Merger for Fooocus v2.1.820

Parameter format for `loras` has changed as JSON.
mrhan1993 · Nov 17, 2023 · a1623c2 · a1623c2
1 parent 3257152
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diff --git a/README.md b/README.md
@@ -4,7 +4,7 @@
 
 FastAPI powered API for [Fooocus](https://github.com/lllyasviel/Fooocus)
 
-Currently loaded Fooocus version: 2.1.806
+Currently loaded Fooocus version: 2.1.820
 
 ### Run with Replicate
 Now you can use Fooocus-API by Replicate, the model is in [konieshadow/fooocus-api](https://replicate.com/konieshadow/fooocus-api).
@@ -76,6 +76,9 @@ All the generation api support for response in PNG bytes directly when request's
 
 All the generation api support async process by pass parameter `async_process` to true. And then use query job api to retrieve progress and generation results.
 
+Break change from v0.3.16:
+* Parameter format for `loras` has changed for the img2img apis (the multipart/form-data requests). Now it requires JSON string.
+
 Break change from v0.3.0:
 * The generation apis won't return `base64` field expect request parameters set `require_base64` to true.
 * The generation apis return a `url` field where the generated image can be requested via a static file url.

diff --git a/fooocus_api_version.py b/fooocus_api_version.py
@@ -1 +1 @@
-version = '0.3.15'
+version = '0.3.16'
diff --git a/fooocusapi/api_utils.py b/fooocusapi/api_utils.py
@@ -96,7 +96,7 @@ def req_to_params(req: Text2ImgRequest) -> ImageGenerationParams:
             print(f"[Warning] Wrong inpaint_engine input: {adp.inpaint_engine}, using default")
             adp.inpaint_engine = flags.default_inpaint_engine_version
 
-        advanced_params = [adp.adm_scaler_positive, adp.adm_scaler_negative, adp.adm_scaler_end, adp.adaptive_cfg, adp.sampler_name,
+        advanced_params = [adp.disable_preview, adp.adm_scaler_positive, adp.adm_scaler_negative, adp.adm_scaler_end, adp.adaptive_cfg, adp.sampler_name,
                                 adp.scheduler_name, False, adp.overwrite_step, adp.overwrite_switch, adp.overwrite_width, adp.overwrite_height,
                                 adp.overwrite_vary_strength, adp.overwrite_upscale_strength,
                                 adp.mixing_image_prompt_and_vary_upscale, adp.mixing_image_prompt_and_inpaint,

diff --git a/fooocusapi/models.py b/fooocusapi/models.py
@@ -2,16 +2,17 @@
 from fastapi import Form, UploadFile
 from fastapi.params import File
 from fastapi.exceptions import RequestValidationError
-from pydantic import BaseModel, ConfigDict, Field, ValidationError, parse_obj_as
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, ValidationError, parse_obj_as
 from typing import List
 from enum import Enum
 
 from pydantic_core import InitErrorDetails
-from fooocusapi.parameters import GenerationFinishReason, defualt_styles, default_base_model_name, default_refiner_model_name, default_refiner_switch, default_lora_name, default_lora_weight, default_cfg_scale, default_prompt_negative, default_aspect_ratio, default_sampler, default_scheduler
+from fooocusapi.parameters import GenerationFinishReason, defualt_styles, default_base_model_name, default_refiner_model_name, default_refiner_switch, default_loras, default_cfg_scale, default_prompt_negative, default_aspect_ratio, default_sampler, default_scheduler
 from fooocusapi.task_queue import TaskType
 import modules.flags as flags
 
 
+
 class Lora(BaseModel):
     model_name: str
     weight: float = Field(default=0.5, ge=-2, le=2)
@@ -21,6 +22,11 @@ class Lora(BaseModel):
     )
 
 
+LoraList = TypeAdapter(List[Lora])
+default_loras_model = [Lora(model_name=l[0], weight=l[1]) for l in default_loras if l[0] != 'None']
+default_loras_json = LoraList.dump_json(default_loras_model)
+
+
 class PerfomanceSelection(str, Enum):
     speed = 'Speed'
     quality = 'Quality'
@@ -58,6 +64,7 @@ class ImagePrompt(BaseModel):
 
