Fix minor issues on compute daemon found thanks to tests, vastly improve pipeline_for function and support old diffuse method

skynet/dgpu/compute.py

@@ -44,14 +44,14 @@ def prepare_params_for_diffuse(
             _params['image'] = image
             _params['strength'] = float(params['strength'])
 
-        case 'txt2img':
+        case 'txt2img' | 'diffuse':
             ...
 
         case _:
-            raise DGPUComputeError(f'Unknown input_type {input_type}')
+            raise DGPUComputeError(f'Unknown mode {mode}')
 
-    _params['width'] = int(params['width'])
+    # _params['width'] = int(params['width'])
-    _params['height'] = int(params['height'])
+    # _params['height'] = int(params['height'])
 
     return (
         params['prompt'],
@@ -72,7 +72,10 @@ class SkynetMM:
         if 'hf_home' in config:
             self.cache_dir = config['hf_home']
 
-        self.load_model(DEFAULT_INITAL_MODEL, 'txt2img')
+        self._model_name = ''
+        self._model_mode = ''
+
+        # self.load_model(DEFAULT_INITAL_MODEL, 'txt2img')
 
     def log_debug_info(self):
         logging.info('memory summary:')
@@ -90,10 +93,13 @@ class SkynetMM:
         name: str,
         mode: str
     ):
-        logging.info(f'loading model {model_name}...')
+        logging.info(f'loading model {name}...')
         self._model_mode = mode
         self._model_name = name
 
+        if getattr(self, '_model', None):
+            del self._model
+
         gc.collect()
         torch.cuda.empty_cache()
 
@@ -131,7 +137,7 @@ class SkynetMM:
         output_hash = None
         try:
             match method:
-                case 'txt2img' | 'img2img' | 'inpaint':
+                case 'diffuse' | 'txt2img' | 'img2img' | 'inpaint':
                     arguments = prepare_params_for_diffuse(
                         params, method, inputs)
                     prompt, guidance, step, seed, upscaler, extra_params = arguments

skynet/utils.py

@@ -18,6 +18,8 @@ from PIL import Image
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from diffusers import (
     DiffusionPipeline,
+    AutoPipelineForText2Image,
+    AutoPipelineForImage2Image,
     AutoPipelineForInpainting,
     EulerAncestralDiscreteScheduler
 )
@@ -106,11 +108,15 @@ def pipeline_for(
 
     torch.cuda.set_per_process_memory_fraction(mem_fraction)
 
-    if 'inpaint' in mode:
+    match mode:
-        pipe_class = AutoPipelineForInpainting
+        case 'inpaint':
+            pipe_class = AutoPipelineForInpainting
 
-    else:
+        case 'img2img':
-        pipe_class = DiffusionPipeline
+            pipe_class = AutoPipelineForImage2Image
+
+        case 'txt2img' | 'diffuse':
+            pipe_class = AutoPipelineForText2Image
 
     pipe = pipe_class.from_pretrained(
         model, **params)
@@ -121,7 +127,7 @@ def pipeline_for(
     pipe.enable_xformers_memory_efficient_attention()
 
     if over_mem:
-        if 'img2img' not in mode:
+        if mode == 'txt2img':
             pipe.enable_vae_slicing()
             pipe.enable_vae_tiling()
 

tests/test_reqs.py

@@ -4,6 +4,33 @@ from skynet.dgpu.compute import SkynetMM
 from skynet.constants import *
 from skynet.config import *
 
+
+async def test_diffuse(dgpu):
+    conn, mm, daemon = dgpu
+    await conn.cancel_work(0, 'testing')
+
+    daemon._snap['requests'][0] = {}
+    req = {
+        'id': 0,
+        'nonce': 0,
+        'body': json.dumps({
+            "method": "diffuse",
+            "params": {
+                "prompt": "Kronos God Realistic 4k",
+                "model": list(MODELS.keys())[-1],
+                "step": 21,
+                "width": 1024,
+                "height": 1024,
+                "seed": 168402949,
+                "guidance": "7.5"
+            }
+        }),
+        'binary_data': '',
+    }
+
+    await daemon.maybe_serve_one(req)
+
+
 async def test_txt2img(dgpu):
     conn, mm, daemon = dgpu
     await conn.cancel_work(0, 'testing')
@@ -41,7 +68,7 @@ async def test_img2img(dgpu):
         'body': json.dumps({
             "method": "img2img",
             "params": {
-                "prompt": "Kronos God Realistic 4k",
+                "prompt": "a hindu cat god feline god on a house roof",
                 "model": list(MODELS.keys())[-2],
                 "step": 21,
                 "width": 1024,