feat: support for flux

CHANGELOG.md

@@ -6,6 +6,10 @@ SPDX-License-Identifier: AGPL-3.0-or-later
 
 # Changelog
 
+# 4.42.0
+
+* Adds support for the Flux family of models
+
 # 4.41.0
 
 * Adds support for extra backends behind LLM bridges, and for knowing which are validated.

horde/apis/v2/stable.py

@@ -172,6 +172,11 @@ def validate(self):
         if any(model_reference.get_model_baseline(model_name).startswith("stable_cascade") for model_name in self.args.models):
             if "control_type" in self.params:
                 raise e.BadRequest("ControlNet does not work with Stable Cascade currently.", rc="ControlNetMismatch")
+        if any(model_reference.get_model_baseline(model_name).startswith("flux.1") for model_name in self.args.models):
+            if "control_type" in self.params:
+                raise e.BadRequest("ControlNet does not work with Flux currently.", rc="ControlNetMismatch")
+            if self.params.get("hires_fix", False) is True:
+                raise e.BadRequest("HiRes Fix does not work with Flux currently.", rc="HiResMismatch")
         if "loras" in self.params:
             if len(self.params["loras"]) > 5:
                 raise e.BadRequest("You cannot request more than 5 loras per generation.", rc="TooManyLoras")

horde/classes/stable/processing_generation.py

@@ -64,6 +64,9 @@ def get_gen_kudos(self):
             if self.wp.params.get("hires_fix", False):
                 return self.wp.kudos * 7
             return self.wp.kudos * 4
+        if model_reference.get_model_baseline(self.model) in ["flux.1"]:
+            # Flux is double the size of SDXL and much slower, so it gives double the rewards from it.
+            return self.wp.kudos * 8
         return self.wp.kudos
 
     def log_aborted_generation(self):

horde/classes/stable/waiting_prompt.py

@@ -364,18 +364,16 @@ def require_upfront_kudos(self, counted_totals, total_threads):
             max_res = 768
         # We allow everyone to use SDXL up to 1024
         if max_res < 1024 and any(
-            model_reference.get_model_baseline(mn) in ["stable_diffusion_xl", "stable_cascade"] for mn in model_names
+            model_reference.get_model_baseline(mn) in ["stable_diffusion_xl", "stable_cascade", "flux.1"] for mn in model_names
         ):
             max_res = 1024
         if max_res > 1024:
             max_res = 1024
         # Using more than 10 steps with LCM requires upfront kudos
         if self.is_using_lcm() and self.get_accurate_steps() > 10:
             return (True, max_res, False)
-        # Stable Cascade doesn't need so many steps, so we limit it a bit to prevent abuse.
-        if any(model_reference.get_model_baseline(mn) in ["stable_cascade"] for mn in model_names) and self.get_accurate_steps() > 30:
-            return (True, max_res, False)
-        if self.get_accurate_steps() > 50:
+        # Some models don't require a lot of steps, so we check their requirements. The max steps we allow without upfront kudos is 40
+        if any(model_reference.get_model_requirements(mn).get("max_steps",40) > self.get_accurate_steps() for mn in model_names):
             return (True, max_res, False)
         if self.width * self.height > max_res * max_res:
             return (True, max_res, False)
@@ -400,9 +398,7 @@ def downgrade(self, max_resolution):
             # Break, just in case we went too low
             if self.width * self.height < 512 * 512:
                 break
-        max_steps = 50
-        if any(model_reference.get_model_baseline(mn) in ["stable_cascade"] for mn in self.get_model_names()):
-            max_steps = 30
+        max_steps = min(model_reference.get_model_requirements(mn).get("max_steps",30) for mn in self.get_model_names())
         if self.params.get("control_type"):
             max_steps = 20
         if self.is_using_lcm():
@@ -435,7 +431,7 @@ def get_accurate_steps(self):
         if self.params.get("sampler_name", "k_euler_a") in ["k_dpm_adaptive"]:
             # This sampler chooses the steps amount automatically
             # and disregards the steps value from the user
-            # so we just calculate it as an average 50 steps
+            # so we just calculate it as an average 40 steps
             return 40
         steps = self.params["steps"]
         if self.params.get("sampler_name", "k_euler_a") in SECOND_ORDER_SAMPLERS:
@@ -499,6 +495,8 @@ def extrapolate_dry_run_kudos(self):
             return (self.calculate_extra_kudos_burn(kudos) * self.n * 2) + 1
         if model_reference.get_model_baseline(model_name) in ["stable_cascade"]:
             return (self.calculate_extra_kudos_burn(kudos) * self.n * 4) + 1
+        if model_reference.get_model_baseline(model_name) in ["flux.1"]:
+            return (self.calculate_extra_kudos_burn(kudos) * self.n * 8) + 1
         # The +1 is the extra kudos burn per request
         return (self.calculate_extra_kudos_burn(kudos) * self.n) + 1
 

horde/consts.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
-HORDE_VERSION = "4.41.0 "
+HORDE_VERSION = "4.42.0 "
 
 WHITELISTED_SERVICE_IPS = {
     "212.227.227.178",  # Turing Bot

horde/exceptions.py

@@ -150,6 +150,7 @@
     "InvalidTransparencyModel",
     "InvalidTransparencyImg2Img",
     "InvalidTransparencyCN",
+    "HiResMismatch",
 ]
 
 

horde/model_reference.py

@@ -53,6 +53,7 @@ def call_function(self):
                         "stable diffusion 2 512",
                         "stable_diffusion_xl",
                         "stable_cascade",
+                        "flux.1",
                     }:
                         self.stable_diffusion_names.add(model)
                         if self.reference[model].get("nsfw"):