Generalize facetool strength argument

backend/invoke_ai_web_server.py

@@ -319,7 +319,7 @@ def handle_run_postprocessing(
                 elif postprocessing_parameters['type'] == 'gfpgan':
                     image = self.gfpgan.process(
                         image=image,
-                        strength=postprocessing_parameters['gfpgan_strength'],
+                        strength=postprocessing_parameters['facetool_strength'],
                         seed=seed,
                     )
                 else:
@@ -625,7 +625,7 @@ def image_done(image, seed, first_seed):
                         seed=seed,
                     )
                     postprocessing = True
-                    all_parameters['gfpgan_strength'] = gfpgan_parameters[
+                    all_parameters['facetool_strength'] = gfpgan_parameters[
                         'strength'
                     ]
 
@@ -736,12 +736,12 @@ def parameters_to_generated_image_metadata(self, parameters):
             postprocessing = []
 
             # 'postprocessing' is either null or an
-            if 'gfpgan_strength' in parameters:
+            if 'facetool_strength' in parameters:
 
                 postprocessing.append(
                     {
                         'type': 'gfpgan',
-                        'strength': float(parameters['gfpgan_strength']),
+                        'strength': float(parameters['facetool_strength']),
                     }
                 )
 
@@ -838,7 +838,7 @@ def parameters_to_post_processed_image_metadata(
             elif parameters['type'] == 'gfpgan':
                 postprocessing_metadata['type'] = 'gfpgan'
                 postprocessing_metadata['strength'] = parameters[
-                    'gfpgan_strength'
+                    'facetool_strength'
                 ]
             else:
                 raise TypeError(f"Invalid type: {parameters['type']}")

backend/modules/parameters.py

@@ -48,8 +48,14 @@ def parameters_to_command(params):
         switches.append(f'-f {params["strength"]}')
         if "fit" in params and params["fit"] == True:
             switches.append(f"--fit")
-    if "gfpgan_strength" in params and params["gfpgan_strength"]:
+    if "facetool" in params:
+        switches.append(f'-ft {params["facetool"]}')
+    if "facetool_strength" in params and params["facetool_strength"]:
+        switches.append(f'-G {params["facetool_strength"]}')
+    elif "gfpgan_strength" in params and params["gfpgan_strength"]:
         switches.append(f'-G {params["gfpgan_strength"]}')
+    if "codeformer_fidelity" in params:
+        switches.append(f'-cf {params["codeformer_fidelity"]}')
     if "upscale" in params and params["upscale"]:
         switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
     if "variation_amount" in params and params["variation_amount"] > 0:

backend/server.py

@@ -349,7 +349,7 @@ def handle_run_gfpgan_event(original_image, gfpgan_parameters):
     eventlet.sleep(0)
 
     image = gfpgan.process(
-        image=image, strength=gfpgan_parameters["gfpgan_strength"], seed=seed
+        image=image, strength=gfpgan_parameters["facetool_strength"], seed=seed
     )
 
     progress["currentStatus"] = "Saving image"
@@ -464,7 +464,7 @@ def parameters_to_post_processed_image_metadata(parameters, original_image_path,
         image["strength"] = parameters["upscale"][1]
     elif type == "gfpgan":
         image["type"] = "gfpgan"
-        image["strength"] = parameters["gfpgan_strength"]
+        image["strength"] = parameters["facetool_strength"]
     else:
         raise TypeError(f"Invalid type: {type}")
 
@@ -506,10 +506,10 @@ def parameters_to_generated_image_metadata(parameters):
     postprocessing = []
 
     # 'postprocessing' is either null or an
-    if "gfpgan_strength" in parameters:
+    if "facetool_strength" in parameters:
 
         postprocessing.append(
-            {"type": "gfpgan", "strength": float(parameters["gfpgan_strength"])}
+            {"type": "gfpgan", "strength": float(parameters["facetool_strength"])}
         )
 
     if "upscale" in parameters:
@@ -752,7 +752,7 @@ def image_done(image, seed, first_seed):
                 image=image, strength=gfpgan_parameters["strength"], seed=seed
             )
             postprocessing = True
-            all_parameters["gfpgan_strength"] = gfpgan_parameters["strength"]
+            all_parameters["facetool_strength"] = gfpgan_parameters["strength"]
 
