|
|
steps,
|
|
strength,
|
|
guidance_scale,
|
|
seed,
|
|
batch_count,
|
|
batch_size,
|
|
scheduler,
|
|
img2img_custom_model,
|
|
img2img_hf_model_id,
|
|
custom_vae,
|
|
precision,
|
|
device,
|
|
max_length,
|
|
use_stencil,
|
|
save_metadata_to_json,
|
|
save_metadata_to_png,
|
|
lora_weights,
|
|
lora_hf_id,
|
|
ondemand,
|
|
repeatable_seeds,
|
|
],
|
|
outputs=[img2img_gallery, std_output, img2img_status],
|
|
show_progress="minimal" if args.progress_bar else "none",
|
|
)
|
|
|
|
status_kwargs = dict(
|
|
fn=lambda bc, bs: status_label("Image-to-Image", 0, bc, bs),
|
|
inputs=[batch_count, batch_size],
|
|
outputs=img2img_status,
|
|
)
|
|
|
|
prompt_submit = prompt.submit(**status_kwargs).then(**kwargs)
|
|
neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(
|
|
**kwargs
|
|
)
|
|
generate_click = stable_diffusion.click(**status_kwargs).then(**kwargs)
|
|
stop_batch.click(
|
|
fn=cancel_sd,
|
|
cancels=[prompt_submit, neg_prompt_submit, generate_click],
|
|
)
|