better defaults

README.md

@@ -28,8 +28,9 @@ python -m venv venv
 source venv/bin/activate
 pip3 install -r requirements.txt
 cd frontend && npm install && npm run build && cd ..
-python run.py --reload --pipeline controlnet
-```
+# fastest pipeline
+python run.py --reload --pipeline img2imgSD21Turbo 
+ ```
 
 # Pipelines
 You can build your own pipeline following examples here [here](pipelines),

app.py

@@ -12,7 +12,7 @@ print("TORCH_DTYPE:", torch_dtype)
 print("PIPELINE:", args.pipeline)
 print("SAFETY_CHECKER:", args.safety_checker)
 print("TORCH_COMPILE:", args.torch_compile)
-print("USE_TAESD:", args.use_taesd)
+print("USE_TAESD:", args.taesd)
 print("COMPEL:", args.compel)
 print("DEBUG:", args.debug)
 

config.py

@@ -12,7 +12,7 @@ class Args(NamedTuple):
     timeout: float
     safety_checker: bool
     torch_compile: bool
-    use_taesd: bool
+    taesd: bool
     pipeline: str
     ssl_certfile: str
     ssl_keyfile: str
@@ -24,7 +24,7 @@ MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
 TIMEOUT = float(os.environ.get("TIMEOUT", 0))
 SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None) == "True"
 TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None) == "True"
-USE_TAESD = os.environ.get("USE_TAESD", None) == "True"
+USE_TAESD = os.environ.get("USE_TAESD", "True") == "True"
 default_host = os.getenv("HOST", "0.0.0.0")
 default_port = int(os.getenv("PORT", "7860"))
 default_mode = os.getenv("MODE", "default")
@@ -38,7 +38,7 @@ parser.add_argument(
 )
 parser.add_argument(
     "--max-queue-size",
-    "--max_queue_size",
+    dest="max_queue_size",
     type=int,
     default=MAX_QUEUE_SIZE,
     help="Max Queue Size",
@@ -46,23 +46,28 @@ parser.add_argument(
 parser.add_argument("--timeout", type=float, default=TIMEOUT, help="Timeout")
 parser.add_argument(
     "--safety-checker",
-    "--safety_checker",
+    dest="safety_checker",
     action="store_true",
     default=SAFETY_CHECKER,
     help="Safety Checker",
 )
 parser.add_argument(
     "--torch-compile",
-    "--torch_compile",
+    dest="torch_compile",
     action="store_true",
     default=TORCH_COMPILE,
     help="Torch Compile",
 )
 parser.add_argument(
-    "--use-taesd",
-    "--use_taesd",
+    "--taesd",
+    dest="taesd",
     action="store_true",
-    default=USE_TAESD,
+    help="Use Tiny Autoencoder",
+)
+parser.add_argument(
+    "--no-taesd",
+    dest="taesd",
+    action="store_false",
     help="Use Tiny Autoencoder",
 )
 parser.add_argument(
@@ -73,14 +78,14 @@ parser.add_argument(
 )
 parser.add_argument(
     "--ssl-certfile",
-    "--ssl_certfile",
+    dest="ssl_certfile",
     type=str,
     default=None,
     help="SSL certfile",
 )
 parser.add_argument(
     "--ssl-keyfile",
-    "--ssl_keyfile",
+    dest="ssl_keyfile",
     type=str,
     default=None,
     help="SSL keyfile",
@@ -97,5 +102,6 @@ parser.add_argument(
     default=False,
     help="Compel",
 )
+parser.set_defaults(taesd=USE_TAESD)
 
 args = Args(**vars(parser.parse_args()))

pipelines/controlnelSD21Turbo.py

@@ -176,7 +176,7 @@ class Pipeline:
                 safety_checker=None,
             )
 
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)
@@ -196,7 +196,7 @@ class Pipeline:
             text_encoder=self.pipe.text_encoder,
             truncate_long_prompts=True,
         )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/controlnet.py

@@ -169,7 +169,7 @@ class Pipeline:
                 safety_checker=None,
                 controlnet=controlnet_canny,
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/controlnetLoraSD15.py

@@ -202,7 +202,7 @@ class Pipeline:
             if psutil.virtual_memory().total < 64 * 1024**3:
                 pipe.enable_attention_slicing()
 
-            if args.use_taesd:
+            if args.taesd:
                 pipe.vae = AutoencoderTiny.from_pretrained(
                     taesd_model, torch_dtype=torch_dtype, use_safetensors=True
                 ).to(device)

pipelines/controlnetLoraSDXL.py

@@ -211,7 +211,7 @@ class Pipeline:
             returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
             requires_pooled=[False, True],
         )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/controlnetSDXLTurbo.py

@@ -199,7 +199,7 @@ class Pipeline:
             returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
             requires_pooled=[False, True],
         )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/controlnetSegmindVegaRT.py

@@ -208,7 +208,7 @@ class Pipeline:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
                 requires_pooled=[False, True],
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/img2img.py

@@ -102,7 +102,7 @@ class Pipeline:
                 base_model,
                 safety_checker=None,
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/img2imgSD21Turbo.py

@@ -99,7 +99,7 @@ class Pipeline:
                 base_model,
                 safety_checker=None,
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)
@@ -158,7 +158,7 @@ class Pipeline:
             generator=generator,
             strength=strength,
             num_inference_steps=steps,
-            guidance_scale=1.0,
+            guidance_scale=1.1,
             width=params.width,
             height=params.height,
             output_type="pil",

pipelines/img2imgSDXLTurbo.py

@@ -110,7 +110,7 @@ class Pipeline:
                 base_model,
                 safety_checker=None,
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/img2imgSegmindVegaRT.py

@@ -116,7 +116,7 @@ class Pipeline:
                 safety_checker=None,
                 variant="fp16",
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/txt2img.py

@@ -85,7 +85,7 @@ class Pipeline:
             self.pipe = DiffusionPipeline.from_pretrained(
                 base_model, safety_checker=None
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/txt2imgLora.py

@@ -92,7 +92,7 @@ class Pipeline:
             self.pipe = DiffusionPipeline.from_pretrained(
                 base_model, safety_checker=None
             )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)

pipelines/txt2imgLoraSDXL.py

@@ -123,7 +123,7 @@ class Pipeline:
             returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
             requires_pooled=[False, True],
         )
-        if args.use_taesd:
+        if args.taesd:
             self.pipe.vae = AutoencoderTiny.from_pretrained(
                 taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             ).to(device)