         progress["currentStatus"] = "Saving image"
         socketio.emit("progressUpdate", progress)

docs/features/CLI.md

@@ -154,7 +154,9 @@ Here are the invoke> command that apply to txt2img:
 | --log_tokenization | -t        | False               | Display a color-coded list of the parsed tokens derived from the prompt |
 | --skip_normalization| -x       | False               | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) |
 | --upscale <int> <float> | -U <int> <float> | -U 1 0.75| Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75. |
-| --gfpgan_strength <float>  | -G <float> | -G0        | Fix faces using the GFPGAN algorithm; argument indicates how hard the algorithm should try (0.0-1.0) |
+| --facetool_strength <float>  | -G <float> | -G0        | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) |
+| --facetool <name> | -ft <name> | -ft gfpgan | Select face restoration algorithm to use: gfpgan, codeformer |
+| --codeformer_fidelity | -cf <float> | 0.75 | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality |
 | --save_original    | -save_orig| False               | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
 | --variation <float>  |-v<float>| 0.0                 | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with -S<seed> and -n<int> to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
 | --with_variations <pattern> | -V<pattern>| None      | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |

docs/features/POSTPROCESS.md

@@ -69,7 +69,7 @@ If you do not explicitly specify an upscaling_strength, it will default to 0.75.
 
 ### Face Restoration
 
-`-G : <gfpgan_strength>`
+`-G : <facetool_strength>`
 
 This prompt argument controls the strength of the face restoration that is being
 applied. Similar to upscaling, values between `0.5 to 0.8` are recommended.

frontend/dist/index.html

@@ -6,7 +6,7 @@
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>InvokeAI - A Stable Diffusion Toolkit</title>
   <link rel="shortcut icon" type="icon" href="/assets/favicon.0d253ced.ico" />
-  <script type="module" crossorigin src="/assets/index.560edd47.js"></script>
+  <script type="module" crossorigin src="/assets/index.ea68b5f5.js"></script>
   <link rel="stylesheet" href="/assets/index.58175ea1.css">
 </head>
 

frontend/src/app/socketio/emitters.ts

@@ -76,7 +76,7 @@ const makeSocketIOEmitters = (
       const { gfpganStrength } = getState().options;
 
       const gfpganParameters = {
-        gfpgan_strength: gfpganStrength,
+        facetool_strength: gfpganStrength,
       };
       socketio.emit('runPostprocessing', imageToProcess, {
         type: 'gfpgan',

frontend/src/common/util/parameterTranslation.ts

@@ -129,7 +129,7 @@ export const backendToFrontendParameters = (parameters: {
     progress_images,
     variation_amount,
     with_variations,
-    gfpgan_strength,
+    facetool_strength,
     upscale,
     init_img,
     init_mask,
@@ -154,9 +154,9 @@ export const backendToFrontendParameters = (parameters: {
     }
   }
 
-  if (gfpgan_strength > 0) {
+  if (facetool_strength > 0) {
     options.shouldRunGFPGAN = true;
-    options.gfpganStrength = gfpgan_strength;
+    options.gfpganStrength = facetool_strength;
   }
 
   if (upscale) {

ldm/generate.py

@@ -251,7 +251,7 @@ def prompt2image(
             embiggen_tiles =    None,
             # these are specific to GFPGAN/ESRGAN
             facetool         = None,
-            gfpgan_strength  = 0,
+            facetool_strength  = 0,
             codeformer_fidelity = None,
             save_original    = False,
             upscale          = None,
@@ -274,7 +274,7 @@ def prompt2image(
            hires_fix                        // whether the Hires Fix should be applied during generation
            init_img                        // path to an initial image
            strength                        // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
-           gfpgan_strength                 // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
+           facetool_strength               // strength for GFPGAN/CodeFormer. 0.0 preserves image exactly, 1.0 replaces it completely
            ddim_eta                        // image randomness (eta=0.0 means the same seed always produces the same image)
            step_callback                   // a function or method that will be called each step
            image_callback                  // a function or method that will be called each time an image is generated
@@ -417,11 +417,11 @@ def process_image(image,seed):
                                     reference_image_path = init_color,
                                     image_callback       = image_callback)
 