 
 class AdvancedParams(BaseModel):
+    disable_preview: bool = Field(False, description="Disable preview during generation")
     adm_scaler_positive: float = Field(1.5, description="Positive ADM Guidance Scaler", ge=0.1, le=3.0)
     adm_scaler_negative: float = Field(0.8, description="Negative ADM Guidance Scaler", ge=0.1, le=3.0)
     adm_scaler_end: float = Field(0.3, description="ADM Guidance End At Step", ge=0.0, le=1.0)
@@ -100,8 +107,7 @@ class Text2ImgRequest(BaseModel):
     base_model_name: str = default_base_model_name
     refiner_model_name: str = default_refiner_model_name
     refiner_switch: float = Field(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0)
-    loras: List[Lora] = Field(default=[
-        Lora(model_name=default_lora_name, weight=default_lora_weight)])
+    loras: List[Lora] = Field(default=default_loras_model)
     advanced_params: AdvancedParams | None = Field(deafult=None, description="Advanced parameters")
     require_base64: bool = Field(default=False, description="Return base64 data of generated image")
     async_process: bool = Field(default=False, description="Set to true will run async and return job info for retrieve generataion result later")
@@ -128,17 +134,8 @@ def as_form(cls, input_image: UploadFile = Form(description="Init image for upsa
                 base_model_name: str = Form(default_base_model_name),
                 refiner_model_name: str = Form(default_refiner_model_name),
                 refiner_switch: float = Form(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0),
-                l1: str | None = Form(default_lora_name),
-                w1: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l2: str | None = Form(None),
-                w2: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l3: str | None = Form(None),
-                w3: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l4: str | None = Form(None),
-                w4: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l5: str | None = Form(None),
-                w5: float = Form(default=default_lora_weight, ge=-2, le=2),
-                advanced_params: str| None = Form(default=None, description="Advanced parameters in JSON"),
+                loras: str | None = Form(default=default_loras_json, description='Lora config in JSON. Format as [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]'),
+                advanced_params: str | None = Form(default=None, description="Advanced parameters in JSON"),
                 require_base64: bool = Form(default=False, description="Return base64 data of generated image"),
                 async_process: bool = Form(default=False, description="Set to true will run async and return job info for retrieve generataion result later"),
                 ):
@@ -149,15 +146,16 @@ def as_form(cls, input_image: UploadFile = Form(description="Init image for upsa
                     style = s.strip()
                     style_selection_arr.append(style)
 
-        loras: List[Lora] = []
-        lora_config = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
-        for config in lora_config:
-            lora_model, lora_weight = config
-            if lora_model is not None and len(lora_model) > 0:
-                loras.append(Lora(model_name=lora_model, weight=lora_weight))
+        loras_model: List[Lora] = []
+        if loras is not None and len(loras) > 0:
+            try:
+                loras_model = LoraList.validate_json(loras)
+            except ValidationError as ve:
+                errs = ve.errors()
+                raise RequestValidationError(errors=[errs])
 
         advanced_params_obj = None
-        if advanced_params != None and len(advanced_params) > 0:
+        if advanced_params is not None and len(advanced_params) > 0:
             try:
                 advanced_params_obj = AdvancedParams.__pydantic_validator__.validate_json(advanced_params)
             except ValidationError as ve:
@@ -168,7 +166,7 @@ def as_form(cls, input_image: UploadFile = Form(description="Init image for upsa
                    performance_selection=performance_selection, aspect_ratios_selection=aspect_ratios_selection,
                    image_number=image_number, image_seed=image_seed, sharpness=sharpness, guidance_scale=guidance_scale,
                    base_model_name=base_model_name, refiner_model_name=refiner_model_name, refiner_switch=refiner_switch,
-                   loras=loras, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
+                   loras=loras_model, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
 
 
 class ImgInpaintOrOutpaintRequest(Text2ImgRequest):
@@ -196,16 +194,7 @@ def as_form(cls, input_image: UploadFile = Form(description="Init image for inpa
                 base_model_name: str = Form(default_base_model_name),
                 refiner_model_name: str = Form(default_refiner_model_name),
                 refiner_switch: float = Form(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0),
-                l1: str | None = Form(default_lora_name),
-                w1: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l2: str | None = Form(None),
-                w2: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l3: str | None = Form(None),
-                w3: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l4: str | None = Form(None),
-                w4: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l5: str | None = Form(None),
-                w5: float = Form(default=default_lora_weight, ge=-2, le=2),
+                loras: str | None = Form(default=default_loras_json, description='Lora config in JSON. Format as [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]'),
                 advanced_params: str| None = Form(default=None, description="Advanced parameters in JSON"),
                 require_base64: bool = Form(default=False, description="Return base64 data of generated image"),
                 async_process: bool = Form(default=False, description="Set to true will run async and return job info for retrieve generataion result later"),
@@ -233,15 +222,16 @@ def as_form(cls, input_image: UploadFile = Form(description="Init image for inpa
                     style = s.strip()
                     style_selection_arr.append(style)
 
