extensions/openai: Fixes for: embeddings, tokens, better errors. +Docs update, +Images, +logit_bias/logprobs, +more. (#3122)

extensions/openai/images.py

@@ -9,12 +9,16 @@ def generations(prompt: str, size: str, response_format: str, n: int):
     # Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E
     # the results will be limited and likely poor. SD has hundreds of models and dozens of settings.
     # If you want high quality tailored results you should just use the Stable Diffusion API directly.
-    # it's too general an API to try and shape the result with specific tags like "masterpiece", etc,
-    # Will probably work best with the stock SD models.
-    # SD configuration is beyond the scope of this API.
+    # it's too general an API to try and shape the result with specific tags like negative prompts
+    # or "masterpiece", etc. SD configuration is beyond the scope of this API.
     # At this point I will not add the edits and variations endpoints (ie. img2img) because they
     # require changing the form data handling to accept multipart form data, also to properly support
     # url return types will require file management and a web serving files... Perhaps later!
+    base_model_size = 512 if not 'SD_BASE_MODEL_SIZE' in os.environ else int(os.environ.get('SD_BASE_MODEL_SIZE', 512))
+    sd_defaults = {
+        'sampler_name': 'DPM++ 2M Karras',  # vast improvement
+        'steps': 30,
+    }
 
     width, height = [int(x) for x in size.split('x')]  # ignore the restrictions on size
 
@@ -24,8 +28,21 @@ def generations(prompt: str, size: str, response_format: str, n: int):
         'width': width,
         'height': height,
         'batch_size': n,
-        'restore_faces': True,  # slightly less horrible
     }
+    payload.update(sd_defaults)
+
+    scale = min(width, height) / base_model_size
+    if scale >= 1.2:
+        # for better performance with the default size (1024), and larger res.
+        scaler = {
+            'width': width // scale,
+            'height': height // scale,
+            'hr_scale': scale,
+            'enable_hr': True,
+            'hr_upscaler': 'Latent',
+            'denoising_strength': 0.68,
+        }
+        payload.update(scaler)
 
     resp = {
         'created': int(time.time()),
@@ -38,7 +55,8 @@ def generations(prompt: str, size: str, response_format: str, n: int):
     response = requests.post(url=sd_url, json=payload)
     r = response.json()
     if response.status_code != 200 or 'images' not in r:
-        raise ServiceUnavailableError(r.get('detail', [{'msg': 'Unknown error calling Stable Diffusion'}])[0]['msg'], code=response.status_code)
+        print(r)
+        raise ServiceUnavailableError(r.get('error', 'Unknown error calling Stable Diffusion'), code=response.status_code, internal_message=r.get('errors',None))
     # r['parameters']...
     for b64_json in r['images']:
         if response_format == 'b64_json':