-            if upscale is not None or gfpgan_strength > 0:
+            if upscale is not None or facetool_strength > 0:
                 self.upscale_and_reconstruct(results,
                                              upscale        = upscale,
                                              facetool       = facetool,
-                                             strength       = gfpgan_strength,
+                                             strength       = facetool_strength,
                                              codeformer_fidelity = codeformer_fidelity,
                                              save_original  = save_original,
                                              image_callback = image_callback)
@@ -464,7 +464,7 @@ def apply_postprocessor(
             self,
             image_path,
             tool                = 'gfpgan',  # one of 'upscale', 'gfpgan', 'codeformer', 'outpaint', or 'embiggen'
-            gfpgan_strength     = 0.0,
+            facetool_strength   = 0.0,
             codeformer_fidelity = 0.75,
             upscale             = None,
             out_direction       = None,
@@ -511,11 +511,11 @@ def apply_postprocessor(
                 facetool = 'codeformer'
             elif tool == 'upscale':
                 facetool = 'gfpgan'   # but won't be run
-                gfpgan_strength = 0
+                facetool_strength = 0
             return self.upscale_and_reconstruct(
                 [[image,seed]],
                 facetool = facetool,
-                strength = gfpgan_strength,
+                strength = facetool_strength,
                 codeformer_fidelity = codeformer_fidelity,
                 save_original = save_original,
                 upscale = upscale,

ldm/invoke/args.py

@@ -242,9 +242,15 @@ def dream_prompt_str(self,**kwargs):
         else:
             switches.append(f'-A {a["sampler_name"]}')
 
-        # gfpgan-specific parameters
-        if a['gfpgan_strength']:
+        # facetool-specific parameters
+        if a['facetool']:
+            switches.append(f'-ft {a["facetool"]}')
+        if a['facetool_strength']:
+            switches.append(f'-G {a["facetool_strength"]}')
+        elif a['gfpgan_strength']:
             switches.append(f'-G {a["gfpgan_strength"]}')
+        if a['codeformer_fidelity']:
+            switches.append(f'-cf {a["codeformer_fidelity"]}')
 
         if a['outcrop']:
             switches.append(f'-c {" ".join([str(u) for u in a["outcrop"]])}')
@@ -699,6 +705,7 @@ def _create_dream_cmd_parser(self):
         )
         postprocessing_group.add_argument(
             '-G',
+            '--facetool_strength',
             '--gfpgan_strength',
             type=float,
             help='The strength at which to apply the face restoration to the result.',

ldm/invoke/readline.py

@@ -42,7 +42,9 @@
     '--embedding_path',
     '--device',
     '--grid','-g',
-    '--gfpgan_strength','-G',
+    '--facetool','-ft',
+    '--facetool_strength','-G',
+    '--codeformer_fidelity','-cf',
     '--upscale','-U',
     '-save_orig','--save_original',
     '--skip_normalize','-x',

ldm/invoke/server.py

@@ -31,7 +31,7 @@ def build_opt(post_data, seed, gfpgan_model_exists):
     setattr(opt, 'embiggen', None)
     setattr(opt, 'embiggen_tiles', None)
 
-    setattr(opt, 'gfpgan_strength', float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0)
+    setattr(opt, 'facetool_strength', float(post_data['facetool_strength']) if gfpgan_model_exists else 0)
     setattr(opt, 'upscale', [int(post_data['upscale_level']), float(post_data['upscale_strength'])] if post_data['upscale_level'] != '' else None)
     setattr(opt, 'progress_images', 'progress_images' in post_data)
     setattr(opt, 'seed', None if int(post_data['seed']) == -1 else int(post_data['seed']))
@@ -197,7 +197,7 @@ def image_done(image, seed, upscaled=False, first_seed=None):
                 ) + '\n',"utf-8"))
 