-        loras: List[Lora] = []
-        lora_config = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
-        for config in lora_config:
-            lora_model, lora_weight = config
-            if lora_model is not None and len(lora_model) > 0:
-                loras.append(Lora(model_name=lora_model, weight=lora_weight))
+        loras_model: List[Lora] = []
+        if loras is not None and len(loras) > 0:
+            try:
+                loras_model = LoraList.validate_json(loras)
+            except ValidationError as ve:
+                errs = ve.errors()
+                raise RequestValidationError(errors=[errs])
 
         advanced_params_obj = None
-        if advanced_params != None and len(advanced_params) > 0:
+        if advanced_params is not None and len(advanced_params) > 0:
             try:
                 advanced_params_obj = AdvancedParams.__pydantic_validator__.validate_json(advanced_params)
             except ValidationError as ve:
@@ -252,7 +242,7 @@ def as_form(cls, input_image: UploadFile = Form(description="Init image for inpa
                    performance_selection=performance_selection, aspect_ratios_selection=aspect_ratios_selection,
                    image_number=image_number, image_seed=image_seed, sharpness=sharpness, guidance_scale=guidance_scale,
                    base_model_name=base_model_name, refiner_model_name=refiner_model_name, refiner_switch=refiner_switch,
-                   loras=loras, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
+                   loras=loras_model, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
 
 
 class ImgPromptRequest(Text2ImgRequest):
@@ -304,16 +294,7 @@ def as_form(cls, cn_img1: UploadFile = Form(File(None), description="Input image
                 base_model_name: str = Form(default_base_model_name),
                 refiner_model_name: str = Form(default_refiner_model_name),
                 refiner_switch: float = Form(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0),
-                l1: str | None = Form(default_lora_name),
-                w1: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l2: str | None = Form(None),
-                w2: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l3: str | None = Form(None),
-                w3: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l4: str | None = Form(None),
-                w4: float = Form(default=default_lora_weight, ge=-2, le=2),
-                l5: str | None = Form(None),
-                w5: float = Form(default=default_lora_weight, ge=-2, le=2),
+                loras: str | None = Form(default=default_loras_json, description='Lora config in JSON. Format as [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]'),
                 advanced_params: str| None = Form(default=None, description="Advanced parameters in JSON"),
                 require_base64: bool = Form(default=False, description="Return base64 data of generated image"),
                 async_process: bool = Form(default=False, description="Set to true will run async and return job info for retrieve generataion result later"),
@@ -346,15 +327,16 @@ def as_form(cls, cn_img1: UploadFile = Form(File(None), description="Input image
                     style = s.strip()
                     style_selection_arr.append(style)
 
-        loras: List[Lora] = []
-        lora_config = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
-        for config in lora_config:
-            lora_model, lora_weight = config
-            if lora_model is not None and len(lora_model) > 0:
-                loras.append(Lora(model_name=lora_model, weight=lora_weight))
+        loras_model: List[Lora] = []
+        if loras is not None and len(loras) > 0:
+            try:
+                loras_model = LoraList.validate_json(loras)
+            except ValidationError as ve:
+                errs = ve.errors()
+                raise RequestValidationError(errors=[errs])
 
         advanced_params_obj = None
-        if advanced_params != None and len(advanced_params) > 0:
+        if advanced_params is not None and len(advanced_params) > 0:
             try:
                 advanced_params_obj = AdvancedParams.__pydantic_validator__.validate_json(advanced_params)
             except ValidationError as ve:
@@ -365,7 +347,7 @@ def as_form(cls, cn_img1: UploadFile = Form(File(None), description="Input image
                    performance_selection=performance_selection, aspect_ratios_selection=aspect_ratios_selection,
                    image_number=image_number, image_seed=image_seed, sharpness=sharpness, guidance_scale=guidance_scale,
                    base_model_name=base_model_name, refiner_model_name=refiner_model_name, refiner_switch=refiner_switch,
-                   loras=loras, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
+                   loras=loras_model, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
 
 
 class GeneratedImageResult(BaseModel):

diff --git a/fooocusapi/parameters.py b/fooocusapi/parameters.py
@@ -12,6 +12,7 @@
 default_base_model_name = 'juggernautXL_version6Rundiffusion.safetensors'
 default_refiner_model_name = 'None'
 default_refiner_switch = 0.5
+default_loras = [['sd_xl_offset_example-lora_1.0.safetensors', 0.1]]
 default_lora_name = 'sd_xl_offset_example-lora_1.0.safetensors'
 default_lora_weight = 0.1
 default_cfg_scale = 4.0
@@ -60,6 +61,10 @@
 ]
 
