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Use only realesrgan_x4 (not anime) for upscaling in codeformer

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cmdr2 2023-06-07 16:37:44 +05:30
parent e23f66a697
commit 267c7b85ea
1 changed files with 40 additions and 24 deletions
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64
ui/easydiffusion/renderer.py
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@ -7,10 +7,12 @@ from easydiffusion import device_manager
from easydiffusion.types import GenerateImageRequest
from easydiffusion.types import Image as ResponseImage
from easydiffusion.types import Response, TaskData, UserInitiatedStop
from easydiffusion.model_manager import DEFAULT_MODELS, resolve_model_to_use
from easydiffusion.utils import get_printable_request, log, save_images_to_disk
from sdkit import Context
from sdkit.filter import apply_filters
from sdkit.generate import generate_images
from sdkit.models import load_model
from sdkit.utils import (
diffusers_latent_samples_to_images,
gc,
@ -157,37 +159,51 @@ def filter_images(req: GenerateImageRequest, task_data: TaskData, images: list,
if user_stopped:
return images
filters_to_apply = []
filter_params = {}
if task_data.block_nsfw:
filters_to_apply.append("nsfw_checker")
if task_data.use_face_correction and "codeformer" in task_data.use_face_correction.lower():
filters_to_apply.append("codeformer")
images = apply_filters(context, "nsfw_checker", images)
filter_params["upscale_faces"] = task_data.codeformer_upscale_faces
filter_params["codeformer_fidelity"] = task_data.codeformer_fidelity
if task_data.use_face_correction and "codeformer" in task_data.use_face_correction.lower():
default_realesrgan = DEFAULT_MODELS["realesrgan"][0]["file_name"]
prev_realesrgan_path = None
if task_data.codeformer_upscale_faces and default_realesrgan not in context.model_paths["realesrgan"]:
prev_realesrgan_path = context.model_paths["realesrgan"]
context.model_paths["realesrgan"] = resolve_model_to_use(default_realesrgan, "realesrgan")
load_model(context, "realesrgan")
try:
images = apply_filters(
context,
"codeformer",
images,
upscale_faces=task_data.codeformer_upscale_faces,
codeformer_fidelity=task_data.codeformer_fidelity,
)
finally:
if prev_realesrgan_path:
context.model_paths["realesrgan"] = prev_realesrgan_path
load_model(context, "realesrgan")
elif task_data.use_face_correction and "gfpgan" in task_data.use_face_correction.lower():
filters_to_apply.append("gfpgan")
images = apply_filters(context, "gfpgan", images)
if task_data.use_upscale:
if "realesrgan" in task_data.use_upscale.lower():
filters_to_apply.append("realesrgan")
images = apply_filters(context, "realesrgan", images, scale=task_data.upscale_amount)
elif task_data.use_upscale == "latent_upscaler":
filters_to_apply.append("latent_upscaler")
images = apply_filters(
context,
"latent_upscaler",
images,
scale=task_data.upscale_amount,
latent_upscaler_options={
"prompt": req.prompt,
"negative_prompt": req.negative_prompt,
"seed": req.seed,
"num_inference_steps": task_data.latent_upscaler_steps,
"guidance_scale": 0,
},
)
filter_params["latent_upscaler_options"] = {
"prompt": req.prompt,
"negative_prompt": req.negative_prompt,
"seed": req.seed,
"num_inference_steps": task_data.latent_upscaler_steps,
"guidance_scale": 0,
}
filter_params["scale"] = task_data.upscale_amount
if len(filters_to_apply) == 0:
return images
return apply_filters(context, filters_to_apply, images, **filter_params)
return images
def construct_response(images: list, seeds: list, task_data: TaskData, base_seed: int):