             # control state of the "postprocessing..." message
-            upscaling_requested = opt.upscale or opt.gfpgan_strength > 0
+            upscaling_requested = opt.upscale or opt.facetool_strength > 0
             nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
             nonlocal images_upscaled  # NB: Is this bad python style? It is typical usage in a perl closure.
             if upscaled:

scripts/invoke.py

@@ -400,7 +400,7 @@ def do_postprocess (gen, opt, callback):
         file_path = os.path.join(opt.outdir,file_path)
 
     tool=None
-    if opt.gfpgan_strength > 0:
+    if opt.facetool_strength > 0:
         tool = opt.facetool
     elif opt.embiggen:
         tool = 'embiggen'
@@ -416,7 +416,7 @@ def do_postprocess (gen, opt, callback):
         gen.apply_postprocessor(
             image_path      = file_path,
             tool            = tool,
-            gfpgan_strength = opt.gfpgan_strength,
+            facetool_strength = opt.facetool_strength,
             codeformer_fidelity = opt.codeformer_fidelity,
             save_original       = opt.save_original,
             upscale             = opt.upscale,

server/models.py

@@ -42,7 +42,7 @@ class DreamBase():
 
   # GFPGAN
   enable_gfpgan: bool
-  gfpgan_strength: float = 0
+  facetool_strength: float = 0
 
   # Upscale
   enable_upscale: bool
@@ -98,7 +98,7 @@ def parse_json(self, j, new_instance=False):
     # GFPGAN
     self.enable_gfpgan = 'enable_gfpgan' in j and bool(j.get('enable_gfpgan'))
     if self.enable_gfpgan:
-      self.gfpgan_strength = float(j.get('gfpgan_strength'))
+      self.facetool_strength = float(j.get('facetool_strength'))
 
     # Upscale
     self.enable_upscale = 'enable_upscale' in j and bool(j.get('enable_upscale'))

server/services.py

@@ -334,11 +334,11 @@ def __generate(self, jobRequest: JobRequest):
       # TODO: Support no generation (just upscaling/gfpgan)
 
       upscale = None if not jobRequest.enable_upscale else jobRequest.upscale
-      gfpgan_strength = 0 if not jobRequest.enable_gfpgan else jobRequest.gfpgan_strength
+      facetool_strength = 0 if not jobRequest.enable_gfpgan else jobRequest.facetool_strength
 
       if not jobRequest.enable_generate:
         # If not generating, check if we're upscaling or running gfpgan
-        if not upscale and not gfpgan_strength:
+        if not upscale and not facetool_strength:
           # Invalid settings (TODO: Add message to help user)
           raise CanceledException()
 
@@ -347,7 +347,7 @@ def __generate(self, jobRequest: JobRequest):
         self.__model.upscale_and_reconstruct(
           image_list = [[image,0]],
           upscale = upscale,
-          strength = gfpgan_strength,
+          strength = facetool_strength,
           save_original = False,
           image_callback = lambda image, seed, upscaled=False: self.__on_image_result(jobRequest, image, seed, upscaled))
 
@@ -371,7 +371,7 @@ def __generate(self, jobRequest: JobRequest):
           steps            = jobRequest.steps,
           variation_amount = jobRequest.variation_amount,
           with_variations  = jobRequest.with_variations,
-          gfpgan_strength  = gfpgan_strength,
+          facetool_strength = facetool_strength,
           upscale          = upscale,
           sampler_name     = jobRequest.sampler_name,
           seamless         = jobRequest.seamless,

static/dream_web/index.html

@@ -144,8 +144,8 @@ <h1>Stable Diffusion Dream Server</h1>
             <input type="checkbox" name="enable_gfpgan" id="enable_gfpgan">
             <label for="enable_gfpgan">Enable gfpgan</label>
           </legend>
-          <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength:</label>
-          <input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
+          <label title="Strength of the gfpgan (face fixing) algorithm." for="facetool_strength">GPFGAN Strength:</label>
+          <input value="0.8" min="0" max="1" type="number" id="facetool_strength" name="facetool_strength" step="0.05">
         </fieldset>
         <fieldset id="upscale">
           <legend>

static/legacy_web/index.html

@@ -100,8 +100,8 @@ <h1>Stable Diffusion Dream Server</h1>
 	</fieldset>
         <fieldset id="gfpgan">
 	  <div class="section-header">Post-processing options</div>
-          <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
-          <input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
+          <label title="Strength of the gfpgan (face fixing) algorithm." for="facetool_strength">GPFGAN Strength (0 to disable):</label>
+          <input value="0.0" min="0" max="1" type="number" id="facetool_strength" name="facetool_strength" step="0.1">
           <label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
           <select id="upscale_level" name="upscale_level" value="">
             <option value="" selected>None</option>