 
+def get_aspect_ratio_value(label: str) -> str:
+    return label.split(' ')[0].replace('*', '×')
+
+
 class GenerationFinishReason(str, Enum):
     success = 'SUCCESS'
     queue_is_full = 'QUEUE_IS_FULL'
@@ -113,6 +118,7 @@ def __init__(self, prompt: str,
         self.inpaint_input_image = inpaint_input_image
         self.image_prompts = image_prompts
         if advanced_params is None:
+            disable_preview = False
             adm_scaler_positive = 1.5
             adm_scaler_negative = 0.8
             adm_scaler_end = 0.3
@@ -137,7 +143,7 @@ def __init__(self, prompt: str,
             refiner_swap_method = 'joint'
             freeu_enabled = False
             freeu_b1, freeu_b2, freeu_s1, freeu_s2 = [None] * 4
-            self.advanced_params = [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,
+            self.advanced_params = [disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,
                                scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height,
                                overwrite_vary_strength, overwrite_upscale_strength,
                                mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint,

diff --git a/fooocusapi/repositories_versions.py b/fooocusapi/repositories_versions.py
@@ -1,5 +1,5 @@
 import os
 
-fooocus_version = '2.1.806'
+fooocus_version = '2.1.820'
 fooocus_commit_hash = os.environ.get(
-    'FOOOCUS_COMMIT_HASH', "c9a5e729d91ba3772d421a5db7bf79497011e59f")
+    'FOOOCUS_COMMIT_HASH', "3b97e49dd814a43fc75a26b2495603e38b8b2f62")
diff --git a/fooocusapi/worker.py b/fooocusapi/worker.py
@@ -385,7 +385,7 @@ def yield_result(imgs, tasks):
 
             for i, t in enumerate(tasks):
                 if abs(float(cfg_scale) - 1.0) < 1e-4:
-                    progressbar(10, f'Skipped negative #{i + 1} ...')
+                    # progressbar(10, f'Skipped negative #{i + 1} ...')
                     t['uc'] = pipeline.clone_cond(t['c'])
                 else:
                     progressbar(10, f'Encoding negative #{i + 1} ...')

diff --git a/main.py b/main.py
@@ -284,17 +284,16 @@ def prepare_environments(args) -> bool:
     import modules.config as config
     import modules.flags as flags
     import fooocusapi.parameters as parameters
-    parameters.default_inpaint_engine_version = flags.default_inpaint_engine_version
+    parameters.default_inpaint_engine_version = config.default_inpaint_engine_version
     parameters.defualt_styles = config.default_styles
     parameters.default_base_model_name = config.default_base_model_name
     parameters.default_refiner_model_name = config.default_refiner_model_name
     parameters.default_refiner_switch = config.default_refiner_switch
-    parameters.default_lora_name = config.default_lora_name
-    parameters.default_lora_weight = config.default_lora_weight
+    parameters.default_loras = config.default_loras
     parameters.default_cfg_scale = config.default_cfg_scale
     parameters.default_prompt_negative = config.default_prompt_negative
-    parameters.default_aspect_ratio = config.default_aspect_ratio.replace('*', '×')
-    parameters.available_aspect_ratios = [a.replace('*', '×') for a in config.available_aspect_ratios]
+    parameters.default_aspect_ratio = parameters.get_aspect_ratio_value(config.default_aspect_ratio)
+    parameters.available_aspect_ratios = [parameters.get_aspect_ratio_value(a) for a in config.available_aspect_ratios]
 
     ini_cbh_args()
 
@@ -364,7 +363,7 @@ def preplaod_pipeline():
                         default='info', help="Log info for Uvicorn, default: info")
     parser.add_argument("--sync-repo", default=None,
                         help="Sync dependent git repositories to local, 'skip' for skip sync action, 'only' for only do the sync action and not launch app")
-    parser.add_argument("--disable-private-log", default=False, action="store_true", help="Disable Fooocus private log, won't save output files (include generated image files)")
+    parser.add_argument("--disable-private-log", default=False, action="store_true", help="Disable Fooocus image log, won't save output files (include generated image files)")
     parser.add_argument("--skip-pip", default=False, action="store_true", help="Skip automatic pip install when setup")
     parser.add_argument("--preload-pipeline", default=False, action="store_true", help="Preload pipeline before start http server")
     parser.add_argument("--queue-size", type=int, default=3, help="Working queue size, default: 3, generation requests exceeding working queue size will return failure")